  Bi-layer metallic tube (BMT) plays an extremely crucial role in engineering
applications, with rotary draw bending (RDB) the high-precision bending
processing can be achieved, however, the product will further springback. Due
to the complex structure of BMT and the high cost of dataset acquisi-tion, the
existing methods based on mechanism research and machine learn-ing cannot meet
the engineering requirements of springback prediction. Based on the preliminary
mechanism analysis, a physical logic enhanced network (PE-NET) is proposed. The
architecture includes ES-NET which equivalent the BMT to the single-layer tube,
and SP-NET for the final predic-tion of springback with sufficient single-layer
tube samples. Specifically, in the first stage, with the theory-driven
pre-exploration and the data-driven pretraining, the ES-NET and SP-NET are
constructed, respectively. In the second stage, under the physical logic, the
PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small
sample BMT dataset and composite loss function. The validity and stability of
the proposed method are verified by the FE simulation dataset, the small-sample
dataset BMT springback angle prediction is achieved, and the method potential
in inter-pretability and engineering applications are demonstrated.


  Considering a nonlinear charged black hole as a thermodynamics system, we
study the geometric description of its phase transitions. Using the formalism
of geometrothermodynamics we show that the geometry of the space of
thermodynamic equilibrium states of this kind of black holes is related with
information about thermodynamic interaction, critical points and phase
transitions structure. Our results indicate that the equilibrium manifold of
this black hole is curved and that curvature singularities appear exactly at
those places where first and second order phase transitions occur.


  We implement the Bogoliubov-de Gennes (BdG) equation in real-space using the
screened Korringa-Kohn-Rostoker (KKR) method. This allows us to solve,
self-consistently, the superconducting state for 3d crystals including
substitutional impurities with a full normal-state DFT band structure. We apply
the theoretical framework to bulk Nb with impurities. Without impurities, Nb
has an anisotropic gap structure with two distinct peaks around the Fermi
level. In the presence of non-magnetic impurities those peaks are broadened due
to the scattering between the two bulk superconducting gaps, however the peaks
remain separated. As a second example of self-consistent real-space solutions
of the BdG equations we examine superconducting clusters embedded within a
non-superconducting bulk metallic host. This allows us to estimate the
coherence length of the superconductor and we show that, within our framework,
the coherence length of the superconductor is related to the inverse of the gap
size, just as in bulk BCS theory.


  The object of this paper is to introduce and study the concept of
quasi-geometric infinite divisibility for distributions on $\bf R_+$. These
distributions arise as mixing distributions of (discrete) geometric infinitely
divisible Poisson mixtures. Several characterizations and closure properties
are presented. A connection between quasi-geometric infinite divisibility and
log-convex (log-concave) distributions is established. A generalized notion of
quasi-infinite divisibility is also discussed.


  We formulate a question regarding uniform versions of "large Galois image
properties" for modular abelian varieties of higher dimension, generalizing the
well-known case of elliptic curves. We then answer our question affirmatively
in the exceptional image case, and provide lower estimates for uniform bounds
in the remaining cases.


  Two eclipsing binaries in the USco association have recently yielded precise
values of masses and radii for 4 low-mass members of the association. Standard
evolution models would require these dM4.5 - dM5 stars to have ages which are
younger than the ages of more massive stars in the association by factors which
appear (in extreme cases) to be as large as ~3. Are the stars in the
association therefore non-coeval? We suggest that the answer is No: by
incorporating the effects of magnetic inhibition of convective onset, we show
that the stars in USco can be restored to coevality provided that the 4
low-mass member stars have vertical surface fields in the range 200 - 700 G.
Fields of such magnitude have already been measured on the surface of certain
solar-type stars in other young clusters.


  Geomagnetic secular acceleration is a unique window on the dynamics taking
place in Earth's core. In this study, the behaviours of the secular
acceleration and underlying core dynamics are examined in new numerical
simulations of the geodynamo that reside on a theoretical path in parameter
space connecting the region where most classical models are found to the
natural conditions. The typical time scale for geomagnetic acceleration is
found to be invariant along this path, at a value close to 10 years that
matches Earth's core estimates. Despite this invariance, the spatio-temporal
properties of secular acceleration show significant variability along the path,
with an asymptotic regime of rapid rotation reached after 30% of this path
(corresponding to a model Ekman number $E=3~10^{-7}$). In this regime, the
energy of secular acceleration is entirely found at periods longer than that of
planetary rotation, and the underlying flow acceleration patterns acquire a
two-dimensional columnar structure representative of the rapid rotation limit.
The spatial pattern of the secular acceleration at the core-mantle boundary
shows significant localisation of energy within an equatorial belt. Rapid
hydromagnetic wave dynamics is absent at the start of the path but can be
clearly exhibited in the asymptotic regime. This study reports on ubiquitous
axisymmetric geostrophic torsional waves of weak amplitude relatively to
convective transport, and also stronger, laterally limited, quasi-geostrophic
Alfv\'en waves propagating in the cylindrical radial direction from the tip of
convective plumes towards the core-mantle boundary. Quasi-geostrophic Alfv\'en
waves are shown to be an important carrier of flow acceleration to the core
surface that links with the generation of strong, short-lived and intermittent
equatorial pulses in the secular acceleration energy.


  In this work, it is demonstrated that the Earth's preseismic electric field,
which is registered by a pair of electrodes in contact to the ground surface at
certain distance from the epicentral area, corresponds to the gradient of the
total field generated in the focal area as a function of time and distance from
it. The original form of the generated preseismic field follows closely the
theoretical piezoelectric potential form. The later is obtained after
integration in time of the original registered potential grad data values.
Consequently, the time of occurrence of the imminent earthquake (collapsing of
the rock formation) is estimated by the classical laws of rock fracturing based
on theoretical Rock Mechanics. The methodology has been tested on the grad
potential data, registered in Greece, before four large EQs which took place in
the regional area as follows: Izmit, Turkey EQ (M = 7.8R, 17th August, 1999),
Milos, Greece EQ (M = 5.6R, 21st May, 2002), Kythira Greece EQ (M = 6.9R, 8th
January, 2006) and Methoni, Greece EQ (M = 6.7R, 14th February, 2008). The
obtained results prove the validity of the postulated methodology.


  The growth of Aeromonas hydrophila and aerobic natural flora (APC) on
gilthead seabream surfaces was evaluated during the refrigerated storage (21
days). The related growth curves were compared with those obtained by a
conventional third order predictive model obtaining a low agreement between
observed and predicted data (Root Mean Squared Error = 1.77 for Aeromonas
hydrophila and 0.64 for APC). The Lotka-Volterra interspecific competition
model was used in order to calculate the degree of interaction between the two
bacterial populations (\beta_{Ah/APC} and \beta{APC/Ah}, respectively, the
interspecific competition coefficients of APC on Aeromonas hydrophila and
vice-versa). Afterwards, the Lotka-Volterra equations were applied as tertiary
predictive model, taking into account, simultaneously, the environmental
fluctuations and the bacterial interspecific competition. This approach allowed
to obtain a best fitting to the observed mean growth curves with a Root Mean
Squared Error of 0.09 for Aeromonas hydrophila and 0.28 for APC. Finally,
authors carry out some considerations about the necessary use of competitive
models in the context of the new trends in predictive microbiology.


  Spline functions have long been used in numerical solution of differential
equations. Recently it revives as isogeometric analysis, which offers
integration of finite element analysis and NURBS based CAD into a single
unified process. Usually many NURBS pieces are needed to build geometrically
continuous CAD models. In this paper, we introduce some splines defined on
branched covering of sphere, torus or general domains of $R^2$, which are
called branched splines in this paper. A single piece of such splines is enough
to build some complex CAD models. Multiresolution analysis on surfaces of high
genus built from such splines can be carried out naturally. CAD and FEA are
integrated directly on such models. A theoretical framework is presented in
this paper, together with some simple examples.


  Nematicity in the heavily hole-doped iron pnictide superconductors remains
controversial. Sizeable nematic fluctuations and even nematic orders far from a
magnetic instability were declared in RbFe$_2$As$_2$ and its sister compounds.
Here we report a systematic elastoresistance study of series of isovalent- and
electron-doped KFe$_2$As$_2$ crystals. We found divergent elastoresistance upon
cooling for all the crystals along their [110] direction. The amplitude of
elastoresistivity diverges if K is substituted with larger ions or if the
system is driven towards a Lifshitz transition. However, we conclude none of
them necessarily indicates an independent nematic critical point. Instead, the
increased nematicity can be associated with another electronic criticality. In
particular, we propose a mechanism how elastoresistivity is enhanced at a
Lifshitz transition.


  Does the remote measurement-disturbance of the quantum state of a system $B$
by measurement on system $A$ entangled with $B$, constitute a real disturbance
-- i.e., an objective alteration -- of $B$ in an operational sense? Employing
information theoretic criteria motivated by operational considerations alone,
we argue that the disturbance in question is real for a subset of steerable
correlations. This result highlights the distinction between quantum
no-signaling and relativistic signal-locality. It furthermore suggests a
natural reason why a convex operational theory should be non-signaling: namely,
to ensure the consistency between the properties of reduced systems and those
of single systems.


  We investigate parabolic Muckenhoupt weights and functions of bounded mean
oscillation (BMO) related to nonlinear parabolic partial differential
equations. The main result gives a full characterization of weak and strong
type weighted norm inequalities for parabolic forward in time maximal
operators. In addition, we give a Jones type factorization result for the
parabolic Muckenhoupt weights and a Coifman-Rochberg type characterization of
the parabolic BMO from Moser's seminal paper through parabolic Muckenhoupt
weights and maximal functions.


  In this work we present the Scaled QUantum IDentifier (SQUID), an open-source
framework for exploring hybrid Quantum-Classical algorithms for classification
problems. The classical infrastructure is based on PyTorch and we provide a
standardized design to implement a variety of quantum models with the
capability of back-propagation for efficient training. We present the structure
of our framework and provide examples of using SQUID in a standard binary
classification problem from the popular MNIST dataset. In particular, we
highlight the implications for scalability for gradient-based optimization of
quantum models on the choice of output for variational quantum models.


