  Using complex notation, we present new simple expressions for two pairs of
complex supercharges in HKT supersymmetric sigma models. The second pair of
supercharges depends on the holomorphic antisymmetric "hypercomplex structure"
tensor which plays the same role for the HKT models as the complex structure
tensor for the Kaehler models. When the Hamiltonian and supercharges commute
with the momenta conjugate to the imaginary parts of the complex coordinates,
one can perform a Hamiltonian reduction. The models thus obtained represent a
special class of quasicomplex sigma models introduced recently by Ivanov and
Smilga.


  If $S$ is a subset of an abelian group $G$, the polychromatic number of $S$
in $G$ is the largest integer $k$ so that there is a $k-$coloring of the
elements of $G$ such that every translate of $S$ in $G$ gets all $k$ colors. We
determine the polychromatic number of all sets of size 2 or 3 in the group of
integers mod n.


  Despite the multifaceted recent advances in interventional causal
representation learning (CRL), they primarily focus on the stylized assumption
of single-node interventions. This assumption is not valid in a wide range of
applications, and generally, the subset of nodes intervened in an
interventional environment is fully unknown. This paper focuses on
interventional CRL under unknown multi-node (UMN) interventional environments
and establishes the first identifiability results for general latent causal
models (parametric or nonparametric) under stochastic interventions (soft or
hard) and linear transformation from the latent to observed space.
Specifically, it is established that given sufficiently diverse interventional
environments, (i) identifiability up to ancestors is possible using only soft
interventions, and (ii) perfect identifiability is possible using hard
interventions. Remarkably, these guarantees match the best-known results for
more restrictive single-node interventions. Furthermore, CRL algorithms are
also provided that achieve the identifiability guarantees. A central step in
designing these algorithms is establishing the relationships between UMN
interventional CRL and score functions associated with the statistical models
of different interventional environments. Establishing these relationships also
serves as constructive proof of the identifiability guarantees.


  We review the status of the electric dipole moment (EDM) of neutron in the
Standard Model (SM). The contributions of the strong and electroweak
interactions are discussed seperately. In each case the structure of the
Lagrangian and the sources of CP violation are specified, and calculational
details are given subsequently. These two contributions to the neutron EDM
exist in any extension of the SM including supersymmetry, two--doublet models
as well as models with more than three generations of fermions. We do not
discuss the EDM in such extensions; however, we briefly summarize their
predictions with a detailed account of the related literature.


  The Weakly Interacting Massive Particles (WIMPs) so far remain one of the
most popular candidates for dark matter. If captured gravitationally inside the
core of the Sun, these WIMPs may produce high energy neutrinos as the end
product in case they undergo self annihilations at the solar core. In this
work, we address the detectability of such neutrinos at the proposed KM3NeT
detector. Upper bounds of the detection rate for such neutrinos at KM3NeT are
computed for the case of a generic dark matter scenario and also when specific
models for particle dark matter are chosen. In this work, upper bounds of muon
event rates for different annihilating dark matter masses are computed for each
of the cases of dark matter annihilation channels (e.g. $b\bar{b}~, W^+W^-,
Z\bar{Z} $ etc). These upper bounds are also computed by considering the dark
matter scattering cross-section using upper bounds obtained from PandaX-4T
direct dark matter search experiment.


  Filter or screening methods are often used as a preprocessing step for
reducing the number of variables used by a learning algorithm in obtaining a
classification or regression model. While there are many such filter methods,
there is a need for an objective evaluation of these methods. Such an
evaluation is needed to compare them with each other and also to answer whether
they are at all useful, or a learning algorithm could do a better job without
them. For this purpose, many popular screening methods are partnered in this
paper with three regression learners and five classification learners and
evaluated on ten real datasets to obtain accuracy criteria such as R-square and
area under the ROC curve (AUC). The obtained results are compared through curve
plots and comparison tables in order to find out whether screening methods help
improve the performance of learning algorithms and how they fare with each
other. Our findings revealed that the screening methods were useful in
improving the prediction of the best learner on two regression and two
classification datasets out of the ten datasets evaluated.


  A Banach space $X$ with a Schauder basis is defined to have the restricted
quotient hereditarily indecomposable (QHI) property if $X/Y$ is hereditarily
indecomposable (HI) for any infinite codimensional subspace $Y$ with a
successive finite-dimensional decomposition on the basis of $X$. A reflexive
space with the restricted QHI property is in particular HI, has HI dual, and is
saturated with subspaces which are HI and have HI dual.
  The following dichotomy theorem is proved: any infinite dimensional Banach
space contains a quotient of subspace which either has an unconditional basis,
or has the restricted QHI property.


  We consider bimodal planar switched linear systems and obtain dwell time
bounds which guarantee their asymptotic stability. The dwell time bound
obtained is a smooth function of the eigenvectors and eigenvalues of the
subsystem matrices. An optimal scaling of the eigenvectors is used to
strengthen the dwell time bound. A comparison of our bounds with the dwell time
bounds in the existing literature is also presented.


  A new transform pair which can be used to solve mixed boundary value problems
for Laplace's equation and the complex Helmholtz equation in bounded convex
planar domains is presented. This work is an extension of Crowdy (2015, CMFT,
15, 655--687) where new transform techniques were developed for boundary value
problems for Laplace's equation in circular domains. The key ingredient of the
method is the analysis of the so called global relation which provides a
coupling of integral transforms of the given boundary data and of the unknown
boundary values. Three problems which involve mixed boundary conditions are
solved in detail, as well as numerically implemented, to illustrate how to
apply the new approach.


  This paper extends the result in \cite{HNS15} to Triebel-Lizorkin-Morrey
spaces which contains $4$ parameters $p,q,r,s$. This paper reinforces our
earlier paper \cite{HNS15} by Nakamura, the first and the third authors in two
different directions. First, we include the smoothness parameter $s$ and the
second smoothness parameter $r$. In \cite{HNS15} we assumed $s=0$ and $r=2$.
Here we relax the conditions on $s$ and $r$ to $s \in {\mathbb R}$ and $1 < r
\le \infty$. Second, we apply a formula obtained by Bergh in 1978 to prove our
main theorem without using the underlying sequence spaces.


  We study the problem of contextual search, a multidimensional generalization
of binary search that captures many problems in contextual decision-making. In
contextual search, a learner is trying to learn the value of a hidden vector $v
\in [0,1]^d$. Every round the learner is provided an adversarially-chosen
context $u_t \in \mathbb{R}^d$, submits a guess $p_t$ for the value of $\langle
u_t, v\rangle$, learns whether $p_t < \langle u_t, v\rangle$, and incurs loss
$\ell(\langle u_t, v\rangle, p_t)$ (for some loss function $\ell$). The
learner's goal is to minimize their total loss over the course of $T$ rounds.
  We present an algorithm for the contextual search problem for the symmetric
loss function $\ell(\theta, p) = |\theta - p|$ that achieves $O_{d}(1)$ total
loss. We present a new algorithm for the dynamic pricing problem (which can be
realized as a special case of the contextual search problem) that achieves
$O_{d}(\log \log T)$ total loss, improving on the previous best known upper
bounds of $O_{d}(\log T)$ and matching the known lower bounds (up to a
polynomial dependence on $d$). Both algorithms make significant use of ideas
from the field of integral geometry, most notably the notion of intrinsic
volumes of a convex set. To the best of our knowledge this is the first
application of intrinsic volumes to algorithm design.


  We consider Boltzmann random triangulations coupled to the Ising model on
their faces, under Dobrushin boundary conditions and at the critical point of
the model. The first part of this paper computes explicitly the partition
function of this model by solving its Tutte's equation, extending a previous
result by Bernardi and Bousquet-M\'elou to the model with Dobrushin boundary
conditions. We show that the perimeter exponent of the model is 7/3 in contrast
to the exponent 5/2 for uniform triangulations. In the second part, we show
that the model has a local limit in distribution when the two components of the
Dobrushin boundary tend to infinity one after the other. The local limit is
constructed explicitly using the peeling process along an Ising interface.
Moreover, we show that the main interface in the local limit touches the
(infinite) boundary almost surely only finitely many times, a behavior opposite
to that of the Bernoulli percolation on uniform maps. Some scaling limits
closely related to the perimeters of finite clusters are also obtained.


  Fine-grained entity typing (FET) is an essential task in natural language
processing that aims to assign semantic types to entities in text. However, FET
poses a major challenge known as the noise labeling problem, whereby current
methods rely on estimating noise distribution to identify noisy labels but are
confused by diverse noise distribution deviation. To address this limitation,
we introduce Co-Prediction Prompt Tuning for noise correction in FET, which
leverages multiple prediction results to identify and correct noisy labels.
Specifically, we integrate prediction results to recall labeled labels and
utilize a differentiated margin to identify inaccurate labels. Moreover, we
design an optimization objective concerning divergent co-predictions during
fine-tuning, ensuring that the model captures sufficient information and
maintains robustness in noise identification. Experimental results on three
widely-used FET datasets demonstrate that our noise correction approach
significantly enhances the quality of various types of training samples,
including those annotated using distant supervision, ChatGPT, and
crowdsourcing.


  We investigate the combined effects of weak disorder and a two-dimensional
(2D) optical lattice on the collective excitations of a harmonically trapped
Bose-Einstein condensate (BEC) at zero temperature. Accordingly, we generalize
the hydrodynamic equations of superfluid for a weakly interacting Bose gas in a
2D optical lattice to include the effects of weak disorder. Our analytical
results for the collective frequencies beyond the mean-field approximation
reveal the peculiar role of disorder, interplaying with the 2D optical lattice
and interatomic interaction, on elementary excitations along the 3D to 1D
dimensional crossover. In particular, consequences of disorder on the phonon
propagation and surface modes are analyzed in detail. The experimental scenario
is also proposed.


  Soft Computing techniques have opened up new avenues to the forecasters of
complex systems. Atmosphere is a complex system and all the atmospheric
parameters carry different degrees of complexity within themselves. Endeavor of
the present research is to adopt Artificial Neural Network as a Soft Computing
technique to anticipate the average monsoon rainfall over India. After a
rigorous study, the said Neural Net technique has been found adroit and results
have been compared with those obtained through conventional techniques.


