  We consider mechanisms for markets that are double-sided and have players
with multi-dimensional strategic spaces on at least one side. The players of
the market are strategic, and act to optimize their own utilities. The
mechanism designer, on the other hand, aims to optimize a social goal, i.e.,
the gain from trade. We focus on one example of this setting which is motivated
by the foreseeable future form of online advertising.
  Online advertising currently supports some of the most important Internet
services, including: search, social media and user generated content sites. To
overcome privacy concerns, it has been suggested to introduce user information
markets through information brokers into the online advertising ecosystem. Such
markets give users control over which data get shared in the online advertising
exchange. We describe a model for the above foreseeable future form of online
advertising, and design two mechanisms for the exchange of this model: a
deterministic mechanism which is related to the vast literature on mechanism
design through trade reduction and allows players with a multi-dimensional
strategic space, and a randomized mechanism which can handle a more general
version of the model.


  Mathematical equivalence between statistical mechanics and machine learning
theory has been known since the 20th century, and researches based on such
equivalence have provided novel methodology in both theoretical physics and
statistical learning theory. For example, algebraic approach in statistical
mechanics such as operator algebra enables us to analyze phase transition
phenomena mathematically. In this paper, for theoretical physicists who are
interested in artificial intelligence, we review and prospect algebraic
researches in machine learning theory. If a learning machine has hierarchical
structure or latent variables, then the random Hamiltonian cannot be expressed
by any quadratic perturbation because it has singularities. To study an
equilibrium state defined by such a singular random Hamiltonian, algebraic
approach is necessary to derive asymptotic form of the free energy and the
generalization error. We also introduce the most recent advance, in fact,
theoretical foundation for alignment of artificial intelligence is now being
constructed based on algebraic learning theory. This paper is devoted to the
memory of Professor Huzihiro Araki who is a pioneer founder of algebraic
research in both statistical mechanics and quantum field theory.


  Although recent neural text-to-speech (TTS) systems have achieved
high-quality speech synthesis, there are cases where a TTS system generates
low-quality speech, mainly caused by limited training data or information loss
during knowledge distillation. Therefore, we propose a novel method to improve
speech quality by training a TTS model under the supervision of perceptual
loss, which measures the distance between the maximum possible speech quality
score and the predicted one. We first pre-train a mean opinion score (MOS)
prediction model and then train a TTS model to maximize the MOS of synthesized
speech using the pre-trained MOS prediction model. The proposed method can be
applied independently regardless of the TTS model architecture or the cause of
speech quality degradation and efficiently without increasing the inference
time or model complexity. The evaluation results for the MOS and phone error
rate demonstrate that our proposed approach improves previous models in terms
of both naturalness and intelligibility.


  A thermoelectric generator converts a temperature difference into electrical
energy. Its energy conversion efficiency is determined by the steady-state
temperature distribution inside the generator. By assuming the thermoelectric
material in the generator has a temperature-independent Seebeck coefficient and
the generator is one-dimensional, we show that the second-order
integro-differential equation describing the inside temperature distribution
has a unique solution for any given ratio of external load resistance to the
internal resistance. Hence the efficiency is well defined. Furthermore, we show
the efficiency has an explicit formula in terms of the temperature-dependent
thermal conductivity and electrical resistivity of the thermoelectric material.
On the other hand, if we impose an external load resistance value, not the
ratio, then the integro-differential equation can have multiple solutions.


  Pedestrian route choice is a complex, situation- and population-dependent
issue. In this contribution an example is presented, where pedestrians can
choose among two seemingly similar alternatives. The choice ratio is not even
close to being balanced, but almost all pedestrians choose the same
alternative. A number of possible causes for this are given.


  We propose an experiment utilizing an array of cooled micro-cantilevers
coupled to a sample of ultra-cold atoms trapped near a micro-fabricated
surface. The cantilevers allow individual lattice site addressing for atomic
state control and readout, and potentially may be useful in optical lattice
quantum computation schemes. Assuming resonators can be cooled to their
vibrational ground state, the implementation of a two-qubit controlled-NOT gate
with atomic internal states and the motional states of the resonator is
described. We also consider a protocol for entangling two or more cantilevers
on the atom chip with different resonance frequencies, using the trapped atoms
as an intermediary. Although similar experiments could be carried out with
magnetic microchip traps, the optical confinement scheme we consider may
exhibit reduced near-field magnetic noise and decoherence. Prospects for using
this novel system for tests of quantum mechanics at macroscopic scales or
quantum information processing are discussed.


  Terahertz (THz) metamaterials have been developed for THz sensing, detection,
imaging, and many other functions due to their unusual absorbers. However, the
unusual absorption spectra change with different incident angles. Thus, we
designed and fabricated a focal plane array with metal-insulator-metal (MIM)
structure metamaterial absorbers for further research. The absorption spectrum
with incident angles from 20 to 60 was measured using THz time-domain
spectroscopy (THz-TDS), and the experimental results reveal that the absorption
spectrum changes with incident angle variations. A basic analytical asymmetric
peak model for extracting absorption-frequency characteristics was developed in
this study to quantitatively explore this variation in the absorber behavior
with incident angles. The best result was that the frequency corresponding to
the highest absorption can be easily found using this peak model. The
experimental data was coherent with the validation of the asymmetric peak
model. Moreover, a second model to quantitatively relate parameters to the
incident angle was discovered, allowing for the prediction of absorption
spectrum shifts and changes. The absorption spectrum was predicted to have a
valley-like absorption curve at particular incident angles based on the
secondary models deduction. The proposed extraction method's essential feature
is that it can be applied to any physics-based MIM metamaterial system. Such a
model will guide the design and optimization of THz metamaterial absorbers,
sensors, imagers, and many others.


  We discuss whether the NuTeV anomaly can be explained, compatibly with all
other data, by QCD effects (maybe, if the strange sea is asymmetric, or there
is a tiny violation of isospin), new physics in propagators or couplings of the
vector bosons (not really), loops of supersymmetric particles (no), dimension
six operators (yes, for one specific SU(2)-invariant operator), leptoquarks
(not in a minimal way), extra U(1) gauge bosons (maybe: an unmixed Z' coupled
to B-3L_mu also increases the muon g-2 by about 10^{-9} and gives a `burst' to
cosmic rays above the GZK cutoff).


  The coherent stochastic resonance is observed and studied with multi-step
periodic signal in continuous medium having two absorbing boundaries. The
general features of this process are exihibited. The universal features at the
resonance point are demonstrated. The kinetic behaviors around the resonance
point are also presented.


  The co-evolution between central supermassive black holes (BH), their host
galaxies, and dark matter halos is still a matter of intense debate. Present
theoretical models suffer from large uncertainties and degeneracies, for
example, between the fraction of accreting sources and their characteristic
accretion rate. In recent work we showed that Active Galactic Nuclei (AGN)
clustering represents a powerful tool to break degeneracies when analysed in
terms of mean BH mass, and that AGN bias at fixed stellar mass is largely
independent of most of the input parameters, such as the AGN duty cycle and the
mean scaling between BH mass and host galaxy stellar mass. In this paper we
take advantage of our improved semi-empirical methodology and recent clustering
data derived from large AGN samples at $z \sim 1.2$, demonstrate that the AGN
bias as a function of host galaxy stellar mass is a crucial diagnostic of the
BH--galaxy connection, and is highly dependent on the scatter around the BH
mass--galaxy mass scaling relation and on the relative fraction of satellite
and central active BHs. Current data at $z \sim 1.2$ favour relatively high
values of AGN in satellites, pointing to a major role of disc instabilities in
triggering AGN, unless a high minimum host halo mass is assumed. The data are
not decisive on the magnitude/covariance of the BH-galaxy scatter at $z \sim
1.2$ and intermediate host masses $M_\mathrm{star} \lesssim 10^{11}
\,\mathrm{M}_\odot$. However, future surveys like Euclid/LSST will be pivotal
in shedding light on the BH--galaxy co-evolution.


  The study of polarity in computation has revealed that an "ideal" programming
language combines both call-by-value and call-by-name evaluation; the two
calling conventions are each ideal for half the types in a programming
language. But this binary choice leaves out call-by-need which is used in
practice to implement lazy-by-default languages like Haskell. We show how the
notion of polarity can be extended beyond the value/name dichotomy to include
call-by-need by adding a mechanism for sharing which is enough to compile a
Haskell-like functional language with user-defined types. The key to capturing
sharing in this mixed-evaluation setting is to generalize the usual notion of
polarity "shifts:" rather than just two shifts (between positive and negative)
we have a family of four dual shifts.
  We expand on this idea of logical duality -- "and" is dual to "or;" proof is
dual to refutation -- for the purpose of compiling a variety of types. Based on
a general notion of data and codata, we show how classical connectives can be
used to encode a wide range of built-in and user-defined types. In contrast
with an intuitionistic logic corresponding to pure functional programming,
these classical connectives bring more of the pleasant symmetries of classical
logic to the computationally-relevant, constructive setting. In particular, an
involutive pair of negations bridges the gulf between the wide-spread notions
of parametric polymorphism and abstract data types in programming languages. To
complete the study of duality in compilation, we also consider the dual to
call-by-need evaluation, which shares the computation within the control flow
of a program instead of computation within the information flow.


  In this work, physical dealloying was explored as a simple and green method
to microstructure the surface of commercial brass for pool boiling heat
transfer coefficient enhancement. Three samples were dealloyed for 0.5, 1 and 3
hours at 650 C, turning the smooth surface into a porous one with a depth of
175, 200 and 223 um. The boiling experiments carried out in ethanol at 78 C
have shown, that the maximum enhancement of heat transfer coefficient between
110 and 150% was achieved for the sample dealloyed for 0.5 h. Longer intervals
of dealloying reduce boiling performance, but it is still much higher compared
to smooth brass. This simple method can be customized for various thermal
management equipment, such as conventional, plate and micro heat exchangers,
all types of heat pipes, HVAC equipment etc., where the heat transfer occurs
with phase change.