  We present a nonlinear post-Friedmann framework for structure formation,
generalizing to cosmology the weak-field (post-Minkowskian) approximation,
unifying the treatment of small and large scales. We consider a universe filled
with a pressureless fluid and a cosmological constant $\Lambda$, the theory of
gravity is Einstein's general relativity and the background is the standard
flat $\Lambda$CDM cosmological model.
  We expand the metric and the energy-momentum tensor in powers of $1/c$,
keeping the matter density and peculiar velocity as exact fundamental
variables. We assume the Poisson gauge, including scalar and tensor modes up to
$1/c^4$ order and vector modes up to $1/c^5$ terms. Through a redefinition of
the scalar potentials as a resummation of the metric contributions at different
orders, we obtain a complete set of nonlinear equations, providing a unified
framework to study structure formation from small to superhorizon scales, from
the nonlinear Newtonian to the linear relativistic regime. We explicitly show
the validity of our scheme in the two limits: at leading order we recover the
fully nonlinear equations of Newtonian cosmology; when linearized, our
equations become those for scalar and vector modes of first-order relativistic
perturbation theory in the Poisson gauge. Tensor modes are nondynamical at the
$1/c^4$ order we consider: they are purely nonlinear and describe a distortion
of the spatial slices determined at this order by a constraint, quadratic in
the scalar and vector variables. The main results of our analysis are as
follows: (a) at leading order a purely Newtonian nonlinear energy current
sources a frame-dragging gravitomagnetic vector potential, and (b) in the
leading-order Newtonian regime and in the linear relativistic regime the two
scalar metric potentials are the same, while the nonlinearity of general
relativity makes them different.


  The classic fair division problems assume the resources to be allocated are
either divisible or indivisible, or contain a mixture of both, but the agents
always have a predetermined and uncontroversial agreement on the
(in)divisibility of the resources. In this paper, we propose and study a new
model for fair division in which agents have their own subjective divisibility
over the goods to be allocated. That is, some agents may find a good to be
indivisible and get utilities only if they receive the whole good, while others
may consider the same good to be divisible and thus can extract utilities
according to the fraction of the good they receive. We investigate fairness
properties that can be achieved when agents have subjective divisibility.
First, we consider the maximin share (MMS) guarantee and show that the
worst-case MMS approximation guarantee is at most $2/3$ for $n \geq 2$ agents
and this ratio is tight in the two- and three-agent cases. This is in contrast
to the classic fair division settings involving two or three agents. We also
give an algorithm that produces a $1/2$-MMS allocation for an arbitrary number
of agents. Second, we adapt the notion of envy-freeness for mixed goods (EFM)
to our model and show that EFM is incompatible with non-wastefulness, a rather
weak economic efficiency notion. On the positive side, we prove that an EFM and
non-wasteful allocation always exists for two agents if at most one good is
discarded.


  The Folksodriven framework makes it possible for data scientists to define an
ontology environment where searching for buried patterns that have some kind of
predictive power to build predictive models more effectively. It accomplishes
this through an abstractions that isolate parameters of the predictive modeling
process searching for patterns and designing the feature set, too. To reflect
the evolving knowledge, this paper considers ontologies based on folksonomies
according to a new concept structure called "Folksodriven" to represent
folksonomies. So, the studies on the transformational regulation of the
Folksodriven tags are regarded to be important for adaptive folksonomies
classifications in an evolving environment used by Intelligent Systems to
represent the knowledge sharing. Folksodriven tags are used to categorize
salient data points so they can be fed to a machine-learning system and
"featurizing" the data.


  When a planner must decide whether it has enough evidence to make a decision
based on probability, it faces the sample size problem. Current planners using
probabilities need not deal with this problem because they do not generate
their probabilities from observations. This paper presents an event based
language in which the planner's probabilities are calculated from the binomial
random variable generated by the observed ratio of one type of event to
another. Such probabilities are subject to error, so the planner must
introspect about their validity. Inferences about the probability of these
events can be made using statistics. Inferences about the validity of the
approximations can be made using interval estimation. Interval estimation
allows the planner to avoid making choices that are only weakly supported by
the planner's evidence.


  This investigation delves into the intricate entanglement dynamics exhibited
by qubit-qutrit systems when subjected to the intricate dynamics of both the
XXX isotropic Heisenberg model and the anisotropic XYZ model. These models are
enriched by the presence of Dzyaloshinskii-Moriya (DM) interactions and
external magnetic fields, creating a complex interplay of quantum phenomena.
The exploration commences by initializing the system in a superposition of
spin-coherent states, setting the stage for a nuanced analysis of entanglement
evolution over time. By employing the negativity metric as the pivotal
criterion for assessing entanglement, we gain insights into the subtle
relationships between DM interactions, external magnetic fields, and the
entanglement dynamics within this composite system. Through this comprehensive
investigation, we aim to unravel the intricate dynamics and behaviors that
emerge from the interplay of these fundamental elements, shedding light on the
underlying principles governing quantum entanglement in this context.


  We propose a simple model that aims at describing, in a stylized manner, how
local breakdowns due unbalances or congestion propagate in real dynamical
networks. The model converges to a self-organized critical stationary state in
which the network shapes itself as a consequence of avalanches of rewiring
processes. Depending on the model's specification, we obtain either single
scale or scale-free networks. We characterize in detail the relation between
the statistical properties of the network and the nature of the critical state,
by computing the critical exponents. The model also displays a non-trivial,
sudden, collapse to a complete network.


  Mini-batch optimization has proven to be a powerful paradigm for large-scale
learning. However, the state of the art parallel mini-batch algorithms assume
synchronous operation or cyclic update orders. When worker nodes are
heterogeneous (due to different computational capabilities or different
communication delays), synchronous and cyclic operations are inefficient since
they will leave workers idle waiting for the slower nodes to complete their
computations. In this paper, we propose an asynchronous mini-batch algorithm
for regularized stochastic optimization problems with smooth loss functions
that eliminates idle waiting and allows workers to run at their maximal update
rates. We show that by suitably choosing the step-size values, the algorithm
achieves a rate of the order $O(1/\sqrt{T})$ for general convex regularization
functions, and the rate $O(1/T)$ for strongly convex regularization functions,
where $T$ is the number of iterations. In both cases, the impact of asynchrony
on the convergence rate of our algorithm is asymptotically negligible, and a
near-linear speedup in the number of workers can be expected. Theoretical
results are confirmed in real implementations on a distributed computing
infrastructure.


  We consider estimating the parameter associated with the qubit depolarizing
channel when the available initial states that might be employed are mixed. We
use quantum Fisher information as a measure of the accuracy of estimation to
compare protocols which use collections of qubits in product states to one in
which the qubits are in a correlated state. We show that, for certain parameter
values and initial states, the correlated state protocol can yield a greater
accuracy per channel invocation than the product state protocols. We show that,
for some parameters and initial states, using more than two qubits and channel
invocations is advantageous. These results stand in contrast to the known
optimal case that uses pure initial states and a single channel invocation on a
pair of entangled qubits.


  Linear-time pattern matching engines have seen promising results using Finite
Automata (FA) as their computation model. Among different FA variants,
deterministic (DFA) and non-deterministic (NFA) are the most commonly used
computation models for FA-based pattern matching engines. Moreover, NFA is the
prevalent model in pattern matching engines on spatial architectures. The
reasons are: i) DFA size, as in #states, can be exponential compared to
equivalent NFA, ii) DFA cannot exploit the massive parallelism available on
spatial architectures. This paper performs an empirical study on the #state of
minimized DFA and optimized NFA across a diverse set of real-world benchmarks
and shows that if distinct DFAs are generated for distinct patterns, #states of
minimized DFA are typically equal to their equivalent optimized NFA. However,
NFA is more robust in maintaining the low #states for some benchmarks. Thus,
the choice of NFA vs. DFA for spatial architecture is less important than the
need to generate distinct DFAs for each pattern and support these distinct
DFAs' parallel processing. Finally, this paper presents a throughput study for
von Neumann's architecture-based (CPU) vs. spatial architecture-based (FPGA)
automata processing engines. The study shows that, based on the workload,
neither CPU-based automata processing engine nor FPGA-based automata processing
engine is the clear winner. If #patterns matched per workload increases, the
CPU-based automata processing engine's throughput decreases. On the other hand,
the FPGA-based automata processing engine lacks the memory spilling option;
hence, it fails to accommodate an entire automata if it does not fit into
FPGA's logic fabric. In the best-case scenario, the CPU has a 4.5x speedup over
the FPGA, while for some benchmarks, the FPGA has a 32,530x speedup over the
CPU.


  We propose a flexible method for estimating luminosity functions (LFs) based
on kernel density estimation (KDE), the most popular nonparametric density
estimation approach developed in modern statistics, to overcome issues
surrounding binning of LFs. One challenge in applying KDE to LFs is how to
treat the boundary bias problem, since astronomical surveys usually obtain
truncated samples predominantly due to the flux-density limits of surveys. We
use two solutions, the transformation KDE method ($\hat{\phi}_{\mathrm{t}}$),
and the transformation-reflection KDE method ($\hat{\phi}_{\mathrm{tr}}$) to
reduce the boundary bias. We develop a new likelihood cross-validation
criterion for selecting optimal bandwidths, based on which, the posterior
probability distribution of bandwidth and transformation parameters for
$\hat{\phi}_{\mathrm{t}}$ and $\hat{\phi}_{\mathrm{tr}}$ are derived within a
Markov chain Monte Carlo (MCMC) sampling procedure. The simulation result shows
that $\hat{\phi}_{\mathrm{t}}$ and $\hat{\phi}_{\mathrm{tr}}$ perform better
than the traditional binned method, especially in the sparse data regime around
the flux-limit of a survey or at the bright-end of the LF. To further improve
the performance of our KDE methods, we develop the transformation-reflection
adaptive KDE approach ($\hat{\phi}_{\mathrm{tra}}$). Monte Carlo simulations
suggest that it has a good stability and reliability in performance, and is
around an order of magnitude more accurate than using the binned method. By
applying our adaptive KDE method to a quasar sample, we find that it achieves
estimates comparable to the rigorous determination by a previous work, while
making far fewer assumptions about the LF. The KDE method we develop has the
advantages of both parametric and non-parametric methods.


  We found that the Wigner-Yanase skew information, which has been recently
proposed as a measure of coherence in [Phys. Rev. Lett. \textbf{113},
170401(2014)], can increase under a class of operations which may be
interpreted as incoherent following the framework of Baumgratz et al., while
being phase sensitive.