  We introduce a criterion called the Devron property that a discrete dynamical
system can possess. The Devron property is said to occur when a class of highly
singular inputs of a mapping F are carried by some iterate of $F^{-1}$ to a
class of highly singular inputs of $F^{-1}$. The inspiration for this
definition is the discovery by R. Schwartz that the pentagram map exhibits this
kind of behavior. We investigate occurrences of the Devron property in a number
of different dynamical systems.


  In this paper we construct some multi-time geometrical extensions of the
KCC-invariants, which characterize a given second-order system of PDEs on the
1-jet space $J^1(T,M)$. A theorem of characterization of these multi-time
geometrical KCC-invariants is given.


  We modify the Schr\"{o}dinger equation in a way that preserves its main
properties but makes use of higher order derivative terms. Although the
modification represents an analogy to the Doebner-Goldin modification, it can
differ from it quite distinctively. A particular model of this modification
including derivatives up to the fourth order is examined in greater detail. We
observe that a special variant of this model partially retains the linear
superposition principle for the wave packets of standard quantum mechanics
remain solutions to it. It is a peculiarity of this variant that a periodic
structure emerges naturally from its equations. As a result, a free particle,
in addition to a plane wave solution, can possess band solutions. It is argued
that this can give rise to well-focused particle trajectories. Owing to this
peculiarity, when interpreted outside quantum theory, the equations of this
modification could also be used to model pattern formation phenomena.


  One of the most essential aspects of cuprate superconductors is a large
pseudogap coexisting with a superconducting gap, then some anomalous properties
can be understood in terms of the formation of the pseudogap. Within the
kinetic energy driven superconducting mechanism, the effect of the pseudogap on
the infrared response of cuprate superconductors in the superconducting-state
is studied. By considering the interplay between the superconducting gap and
pseudogap, the electron current-current correlation function is evaluated based
on the linear response approach and it then is employed to calculate
finite-frequency conductivity. It is shown that in the underdoped and optimally
doped regimes, the transfer of the part of the low-energy spectral weight of
the conductivity spectrum to the higher energy region to form a midinfrared
band is intrinsically associated with the presence of the pseudogap.


  The multiple T-value, which is a variant of multiple zeta value of level two,
is introduced by Kaneko and Tsumura. We show that the generating function of a
weighted sum of the multiple T-values of fixed weight and depth is given in
terms of the multiple T-values of depth one by solving a differential equation
of Heun type.


  Branching in vascular networks and in overall organismic form is one of the
most common and ancient features of multicellular plants, fungi, and animals.
By combining machine-learning techniques with new theory that relates vascular
form to metabolic function, we enable novel classification of diverse branching
networks--mouse lung, human head and torso, angiosperm and gymnosperm plants.
We find that ratios of limb radii--which dictate essential biologic functions
related to resource transport and supply--are best at distinguishing branching
networks. We also show how variation in vascular and branching geometry
persists despite observing a convergent relationship across organisms for how
metabolic rate depends on body mass.


  We present high-resolution X-ray spectra from the young supernova remnant Cas
A using a 70-ks observation taken by the Chandra High Energy Transmission
Grating Spectrometer (HETGS). Line emission, dominated by Si and S ions, is
used for high-resolution spectral analysis of many bright, narrow regions of
Cas A to examine their kinematics and plasma state. These data allow a 3D
reconstruction using the unprecedented X-ray kinematic results: we derive
unambiguous Doppler shifts for these selected regions, with values ranging
between -2500 and +4000 km/s. Plasma diagnostics of these regions, derived from
line ratios of resolved He-like triplet lines and H-like lines of Si, indicate
temperatures largely around 1 keV, which we model as O-rich reverse-shocked
ejecta. The ionization age also does not vary considerably over these regions
of the remnant. The gratings analysis was complemented by the non-dispersed
spectra from the same dataset, which provided information on emission measure
and elemental abundances for the selected Cas A regions. The derived electron
density of X-ray emitting ejecta varies from 20 to 200 cm^{-3}. The measured
abundances of Mg, Si, S and Ca are consistent with O being the dominant element
in the Cas A plasma. With a diameter of 5 arcmin, Cas A is the largest source
observed with the HETGS to date. We, therefore, describe the technique we use
and some of the challenges we face in the HETGS data reduction from such an
extended, complex object.


  Various laboratory-based experiments are underway attempting to detect dark
matter directly. The event rates and detailed signals expected in these
experiments depend on the dark matter phase space distribution on
sub-milliparsec scales. These scales are many orders of magnitude smaller than
those that can be resolved by conventional N-body simulations, so one cannot
hope to use such tools to investigate the effect of mergers in the history of
the Milky Way on the detailed phase-space structure probed by the current
experiments. In this paper we present an alternative approach to investigating
the results of such mergers, by studying a simplified model for a merger of a
sub-halo with a larger parent halo. With an appropriate choice of parent halo
potential, the evolution of material from the sub-halo can be expressed
analytically in action-angle variables, so it is possible to obtain its entire
orbit history very rapidly without numerical integration. Furthermore by
evolving backwards in time, we can obtain arbitrarily-high spatial resolution
for the current velocity distribution at a fixed point. Although this model
cannot provide a detailed quantitative comparison with the Milky Way, its
properties are sufficiently generic that it offers qualitative insight into the
expected structure arising from a merger at a resolution that cannot be
approached with full numerical simulations. Preliminary results indicate that
the velocity-space distribution of dark matter particles remains characterized
by discrete and well-defined peaks over an extended period of time, both for
single and multi-merging systems, in contrast to the simple smooth velocity
distributions sometimes assumed in predicting laboratory experiment detection
rates.


  We study multiplayer quantitative reachability games played on a finite
directed graph, where the objective of each player is to reach his target set
of vertices as quickly as possible. Instead of the well-known notion of Nash
equilibrium (NE), we focus on the notion of subgame perfect equilibrium (SPE),
a refinement of NE well-suited in the framework of games played on graphs. It
is known that there always exists an SPE in quantitative reachability games and
that the constrained existence problem is decidable. We here prove that this
problem is PSPACE-complete. To obtain this result, we propose a new algorithm
that iteratively builds a set of constraints characterizing the set of SPE
outcomes in quantitative reachability games. This set of constraints is
obtained by iterating an operator that reinforces the constraints up to
obtaining a fixpoint. With this fixpoint, the set of SPE outcomes can be
represented by a finite graph of size at most exponential. A careful inspection
of the computation allows us to establish PSPACE membership.


  The quaternion Fourier transform (QFT) satisfies some uncertainty principles
similar to the Euclidean Fourier transform. In this paper, we establish
Miyachi's theorem for this transform.


  We study the Maximum Budgeted Allocation problem, i.e., the problem of
selling a set of $m$ indivisible goods to $n$ players, each with a separate
budget, such that we maximize the collected revenue. Since the natural
assignment LP is known to have an integrality gap of $\frac{3}{4}$, which
matches the best known approximation algorithms, our main focus is to improve
our understanding of the stronger configuration LP relaxation. In this
direction, we prove that the integrality gap of the configuration LP is
strictly better than $\frac{3}{4}$, and provide corresponding polynomial time
roundings, in the following restrictions of the problem: (i) the Restricted
Budgeted Allocation problem, in which all the players have the same budget and
every item has the same value for any player it can be sold to, and (ii) the
graph MBA problem, in which an item can be assigned to at most 2 players.
Finally, we improve the best known upper bound on the integrality gap for the
general case from $\frac{5}{6}$ to $2\sqrt{2}-2\approx 0.828$ and also prove
hardness of approximation results for both cases.


  Given a simple graph $G$ and an integer $k$, the goal of $k$-Clique problem
is to decide if $G$ contains a complete subgraph of size $k$. We say an
algorithm approximates $k$-Clique within a factor $g(k)$ if it can find a
clique of size at least $k / g(k)$ when $G$ is guaranteed to have a $k$-clique.
Recently, it was shown that approximating $k$-Clique within a constant factor
is W[1]-hard [Lin21].
  We study the approximation of $k$-Clique under the Exponential Time
Hypothesis (ETH). The reduction of [Lin21] already implies an
$n^{\Omega(\sqrt[6]{\log k})}$-time lower bound under ETH. We improve this
lower bound to $n^{\Omega(\log k)}$. Using the gap-amplification technique by
expander graphs, we also prove that there is no $k^{o(1)}$ factor
FPT-approximation algorithm for $k$-Clique under ETH.
  We also suggest a new way to prove the Parameterized Inapproximability
Hypothesis (PIH) under ETH. We show that if there is no $n^{O(\frac{k}{\log
k})}$ algorithm to approximate $k$-Clique within a constant factor, then PIH is
true.


  Given a submersion $\phi: M \to N$, where $M$ is Riemannian, we construct a
stochastic process $X$ on $M$ such that the image $Y:=\phi(X)$ is a (reversed,
scaled) mean curvature flow of the fibers of the submersion. The model example
is the mapping $\pi: GL(n) \to GL(n)/O(n)$, whose image is equivalent to the
space of $n$-by-$n$ positive definite matrices, $\pdef$, and the said MCF has
deterministic image. We are able to compute explicitly the mean curvature (and
hence the drift term) of the fibers w.r.t. this map, (i) under diagonalization
and (ii) in matrix entries, writing mean curvature as the gradient of log
volume of orbits. As a consequence, we are able to write down Brownian motions
explicitly on several common homogeneous spaces, such as Poincar\'e's upper
half plane and the Bures-Wasserstein geometry on $\pdef$, on which we can see
the eigenvalue processes of Brownian motion reminiscent of Dyson's Brownian
motion.
  By choosing the background metric via natural $GL(n)$ action, we arrive at an
invariant control system on the $GL(n)$-homogenous space $GL(n)/O(n)$. We
investigate feasibility of developing stochastic algorithms using the mean
curvature flow.
  KEY WORDS: mean curvature flow, gradient flow, Brownian motion, Riemannian
submersion, random matrix, eigenvalue processes, geometry of positive definite
matrices, stochastic algorithm, control theory on homogeneous space


  The CARMA 1.3 mm polarization system consists of dual-polarization receivers
that are sensitive to right- (R) and left-circular (L) polarization, and a
spectral-line correlator that measures all four cross polarizations (RR, LL,
LR, RL) on each of the 105 baselines connecting the 15 telescopes. Each
receiver comprises a single feed horn, a waveguide circular polarizer, an
orthomode transducer (OMT), two heterodyne mixers, and two low-noise amplifiers
(LNAs), all mounted in a cryogenically cooled dewar. Here we review the basics
of polarization observations, describe the construction and performance of key
receiver components (circular polarizer, OMT, and mixers -- but not the
correlator), and discuss in detail the calibration of the system, particularly
the calibration of the R-L phase offsets and the polarization leakage
corrections. The absolute accuracy of polarization position angle measurements
was checked by mapping the radial polarization pattern across the disk of Mars.
Transferring the Mars calibration to the well known polarization calibrator
3C286, we find a polarization position angle of $\chi = 39.2 \pm 1^{\circ}$ for
3C286 at 225 GHz, consistent with other observations at millimeter wavelengths.
Finally, we consider what limitations in accuracy are expected due to the
signal-to-noise ratio, dynamic range, and primary beam polarization.