  The lack of computationally inexpensive and accurate ab-initio based
methodologies to predict lattice thermal conductivity, without computing the
anharmonic force constants or time-consuming ab-initio molecular dynamics, is
one of the obstacles preventing the accelerated discovery of new high or low
thermal conductivity materials. The Slack equation is the best alternative to
other more expensive methodologies but is highly dependent on two variables:
the acoustic Debye temperature, $\theta_a$, and the Gr\"{u}neisen parameter,
$\gamma$. Furthermore, different definitions can be used for these two
quantities depending on the model or approximation. In this article, we present
a combinatorial approach to elucidate which definitions of both variables
produce the best predictions of the lattice thermal conductivity, $\kappa_l$. A
set of 42 compounds was used to test accuracy and robustness of all possible
combinations. This approach is ideal for obtaining more accurate values than
fast screening models based on the Debye model, while being significantly less
expensive than methodologies that solve the Boltzmann transport equation.


  We have studied the temperature dependent thermopower of gate-defined,
lateral quantum dots in the Coulomb blockade regime using an electron heating
technique. The line shape of the thermopower oscillations depends strongly on
the contributing tunneling processes. Between 1.5 K and 40 mK a crossover from
a pure sawtooth- to an intermitted sawtooth-like line shape is observed. The
latter is attributed to the increasing dominance of cotunneling processes in
the Coulomb blockade regime at low temperatures.


  We show that for certain arithmetic groups, geometrically finite subgroups
are the intersection of finite index subgroups containing them. Examples are
the Bianchi groups and the Seifert-Weber dodecahedral space. In particular, for
manifolds commensurable with these groups, immersed incompressible surfaces
lift to embeddings in a finite sheeted covering.


  Let $G=(V, E)$ be a graph, where $V$ and $E$ are the vertex and edge sets,
respectively. For two disjoint subsets $A$ and $B$ of $V$, we say $A$
\textit{dominates} $B$ if every vertex of $B$ is adjacent to at least one
vertex of $A$ in $G$. A vertex partition $\pi = \{V_1, V_2, \ldots, V_k\}$ of
$G$ is called a \emph{transitive $k$-partition} if $V_i$ dominates $V_j$ for
all $i,j$, where $1\leq i<j\leq k$. The maximum integer $k$ for which the above
partition exists is called \emph{transitivity} of $G$ and it is denoted by
$Tr(G)$. The \textsc{Maximum Transitivity Problem} is to find a transitive
partition of a given graph with the maximum number of partitions. It was known
that the decision version of \textsc{Maximum Transitivity Problem} is
NP-complete for chordal graphs [Iterated colorings of graphs, \emph{Discrete
Mathematics}, 278, 2004]. In this paper, we first prove that this problem can
be solved in linear time for \emph{split graphs} and for the \emph{complement
of bipartite chain graphs}, two subclasses of chordal graphs. We also discuss
Nordhaus-Gaddum type relations for transitivity and provide counterexamples for
an open problem posed by J. T. Hedetniemi and S. T. Hedetniemi [The
transitivity of a graph, \emph{J. Combin. Math. Combin. Comput}, 104, 2018].
Finally, we characterize transitively critical graphs having fixed
transitivity.


  This paper proposes a vision-based framework for a 7-degree-of-freedom
robotic manipulator, with the primary objective of facilitating its capacity to
acquire information from human hand demonstrations for the execution of
dexterous pick-and-place tasks. Most existing works only focus on the position
demonstration without considering the orientations. In this paper, by employing
a single depth camera, MediaPipe is applied to generate the three-dimensional
coordinates of a human hand, thereby comprehensively recording the hand's
motion, encompassing the trajectory of the wrist, orientation of the hand, and
the grasp motion. A mean filter is applied during data pre-processing to smooth
the raw data. The demonstration is designed to pick up an object at a specific
angle, navigate around obstacles in its path and subsequently, deposit it
within a sloped container. The robotic system demonstrates its learning
capabilities, facilitated by the implementation of Dynamic Movement Primitives,
enabling the assimilation of user actions into its trajectories with different
start and end poi


  Partial differential equation (PDE) models for infectious disease have
received renewed interest in recent years. Most models of this type extend
classical compartmental formulations with additional terms accounting for
spatial dynamics, with Fickian diffusion being the most common such term.
However, while diffusion may be appropriate for modeling vector-borne diseases,
or diseases among plants or wildlife, the spatial propagation of airborne
diseases in human populations is heavily dependent on human contact and
mobility patterns, which are not necessarily well-described by diffusion. By
including an additional chemotaxis-inspired term, in which the infection is
propagated along the positive gradient of the susceptible population (from
regions of low- to high-density of susceptibles), one may provide a more
suitable description of these dynamics. This article introduces and analyzes a
mathematical model of infectious disease incorporating a modified
chemotaxis-type term. The model is analyzed mathematically and the
well-posedness of the resulting PDE system is demonstrated. A series of
numerical simulations are provided, demonstrating the ability of the model to
naturally capture important phenomena not easily observed in standard diffusion
models, including propagation over long spatial distances over short time
scales and the emergence of localized infection hotspots


  Gromov-Witten theory is used to define an enumerative geometry of curves in
Calabi-Yau 5-folds. We find recursions for meeting numbers of genus 0 curves,
and we determine the contributions of moving multiple covers of genus 0 curves
to the genus 1 Gromov-Witten invariants. The resulting invariants, conjectured
to be integral, are analogous to the previously defined BPS counts for
Calabi-Yau 3 and 4-folds. We comment on the situation in higher dimensions
where new issues arise.
  Two main examples are considered: the local Calabi-Yau P^2 with balanced
normal bundle 3O(-1) and the compact Calabi-Yau hypersurface X_7 in P^6. In the
former case, a closed form for our integer invariants has been conjectured by
G. Martin. In the latter case, we recover in low degrees the classical
enumeration of elliptic curves by Ellingsrud and Stromme.


  We describe the development of a tracking device, mounted on an
epi-fluorescent inverted microscope, suited to obtain time resolved 3D
Lagrangian tracks of fluorescent passive or active micro-objects in
micro-fluidic devices. The system is based on real-time image processing,
determining the displacement of a x,y mechanical stage to keep the chosen
object at a fixed position in the observation frame. The z displacement is
based on the refocusing of the fluorescent object determining the displacement
of a piezo mover keeping the moving object in focus. Track coordinates of the
object with respect to the micro-fluidic device, as well as images of the
object are obtained at a frequency of several tenths of Hertz. This device is
particularly well adapted to obtain trajectories of motile micro-organisms in
micro-fluidic devices with or without flow.


  Cloud-Radio Access Network (C-RAN) is characterized by a hierarchical
structure in which the baseband processing functionalities of remote radio
heads (RRHs) are implemented by means of cloud computing at a Central Unit
(CU). A key limitation of C-RANs is given by the capacity constraints of the
fronthaul links connecting RRHs to the CU. In this letter, the impact of this
architectural constraint is investigated for the fundamental functions of
random access and active User Equipment (UE) identification in the presence of
a potentially massive number of UEs. In particular, the standard C-RAN approach
based on quantize-and-forward and centralized detection is compared to a scheme
based on an alternative CU-RRH functional split that enables local detection.
Both techniques leverage Bayesian sparse detection. Numerical results
illustrate the relative merits of the two schemes as a function of the system
parameters.


  The theories of strings and $D$-branes have motivated the development of non
Abelian cohomology techniques in differential geometry, on the purpose to find
a geometric interpretation of characteristic classes. The spaces studied here,
like orbifolds are not often smooth. In classical differential geometry, non
smooth spaces appear also naturally, for example in the theory of foliations,
the space of leaves can be an orbifold with singularities. The scheme to study
these structures is identical: classical tools used in differential geometry,
like connections, curvature are adapted. The purpose of this paper is to
present the notion of differential category which unifies all these points of
view. This enables us to provide a geometric interpretation of 5-characteristic
classes, and to interpret classical problems which appear in the theory of
$G$-structures by using gerbes.


  We shall prove a semi-negative curvature property for a manifold with a flat
admissible Higgs bundle.


  The Mapper produces a compact summary of high dimensional data as a
simplicial complex. We study the problem of quantifying the interestingness of
subpopulations in a Mapper, which appear as long paths, flares, or loops.
First, we create a weighted directed graph G using the 1-skeleton of the
Mapper. We use the average values at the vertices of a target function to
direct edges (from low to high). The difference between the average values at
vertices (high-low) is set as the edge's weight. Covariation of the remaining h
functions (independent variables) is captured by a h-bit binary signature
assigned to the edge. An interesting path in G is a directed path whose edges
all have the same signature. We define the interestingness score of such a path
as a sum of its edge weights multiplied by a nonlinear function of their ranks
in the path.
  Second, we study three optimization problems on this graph G. In the problem
Max-IP, we seek an interesting path in G with the maximum interestingness
score. We show that Max-IP is NP-complete. For the special case when G is a
directed acyclic graph (DAG), we show that Max-IP can be solved in polynomial
time - in O(mnd_i) where d_i is the maximum indegree of a vertex in G.
  In the more general problem IP, the goal is to find a collection of
edge-disjoint interesting paths such that the overall sum of their
interestingness scores is maximized. We also study a variant of IP termed k-IP,
where the goal is to identify a collection of edge-disjoint interesting paths
each with k edges, and their total interestingness score is maximized. While
k-IP can be solved in polynomial time for k <= 2, we show k-IP is NP-complete
for k >= 3 even when G is a DAG. We develop polynomial time heuristics for IP
and k-IP on DAGs.


  Despite the rapid expansion of smart grids and large volumes of data at the
individual consumer level, there are still various cases where adequate data
collection to train accurate load forecasting models is challenging or even
impossible. This paper proposes adapting an established model-agnostic
meta-learning algorithm for short-term load forecasting in the context of
few-shot learning. Specifically, the proposed method can rapidly adapt and
generalize within any unknown load time series of arbitrary length using only
minimal training samples. In this context, the meta-learning model learns an
optimal set of initial parameters for a base-level learner recurrent neural
network. The proposed model is evaluated using a dataset of historical load
consumption data from real-world consumers. Despite the examined load series'
short length, it produces accurate forecasts outperforming transfer learning
and task-specific machine learning methods by $12.5\%$. To enhance robustness
and fairness during model evaluation, a novel metric, mean average log
percentage error, is proposed that alleviates the bias introduced by the
commonly used MAPE metric. Finally, a series of studies to evaluate the model's
robustness under different hyperparameters and time series lengths is also
conducted, demonstrating that the proposed approach consistently outperforms
all other models.


