  We use transient absorption spectroscopy to monitor the ionization and
dissociation products following two-photon excitation of pure liquid water. The
two decay mechanisms occur with similar yield for an excitation energy of 9.3
eV, whereas the major channel at 8.3 eV is dissociation. The geminate
recombination kinetics of the H and OH fragments, which can be followed in the
transient absorption probed at 267 nm, provide a window on the dissociation
dynamics at the lower excitation energy. Modeling the OH geminate recombination
indicates that the dissociating H atoms have enough kinetic energy to escape
the solvent cage and one or two additional solvent shells. The average initial
separation of H and OH fragments is 0.7+-0.2 nm. Our observation suggests that
the hydrogen bonding environment does not prevent direct dissociation of an O-H
bond in the excited state. We discuss the implications of our measurement for
the excited state dynamics of liquid water and explore the role of those
dynamics in the ionization mechanism at low excitation energies.


  We define a notion of category enriched over an oplax monoidal category $V$,
extending the usual definition of category enriched over a monoidal category.
Even though oplax monoidal structures involve infinitely many functors $V^n\to
V$, defining categories enriched over $V$ only requires the lower arity maps
$(n \leq 3)$, similarly to the monoidal case. The focal point of the enrichment
theory shifts, in the oplax case, from the notion of $V$-category (given by
collections of objects and hom-objects together with composition and unit maps)
to the one of categories enriched over $V$ (genuine categories equipped with
additional structures). One of the merits of the notion of categories enriched
over $V$ is that it becomes straightforward to define enriched functors and
natural transformations. We show moreover that the resulting 2-category
$\mathsf{Cat}_V$ can be put in correspondence (via the theory of distributors)
with the 2-category of modules over $V$. We give an example of such an enriched
category in the framework of operads: every cocomplete symmetric monoidal
category $C$ is enriched over the category of sequences in $C$ endowed with an
oplax monoidal structure stemming from the usual operadic composition product,
whose monoids are still the operads. As an application of the study of the
2-functor $V\mapsto\mathsf{Cat}_V$, we show that when $V$ is also endowed with
a compatible lax monoidal structure - thus forming a lax-oplax duoidal category
- the 2-category $\mathsf{Cat}_V$ inherits a lax 2-monoidal structure, thereby
generalising the corresponding result when the enrichment base is a braided
monoidal category. We illustrate this result by discussing the lax-oplax
structure on the category of $(R^\mathrm{e}, R^\mathrm{e})$-bimodules, whose
bimonoids are the bialgebroids. We also comment on the relations with other
enrichment theories (monoidal, multicategories, skew and lax).


  Starting with the many-body Schr\"odinger equation we derive new rate
equations for resonant transport in quantum dots linked by ballistic channels
with high density of states. The charging and the Pauli exclusion principle
effects were taken into account. It is shown that the current in such a system
displays quantum coherence effects, even if the dots are away one from another.
A comparative analysis of quantum coherence effects in coupled and separated
dots is presented. The rate equations are extended for description of coherent
and incoherent transport in arbitrary multi-dot systems. It is demonstrated
that new rate equations constitute a generalization of the well-known optical
Bloch equations.


  Smith homomorphisms are maps between bordism groups that change both the
dimension and the tangential structure. We give a completely general account of
Smith homomorphisms, unifying the many examples in the literature. We provide
three definitions of Smith homomorphisms, including as maps of Thom spectra,
and show they are equivalent. Using this, we identify the cofiber of the
spectrum-level Smith map and extend the Smith homomorphism to a long exact
sequence of bordism groups, which is a powerful computation tool. We discuss
several examples of this long exact sequence, relating them to known
constructions such as Wood's and Wall's sequences. Furthermore, taking Anderson
duals yields a long exact sequence of invertible field theories, which has a
rich physical interpretation. We developed the theory in this paper with
applications in mind to symmetry breaking in quantum field theory, which we
study in a companion paper.