  Many protein design applications, such as binder or enzyme design, require
scaffolding a structural motif with high precision. Generative modelling
paradigms based on denoising diffusion processes emerged as a leading candidate
to address this motif scaffolding problem and have shown early experimental
success in some cases. In the diffusion paradigm, motif scaffolding is treated
as a conditional generation task, and several conditional generation protocols
were proposed or imported from the Computer Vision literature. However, most of
these protocols are motivated heuristically, e.g. via analogies to Langevin
dynamics, and lack a unifying framework, obscuring connections between the
different approaches. In this work, we unify conditional training and
conditional sampling procedures under one common framework based on the
mathematically well-understood Doob's h-transform. This new perspective allows
us to draw connections between existing methods and propose a new variation on
existing conditional training protocols. We illustrate the effectiveness of
this new protocol in both, image outpainting and motif scaffolding and find
that it outperforms standard methods.


  Many extensions of the Standard Model predict new resonances decaying to a
$Z$, $W$, or Higgs boson and a photon. This paper presents a search for such
resonances produced in $pp$ collisions at $\sqrt{s} = 13$ $\mathrm{TeV}$ using
a dataset with an integrated luminosity of 36.1 fb$^{-1}$ collected by the
ATLAS detector at the Large Hadron Collider. The $Z/W/H$ bosons are identified
through their decays to hadrons. The data are found to be consistent with the
Standard Model expectation in the entire investigated mass range. Upper limits
are set on the production cross section times branching fraction for resonance
decays to $Z/W+\gamma$ in the mass range from 1.0 to 6.8 $\mathrm{TeV}$, and
for the first time into $H+\gamma$ in the mass range from 1.0 to 3.0
$\mathrm{TeV}$.


  In this chapter, we present a strategy and the technics to approach a
scientific field from a set of articles gathered from the bibliographic
database, Web of Science. The strategy is based on methods developed to analyze
social networks. We illustrate the use of such strategy in studying the
calmodulin field. Such method allows to structure a huge number of articles
when writing a review, to detect the key opinion leaders in a given field and
to locate his own research topic in the landscape of the themes deciphered by
our own community. We show that the free software VosViewer may be used without
knowledge in computing science and with a short learning period. iii.


  Secure multiparty computation (MPC) allows joint privacy-preserving
computations on data of multiple parties. Although MPC has been studied
substantially, building solutions that are practical in terms of computation
and communication cost is still a major challenge. In this paper, we
investigate the practical usefulness of MPC for multi-domain network security
and monitoring. We first optimize MPC comparison operations for processing high
volume data in near real-time. We then design privacy-preserving protocols for
event correlation and aggregation of network traffic statistics, such as
addition of volume metrics, computation of feature entropy, and distinct item
count. Optimizing performance of parallel invocations, we implement our
protocols along with a complete set of basic operations in a library called
SEPIA. We evaluate the running time and bandwidth requirements of our protocols
in realistic settings on a local cluster as well as on PlanetLab and show that
they work in near real-time for up to 140 input providers and 9 computation
nodes. Compared to implementations using existing general-purpose MPC
frameworks, our protocols are significantly faster, requiring, for example, 3
minutes for a task that takes 2 days with general-purpose frameworks. This
improvement paves the way for new applications of MPC in the area of
networking. Finally, we run SEPIA's protocols on real traffic traces of 17
networks and show how they provide new possibilities for distributed
troubleshooting and early anomaly detection.


  Video steganography is the art of unobtrusively concealing secret data in a
cover video and then recovering the secret data through a decoding protocol at
the receiver end. Although several attempts have been made, most of them are
limited to low-capacity and fixed steganography. To rectify these weaknesses,
we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN)
in this paper. For large-capacity, we present a reversible pipeline to perform
multiple videos hiding and recovering through a single invertible neural
network (INN). Our method can hide/recover 7 secret videos in/from 1 cover
video with promising performance. For flexibility, we propose a
key-controllable scheme, enabling different receivers to recover particular
secret videos from the same cover video through specific keys. Moreover, we
further improve the flexibility by proposing a scalable strategy in multiple
videos hiding, which can hide variable numbers of secret videos in a cover
video with a single model and a single training session. Extensive experiments
demonstrate that with the significant improvement of the video steganography
performance, our proposed LF-VSN has high security, large hiding capacity, and
flexibility. The source code is available at https://github.com/MC-E/LF-VSN.


  The present work investigates several models of a single real scalar field,
engendering kinetic term of the Dirac-Born-Infeld type. Such theories introduce
nonlinearities to the kinetic part of the Lagrangian, which presents a square
root restricting the field evolution and including additional powers in
derivatives of the scalar field, controlled by a real parameter. In order to
obtain topological solutions analytically, we propose a first-order framework
that simplifies the equation of motion ensuring solutions that are linearly
stable. This is implemented using the deformation method, and we introduce
examples presenting two categories of potentials, one having polynomial
interactions and the other with nonpolynomial interactions. We also explore how
the Dirac-Born-Infeld kinetic term affects the properties of the solutions. In
particular, we note that the kinklike solutions are similar to the ones
obtained through models with standard kinetic term and canonical potential, but
their energy densities and stability potentials vary according to the parameter
introduced to control the new models.


  Due to its high performance and decreasing cost per bit, flash is becoming
the main storage medium in datacenters for hot data. However, flash endurance
is a perpetual problem, and due to technology trends, subsequent generations of
flash devices exhibit progressively shorter lifetimes before they experience
uncorrectable bit errors.
  In this paper we propose extending flash lifetime by allowing devices to
expose higher bit error rates. To do so, we present DIRECT, a novel set of
policies that leverages latent redundancy in distributed storage systems to
recover from bit corruption errors with minimal performance and recovery
overhead. In doing so, DIRECT can significantly extend the lifetime of flash
devices by effectively utilizing these devices even after they begin exposing
bit errors.
  We implemented DIRECT on two real-world storage systems: ZippyDB, a
distributed key-value store backed by RocksDB, and HDFS, a distributed file
system. When tested on production traces at Facebook, DIRECT reduces
application-visible error rates in ZippyDB by more than 10^2 and recovery time
by more than 10^4. DIRECT also allows HDFS to tolerate a 10^4--10^5 higher bit
error rate without experiencing application-visible errors.


  Runtime reconfiguration is a fundamental requirement of many
telecommunication applications which also has been addressed by management
standards like CMIP, 3GPP TS 32.602, and NETCONF. Two basic commands considered
by these standards are CREATE and DELETE which operate on managed objects
inside an application. The available configuration management technologies,
like JMX, OSGi, and Fractal, do not support the CREATE and DELETE
reconfiguration commands of the telecommunication standards. In this paper, we
introduce a novel technology, called RSCM, for development of runtime
reconfigurable applications complying with the telecommunication standards. The
RSCM subagent takes the responsibility of loading the application from the
configuration file, executing the runtime reconfiguration commands (including
CREATE and DELETE), enforcing validity of the configuration state, and updating
the configuration file according to the latest reconfiguration commands. We
exploit the modular and object oriented features of the XML technology for
storing the configuration state of a program in a configuration file. The
software development process is tailored such that the design of XML schemas of
managed classes is performed parallel to the design of software classes. In
addition, a novel programming approach based on indirect referencing is
proposed which allows safe and almost immediate deletion of managed objects at
runtime. This indirect referencing mechanism affects the implementation of
associations in class diagrams and prevents methods of a class to use the this
pointer freely. The RSCM technology has been successfully used in several
commercial telecommunication applications; including an SMS service center, an
SMS gateway, and an SMS hub.


  We put forward a model, or rather a relatively broad class of models, beyond
the Standard Model based on the two main assumptions: MPP) The coupling
constants should be fixed such as to ensure that there be many ``vacuum
states'', i.e. Lorentz invariant states of the fields, with the same energy
density. Anti-GUT) At high energy, above an essential desert of only Standard
Model interactions, there is the bigger gauge group $SMG^3\times U(1)_f$ which
means that each family of quarks and leptons has its own set of gauge bosons
analogous to those in the Standard Model itself, and then there is one extra
abelian gauge boson called $U(1)_f$. In addition we make some further more
phenomenological assumptions. We succeed in fitting order of magnitudewise most
of the (effective) Yukawa couplings observed in the Standard Model as quark and
lepton masses and mixing angles. We have more accurate numbers for the three
fine-structure constants and the top and Higgs masses, as well as some
suggestive understanding of the fine-tuning wonders for the cosmological
constant and the $\Theta$-angles. CP-violation is predicted a bit low, but
order of magnitudewise in agreement with experiment. In summary, we obtain at
least an order of magnitudewise understanding of the Standard Model parameters
within our scheme, except for the mysteriously low weak scale compared to the
Planck scale, with only 4 continuous parameters being fitted, in addition to
our somewhat more discrete choices.


  This paper initiates the investigation of the family of
$(G,s)$-geodesic-transitive digraphs with $s\geq 2$. We first give a global
analysis by providing a reduction result. Let $\Gamma$ be such a digraph and
let $N$ be a normal subgroup of $G$ maximal with respect to having at least $3$
orbits. Then the quotient digraph $\Gamma_N$ is $(G/N,s')$-geodesic-transitive
where $s'=\min\{s,\diam(\Gamma_N)\}$, $G/N$ is either quasiprimitive or
bi-quasiprimitive on $V(\Gamma_N)$, and $\Gamma_N$ is either directed or an
undirected complete graph. Moreover, it is further shown that if $\Gamma $ is
not $(G,2)$-arc-transitive, then $G/N$ is quasiprimitive on $V(\Gamma_N)$.
  On the other hand, we also consider the case that the normal subgroup $N$ of
$G$ has one orbit on the vertex set. We show that if $N$ is regular on
$V(\Gamma)$, then $\Gamma$ is a circuit, and particularly each
$(G,s)$-geodesic-transitive normal Cayley digraph with $s\geq 2$, is a circuit.
  Finally, we investigate $(G,2)$-geodesic-transitive digraphs with either
valency at most 5 or diameter at most 2. Let $\Gamma$ be a
$(G,2)$-geodesic-transitive digraph. It is proved that: if $\Gamma$ has valency
at most $5$, then $\Gamma$ is $(G,2)$-arc-transitive; if $\Gamma$ has diameter
$2$, then $\Gamma$ is a balanced incomplete block design with the Hadamard
parameters.


