  We study the AJ conjecture that relates the A-polynomial and the colored
Jones polynomial of a knot in $S^3$. We confirm the AJ conjecture for
$(r,2)$-cables of the $m$-twist knot, for all odd integers $r$ satisfying
$\begin{cases} (r+8)(r-8m)>0 &{if~} m> 0, \\ r(r+8m-4)>0 &{if~} m<0.\end{cases}
$


  In this contribution to the panel discussion on ``The Future of Hadron B
Experiments'' held at the 8th International Conference on B Physics at Hadron
Machines (Beauty 2002) at Santiago de Compostela, Spain, we explore the physics
potential for B physics at CDF and DO in five years and beyond. After a brief
introduction to precision flavour physics, we concentrate our discussion on the
future of CP violation by evaluating the prospects for measuring the CKM angles
beta, gamma and alpha at the Tevatron Collider experiments CDF and DO by the
end of Run II.


  Ordinary algebra of formal power series in one variable is convenient to
study by means of the algebra of Riordan matrices and the Riordan group. In
this paper we consider algebra of formal power series without constant term,
isomorphic to the algebra of formal Dirichlet series. To study it, we introduce
matrices, similar to the Riordan matrices. As a result, some analogies between
two algebras becomes visible. For example, the Bell polynomials (polynomials of
partitions of number $n$ into $m$ parts) play a certain role in the ordinary
algebra. Similar polynomials (polynomials of decompositions of number $n$ into
$m$ factors) play a similar role in the considered algebra. Analog of the
Lagrange series for the considered algebra is also exists. In connection with
this analogy, we introduce matrix group, similar to the Riordan group and
called the Riordan-Dirichlet group. As an example, we consider analog of the
Abel's identities for this group.


  The Covid-19 outbreak of 2020 has required many governments to develop
mathematical-statistical models of the outbreak for policy and planning
purposes. This work provides a tutorial on building a compartmental model using
Susceptibles, Exposed, Infected, Recovered and Deaths status through time. A
Bayesian Framework is utilized to perform both parameter estimation and
predictions. This model uses interventions to quantify the impact of various
government attempts to slow the spread of the virus. Predictions are also made
to determine when the peak Active Infections will occur.


  Program visualizations help to form useful mental models of how programs
work, and to reason and debug code. But these visualizations exist at a fixed
level of abstraction, e.g., line-by-line. In contrast, programmers switch
between many levels of abstraction when inspecting program behavior. Based on
results from a formative study of hand-designed program visualizations, we
designed CrossCode, a web-based program visualization system for JavaScript
that leverages structural cues in syntax, control flow, and data flow to
aggregate and navigate program execution across multiple levels of abstraction.
In an exploratory qualitative study with experts, we found that CrossCode
enabled participants to maintain a strong sense of place in program execution,
was conducive to explaining program behavior, and helped track changes and
updates to the program state.


  We analyse the extraction of a coherent atomic beam from a trapped
Bose-Einstein condensate using a rf transition to a non-trapping state at T=0
K. Our quantum treatment fully takes gravity into account but neglects all
interactions in the free falling beam. We obtain an analytical expression of
the output rate and of the wave function of the extracted beam, i.e. the output
mode of the ``atom laser''. Our model reproduces satisfactorily experimental
data without any adjustable parameter.


  A group is Markov if it admits a prefix-closed regular language of unique
representatives with respect to some generating set, and strongly Markov if it
admits such a language of unique minimal-length representatives over every
generating set. This paper considers the natural generalizations of these
concepts to semigroups and monoids. Two distinct potential generalizations to
monoids are shown to be equivalent. Various interesting examples are presented,
including an example of a non-Markov monoid that nevertheless admits a regular
language of unique representatives over any generating set. It is shown that
all finitely generated commutative semigroups are strongly Markov, but that
finitely generated subsemigroups of virtually abelian or polycyclic groups need
not be. Potential connections with word-hyperbolic semigroups are investigated.
A study is made of the interaction of the classes of Markov and strongly Markov
semigroups with direct products, free products, and finite-index subsemigroups
and extensions. Several questions are posed.


  In this work, we propose a heuristic genetic algorithm (GA) for pruning
convolutional neural networks (CNNs) according to the multi-objective trade-off
among error, computation and sparsity. In our experiments, we apply our
approach to prune pre-trained LeNet across the MNIST dataset, which reduces
95.42% parameter size and achieves 16$\times$ speedups of convolutional layer
computation with tiny accuracy loss by laying emphasis on sparsity and
computation, respectively. Our empirical study suggests that GA is an
alternative pruning approach for obtaining a competitive compression
performance. Additionally, compared with state-of-the-art approaches, GA is
capable of automatically pruning CNNs based on the multi-objective importance
by a pre-defined fitness function.


  Recent years have seen a dramatic increase in the microarchitectural
complexity of processors. This increase in complexity presents a twofold
challenge for the field of computer architecture. First, no individual
architect can fully comprehend the complexity of the entire microarchitecture
of the core. This leads to increasingly specialized architects, who treat parts
of the core outside their particular expertise as black boxes. Second, with
increasing complexity, the field becomes decreasingly accessible to new
students of the field. When learning core microarchitecture, new students must
first learn the big picture of how the system works in order to understand how
the pieces all fit together. The tools used to study microarchitecture
experience a similar struggle. As with the microarchitectures they simulate, an
increase in complexity reduces accessibility to new users.
  In this work, we present ChampSim. ChampSim uses a modular design and
configurable structure to achieve a low barrier to entry into the field of
microarchitecural simulation. ChampSim has shown itself to be useful in
multiple areas of research, competition, and education. In this way, we seek to
promote access and inclusion despite the increasing complexity of the field of
computer architecture.


  It is shown by rigorous ab initio calculations that half-Heusler alloys of
transition metals and $d^0$ metals, defined by the valence electronic
configuration $ns^{1,2},(n-1)d^0$, can produce all kinds of half-metallic
behavior including the elusive Dirac half-semimetallicity that is reported for
the first time in the real 3D material CoKSb. Together with the predicted
magnetic and chemical stability, this paves the way for massless and
dissipationless spintronics of the future. Furthermore, the introduction of
$d^0$ atoms is shown to stabilize the otherwise instable chemical structure of
zinc-blende transition metal pnictides and chalcogeneides without altering the
$p$-$d$ exchange that is mainly responsible for their half-metallicity,
therefore, making their application in spintronic devices feasible.


  Eigen-channel approach to heavy-ion fusion reactions is exact only when the
excitation energy of the intrinsic motion is zero. In order to take into
account effects of finite excitation energy, we introduce an energy dependence
to weight factors in the eigen-channel approximation. Using two channel
problem, we show that the weight factors are slowly changing functions of
incident energy. This suggests that the concept of the fusion barrier
distribution still holds to a good approximation even when the excitation
energy of the intrinsic motion is finite. A transition to the adiabatic
tunneling, where the coupling leads to a static potential renormalization, is
also discussed.


  The de Haas-van Alphen-frequencies as well as the effective masses for a
magnetic field parallel to the crystallographic c-axis are calculated within
the local spin density approximation (LSDA) for MgB2 and TaB2. In addition, we
analyze the plasma frequencies computed for each Fermi surface sheet. We find a
large anisotropy of Fermi velocities in MgB2 in difference to the nearly
isotropic behavior in TaB2. We compare calculations performed within the
relativistic non-full potential augmented-spherical-wave (ASW) scheme and the
scalar-relativistic full potential local orbital (FPLO) scheme. A significant
dependence for small cross sections on the bandstructure method is found. The
comparison with the first available experimental de Haas-van Alphen-data by
Yelland et al. (Ref. 19) shows deviations from the electronic structure
calculated within both L(S)DA approaches although the cross section predicted
by FPLO are closer to the experimental data. The elucidation of the relevant
many-body effects beyond the standard LDA is considered as a possible key
problem to understand the superconductivity in MgB2.


  The question raised by Shimony and Stein is examined and used to explain in
more detail a key point of my proof that any theory that conforms to certain
general ideas of orthodox relativistic quantum field theory must permit
transfers of information over spacelike intervals. It is also explained why
this result is not a problem for relativistic quantum theory, but, on the
contrary, opens the door to a satisfactory realistic relativistic quantum
theory based on the ideas of Tomonaga, Schwinger, and von Neumann.


  Fudenberg and Harris' stochastic version of the classical replicator dynamics
is considered. The behavior of this diffusion process in the presence of an
evolutionarily stable strategy is investigated. Moreover, extinction of
dominated strategies and stochastic stability of strict Nash equilibria are
studied. The general results are illustrated in connection with a discrete war
of attrition. A persistence result for the maximum effort strategy is obtained
and an explicit expression for the evolutionarily stable strategy is derived.


  The study of frequency-hopping sequences (FHSs) has been focused on the
establishment of theoretical bounds for the parameters of FHSs as well as on
the construction of optimal FHSs with respect to the bounds. Peng and Fan
(2004) derived two lower bounds on the maximum nontrivial Hamming correlation
of an FHS set, which is an important indicator in measuring the performance of
an FHS set employed in practice.
  In this paper, we obtain two main results. We study the construction of new
optimal frequency-hopping sequence sets by using cyclic codes over finite
fields. Let $\mathcal{C}$ be a cyclic code of length $n$ over a finite field
$\mathbb{F}_q$ such that $\mathcal{C}$ contains the one-dimensional subcode $
\mathcal{C}_0=\{(\alpha,\alpha,\cdots,\alpha)\in \mathbb{F}_q^n\,|\,\alpha\in
\mathbb{F}_q\}. $ Two codewords of $\mathcal{C}$ are said to be equivalent if
one can be obtained from the other through applying the cyclic shift a certain
number of times. We present a necessary and sufficient condition under which
the equivalence class of any codeword in $\mathcal{C}\setminus\mathcal{C}_0$
has size $n$. This result addresses an open question raised by Ding {\it et
al.} in \cite{Ding09}. As a consequence, three new classes of optimal FHS sets
with respect to the Singleton bound are obtained, some of which are also
optimal with respect to the Peng-Fan bound at the same time. We also show that
the two Peng-Fan bounds are, in fact, identical.


  In this paper, we propose new coupled codes constructed by overlapping
circular spatially-coupled low-density parity-check (SC-LDPC) codes, which show
better asymptotic and finite-length decoding performance compared to the
conventional SC-LDPC codes. The performance improvement comes from the property
that the proposed codes effectively split into two separated SC-LDPC codes with
shorter chain length during the decoding process. We verify that the property
of the proposed codes is valid in asymptotic setting via analysis tools such as
the density evolution and the expected graph evolution. Experimental results
show that the proposed codes also outperform the conventional SC-LDPC codes in
terms of the finite-length performance under belief propagation decoding.