  Let k be an infinite field, I an infinite set, V a k-vector-space, and
g:k^I\to V a k-linear map. It is shown that if dim_k(V) is not too large (under
various hypotheses on card(k) and card(I), if it is finite, respectively
countable, respectively < card(k)), then ker(g) must contain elements
(u_i)_{i\in I} with all but finitely many components u_i nonzero.
  These results are used to prove that any homomorphism from a direct product
\prod_I A_i of not-necessarily-associative algebras A_i onto an algebra B,
where dim_k(B) is not too large (in the same senses) must factor through the
projection of \prod_I A_i onto the product of finitely many of the A_i, modulo
a map into the subalgebra \{b\in B | bB=Bb=\{0\}\}\subseteq B.
  Detailed consequences are noted in the case where the A_i are Lie algebras.


  Even to a simple and short news headline, readers react in a multitude of
ways: cognitively (e.g. inferring the writer's intent), emotionally (e.g.
feeling distrust), and behaviorally (e.g. sharing the news with their friends).
Such reactions are instantaneous and yet complex, as they rely on factors that
go beyond interpreting factual content of news. We propose Misinfo Reaction
Frames (MRF), a pragmatic formalism for modeling how readers might react to a
news headline. In contrast to categorical schema, our free-text dimensions
provide a more nuanced way of understanding intent beyond being benign or
malicious. We also introduce a Misinfo Reaction Frames corpus, a crowdsourced
dataset of reactions to over 25k news headlines focusing on global crises: the
Covid-19 pandemic, climate change, and cancer. Empirical results confirm that
it is indeed possible for neural models to predict the prominent patterns of
readers' reactions to previously unseen news headlines. Additionally, our user
study shows that displaying machine-generated MRF implications alongside news
headlines to readers can increase their trust in real news while decreasing
their trust in misinformation. Our work demonstrates the feasibility and
importance of pragmatic inferences on news headlines to help enhance AI-guided
misinformation detection and mitigation.


  In the present work, we describe a cryogenic setup for studies of
wavelength-shifting materials for optimised light collection in noble element
radiation detectors, and discuss the commissioning results. This SiPM-based
setup uses alpha induced scintillation in gaseous argon as the vacuum
ultraviolet light source with the goal of characterising materials, such as
polyethylene naphthalate (PEN) and tetraphenyl butadiene (TPB), in terms of
their wavelength-shifting efficiency. Further extensions of the system are
currently being studied. The foreseen upgrades are expected to allow the study
of GEM-like structures potentially interesting for rare-event searches. The
design of the setup will be addressed along with the first results.


  We consider the problem of failure detection in dynamic networks such as
MANETs. Unreliable failure detectors are classical mechanisms which provide
information about process failures. However, most of current implementations
consider that the network is fully connected and that the initial number of
nodes of the system is known. This assumption is not applicable to dynamic
environments. Furthermore, such implementations are usually timer-based while
in dynamic networks there is no upper bound for communication delays since
nodes can move. This paper presents an asynchronous implementation of a failure
detector for unknown and mobile networks. Our approach does not rely on timers
and neither the composition nor the number of nodes in the system are known. We
prove that our algorithm can implement failure detectors of class <>S when
behavioral properties and connectivity conditions are satisfied by the
underlying system.


  In this paper, we present a domain specific process to assist the
verification of observer-based fault detection software. Observer-based fault
detection systems, like control systems, yield invariant properties of
quadratic types. These quadratic invariants express both safety properties of
the software such as the boundedness of the states and correctness properties
such as the absence of false alarms from the fault detector. We seek to
leverage these quadratic invariants, in an automated fashion, for the formal
verification of the fault detection software. The approach, referred to as the
credible autocoding framework [1], can be characterized as autocoding with
proofs. The process starts with the fault detector model, along with its safety
and correctness properties, all expressed formally in a synchronous modeling
environment such as Simulink. The model is then transformed by a prototype
credible autocoder into both code and analyzable annotations for the code. We
demonstrate the credible autocoding process on a running example of an output
observer fault detector for a 3-degrees-of-freedom (3DOF) helicopter control
system.