  Using a recent result of Salberger, we establish the paucity of non-trivial
positive integer solutions to a certain system of diagonal Diophantine
equations.


  In this paper, we provide for the first time an automated,
correct-by-construction, controller synthesis scheme for a class of infinite
dimensional stochastic systems, namely, retarded jump-diffusion systems. First,
we construct finite abstractions approximately bisimilar to non-probabilistic
retarded systems corresponding to the original systems having some stability
property, namely, incremental input-to-state stability. Then, we provide a
result on quantifying the distance between output trajectory of the obtained
finite abstraction and that of the original retarded jump-diffusion system in a
probabilistic setting. Using the proposed result, one can refine the control
policy synthesized using finite abstractions to the original systems while
providing guarantee on the probability of satisfaction of high-level
requirements. Moreover, we provide sufficient conditions for the proposed
notion of incremental stability in terms of the existence of incremental
Lyapunov functions which reduce to some matrix inequalities for the linear
systems. Finally, the effectiveness of the proposed results is illustrated by
synthesizing a controller regulating the temperatures in a ten-room building
modelled as a delayed jump-diffusion system.


  An algorithm to classify a general Hermitian matrix according to its
signature (positive semi-definite, negative or indefinite) is presented. It
builds on the Quantum Phase Estimation algorithm, which stores the sign of the
eigenvalues of a Hermitian matrix in one ancillary qubit. The signature of the
matrix is extracted from the mean value of a spin operator in this single
ancillary qubit. The algorithm is probabilistic, but it shows good performance,
achieving 97% of correct classifications with few qubits. The computational
cost scales comparably to the classical one in the case of a generic matrix,
but improves significantly for restricted classes of matrices like $k$-local or
sparse hamiltonians.


  An almost-direct product of free groups is an iterated semidirect product of
finitely generated free groups in which the action of the constituent free
groups on the homology of one another is trivial. We determine the structure of
the cohomology ring of such a group. This is used to analyze the topological
complexity of the associated Eilenberg-Mac Lane space.


  Variability of active galactic nuclei is not well understood. One possible
explanation is existence of supermassive binary black holes (SMBBH) in their
centres. It is expected that major mergers are common in the Universe. It is
expected that each supermassive black hole of every galaxy eventually finish as
a SMBBH system in the core of newly formed galaxy. Here we model the emission
line profiles of active galactic nuclei (AGN) assuming that the flux and
emission line shapes variation are induced by supermassive binary black hole
systems (SMBBH). We assume that accreting gas inside of circumbinary (CB) disk
is photo ionized by mini accretion disk emission around each SMBBH. We
calculate variations of emission line flux, shifts and shapes for different
parameters of SMBBH orbits. We consider cases with different masses and
inclinations for circular orbits and measure the effect to the shape of
emission line profiles and flux variability.


  HaloSat is a small satellite (CubeSat) designed to map soft X-ray oxygen line
emission across the sky in order to constrain the mass and spatial distribution
of hot gas in the Milky Way. The goal of HaloSat is to help determine if hot
gas gravitationally bound to individual galaxies makes a significant
contribution to the cosmological baryon budget. HaloSat was deployed from the
International Space Station in July 2018 and began routine science operations
in October 2018. We describe the goals and design of the mission, the on-orbit
performance of the science instrument, and initial observations.


  Previous work relating the flavour variation of the chiral order parameters
$F_\pi$, $<\bar u u>$ and S-wave scattering data, based on chiral sum rules and
chiral perturbation theory at order $p^4$, is extended to include $O(p^6)$
corrections. The finding of a significant decrease of these order parameters,
particularly $<\bar u u>$, with the number of flavours increasing from $N_F=2$
to $N_F=3$ is confirmed, modulo an assumption on the convergence of the chiral
expansion. The connection between scalar resonance physics and the phase
structure of the chiral vacuum is also illustrated on the basis of the linear
sigma-model. We allow for a very general symmetry breaking sector compatible
with softness. The result depends strongly on the input scalar meson masses, in
particular, on the presence, or not, of a light sigma.}


  An odd wheel graph is a graph formed by connecting a new vertex to all
vertices of an odd cycle. We answer a question of Rosenfeld and Le by showing
that odd wheels cannot be drawn in the plane such that the lengths of the edges
are odd integers.


  We introduce a novel approach for rendering static and dynamic 3D neural
signed distance functions (SDF) in real-time. We rely on nested neighborhoods
of zero-level sets of neural SDFs, and mappings between them. This framework
supports animations and achieves real-time performance without the use of
spatial data-structures. It consists of three uncoupled algorithms representing
the rendering steps. The multiscale sphere tracing focuses on minimizing
iteration time by using coarse approximations on earlier iterations. The neural
normal mapping transfers details from a fine neural SDF to a surface nested on
a neighborhood of its zero-level set. It is smooth and it does not depend on
surface parametrizations. As a result, it can be used to fetch smooth normals
for discrete surfaces such as meshes and to skip later iterations when sphere
tracing level sets. Finally, we propose an algorithm for analytic normal
calculation for MLPs and describe ways to obtain sequences of neural SDFs to
use with the algorithms.


  We study the online budgeted allocation (also called ADWORDS) problem, where
a set of impressions arriving online are allocated to a set of
budget-constrained advertisers to maximize revenue. Motivated by connections to
Internet advertising, several variants of this problem have been studied since
the seminal work of Mehta, Saberi, Vazirani, and Vazirani (FOCS 2005). However,
this entire body of work focuses on a single budget for every advertising
campaign, whereas in order to fully represent the actual agenda of an
advertiser, an advertising budget should be expressible over multiple tiers of
user-attribute granularity. A simple example is an advertising campaign that is
constrained by an overall budget but is also accompanied by a set of
sub-budgets for each target demographic. In such a contract scheme, an
advertiser can specify their true user-targeting goals, allowing the publisher
to fulfill them through relevant allocations.
  In this paper, we give a complete characterization of the ADWORDS problem for
general advertising budgets. In the most general setting, we show that, unlike
in the single-budget ADWORDS problem, obtaining a constant competitive ratio is
impossible and give asymptotically tight upper and lower bounds. However for
our main result, we observe that in many real-world scenarios (as in the above
example), multi-tier budgets have a laminar structure, since most relevant
consumer or product classifications are hierarchical. For laminar budgets, we
obtain a competitive ratio of e/(e-1) in the small bids case, which matches the
best known ADWORDS result for single budgets. Our algorithm has a primal-dual
structure and generalizes the primal-dual analysis for single- budget ADWORDS
first given by Buchbinder, Jain, and Naor (ESA 2007).


  We consider an asymptotic regime for two-dimensional ferromagnetic films that
is consistent with the formation of transition layers (N\'eel walls). We first
establish compactness of S2-valued magnetizations in the energetic regime of
N\'eel walls and characterize the set of accumulation points. We then prove
that N\'eel walls are asymptotically the unique energy minimizing
configurations. We finally study the corresponding dynamical issues, namely the
compactness properties of the magnetizations under the flow of the
Landau-Lifshitz-Gilbert equation.


  We develop Chiral Perturbation Theory for chirally broken theories with
fermions in two different representations of the gauge group. Any such theory
has a non-anomalous singlet $U(1)_A$ symmetry, yielding an additional
Nambu-Goldstone boson when spontaneously broken. We calculate the
next-to-leading order corrections for the pseudoscalar masses and decay
constants, which include the singlet Nambu-Goldstone boson, as well as for the
two condensates. The results can be generalized to more than two
representations.


  We consider the discretized model of a driven string with an anharmonic
elastic energy, in a two dimensional random potential, as introduced by Rosso
and Krauth. Using finite size scaling, we numerically compute the roughness of
the string in a uniform applied force at the critical depinning threshold. By
considering a string with a net average tilt, we demonstrate that the
anharmonic elastic energy crosses the model over to the quenched KPZ
universality class, in agreement with recent theoretical predictions.


  Using a single functional form which is able to represent several different
classes of statistical distributions, we introduce a preliminary study of the
ferromagnetic Ising model on the cubic lattices under the influence of
non-Gaussian local external magnetic field. Specifically, depending on the
value of the tail parameter, $\tau $ ($\tau < 3$), we assign a quenched random
field that can be platykurtic (sub-Gaussian) or leptokurtic (fat-tailed) form.
For $\tau< 5/3$, such distributions have finite standard deviation and they are
either the Student-$t$ ($1< \tau< 5/3$) or the $r$-distribution ($\tau< 1$)
extended to all plausible real degrees of freedom with the Gaussian being
retrieved in the limit $\tau \rightarrow 1$. Otherwise, the distribution has
got the same asymptotic power-law behaviour as the $\alpha$-stable L\'{e}vy
distribution with $\alpha = (3 - \tau)/(\tau - 1)$. The uniform distribution is
achieved in the limit $\tau \rightarrow \infty$. Our results purport the
existence of ferromagnetic order at finite temperatures for all the studied
values of $\tau$ with some mean-field predictions surviving in the
three-dimensional case.


  Three dimensional dunes of crescentic shape, called barchans, are commonly
found on Earth and other planetary environments. In the great majority of
cases, barchans are organized in large fields in which corridors of
size-selected barchans are observed, and where barchan-barchan interactions
play an important role in size regulation. Previous studies shed light on the
interactions between barchans by making use of monodisperse particles, but
dunes in nature consist, however, of polydisperse grains. In this paper, we
investigate the binary interactions of barchans consisting of (i) bidisperse
mixtures of grains and (ii) different monodisperse grains (one type for each
barchan). We performed experiments in a water channel where grains of different
sizes were poured inside forming two barchans that interacted with each other
while filmed by a camera, and we obtained their morphology from image
processing. We observed that a transient stripe appears over the dunes in cases
of bidisperse mixtures, that interaction patterns vary with concentrations, and
that different interactions exist when each barchan consists of different
monodisperse grains. Interestingly, we found the conditions for a collision in
which the upstream barchan is larger than the downstream one, and we propose a
timescale for the interactions of both monodisperse and bidisperse barchans.
Our results represent a new step toward understanding complex barchanoid
structures found on Earth, Mars and other celestial bodies.