  The time and size distribution of the waves of topplings in the Abelian
sandpile model are expressed as the first arrival at the origin distribution
for a scale invariant, time inhomogeneous Fokker-Planck equation. Assuming a
linear conjecture for the the time inhomogeneity exponent as function of loop
erased random walk (LERW) critical exponent, suggested by numerical results,
this approach allows to estimate the lower critical dimension of the model and
the exact value of the critical exponent for LERW in three dimension. The
avalanche size distribution in two dimensions is found to be the difference
between two closed power laws.


  We formulate N=4 supersymmetric Yang-Mills theory in terms of soft-collinear
effective theory. The effective Lagrangian in soft-collinear effective theory
is developed according to the power counting by a small parameter \eta \sim
p_{\perp}/Q. All the particles in this theory are in the adjoint representation
of the SU(N) gauge group, and we derive the collinear gauge-invariant
Lagrangian in the adjoint and fundamental representations respectively.
  We consider collinear and ultrasoft Wilson lines in this theory, and show the
ultrasoft factorization of the collinear Lagrangian by redefining the collinear
fields with the use of the ultrasoft Wilson lines. The vertex correction for a
vector fermion current at one loop is explicitly presented as an example to
illustrate how the computation is performed in the effective theory.


  We report the trapping of ultracold neutral $ \text{Rb}$ atoms and $
\text{Ba}^+ $ ions in a common optical potential in absence of any
radiofrequency (RF) fields. We prepare $ \text{Ba}^+ $ at $ 370 ~ \mu K $ and
demonstrate efficient sympathetic cooling by $100 ~ \mu K $ after one
collision. Our approach is currently limited by the $ \text{Rb}$ density and
related three-body losses, but it overcomes the fundamental limitation in RF
traps set by RF-driven, micromotion-induced heating. It is applicable to a wide
range of ion-atom species, and may enable novel ultracold chemistry experiments
and complex many-body dynamics.


  A three-dimensional, multifluid simulation of a giant planet's magnetospheric
interaction with steady-state stellar wind from a Sun-like star was performed
for four different orbital semi-major axes - 10, 5, 1 and 0.2 AU. We simulate
the effect of the increasing, steady-state stellar wind pressure related to the
planetary orbital semi-major axis on the global magnetospheric dynamics for a
Saturn-like planet, including an Enceladus-like plasma torus. Mass loss
processes are shown to vary with orbital distance, with the centrifugal
interchange instability displayed only in the 10 AU and 5 AU cases which reach
a state of mass loss equilibrium more slowly than the 1 AU or 0.2 AU cases. The
compression of the magnetosphere in the 1 AU and 0.2 AU cases contributes to
the quenching of the interchange process by increasing the ratio of total
plasma thermal energy to corotational energy. The strength of field-aligned
currents (FAC), associated with auroral radio emissions, are shown to increase
in magnitude and latitudinal coverage with a corresponding shift equatorward
from increased dynamic ram pressure experienced in the hotter orbits. Similar
to observed hot Jovian planets, the warm exo-Saturn simulated in the current
work shows enhanced ion density in the magnetosheath and magnetopause regions,
as well as the plasma torus which could contribute to altered transit signals,
suggesting that for planets in warmer ($>$0.1 AU) orbits, planetary magnetic
field strengths and possibly exomoons - via the plasma torus - could be
observable with future missions.


  A central challenge in Gravitational Wave Astronomy is identifying weak
signals in the presence of non-stationary and non-Gaussian noise. The
separation of gravitational wave signals from noise requires good models for
both. When accurate signal models are available, such as for binary Neutron
star systems, it is possible to make robust detection statements even when the
noise is poorly understood. In contrast, searches for "un-modeled" transient
signals are strongly impacted by the methods used to characterize the noise.
Here we take a Bayesian approach and introduce a multi-component, variable
dimension, parameterized noise model that explicitly accounts for
non-stationarity and non-Gaussianity in data from interferometric gravitational
wave detectors. Instrumental transients (glitches) and burst sources of
gravitational waves are modeled using a Morlet-Gabor continuous wavelet frame.
The number and placement of the wavelets is determined by a trans-dimensional
Reversible Jump Markov Chain Monte Carlo algorithm. The Gaussian component of
the noise and sharp line features in the noise spectrum are modeled using the
BayesLine algorithm, which operates in concert with the wavelet model.


  Cyclotomic polylogarithms are reviewed and new results concerning the special
constants that occur are presented. This also allows some comments on previous
literature results using PSLQ.


  The spin orientations of spinning binary black hole (BBH) mergers detected by
ground-based gravitational wave detectors such as LIGO and Virgo can provide
important clues about the formation of such binaries. However, these spin
tilts, i.e., the angles between the spin vector of each black hole and the
binary's orbital angular momentum vector, can change due to precessional
effects as the black holes evolve from a large separation to their merger. The
tilts inferred at a frequency in the sensitive band of the detectors by
comparing the signal with theoretical waveforms can thus be significantly
different from the tilts when the binary originally formed. These tilts at the
binary's formation are well approximated in many scenarios by evolving the BBH
backwards in time to a formally infinite separation. Using the tilts at
infinite separation also places all binaries on an equal footing in analyzing
their population properties. In this paper, we perform parameter estimation for
simulated BBHs and investigate the differences between the tilts one infers
directly close to merger and those obtained by evolving back to infinite
separation. We select simulated observations such that their configurations
show particularly large differences in their orientations close to merger and
at infinity. While these differences may be buried in the statistical noise for
current detections, we show that in future plus-era (A$+$ and Virgo$+$)
detectors, they can be easily distinguished in some cases. We also consider the
tilts at infinity for BBHs in various spin morphologies and at the endpoint of
the up-down instability. In particular, we find that we are able to easily
identify the up-down instability cases as such from the tilts at infinity.


  We establish that for every function $u \in L^1_\mathrm{loc}(\Omega)$ whose
distributional Laplacian $\Delta u$ is a signed Borel measure in an open set
$\Omega$ in $\mathbb{R}^{N}$, the distributional gradient $\nabla u$ is
differentiable almost everywhere in $\Omega$ with respect to the
weak-$L^{\frac{N}{N-1}}$ Marcinkiewicz norm. We show in addition that the
absolutely continuous part of $\Delta u$ with respect to the Lebesgue measure
equals zero almost everywhere on the level sets $\{u = \alpha\}$ and $\{\nabla
u = e\}$, for every $\alpha \in \mathbb{R}$ and $e \in \mathbb{R}^N$. Our
proofs rely on an adaptation of Calder\'on and Zygmund's singular-integral
estimates inspired by subsequent work by Hajlasz.


  An absolutely representing family of subspaces is a natural generalization of
an absolutely representing system of subspaces and absolutely representing
system (of elements). We obtain necessary an (or) sufficient conditions for a
family of subspaces to be an absolutely representing family of subspaces and
study properties of absolutely representing families of subspaces in Banach
spaces. As an example, we study families of subspaces spanned by exponents.


  We study reconfiguration problems for cliques in a graph, which determine
whether there exists a sequence of cliques that transforms a given clique into
another one in a step-by-step fashion. As one step of a transformation, we
consider three different types of rules, which are defined and studied in
reconfiguration problems for independent sets. We first prove that all the
three rules are equivalent in cliques. We then show that the problems are
PSPACE-complete for perfect graphs, while we give polynomial-time algorithms
for several classes of graphs, such as even-hole-free graphs and cographs. In
particular, the shortest variant, which computes the shortest length of a
desired sequence, can be solved in polynomial time for chordal graphs,
bipartite graphs, planar graphs, and bounded treewidth graphs.


  Neural networks have been successfully used for classification tasks in a
rapidly growing number of practical applications. Despite their popularity and
widespread use, there are still many aspects of training and classification
that are not well understood. In this paper we aim to provide some new insights
into training and classification by analyzing neural networks from a
feature-space perspective. We review and explain the formation of decision
regions and study some of their combinatorial aspects. We place a particular
emphasis on the connections between the neural network weight and bias terms
and properties of decision boundaries and other regions that exhibit varying
levels of classification confidence. We show how the error backpropagates in
these regions and emphasize the important role they have in the formation of
gradients. These findings expose the connections between scaling of the weight
parameters and the density of the training samples. This sheds more light on
the vanishing gradient problem, explains the need for regularization, and
suggests an approach for subsampling training data to improve performance.


  We first recall Grothendieck's notion of n-truncated Barsotti-Tate group.
Such groups form an algebraic stack over the integers. The problem is to give
an illuminating description of its reductions modulo powers of p. A related
problem is to construct analogs of these reductions related to general Shimura
varieties with good reduction at p. We discuss some conjectures on this subject
based on the theory of prismatic cohomology.


  The quantum max-flow quantifies the maximal possible entanglement between two
regions of a tensor network state for a fixed graph and fixed bond dimensions.
In this work, we calculate the quantum max-flow exactly in the case of the
bridge graph. The result is achieved by drawing connections to the theory of
prehomogenous tensor and the representation theory of quivers. Further, we
highlight relations to invariant theory and to algebraic statistics.


  In the low energy domain of four-dimensional SU(2) Yang-Mills theory the spin
and the charge of the gauge field can become separated from each other. The
ensuing field variables describe the interacting dynamics between a version of
the O(3) nonlinear $\sigma$-model and a nonlinear Grassmannian $\sigma$-model,
both of which may support closed knotted strings as stable solitons. Lorentz
transformations act projectively in the O(3) model which breaks global internal
rotation symmetry and removes massless Goldstone bosons from the particle
spectrum. The entire Yang-Mills Lagrangian can be recast into a generally
covariant form with a conformally flat metric tensor. The result contains the
Einstein-Hilbert Lagrangian together with a nonvanishing cosmological constant,
and insinuates the presence of a novel dimensionfull parameter in the
Yang-Mills theory.