  An $n$-dimensional Hartogs domain $D_F$ with strongly pseudoconvex boundary
can be equipped with a natural \K metric $g_F$. In this paper we prove that if
$g_F$ is an extremal \K metric then $(D_F, g_F)$ is biholomorphically isometric
to the $n$-dimensional complex hyperbolic space.


  In 1968, R. Steinberg proved a theorem stating that the exterior powers of an
irreducible reflection representation of a Euclidean reflection group are again
irreducible and pairwise non-isomorphic. We extend this result to a more
general context where the inner product invariant under the group action may
not necessarily exist.


  In this paper, cylindric partitions into profiles $c=(1,1)$ and $c=(2,0)$ are
considered. The generating functions into unrestricted cylindric partitions and
cylindric partitions into distinct parts with these profiles are constructed.
The constructions are combinatorial and they connect the cylindric partitions
with ordinary partitions.


  We study some aspects of the Kaluza-Klein Melvin solution in M-theory. The
associated magnetic field has a maximal critical value $B=\pm 1/R$ where $R$ is
the radius of the compactification circle. It is argued that the Melvin
background of type IIA with magnetic field $B$ and of type 0A with magnetic
field $B'=B-1/R$ are equivalent. Evidence for this conjecture is provided using
a further circle compactification and a `9-11' flip. We show that partition
functions of nine-dimensional type IIA strings and of a $(-1)^F\sigma_{1/2}$
type IIA orbifold both with NS-NS Melvin fluxtubes are related by such shift of
the magnetic field. Then the instabilities of both IIA and 0A Melvin solutions
are analyzed. For each theory there is an instanton associated to the decay of
spacetime. In the IIA case the decay mode is associated to the nucleation of
$D6/D\bar{6}$-brane pairs, while in the 0A case spacetime decays through
Witten's bubble production.


  We prove that the reflection equation (RE) algebra $\La_R$ associated with a
finite dimensional representation of a quasitriangular Hopf algebra $\Ha$ is
twist-equivalent to the corresponding Faddeev-Reshetikhin-Takhtajan (FRT)
algebra. We show that $\La_R$ is a module algebra over the twisted tensor
square \twist{$\Ha$}{$\Ha$} and the double $\D(\Ha)$. We define FRT- and
RE-type algebras and apply them to the problem of equivariant quantization on
Lie groups and matrix spaces.


  An analytical solution for symmetric Skyrmion was proposed for the SU(2)
Skyrme model, which take the form of the hybrid form of a kink-like solution
and that given by the instanton method. The static properties of nucleons was
then computed within the framework of collective quantization of the Skyrme
model, with a good agreement with that given by the exact numeric solution. The
comparisons with the previous results as well as the experimental values are
also given.


  High precision spectrometry of galactic cosmic rays (CR) has revealed the
lack of our understanding of how different CR elements are extracted from the
supernova environments to be further accelerated in their shocks. Comparing the
spectra of accelerated particles with different mass to charge ratios is a
powerful tool for studying the physics of particle injection into the diffusive
shock acceleration (DSA). Recent AMS-02 demonstration of the similarity of
He/$p$, C/$p$, and O/$p$ rigidity spectra has provided new evidence that
injection is a mass-to-charge dependent process. We performed hybrid
simulations of collisionless shocks and analyzed a joint injection of $p$ and
He$^{2+}$ in conjunction with upstream waves they generate. By implication, our
results equally apply to C and O fully ionized ions, since they have similar
mass to charge ratios. By convolving the time-dependent injection rates of $p$
and He, obtained from the simulations, with a decreasing shock strength over
the active life of SNRs, we generate the integrated SNR spectra for $p$ and He.
These spectra are consistent with the AMS-02 and Pamela data and earlier
theoretical predictions. Our interpretation of the elemental anomaly is
therefore intrinsic to collisionless shock mechanisms and does not require
additional assumptions, such as the contributions from several different SNRs,
their inhomogeneous environments or acceleration from grains.


  Synchrotron photon spectrum in heavy-ion collisions is computed taking into
account the spatial and temporal structure of magnetic field. It is found that
a significant fraction of photon excess in heavy-ion collisions in the region
$k_\bot=1-3$GeV can be attributed to the synchrotron radiation. Azimuthal
anisotropy of the synchrotron photon spectrum is characterized by the Fourier
coefficients $v_2=4/7$ and $v_4=1/10$ that are independent of photon momentum
and centrality.


  We propose that the periodic fast radio bursts of FRB 180916.J0158+65 are
sourced by axion emission (mass $m_{a} \sim 10^{-14}$ eV) from cosmic
superstrings. Some of the emitted axions are converted to photons by magnetic
fields as they travel along the line of sight to Earth. An impulsive burst of
axion emission generates a photon signal typically lasting for milliseconds and
varying with frequency in the observed manner. We find a range of parameters in
our cosmic string network model consistent with the properties of FRB
180916.J0158+65. We suggest followup gravitational wave observations to test
our model.


  We show a flow-augmentation algorithm in directed graphs: There exists a
randomized polynomial-time algorithm that, given a directed graph $G$, two
vertices $s,t \in V(G)$, and an integer $k$, adds (randomly) to $G$ a number of
arcs such that for every minimal $st$-cut $Z$ in $G$ of size at most $k$, with
probability $2^{-\mathrm{poly}(k)}$ the set $Z$ becomes a minimum $st$-cut in
the resulting graph. We also provide a deterministic counterpart of this
procedure.
  The directed flow-augmentation tool allows us to prove fixed-parameter
tractability of a number of problems parameterized by the cardinality of the
deletion set, whose parameterized complexity status was repeatedly posed as
open problems: (1) Chain SAT, defined by Chitnis, Egri, and Marx [ESA'13,
Algorithmica'17], (2) a number of weighted variants of classic directed cut
problems, such as Weighted $st$-Cut} or Weighted Directed Feedback Vertex Set.
By proving that Chain SAT is FPT, we confirm a conjecture of Chitnis, Egri, and
Marx that, for any graph $H$, if the List $H$-Coloring problem is
polynomial-time solvable, then the corresponding vertex-deletion problem is
fixed-parameter tractable.


  Over the summer of 2007, we obtained 1191 hours of `drift-scan' pulsar search
observations with the Green Bank Telescope at a radio frequency of 350 MHz.
Here we describe the survey setup, search procedure, and the discovery and
follow-up timing of thirteen pulsars. Among the new discoveries, one (PSR
J1623-0841) was discovered only through its single pulses, two (PSRs J1327-0755
and J1737-0814) are millisecond pulsars, and another (PSR J2222-0137) is a
mildly recycled pulsar. PSR J1327-0755 is a 2.7 ms pulsar at a DM of 27.9 pc
cm^{-3} in a 8.7 day orbit with a minimum companion mass of 0.22 solar mass.
PSR J1737-0814 is a 4.2 ms pulsar at a DM of 55.3 pc cm^{-3} in a 79.3 day
orbit with a minimum companion mass of 0.06 solar mass. PSR J2222-0137 is a
32.8 ms pulsar at a very low DM of 3.27 pc cm^{-3} in a 2.4 day orbit with a
minimum companion mass of 1.11 solar mass. It is most likely a white
dwarf-neutron star system or an unusual low-eccentricity double neutron star
system. Ten other pulsars discovered in this survey are reported in the
companion paper Lynch et al. 2012.


  We present our methods to fit the two point correlators for light, strange,
and charmed pseudoscalar meson physics with the highly improved staggered quark
(HISQ) action. We make use of the least-squares fit including the full
covariance matrix of the correlators and including Gaussian constraints on some
parameters. We fit the correlators on a variety of the HISQ ensembles. The
lattice spacing ranges from 0.15 fm down to 0.06 fm. The light sea quark mass
ranges from 0.2 times the strange quark mass down to the physical light quark
mass. The HISQ ensembles also include lattices with different volumes and with
unphysical values of the strange quark mass. We use the results from this work
to obtain our preliminary results of $f_D$, $f_{D_s}$, $f_{D_s}/f_{D}$, and
ratios of quark masses presented in another talk [1].


  We present a simple and physically compelling boundary condition
regularization scheme in the framework of effective field theory as applied to
nucleon-nucleon interaction. It is free of off-shell ambiguities and
ultraviolet divergences and provides finite results at any step of the
calculation. Low energy constants and their non-perturbative evolution can
directly be obtained from experimental threshold parameters in a completely
unique and model independent way when the long range explicit pion effects are
removed. This allows to compute scattering phase shifts which are, by
construction consistent with effective range expansion to a given order in the
CM momentum and are free from finite cut-off artifacts. We illustrate how the
method works in the $^1S_0$ channel for the One Pion Exchange potential.


  Deep learning has made significant progress in the past decade, and
demonstrates potential to solve problems with extensive social impact. In
high-stakes decision making areas such as law, experts often require
interpretability for automatic systems to be utilized in practical settings. In
this work, we attempt to address these requirements applied to the important
problem of legal citation prediction (LCP). We design the task with parallels
to the thought-process of lawyers, i.e., with reference to both precedents and
legislative provisions. After initial experimental results, we refine the
target citation predictions with the feedback of legal experts. Additionally,
we introduce a prototype architecture to add interpretability, achieving strong
performance while adhering to decision parameters used by lawyers. Our study
builds on and leverages the state-of-the-art language processing models for
law, while addressing vital considerations for high-stakes tasks with practical
societal impact.


  The dependence of Brans-Dicke (BD) parameter upon the scalar field, for
different cosmological era of the expanding universe, has been explored. The
time dependence of the scalar field has been determined and thereby the
explicit time dependence of the BD parameter has been obtained. Experimental
observations regarding the time dependence of the gravitational constant has
been considered for this study. The scale factor, Hubble parameter,
deceleration parameter, matter density, gravitational constant and the density
parameters for matter and dark energy have been expressed in terms of the BD
parameter and its time derivatives, showing its role in cosmic expansion.


  The performance of various systems of the Next Linear Collider (NLC) have
been studied in terms of ground motion using recently developed models. In
particular, the performance of the beam delivery system is discussed. Plans to
evaluate the operation of the main linac beam-based alignment and feedback
systems are also outlined.