  In this paper we develop a general formalism of a path approach for
non-equilibrium statistical mechanics. Firstly, we consider the classical Gibbs
approach for states and find that this formalism is ineffective for
non-equilibrium phenomena because it is based on a distribution of
probabilities indirectly. Secondly, we develop a path formalism which is
directly based on the distribution of probabilities and therefore significantly
simplifies the analytical approach. The new formalism requires generalizing the
'static', state quantities of a system, like entropy or free energy potential,
to their path analogues. Also we obtain a path balance equation and an equation
of equilibrium path. For the distribution of probabilities we obtain a
functional dependence similar to the Feynman's path integral formalism of
action, only now the role of a Hamiltonian is played by the state entropy. For
the production of the state free energy we illustrate a significant dependence
on the type of system's connectivity.


  Environmental effects, such as stellar fly-bys and external irradiation, are
thought to affect the evolution of protoplanetary disks in clustered star
formation. Previous ALMA images at 225 GHz of the ISO-Oph 2 binary revealed a
peculiar morphology in the disk of the primary, perhaps due to a possible
fly-by with the secondary. Here we report on new ALMA continuum observations of
this system at 97.5 GHz, 145 GHz and 405 GHz, which reveal strong morphological
variations. Multi-frequency positional alignment allows to interpret these
spectral variations in terms of underlying physical conditions. ISO-Oph 2A is
remarkably offset from the centroid of its ring, at all frequencies, and the
disk is lopsided, pointing at gravitational interactions. However, the dust
temperature also varies in azimuth, with two peaks whose direction connects
with HD 147889, the earliest-type star in the Ophiuchus complex, suggesting
that it is the dominant heat source. The stellar environment of ISO-Oph 2
appears to drive both its density structure and its thermal balance.Simon
Casassus, Lucas Cieza, Miguel C\'arcamo, \'Alvaro Ribas, Valentin Christiaens,
Abigali Rodr\'iguez-Jim\'enez, Carla Arce-Tord, Trisha Bhowmik, Prachi Chavan,
Camilo Gonz\'alez-Ruilova, Rafael Mart\'inez-Brunner, Valeria Guidotti,
Mauricio Leiva


  In this paper, we introduce the notion of $L^2$-subgroup rigid groups and
demonstrate that free groups are $L^2$-subgroup rigid. As a consequence, we
establish the equivalence between compressibility, inertness, strong inertness,
and $L^2$-independence for a finitely generated subgroup of a free group,
confirming a conjecture by Dicks and Ventura as well as the one by Antolin and
Jaikin-Zapirain.


  We present a computer code for calculating near- and far-field
electromagnetic properties of multilayered spheres. STRATIFY is one-of-a-kind
open-source package which allows for the efficient calculation of
electromagnetic near-field, energy density, total electromagnetic energy,
radiative and non-radiative decay rates of a dipole emitter located in any
(non-absorbing) shell (including a host medium), and fundamental cross-sections
of a multilayered sphere, all within a single program. The developed software
is typically more than 50 times faster than freely available packages based on
boundary-element-method. Because of its speed and broad applicability, our
package is a valuable tool for analysis of numerous light scattering problems,
including, but not limited to fluorescence enhancement, upconversion,
downconversion, second harmonic generation, surface enhanced Raman
spectroscopy. The software is available for download from GitLab
https://gitlab.com/iliarasskazov/stratify


  The self-gravitating gas in the Newtonian limit is studied in the presence of
dark energy with a linear and constant equation of state. Entropy extremization
associates to the isothermal Boltzmann distribution an effective density that
includes `dark energy particles', which either strengthen or weaken mutual
gravitational attraction, in case of quintessence or phantom dark energy,
respectively, that satisfy a linear equation of state. Stability is studied for
microcanonical (fixed energy) and canonical (fixed temperature) ensembles.
Compared to the previously studied cosmological constant case, in the present
work it is found that quintessence increases, while phantom dark energy
decreases the instability domain under gravitational collapse. Thus, structures
are more easily formed in a quintessence rather than in a phantom dominated
Universe. Assuming that galaxy clusters are spherical, nearly isothermal and in
hydrostatic equilibrium we find that dark energy with a linear and constant
equation of state, for fixed radius, mass and temperature, steepens their total
density profile. In case of a cosmological constant, this effect accounts for a
1.5% increase in the density contrast, that is the center to edge density ratio
of the cluster. We also propose a method to constrain phantom dark energy.


  CW Leo has been observed six times between October 2009 and June 2012 with
the SPIRE instrument on board the Herschel satellite. Variability has been
detected in the flux emitted by the central star with a period of 639 \pm 4
days, in good agreement with determinations in the literature. Variability is
also detected in the bow shock around CW Leo that had previously been detected
in the ultraviolet and Herschel PACS/SPIRE data. Although difficult to prove
directly, our working hypothesis is that this variability is directly related
to that of the central star. In this case, fitting a sine curve with the period
fixed to 639 days results in a time-lag in the variability between bow shock
and the central star of 402 \pm 37 days. The orientation of the bow shock
relative to the plane of the sky is unknown (but see below). For an inclination
angle of zero degrees, the observed time-lag translates into a distance to CW
Leo of 130 \pm 13 pc, and for non-zero inclination angles the distance is
smaller. Fitting the shape of the bow shock with an analytical model (Wilkin
1996), the effect of the inclination angle on the distance may be estimated.
Making the additional assumption that the relative peculiar velocity between
the interstellar medium (ISM) and CW Leo is determined entirely by the star
space velocity with respect to the local standard of rest (i.e. a stationary
ISM), the inclination angle is found to be (-33.3 \pm 0.8) degrees based on the
observed proper motion and radial velocity. Using the Wilkin model, our current
best estimate of the distance to CW Leo is 123 \pm 14 pc. For a distance of 123
pc, we derive a mean luminosity of 7790 \pm 150 Lsol (internal error).


  Using data from contact maps of the DNA-polymer of $E. Coli$ (at kilobase
pair resolution) as an input to our model, we introduce cross-links between
monomers in a bead-spring model of a ring polymer at very specific points along
the chain. By suitable Monte Carlo Simulations, we show that the presence of
these cross-links leads to a particular architecture and organization of the
chain at large (micron) length scales of the DNA. We also investigate the
structure of a ring polymer with an equal number of cross-links at random
positions along the chain. We find that though the polymer does get organized
at the large length scales, the nature of the organization is quite different
from the organization observed with cross-links at specific biologically
determined positions. We used the contact map of $E. Coli$ bacteria which has
around $4.6$ million base pairs in a single circular chromosome. In our
coarse-grained flexible ring polymer model, we used $4642$ monomer beads and
observed that around $80$ cross-links are enough to induce the large-scale
organization of the molecule accounting for statistical fluctuations caused by
thermal energy. The length of a DNA chain of an even simple bacterial cell such
as $E. Coli$ is much longer than typical proteins, hence we avoided methods
used to tackle protein folding problems. We define new suitable quantities to
identify large scale structure of a polymer chain with a few cross-links.


  We discuss systematically two versions of confidence regions: those based on
p-values and those based on e-values, a recent alternative to p-values. Both
versions can be applied to multiple hypothesis testing, and in this paper we
are interested in procedures that control the number of false discoveries under
arbitrary dependence between the base p- or e-values. We introduce a procedure
that is based on e-values and show that it is efficient both computationally
and statistically using simulated and real-world datasets. Comparison with the
corresponding standard procedure based on p-values is not straightforward, but
there are indications that the new one performs significantly better in some
situations.


  The seminal work of Morgan and Rubin (2012) considers rerandomization for all
the units at one time. In practice, however, experimenters may have to
rerandomize units sequentially. For example, a clinician studying a rare
disease may be unable to wait to perform an experiment until all the
experimental units are recruited. Our work offers a mathematical framework for
sequential rerandomization designs, where the experimental units are enrolled
in groups. We formulate an adaptive rerandomization procedure for balancing
treatment/control assignments over some continuous or binary covariates, using
Mahalanobis distance as the imbalance measure. We prove in our key result,
Theorem 3, that given the same number of rerandomizations (in expected value),
under certain mild assumptions, sequential rerandomization achieves better
covariate balance than rerandomization at one time.


  Candlesticks are graphical representations of price movements for a given
period. The traders can discovery the trend of the asset by looking at the
candlestick patterns. Although deep convolutional neural networks have achieved
great success for recognizing the candlestick patterns, their reasoning hides
inside a black box. The traders cannot make sure what the model has learned. In
this contribution, we provide a framework which is to explain the reasoning of
the learned model determining the specific candlestick patterns of time series.
Based on the local search adversarial attacks, we show that the learned model
perceives the pattern of the candlesticks in a way similar to the human trader.


  We investigate the phase space of topological black hole solutions of
${\mathfrak {su}}(N)$ Einstein-Yang-Mills theory in anti-de Sitter space with a
purely magnetic gauge potential. The gauge field is described by $N-1$ magnetic
gauge field functions $\omega_{j}$, $j=1,\ldots , N-1$. For ${\mathfrak
{su}}(2)$ gauge group, the function $\omega_{1}$ has no zeros. This is no
longer the case when we consider a larger gauge group. The phase space of
topological black holes is considerably simpler than for the corresponding
spherically symmetric black holes, but for $N>2$ and a flat event horizon,
there exist solutions where at least one of the $\omega_{j}$ functions has one
or more zeros. For most of the solutions, all the $\omega_{j}$ functions have
no zeros, and at least some of these are linearly stable.