  A fundamental understanding of the network architecture of the brain is
necessary for the further development of theories explicating circuit function.
Perhaps as a derivative of its initial application to abstract informational
systems, network science provides many methods and summary statistics that
address the network's topological characteristics with little or no thought to
its physical instantiation. Recent progress has capitalized on quantitative
tools from network science to parsimoniously describe and predict neural
activity and connectivity across multiple spatial and temporal scales. Yet, for
embedded systems, physical laws can directly constrain processes of network
growth, development, and function, and an appreciation of those physical laws
is therefore critical to an understanding of the system. Recent evidence
demonstrates that the constraints imposed by the physical shape of the brain,
and by the mechanical forces at play in its development, have marked effects on
the observed network topology and function. Here, we review the rules imposed
by space on the development of neural networks and show that these rules give
rise to a specific set of complex topologies. We present evidence that these
fundamental wiring rules affect the repertoire of neural dynamics that can
emerge from the system, and thereby inform our understanding of network
dysfunction in disease. We also discuss several computational tools,
mathematical models, and algorithms that have proven useful in delineating the
effects of spatial embedding on a given networked system and are important
considerations for addressing future problems in network neuroscience. Finally,
we outline several open questions regarding the network architectures that
support circuit function, the answers to which will require a thorough and
honest appraisal of the role of physical space in brain network anatomy and
physiology.


  We analyze annual revenues and earnings data for the 500 largest-revenue U.S.
companies during the period 1954-2007. We find that mean year profits are
proportional to mean year revenues, exception made for few anomalous years,
from which we postulate a linear relation between company expected mean profit
and revenue. Mean annual revenues are used to scale both company profits and
revenues. Annual profit fluctuations are obtained as difference between actual
annual profit and its expected mean value, scaled by a power of the revenue to
get a stationary behavior as a function of revenue. We find that profit
fluctuations are broadly distributed having approximate power-law tails with a
Levy-type exponent $\alpha \simeq 1.7$, from which we derive the associated
break-even probability distribution. The predictions are compared with
empirical data.


  The elastic scattering $^{12}$C(p,po) Excitation Function (EF) was measured
in the energy range Ep =16 - 19.5 MeV with resolution about 10 keV by means of
innovative approach - MSS - the Method of Spectra Superposition at the 14-angle
Magnetic Spectrograph (Apelsin) at beam with an energy spread of about 200 keV
from U-150 cyclotron of INP Ulugbek (Tashkent) of Science Academy of Uzbekistan
[1-8]. The obtained EF has a reach structure of anomalies in precise agreement
with thresholds and levels data [2,9-12]. Measurements were done on the 12C
self-supporting target with thickness 13 mg/cm2 and an area of 1 cm2 in the
center of Apelsin. The 20-step Energy Moderator controlled the proton energy,
providing a 3.5 MeV wide Ep interval with no readjustment of cyclotron and all
the ionic-optics on the 41-meter-long beam-pipe. Energy resolution of Apelsin
for protons is better than 5 keV along all focal plane, where particle-product
were detected by specially designed coordinate-sensitive gas mixture MWPC with
two particle counters in coincidence. The acquisition system was based on IBM
PC online with a fast CAMAC branch (custom designed at St-Petersburg PNPI) with
fast TDC modules and other fast electronics providing immediate selection and
accumulations of events from MWPCs. The NMR-monitor system stabilized
spectrographs magnetic field to 3ppm [1,9]. The EF of 12C (p,po) scattering has
many resonances that precisely correspond to g.s. and levels of well-known
product-nuclei. But 80 percent of anomalies did not have any explanation. A new
concept is proposed to explain all unrecognized anomalies. Two expressions
Writing I and Writing II proposed to explain the unknown peaks which
corresponds to population of A13 phase volume states. The results of the
proposed explanation look very promising (Fig. 13). Concept of the Combinative
Isobaric Resonances (CIRs) proposed


  Stock return predictability is an important research theme as it reflects our
economic and social organization, and significant efforts are made to explain
the dynamism therein. Statistics of strong explanative power, called "factor"
have been proposed to summarize the essence of predictive stock returns.
Although machine learning methods are increasingly popular in stock return
prediction, an inference of the stock returns is highly elusive, and still most
investors, if partly, rely on their intuition to build a better decision
making. The challenge here is to make an investment strategy that is consistent
over a reasonably long period, with the minimum human decision on the entire
process. To this end, we propose a new stock return prediction framework that
we call Ranked Information Coefficient Neural Network (RIC-NN). RIC-NN is a
deep learning approach and includes the following three novel ideas: (1)
nonlinear multi-factor approach, (2) stopping criteria with ranked information
coefficient (rank IC), and (3) deep transfer learning among multiple regions.
Experimental comparison with the stocks in the Morgan Stanley Capital
International (MSCI) indices shows that RIC-NN outperforms not only
off-the-shelf machine learning methods but also the average return of major
equity investment funds in the last fourteen years.


  Analysis of mobile app reviews has shown its important role in requirement
engineering, software maintenance and evolution of mobile apps. Mobile app
developers check their users' reviews frequently to clarify the issues
experienced by users or capture the new issues that are introduced due to a
recent app update. App reviews have a dynamic nature and their discussed topics
change over time. The changes in the topics among collected reviews for
different versions of an app can reveal important issues about the app update.
A main technique in this analysis is using topic modeling algorithms. However,
app reviews are short texts and it is challenging to unveil their latent topics
over time. Conventional topic models suffer from the sparsity of word
co-occurrence patterns while inferring topics for short texts. Furthermore,
these algorithms cannot capture topics over numerous consecutive time-slices.
Online topic modeling algorithms speed up the inference of topic models for the
texts collected in the latest time-slice by saving a fraction of data from the
previous time-slice. But these algorithms do not analyze the statistical-data
of all the previous time-slices, which can confer contributions to the topic
distribution of the current time-slice.
  We propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in
short texts adaptively. AOBTM alleviates the sparsity problem in short-texts
and considers the statistical-data for an optimal number of previous
time-slices. We also propose parallel algorithms to automatically determine the
optimal number of topics and the best number of previous versions that should
be considered in topic inference phase. Automatic evaluation on collections of
app reviews and real-world short text datasets confirm that AOBTM can find more
coherent topics and outperforms the state-of-the-art baselines.


  Suppose a gambler starts with a fortune in (0,1) and wishes to attain a
fortune of 1 by making a sequence of bets. Assume thay whenever the gambler
stakes the amount s, the gambler's fortune increases by s with probability w
and decreases by s with probability 1 - w, where w < 1/2. Dubins and Savage
showed that the optimal strategy, which they called "bold play", is always to
stake min{f, 1-f}, where f is the gambler's current fortune. Here we consider
the problem in which the gambler may stake no more than l at one time. We show
that the bold strategy of always betting min{l, f, 1-f} is not optimal if l is
irrational, extending a result of Heath, Pruitt, and Sudderth.


  Thanks to its capability to provide a uniform service rate for the User
Equipments (UEs), Cell-free (CF) massive Multiple-Input, Multiple-Output
(mMIMO), has recently attracted considerable attention, both in academia and in
industry, and so is considered as one of the potential technologies for
beyond-5G and 6G. However, the reuse of the same pilot signals by multiple
users can create the so-called pilot contamination problem, which can hinder
the CF mMIMO from unlocking its full performance. In this paper, we address the
challenge by formulating the pilot assignment as a maximally diverse clustering
problem and propose an efficient yet straightforward repulsive clustering-based
pilot assignment scheme to mitigate the effects of pilot contamination on CF
mMIMO. The numerical results show the superiority of the proposed technique
compared to some other methods with respect to the achieved uplink per-user
rate.


  We investigate the stabilizing effects of the magnetic fields in the
linearized magnetic Rayleigh-Taylor (RT) problem of a nonhomogeneous
incompressible viscous magnetohydrodynamic fluid of zero resistivity in the
presence of a uniform gravitational field in a three-dimensional bounded
domain, in which the velocity of the fluid is non-slip on the boundary. By
adapting a modified variational method and careful deriving \emph{a priori}
estimates, we establish a criterion for the instability/stability of the
linearized problem around a magnetic RT equilibrium state. In the criterion, we
find a new phenomenon that a sufficiently strong horizontal magnetic field has
the same stabilizing effect as that of the vertical magnetic field on growth of
the magnetic RT instability. In addition, we further study the corresponding
compressible case, i.e., the Parker (or magnetic buoyancy) problem, for which
the strength of a horizontal magnetic field decreases with height, and also
show the stabilizing effect of a sufficiently large magnetic field.


  In the era of rapid digitalization and artificial intelligence advancements,
the development of DeepFake technology has posed significant security and
privacy concerns. This paper presents an effective measure to assess the visual
realism of DeepFake videos. We utilize an ensemble of two Convolutional Neural
Network (CNN) models: Eva and ConvNext. These models have been trained on the
DeepFake Game Competition (DFGC) 2022 dataset and aim to predict Mean Opinion
Scores (MOS) from DeepFake videos based on features extracted from sequences of
frames. Our method secured the third place in the recent DFGC on Visual Realism
Assessment held in conjunction with the 2023 International Joint Conference on
Biometrics (IJCB 2023). We provide an over\-view of the models, data
preprocessing, and training procedures. We also report the performance of our
models against the competition's baseline model and discuss the implications of
our findings.


  The first goal of Vibration-Transit (V-T) theory was to construct a tractable
approximate Hamiltonian from which the equilibrium thermodynamic properties of
monatomic liquids can be calculated. The Hamiltonian for vibrations in an
infinitely extended harmonic random valley, together with the universal
multiplicity of such valleys, gives an accurate first-principles account of the
measured thermodynamic properties of the elemental liquids at melt. In the
present paper, V-T theory is extended to non-equilibrium properties, through an
application to the dynamic structure factor S(q,w). It was previously shown
that the vibrational contribution alone accurately accounts for the Brillouin
peak dispersion curve for liquid sodium, as compared both with MD calculations
and inelastic x-ray scattering data. Here it is argued that the major effects
of transits will be to disrupt correlations within the normal mode vibrational
motion, and to provide an additional source of inelastic scattering. We
construct a parameterized model for these effects, and show that it is capable
of fitting MD results for S(q,w) in liquid sodium. A small discrepancy between
model and MD at large q is attributed to multimode vibrational scattering. In
comparison, mode coupling theory formulates S(q,w) in terms of processes
through which density fluctuations decay. While mode coupling theory is also
capable of modeling S(q,w) very well, V-T theory is the more universal since it
expresses all statistical averages, thermodynamic functions and time
correlation functions alike, in terms of the same motional constituents,
vibrations and transits.