  The study of finite approximations of probability measures has a long
history. In (Xu and Berger, 2017), the authors focus on constrained finite
approximations and, in particular, uniform ones in dimension $d=1$. The present
paper gives an elementary construction of a uniform decomposition of
probability measures in dimension $d\geq 1$. This decomposition is then used to
give upper-bounds on the rate of convergence of the optimal uniform
approximation error. These bounds appear to be the generalization of the ones
obtained in (Xu and Berger, 2017) and to be sharp for generic probability
measures.


  We present charged particle densities as a function of pseudorapidity and
collision centrality for the 197Au+197Au reaction at Sqrt{s_NN}=200 GeV. For
the 5% most central events we obtain dN_ch/deta(eta=0) = 625 +/- 55 and
N_ch(-4.7<= eta <= 4.7) = 4630+-370, i.e. 14% and 21% increases, respectively,
relative to Sqrt{s_NN}=130 GeV collisions. Charged-particle production per pair
of participant nucleons is found to increase from peripheral to central
collisions around mid-rapidity. These results constrain current models of
particle production at the highest RHIC energy.


  Multicomponent dark matter with self-interactions, which allows for
inter-conversions of species into one another, is a promising paradigm that is
known to successfully and simultaneously resolve major problems of the
conventional $\Lambda$CDM cosmology at galactic and sub-galactic scales. In
this paper, we present $N$-body simulations of the simplest two-component
(2cDM) model aimed at studying the distribution of dark matter halos with
masses $M\lesssim10^{12}M_\odot$. In particular, we investigate how the maximum
circular velocity function of the halos is affected by the velocity dependence
of the self-interaction cross-sections, $\sigma(v)\propto v^a$, and compare
them with available observational data. The results demonstrate that the 2cDM
paradigm with the range of self-interaction cross-section per particle mass
(evaluated at $v=100$ km s$^{-1}$) of $0.01\lesssim \sigma_0/m\lesssim 1 $
cm$^2$g$^{-1}$ and the mass degeneracy $\Delta m/m\sim 10^{-7}-10^{-8}$ is
robustly resolving the substructure and too-big-to-fail problems by suppressing
the substructure having small maximum circular velocities, $V_{\rm
max}\lesssim100$ km s$^{-1}$. We also discuss the disagreement between the
radial distribution of dwarfs in a host halo observed in the Local Group and
simulated with CDM. This can be considered as one more small-scale problem of
CDM. We demonstrate that such a disagreement is alleviated in 2cDM. Finally,
the computed matter power-spectra of the 2cDM structure indicate the model's
consistency with the existing Ly-$\alpha$ forest constraints.


  Galaxies modeled as singular isothermal ellipsoids with an axis ratio
distribution similar to the observed axis ratio distribution of E and S0
galaxies are statistically consistent with both the observed numbers of
two-image and four-image lenses and the inferred ellipticities of individual
lenses. However, no four-image lens is well fit by the model (typical
$\chi^2/N_{dof} \sim 20$), the axis ratio of the model can be significantly
different from that of the observed lens galaxy, and the major axes of the
model and the galaxy may be slightly misaligned. We found that models with a
second, independent, external shear axis could fit the data well (typical
$\chi^2/N_{dof} \sim 1$), while adding the same number of extra parameters to
the radial mass distribution does not produce such a dramatic improvement in
the fit. An independent shear axis can be produced by misalignments between the
luminous galaxy and its dark matter halo, or by external shear perturbations
due to galaxies and clusters correlated with the primary lens or along the line
of sight. We estimate that the external shear perturbations have no significant
effect on the expected numbers of two-image and four-image lenses, but that
they can be important perturbations in individual lens models. However, the
amplitudes of the external shears required to produce the good fits are larger
than our estimates for typical external shear perturbations (10-15% shear
instead of 1-3% shear) suggesting that the origin of the extra angular
structure must be intrinsic to the primary lens galaxy in most cases.


  We prove some results about the model theory of fields with a derivation of
the Frobenius map, especially that the model companion of this theory is
axiomatizable by axioms used by Wood in the case of the theory
$\operatorname{DCF}_p$ and that it eliminates quantifiers after adding the
inverse of the Frobenius map to the language. This strengthens the results from
[4]. As a by-product, we get a new geometric axiomatization of this model
companion. Along the way we also prove a quantifier elimination result, which
holds in a much more general context and we suggest a way of giving
"one-dimensional" axiomatizations for model companions of some theories of
fields with operators.


  We report a measurement of the CP-violating parameters in B0->K0S K0S decays
based on a data sample of 657 million BBbar pairs collected at the Y(4S)
resonance with the Belle detector at the KEKB asymmetric-energy e+e- collider.
In this study, one neutral B meson is fully reconstructed in the B0->K0S K0S
decay mode, and the flavor of the accompanying B meson is identified by its
decay products. The CP-violating parameters are measured from the asymmetry in
the distributions of the proper-time interval between the two B decays: S =
-0.38 +0.69-0.77(stat) +-0.09(syst) and A = -0.38 +-0.38(stat) +-0.05(syst).


  Wrong labeling problem and long-tail relations are two main challenges caused
by distant supervision in relation extraction. Recent works alleviate the wrong
labeling by selective attention via multi-instance learning, but cannot well
handle long-tail relations even if hierarchies of the relations are introduced
to share knowledge. In this work, we propose a novel neural network,
Collaborating Relation-augmented Attention (CoRA), to handle both the wrong
labeling and long-tail relations. Particularly, we first propose
relation-augmented attention network as base model. It operates on sentence bag
with a sentence-to-relation attention to minimize the effect of wrong labeling.
Then, facilitated by the proposed base model, we introduce collaborating
relation features shared among relations in the hierarchies to promote the
relation-augmenting process and balance the training data for long-tail
relations. Besides the main training objective to predict the relation of a
sentence bag, an auxiliary objective is utilized to guide the
relation-augmenting process for a more accurate bag-level representation. In
the experiments on the popular benchmark dataset NYT, the proposed CoRA
improves the prior state-of-the-art performance by a large margin in terms of
Precision@N, AUC and Hits@K. Further analyses verify its superior capability in
handling long-tail relations in contrast to the competitors.


  Deep learning has significantly advanced the field of gastrointestinal
vision, enhancing disease diagnosis capabilities. One major challenge in
automating diagnosis within gastrointestinal settings is the detection of
abnormal cases in endoscopic images. Due to the sparsity of data, this process
of distinguishing normal from abnormal cases has faced significant challenges,
particularly with rare and unseen conditions. To address this issue, we frame
abnormality detection as an out-of-distribution (OOD) detection problem. In
this setup, a model trained on In-Distribution (ID) data, which represents a
healthy GI tract, can accurately identify healthy cases, while abnormalities
are detected as OOD, regardless of their class. We introduce a test-time
augmentation segment into the OOD detection pipeline, which enhances the
distinction between ID and OOD examples, thereby improving the effectiveness of
existing OOD methods with the same model. This augmentation shifts the pixel
space, which translates into a more distinct semantic representation for OOD
examples compared to ID examples. We evaluated our method against existing
state-of-the-art OOD scores, showing improvements with test-time augmentation
over the baseline approach.


  Long-time coherent integration (LTCI) is one of the most important techniques
to improve radar detection performance of weak targets. However, for the
targets moving with constant Cartesian velocities (CCV), the existing LTCI
methods based on polynomial motion models suffer from limited integration time
and coverage of target speed due to model mismatch. Here, a novel generalized
Radon Fourier transform method for CCV targets is presented, based on the
accurate range evolving model, which is a square root of a polynomial with
terms up to the second order with target speed as the factor. The accurate
model instead of approximate polynomial models used in the proposed method
enables effective energy integration on characteristic invariant with feasible
computational complexity. The target samplings are collected and the phase
fluctuation among pulses is compensated according to the accurate range model.
The high order range migration and complex Doppler frequency migration caused
by the highly nonlinear signal are eliminated simultaneously. Integration
results demonstrate that the proposed method can not only achieve effective
coherent integration of CCV targets regardless of target speed and coherent
processing interval, but also provide additional observation and resolution in
speed domain.


  In this paper necessary and sufficient conditions are presented for heat
kernel upper bounds for random walks on weighted graphs. Several equivalent
conditions are given in the form of isoperimetric inequalities.


  We consider a linear non-autonomous evolutionary Cauchy problem
\begin{equation} \dot{u} (t)+A(t)u(t)=f(t) \hbox{ for }\ \hbox{a.e. t}\in
[0,T],\quad u(0)=u_0, \end{equation} where the operator $A(t)$ arises from a
time depending sesquilinear form $a(t,.,.)$ on a Hilbert space $H$ with
constant domain $V.$ Recently a result on $L^2$-maximal regularity in $H,$
i.e., for each given $f\in L^2(0,T,H)$ and $u_0 \in V$ the problem above has a
unique solution $u\in L^2(0,T,V)\cap H^1(0,T,H),$ is proved in [10] under the
assumption that $a$ is symmetric and of bounded variation. The aim of this
paper is to prove that the solutions of an approximate non-autonomous Cauchy
problem in which $a$ is symmetric and piecewise affine are closed to the
solutions of that governed by symmetric and of bounded variation form. In
particular, this provide an alternative proof of the result in [10] on
$L^2$-maximal regularity in $H.$


  e have investigated the magnetic properties of YbRu$_{2}$Ge$_{2}$ by means of
magnetic susceptibility $\chi$(T), specific heat C(T) and electrical
resistivity $\rho$(T) measurements performed on flux grown single crystals. The
Curie-Weiss behavior of $\chi$(T) along the easy plane, the large magnetic
entropy at low temperatures and the weak Kondo like increase in $\rho$(T)
proves a stable trivalent Yb state. Anomalies in C(T), $\rho$(T) and $\chi$(T)
at T$_{0}$ = 10.2 K, T$_{1}$ = 6.5 K and T$_{2}$ = 5.7 K evidence complex
ordering phenomena, T$_{0}$ being larger than the highest Yb magnetic ordering
temperature found up to now. The magnetic entropy just above T$_{0}$ amounts to
almost Rln4, indicating that the crystal electric field (CEF) ground state is a
quasiquartet instead of the expected doublet. The behavior at T$_{0}$ is rather
unusual and suggest that this transition is related to quadrupolar ordering,
being a consequence of the CEF quasiquartet ground state. The combination of a
quasiquartet CEF ground state, a high ordering temperature, and the relevance
of quadrupolar interactions makes YbRu$_{2}$Ge$_{2}$ a rather unique system
among Yb based compounds.