  We examine the selection effects that determine how the population of
inspiraling binary compact objects (BCOs) is reflected by those potentially
observed with ground-based interferometers like LIGO. We lay the ground-work
for the interpretation of future observations in terms of constraints on the
real population and, correspondingly, binary star evolution models.
  To determine the extra-galactic population of inspiraling binaries we combine
data on distance and blue luminosity from galaxy catalogs with current models
of the galactic BCO mass distribution to simulate the physical distribution of
binaries in the nearby universe. We use Monte Carlo methods to determine the
fraction of binaries observable by the LIGO detectors from each galaxy as a
function of the BCO chirp mass. We examine separately the role of source
distance, sky position, time of detection, and binary system chirp mass on
detection efficiency and selection effects relevant to the three LIGO
detectors. Finally, we discuss the implications of the nearby geography of
space on anticipated GW detection and compare our results to previous studies,
which have assumed uniform galaxy volume density and fixed chirp mass for
binary compact objects.
  From these considerations, actual BCO inspiral observations or significant
upper limits on the coalescence rate anticipated in the near future by
ground-based interferometers can be used to improve our knowledge of the
galactic binary inspiral rate and to constrain models of radio pulsar
characteristics and binary star evolution channels leading to neutron star or
black hole binaries.


  An equilibrium reversible cycle with a certain engine to transduce the energy
of any chemical reaction into mechanical energy is proposed. The efficiency for
chemical energy transduction is also defined so as to be compared with Carnot
efficiency. Relevance to the study of protein motors is discussed. KEYWORDS:
Chemical thermodynamics, Engine, Efficiency, Molecular machine.


  In the framework of the isospin-dependent Boltzmann-Uehling-Uhlenbeck
transport model, effect of the momentum dependence of nuclear symmetry
potential on nuclear transverse and elliptic flows in the neutron-rich reaction
$^{132}$Sn+$^{124}$Sn at a beam energy of 400 MeV/nucleon is studied. We find
that the momentum dependence of nuclear symmetry potential affects the rapidity
distribution of the free neutron to proton ratio, the neutron and the proton
transverse flows as a function of rapidity. The momentum dependence of nuclear
symmetry potential affects the neutron-proton differential transverse flow more
evidently than the difference of neutron and proton transverse flows as well as
the difference of proton and neutron elliptic flows. It is thus better to probe
the symmetry energy by using the difference of neutron and proton flows since
the momentum dependence of nuclear symmetry potential is still an open
question. And it is better to probe the momentum dependence of nuclear symmetry
potential by using the neutron-proton differential transverse flow and the
rapidity distribution of the free neutron to proton ratio.


  This paper is a continuation and elaboration of our work quant-ph/0206057
(Nucl. Phys. B, 1968, 7, 79) where some approach to the variable-mass problem
were proposed. Here we have found a concret realization of irreducible
representations of the inhomogeneous group P(1,n) - the group of translations
and rotations in (1+n)-dimensional Minkowski space in two classes (when
P_0^2-P_k^2>0 and P_0^2-P_k^2<0). All the P(1,n)-invariant equations of the
Schrodinger-Foldy type are written down. Some questions of a physical
interpretation of the quantum, mechanical scheme based on the inhomogeneous de
Sitter group P(1,n) are discussed.


  In repeated Measure Designs with multiple groups, the primary purpose is to
compare different groups in various aspects. For several reasons, the number of
measurements and therefore the dimension of the observation vectors can depend
on the group, making the usage of existing approaches impossible. We develop an
approach which can be used not only for a possibly increasing number of groups
$a$, but also for group-depending dimension $d_i$, which is allowed to go to
infinity. This is a unique high-dimensional asymptotic framework impressing
through its variety and do without usual conditions on the relation between
sample size and dimension. It especially includes settings with fixed
dimensions in some groups and increasing dimensions in other ones, which can be
seen as semi-high-dimensional. To find a appropriate statistic test new and
innovative estimators are developed, which can be used under these diverse
settings on $a,d_i$ and $n_i$ without any adjustments. We investigated the
asymptotic distribution of a quadratic-form-based test statistic and developed
an asymptotic correct test. Finally, an extensive simulation study is conducted
to investigate the role of the single group's dimension.


  Mesh optimization procedures are generally a combination of node smoothing
and discrete operations which affect a small number of elements to improve the
quality of the overall mesh. These procedures are useful as a post-processing
step in mesh generation procedures and in applications such as fluid
simulations with severely deforming domains. In order to perform high-order
mesh optimization, these ingredients must also be extended to high-order
(curved) meshes. In this work, we present a method to perform local element
operations on curved meshes. The mesh operations discussed in this work are
edge/face swaps, edge collapses, and edge splitting (more generally refinement)
for triangular and tetrahedral meshes. These local operations are performed by
first identifying the patch of elements which contain the edge/face being acted
on, performing the operation as a straight-sided one by placing the high-order
nodes via an isoparametric mapping from the master element, and smoothing the
high-order nodes on the elements in the patch by minimizing a Jacobian-based
high-order mesh distortion measure. Since the initial straight-sided guess from
the placement of the nodes via the isoparametric mapping frequently results in
invalid elements, the distortion measure must be regularized which allows for
mesh untangling for the optimization to succeed. We present several examples in
2D and 3D to demonstrate these local operations and how they can be combined
with a high-order node smoothing procedure to maintain mesh quality when faced
with severe deformations.


  As one of the most violent activities in the solar atmosphere, white-light
flares (WLFs) are generally known for their enhanced white-light (or continuum)
emission, which primarily originates in the solar lower atmosphere. However, we
know little about how white-light emission is produced. In this study, we aim
to investigate the response of the continua at 3600\AA\ and 4250\AA\ and also
the H$\alpha$ and Ly$\alpha$ lines during WLFs modeled with radiative
hydrodynamics simulations. We take non-thermal electron beams as the energy
source for the WLFs in two different initial atmospheres and vary their
parameters. Our results show that the model with non-thermal electron beam
heating can clearly show enhancements in the continua at 3600\AA\ and 4250\AA\
as well as in the H$\alpha$ and Ly$\alpha$ lines. A larger electron beam flux,
a smaller spectral index, or a penumbral initial atmosphere leads to a stronger
emission increase at 3600\AA, 4250\AA\ and in the H$\alpha$ line. For the
Ly$\alpha$ line, however, it is more preferably enhanced in a quiet-Sun initial
atmosphere with a larger spectral index of the electron beam. It is also
notable that the continua at 3600\AA\ and 4250\AA\ and the H$\alpha$ line
exhibit a dimming at the beginning of the heating and reach their peak
emissions later than the peak time of the heating function, while the
Ly$\alpha$ line does not show such behaviors. These results can be served as a
reference for analyzing future WLF observations.


  The performance of a quantum information processor depends on the precise
control of phases introduced into the system during quantum gate operations. As
the number of operations increases with the complexity of a computation, the
phases of gates at different locations and different times must be controlled,
which can be challenging for optically-driven operations. We circumvent this
issue by demonstrating an entangling gate between two trapped atomic ions that
is insensitive to the optical phases of the driving fields, while using a
common master reference clock for all coherent qubit operations. Such
techniques may be crucial for scaling to large quantum information processors
in many physical platforms.


  Primordial black holes (PBH) have been shown to arise from high peaks in the
matter power spectra of multi-field models of inflation. Here we show, with a
simple toy model, that it is also possible to generate a peak in the curvature
power spectrum of single-field inflation. We assume that the effective dynamics
of the inflaton field presents a near-inflection point which slows down the
field right before the end of inflation and gives rise to a prominent spike in
the fluctuation power spectrum at scales much smaller than those probed by
Cosmic Microwave Background (CMB) and Large Scale Structure (LSS) observations.
This peak will give rise, upon reentry during the radiation era, to PBH via
gravitational collapse. The mass and abundance of these PBH is such that they
could constitute the totality of the Dark Matter today. We satisfy all CMB and
LSS constraints and predict a very broad range of PBH masses. Some of these PBH
are light enough that they will evaporate before structure formation, leaving
behind a large curvature fluctuation on small scales. This broad mass
distribution of PBH as Dark Matter will be tested in the future by AdvLIGO and
LISA interferometers.


  We present a method for modelling the cluster-galaxy correlation function in
redshift-space, down to ~ Mpc scales. The method builds upon the so-called
Galaxy Infall Kinematics (GIK) model, a parametric model for the pairwise
velocities of galaxies with respect to nearby galaxy clusters. We fit the
parameters of the GIK model to a suite of simulations run with different
cosmologies, and use Gaussian Processes to emulate how the GIK parameters
depend upon cosmology. This emulator can then be combined with knowledge of the
real-space clustering of clusters and galaxies, to predict the cluster-galaxy
correlation function in redshift space. Fitting this model to an observed
correlation function enables the extraction of cosmological parameter
constraints, and we present forecasts for a DESI-like survey. We also perform
tests of the robustness of our constraints from fitting to mock data extracted
from N-body simulations, finding that fitting to scales < 3 Mpc/h leads to a
biased inference on cosmology, due to model misspecification on these scales.
Finally, we discuss what steps will need to be taken in order to apply our
method to real data.


  Feedback is essential for learning a new skill or improving one's current
skill-level. However, current methods for skill-assessment from video only
provide scores or compare demonstrations, leaving the burden of knowing what to
do differently on the user. We introduce a novel method to generate actionable
feedback from video of a person doing a physical activity, such as basketball
or soccer. Our method takes a video demonstration and its accompanying 3D body
pose and generates (1) free-form expert commentary describing what the person
is doing well and what they could improve, and (2) a visual expert
demonstration that incorporates the required corrections. We show how to
leverage Ego-Exo4D's videos of skilled activity and expert commentary together
with a strong language model to create a weakly-supervised training dataset for
this task, and we devise a multimodal video-language model to infer coaching
feedback. Our method is able to reason across multi-modal input combinations to
output full-spectrum, actionable coaching -- expert commentary, expert video
retrieval, and the first-of-its-kind expert pose generation -- outperforming
strong vision-language models on both established metrics and human preference
studies.