  We have studied the normal modes of hydrogenated and oxidized silicon
nanocrystals, namely SiH4 (silan), H2SiO (silanon), Si10H16 and Si10H14O. The
small clusters (SiH4 and H2SiO) have been used for convergence tests and their
bondlengths and frequencies have been compared with experimental and
theoretical reference data. For the large clusters (Si10H16 and Si10H14O) we
have investigated the vibrational density of states where we have identified
the oxygen-related spectral features. The vibrational modes have been also
analyzed with respect to the displacement patterns. The calculations have been
carried out within the density-functional and density-functional perturbation
theory using the local-density approximation.


  By exploiting a bipartite network representation of the relationships between
mutual funds and portfolio holdings, we propose an indicator that we derive
from the analysis of the network, labelled the Average Commonality Coefficient
(ACC), which measures how frequently the assets in the fund portfolio are
present in the portfolios of the other funds of the market. This indicator
reflects the investment behavior of funds' managers as a function of the
popularity of the assets they held. We show that $ACC$ provides useful
information to discriminate between funds investing in niche markets and those
investing in more popular assets. More importantly, we find that $ACC$ is able
to provide indication on the performance of the funds. In particular, we find
that funds investing in less popular assets generally outperform those
investing in more popular financial instruments, even when correcting for
standard factors. Moreover, funds with a low $ACC$ have been less affected by
the 2007-08 global financial crisis, likely because less exposed to fire sales
spillovers.


  The ability to generalize out-of-domain (OOD) is an important goal for deep
neural network development, and researchers have proposed many high-performing
OOD generalization methods from various foundations. While many OOD algorithms
perform well in various scenarios, these systems are evaluated as
``black-boxes''. Instead, we propose a flexible framework that evaluates OOD
systems with finer granularity using a probing module that predicts the
originating domain from intermediate representations. We find that
representations always encode some information about the domain. While the
layerwise encoding patterns remain largely stable across different OOD
algorithms, they vary across the datasets. For example, the information about
rotation (on RotatedMNIST) is the most visible on the lower layers, while the
information about style (on VLCS and PACS) is the most visible on the middle
layers. In addition, the high probing results correlate to the domain
generalization performances, leading to further directions in developing OOD
generalization systems.


  Canonical quantization of abelian BF-type topological field theory coupled to
extended sources on generic d-dimensional manifolds and with curved line
bundles is studied. Sheaf cohomology is used to construct the appropriate
topological extension of the action and the topological flux quantization
conditions, in terms of the Cech cohomology of the underlying spatial manifold,
as required for topological invariance of the quantum field theory. The
wavefunctions are found in the Hamiltonian formalism and are shown to carry
multi-dimensional representations of various topological groups of the space.
Expressions for generalized linking numbers in any dimension are thereby
derived. In particular, new global aspects of motion group presentations are
obtained in any dimension. Applications to quantum exchange statistics of
objects in various dimensionalities are also discussed.


  The single-source unsplittable flow (SSUF) problem asks to send flow from a
common source to different terminals with unrelated demands, each terminal
being served through a single path. One of the most heavily studied SSUF
objectives is to minimize the violation of some given arc capacities. A seminal
result of Dinitz, Garg, and Goemans showed that, whenever a fractional flow
exists respecting the capacities, then there is an unsplittable one violating
the capacities by at most the maximum demand. Goemans conjectured a very
natural cost version of the same result, where the unsplittable flow is
required to be no more expensive than the fractional one. This intriguing
conjecture remains open. More so, there are arguably no non-trivial graph
classes for which it is known to hold.
  We show that a slight weakening of it (with at most twice as large
violations) holds for planar graphs. Our result is based on a connection to a
highly structured discrepancy problem, whose repeated resolution allows us to
successively reduce the number of paths used for each terminal, until we obtain
an unsplittable flow. Moreover, our techniques also extend to simultaneous
upper and lower bounds on the flow values. This also affirmatively answers a
conjecture of Morell and Skutella for planar SSUF.


  Single image super resolution (SISR) is to reconstruct a high resolution
image from a single low resolution image. The SISR task has been a very
attractive research topic over the last two decades. In recent years,
convolutional neural network (CNN) based models have achieved great performance
on SISR task. Despite the breakthroughs achieved by using CNN models, there are
still some problems remaining unsolved, such as how to recover high frequency
details of high resolution images. Previous CNN based models always use a pixel
wise loss, such as l2 loss. Although the high resolution images constructed by
these models have high peak signal-to-noise ratio (PSNR), they often tend to be
blurry and lack high-frequency details, especially at a large scaling factor.
In this paper, we build a super resolution perceptual generative adversarial
network (SRPGAN) framework for SISR tasks. In the framework, we propose a
robust perceptual loss based on the discriminator of the built SRPGAN model. We
use the Charbonnier loss function to build the content loss and combine it with
the proposed perceptual loss and the adversarial loss. Compared with other
state-of-the-art methods, our method has demonstrated great ability to
construct images with sharp edges and rich details. We also evaluate our method
on different benchmarks and compare it with previous CNN based methods. The
results show that our method can achieve much higher structural similarity
index (SSIM) scores on most of the benchmarks than the previous state-of-art
methods.


  We consider a self-avoiding walk model of polymer adsorption where the
adsorbed polymer can be desorbed by the application of a force, concentrating
on the case of the square lattice. Using series analysis methods we investigate
the behaviour of the free energy of the system when there is an attractive
potential $\epsilon$ with the surface and a force $f$ applied at the last
vertex, normal to the surface, and extract the phase boundary between the
ballistic and adsorbed phases. We believe this to be exact to graphical
accuracy. We give precise estimates of the location of the transition from the
free phase to the ballistic phase, which we find to be at $y_c=\exp(f/k_B
T_c)=1$, and from the free phase to the adsorbed phase, which we estimate to be
at $a_c=\exp(-\epsilon/k_B T_c)=1.775615 \pm 0.000005$. In addition we prove
that the phase transition from the ballistic to the adsorbed phase is first
order.


  Analytic approximations for the radial distribution function, the structure
factor, and the equation of state of hard-core fluids in fractal dimension $d$
($1 \leq d \leq 3$) are developed as heuristic interpolations from the
knowledge of the exact and Percus-Yevick results for the hard-rod and
hard-sphere fluids, respectively. In order to assess their value, such
approximate results are compared with those of recent Monte Carlo simulations
and numerical solutions of the Percus-Yevick equation for fractal dimension [M.
Heinen et al., Phys. Rev. Lett. \textbf{115}, 097801 (2015)], a good agreement
being observed.


  The intermediate age star cluster W3 (age ~ 300-500 Myr) in NGC 7252 is the
most luminous star cluster known to date with a dynamical mass estimate of 8
+/- 2 x 10^7 M_sun. With an effective radius of about 17.5 pc and a velocity
dispersion of 45 km/s this object may be viewed as one of the recently
discovered ultra-compact dwarf galaxies (UCDs). Its intermediate age, however,
precludes an origin as a stripped nucleated dwarf elliptical galaxy. The galaxy
NGC 7252 is a merger remnant from two gas-rich disc galaxies. Interactions
between two gas-rich galaxies lead to bursts of intense star formation. The age
of the interaction and the age of W3 are in good agreement, suggesting that was
W3 probably formed in the starburst. We propose a formation scenario for W3.
Observations of interacting galaxies reveal regions of strong star formation
forming dozens up to hundreds of star cluster in confined regions of up to
several hundred parsec in diameter. The total mass of new stars in these
regions can reach 10^7 or even 10^8 M_sun. By means of numerical simulations we
have shown that the star clusters in these regions merge on short time-scales
of a few Myr up to a few hundred Myr. We apply this scenario to W3 and predict
properties which could be observable and the future evolution of this object.
This work lends credence to the notion that at least some of the UCDs may be
evolved star cluster complexes formed during early hierarchical mergers.


  We present a comprehensive study of the information rate loss of the max-log
approximation for $M$-ary pulse-amplitude modulation (PAM) in a bit-interleaved
coded modulation (BICM) system. It is widely assumed that the calculation of
L-values using the max-log approximation leads to an information loss. We prove
that this assumption is correct for all $M$-PAM constellations and labelings
with the exception of a symmetric 4-PAM constellation labeled with a Gray code.
We also show that for max-log L-values, the BICM generalized mutual information
(GMI), which is an achievable rate for a standard BICM decoder, is too
pessimistic. In particular, it is proved that the so-called "harmonized" GMI,
which can be seen as the sum of bit-level GMIs, is achievable without any
modifications to the decoder. We then study how bit-level channel
symmetrization and mixing affect the mutual information (MI) and the GMI for
max-log L-values. Our results show that these operations, which are often used
when analyzing BICM systems, preserve the GMI. However, this is not necessarily
the case when the MI is considered. Necessary and sufficient conditions under
which these operations preserve the MI are provided.


  Historically, females were excluded from clinical research due to their
reproductive roles, hindering medical understanding and healthcare quality.
Despite guidelines promoting equal participation, females are underrepresented
in exercise science, perpetuating misconceptions about female physiology. Even
less attention has been given to exercise in the pregnant population. Research
on pregnancy and exercise has evolved considerably from the initial bedrest
prescriptions but concerns about exercise risks during pregnancy persisted for
many decades. Recent guidelines endorse moderate-intensity physical activity
during pregnancy, supported by considerable evidence of its safety and
benefits. Mental health during pregnancy, often overlooked, is gaining
traction, with exercise showing promise in reducing depression and anxiety.
While pregnancy guidelines recommend moderate-intensity physical activity,
there remains limited understanding of optimal frequency, intensity, type and
time (duration) for extremes like elite athletes or those with complications.
Female participation in elite sport and physically demanding jobs is rising,
but research on their specific needs is lacking. Traditional practices like bed
rest for high-risk pregnancies are being questioned, as evidence suggests it
may not improve outcomes. Historical neglect of gestational parents in research
perpetuated stereotypes of female frailty, but recent years have seen a shift
towards recognizing the benefits of an active pregnancy. Closing knowledge gaps
and inclusivity in research are crucial for ensuring guidelines reflect the
diverse needs of gestational parents. Therefore, the purpose of this review is
to summarize the evolution of exercise physiology and pregnancy research along
with future directions for this novel field.