  We report the results of a SEST 1.2 mm continuum emission survey toward
regions previously identified as harbouring a methanol maser and/or an UC HII
region, typically indicative of massive star formation. Emission is detected
toward all of the methanol maser and UC HII regions targeted, implying that
these objescts are associated with cold, deeply embedded objects.
  We have also identified a large number (253) of sources within the SIMBA
maps, which are devoid of maser and radio continuum emission. These `mm-only'
cores may be an entirely new class of source that represents an earlier stage
in the evolution of massive stars, prior to the onset of methanol maser
emission. Or, they may harbour protoclusters, which do not contain any high
mass stars (i.e. below the HII). Alternatively, they may represent a
cross-section of both, where the more massive mm-only cores are a precursor to
the methanol maser and the least massive mm-only cores will form intermediate
mass stars.
  Analysis of the mm-only sources shows that they are less massive ({\= M} =
0.9 x 10^{3} Msun), and smaller ({\= R} = 0.4 pc) than sources with methanol
maser and/or radio continuum emission, which collectively have a mean mass of
2.5 x 10^{3} Msun and a mean radius of 0.7 pc.


  Under fairly general conditions, we show that families of integer-valued
polynomial-like multiplicative functions are uniformly distributed in coprime
residue classes mod $p$, where $p$ is a growing prime (or nearly prime)
modulus. This can be seen as complementary to work of Narkiewicz, who obtained
comprehensive results for fixed moduli.


  The Function-as-a-Service (FaaS) paradigm has a lot of potential as a
computing model for fog environments comprising both cloud and edge nodes. When
the request rate exceeds capacity limits at the edge, some functions need to be
offloaded from the edge towards the cloud.
  In this position paper, we propose an auction-based approach in which
application developers bid on resources. This allows fog nodes to make a local
decision about which functions to offload while maximizing revenue. For a first
evaluation of our approach, we use simulation.


  The Delayed-Choice Quantum Eraser experiment is commonly interpreted as
implying that in quantum mechanics a choice made at one time can influence an
earlier event. We here suggest an extension of the experiment that results in a
paradox when interpreted using a local realist interpretation combined with
backward causation ("retrocausality"). We argue that resolving the paradox
requires giving up the idea that, in quantum mechanics, a choice can influence
the past, and that it instead requires a violation of Statistical Independence
without retrocausality. We speculate what the outcome of the experiment would
be.


  We investigate manifolds with boundary in noncommutative geometry. Spectral
triples associated to a symmetric differential operator and a local boundary
condition are constructed. For a classical Dirac operator with a chiral
boundary condition, we show that there is no tadpole.


  The validity of using QCD perturbation theory to generate dynamically the
parton distribution functions of hadrons, starting from a valencelike input at
low $Q^2$, is discussed. In particular, we consider the prescription of Barone
{\em et al} who evolve from $Q^2 = 0$, and that of Gl\"uck {\em et al} who
start evolution from $Q^2 \simeq (2\Lambda_{QCD})^2$.


  The proton ordering model of the phase transition and physical properties of
antiferroelectric crystals of squaric acid is modified by taking into account
the influence of diagonal lattice strains and of the local geometry of hydrogen
bonds, namely of the distance $\delta$ between the H-sites on a bond. Thermal
expansion, the spontaneous strain $\varepsilon_1-\varepsilon_3$, and specific
heat of squaric acid are well described by the proposed model. However, a
consistent description of hydrostatic pressure influence on the transition
temperature is possible only with further modifications of the model.


  The paper deals with the automatic analysis of real-life telephone
conversations between an agent and a customer of a customer care service (ccs).
The application domain is the public transportation system in Paris and the
purpose is to collect statistics about customer problems in order to monitor
the service and decide priorities on the intervention for improving user
satisfaction. Of primary importance for the analysis is the detection of themes
that are the object of customer problems. Themes are defined in the application
requirements and are part of the application ontology that is implicit in the
ccs documentation. Due to variety of customer population, the structure of
conversations with an agent is unpredictable. A conversation may be about one
or more themes. Theme mentions can be interleaved with mentions of facts that
are irrelevant for the application purpose. Furthermore, in certain
conversations theme mentions are localized in specific conversation segments
while in other conversations mentions cannot be localized. As a consequence,
approaches to feature extraction with and without mention localization are
considered. Application domain relevant themes identified by an automatic
procedure are expressed by specific sentences whose words are hypothesized by
an automatic speech recognition (asr) system. The asr system is error prone.
The word error rates can be very high for many reasons. Among them it is worth
mentioning unpredictable background noise, speaker accent, and various types of
speech disfluencies. As the application task requires the composition of
proportions of theme mentions, a sequential decision strategy is introduced in
this paper for performing a survey of the large amount of conversations made
available in a given time period. The strategy has to sample the conversations
to form a survey containing enough data analyzed with high accuracy so that
proportions can be estimated with sufficient accuracy. Due to the unpredictable
type of theme mentions, it is appropriate to consider methods for theme
hypothesization based on global as well as local feature extraction. Two
systems based on each type of feature extraction will be considered by the
strategy. One of the four methods is novel. It is based on a new definition of
density of theme mentions and on the localization of high density zones whose
boundaries do not need to be precisely detected. The sequential decision
strategy starts by grouping theme hypotheses into sets of different expected
accuracy and coverage levels. For those sets for which accuracy can be improved
with a consequent increase of coverage a new system with new features is
introduced. Its execution is triggered only when specific preconditions are met
on the hypotheses generated by the basic four systems. Experimental results are
provided on a corpus collected in the call center of the Paris transportation
system known as ratp. The results show that surveys with high accuracy and
coverage can be composed with the proposed strategy and systems. This makes it
possible to apply a previously published proportion estimation approach that
takes into account hypothesization errors .


  Five parameters of a model of a polymer electrolyte membrane fuel cell
cathode (the porosity of the gas diffusion layer, the porosity of the catalyst
layer, the exchange current density of the oxygen reduction reaction, the
effective ionic conductivity of the electrolyte, and the ratio of the effective
diffusion coefficient of oxygen in a flooded spherical agglomerate particle to
the squared particle radius) were determined by the least square fitting of
experimental polarization curves. The values of parameters obtained in this
work indicate that ionic conduction and gas-phase transport are two processes
significantly influencing the performance of PEMFC air cathodes. While ionic
conduction influences cathode performance over a wide range of current
densities, gas-phase transport influences cathode performance only at high
current densities.


  We present a novel methodology that utilizes 4-Dimensional (4D) space
deformation to simulate a magnification lens on versatile volume datasets and
textured solid models. Compared with other magnification methods (e.g.,
geometric optics, mesh editing), 4D differential geometry theory and its
practices are much more flexible and powerful for preserving shape features
(i.e., minimizing angle distortion), and easier to adapt to versatile solid
models. The primary advantage of 4D space lies at the following fact: we can
now easily magnify the volume of regions of interest (ROIs) from the additional
dimension, while keeping the rest region unchanged. To achieve this primary
goal, we first embed a 3D volumetric input into 4D space and magnify ROIs in
the 4th dimension. Then we flatten the 4D shape back into 3D space to
accommodate other typical applications in the real 3D world. In order to
enforce distortion minimization, in both steps we devise the high dimensional
geometry techniques based on rigorous 4D geometry theory for 3D/4D mapping back
and forth to amend the distortion. Our system can preserve not only focus
region, but also context region and global shape. We demonstrate the
effectiveness, robustness, and efficacy of our framework with a variety of
models ranging from tetrahedral meshes to volume datasets.


  We study the effect of shear flow on homogeneous crystal nucleation, using
Brownian Dynamics simulations in combination with an umbrella sampling like
technique. The symmetry breaking due to shear results in anisotropic radial
distribution functions. The homogeneous shear rate suppresses crystal
nucleation and leads to an increase of the size of the critical nucleus. These
observations can be described by a simple, phenomenological extension of
classical nucleation theory. In addition, we find that nuclei have a
preferential orientation with respect to the direction of shear. On average the
longest dimension of a nucleus is along the vorticity direction, while the
shortest dimension is preferably perpendicular to that and slightly tilted with
respect to the gradient direction.


  Walgate and Scott have determined the maximum number of generic pure quantum
states that can be unambiguously discriminated by an LOCC measurement [Journal
of Physics A: Mathematical and Theoretical, 41:375305, 08 2008]. In this work,
we determine this number in a more general setting in which the local parties
have access to pre-shared entanglement in the form of a resource state. We find
that, for an arbitrary pure resource state, this number is equal to the Krull
dimension of (the closure of) the set of pure states obtainable from the
resource state by SLOCC. Surprisingly, a generic resource state maximizes this
number.
  Local state discrimination is closely related to the topic of entangled
subspaces, which we study in its own right. We introduce $r$-entangled
subspaces, which naturally generalize previously studied spaces to higher
multipartite entanglement. We use algebraic-geometric methods to determine the
maximum dimension of an $r$-entangled subspace, and present novel explicit
constructions of such spaces. We obtain similar results for symmetric and
antisymmetric $r$-entangled subspaces, which correspond to entangled subspaces
of bosonic and fermionic systems, respectively.


  Terminal devices (TDs) connect to networks through access points (APs)
integrated into the edge server. This provides a prerequisite for TDs to upload
tasks to cloud data centers or offload them to edge servers for execution. In
this process, signal coverage, data transmission, and task execution consume
energy, and the energy consumption of signal coverage increases sharply as the
radius increases. Lower power leads to less energy consumption in a given time
segment. Thus, power control for APs is essential for reducing energy
consumption. Our objective is to determine the power assignment for each AP
with same capacity constraints such that all TDs are covered, and the total
power is minimized. We define this problem as a \emph{minimum power capacitated
cover } (MPCC) problem and present a \emph{minimum local ratio} (MLR) power
control approach for this problem to obtain accurate results in polynomial
time. Power assignments are chosen in a sequence of rounds. In each round, we
choose the power assignment that minimizes the ratio of its power to the number
of currently uncovered TDs it contains. In the event of a tie, we pick an
arbitrary power assignment that achieves the minimum ratio. We continue
choosing power assignments until all TDs are covered. Finally, various
experiments verify that this method can outperform another greedy-based way.