  Recent experiments with Yb-173 and Sr-87 isotopes provide new possibilities
to study high spin two-orbital systems. Within these experiments part of the
atoms are excited to a higher energy metastable electronic state mimicking an
additional internal (orbital) degree of freedom. The interaction between the
atoms depends on the orbital states, therefore four different scattering
channels can be identified in the system characterized by four independent
couplings. When the system is confined into a one-dimensional chain the
scattering lengths can be tuned by changing the transverse confinement, and
driven through four resonances. Using the new available experimental data of
the scattering lengths we analyze the phase diagram of the one-dimensional
system as the couplings are tuned via transverse confinement, and the
populations of the two orbital states are changed. We found that three orders
compete showing power law decay: a state with dominant density wave
fluctuations, another one with spin density fluctuations, and a third one
characterized by exotic Fulde-Ferrell-Larkin-Ovchinnikov-like pairs consisting
one atom in the electronic ground state and one in the excited state. We also
show that sufficiently close to the resonances the compressibility of the
system starts to diverge indicating that the emerging order is unstable and
collapses to a phase separated state with a first order phase transition.


  This paper develops a new video compression approach based on underdetermined
blind source separation. Underdetermined blind source separation, which can be
used to efficiently enhance the video compression ratio, is combined with
various off-the-shelf codecs in this paper. Combining with MPEG-2, video
compression ratio could be improved slightly more than 33%. As for combing with
H.264, 4X~12X more compression ratio could be achieved with acceptable PSNR,
according to different kinds of video sequences.


  As hundreds of gas giant planets have been discovered, we study how these
planets form and evolve in different stellar environments, specifically in
multiple stellar systems. In such systems, stellar companions may have a
profound influence on gas giant planet formation and evolution via several
dynamical effects such as truncation and perturbation. We select 84 Kepler
Objects of Interest (KOIs) with gas giant planet candidates. We obtain
high-angular resolution images using telescopes with adaptive optics (AO)
systems. Together with the AO data, we use archival radial velocity data and
dynamical analysis to constrain the presence of stellar companions. We detect
59 stellar companions around 40 KOIs for which we develop methods of testing
their physical association. These methods are based on color information and
galactic stellar population statistics. We find evidence of suppressive planet
formation within 20 AU by comparing stellar multiplicity. The stellar
multiplicity rate for planet host stars is 0$^{+5}_{-0}$\% within 20 AU. In
comparison, the stellar multiplicity rate is 18\%$\pm$2\% for the control
sample, i.e., field stars in the solar neighborhood. The stellar multiplicity
rate for planet host stars is 34\%$\pm$8\% for separations between 20 and 200
AU, which is higher than the control sample at 12\%$\pm$2\%. Beyond 200 AU,
stellar multiplicity rates are comparable between planet host stars and the
control sample. We discuss the implications of the results to gas giant planet
formation and evolution.


  Traditional steganographic techniques have often relied on manually crafted
attributes related to image residuals. These methods demand a significant level
of expertise and face challenges in integrating diverse image residual
characteristics. In this paper, we introduce an innovative deep learning-based
methodology that seamlessly integrates image residuals, residual distances, and
image local variance to autonomously learn embedding probabilities. Our
framework includes an embedding probability generator and three pivotal guiding
components: Residual guidance strives to facilitate embedding in
complex-textured areas. Residual distance guidance aims to minimize the
residual differences between cover and stego images. Local variance guidance
effectively safeguards against modifications in regions characterized by
uncomplicated or uniform textures. The three components collectively guide the
learning process, enhancing the security performance. Comprehensive
experimental findings underscore the superiority of our approach when compared
to traditional steganographic methods and randomly initialized ReLOAD in the
spatial domain.


  Let $m$ be a bounded function and $\alpha$ a nonnegative parameter. This
article is concerned with the first eigenvalue $\lambda\_\alpha(m)$ of the
drifted Laplacian type operator $\mathcal L\_m$ given by $\mathcal L\_m(u)=
-\operatorname{div} \left((1+\alpha m)\nabla u\right)-mu$ on a smooth bounded
domain, with Dirichlet boundary conditions. Assuming uniform pointwise and
integral bounds on $m$, we investigate the issue of minimizing
$\lambda\_\alpha(m)$ with respect to $m$. Such a problem is related to the
so-called "two phase extremal eigenvalue problem" and arises naturally, for
instance in population dynamics where it is related to the survival ability of
a species in a domain. We prove that unless the domain is a ball, this problem
has no "regular" solution. We then provide a careful analysis in the case of a
ball by: (1) characterizing the solution among all radially symmetric resources
distributions, with the help of a new method involving a homogenized version of
the problem; (2) proving in a more general setting, a stability result for the
centered distribution of resources with the help of a monotonicity principle
for second order shape derivatives which significantly simplifies the analysis.


  The response of microbes to external signals is mediated by biochemical
networks with intrinsic time scales. These time scales give rise to a memory
that impacts cellular behaviour. Here we study theoretically the role of
cellular memory in Escherichia coli chemotaxis. Using an agent-based model, we
show that cells with memory navigating rugged chemoattractant landscapes can
enhance their drift speed by extracting information from environmental
correlations. Maximal advantage is achieved when the memory is comparable to
the time scale of fluctuations as perceived during swimming. We derive an
analytical approximation for the drift velocity in rugged landscapes that
explains the enhanced velocity, and recovers standard Keller-Segel
gradient-sensing results in the limits when memory and fluctuation time scales
are well separated. Our numerics also show that cellular memory can induce
bet-hedging at the population level resulting in long-lived multi-modal
distributions in heterogeneous landscapes.


  Recently, a new probabilistic "data fusion" framework based on Bayesian
principles has been developed on JET and W7-AS. The Bayesian analysis framework
folds in uncertainties and inter-dependencies in the diagnostic data and signal
forward-models, together with prior knowledge of the state of the plasma, to
yield predictions of internal magnetic structure. A feature of the framework,
known as MINERVA (J. Svensson, A. Werner, Plasma Physics and Controlled Fusion
50, 085022, 2008), is the inference of magnetic flux surfaces without the use
of a force balance model. We discuss results from a new project to develop
Bayesian inversion tools that aim to (1) distinguish between competing
equilibrium theories, which capture different physics, using the MAST spherical
tokamak; and (2) test the predictions of MHD theory, particularly mode
structure, using the H-1 Heliac.


  We present a photoemission study of La$_{0.8-x}$Eu$_{0.2}$Sr$_x$CuO$_{4}$
with doping level $x$=1/8, where the charge carriers are expected to order
forming static stripes. Though the local probes in direct space seem to be
consistent with this idea, there has been little evidence found for such
ordering in quasiparticle dispersions. We show that the Fermi surface topology
of the 1/8 compound develops notable deviations from that observed for La$_{2-
x}$Sr$_x$CuO$_{4}$ in a way consistent with the FS reconstruction expected for
the scattering on the antiphase stripe order.


  We compute the space of $L^2$ harmonic forms (outside the middle degrees) on
negatively curved Kaehler manifolds of finite volume.


  Fermionic Gaussian states are eigenstates of quadratic Hamiltonians and
widely used in quantum many-body problems. We propose a highly efficient
algorithm that converts fermionic Gaussian states to matrix product states. It
can be formulated for finite-size systems without translation invariance, but
becomes particularly appealing when applied to infinite systems with
translation invariance. If the ground states of a topologically ordered system
on infinite cylinders are expressed as matrix product states, then the fixed
points of the transfer matrix can be harnessed to filter out the anyon
eigenbasis, also known as minimally entangled states. This allows for efficient
computation of universal properties such as entanglement spectrum and modular
matrices. The potential of our method is demonstrated by numerical calculations
in two chiral spin liquids that have the same topological orders as the bosonic
Laughlin and Moore-Read states, respectively. The anyon eigenbasis for the
first one has been worked out before and serves as a useful benchmark. The
anyon eigenbasis of the second one is, however, not transparent and its
successful construction provides a nontrivial corroboration of our method.


  We present a spectroscopic analysis of a 1-year intensive monitoring campaign
of the 22-Myr old planet-hosting M dwarf AU Mic using the HARPS spectrograph.
In a companion paper, we reported detections of the planet radial velocity (RV)
signatures of the two close-in transiting planets of the system, with
respective semi-amplitudes of 5.8 $\pm$ 2.5 m/s and 8.5 $\pm$ 2.5 m/s for AU
Mic b and AU Mic c. Here, we perform an independent measurement of the RV
semi-amplitude of AU Mic c using Doppler imaging to simultaneously model the
activity-induced distortions and the planet-induced shifts in the line
profiles. The resulting semi-amplitude of 13.3 $\pm$ 4.1 m/s for AU Mic c
reinforces the idea that the planet features a surprisingly large inner
density, in tension with current standard models of core accretion. Our
brightness maps feature significantly higher spot coverage and lower level of
differential rotation than the brightness maps obtained in late 2019 with the
SPIRou spectropolarimeter, suggesting that the stellar magnetic activity has
evolved dramatically over a $\sim$1-yr time span. Additionally, we report a
3-$\sigma$ detection of a modulation at 8.33 $\pm$ 0.04 d of the He I D3
(587.562 nm) emission flux, close to the 8.46-d orbital period of AU Mic b. The
power of this emission (a few 10$^{17}$ W) is consistent with 3D
magnetohydrodynamical simulations of the interaction between stellar wind and
the close-in planet if the latter hosts a magnetic field of $\sim$10 G.
Spectropolarimetric observations of the star are needed to firmly elucidate the
origin of the observed chromospheric variability.


  In this paper, we propose a new task of machine translation (MT), which is
based on no parallel sentences but can refer to a ground-truth bilingual
dictionary. Motivated by the ability of a monolingual speaker learning to
translate via looking up the bilingual dictionary, we propose the task to see
how much potential an MT system can attain using the bilingual dictionary and
large scale monolingual corpora, while is independent on parallel sentences. We
propose anchored training (AT) to tackle the task. AT uses the bilingual
dictionary to establish anchoring points for closing the gap between source
language and target language. Experiments on various language pairs show that
our approaches are significantly better than various baselines, including
dictionary-based word-by-word translation, dictionary-supervised cross-lingual
word embedding transformation, and unsupervised MT. On distant language pairs
that are hard for unsupervised MT to perform well, AT performs remarkably
better, achieving performances comparable to supervised SMT trained on more
than 4M parallel sentences.