  Present and prospective fundamental symmetry tests with nucleons, nuclei and
atoms are probing for possible new physics at the TeV scale and beyond. These
ongoing and proposed table-top as well as accelerator-based experiments are
thus a vital component of the Intensity Frontier. At the same time, these tests
provide increasingly sophisticated probes of long-distance strong interactions
that are responsible for the structure of nucleons and nuclei. In this
community report, some of the most compelling opportunities with nucleons,
nuclei and atoms are summarized, drawing largely on input received from the
nuclear and atomic physics communities. In particular, this report includes
many contributions submitted to two recent Intensity Frontier Workshops.


  An obituary for Alexander Gordon which will appear in the Journal of Spectral
Theory


  I review the recent progress in computing inclusive B decay widths and
spectra and its implications on the interpretation of measurements from the B
factories. I discuss the inclusive charmless semileptonic decay, B -> X_u l nu,
which provides the most robust determination of the CKM parameter |V_{ub}|, and
the rare radiative decay, B -> X_s gamma, which constrains flavor violation
beyond the Standard Model. I demonstrate that precise predictions for the
experimentally-relevant branching fractions can be derived from resummed
perturbation theory and explain the way in which the resummation further
provides guidance in parametrizing non-perturbative Fermi-motion effects.
Finally I address the comparison between theory and data and discuss future
prospects.


  This work deals with Abelian Chern-Simons vortices interacting with magnetic
impurities. We compute static solutions with winding numbers zero and one.
Then, we develop a numerical algorithm to simulate their collisions. Collisions
between a vortex with winding number two and a magnetic impurity are also
performed. All scattering results are interpreted in terms of the moduli space
approximation and compared with the Abelian Maxwell-Higgs model.


  We present general numerical methods to construct witness operators for
entanglement detection and estimation of the fidelity. Our methods are applied
to detecting entanglement in the vicinity of a six-qubit Dicke state with three
excitations and also to further entangled symmetric states. All our witnesses
are designed to keep the measurement effort small. We present also general
results on the efficient local decomposition of permutationally invariant
operators, which makes it possible to measure projectors to symmetric states
efficiently


  The Kitaev spin liquid model on honeycomb lattice offers an intriguing
feature that encapsulates both Abelian and non-Abelian anyons. Recent studies
suggest that the comprehensive phase diagram of possible generalized Kitaev
model largely depends on the specific details of the discrete lattice, which
somewhat deviates from the traditional understanding of "topological" phases.
In this paper, we propose an adapted version of the Kitaev spin liquid model on
arbitrary planar lattices. Our revised model recovers the toric code model
under certain parameter selections within the Hamiltonian terms. Our research
indicates that changes in parameters can initiate the emergence of holes,
domain walls, or twist defects. Notably, the twist defect, which presents as a
lattice dislocation defect, exhibits non-Abelian braiding statistics upon
tuning the coefficients of the Hamiltonian on a standard translationally
invariant lattice. Additionally, we illustrate that the creation, movement, and
fusion of these defects can be accomplished through natural time evolution by
linearly interpolating the static Hamiltonian. These defects demonstrate the
Ising anyon fusion rule as anticipated. Our findings hint at possible
implementation in actual physical materials owing to a more realistically
achievable two-body interaction.


  Constraint programming (CP) has been used with great success to tackle a wide
variety of constraint satisfaction problems which are computationally
intractable in general. Global constraints are one of the important factors
behind the success of CP. In this paper, we study a new global constraint, the
multiset ordering constraint, which is shown to be useful in symmetry breaking
and searching for leximin optimal solutions in CP. We propose efficient and
effective filtering algorithms for propagating this global constraint. We show
that the algorithms are sound and complete and we discuss possible extensions.
We also consider alternative propagation methods based on existing constraints
in CP toolkits. Our experimental results on a number of benchmark problems
demonstrate that propagating the multiset ordering constraint via a dedicated
algorithm can be very beneficial.


  It has been shown in the literature that detections of gravitational waves
(GWs) emitted by binary sources can provide measurements of luminosity
distance. The events followed by electromagnetic counterparts are, then,
suitable for probing the distance-redshift relation and doing cosmological
parameter estimation, as well as investigating modified gravity (MG) models. In
the context of effective approaches to MG equivalent to Horndeski, this GW
distance differs from the standard electromagnetic luminosity distance due to
the presence of a modified friction in the wave propagation. Here, we
investigate how precisely the future-planned interferometer Einstein Telescope
will probe such deviations from General Relativity, considering
phenomenological parametrizations for both the dark energy equation of state
and the GW friction. Despite being an independent test of gravity, for $f(R)$
in particular, we conclude that it may provide weaker constraints than the
current available ones.


  We report a novel insulator-insulator transition arising from the internal
charge degrees of freedom in the two-dimensional quarter-filled organic salt
beta-(meso-DMBEDT-TTF)2PF6. The optical conductivity spectra above Tc = 70 K
display a prominent feature of the dimer-Mott insulator, characterized by a
substantial growth of a dimer peak near 0.6 eV with decreasing temperature. The
dimer-peak growth is rapidly quenched as soon as a peak of the charge order
shows up below Tc, indicating a competition between the two insulating phases.
Our infrared imaging spectroscopy has further revealed a spatially competitive
electronic phases far below Tc, suggesting a nature of quantum phase transition
driven by material-parameter variations.


  Suppose that $A$, $B$ and $S$ are non-empty subsets of a finite abelian group
$G$. Then the generalized restricted sumset $$ A\stackrel{S}+B:=\{a+b:\,a\in
A,\ b\in B,\ a-b\not\in S\} $$ contains at least $$ \min\{|A|+|B|-3|S|,p(G)\}
$$ elements, where $p(G)$ is the least prime factor of $|G|$. Further, we also
have $$ |A\stackrel{S}+B|\geq \min\{|A|+|B|-|S|-2,p(G)\}, $$ provided that both
$|A|$ and $|B|$ are large with respect to $|S|$.


  Dealing with irregular domains, graph signal processing (GSP) has attracted
much attention especially in brain imaging analysis. Motor imagery tasks are
extensively utilized in brain-computer interface (BCI) systems that perform
classification using features extracted from Electroencephalogram signals. In
this paper, a GSP-based approach is presented for two-class motor imagery tasks
classification. The proposed method exploits simultaneous diagonalization of
two matrices that quantify the covariance structure of graph spectral
representation of data from each class, providing a discriminative subspace
where distinctive features are extracted from the data. The performance of the
proposed method was evaluated on Dataset IVa from BCI Competition III.
Experimental results show that the proposed method outperforms two
state-of-the-art alternative methods.


  Possibility of magnetic energy accumulation inside silicon nanoparticles at
the conditions of resonant optical responses is investigated theoretically. The
magnetic field distributions inside silicon nanocylinders with and without
coaxial through holes are calculated using full-wave numerical approach. It is
demonstrated that such systems can be used for control and manipulation of
optical magnetic fields providing their enhancement up to 26 times at the
condition of optical resonances. Obtained results can be used for realization
of nanoantennas and nanolasers, in which magnetic optical transitions play
significant roles.


  Neutral beam injection or ion cyclotron resonance heating induces pressure
anisotropy. The axisymmetric plasma equilibrium code HELENA has been upgraded
to include anisotropy and toroidal flow. With both analytical and numerical
methods, we have studied the determinant factors in anisotropic equilibria and
their impact on flux surfaces, magnetic axis shift, the displacement of
pressures and density contours from flux surface. With $p_\parallel/p_\perp
\approx 1.5$, $p_\perp$ can vary 20% on $s=0.5$ flux surface, in a MAST like
equilibrium. We have also re-evaluated the widely applied approximation to
anisotropy in which $p^*=(p_\parallel + p_\perp)/2$, the average of parallel
and perpendicular pressure, is taken as the approximate isotropic pressure. We
find the reconstructions of the same MAST discharge with $p_\parallel/p_\perp
\approx 1.25$, using isotropic and anisotropic model respectively, to have a 3%
difference in toroidal field but a 66% difference in poloidal current.


  Renormalization is often described as the removal or "integrating out" of
high energy degrees of freedom. In the context of quantum matter, one might
suspect that quantum entanglement provides a sharp way to characterize such a
loss of degrees of freedom. Indeed, for quantum many-body systems with Lorentz
invariance, such entanglement monotones have been proven to exist in one, two,
and three spatial dimensions. In each dimension d, a certain term in the
entanglement entropy of a d-ball decreases along renormalization group (RG)
flows. Given that most quantum many-body systems available in the laboratory
are not Lorentz invariant, it is important to generalize these results if
possible. In this work we demonstrate the impossibility of a wide variety of
such generalizations. We do this by exhibiting a series of counterexamples with
understood renormalization group flows which violate entanglement RG
monotonicity. We discuss bosons at finite density, fermions at finite density,
and majorization in Lorentz invariant theories, among other results.


  We augment adversarial training (AT) with worst case adversarial training
(WCAT) which improves adversarial robustness by 11% over the current
state-of-the-art result in the $\ell_2$ norm on CIFAR-10. We obtain verifiable
average case and worst case robustness guarantees, based on the expected and
maximum values of the norm of the gradient of the loss. We interpret
adversarial training as Total Variation Regularization, which is a fundamental
tool in mathematical image processing, and WCAT as Lipschitz regularization.


  The recently proposed data augmentation TransMix employs attention labels to
help visual transformers (ViT) achieve better robustness and performance.
However, TransMix is deficient in two aspects: 1) The image cropping method of
TransMix may not be suitable for ViTs. 2) At the early stage of training, the
model produces unreliable attention maps. TransMix uses unreliable attention
maps to compute mixed attention labels that can affect the model. To address
the aforementioned issues, we propose MaskMix and Progressive Attention
Labeling (PAL) in image and label space, respectively. In detail, from the
perspective of image space, we design MaskMix, which mixes two images based on
a patch-like grid mask. In particular, the size of each mask patch is
adjustable and is a multiple of the image patch size, which ensures each image
patch comes from only one image and contains more global contents. From the
perspective of label space, we design PAL, which utilizes a progressive factor
to dynamically re-weight the attention weights of the mixed attention label.
Finally, we combine MaskMix and Progressive Attention Labeling as our new data
augmentation method, named MixPro. The experimental results show that our
method can improve various ViT-based models at scales on ImageNet
classification (73.8\% top-1 accuracy based on DeiT-T for 300 epochs). After
being pre-trained with MixPro on ImageNet, the ViT-based models also
demonstrate better transferability to semantic segmentation, object detection,
and instance segmentation. Furthermore, compared to TransMix, MixPro also shows
stronger robustness on several benchmarks. The code is available at
https://github.com/fistyee/MixPro.