  The driven Dicke model, wherein an ensemble of atoms is driven by an external
field and undergoes collective spontaneous emission due to coupling to a leaky
cavity mode, is a paradigmatic example of a system exhibiting a
driven-dissipative phase transition as a function of driving strength.
Recently, a similar phenomenon was experimentally observed, not in a cavity
setting, but rather in a free-space atomic ensemble. The reason why similar
behavior should emerge in free space is not obvious, as the system interacts
with a continuum of optical modes, which encodes light propagation effects.
Here, we present and solve a simple model to explain the behavior of the
free-space system, based on the one-dimensional Maxwell-Bloch equations. On one
hand, we show that a free-space ensemble at a low optical depth can exhibit
similar behavior as the cavity system, as spatial propagation effects are
negligible. On the other hand, in the thermodynamic limit of large atom number,
we show that certain observables such as the transmittance or the atomic
excited population exhibit non-analytic behavior as a function of the driving
intensity, reminiscent of a phase transition. However, a closer analysis
reveals that the atomic properties are highly inhomogeneous in space, and based
on this we argue that the free-space system does not undergo a phase transition
but rather a ``phase separation", roughly speaking, between saturated and
unsaturated regions.


  We analyse various flow coefficients of anisotropic momentum distribution of
final state particles in mid-central ($b$ $=$ 5--9 $fm$) Au + Au collisions in
the beam energy range $\rm E_{\rm Lab}$ $=$ $1A -158A$ GeV. Different variants
of the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model, namely the
pure transport (cascade) mode and the hybrid mode, are employed for this
investigation. In the hybrid UrQMD model, the ideal hydrodynamical evolution is
integrated with the pure transport calculation for description of the evolution
of the fireball. We opt for the different available equations of state (EoS)
replicating the hadronic as well as partonic degrees of freedom together with
possible phase transitions, viz. hadron gas, chiral + deconfinement EoS and bag
model EoS, to investigate their effect on the properties of the final state
particles. We also attempt to gain insights about the dynamics of the medium by
studying different features of particle production such as particle ratios and
net-proton rapidity distribution. The results and conclusions drawn here would
be useful to understand the response of various observables to the underlying
physics of the model as well as to make comparisons with the upcoming
measurements of the future experiments at Facility for Antiproton and Ion
Research (FAIR) and Nuclotron-based Ion Collider fAcility (NICA).


  We study the gravitationally-dominated, accretion-driven evolution of a
prestellar core. In our model, as the core's density increases, it remains
immersed in a constant-density environment and so it accretes from this
environment, increasing its mass and reducing its Jeans length. Assuming a
power-law density profile $\rho \propto r^{-p}$, we compute the rate of change
of the slope $p$, and show that the value $p=2$ is stationary, and furthermore,
an attractor. The radial profile of the Jeans length scales as $r^{p/2}$,
implying that, for $p<2$, there is a radius below which the region is smaller
than its Jeans length, thus appearing gravitationally stable and in need of
pressure confinement, while, in reality, it is part of a larger-scale collapse
and is undergoing compression by the infalling material. In this region, the
infall speed decreases towards the center, eventually becoming subsonic, thus
appearing "coherent", without the need for turbulence dissipation. We present a
compilation of observational determinations of density profiles in dense cores
and show that the distribution of their slopes peaks at $p \sim 1.7$--1.9,
supporting the notion that the profile steepens over time. Finally, we discuss
the case of magnetic support in a core in which the field scales as $B \propto
\rho^\beta$. For the expected value of $\beta = 2/3$, this implies that the
mass to magnetic flux ratio also decreases towards the central parts of the
cores, making them appear magnetically supported, while in reality they may be
part of larger collapsing supercritical region. We conclude that local
signatures of either thermal or magnetic support are not conclusive evidence of
stability, that the gravitational instability of a region must be established
at the large scales, and that the prestellar stage of collapse is dynamic
rather than quasistatic.


  We study the moduli spaces of flat surfaces with prescribed conical
singularities. Veech showed that these spaces are diffeomorphic to the moduli
spaces of marked Riemann surfaces, and endowed with a natural volume form
depending on the orders of the singularities. We show that the volumes of these
spaces are finite. Moreover we show that they are explicitely computable by
induction on the Euler characteristics of the punctured surface for almost all
orders of the singularities.


  It is well known that the generative adversarial nets (GANs) are remarkably
difficult to train. The recently proposed Wasserstein GAN (WGAN) creates
principled research directions towards addressing these issues. But we found in
practice that gradient penalty WGANs (GP-WGANs) still suffer from training
instability. In this paper, we combine a Total Variational (TV) regularizing
term into the WGAN formulation instead of weight clipping or gradient penalty,
which implies that the Lipschitz constraint is enforced on the critic network.
Our proposed method is more stable at training than GP-WGANs and works well
across varied GAN architectures. We also present a method to control the
trade-off between image diversity and visual quality. It does not bring any
computation burden.


  Treating the MSSM as an effective theory below a threshold scale Lambda, we
study the consequences of having dimension-five operators in the superpotential
for flavour and CP-violating processes. Below the supersymmetric threshold such
terms generate flavour changing and/or CP-odd effective operators of dimension
six composed from the Standard Model fermions, that have the interesting
property of decoupling linearly with the threshold scale, i.e. as 1/(Lambda
m_soft), where m_soft is the scale of soft supersymmetry breaking. The
assumption of weak-scale supersymmetry, together with the stringent limits on
electric dipole moments and lepton flavour-violating processes, then provides
sensitivity to Lambda as high as 10^7-10^9 GeV. We discuss the varying
sensitivity to these scales within several MSSM benchmark scenarios and also
outline the classes of UV physics which could generate these operators.


  We consider a class of resource allocation problems given a set of
unconditional constraints whose objective function satisfies Bellman's
optimality principle. Such problems are ubiquitous in wireless communication,
signal processing, and networking. These constrained combinatorial optimization
problems are, in general, NP-Hard. This paper proposes two algorithms to solve
this class of problems using a dynamic programming framework assisted by an
information-theoretic measure. We demonstrate that the proposed algorithms
ensure optimal solutions under carefully chosen conditions and use
significantly reduced computational resources. We substantiate our claims by
solving the power-constrained bit allocation problem in 5G massive
Multiple-Input Multiple-Output receivers using the proposed approach.


  Cloud service providers are often trusted to be genuine, the damage caused by
being discovered to be attacking their own customers outweighs any benefits
such attacks could reap. On the other hand, it is expected that some cloud
service users may be actively malicious. In such an open system, each location
may run code which has been developed independently of other locations (and
which may be secret). In this paper, we present a typed language which ensures
that the access restrictions put on data on a particular device will be
observed by all other devices running typed code. Untyped, compromised devices
can still interact with typed devices without being able to violate the
policies, except in the case when a policy directly places trust in untyped
locations. Importantly, our type system does not need a middleware layer or all
users to register with a preexisting PKI, and it allows for devices to
dynamically create new identities. The confidentiality property guaranteed by
the language is defined for any kind of intruder: we consider labeled
bisimilarity i.e. an attacker cannot distinguish two scenarios that differ by
the change of a protected value. This shows our main result that, for a device
that runs well typed code and only places trust in other well typed devices,
programming errors cannot cause a data leakage.


  Attention mechanism is one of the most successful techniques in deep learning
based Natural Language Processing (NLP). The transformer network architecture
is completely based on attention mechanisms, and it outperforms
sequence-to-sequence models in neural machine translation without recurrent and
convolutional layers. Grapheme-to-phoneme (G2P) conversion is a task of
converting letters (grapheme sequence) to their pronunciations (phoneme
sequence). It plays a significant role in text-to-speech (TTS) and automatic
speech recognition (ASR) systems. In this paper, we investigate the application
of transformer architecture to G2P conversion and compare its performance with
recurrent and convolutional neural network based approaches. Phoneme and word
error rates are evaluated on the CMUDict dataset for US English and the NetTalk
dataset. The results show that transformer based G2P outperforms the
convolutional-based approach in terms of word error rate and our results
significantly exceeded previous recurrent approaches (without attention)
regarding word and phoneme error rates on both datasets. Furthermore, the size
of the proposed model is much smaller than the size of the previous approaches.


  Highways always have a huge impact on the environment, quantifying the level
of pollution and calculating the traffic capacity under environmental
constraints is an important part of practicing environmental protection.
Available traffic capacity methods do not focus on the traffic emissions impact
on road region environment. To fill this research gap, this paper proposes a
method consisting of Computational Fluid Dynamics (CFD) model and Traffic Flow
Field and the Gas Flow Field coupled model for the traffic capacity model. The
COPERT 5 model is adopted to simulate the traffic emissions on highways with
field measurement data and traffic conditions as initial conditions. Then,
these are served as the inputs, set and further calculated using coupled model
in the CFD numerical simulation phase. Finally, modeling the Traffic Capacity
model and calculating the traffic capacity. The experiment results in G30
Yongshan highway section demonstrate that, with the proposed method, the CFD
simulation model for traffic emissions in highway region can reflect the
practical conditions (the overall error is within 10%), while the traffic
capacity can be well performance with the traffic capacity model under the
atmospheric pollution constraints. The annual average traffic capacity of G30
Yongshan is 739,800 vehicles, and SO2 under different constraints has the
greatest limitation on traffic capacity (90,9662 vehicles), which is the most
dominant impact in the environmental constraints of the road region, rest are
ranked in: NO2 (246,5373), PM2.5 (323,6022), CO (771,8717), CO2 (803,7217), and
PM10 (2202,0013).


  This article, based on joint work with Gabriel Carroll, Andy Itsara, Ian Le,
Gregg Musiker, Gregory Price, Dylan Thurston, and Rui Viana, presents a
combinatorial model based on perfect matchings that explains the symmetries of
the numerical arrays that Conway and Coxeter dubbed frieze patterns. This
matchings model is a combinatorial interpretation of Fomin and Zelevinsky's
cluster algebras of type A. One can derive from the matchings model an
enumerative meaning for the Markoff numbers, and prove that the associated
Laurent polynomials have positive coefficients as was conjectured (much more
generally) by Fomin and Zelevinsky. Most of this research was conducted under
the auspices of REACH (Research Experiences in Algebraic Combinatorics at
Harvard).


  Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bound
algorithm. Recently, Learning to imitate fast approximations of the expert
strong branching heuristic has gained attention due to its success in reducing
the running time for solving MIPs. However, existing learning-to-branch methods
assume that the entire training data is available in a single session of
training. This assumption is often not true, and if the training data is
supplied in continual fashion over time, existing techniques suffer from
catastrophic forgetting. In this work, we study the hitherto unexplored
paradigm of Lifelong Learning to Branch on Mixed Integer Programs. To mitigate
catastrophic forgetting, we propose LIMIP, which is powered by the idea of
modeling an MIP instance in the form of a bipartite graph, which we map to an
embedding space using a bipartite Graph Attention Network. This rich embedding
space avoids catastrophic forgetting through the application of knowledge
distillation and elastic weight consolidation, wherein we learn the parameters
key towards retaining efficacy and are therefore protected from significant
drift. We evaluate LIMIP on a series of NP-hard problems and establish that in
comparison to existing baselines, LIMIP is up to 50% better when confronted
with lifelong learning.


  In many published articles isobaric behavior in DAC chambers has been
reported On the other hand, a clear isochoric behavior in the chambers were
reported. Thus, Isochoric or isobaric condition remain an open question. In
isochoric condition, upon increasing the temperature, at each pressure, the
examined sample and the PTM both are subject to increases of their volumes
which is suppressed by the chamber finite volume, provoking an increase in the
thermal pressure over the whole system. For this reason, the pressure scale
reported in DAC experiments do not represent the actual pressure experienced by
the sample in the cell. The different response of the PTMs to P,T changes is
the reason for the variety of melting curves reported in the literature. It is
concluded that the pressure scale in DAC experiments need a correction taking
in to account the thermal pressure shift. Trusting the first principals DFT- Z
methodology it is shown that the melting curve derived by this method should
serve as an anchor for the pressure scale correction.


  In a large-scale computing cluster, the job completions can be substantially
delayed due to two sources of variability, namely, variability in the job size
and that in the machine service capacity. To tackle this issue, existing works
have proposed various scheduling algorithms which exploit redundancy wherein a
job runs on multiple servers until the first completes. In this paper, we
explore the impact of variability in the machine service capacity and adopt a
rigorous analytical approach to design scheduling algorithms using redundancy
and checkpointing. We design several online scheduling algorithms which can
dynamically vary the number of redundant copies for jobs. We also provide new
theoretical performance bounds for these algorithms in terms of the overall job
flowtime by introducing the notion of a speedup function, based on which a
novel potential function can be defined to enable the corresponding competitive
ratio analysis. In particular, by adopting the online primal-dual fitting
approach, we prove that our SRPT+R Algorithm in a non-multitasking cluster is
$(1+\epsilon)$-speed, $\ O(\frac{1}{\epsilon})$-competitive. We also show that
our proposed Fair+R and LAPS+R($\beta$) Algorithms for a multitasking cluster
are $(4+\epsilon)$-speed, $\ O(\frac{1}{\epsilon})$-competitive and {($2 +
2\beta + 2\epsilon)$-speed $O(\frac{1}{\beta \epsilon})$-competitive}
respectively. We demonstrate via extensive simulations that our proposed
algorithms can significantly reduce job flowtime under both the
non-multitasking and multitasking modes.


  In this paper, we evaluate, up to QCD next-to-next-to-next-to-leading order,
the $B_c^*$ decay constant, which, within the nonrelativistic QCD (NRQCD)
framework, is factorized as the product of the short-distance coefficient (SDC)
and the long-distance matrix element. For the first time, the renormalization
constant and the anomalous dimension for the NRQCD vector current composed of
$c\bar{b}$, which are functions of the charm quark mass $m_c$, the bottom quark
mass $m_b$ and the factorization scale $\mu_\Lambda$, are obtained analytically
at $\mathcal{O}(\alpha_s^2)$ and $\mathcal{O}(\alpha_s^3)$. The SDC is
calculated up to $\mathcal{O}(\alpha_s^3)$ in perturbative expansion.
Explicitly, the $\mathcal{O}(\alpha_s^2)$ correction to the SDC is analytically
calculated in terms of logarithmic and polylogarithmic functions of $r\equiv
m_b/m_c$, and the $\mathcal{O}(\alpha_s^3)$ correction to the SDC is
numerically calculated at a series of values of $r$, ranging from $2.1$ to
$4.0$. Surprisingly, we find that the nontrivial part of the SDC at
$\mathcal{O}(\alpha_s^3)$ can be well estimated by a linear function of $r$. In
addition, We find that the $\mathcal{O}(\alpha_s^2)$ and
$\mathcal{O}(\alpha_s^3)$ corrections to the decay constant and decay width are
considerable and very significant, which indicates a very poor convergence for
the perturbative expansion.


  We discuss the calculation of the nf-contributions to the two-loop amplitude
for e+ e- -> q qbar g. The calculation uses an efficient method based on nested
sums. The result is presented in terms of multiple polylogarithms with simple
arguments, which allow for analytic continuation in a straightforward manner.


  We propose a new distributed-computing model, inspired by permissionless
distributed systems such as Bitcoin and Ethereum, that allows studying
permissionless consensus in a mathematically regular setting. Like in the
sleepy model of Pass and Shi, we consider a synchronous, round-by-round
message-passing system in which the set of online processors changes each
round. Unlike the sleepy model, the set of processors may be infinite.
Moreover, processors never fail; instead, an adversary can temporarily or
permanently impersonate some processors. Finally, processors have access to a
strong form of message-authentication that authenticates not only the sender of
a message but also the round in which the message was sent.
  Assuming that, each round, the adversary impersonates less than 1/2 of the
online processors, we present two consensus algorithms. The first ensures
deterministic safety and constant latency in expectation, assuming a
probabilistic leader-election oracle. The second ensures deterministic safety
and deterministic liveness assuming irrevocable impersonation and
eventually-stabilizing participation.
  The model is unrealistic in full generality. However, if we assume finitely
many processes and that the set of faulty processes remains constant, the model
coincides with a practically-motivated model: the static version of the sleepy
model.


  The diversity of structures in the Universe (from the smallest galaxies to
the largest superclusters) has formed under the pull of gravity from the tiny
primordial perturbations that we see imprinted in the cosmic microwave
background. A quantitative description of this process would require
description of motion of zillions of dark matter particles. This impossible
task is usually circumvented by coarse-graining the problem: one either
considers a Newtonian dynamics of "particles" with macroscopically large masses
or approximates the dark matter distribution with a continuous density field.
There is no closed system of equations for the evolution of the matter density
field alone and instead it should still be discretized at each timestep. In
this work we describe a method of solving the full 6-dimensional Vlasov-Poisson
equation via a system of auxiliary Schroedinger-like equations. The complexity
of the problem gets shifted into the choice of the number and shape of the
initial wavefunctions that should only be specified at the beginning of the
computation (we stress that these wavefunctions have nothing to do with quantum
nature of the actual dark matter particles). We discuss different prescriptions
to generate the initial wave functions from the initial conditions and
demonstrate the validity of the technique on two simple test cases. This new
simulation algorithm can in principle be used on an arbitrary distribution
function, enabling the simulation of warm and hot dark matter structure
formation scenarios.


  This paper studies algebraic properties of Hermitian solutions and Hermitian
definite solutions of the two types of matrix equation $AX = B$ and $AXA^* =
B$. We first establish a variety of rank and inertia formulas for calculating
the maximal and minimal ranks and inertias of Hermitian solutions and Hermitian
definite solutions of the matrix equations $AX = B$ and $AXA^* = B$, and then
use them to characterize many qualities and inequalities for Hermitian
solutions and Hermitian definite solutions of the two matrix equations and
their variations.


  In the present paper we consider Laplace deconvolution for discrete noisy
data observed on the interval whose length may increase with a sample size.
Although this problem arises in a variety of applications, to the best of our
knowledge, it has been given very little attention by the statistical
community. Our objective is to fill this gap and provide statistical treatment
of Laplace deconvolution problem with noisy discrete data. The main
contribution of the paper is explicit construction of an asymptotically
rate-optimal (in the minimax sense) Laplace deconvolution estimator which is
adaptive to the regularity of the unknown function. We show that the original
Laplace deconvolution problem can be reduced to nonparametric estimation of a
regression function and its derivatives on the interval of growing length T_n.
Whereas the forms of the estimators remain standard, the choices of the
parameters and the minimax convergence rates, which are expressed in terms of
T_n^2/n in this case, are affected by the asymptotic growth of the length of
the interval.
  We derive an adaptive kernel estimator of the function of interest, and
establish its asymptotic minimaxity over a range of Sobolev classes. We
illustrate the theory by examples of construction of explicit expressions of
Laplace deconvolution estimators. A simulation study shows that, in addition to
providing asymptotic optimality as the number of observations turns to
infinity, the proposed estimator demonstrates good performance in finite sample
examples.


  We prove the linear stability of Schwarzschild-Tangherlini spacetimes and
their Anti-de Sitter counterparts under Ricci flow for a special class of
perturbations. This is useful in the choice of suitable initial conditions in
numerical Ricci-flow-based algorithms for obtaining new solutions to the
Einstein equation when the cosmological constant is zero or negative. The Ricci
flow is a first-order renormalization group (RG) flow in string theory, and its
solutions are believed to approximate string field theory processes in certain
cases. Thus this result offers insights into the off-shell stability of these
Euclidean black hole geometries in string theory, as well as in the Euclidean
path integral approach to quantum gravity.


  We study approximate quantum low-density parity-check (QLDPC) codes, which
are approximate quantum error-correcting codes specified as the ground space of
a frustration-free local Hamiltonian, whose terms do not necessarily commute.
Such codes generalize stabilizer QLDPC codes, which are exact quantum
error-correcting codes with sparse, low-weight stabilizer generators (i.e. each
stabilizer generator acts on a few qubits, and each qubit participates in a few
stabilizer generators). Our investigation is motivated by an important question
in Hamiltonian complexity and quantum coding theory: do stabilizer QLDPC codes
with constant rate, linear distance, and constant-weight stabilizers exist?
  We show that obtaining such optimal scaling of parameters (modulo
polylogarithmic corrections) is possible if we go beyond stabilizer codes: we
prove the existence of a family of $[[N,k,d,\varepsilon]]$ approximate QLDPC
codes that encode $k = \widetilde{\Omega}(N)$ logical qubits into $N$ physical
qubits with distance $d = \widetilde{\Omega}(N)$ and approximation infidelity
$\varepsilon = \mathcal{O}(1/\textrm{polylog}(N))$. The code space is
stabilized by a set of 10-local noncommuting projectors, with each physical
qubit only participating in $\mathcal{O}(\textrm{polylog} N)$ projectors. We
prove the existence of an efficient encoding map, and we show that arbitrary
Pauli errors can be locally detected by circuits of polylogarithmic depth.
Finally, we show that the spectral gap of the code Hamiltonian is
$\widetilde{\Omega}(N^{-3.09})$ by analyzing a spacetime circuit-to-Hamiltonian
construction for a bitonic sorting network architecture that is spatially local
in $\textrm{polylog}(N)$ dimensions.