  Machine Intelligence (MI) technologies have revolutionized the design and
applications of computational intelligence systems, by introducing remarkable
scientific and technological enhancements across domains. MI can improve
Internet of Things (IoT) in several ways, such as optimizing the management of
large volumes of data or improving automation and transmission in large-scale
IoT deployments. When considering MI in the IoT context, MI services deployment
must account for the latency demands and network bandwidth requirements. To
this extent, moving the intelligence towards the IoT end-device aims to address
such requirements and introduces the notion of Distributed MI (D-MI) also in
the IoT context. However, current D-MI deployments are limited by the lack of
MI interoperability. Currently, the intelligence is tightly bound to the
application that exploits it, limiting the provisioning of that specific
intelligence service to additional applications. The objective of this article
is to propose a novel approach to cope with such constraints. It focuses on
decoupling the intelligence from the application by revising the traditional
device's stack and introducing an intelligence layer that provides services to
the overlying application layer. This paradigm aims to provide final users with
more control and accessibility of intelligence services by boosting providers'
incentives to develop solutions that could theoretically reach any device.
Based on the definition of this emerging paradigm, we explore several aspects
related to the intelligence distribution and its impact in the whole MI
ecosystem.


  We analyze Niels Bohr's proposed two-slit interference experiment with highly
charged particles that argues that the consistency of elementary quantum
mechanics requires that the electromagnetic field must be quantized. In the
experiment a particle's path through the slits is determined by measuring the
Coulomb field that it produces at large distances; under these conditions the
interference pattern must be suppressed. The key is that as the particle's
trajectory is bent in diffraction by the slits it must radiate and the
radiation must carry away phase information. Thus the radiation field must be a
quantized dynamical degree of freedom. On the other hand, if one similarly
tries to determine the path of a massive particle through an inferometer by
measuring the Newtonian gravitational potential the particle produces, the
interference pattern would have to be finer than the Planck length and thus
undiscernable. Unlike for the electromagnetic field, Bohr's argument does not
imply that the gravitational field must be quantized.


  This thesis is concerned with distributed control and coordination of
networks consisting of multiple, potentially mobile, agents. This is motivated
mainly by the emergence of large scale networks characterized by the lack of
centralized access to information and time-varying connectivity. Control and
optimization algorithms deployed in such networks should be completely
distributed, relying only on local observations and information, and robust
against unexpected changes in topology such as link failures. We will describe
protocols to solve certain control and signal processing problems in this
setting. We will demonstrate that a key challenge for such systems is the
problem of computing averages in a decentralized way. Namely, we will show that
a number of distributed control and signal processing problems can be solved
straightforwardly if solutions to the averaging problem are available. The rest
of the thesis will be concerned with algorithms for the averaging problem and
its generalizations. We will (i) derive the fastest known averaging algorithms
in a variety of settings and subject to a variety of communication and storage
constraints (ii) prove a lower bound identifying a fundamental barrier for
averaging algorithms (iii) propose a new model for distributed function
computation which reflects the constraints facing many large-scale networks,
and nearly characterize the general class of functions which can be computed in
this model.


  We consider the unoriented two-dimensional Abelian sandpile model on the
half-plane with open and closed boundary conditions. We show that the operator
effecting the change from closed to open, or from open to closed, is a boundary
primary field of weight -1/8, belonging to a c=-2 logarithmic conformal field
theory.


  The transfer of conserved charges through insulating matter via smooth
deformations of the Hamiltonian is known as quantum adiabatic, or Thouless,
pumping. Central to this phenomenon are Hamiltonians whose insulating gap is
controlled by a multi-dimensional (usually two-dimensional) parameter space in
which paths can be defined for adiabatic changes in the Hamiltonian, i.e.,
without closing the gap. Here, we extend the concept of Thouless pumps of band
insulators by considering a larger, three-dimensional parameter space. We show
that the connectivity of this parameter space is crucial for defining quantum
pumps, demonstrating that, as opposed to the conventional two-dimensional case,
pumped quantities depend not only on the initial and final points of
Hamiltonian evolution but also on the class of the chosen path and preserved
symmetries. As such, we distinguish the scenarios of closed/open paths of
Hamiltonian evolution, finding that different closed cycles can lead to the
pumping of different quantum numbers, and that different open paths may point
to distinct scenarios for surface physics. As explicit examples, we consider
models similar to simple models used to describe topological insulators, but
with doubled degrees of freedom compared to a minimal topological insulator
model. The extra fermionic flavors from doubling allow for extra gapping
terms/adiabatic parameters - besides the usual topological mass which preserves
the topology-protecting discrete symmetries - generating an enlarged adiabatic
parameter-space. We consider cases in one and three \emph{spatial} dimensions,
and our results in three dimensions may be realized in the context of
crystalline topological insulators, as we briefly discuss.


  We study supersymmetric and non-supersymmetric extremal black holes obtained
in Type IIA string theory compactified on K3 X T^2, with duality group
O(6,22,Z) X SL(2,Z). In the Cardy limit an internal circle combines with the
AdS_2 component in the near horizon geometry to give a BTZ black hole whose
entropy is given by the Cardy formula. We study black holes carrying D0-D4 and
D0-D6 brane charges. We find, both in the supersymmetric and non-supersymmetric
cases, that a generic set of charges cannot be brought to the Cardy limit using
the duality symmetries. In the non-supersymmetric case, unlike the
supersymmetric one, we find that when the charges are large, a small fractional
change in them always allows the charges to be taken to the Cardy limit. These
results could lead to a microscopic determination of the entropy for extremal
non-supersymmetric black holes, including rotating cases like the extreme Kerr
black hole in four dimensions.


  This work investigates the motion of a non-relativistic charged particle
within the spacetime of a global monopole. We introduce the Schr\"odinger
equation to describe the particle's motion with two interactions by considering
the Kratzer and the screened modified Kratzer potential. The problem's
eigenfunctions and eigenvalues are obtained by deriving and solving the radial
equation. The effective potential encompasses both the Kratzer and
electrostatic self-interaction potential and leads to bound states solutions.
The energy spectrum is investigated, particularly emphasizing its dependence on
the system's physical parameters. The screened modified Kratzer potential and
the screened self-interaction potential reveal an important role in influencing
both the effective potential and the energy spectrum. Additionally, it also
accommodates the existence of bound states. All these behaviors are illustrated
with graphs and discussed in detail.


  [...] The aim of this paper is to study the precision and theoretical biases
in the age determinations of halo stars adopting both asteroseismic and classic
observational constraints. [...] We adopt the well-tested SCEPtER pipeline,
covering evolutionary phases up to the red giant branch (RGB). The fitting
grids contain stars with mass in the range of [0.7; 1.0] $M_{\odot}$ and
metallicity [Fe/H] from -2.5 to -0.5. We investigate several scenarios
characterised by different adopted observational uncertainties. We also assess
the impact of systematic discrepancies between the recovery grid models and
target stars by computing several synthetic grids of stellar models with
perturbed input physics. In our reference scenario, we recover ages for stars
in the main sequence (MS) or subgiant branch (SGB) with a typical 10\%--20\%
precision, while we recover those of RGB stars with a precision of about 60\%.
However, adopting tighter constraints on asteroseismic parameters, the age
precision in RGB improved to 20\%, while few modifications occur in the other
analysed evolutionary phases. A systematic discrepancies between grid models
and target stars shows that a mismatch in the mixing-length parameter value
leads to significant bias in the age estimations for MS stars (about 10\%), but
this bias is smaller for SGB and RGB stars. Neglecting the microscopic
diffusion effect in the recovery grid leads to a typical 40\% bias in age
estimates for stars on the MS. Finally, we applied the technique to stars in
globular clusters. We find a precision in age estimates of around 20\% for MS
stars and up to 40\% for RGB stars, greater than those obtained with classical
methods. We demonstrate the method on stars of the cluster M4, obtaining a
cluster age of $11.9 \pm 1.5$ Gyr and a mass at the turn-of off $0.86 \pm 0.04$
$M_{\odot}$, which are in good agreement with literature results.


  Target-speaker speech recognition aims to recognize target-speaker speech
from noisy environments with background noise and interfering speakers. This
work presents a joint framework that combines time-domain target-speaker speech
extraction and Recurrent Neural Network Transducer (RNN-T). To stabilize the
joint-training, we propose a multi-stage training strategy that pre-trains and
fine-tunes each module in the system before joint-training. Meanwhile, speaker
identity and speech enhancement uncertainty measures are proposed to compensate
for residual noise and artifacts from the target speech extraction module.
Compared to a recognizer fine-tuned with a target speech extraction model, our
experiments show that adding the neural uncertainty module significantly
reduces 17% relative Character Error Rate (CER) on multi-speaker signals with
background noise. The multi-condition experiments indicate that our method can
achieve 9% relative performance gain in the noisy condition while maintaining
the performance in the clean condition.


  Given a triangulated category over a field $K$ and a field extension $L/K$,
we investigate how one can construct a triangulated category over $L$. Our
approach produces the derived category of the base change scheme $X_L$ if the
category one starts with is the bounded derived category of a smooth projective
variety $X$ over $K$ and the field extension is finite and Galois. We also
investigate how the dimension of a triangulated category behaves under scalar
extensions.


  Due to complexity and invisibility of human organs, diagnosticians need to
analyze medical images to determine where the lesion region is, and which kind
of disease is, in order to make precise diagnoses. For satisfying clinical
purposes through analyzing medical images, registration plays an essential
role. For instance, in Image-Guided Interventions (IGI) and computer-aided
surgeries, patient anatomy is registered to preoperative images to guide
surgeons complete procedures. Medical image registration is also very useful in
surgical planning, monitoring disease progression and for atlas construction.
Due to the significance, the theories, methods, and implementation method of
image registration constitute fundamental knowledge in educational training for
medical specialists. In this chapter, we focus on image registration of a
specific human organ, i.e. the lung, which is prone to be lesioned. For
pulmonary image registration, the improvement of the accuracy and how to obtain
it in order to achieve clinical purposes represents an important problem which
should seriously be addressed. In this chapter, we provide a survey which
focuses on the role of image registration in educational training together with
the state-of-the-art of pulmonary image registration. In the first part, we
describe clinical applications of image registration introducing artificial
organs in Simulation-based Education. In the second part, we summarize the
common methods used in pulmonary image registration and analyze popular papers
to obtain a survey of pulmonary image registration.