  This paper presents the results of empirical research conducted during March
to September 2009. The study focused on the influence of virtual research and
development teams within Malaysian manufacturing small and medium sized
enterprises (SMEs). The specific objective of the study is better understanding
of the application of collaborative technologies in business, to find the
effective factors to assist SMEs to remain competitive in the future. The paper
stresses to find an answer for a question Is there any relationship between
company size, Internet connection facility and virtuality?. The survey data
shows SMEs are now technologically capable of performing the virtual
collaborative team, but the infrastructure usage is less. SMEs now have the
necessary technology to begin the implementation process of collaboration tools
to reduce research and development time, costs and increase productivity. So,
the manager of R and D should take the potentials of virtual teams into
account.


  In Oste and Van der Jeugt, SIGMA, 12 (2016) we classified all pairs of
recurrence relations in which two (dual) Hahn polynomials with different
parameters appear. Such pairs are referred to as (dual) Hahn doubles, and the
same technique was then applied to obtain all Racah doubles. We now consider a
special case concerning the doubles related to Racah polynomials. This gives
rise to an interesting class of two-diagonal matrices with closed form
expressions for the eigenvalues. Just as it was the case for (dual) Hahn
doubles, the resulting two-diagonal matrix can be used to construct a finite
oscillator model. We discuss some properties of this oscillator model, give its
(discrete) position wavefunctions explicitly, and illustrate their behaviour by
means of some plots.


  We review recent progress in the analytic study of random matrix models
suggested by noncommutative geometry. One considers fuzzy spectral triples
where the space of possible Dirac operators is assigned a probability
distribution. These ensembles of Dirac operators are constructed as toy models
of Euclidean quantum gravity on finite noncommutative spaces and display many
interesting properties. The ensembles exhibit spectral phase transitions, and
near these phase transitions they show manifold-like behavior. In certain cases
one can recover Liouville quantum gravity in the double scaling limit. We
highlight examples where bootstrap techniques, Coulomb gas methods, and
Topological Recursion are applicable.


  Beyond the second-order Born approximation, we develop an improved master
equation approach to quantum transport by virtue of a self-consistent Born
approximation. The basic idea is replacing the free Green's function in the
tunneling self-energy by an effective reduced propagator under the Born
approximation. We found that the effect of this simple improvement is
remarkable, for instance, it can not only recover the exact result of
noninteracting transport under arbitrary voltages, but also predict the
challenging nonequilibrium Kondo effect. In addition to having an elegant
structure, the application convenience and accuracy of the proposed scheme, as
demonstrated by the examples in this work, may suggest it a useful tool for
quantum transports.


  We report a comprehensive study of the complex AC conductivity of thin
effectively 2D amorphous superconducting InO_x films at zero applied field.
Below a temperature scale T_c0 where the superconducting order parameter
amplitude becomes well defined, there is a temperature where both the
generalized superfluid stiffness acquires a frequency dependence and the DC
mangetoresistance becomes linear in field. We associate this with a transition
of the Kosterlitz-Thouless-Berezinskii (KTB) type. At our measurement
frequencies the superfluid stiffness at T_KTB is found to be larger than the
universal value. Although this may be understood with a vortex dielectric
constant of epsilon_v ~ 1.9 within the usual KTB theory, this is a relatively
large value and indicates that such a system may be out of the domain of
applicability of the low-fugacity (low vortex density) KTB treatment. This
opens up the possibility that at least some of the discrepancy from a
non-universal magnitude is intrinsic. Our finite frequency measurements allow
us access to a number of other phenomena concerning the charge dynamics in
superconducting thin films, including an enhanced conductivity near the
amplitude fluctuation temperature T_c0 and a finite dissipation at low
temperature which appears to be a universal aspect of highly disordered
superconducting films.