  This article describes a method to build syntactical dependencies starting
from the phrase structure parsing process. The goal is to obtain all the
information needed for a detailled semantical analysis. Interaction Grammars
are used for parsing; the saturation of polarities which is the core of this
formalism can be mapped to dependency relation. Formally, graph patterns are
used to express the set of constraints which control dependency creations.


  The Feynman-Vernon path integral formalism is used to derive the density
matrix of a quantum oscillator that is linearly coupled to an environmental
reservoir. Although low-temperature reservoirs thermalize the oscillator to the
usual Boltzmann distribution, reservoirs at intermediate temperatures reduce
this distribution to a single, coherent ground state. Associated with this
state is an imaginary frequency indicating an environment which absorbs energy
from the oscillator through the suppression of all excited modes. Further
increase of the environmental temperature results again in the thermalization
of the quantum oscillator to the expected Boltzmann distribution.
Qualitatively, this result could account for high-temperature quantum effects
including the superconducting properties of graphite grains as well as the
quantum coherence observed in photosynthetic systems.


  An integral representation result is obtained for the relaxation of a class
of energy functionals depend- ing on two vector fields with different
behaviors, which may appear in the context of image decomposition and
thermochemical equilibrium problems.


  With the rapid development of measurement technology, LiDAR and depth cameras
are widely used in the perception of the 3D environment. Recent learning based
methods for robot perception most focus on the image or video, but deep
learning methods for dynamic 3D point cloud sequences are underexplored.
Therefore, developing efficient and accurate perception method compatible with
these advanced instruments is pivotal to autonomous driving and service robots.
An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv)
is proposed in this paper to process dynamic 3D point cloud sequences. The
proposed convolution operation builds a regular receptive field around each
point by setting several virtual anchors around each point. The features of
neighborhood points are firstly aggregated to each anchor based on the
spatio-temporal attention mechanism. Then, anchor-based 3D convolution is
adopted to aggregate these anchors' features to the core points. The proposed
method makes better use of the structured information within the local region
and learns spatio-temporal embedding features from dynamic 3D point cloud
sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural
Networks (ASTA3DCNNs) are built for classification and segmentation tasks based
on the proposed ASTA3DConv and evaluated on action recognition and semantic
segmentation tasks. The experiments and ablation studies on MSRAction3D and
Synthia datasets demonstrate the superior performance and effectiveness of our
method for dynamic 3D point cloud sequences. Our method achieves the
state-of-the-art performance among the methods with dynamic 3D point cloud
sequences as input on MSRAction3D and Synthia datasets.


  Spin-dependent deconfined interaction in the Q\barQ system is derived from
the field correlators known from lattice and analytic calculations. As a result
hyperfine splitting is found numerically for charmonium, bottomonium and
strangeonium in the range T_c<T<2T_c. Spin-orbit interaction due to magnetic
correlators (the Thomas term) is able to produce numerous Q\barQ bound states
with accumulation point at M=m_Q+m_\barQ. Possible influence of these effects
on the thermodynamics of quark-gluon plasma is discussed.


  Different communities rely heavily on software, but use quite different
software development practices. {\bf Objective}: We wanted to measure the state
of the practice in the area of statistical software for psychology to
understand how it compares to best practices. {\bf Method}: We compared and
ranked 30 software tools with respect to adherence to best software engineering
practices on items that could be measured by end-users. {\bf Results} We found
that R packages use quite good practices, that while commercial packages were
quite usable, many aspects of their development is too opaque to be measures,
and that research projects vary a lot in their practices. {\bf Conclusion} We
recommend that more organizations adopt practices similar to those used by CRAN
to facilitate success, even for small teams. We also recommend close coupling
of source code and documentation, to improve verifiability.


  We offer a new, gradual approach to the largest girth problem for cubic
graphs. It is easily observed that the largest possible girth of all $n$-vertex
cubic graphs is attained by a $2$-connected graph $G=(V,E)$. By Petersen's
graph theorem, $E$ is the disjoint union of a $2$-factor and a perfect matching
$M$. We refer to the edges of $M$ as chords and classify the cycles in $G$ by
their number of chords. We define $\gamma_k(n)$ to be the largest integer $g$
such that every cubic $n$-vertex graph with a given perfect matching $M$ has a
cycle of length at most $g$ with at most $k$ chords. Here we determine this
function up to small additive constant for $k= 1, 2$ and up to a small
multiplicative constant for larger $k$.


  We reanalyze and expand upon models proposed in 2015 for linear dilaton black
holes, and use them to test several speculative ideas about black hole physics.
We examine ideas based on the definition of quantum extremal surfaces in
quantum field theory in curved space-time. The low energy effective field
theory of our model is the large N CGHS model, which includes the one loop
effects that are taken into account in the "island" proposal for understanding
the Page curve. Contrary to the results of the island analysis, that solution
leads to a singular geometry for the evaporated black hole. If the singularity
obeys Cosmic Censorship then Hawking evaporation leaves behind a remnant object
with a finite fraction of the black hole entropy. If the singularity becomes
naked at some point, boundary conditions on a time-like line emanating from
that point can produce a sensible model where we expect a Page curve. We show
that the fully UV complete model gives a correct Page curve, as it must since
the model is manifestly unitary. Recent result on replicawormholes suggest that
the island formula, which appears to involve only one loop computations, in
fact encodes non-perturbative contributions to the gravitational path integral.
The question of why Euclidean gravity computations can capture information
about microscopic states of quantum gravity remains mysterious. In a
speculative coda to the paper we suggest that the proper way of understanding
the relation between Euclidean gravity path integrals and quantum spectra is
via a statistical approach to Jacobson's interpretation of general relativistic
field equations as the hydrodynamic equations of the area law for the maximal
entropy of causal diamonds.


  The field of Distributed Constraint Optimization has gained momentum in
recent years thanks to its ability to address various applications related to
multi-agent cooperation. While techniques to solve Distributed Constraint
Optimization Problems (DCOPs) are abundant and have matured substantially since
the field inception, the number of DCOP realistic applications and benchmark
used to asses the performance of DCOP algorithms is lagging behind. To contrast
this background we (i) introduce the Smart Home Device Scheduling (SHDS)
problem, which describe the problem of coordinating smart devices schedules
across multiple homes as a multi-agent system, (ii) detail the physical models
adopted to simulate smart sensors, smart actuators, and homes environments, and
(iii) introduce a DCOP realistic benchmark for SHDS problems.


  We show that on superconducting spherical nanoshells, the co-existence of the
Meissner state with a variety of vortex patterns drives the phase transition to
higher magnetic fields. The spherical geometry leads to a Magnus-Lorentz force
pushing the nucleating vortices and antivortices towards the poles, overcoming
local pinning centers, preventing vortex-antivortex recombination and leading
to the appearance of a Meissner belt around the sphere's equator. In
sufficiently small and thin spherical shells paramagnetic vortex states can be
stable, enabling spatial separation of freely moving shells with different
radii and vorticity in an inhomogeneous external magnetic field.


  Ge$_{1-x}$Sn$_x$ semiconductors hold the premise for large-scale, monolithic
mid-infrared photonics and optoelectronics. However, despite the successful
demonstration of several Ge$_{1-x}$Sn$_x$-based photodetectors and emitters,
key fundamental properties of this material system are yet to be fully explored
and understood. In particular, little is known about the role of the material
properties in controlling the recombination mechanisms and their consequences
on the carrier lifetime. Evaluating the latter is in fact fraught with large
uncertainties that are exacerbated by the difficulty to investigate narrow
bandgap semiconductors. To alleviate these limitations, herein we demonstrate
that the radiative carrier lifetime can be obtained from straightforward
excitation power- and temperature- dependent photoluminescence measurements. To
this end, a theoretical framework is introduced to simulate the measured
spectra by combining the band structure calculations from the k.p theory and
the envelope function approximation (EFA) to estimate the absorption and
spontaneous emission. Based on this model, the temperature-dependent emission
from Ge$_{0.83}$Sn$_{0.17}$ samples at a biaxial compressive strain of $-1.3\%$
was investigated. The simulated spectra reproduce accurately the measured data
thereby enabling the evaluation of the steady-state radiative carrier
lifetimes, which are found in the 3-22 ns range for temperatures between 10 and
300 K at an excitation power of 0.9 kW/cm$^2$. For a lower power of 0.07
kW/cm$^2$, the obtained lifetime has a value of 1.9 ns at 4 K. The demonstrated
approach yielding the radiative lifetime from simple emission spectra will
provide valuable inputs to improve the design and modeling of
Ge$_{1-x}$Sn$_x$-based devices.


  Accurate constraints on curvature provide a powerful probe of inflation.
However, curvature constraints based on specific assumptions of dark energy may
lead to unreliable conclusions when used to test inflation models. To avoid
this, it is important to obtain constraints that are independent on assumptions
for dark energy. In this paper, we investigate such constraints on curvature
from the geometrical probe constructed from galaxy-lensing cross-correlations.
We study comprehensively the cross-correlations of galaxy with magnification,
measured from type Ia supernovae's brightnesses ("$g\kappa^{\rm SN}$"), with
shear ("$g\kappa^{\rm g}$"), and with CMB lensing ("$g\kappa^{\rm CMB}$"). We
find for the LSST and Stage IV CMB surveys, "$g\kappa^{\rm SN}$" ,
"$g\kappa^{\rm g}$" and "$g\kappa^{\rm CMB}$" can be detected with
signal-to-noise ratio $S/N=104,\ 2291,\ 1842$ respectively. When combined with
supernovae Hubble diagram ("SN") to constrain curvature, we find galaxy-lensing
cross-correlation becomes increasingly important with more degrees of freedom
allowed in dark energy. Without any priors, we obtain error on $\Omega_K$ of
$0.723$ from "SN + $g\kappa^{\rm SN}$", $0.0417$ from "SN + $g\kappa^{\rm g}$",
and $0.04$ from "SN + $g\kappa^{\rm g}$ + $g\kappa^{\rm CMB}$" for the LSST and
Stage IV CMB surveys. The last one is more competitive than a Stage IV BAO
survey ("BAO"). When galaxy-lensing cross-correlations are added to the
combined probe of "SN + BAO + CMB", where "CMB" stands for Planck measurement
for the CMB acoustic scale, we obtain constraint on $\Omega_K$ of $0.0013$,
which is a factor of 7 improvement from "SN + BAO + CMB". We study improvements
in these results from increasing the high redshift extension of supernovae.