  Recent studies have shown a number of surprising vortex dynamics phenomena
both in low and high temperature superconductors, which include: low frequency
noise, slow voltage oscillations, history dependent dynamic response, memory of
the direction, amplitude, duration, and frequency of the previously applied
current, suppression of a large ac vortex response by a very small dc bias, and
a strong frequency dependence. Taken together, these phenomena are incompatible
with the current understanding of bulk vortex dynamics. We propose a generic
mechanism to account for these observations in terms of the competition between
the injection of a disordered vortex phase through the surface barriers at the
sample edges, and the annealing of this metastable disorder by the transport
current. The model is confirmed by investigating the current distribution
across NbSe2 single crystals using arrays of Hall sensors. For an ac current
only narrow regions near the edges are in the pinned disordered phase resulting
in a large response. In the presence of a dc bias a wide region of the sample
is filled by the disordered phase preventing vortex motion. The resulting
spatial variation of the disorder across the sample acts as an active memory of
the previously applied current sequence.


  Graph structured data is ubiquitous in daily life and scientific areas and
has attracted increasing attention. Graph Neural Networks (GNNs) have been
proved to be effective in modeling graph structured data and many variants of
GNN architectures have been proposed. However, much human effort is often
needed to tune the architecture depending on different datasets. Researchers
naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce
the human effort and achieve generally top-performing GNNs, but their methods
focus more on the architecture search. To understand GNN practitioners'
automated solutions, we organized AutoGraph Challenge at KDD Cup 2020,
emphasizing on automated graph neural networks for node classification. We
received top solutions especially from industrial tech companies like Meituan,
Alibaba and Twitter, which are already open sourced on Github. After detailed
comparisons with solutions from academia, we quantify the gaps between academia
and industry on modeling scope, effectiveness and efficiency, and show that (1)
academia AutoML for Graph solutions focus on GNN architecture search while
industrial solutions, especially the winning ones in the KDD Cup, tend to
obtain an overall solution (2) by neural architecture search only, academia
solutions achieve on average 97.3% accuracy of industrial solutions (3)
academia solutions are cheap to obtain with several GPU hours while industrial
solutions take a few months' labors. Academic solutions also contain much fewer
parameters.


  We consider the problem of learning a non-deterministic probabilistic system
consistent with a given finite set of positive and negative tree samples.
Consistency is defined with respect to strong simulation conformance. We
propose learning algorithms that use traditional and a new "stochastic"
state-space partitioning, the latter resulting in the minimum number of states.
We then use them to solve the problem of "active learning", that uses a
knowledgeable teacher to generate samples as counterexamples to simulation
equivalence queries. We show that the problem is undecidable in general, but
that it becomes decidable under a suitable condition on the teacher which comes
naturally from the way samples are generated from failed simulation checks. The
latter problem is shown to be undecidable if we impose an additional condition
on the learner to always conjecture a "minimum state" hypothesis. We therefore
propose a semi-algorithm using stochastic partitions. Finally, we apply the
proposed (semi-) algorithms to infer intermediate assumptions in an automated
assume-guarantee verification framework for probabilistic systems.


  We derive a complete asymptotic expansion of generalized Witten integrals for
Hamiltonian circle actions on arbitrary symplectic manifolds, characterizing
the coefficients in the expansion as integrals over the symplectic strata of
the corresponding Marsden-Weinstein reduced space and distributions on the Lie
algebra. The obtained coefficients involve singular contributions of the
lower-dimensional strata related to numerical invariants of the fixed-point
set.


  We propose a novel cohesive subgraph model called $\tau$-strengthened
$(\alpha,\beta)$-core (denoted as $(\alpha,\beta)_{\tau}$-core), which is the
first to consider both tie strength and vertex engagement on bipartite graphs.
An edge is a strong tie if contained in at least $\tau$ butterflies
($2\times2$-bicliques). $(\alpha,\beta)_{\tau}$-core requires each vertex on
the upper or lower level to have at least $\alpha$ or $\beta$ strong ties,
given strength level $\tau$. To retrieve the vertices of
$(\alpha,\beta)_{\tau}$-core optimally, we construct index
$I_{\alpha,\beta,\tau}$ to store all $(\alpha,\beta)_{\tau}$-cores. Effective
optimization techniques are proposed to improve index construction. To make our
idea practical on large graphs, we propose 2D-indexes $I_{\alpha,\beta},
I_{\beta,\tau}$, and $I_{\alpha,\tau}$ that selectively store the vertices of
$(\alpha,\beta)_{\tau}$-core for some $\alpha,\beta$, and $\tau$. The
2D-indexes are more space-efficient and require less construction time, each of
which can support $(\alpha,\beta)_{\tau}$-core queries. As query efficiency
depends on input parameters and the choice of 2D-index, we propose a
learning-based hybrid computation paradigm by training a feed-forward neural
network to predict the optimal choice of 2D-index that minimizes the query
time. Extensive experiments show that ($1$) $(\alpha,\beta)_{\tau}$-core is an
effective model capturing unique and important cohesive subgraphs; ($2$) the
proposed techniques significantly improve the efficiency of index construction
and query processing.


  We introduce the problem of proficiency modeling: Given a user's posts on a
social media platform, the task is to identify the subset of posts or topics
for which the user has some level of proficiency. This enables the filtering
and ranking of social media posts on a given topic as per user proficiency.
Unlike experts on a given topic, proficient users may not have received formal
training and possess years of practical experience, but may be autodidacts,
hobbyists, and people with sustained interest, enabling them to make genuine
and original contributions to discourse. While predicting whether a user is an
expert on a given topic imposes strong constraints on who is a true positive,
proficiency modeling implies a graded scoring, relaxing these constraints. Put
another way, many active social media users can be assumed to possess, or
eventually acquire, some level of proficiency on topics relevant to their
community. We tackle proficiency modeling in an unsupervised manner by
utilizing user embeddings to model engagement with a given topic, as indicated
by a user's preference for authoring related content. We investigate five
alternative approaches to model proficiency, ranging from basic ones to an
advanced, tailored user modeling approach, applied within two real-world
benchmarks for evaluation.


  This paper deals with the existence of optimal transport maps for some
optimal transport problems with a convex but non strictly convex cost. We give
a decomposition strategy to address this issue. As part of our strategy, we
have to treat some transport problems, of independent interest, with a convex
constraint on the displacement. As an illustration of our strategy, we prove
existence of optimal transport maps in the case where the source measure is
absolutely continuous with respect to the Lebesgue measure and the
transportation cost is of the form h(||x-y||) with h strictly convex increasing
and ||. || an arbitrary norm in \R2.


  Solving optimization problems with transient PDE-constraints is
computationally costly due to the number of nonlinear iterations and the cost
of solving large-scale KKT matrices. These matrices scale with the size of the
spatial discretization times the number of time steps. We propose a new two
level domain decomposition preconditioner to solve these linear systems when
constrained by the heat equation. Our approach leverages the observation that
the Schur-complement is elliptic in time, and thus amenable to classical domain
decomposition methods. Further, the application of the preconditioner uses
existing time integration routines to facilitate implementation and maximize
software reuse. The performance of the preconditioner is examined in an
empirical study demonstrating the approach is scalable with respect to the
number of time steps and subdomains.


  A statistical analysis of the impact of the diminishing number of operational
shutters experienced by the JWST/NIRSpec Micro Shutter Array since
Commissioning is presented. It is shown that the number of high priority
science targets that NIRSpec is able to observe simultaneously has so far
decreased by 3.1%. Of greater concern, however, is NIRSpec's diminished ability
to carry out autonomous MSATA target acquisition, which is more sensitive to
the loss of shutters than is the multiplexing. In the flagship case of MSA
observations of deep fields, the number of pointings at which it is not
possible to reach the required minimum number of 5 Valid Reference Stars has
increased from 4.9% to 6.3% and is beginning to become noticeable. Similarly,
the number of higher risk target acquisitions that need to be carried out with
fewer than the maximum allowed number of 8 Reference Stars has grown from 27%
to 31%.


  Studentisation upon rank-based linear estimators is generally considered an
unnecessary topic, due to the domain restriction upon $S_{n}$, which exhibits
constant variance. This assertion is functionally inconsistent with general
analytic practice though. We introduce a general unbiased and minimum variance
estimator upon the Beta-Binomially distributed Kemeny Hilbert space, which
allows for permutation ties to exist and be uniquely measured. As individual
permutation samples now exhibit unique random variance, a sample dependent
variance estimator must now be introduced into the linear model. We derive and
prove the Slutsky conditions to enable $t_{\nu}$-distributed Wald test
statistics to be constructed, while stably exhibiting Gauss-Markov properties
upon finite samples. Simulations demonstrate convergent decisions upon the two
orthonormal Slutsky corrected Wald test statistics, verifying the projective
geometric duality which exists upon the affine-linear Kemeny metric.


  In our previous work arXiv:1704.08675, we pointed out that various multi-cut
solutions exist in the Chern-Simons (CS) matrix models at large-$N$ due to a
curious structure of the saddle point equations. In the ABJM matrix model,
these multi-cut solutions might be regarded as the condensations of the
D2-brane instantons. However many of these multi-cut solutions including the
ones corresponding to the condensations of the D2-brane instantons were
obtained numerically only. In the current work, we propose an ansatz for the
multi-cut solutions which may allow us to derive the analytic expressions for
all these solutions. As a demonstration, we derive several novel analytic
solutions in the pure CS matrix model and the ABJM matrix model. We also
develop the argument for the connection to the instantons.


  We prove a weak version of the cross--product conjecture: ${F}(k+1,\ell)
{F}(k,\ell+1) \geq (\frac12+\varepsilon) {F}(k,\ell) {F}(k+1,\ell+1)$, where
${F}(k,\ell)$ is the number of linear extensions for which the values at fixed
elements $x,y,z$ are $k$ and $\ell$ apart, respectively, and where
$\varepsilon>0$ depends on the poset. We also prove the converse inequality and
disprove the {generalized cross--product conjecture}. The proofs use geometric
inequalities for mixed volumes and combinatorics of words.