  Relying upon our previous treatment of the density matrices for nuclei (in
general, nonrelativistic self-bound finite systems) we are studying a combined
effect of center-of-mass motion and short-range nucleon-nucleon correlations on
the nucleon density and momentum distributions in light nuclei ($^{4}He $ and
$^{16}O$). Their intrinsic ground-state wave functions are constructed in the
so-called fixed center-of-mass approximation, starting with mean-field Slater
determinants modified by some correlator (e.g., after Jastrow or Villars). We
develop the formalism based upon the Cartesian or boson representation, in
which the coordinate and momentum operators are linear combinations of the
creation and annihilation operators for oscillatory quanta in the three
different space directions, and get the own "Tassie-Barker" factors for each
distribution and point out other model-independent results. After this
separation of the center-of-mass motion effects we propose additional analytic
means in order to simplify the subsequent calculations (e.g., within the
Jastrow approach or the unitary correlation operator method). The charge form
factors, densities and momentum distributions of $^{4}He $ and $^{16}O$
evaluated by using the well known cluster expansions are compared with data,
our exact (numerical) results and microscopic calculations.


  We find bounds on the Hilbert space compression of the limit of a directed
metric system of groups. We also give estimates on the Hilbert space
compression of group extensions of a group $H$ by a a word-hyperbolic group or
a group of polynomial growth.


  Two-dimensional magnetic insulators can be promising hosts for topological
magnons. In this study, we show that ABC-stacked honeycomb lattice multilayers
with alternating Dzyaloshinskii-Moriya interaction (DMI) reveal a rich
topological magnon phase diagram. Based on our bandstructure and Berry
curvature calculations, we demonstrate jumps in the thermal Hall behavior that
corroborate with topological phase transitions triggered by adjusting the DMI
and interlayer coupling. We connect the phase diagram of generic multilayers to
a bilayer and a trilayer system. We find an even-odd effect amongst the
multilayers where the even layers show no jump in thermal Hall conductivity,
but the odd layers do. We also observe the presence of topological proximity
effect in our trilayer. Our results offer new schemes to manipulate Chern
numbers and their measurable effects in topological magnonic systems.


  Doubletalk detector (DTD) is essential to keep adaptive filter from diverging
in the presence of near-end speech in acoustic echo cancellation (AEC), and
there was a receiver operating characteristic (ROC) to characterize DTD
performance. However, the traditional ROC for evaluating DTD used a static
time-invariant room acoustic impulse response and could not evaluate DTDs which
distinguish echo path change from doubletalk. We solve these problems by
extending the traditional binary detection ROC to three class, and simulations
show the efficiency of the proposed method.


  Two link diagrams are link homotopic if one can be transformed into the other
by a sequence of Reidemeister moves and self crossing changes. Milnor
introduced invariants under link homotopy called $\bar{\mu}$. Nanophrases,
introduced by Turaev, generalize links. In this paper, we extend the notion of
link homotopy to nanophrases. We also generalize $\bar{\mu}$ to the set of
those nanophrases that correspond to virtual links.


  The quest for high-redshift quasars has led to a series of record-breaking
sources, with the current record holder at $z=7.642$. Here, we show how future
detections of $z>8$ quasars impact the constraints on the parameters for black
hole growth and seed models. Using broad flat priors on the growth parameters
(Eddington ratio $\,f_{\rm Edd}$, duty cycle ${\cal D}$, seed mass $M_{\rm
\bullet, seed}$ and radiative efficiency $\epsilon$), we show that the large
uncertainties in their determination decrease by a factor $\sim 5$ when a
quasar's detection redshift goes from $z=9$ to $z=12$. In this high-redshift
regime, $\epsilon$ tends to the lowest value allowed, and the distribution for
$M_{\rm \bullet, seed}$ peaks well inside the heavy seed domain. Remarkably,
two quasars detected at $z > 7$ with low accretion rates (J1243+0100 and
J0313-1806) already tighten the available parameter space, requiring $M_{\rm
\bullet, seed} > 10^{3.5} \,{\rm M_\odot}$ and $\epsilon < 0.1$. The radiative
efficiency is a crucial unknown, with factor $\sim 2$ changes able to modify
the predicted mass by $\sim 3$ orders of magnitude already at $z\sim 9$. The
competing roles of inefficient accretion (decreasing $\epsilon$) and black hole
spin-up (increasing $\epsilon$) significantly impact growth models. Finally, we
suggest that yields currently predicted by upcoming quasar surveys (e.g.,
Euclid) will be instrumental for determining the most-likely seed mass regime.
For example, assuming thin-disk accretion, a detection of a quasar with
$M_\bullet \sim 10^{10} \,{\rm M_\odot}$ by $z\sim 9-10$ would exclude the
entire parameter space available for light seeds and dramatically reduce the
one for heavy seeds.


  Webpages have been a rich, scalable resource for vision-language and language
only tasks. Yet only pieces of webpages are kept in existing datasets:
image-caption pairs, long text articles, or raw HTML, never all in one place.
Webpage tasks have resultingly received little attention and structured
image-text data left underused. To study multimodal webpage understanding, we
introduce the Wikipedia Webpage suite (WikiWeb2M) containing 2M pages with all
of the associated image, text, and structure data. We verify its utility on
three generative tasks: page description generation, section summarization, and
contextual image captioning. We design a novel attention mechanism Prefix
Global, which selects the most relevant image and text content as global tokens
to attend to the rest of the webpage for context. By using page structure to
separate such tokens, it performs better than full attention with lower
computational complexity. Extensive experiments show that the new data in
WikiWeb2M improves task performance compared to prior work.


  Robust topological information commonly comes in the form of a set of
persistence diagrams, finite measures that are in nature uneasy to affix to
generic machine learning frameworks. We introduce a fast, learnt, unsupervised
vectorization method for measures in Euclidean spaces and use it for reflecting
underlying changes in topological behaviour in machine learning contexts. The
algorithm is simple and efficiently discriminates important space regions where
meaningful differences to the mean measure arise. It is proven to be able to
separate clusters of persistence diagrams. We showcase the strength and
robustness of our approach on a number of applications, from emulous and modern
graph collections where the method reaches state-of-the-art performance to a
geometric synthetic dynamical orbits problem. The proposed methodology comes
with a single high level tuning parameter: the total measure encoding budget.
We provide a completely open access software.


  In this work we propose HAMLET, a novel tract learning algorithm, which,
after training, maps raw diffusion weighted MRI directly onto an image which
simultaneously indicates tract direction and tract presence. The automatic
learning of fiber tracts based on diffusion MRI data is a rather new idea,
which tries to overcome limitations of atlas-based techniques. HAMLET takes a
such an approach. Unlike the current trend in machine learning, HAMLET has only
a small number of free parameters HAMLET is based on spherical tensor algebra
which allows a translation and rotation covariant treatment of the problem.
HAMLET is based on a repeated application of convolutions and non-linearities,
which all respect the rotation covariance. The intrinsic treatment of such
basic image transformations in HAMLET allows the training and generalization of
the algorithm without any additional data augmentation. We demonstrate the
performance of our approach for twelve prominent bundles, and show that the
obtained tract estimates are robust and reliable. It is also shown that the
learned models are portable from one sequence to another.


  Driven by powerful image diffusion models, recent research has achieved the
automatic creation of 3D objects from textual or visual guidance. By performing
score distillation sampling (SDS) iteratively across different views, these
methods succeed in lifting 2D generative prior to the 3D space. However, such a
2D generative image prior bakes the effect of illumination and shadow into the
texture. As a result, material maps optimized by SDS inevitably involve
spurious correlated components. The absence of precise material definition
makes it infeasible to relight the generated assets reasonably in novel scenes,
which limits their application in downstream scenarios. In contrast, humans can
effortlessly circumvent this ambiguity by deducing the material of the object
from its appearance and semantics. Motivated by this insight, we propose
MaterialSeg3D, a 3D asset material generation framework to infer underlying
material from the 2D semantic prior. Based on such a prior model, we devise a
mechanism to parse material in 3D space. We maintain a UV stack, each map of
which is unprojected from a specific viewpoint. After traversing all
viewpoints, we fuse the stack through a weighted voting scheme and then employ
region unification to ensure the coherence of the object parts. To fuel the
learning of semantics prior, we collect a material dataset, named Materialized
Individual Objects (MIO), which features abundant images, diverse categories,
and accurate annotations. Extensive quantitative and qualitative experiments
demonstrate the effectiveness of our method.


  In contemporary distributed systems, logs are produced at an astounding rate,
generating terabytes of data within mere seconds. These logs, containing
pivotal details like system metrics, user actions, and diverse events, are
foundational to the system's consistent and accurate operations. Precise log
ordering becomes indispensable to avert potential ambiguities and discordances
in system functionalities. Apache Kafka, a prevalent distributed message queue,
offers significant solutions to various distributed log processing challenges.
However, it presents an inherent limitation while Kafka ensures the in-order
delivery of messages within a single partition to the consumer, it falls short
in guaranteeing a global order for messages spanning multiple partitions. This
research delves into innovative methodologies to achieve global ordering of
messages within a Kafka topic, aiming to bolster the integrity and consistency
of log processing in distributed systems. Our code is available on GitHub.


  In this paper, we study Bohr's inequality and refined versions of
Bohr-Rogosinski inequalities involving Schwarz functions. Moreover, we
establish a version of multidimensional analogue of Bohr inequality and
Bohr-Rogosinski inequalities involving Schwarz functions. Finally, we establish
a multidimensional analogue of the refined version of Bohr inequalities with
the initial coefficient being zero. All the results are proved to be sharp.