  Ehrhart discovered that the function that counts the number of lattice points
in dilations of an integral polytope is a polynomial. We call the coefficients
of this polynomial Ehrhart coefficients, and say a polytope is Ehrhart positive
if all Ehrhart coefficients are positive (which is not true for all integral
polytopes). The main purpose of this article is to survey interesting families
of polytopes that are known to be Ehrhart positive and discuss the reasons from
which their Ehrhart positivity follows. We also include examples of polytopes
that have negative Ehrhart coefficients and polytopes that are conjectured to
be Ehrhart positive, as well as pose a few relevant questions.


  In the range closest pair problem, we want to construct a data structure
storing a set $S$ of $n$ points in the plane, such that for any axes-parallel
query rectangle $R$, the closest pair in the set $R \cap S$ can be reported.
The currently best result for this problem is by Xue et al.~(SoCG 2018). Their
data structure has size $O(n \log^2 n)$ and query time $O(\log^2 n)$. We show
that a data structure of size $O(n \log n)$ can be constructed in $O(n \log n)$
time, such that queries can be answered in $O(\log n + f \log f)$ time, where
$f$ is the aspect ratio of $R$. Thus, for fat query rectangles, the query time
is $O(\log n)$. This result is obtained by reducing the range closest pair
problem to standard range searching problems on the points of $S$.


  Qualitative calculi play a central role in representing and reasoning about
qualitative spatial and temporal knowledge. This paper studies distributive
subalgebras of qualitative calculi, which are subalgebras in which (weak)
composition distributives over nonempty intersections. It has been proven for
RCC5 and RCC8 that path consistent constraint network over a distributive
subalgebra is always minimal and globally consistent (in the sense of strong
$n$-consistency) in a qualitative sense. The well-known subclass of convex
interval relations provides one such an example of distributive subalgebras.
This paper first gives a characterisation of distributive subalgebras, which
states that the intersection of a set of $n\geq 3$ relations in the subalgebra
is nonempty if and only if the intersection of every two of these relations is
nonempty. We further compute and generate all maximal distributive subalgebras
for Point Algebra, Interval Algebra, RCC5 and RCC8, Cardinal Relation Algebra,
and Rectangle Algebra. Lastly, we establish two nice properties which will play
an important role in efficient reasoning with constraint networks involving a
large number of variables.


  Nonlinear state-space models are powerful tools to describe dynamical
structures in complex time series. In a streaming setting where data are
processed one sample at a time, simultaneous inference of the state and its
nonlinear dynamics has posed significant challenges in practice. We develop a
novel online learning framework, leveraging variational inference and
sequential Monte Carlo, which enables flexible and accurate Bayesian joint
filtering. Our method provides an approximation of the filtering posterior
which can be made arbitrarily close to the true filtering distribution for a
wide class of dynamics models and observation models. Specifically, the
proposed framework can efficiently approximate a posterior over the dynamics
using sparse Gaussian processes, allowing for an interpretable model of the
latent dynamics. Constant time complexity per sample makes our approach
amenable to online learning scenarios and suitable for real-time applications.


  Urban form and growth can be described with fractal dimension, which is a
measurement of space filling of urban evolution. Based on empirical analyses, a
discovery is made that the time series of fractal dimension of urban form can
be treated as a sigmoid function of time. Among various sigmoid functions, the
logistic function is the most probable selection. However, how to use the model
of fractal dimension growth to explain and predict urban growth is a pending
problem remaining to be solved. This paper is devoted to modeling fractal
dimension evolution of different types of cities. A interesting discovery is as
follows: for the cities in developed countries such as UK, USA and Israel, the
comparable fractal dimension values of a city's morphology in different years
can be fitted to the logistic function; while for the cities in developing
countries such as China, the fractal dimension data of urban form can be fitted
to a quadratic logistic function. A generalized logistic function is thus
proposed to model fractal dimension growth of urban form. The logistic model
can also be employed to characterize the change of the dimensions of
multifractals. The generalized logistic models can be used to predict the
missing values of fractal dimension, to estimate the growth rate of the
dimension, and thus to reveal the spatio-temporal process and pattern of a
city's growth. Especially, the models lay a foundation for researching the
correlation between urban form and urbanization and for developing the theory
of spatial replacement dynamics.


  In this paper we introduce a new method for text detection in natural images.
The method comprises two contributions: First, a fast and scalable engine to
generate synthetic images of text in clutter. This engine overlays synthetic
text to existing background images in a natural way, accounting for the local
3D scene geometry. Second, we use the synthetic images to train a
Fully-Convolutional Regression Network (FCRN) which efficiently performs text
detection and bounding-box regression at all locations and multiple scales in
an image. We discuss the relation of FCRN to the recently-introduced YOLO
detector, as well as other end-to-end object detection systems based on deep
learning. The resulting detection network significantly out performs current
methods for text detection in natural images, achieving an F-measure of 84.2%
on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per
second on a GPU.


  Efficient numerical methods are still lacking to probe the unconventional
dynamics of quantum many-body systems under non-unitary evolution. In this
work, we use Faber polynomials to numerically simulate both the dynamics of
non-Hermitian systems and the quantum jumps unravelling of the Lindblad
dynamics. We apply the method to the non-interacting and interacting
Hatano-Nelson models evolving from two different setups: i) a N\'eel state, and
ii) a domain wall. In the first case, we study how interactions preserve the
initial magnetic order against the skin effect. In the second example, we
present numerical evidence of the existence of an effective hydrodynamic
description for the domain-wall melting problem in the non-interacting limit.
Additionally, we investigate both the conditional and unconditional dynamics of
the quantum jump unravelling in two quantum spin chains, which exhibit either
the non-Hermitian or the Liouvillian skin effect. This numerical method
inherently generalises the well-established method based on Chebyshev
polynomials to accommodate non-Hermitian scenarios.


  While neural networks have acted as a strong unifying force in the design of
modern AI systems, the neural network architectures themselves remain highly
heterogeneous due to the variety of tasks to be solved. In this chapter, we
explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used
for explaining the predictions of feed-forward networks to the LSTM
architecture used for sequential data modeling and forecasting. The special
accumulators and gated interactions present in the LSTM require both a new
propagation scheme and an extension of the underlying theoretical framework to
deliver faithful explanations.


  Shear viscosity, though being a fundamental property of all liquids, is
computationally expensive to estimate from equilibrium molecular dynamics
simulations. Recently, Machine Learning (ML) methods have been used to augment
molecular simulations in many contexts, thus showing promise to estimate
viscosity too in a relatively inexpensive manner. However, ML methods face
significant challenges like overfitting when the size of the data set is small,
as is the case with viscosity. In this work, we train several ML models to
predict the shear viscosity of a Lennard-Jones (LJ) fluid, with particular
emphasis on addressing issues arising from a small data set. Specifically, the
issues related to model selection, performance estimation and uncertainty
quantification were investigated. First, we show that the widely used
performance estimation procedure of using a single unseen data set shows a wide
variability on small data sets. In this context, the common practice of using
Cross validation (CV) to select the hyperparameters (model selection) can be
adapted to estimate the generalization error (performance estimation) as well.
We compare two simple CV procedures for their ability to do both model
selection and performance estimation, and find that k-fold CV based procedure
shows a lower variance of error estimates. We discuss the role of performance
metrics in training and evaluation. Finally, Gaussian Process Regression (GPR)
and ensemble methods were used to estimate the uncertainty on individual
predictions. The uncertainty estimates from GPR were also used to construct an
applicability domain using which the ML models provided more reliable
predictions on another small data set generated in this work. Overall, the
procedures prescribed in this work, together, lead to robust ML models for
small data sets.


  In this paper, we study the Galois conjugates of stretch factors of
pseudo-Anosov elements of the mapping class group of a surface. We show that -
except in low-complexity cases - these conjugates are dense in the complex
plane. For this, we use Penner's construction of pseudo-Anosov mapping classes.
As a consequence, we obtain that in a sense there is no restriction on the
location of Galois conjugates of stretch factors arising from Penner's
construction. This complements an earlier result of Shin and the author stating
that Galois conjugates of stretch factors arising from Penner's construction
may never lie on the unit circle.


  A distinct signature for dark matter in the form of weakly interacting
massive particles (WIMPs) would be the detection of a monochromatic spectral
line in the gamma-ray sky. The Fermi-LAT collaboration has searched for such a
line in the energy range from 5 to 300 GeV in five sky regions around the
Galactic centre. No globally significant line is detected, and 95% CL upper
limits on monochromatic-line strengths are presented. The smallest search
region reveals a line-like structure at 133 GeV with a local significance of
2.9 sigma after 4.4 years of data, which translates to less than 1 sigma global
significance from a trial factor of around 200.


  A well-defined notion of chemical compound space (CCS) is essential for
gaining rigorous control of properties through variation of elemental
composition and atomic configurations. Here, we review an atomistic first
principles perspective on CCS. First, CCS is discussed in terms of variational
nuclear charges in the context of conceptual density functional and molecular
grand-canonical ensemble theory. Thereafter, we revisit the notion of compound
pairs, related to each other via "alchemical" interpolations involving
fractional nuclear chargens in the electronic Hamiltonian. We address Taylor
expansions in CCS, property non-linearity, improved predictions using reference
compound pairs, and the ounce-of-gold prize challenge to linearize CCS.
Finally, we turn to machine learning of analytical structure property
relationships in CCS. These relationships correspond to inferred, rather than
derived through variational principle, solutions of the electronic
Schr\"odinger equation.


  We present an apparatus for the study of an ultracold gaseous atomic mixture
of 133Cs and 87Rb. The mixture is prepared using a double magneto-optical trap
(MOT) system in which a two-species pyramid MOT acts as a source of cold atoms
for a `science' MOT. Measurements of the interspecies trap loss rate
coefficients beta_RbCs and beta_CsRb in the science MOT are reported. After the
initial MOT phase, atoms in the mixture are optically pumped into the
magnetically trappable |F=3,m_F=-3> and |F=1,m_F=-1> states of Cs and Rb
(respectively) and loaded into an Ioffe-Pritchard magnetic trap. We demonstrate
a novel technique for limiting the interspecies loss rate in the science MOT by
spatially separating the two trapped atom clouds, which greatly enhances the
number of atoms which can be loaded into the magnetic trap.