  In this paper, we introduce a structure-based neural network architecture,
namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the
temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A
binary classifier leverages the repetitive constellation structure in the
system to perform multi-class detection. The incorporation of RC allows the
RC-Struct to be learned in a purely online fashion with extremely limited pilot
symbols in each OFDM subframe. The binary classifier enables the efficient
utilization of the precious online training symbols and allows an easy
extension to high-order modulations without a substantial increase in
complexity. Experiments show that the introduced RC-Struct outperforms both the
conventional model-based symbol detection approaches and the state-of-the-art
learning-based strategies in terms of bit error rate (BER). The advantages of
RC-Struct over existing methods become more significant when rank and link
adaptation are adopted. The introduced RC-Struct sheds light on combining
communication domain knowledge and learning-based receive processing for
5G/5G-Advanced and Beyond.


  Deep learning as a service (DLaaS) has been intensively studied to facilitate
the wider deployment of the emerging deep learning applications. However, DLaaS
may compromise the privacy of both clients and cloud servers. Although some
privacy preserving deep neural network (DNN) based inference techniques have
been proposed by composing cryptographic primitives, the challenges on
computational efficiency have not been well-addressed due to the complexity of
DNN models and expensive cryptographic primitives. In this paper, we propose a
novel privacy preserving cloud-based DNN inference framework (namely, "PROUD"),
which greatly improves the computational efficiency. Finally, we conduct
extensive experiments on two commonly-used datasets to validate both
effectiveness and efficiency for the PROUD, which also outperforms the
state-of-the-art techniques.


  A three species food chain model is studied analytically as well as
numerically. Integrability of the model is studied using Painleve analysis
while chaotic behaviour is studied using numerical techniques, such as
calculation of Lyapunov exponents, plotting the bifurcation diagram and phase
plots. We correct and critically comment on the wrong results reported recently
on this ecological model, in a paper by Rai ([1995] ``Modelling ecological
systems'', Int. J. Bifurcation and Chaos 5, 537-543).


  The development of thin film transistor (TFTs) based integrated circuits on
flexible substrates promise interesting approaches to human interface systems.
Recently TFTs have been fabricated on textured surfaces such as textiles,
paper, artificially corrugated or dimpled substrates, threads and fibers. This
can result in the TFTs metal-insulator-semiconductor (MIS) stack being
significantly distorted from a planar zero-curvature geometry to having non
zero and potentially high curvature. Although the direct deposition on textured
surfaces do not result in mechanical stress (as for example in bending,
buckling or wrinkling), the geometry of high curvature can significantly
influence the current voltage characteristics of the TFT. Here we present a
closed form analytical model describing the geometrical impact of high
curvature on the electrical performance of the TFT. Models are obtained from
the solution to the Poisson-Boltzmann equation in polar co-ordinates and are
verified using TCAD simulations. The techniques to generalize the results to
adapt to any texture and semiconducting material are also discussed with
experimental verification. This work forms an analytical basis to understand
the impact of curvature on the electrostatics of the MIS stack and the current
voltage characteristics of the TFT.


  String theories with two dimensional space-time target spaces are
characterized by the existence of a ``ground ring'' of operators of spin
$(0,0)$. By understanding this ring, one can understand the symmetries of the
theory and illuminate the relation of the critical string theory to matrix
models. The symmetry groups that arise are, roughly, the area preserving
diffeomorphisms of a two dimensional phase space that preserve the fermi
surface (of the matrix model) and the volume preserving diffeomorphisms of a
three dimensional cone. The three dimensions in question are the matrix
eigenvalue, its canonical momentum, and the time of the matrix model.


  We apply the Hartle formalism to study equilibrium configurations in the
framework of Newtonian gravity. This approach allows one to study in a simple
manner the properties of the interior gravitational field in the case of static
as well as stationary rotating stars in hydrostatic equilibrium. It is shown
that the gravitational equilibrium conditions reduce to a system of ordinary
differential equations which can be integrated numerically. We derive all the
relevant equations up to the second order in the angular velocity. Moreover, we
find explicitly the total mass, the moment of inertia, the quadrupole moment,
the polar and equatorial radii, and the eccentricity of the rotating body. We
also present the procedure to calculate the gravitational Love number. We test
the formalism in the case of white dwarfs and show its compatibility with the
known results in the literature.


  We have measured the inclusive $D^{*\pm}$ production cross section in a
two-photon collision at the TRISTAN $e^+e^-$ collider. The mean $\sqrt{s}$ of
the collider was 57.16 GeV and the integrated luminosity was 150 $pb^{-1}$. The
differential cross section ($d\sigma(D^{*\pm})/dP_T$) was obtained in the $P_T$
range between 1.6 and 6.6 GeV and compared with theoretical predictions, such
as those involving direct and resolved photon processes.


  We outline the construction of the holonomy groupoid of a locally Lie
groupoid and the monodromy groupoid of a Lie groupoid. These specialise to the
well known holonomy and monodromy groupoids of a foliation, when the groupoid
is just an equivalence relation.


  We obtain in arbitrary codimension a removability result on the order of
singularity of Willmore surfaces realising the width of Willmore min-max
problems on spheres. As a consequence, out of the twelve families of non-planar
minimal surfaces in $\mathbb{R}^3$ of total curvature greater than $-12\pi$,
only three of them may occur as conformal images of bubbles in Willmore min-max
problems.


  In $masked\ low-rank\ approximation$, one is given $A \in \mathbb{R}^{n
\times n}$ and binary mask matrix $W \in \{0,1\}^{n \times n}$. The goal is to
find a rank-$k$ matrix $L$ for which: $$cost(L) = \sum_{i=1}^{n} \sum_{j =
1}^{n} W_{i,j} \cdot (A_{i,j} - L_{i,j} )^2 \leq OPT + \epsilon \|A\|_F^2 ,$$
where $OPT = \min_{rank-k\ \hat{L}} cost(\hat L)$ and $\epsilon$ is a given
error parameter. Depending on the choice of $W$, this problem captures factor
analysis, low-rank plus diagonal decomposition, robust PCA, low-rank matrix
completion, low-rank plus block matrix approximation, and many problems. Many
of these problems are NP-hard, and while some algorithms with provable
guarantees are known, they either 1) run in time $n^{\Omega(k^2/\epsilon)}$ or
2) make strong assumptions, e.g., that $A$ is incoherent or that $W$ is random.
  In this work, we show that a common polynomial time heuristic, which simply
sets $A$ to $0$ where $W$ is $0$, and then finds a standard low-rank
approximation, yields bicriteria approximation guarantees for this problem. In
particular, for rank $k' > k$ depending on the $public\ coin\ partition\
number$ of $W$, the heuristic outputs rank-$k'$ $L$ with cost$(L) \leq OPT +
\epsilon \|A\|_F^2$. This partition number is in turn bounded by the
$randomized\ communication\ complexity$ of $W$, when interpreted as a
two-player communication matrix. For many important examples of masked low-rank
approximation, including all those listed above, this result yields bicriteria
approximation guarantees with $k' = k \cdot poly(\log n/\epsilon)$.
  Further, we show that different models of communication yield algorithms for
natural variants of masked low-rank approximation. For example, multi-player
number-in-hand communication complexity connects to masked tensor decomposition
and non-deterministic communication complexity to masked Boolean low-rank
factorization.


  China is the world's second largest economy. After four decades of economic
miracles, China's economy is transitioning into an advanced, knowledge-based
economy. Yet, we still lack a detailed understanding of the skills that underly
the Chinese labor force, and the development and spatial distribution of these
skills. For example, the US standardized skill taxonomy O*NET played an
important role in understanding the dynamics of manufacturing and
knowledge-based work, as well as potential risks from automation and
outsourcing. Here, we use Machine Learning techniques to bridge this gap,
creating China's first workforce skill taxonomy, and map it to O*NET. This
enables us to reveal workforce skill polarization into social-cognitive skills
and sensory-physical skills, and to explore the China's regional inequality in
light of workforce skills, and compare it to traditional metrics such as
education. We build an online tool for the public and policy makers to explore
the skill taxonomy: skills.sysu.edu.cn. We will also make the taxonomy dataset
publicly available for other researchers upon publication.


  We present a calculation of the Mellin moments of the transverse quark spin
densities in the nucleon using lattice QCD. The densities are extracted from
the unpolarized and transversity generalized form factors extrapolated to the
continuum limit using three $N_f=2+1+1$ twisted mass fermion gauge ensembles
simulated with physical quark masses and spanning three lattice spacings. The
first moment of transversely polarized quarks in an unpolarized nucleon shows
an interesting distortion, which can be traced back to the sharp falloff of the
transversity generalized form factor $\bar{B}_{Tn0}(t)$. The isovector tensor
anomalous magnetic moment is determined to be $\kappa_T=1.051(94)$, which
confirms a negative and large Boer-Mulders function, $h_1^{\perp}$, in the
nucleon.


  Large Language Models (LLMs) have achieved unparalleled success across
diverse language modeling tasks in recent years. However, this progress has
also intensified ethical concerns, impacting the deployment of LLMs in everyday
contexts. This paper provides a comprehensive survey of ethical challenges
associated with LLMs, from longstanding issues such as copyright infringement,
systematic bias, and data privacy, to emerging problems like truthfulness and
social norms. We critically analyze existing research aimed at understanding,
examining, and mitigating these ethical risks. Our survey underscores
integrating ethical standards and societal values into the development of LLMs,
thereby guiding the development of responsible and ethically aligned language
models.


  Automated Deduction in Geometry (ADG) is a forum to exchange ideas and views,
to present research results and progress, and to demonstrate software tools at
the intersection between geometry and automated deduction. Relevant topics
include (but are not limited to): polynomial algebra, invariant and
coordinate-free methods; probabilistic, synthetic, and logic approaches,
techniques for automated geometric reasoning from discrete mathematics,
combinatorics, and numerics; interactive theorem proving in geometry; symbolic
and numeric methods for geometric computation, geometric constraint solving,
automated generation/reasoning and manipulation with diagrams; design and
implementation of geometry software, automated theorem provers, special-purpose
tools, experimental studies; applications of ADG in mechanics, geometric
modelling, CAGD/CAD, computer vision, robotics and education.
  Traditionally, the ADG conference is held every two years. The previous
editions of ADG were held in Nanning in 2018, Strasbourg in 2016, Coimbra in
2014, Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006,
Gainesville in 2004, Hagenberg in 2002, Zurich in 2000, Beijing in 1998, and
Toulouse in 1996. The 13th edition of ADG was supposed to be held in 2020 in
Hagenberg, Austria, but due to the COVID-19 pandemic, it was postponed for
2021, and held online (still hosted by RISC Institute, Hagenberg, Austria),
September 15-17, 2021 (https://www.risc.jku.at/conferences/adg2021).