  Internet is fast becoming critically important to commerce, industry and
individuals. Search Engine (SE) is the most vital component for communication
network and also used for discover information for users or people. Search
engine optimization (SEO) is the process that is mostly used to increasing
traffic from free, organic or natural listings on search engines and also helps
to increase website ranking. It includes techniques like link building,
directory submission, classified submission etc. but SMO, on the other hand, is
the process of promoting your website on social media platforms. It includes
techniques like RSS feeds, social news and bookmarking sites, video and
blogging sites, as well as social networking sites, such as Facebook, Twitter,
Google+, Tumblr, Pinterest, Instagram etc.Social media optimization is becoming
increasingly important for search engine optimization, as search engines are
increasingly utilizing the recommendations of users of social networks to rank
pages in the search engine result pages. Since it is more difficult to tip the
influence the search engines in this way. Social Media Optimization (SMO) may
also use to generate traffic on a website, promote your business at the center
of social marketing place and increase ranking.


  In this paper, we propose new spectral viscosity methods based on the
generalized Hermite functions for the solution of nonlinear scalar conservation
laws in the whole line. It is shown rigorously that these schemes converge to
the unique entropy solution by using compensated compactness arguments, under
some conditions. The numerical experiments of the inviscid Burger's equation
support our result, and it verifies the reasonableness of the conditions.


  A method to generalize results from Riemannian Geometry to Finsler geometry
is presented. We use the method to generalize several results that involve only
metric conditions. Between them we show that the topology induced by the
Finsler structure is equivalent to the manifold topology, we provide a new
proof of the Hopf-Rinow theorem in Finsler geometry and we prove the existence
of the center of mass of a convex body when the non-symmetric distance function
comes from a non-reversible Finsler function.


  We report the discovery of a transient equivalent hydrogen column density
with an absorption edge at ~3.8 kiloelectron volts in the spectrum of the
prompt x-ray emission of gamma-ray burst (GRB) 990705. This feature can be
satisfactorily modeled with a photoelectric absorption by a medium located at a
redshift of ~0.86 and with an iron abundance of ~75 times the solar one. The
transient behavior is attributed to the strong ionization produced in the
circumburst medium by the GRB photons. The high iron abundance points to the
existence of a burst environment enriched by a supernova along the line of
sight. The supernova explosion is estimated to have occurred about 10 years
before the burst. Our results agree with models in which GRBs originate from
the collapse of very massive stars and are preceded by a supernova event


  We present HST/NICMOS imaging of the H_2 2.12 \mu m emission in 5 fields in
the Helix Nebula ranging in radial distance from 250-450" from the central
star. The images reveal arcuate structures with their apexes pointing towards
the central star. Comparison of these images with comparable resolution ground
based images reveals that the molecular gas is more highly clumped than the
ionized gas line tracers. From our images, we determine an average number
density of knots in the molecular gas ranging from 162 knots/arcmin^2 in the
denser regions to 18 knots/arcmin^2 in the lower density outer regions. Using
this new number density, we estimate that the total number of knots in the
Helix to be ~23,000 which is a factor of 6.5 larger than previous estimates.
The total neutral gas mass in the Helix is 0.35 M_\odot assuming a mass of
\~1.5x10^{-5} M_\odot for the individual knots. The H_2 intensity, 5-9x10^{-5}
erg s^{-1} cm^{-2} sr^{-1}, remains relatively constant with projected distance
from the central star suggesting a heating mechanism for the molecular gas that
is distributed almost uniformly in the knots throughout the nebula. The
temperature and H_2 2.12 \mu m intensity of the knots can be approximately
explained by photodissociation regions (PDRs) in the individual knots; however,
theoretical PDR models of PN under-predict the intensities of some knots by a
factor of 10.


  Online music services are increasing in popularity. They enable us to analyze
people's music listening behavior based on play logs. Although it is known that
people listen to music based on topic (e.g., rock or jazz), we assume that when
a user is addicted to an artist, s/he chooses the artist's songs regardless of
topic. Based on this assumption, in this paper, we propose a probabilistic
model to analyze people's music listening behavior. Our main contributions are
three-fold. First, to the best of our knowledge, this is the first study
modeling music listening behavior by taking into account the influence of
addiction to artists. Second, by using real-world datasets of play logs, we
showed the effectiveness of our proposed model. Third, we carried out
qualitative experiments and showed that taking addiction into account enables
us to analyze music listening behavior from a new viewpoint in terms of how
people listen to music according to the time of day, how an artist's songs are
listened to by people, etc. We also discuss the possibility of applying the
analysis results to applications such as artist similarity computation and song
recommendation.


  We compute the propagation and scattering of linear gravitational waves off a
Schwarzschild black hole using a numerical code which solves a generalization
of the Zerilli equation to a three dimensional cartesian coordinate system.
Since the solution to this problem is well understood it represents a very good
testbed for evaluating our ability to perform three dimensional computations of
gravitational waves in spacetimes in which a black hole event horizon is
present.


  This paper develops verification methodology for quantum programs, and the
contribution of the paper is two-fold: 1. Sharir, Pnueli and Hart [SIAM J.
Comput. 13(1984)292-314] presented a general method for proving properties of
probabilistic programs, in which a probabilistic program is modeled by a Markov
chain and an assertion on the output distribution is extended into an invariant
assertion on all intermediate distributions. Their method is essentially a
probabilistic generalization of the classical Floyd inductive assertion method.
In this paper, we consider quantum programs modeled by quantum Markov chains
which are defined by super-operators. It is shown that the Sharir-Pnueli-Hart
method can be elegantly generalized to quantum programs by exploiting the
Schr\"odinger-Heisenberg duality between quantum states and observables. In
particular, a completeness theorem for the Sharir-Pnueli-Hart verification
method of quantum programs is established. 2. As indicated by the completeness
theorem, the Sharir-Pnueli-Hart method is in principle effective for verifying
all properties of quantum programs that can be expressed in terms of Hermitian
operators (observables). But it is not feasible for many practical applications
because of the complicated calculation involved in the verification. For the
case of finite-dimensional state spaces, we find a method for verification of
quantum programs much simpler than the Sharir-Pnueli-Hart method by employing
the matrix representation of super-operators and Jordan decomposition of
matrices. In particular, this method enables us to compute easily the average
running time and even to analyze some interesting long-run behaviors of quantum
programs in a finite-dimensional state space.


  A series of simple and low-cost devices for switching, amplifying, and
chirping diode lasers based on current modulation are presented. Direct
modulation of diode laser currents is rarely sufficient to establish precise
amplitude and phase control over light, as its effects on these parameters are
not independent. These devices overcome this limitation by exploiting amplifier
saturation and dramatically outperform commonly used external modulators in key
figures of merit for quantum technological applications. Semiconductor optical
amplifiers operated on either rubidium D line are recast as intensity switches
and shown to achieve ON:OFF ratios $>10^6$ in as little as 50 ns. Current is
switched to a 795 nm wavelength (Rb D1) tapered amplifier to produce optical
pulses of few nanosecond duration and peak powers of 3 W at a similar
extinction ratio. Fast rf pulses are applied directly to a laser diode to shift
its emission frequency by up to 300 MHz in either direction and at a maximum
chirp rate of 150 MHz/ns. Finally, the latter components are combined, yielding
a system that produces watt-level optical pulses with arbitrary frequency
chirps in the given range and <2% residual intensity variation, all within 65
ns upon asynchronous demand. Such systems have broad application in atomic,
molecular, and optical physics, and are of particular interest to fast
experiments simultaneously requiring high power and low noise, for example
quantum memory experiments with atomic vapors.


  Following the reports of candidate chiral doublet bands observed in cesium
isotopes, the possible chiral candidates and the evolution of three-dimensional
rotation in $^{120-134}{\textrm{Cs}}$ are investigated within the microscopic
three-dimensional tilted axis cranking covariant density functional theory
(3DTAC-CDFT). By investigating the evolution of the polar angle $\theta$ and
azimuth angle $\varphi$ as a function of rotational frequency $\hbar\omega$,
the transition from the planar rotation to the chiral rotation has been found
in $^{121-133}{\textrm{Cs}}$. The corresponding critical rotational frequency
$\omega_{\textrm{crit}}$ of the appearance of chiral aplanar rotation decreases
as neutron number increases, which can be attributed to the neutrons in $(gd)$
and $(sd)$ shells having smaller angular momentum components along both the
short and long axes, and larger components along medium axis, respectively. In
comparison, only planar rotation has been obtained in
$^{120,134}{\textrm{Cs}}$. With these interpretations, the obtained
$I\sim\hbar\omega$ and energy spectra as well as $B(M1)/B(E2)$ values show
reasonable agreement with the available experimental data. In addition, the
evolution of quadrupole deformation $\beta$ and triaxial deformation $\gamma$
are also discussed.


  A ferromagnetic insulator (FI) attached to a conventional superconductor (S)
changes drastically the properties of the latter. Specifically, the exchange
field at the FI/S interface leads to a splitting of the superconducting density
of states. If S is a superconducting film, thinner than the superconducting
coherence length, the modification of the density of states occurs over the
whole sample. The co-existence of the exchange splitting and superconducting
correlations in S/FI structures leads to striking transport phenomena that are
of interest for applications in thermoelectricity, superconducting spintronics
and radiation sensors. Here we review the most recent progress in understanding
the transport properties of FI/S structures by presenting a complete
theoretical framework based on the quasiclassical kinetic equations. We discuss
the coupling between the electronic degrees of freedom, charge, spin and
energy, under non-equilibrium conditions and its manifestation in
thermoelectricity and spin-dependent transport.


  We show that every topological surface lamination of a 3-manifold M is
isotopic to one with smoothly immersed leaves. This carries out a project
proposed by Gabai in [Problems in foliations and laminations, AMS/IP Stud. Adv.
Math. 2.2 1--33]. Consequently any such lamination admits the structure of a
Riemann surface lamination, and therefore useful structure theorems of Candel
[Uniformization of surface laminations, Ann. Sci. Ecole Norm. Sup. 26 (1993)
489--516] and Ghys [Dynamique et geometrie complexes, Panoramas et Syntheses 8
(1999)] apply.