  Neural Architecture Search (NAS) for automatically finding the optimal
network architecture has shown some success with competitive performances in
various computer vision tasks. However, NAS in general requires a tremendous
amount of computations. Thus reducing computational cost has emerged as an
important issue. Most of the attempts so far has been based on manual
approaches, and often the architectures developed from such efforts dwell in
the balance of the network optimality and the search cost. Additionally, recent
NAS methods for image restoration generally do not consider dynamic operations
that may transform dimensions of feature maps because of the dimensionality
mismatch in tensor calculations. This can greatly limit NAS in its search for
optimal network structure. To address these issues, we re-frame the optimal
search problem by focusing at component block level. From previous work, it's
been shown that an effective denoising block can be connected in series to
further improve the network performance. By focusing at block level, the search
space of reinforcement learning becomes significantly smaller and evaluation
process can be conducted more rapidly. In addition, we integrate an innovative
dimension matching modules for dealing with spatial and channel-wise mismatch
that may occur in the optimal design search. This allows much flexibility in
optimal network search within the cell block. With these modules, then we
employ reinforcement learning in search of an optimal image denoising network
at a module level. Computational efficiency of our proposed Denoising Prior
Neural Architecture Search (DPNAS) was demonstrated by having it complete an
optimal architecture search for an image restoration task by just one day with
a single GPU.


  The effective-interaction theory has been one of the useful and practical
methods for solving nuclear many-body problems based on the shell model.
Various approaches have been proposed which are constructed in terms of the
so-called $\widehat{Q}$ box and its energy derivatives introduced by Kuo {\it
et al}. In order to find out a method of calculating them we make decomposition
of a full Hilbert space into subspaces (the Krylov subspaces) and transform a
Hamiltonian to a block-tridiagonal form. This transformation brings about much
simplification of the calculation of the $\widehat{Q}$ box. In the previous
work a recursion method has been derived for calculating the $\widehat{Q}$ box
analytically on the basis of such transformation of the Hamiltonian. In the
present study, by extending the recursion method for the $\widehat{Q}$ box, we
derive another recursion relation to calculate the derivatives of the
$\widehat{Q}$ box of arbitrary order. With the $\widehat{Q}$ box and its
derivatives thus determined we apply them to the calculation of the
$E$-independent effective interaction given in the so-called Lee-Suzuki (LS)
method for a system with a degenerate unperturbed energy. We show that the
recursion method can also be applied to the generalized LS scheme for a system
with non-degenerate unperturbed energies. If the Hilbert space is taken to be
sufficiently large, the theory provides an exact way of calculating the
$\widehat{Q}$ box and its derivatives. This approach enables us to perform
recursive calculations for the effective interaction to arbitrary order for
both systems with degenerate and non-degenerate unperturbed energies.


  Large-amplitude magnetization dynamics is substantially more complex compared
to the low-amplitude linear regime, due to the inevitable emergence of
nonlinearities. One of the fundamental nonlinear phenomena is the nonlinear
damping enhancement, which imposes strict limitations on the operation and
efficiency of magnetic nanodevices. In particular, nonlinear damping prevents
excitation of coherent magnetization auto-oscillations driven by the injection
of spin current into spatially extended magnetic regions. Here, we propose and
experimentally demonstrate that nonlinear damping can be controlled by the
ellipticity of magnetization precession. By balancing different contributions
to anisotropy, we minimize the ellipticity and achieve coherent magnetization
oscillations driven by spatially extended spin current injection into a
microscopic magnetic disk. Our results provide a novel route for the
implementation of efficient active spintronic and magnonic devices driven by
spin current.


  In recent work, Gies and Karbstein have discovered that the two-loop
Euler-Heisenberg Lagrangians for scalar and spinor QED have non-vanishing
reducible contributions in addition to the well-studied irreducible ones. This
invalidates previous applications of those Lagrangians to the computation of
the two-loop $N$-photon amplitudes in the low energy limit. Here we compute the
corrections to those amplitudes due to the reducible contributions.


  Referring to recent works concerning the functional renormalization group for
tensorial group fields theories [arXiv:1803.09902] and [arXiv:1809.00247], this
paper gives in-depth explanation for, the ambiguity around the search of fixed
points in the Wetterich flow equation and the Ward-Takahashi identities. We
consider the $U(1)$-tensors models and discuss the non-existence of phase
transition taking into account the Ward-identities as new constraint along the
flow. We prove that the quartic melonic tensorial group fields theories without
closure constraint are devoid with the fixed points and therefore the phase
transitions.


  We show that there are topological obstructions for a noncompact manifold to
admit a Riemannian metric with quadratic curvature decay and a volume growth
which is slower than that of Euclidean space of the same dimension.


  For a finite simplicial graph $\Gamma$, let $A(\Gamma)$ denote the
right-angled Artin group on $\Gamma$. Recently Kim and Koberda introduced the
extension graph $\Gamma^e$ for $\Gamma$, and established the Extension Graph
Theorem: for finite simplicial graphs $\Gamma_1$ and $\Gamma_2$ if $\Gamma_1$
embeds into $\Gamma_2^e$ as an induced subgraph then $A(\Gamma_1)$ embeds into
$A(\Gamma_2)$. In this article we show that the converse of this theorem does
not hold for the case $\Gamma_1$ is the complement of a tree and for the case
$\Gamma_2$ is the complement of a path graph.


  In this paper we give a positive answer to a question of Nash concerning the
arc space of a singularity, for the class of quasi-ordinary hypersurface
singularities, extending to this case previous results and techniques of
Shihoko Ishii.


  The CDF collaboration at Fermilab has recently reported a new precision
measurement of the $W$~boson mass showing a substantial $7\sigma$ deviation
from the Standard Model prediction. Moreover, Fermilab has recently confirmed
the longstanding tension in the $(g-2)_\mu$ measurement. We propose a unified
solution to these deviations within the simplest radiative neutrino mass model:
the Zee model. Our analysis establishes non-trivial links between the origin of
neutrino mass, the $(g-2)_\mu$ anomaly, and the $W$~boson mass shift while
being consistent with lepton flavor violation and all other experimental
constraints. We find that the mass spectrum of the physical scalars must be
hierarchical to be consistent with the $W$~boson mass shift; remarkably, this
is also the key to resolving the $(g-2)_\mu$ tension. Furthermore, this mass
splitting offers a unique same-sign dimuon signal through which our model can
be tested at the LHC.


  The well known phenomenon of exponential contraction for solutions to the
viscous Hamilton-Jacobi equation in the space-periodic setting is based on the
Markov mechanism. However, the corresponding Lyapunov exponent $\lambda(\nu)$
characterizing the exponential rate of contraction depends on the viscosity
$\nu$. The Markov mechanism provides only a lower bound for $\lambda(\nu)$
which vanishes in the limit $\nu \to 0$. At the same time, in the inviscid case
$\nu=0$ one also has exponential contraction based on a completely different
dynamical mechanism. This mechanism is based on hyperbolicity of
action-minimizing orbits for the related Lagrangian variational problem.
  In this paper we consider the discrete time case (kicked forcing), and
establish a uniform lower bound for $\lambda(\nu)$ which is valid for all
$\nu\geq 0$. The proof is based on a nontrivial interplay between the dynamical
and Markov mechanisms for exponential contraction. We combine PDE methods with
the ideas from the Weak KAM theory.


  Based on the planet-satellite dynamics, a new perspective on the birth and
evolution of the Solar System was proposed in 2004. This new perspective has
1:2 Mean Motion Resonance (1:2 MMR, that is Ps/Pj = 2) implicit in itself .
This paper has shown that if we assume the sequential birth of the planets with
the heaviest, i.e. Jupiter, being born the earliest and this axiom is central
to the new perspective then there is an unique initial condition for which 1:2
MMR crossing occurs exactly 300My after the birth of Jupiter. It is shown that
25 My time gap between the birth of Jupiter and Saturn results in the 300 My
time delay in the occurrence of 1:2 MMR. Because the process by which Late
Heavy Bombardment is triggered involves the movement of Neptune over enormous
distances hence almost 200 My time delay occurs between 1:2MMR crossing and the
actual spike in the Meteoritic Shower on all the terrestrial planets as
evidenced by the petrology record on the Moon. The analytical derivation of the
time delay in the spike is a vindication of this new perspective of the
application of planet-satellite dynamics to the birth and evolution of Solar
Systems.


  Exposure of highly deuterated materials to a low-energy (nom. 2 MeV) photon
beam resulted in nuclear activity of both the parent metals of hafnium and
erbium and a witness material (molybdenum) mixed with the reactants. Gamma
spectral analysis of all deuterated materials, ErD2.8-C36D74-Mo and
HfD2-C36D74-Mo, showed that nuclear processes had occurred as shown by unique
gamma signatures. For the deuterated erbium specimens, posttest gamma spectra
showed evidence of radioisotopes of erbium (163Er and 171Er) and of molybdenum
(99Mo and 101Mo) and by beta decay, technetium (99mTc and 101Tc). For the
deuterated hafnium specimens, posttest gamma spectra showed evidence of
radioisotopes of hafnium (180mHf and 181Hf) and molybdenum (99Mo and 101Mo),
and by beta decay, technetium (99mTc and 101Tc). In contrast, when either the
hydrogenated or non-gas-loaded erbium or hafnium materials were exposed to the
gamma flux, the gamma spectra revealed no new isotopes. Neutron activation
materials showed evidence of thermal and epithermal neutrons. CR-39 solid-state
nuclear track detectors showed evidence of fast neutrons with energies between
1.4 and 2.5 MeV and several instances of triple tracks, indicating greater than
10 MeV neutrons. Further study is required to determine the mechanism causing
the nuclear activity


  A resonant photoacoustic cell intended for laser-spectroscopy gas sensing is
represented. This cell is a miniature imitation of a macro-scale banana-shaped
cell developed previously. The parameters, which specify the cavity shape, are
chosen so as not only to provide optimal cell operation at a selected acoustic
resonance but also to reduce substantially the cell sizes. A miniaturized
prototype cell (the volume of acoustic cavity of ~ 5 mm^3) adapted to the
narrow diffraction-limited beam of near-infrared laser is produced and examined
experimentally. The noise-associated measurement error and laser-initiated
signals are studied as functions of modulation frequency. The background signal
and the useful response to light absorption by the gas are analyzed in
measurements of absorption for ammonia in nitrogen flow with the help of a
pigtailed DFB laser diode oscillated near a wavelength of 1.53 um. The
performance of prototype operation at the second longitudinal acoustic
resonance (the resonance frequency of ~ 32.9 kHz, Q-factor of ~ 16.3) is
estimated. The noise-limited minimal detectable absorption normalized to
laser-beam power and detection bandwidth is ~ 8.07*10^(-8) cm^(-1) W Hz^(-1/2).
The amplitude of the background signal is equivalent to an absorption
coefficient of ~ 2.5*10^(-5) cm^(-1). Advantages and drawbacks of the cell
prototype are discussed. Despite low absorption-sensing performance, the
produced miniaturized cell prototype shows a good capability of gas-leak
detection.