  Low rank matrix recovery is the focus of many applications, but it is a
NP-hard problem. A popular way to deal with this problem is to solve its convex
relaxation, the nuclear norm regularized minimization problem (NRM), which
includes LASSO as a special case. There are some regularization parameter
selection results for LASSO in vector case, such as screening rules, which
improve the efficiency of the algorithms. However, there are no corresponding
parameter selection results for NRM in matrix case. In this paper, we build up
a novel rule to choose the regularization parameter for NRM under the help of
duality theory. This rule claims that the regularization parameter can be
easily chosen by feasible points of NRM and its dual problem, when the rank of
the desired solution is no more than a given constant. In particular, we apply
this idea to NRM with least square and Huber functions, and establish the
easily calculated formula of regularization parameters. Finally, we report
numerical results on some signal shapes, which state that our proposed rule
shrinks the interval of the regularization parameter efficiently.


  Cross-Domain Detection (XDD) aims to train an object detector using labeled
image from a source domain but have good performance in the target domain with
only unlabeled images. Existing approaches achieve this either by aligning the
feature maps or the region proposals from the two domains, or by transferring
the style of source images to that of target image. Contrasted with prior work,
this paper provides a complementary solution to align domains by learning the
same auxiliary tasks in both domains simultaneously. These auxiliary tasks push
image from both domains towards shared spaces, which bridges the domain gap.
Specifically, this paper proposes Rotation Prediction and Consistency Learning
(PRCL), a framework complementing existing XDD methods for domain alignment by
leveraging the two auxiliary tasks. The first one encourages the model to
extract region proposals from foreground regions by rotating an image and
predicting the rotation angle from the extracted region proposals. The second
task encourages the model to be robust to changes in the image space by
optimizing the model to make consistent class predictions for region proposals
regardless of image perturbations. Experiments show the detection performance
can be consistently and significantly enhanced by applying the two proposed
tasks to existing XDD methods.


  The precision data on Z boson decays from LEP-I and SLC colliders are
compared with the predictions based on the Minimal Standard Theory. The Born
approximation of the theory is based on three most accurately known
observables: G_mu -- the four fermion coupling constant of muon decay, m_Z --
the mass of the Z boson, and alpha(m_Z) -- the value of the running fine
structure constant at the scale of m_Z. The electroweak loop corrections are
expressed, in addition, in terms of the masses of higgs, m_H, of the top and
bottom quarks, m_t and m_b, and of the strong interaction constant
alpha_s(m_Z). The main emphasis of the review is focused on the
one-electroweak-loop approximation. Two electroweak loops have been calculated
in the literature only partly. Possible manifestations of new physics are
briefly discussed.


  Quantum Relativity is supposed to be a new theory, which locally is a
deformation of Special Relativity, and globally it is a background independent
theory including the main ideas of General Relativity, with hindsight from
Quantum Theory. The qubit viewed as a Hopf monopole bundle is considered as a
unifying gauge "group". Breaking its chiral symmetry is conjectured to yield
gravity as a deformation of electromagnetism. It is already a quantum theory in
the context of Quantum Information Dynamics as a discrete, background
independent theory, unifying classical and quantum physics.
  Based on the above, Quantum Gravity is sketched as an open project.


  An RF ion-funnel technique has been developed to extract ions from a
high-pressure (10 bar) noble-gas environment into vacuum ($10^{-6}$ mbar).
Detailed simulations have been performed and a prototype has been developed for
the purpose of extracting $^{136}$Ba ions from Xe gas with high efficiency.
With this prototype, ions have been extracted for the first time from
high-pressure xenon gas and argon gas. Systematic studies have been carried out
and compared to the simulations. This demonstration of extraction of ions with
mass comparable to that of the gas generating the high-pressure into vacuum has
applications to Ba tagging from a Xe-gas time-projection chamber (TPC) for
double beta decay as well as to the general problem of recovering trace amounts
of an ionized element in a heavy (m$>40$ u) carrier gas.


  We investigate the charge fluctuations of a single-electron box (metallic
grain) coupled to a lead via a smaller quantum dot in the Kondo regime. The
most interesting aspect of this problem resides in the interplay between spin
Kondo physics stemming from the screening of the spin of the small dot and
orbital Kondo physics emerging when charging states of the grain with (charge)
Q=0 and Q=e are almost degenerate. Combining Wilson's numerical
renormalization-group method with perturbative scaling approaches we push
forward our previous work [K. Le Hur and P. Simon, Phys. Rev. B 67, 201308R
(2003)]. We emphasize that for symmetric and slightly asymmetric barriers, the
strong entanglement of charge and spin flip events in this setup inevitably
results in a non trivial stable SU(4) Kondo fixed point near the degeneracy
points of the grain. By analogy with a small dot sandwiched between two leads,
the ground state is Fermi-liquid like which considerably smears out the Coulomb
staircase behavior and hampers the Matveev logarithmic singularity to arise.
Most notably, the associated Kondo temperature $T_K^{SU(4)}$ might be raised
compared to that in the conductance experiments through a small quantum dot
$(\sim 1K)$ which makes the observation of our predictions a priori accessible.
We discuss the robustness of the SU(4) correlated state against the inclusion
of an external magnetic field, a deviation from the degeneracy points,
particle-hole symmetry in the small dot, asymmetric tunnel junctions and
comment on the different crossovers.


  Causal structure discovery from observations can be improved by integrating
background knowledge provided by an expert to reduce the hypothesis space.
Recently, Large Language Models (LLMs) have begun to be considered as sources
of prior information given the low cost of querying them relative to a human
expert. In this work, firstly, we propose a set of metrics for assessing LLM
judgments for causal graph discovery independently of the downstream algorithm.
Secondly, we systematically study a set of prompting designs that allows the
model to specify priors about the structure of the causal graph. Finally, we
present a general methodology for the integration of LLM priors in graph
discovery algorithms, finding that they help improve performance on
common-sense benchmarks and especially when used for assessing edge
directionality. Our work highlights the potential as well as the shortcomings
of the use of LLMs in this problem space.


  The differential production cross section of electrons from semileptonic
heavy-flavour hadron decays has been measured at mid-rapidity ($|y| < 0.5$) in
proton-proton collisions at $\sqrt{s} = 7$ TeV with ALICE at the LHC. Electrons
were measured in the transverse momentum range 0.5 $<p_{\rm T}<$ 8 GeV/$c$.
Predictions from a fixed order perturbative QCD calculation with
next-to-leading-log resummation agree with the data within the theoretical and
experimental uncertainties.


  An algebraic formula for the effective conductivity of a d-dimensional,
two-component chessboard (checkerboard) is proposed. The derivation relies on
the self-duality of the square bond lattice, the principle of universality, and
the analytical capabilities of the Walker Diffusion Method.


  We study the finitary satisfiability problem for first order logic with two
variables and two binary relations, corresponding to the induced successor
relations of two finite linear orders. We show that the problem is decidable in
NEXPTIME.


  Reliance on vast annotations to achieve leading performance severely
restricts the practicality of large-scale point cloud semantic segmentation.
For the purpose of reducing data annotation costs, effective labeling schemes
are developed and contribute to attaining competitive results under weak
supervision strategy. Revisiting current weak label forms, we introduce One
Class One Click (OCOC), a low cost yet informative quasi scene-level label,
which encapsulates point-level and scene-level annotations. An active weakly
supervised framework is proposed to leverage scarce labels by involving weak
supervision from global and local perspectives. Contextual constraints are
imposed by an auxiliary scene classification task, respectively based on global
feature embedding and point-wise prediction aggregation, which restricts the
model prediction merely to OCOC labels. Furthermore, we design a context-aware
pseudo labeling strategy, which effectively supplement point-level supervisory
signals. Finally, an active learning scheme with a uncertainty measure -
temporal output discrepancy is integrated to examine informative samples and
provides guidance on sub-clouds query, which is conducive to quickly attaining
desirable OCOC annotations and reduces the labeling cost to an extremely low
extent. Extensive experimental analysis using three LiDAR benchmarks collected
from airborne, mobile and ground platforms demonstrates that our proposed
method achieves very promising results though subject to scarce labels. It
considerably outperforms genuine scene-level weakly supervised methods by up to
25\% in terms of average F1 score and achieves competitive results against full
supervision schemes. On terrestrial LiDAR dataset - Semantics3D, using
approximately 2\textpertenthousand{} of labels, our method achieves an average
F1 score of 85.2\%, which increases by 11.58\% compared to the baseline model.


  Let $C$ be a convex body and let $S$ be a nondegenerate simplex in ${\mathbb
R}^n$. Denote by $\xi(C;S)$ the minimal $\tau>0$ such that $C$ is a subset of
the simplex $\tau S$. By $\alpha(C;S)$ we mean the minimal $\tau>0$ such that
$C$ is contained in a translate of $\tau S$. Earlier the author has proved the
equalities $\xi(C;S)=(n+1)\max\limits_{1\leq j\leq n+1} \max\limits_{x\in
C}(-\lambda_j(x))+1$ \ (if $C\not\subset S$), \ $\alpha(C;S)=
\sum\limits_{j=1}^{n+1} \max\limits_{x\in C} (-\lambda_j(x))+1.$ Here
$\lambda_j$ are linear functions called the basic Lagrange polynomials
corresponding to $S$. In his previous papers, the author has investigated these
formulae if $C=[0,1]^n$. The present paper is related to the case when $C$
coincides with the unit Euclidean ball $B_n=\{x: \|x\|\leq 1\},$ where
$\|x\|=\left(\sum\limits_{i=1}^n x_i^2 \right)^{1/2}.$ We establish various
relations for $\xi(B_n;S)$ and $\alpha(B_n;S)$, as well as we give their
geometric interpretation.


  In this talk we present the results published recently in Ref. [1], where we
showed how to introduce a quark chemical potential in the overlap Dirac
operator. The resulting operator satisfies a Ginsparg-Wilson relation and has
exact zero modes. It is no longer gamma_5-Hermitian, but its nonreal
eigenvalues still occur in pairs. We compute the spectral density of the
operator on the lattice and show that, for small eigenvalues, the data agree
with analytical predictions of non-Hermitian chiral random matrix theory for
both trivial and nontrivial topology. We also explain an observed change in the
number of zero modes as a function of chemical potential.