  Beyond black holes and neutron stars, new hypothetical compact objects have
been proposed as potential astrophysical entities. In general, their properties
have not yet been fully explored or understood, nor has it been proven whether
or not they exist in nature. They are the so-called $\textit{exotic compact
objects}$, theoretical equilibrium configurations in the strong regime of
gravity that involve new exotic physical phenomena deeply related to
fundamental questions of theoretical physics (e.g., the nature of dark matter,
the formation of singularities, or the presence of horizons). Among these
exotic objects, there are those that require the existence of new fields and
particles beyond the Standard Model, such as boson stars and ultralight bosons;
those that seek to describe the dense equation of state of neutron stars as an
even more extreme state of matter made up of free quarks; or those that exhibit
additional properties related to extensions of General Relativity or even
quantum gravity. However, in order to move from theoretical objects to
astrophysical objects, their dynamics and stability must first be assessed by
performing numerical-relativity simulations under the premise that solutions
that are unstable on dynamical timescales will never completely form, being, at
most, a transient state that will not be able to play any astrophysical role.
Furthermore, numerical simulations also make it possible to extract the
gravitational radiation from relevant astrophysical scenarios, such as the
collapse of exotic stars or binary mergers, which could then be compared with
current and upcoming LIGO-Virgo-KAGRA gravitational-wave detections.


  The stability under phase perturbations of the decay rate of local scalar
oscillatory integrals in two dimensions is analyzed. For a smooth phase S(x,y)
and a smooth perturbation function f(x,y), the decay rate for phase S(x,y) +
tf(x,y) is shown to be the same for all but finitely many t and given an
explicit description. The decay rate of the generic S(x,y) + tf(x,y) is always
at least as fast as that of S(x,y), and the "good" cases where it is the same
as that of S(x,y) are explicitly described. Uniform stability of the decay rate
is proven for S(x,y) + f(x,y) for small enough such good f(x,y), and the
coefficient of the leading term of the asymptotics is shown to be Lipschitz of
some order alpha, again for small enough good perturbations f(x,y).


  Till now, many path planning algorithms have been proposed in the literature.
The objective of these algorithms is to find the quickest path between initial
position to the end position in a certain environment. The complexity of these
algorithms depends on the internal parameters such as motor speed or sensor
range and on other external parameters, including the accuracy of the map, size
of the environment, and the number of obstacles. In this paper, we are giving
information about how path planning algorithm finds the optimal path in an
uneven terrain with a multiple obstacle using TurtleBot3 robot into the Gazebo
environment using Dijkstra's and A(star).


  Recent experiments have shown that the spontaneous activity of young
dissociated neuronal cultures can be described as a process of highly
inhomogeneous nucleation and front propagation due to the localization of noise
activity, i.e., noise focusing. However, the basic understanding of the
mechanisms of noise build-up leading to the nucleation remain an open
fundamental problem. Here we present a minimal dynamical model called
stochastic quorum percolation that can account for the observed phenomena,
while providing a robust theoretical framework. The model reproduces the first
and second order phase--transitions of bursting dynamics and neuronal
avalanches respectively, and captures the profound effect metric correlations
in the network topology can have on the dynamics. The application of our
results to other systems such as in the propagation of infectious diseases and
of rumors is discussed.