  New fractal subset of a rough surface, the ``oceanic coastline'', is defined.
For random Gaussian surfaces with negative Hurst exponent $H<0$, ``oceanic
coastlines'' are mapped to the percolation clusters of the (correlated)
percolation problem. In the case of rough self-affine surfaces ($H \ge 0$), the
fractal dimension of the ``oceanic coastline'' $d_c$ is calculated numerically
as a function of the roughness exponent $H$ (using a novel technique of
minimizing finite-size effects). For H=0, the result $d_c \approx 1.896$
coincides with the analytic value for the percolation problem (91/48),
suggesting a super-universality of $d_c$ for correlated percolation problem.


  Graph representations have increasingly grown in popularity during the last
years. Existing representation learning approaches explicitly encode network
structure. Despite their good performance in downstream processes (e.g., node
classification, link prediction), there is still room for improvement in
different aspects, like efficacy, visualization, and interpretability. In this
paper, we propose, t-PINE, a method that addresses these limitations. Contrary
to baseline methods, which generally learn explicit graph representations by
solely using an adjacency matrix, t-PINE avails a multi-view information graph,
the adjacency matrix represents the first view, and a nearest neighbor
adjacency, computed over the node features, is the second view, in order to
learn explicit and implicit node representations, using the Canonical Polyadic
(a.k.a. CP) decomposition. We argue that the implicit and the explicit mapping
from a higher-dimensional to a lower-dimensional vector space is the key to
learn more useful, highly predictable, and gracefully interpretable
representations. Having good interpretable representations provides a good
guidance to understand how each view contributes to the representation learning
process. In addition, it helps us to exclude unrelated dimensions. Extensive
experiments show that t-PINE drastically outperforms baseline methods by up to
158.6% with respect to Micro-F1, in several multi-label classification
problems, while it has high visualization and interpretability utility.


  We consider a class of N-player stochastic games of multi-dimensional
singular control, in which each player faces a minimization problem of
monotone-follower type with submodular costs. We call these games
"monotone-follower games". In a not necessarily Markovian setting, we establish
the existence of Nash equilibria. Moreover, we introduce a sequence of
approximating games by restricting, for each natural number n, the players'
admissible strategies to the set of Lipschitz processes with Lipschitz constant
bounded by n. We prove that, for each n, there exists a Nash equilibrium of the
approximating game and that the sequence of Nash equilibria converges, in the
Meyer-Zheng sense, to a weak (distributional) Nash equilibrium of the original
game of singular control. As a byproduct, such a convergence also provides
approximation results of the equilibrium values across the two classes of
games. We finally show how our findings can be employed to prove existence of
open-loop Nash equilibria in an N-player stochastic differential game with
singular controls, and we propose an algorithm to determine a Nash equilibrium
for the monotone-follower game.


  We prove the Bisognano-Wichmann and CPT theorems for massive particles
obeying braid group statistics in three-dimensional Minkowski space. We start
from first principles of local relativistic quantum theory, assuming Poincare
covariance and asymptotic completeness. The particle masses must be isolated
points in the mass spectra of the corresponding charged sectors, and may only
be finitely degenerate.


  This paper considers unbalanced multiphase distribution systems with generic
topology and different load models, and extends the Z-bus iterative load-flow
algorithm based on a fixed-point interpretation of the AC load-flow equations.
Explicit conditions for existence and uniqueness of load-flow solutions are
presented. These conditions also guarantee convergence of the load-flow
algorithm to the unique solution. The proposed methodology is applicable to
generic systems featuring (i) wye connections; (ii) ungrounded delta
connections; (iii) a combination of wye-connected and delta-connected
sources/loads; and, (iv) a combination of line-to-line and
line-to-grounded-neutral devices at the secondary of distribution transformers.
Further, a sufficient condition for the non-singularity of the load-flow
Jacobian is proposed. Finally, linear load-flow models are derived, and their
approximation accuracy is analyzed. Theoretical results are corroborated
through experiments on IEEE test feeders.


  Fingerprinting is a popular indoor localization technique since it can
utilize existing infrastructures (e.g., access points). However, its site
survey process is a labor-intensive and time-consuming task, which limits the
application of such systems in practice. In this paper, motivated by the
availability of advanced sensing capabilities in smartphones, we propose a fast
and reliable fingerprint collection method to reduce the time and labor
required for site survey. The proposed method uses a landmark graph-based
method to automatically associate the collected fingerprints, which does not
require active user participation. We will show that besides fast fingerprint
data collection, the proposed method results in accurate location estimate
compared to the state-of-the-art methods. Experimental results show that the
proposed method is an order of magnitude faster than the manual fingerprint
collection method, and using the radio map generated by our method achieves a
much better accuracy compared to the existing methods.


  We determine the energy density $\xi (3/5) n \epsilon_F$ and the gradient
correction $\lambda \hbar^2(\nabla n)^2/(8m n)$ of the extended Thomas-Fermi
(ETF) density functional, where $n$ is number density and $\epsilon_F$ is Fermi
energy, for a trapped two-components Fermi gas with infinite scattering length
(unitary Fermi gas) on the basis of recent diffusion Monte Carlo (DMC)
calculations [Phys. Rev. Lett. {\bf 99}, 233201 (2007)]. In particular we find
that $\xi=0.455$ and $\lambda=0.13$ give the best fit of the DMC data with an
even number $N$ of particles. We also study the odd-even splitting $\gamma
N^{1/9} \hbar \omega$ of the ground-state energy for the unitary gas in a
harmonic trap of frequency $\omega$ determining the constant $\gamma$. Finally
we investigate the effect of the gradient term in the time-dependent ETF model
by introducing generalized Galilei-invariant hydrodynamics equations.


  In this paper, we propose OneQ, the first optimizing compilation framework
for one-way quantum computation towards realistic photonic quantum
architectures. Unlike previous compilation efforts for solid-state qubit
technologies, our innovative framework addresses a unique set of challenges in
photonic quantum computing. Specifically, this includes the dynamic generation
of qubits over time, the need to perform all computation through measurements
instead of relying on 1-qubit and 2-qubit gates, and the fact that photons are
instantaneously destroyed after measurements. As pioneers in this field, we
demonstrate the vast optimization potential of photonic one-way quantum
computing, showcasing the remarkable ability of OneQ to reduce computing
resource requirements by orders of magnitude.


  We develop a spinor equation of the electromagnetic field, which is
equivalent to the Maxwell equation and has a similar form as the Dirac
equation. The spinor is the very conjugate momentum of the vector potential in
the Lagrangian mechanics. In this framework the electromagnetic field described
by the spinor exhibits the SU(2) internal symmetry. The quantization and the
problem of longinal and scalar photons are disscussed.


  At the largest angular scales the presence of a number of unexpected features
has been confirmed by the latest measurements of the cosmic microwave
background (CMB). Among them are the anomalous alignment of the quadrupole and
octopole with each other as well as the stubborn lack of angular correlation on
scales >60deg. We search for correlations between these two phenomena and
demonstrate their absence. A Monte Carlo likelihood analysis confirms previous
studies and shows that the joint likelihood of both anomalies is incompatible
with the best-fit Lambda Cold Dark Matter model at >99.95% C.L. At the same
time, a presumed special axis (the `Axis of Evil') identified on the microwave
sky demands additional contributions to multipole power on top of the
primordial standard inflationary ones. We find that the notion of a preferred
axis in the CMB is misleading and inconsistent with three-year data from the
Wilkinson Microwave Anisotropy Probe (WMAP). Rather the data require a
preferred plane, whereupon the axis is just the normal direction to that plane.
Rotational symmetry within that plane is inconsistent with the observations and
is ruled out at high confidence.


  Nanostructured Fe40Ni40P14B6 alloys ingots of diameter 3~5 mm could be
synthesised by a metastable liquid state spinodal decomposition method. The
molten Fe40Ni40P14B6 alloy was purified by means of the fluxing technique and
thus a large undercooling could be achieved. For undercooling Delta T > 260 K,
the microstructure of the undercooled specimen had exhibited liquid state
spinodal decomposition in the undercooled liquid state. The microstructure
could be described as two intertwining networks with small grains dispersed in
them. For undercooling Delta T > 290 K, the overall microstructure of the
specimen changed into a granular morphology. The average grain sizes of the
small and large grains are ~ 30 nm and ~ 80 nm, respectively. These prepared
samples are soft magnets with saturation magnetization Bs ~0.744 T.


  For a variety with a Whitney stratification by affine spaces, we study
categories of motivic sheaves which are constant mixed Tate along the strata.
We are particularly interested in those cases where the category of mixed Tate
motives over a point is equivalent to the category of finite-dimensional
bigraded vector spaces. Examples of such situations include rational motives on
varieties over finite fields and modules over the spectrum representing the
semisimplification of de Rham cohomology for varieties over the complex
numbers. We show that our categories of stratified mixed Tate motives have a
natural weight structure. Under an additional assumption of pointwise purity
for objects of the heart, tilting gives an equivalence between stratified mixed
Tate sheaves and the bounded homotopy category of the heart of the weight
structure. Specializing to the case of flag varieties, we find natural
geometric interpretations of graded category $\mathcal O$ and Koszul duality.


  We present a new set of zero metallicity models in the range 13-80 $\rm
M_\odot$ together to the associated explosive nucleosynthesis. These models are
fully homogeneous with the solar metallicity set we published in Limongi &
Chieffi (2006) and will be freely available at the web site
http://www.iasf-roma.inaf.it./orfeo/public{\_}html. A comparison between these
yields and an average star that represents the average behavior of most of the
very metal poor stars in the range $\rm -5.0<[Fe/H]<-2.5$ confirms previous
findings that only a fraction of the elemental [X/Fe] may be fitted by the
ejecta of $\it standard$ core collapse supernovae.