  Accurate classification of apoptosis plays an important role in cell biology
research. There are many state-of-the-art approaches which use deep CNNs to
perform the apoptosis classification but these approaches do not account for
the cell interaction. Our paper proposes the Attention Graph spatio-temporal
graph convolutional network to classify the cell death based on the target
cells in the video. This method considers the interaction of multiple target
cells at each time stamp. We model the whole video sequence as a set of graphs
and classify the target cell in the video as dead or alive. Our method
encounters both spatial and temporal relationships.


  We explore the phenomenology of Kaluza-Klein (KK) dark matter in very general
models with universal extra dimensions (UEDs), emphasizing the complementarity
between high-energy colliders and dark matter direct detection experiments. In
models with relatively small mass splittings between the dark matter candidate
and the rest of the (colored) spectrum, the collider sensitivity is diminished,
but direct detection rates are enhanced. UEDs provide a natural framework for
such mass degeneracies. We consider both 5-dimensional and 6-dimensional
non-minimal UED models, and discuss the detection prospects for various KK dark
matter candidates: the KK photon $\gamma_1$, the KK $Z$-boson $Z_1$, the KK
Higgs boson $H_1$ and the spinless KK photon $\gamma_H$. We combine collider
limits such as electroweak precision data and expected LHC reach, with
cosmological constraints from WMAP, and the sensitivity of current or planned
direct detection experiments. Allowing for general mass splittings, we show
that neither colliders, nor direct detection experiments by themselves can
explore all of the relevant KK dark matter parameter space. Nevertheless, they
probe different parameter space regions, and the combination of the two types
of constraints can be quite powerful. For example, in the case of $\gamma_1$ in
5D UEDs the relevant parameter space will be almost completely covered by the
combined LHC and direct detection sensitivities expected in the near future.


  This study examines opinion instability among individuals from different
ethnic groups (White, Latino, and Asian Americans) by analyzing measurement
errors in survey measures. Using a multi-wave panel dataset of college students
and employing generalizability theory, the study uncovers significant patterns.
The results reveal that White students exhibit higher attitude reliability,
characterized by larger variances in true opinions and smaller measurement
errors. In contrast, Latino and Asian American students display lower attitude
stability, with lower variances in true opinions and higher variances in both
item-specific and measurement errors. Disparities in political socialization
and issue concerns contribute to the observed attitude instability among Latino
and Asian American students. Moreover, Asian American and Latino respondents
require a greater number of survey items to mitigate measurement error compared
to their White counterparts. However, the impact of multiple waves of surveys
on improving reliability is limited for Latino and Asian American students
compared to White students. These findings deepen our understanding of attitude
instability across ethnic groups and underscore the importance of further
research in this area.


  Background and Purpose: Convolutional neural network is widely used for image
recognition in the medical area at nowadays. However, overall accuracy in
predicting lung tumor is low and the processing time is high as the error
occurred while reconstructing the CT image. The aim of this work is to increase
the overall prediction accuracy along with reducing processing time by using
multispace image in pooling layer of convolution neural network. Methodology:
The proposed method has the autoencoder system to improve the overall accuracy,
and to predict lung cancer by using multispace image in pooling layer of
convolution neural network and Adam Algorithm for optimization. First, the CT
images were pre-processed by feeding image to the convolution filter and down
sampled by using max pooling. Then, features are extracted using the
autoencoder model based on convolutional neural network and multispace image
reconstruction technique is used to reduce error while reconstructing the image
which then results improved accuracy to predict lung nodule. Finally, the
reconstructed images are taken as input for SoftMax classifier to classify the
CT images. Results: The state-of-art and proposed solutions were processed in
Python Tensor Flow and It provides significant increase in accuracy in
classification of lung cancer to 99.5 from 98.9 and decrease in processing time
from 10 frames/second to 12 seconds/second. Conclusion: The proposed solution
provides high classification accuracy along with less processing time compared
to the state of art. For future research, large dataset can be implemented, and
low pixel image can be processed to evaluate the classification


  TCP proxies are basic building blocks for many advanced middleboxes. In this
paper we present Miniproxy, a TCP proxy built on top of a specialized
minimalistic cloud operating system. Miniproxy's connection handling
performance is comparable to that of full-fledged GNU/Linux TCP proxy
implementations, but its minimalistic footprint enables new use cases.
Specifically, Miniproxy requires as little as 6 MB to run and boots in tens of
milliseconds, enabling massive consolidation, on-the-fly instantiation and edge
cloud computing scenarios. We demonstrate the benefits of Miniproxy by
implementing and evaluating a TCP acceleration use case.


  An operator satisfies the Global Comparison Property if anytime a function
touches another from above at some point, then the operator preserves the
ordering at the point of contact. This is characteristic of degenerate elliptic
operators, including nonlocal and nonlinear ones. In previous work, the authors
considered such operators in Riemannian manifolds and proved they can be
represented by a min-max formula in terms of L\'evy operators. In this note we
revisit this theory in the context of Euclidean space. With the intricacies of
the general Riemannian setting gone, the ideas behind the original proof of the
min-max representation become clearer. Moreover, we prove new results regarding
operators that commute with translations or which otherwise enjoy some spatial
regularity.


  This letter introduces HOPPY, an open-source, low-cost, robust, and modular
kit for robotics education. The robot dynamically hops around a rotating gantry
with a fixed base. The kit lowers the entry barrier for studying dynamic robots
and legged locomotion in real systems. The kit bridges the theoretical content
of fundamental robotic courses and real dynamic robots by facilitating and
guiding the software and hardware integration. This letter describes the topics
which can be studied using the kit, lists its components, discusses best
practices for implementation, presents results from experiments with the
simulator and the real system, and suggests further improvements. A simple
controller is described to achieve velocities up to 2m/s, navigate small
objects, and mitigate external disturbances (kicks). HOPPY was utilized as the
topic of a semester-long project for the Robot Dynamics and Control course at
the University of Illinois at Urbana-Champaign. Students provided an
overwhelmingly positive feedback from the hands-on activities during the course
and the instructors will continue to improve the kit for upcoming semesters.


  If the variance of a short range Gaussian random potential grows like the
volume its quenched thermodynamic limit is reached monotonically.


  Using one-dimensional spin-1/2 systems as prototypes of quantum many-body
systems, we study the emergence of quantum chaos. The main purpose of this work
is to answer the following question: how does the spin-orbit interaction, as a
pure quantum interaction, may lead to the onset of quantum chaos? We consider
three integrable spin-1/2 systems: the Ising, the XX, and the XXZ limits, and
analyze whether quantum chaos develops or not after the addition of the
Dzyaloshinskii-Moriya interaction. We find that, depending on the strength of
the anisotropy parameter, the answer is positive for the XXZ and Ising models,
while no such evidence is observed for the XX model. We also discuss the
relationship between quantum chaos and thermalization.


  We consider the problem of non-uniform vote aggregation, and in particular,
the algorithmic aspects associated with the aggregation process. For a novel
class of weighted distance measures on votes, we present two different
aggregation methods. The first algorithm is based on approximating the weighted
distance measure by Spearman's footrule distance, with provable constant
approximation guarantees. The second algorithm is based on a non-uniform Markov
chain method inspired by PageRank, for which currently only heuristic
guarantees are known. We illustrate the performance of the proposed algorithms
on a number of distance measures for which the optimal solution may be easily
computed.


  We present a Compression Tool, "GenBit Compress", for genetic sequences based
on our new proposed "GenBit Compress Algorithm". Our Tool achieves the best
compression ratios for Entire Genome (DNA sequences) . Significantly better
compression results show that GenBit compress algorithm is the best among the
remaining Genome compression algorithms for non-repetitive DNA sequences in
Genomes. The standard Compression algorithms such as gzip or compress cannot
compress DNA sequences but only expand them in size. In this paper we consider
the problem of DNA compression. It is well known that one of the main features
of DNA Sequences is that they contain substrings which are duplicated except
for a few random Mutations. For this reason most DNA compressors work by
searching and encoding approximate repeats. We depart from this strategy by
searching and encoding only exact repeats. our proposed algorithm achieves the
best compression ratio for DNA sequences for larger genome. As long as 8 lakh
characters can be given as input While achieving the best compression ratios
for DNA sequences, our new GenBit Compress program significantly improves the
running time of all previous DNA compressors. Assigning binary bits for
fragments of DNA sequence is also a unique concept introduced in this program
for the first time in DNA compression.


  We present multi-modal adversarial autoencoders for recommendation and
evaluate them on two different tasks: citation recommendation and subject label
recommendation. We analyze the effects of adversarial regularization, sparsity,
and different input modalities. By conducting 408 experiments, we show that
adversarial regularization consistently improves the performance of
autoencoders for recommendation. We demonstrate, however, that the two tasks
differ in the semantics of item co-occurrence in the sense that item
co-occurrence resembles relatedness in case of citations, yet implies diversity
in case of subject labels. Our results reveal that supplying the partial item
set as input is only helpful, when item co-occurrence resembles relatedness.
When facing a new recommendation task it is therefore crucial to consider the
semantics of item co-occurrence for the choice of an appropriate model.


  We analyze international co-authorship relations in the Social Science
Citation Index 2011 using all citable items in the DVD-version of this index.
Network statistics indicate four groups of nations: (i) an Asian-Pacific one to
which all Anglo-Saxon nations (including the UK and Ireland) are attributed;
(ii) a continental European one including also the Latin-American countries;
(iii) the Scandinavian nations; and (iv) a community of African nations. Within
the EU-28 (including Croatia), eleven of the EU-15 states have dominant
positions. Collapsing the EU-28 into a single node leads to a bi-polar
structure between the US and EU-28; China is part of the US-pole. We develop an
information-theoretical test to distinguish whether international
collaborations or domestic collaborations prevail; the results are mixed, but
the international dimension is more important than the national one in the
aggregated sets (this was found in both SSCI and SCI). In France, however, the
national distribution is more important than the international one, while the
reverse is true for most European nations in the core group (UK, Germany, the
Netherlands, etc.). Decomposition of the USA in terms of states shows a
similarly mixed result; more US states are domestically oriented in SSCI,
whereas more internationally in SCI. The international networks have grown
during the last decades in addition to the national ones, but not by replacing
them.