  Measurements of strong gravitational lensing jointly with type Ia supernovae
(SNe Ia) observations have been used to test the validity of the cosmic
distance duality relation (CDDR), $D_L(z)/[(1+z)^2D_A(z)]=\eta=1$, where
$D_L(z)$ and $D_A(z)$ are the luminosity and the angular diameter distances to
a given redshift $z$, respectively. However, several lensing systems lie in the
interval $1.4 \leq z \leq 3.6$ i.e., beyond the redshift range of current SNe
Ia compilations ($z \approx 1.50$), which prevents this kind of test to be
fully explored. In this paper, we circumvent this problem by testing the CDDR
considering observations of strong gravitational lensing along with SNe Ia and
{ a subsample from} the latest gamma-ray burst distance modulus data, whose
redshift range is $0.033 \leq z \leq 9.3$. { We parameterize their luminosity
distances with a second degree polynomial function and search for possible
deviations from the CDDR validity by using four different $\eta(z)$ functions:
$\eta(z)=1+\eta_0z$, $\eta(z)=1+\eta_0z/(1+z)$, $\eta(z)=(1+z)^{\eta_0}$ and
$\eta(z)=1+\eta_0\ln(1+z)$. Unlike previous tests done at redshifts lower than
$1.50$, the likelihood for $\eta_0$ depends strongly on the $\eta(z)$ function
considered, but we find no significant deviation from the CDDR validity
($\eta_0=0$). However, our analyses also point to the fact that caution is
needed when one fits data in higher redshifts to test the CDDR as well as a
better understanding of the mass distribution of lenses also is required for
more accurate results.


  For field theories with a topological charge Q, it is often of interest to
measure the topological susceptibility chi_t = ( < Q^2 > - < Q >^2 ) / V. If we
manage to perform a Monte Carlo simulation where Q changes frequently, chi_t
can be evaluated directly. However, for local update algorithms and fine
lattices, the auto-correlation time with respect to Q tends to be extremely
long, which invalidates the direct approach. Nevertheless, the measurement of
chi_t is still feasible, even when the entire Markov chain is topologically
frozen. We test a method for this purpose, based on the correlation of the
topological charge density, as suggested by Aoki, Fukaya, Hashimoto and Onogi.
Our studies in non-linear sigma-models and in 2d Abelian gauge theory yield
accurate results for chi_t, which confirm that the method is applicable. We
also obtain promising results in 4d SU(2) Yang-Mills theory, which suggest the
applicability of this method in QCD.


  We consider the initial value problem for the Dirac-Klein-Gordon equations in
two space dimensions. Global regularity for $C^\infty$ data was proved by
Gr\"unrock and Pecher. Here we consider analytic data, proving that if the
initial radius of analyticity is $\sigma_0 > 0$, then for later times $t > 0$
the radius of analyticity obeys a lower bound $\sigma(t) \ge \sigma_0
\exp(-At)$. This provides information about the possible dynamics of the
complex singularities of the holomorphic extension of the solution at time $t$.
The proof relies on an analytic version of Bourgain's Fourier restriction norm
method, multilinear space-time estimates of null form type and an approximate
conservation of charge.


  How do matching of spouses and the nature of work jointly shape the
distribution of COVID-19 health risks? To address this question, I study the
association between the incidence of COVID-19 and the degree of spousal sorting
into occupations that differ by contact intensity at the workplace. The
mechanism, that I explore, implies that the higher degree of positive spousal
sorting mitigates intra-household contagion and this translates into a smaller
number of individuals exposed to COVID-19 risk. Using the U.S. data at the
state level, I argue that spousal sorting is an important factor for
understanding the disparities in the prevalence of COVID-19 during the early
stages of the pandemic. First, I document that it creates about two-thirds of
the U.S. dual-earner couples that are exposed to higher COVID-19 health risk
due to within-household transmission. Moreover, I uncover substantial
heterogeneity in the degree of spousal sorting by state. Next, for the first
week of April 2020, I estimate that a one standard deviation increase in the
measure of spousal sorting is associated with a 30% reduction in the total
number of cases per 100000 inhabitants and a 39.3% decline in the total number
of deaths per 100000 inhabitants. Furthermore, I find substantial temporal
heterogeneity as the coefficients decline in magnitude over time. My results
speak to the importance of policies that allow mitigating intra-household
contagion.


  Software Defined Networking is currently revolutionizing computer networking
by decoupling the network control (control plane) from the forwarding functions
(data plane) enabling the network control to become directly programmable and
the underlying infrastructure to be abstracted for applications and network
services. Next to the well-known OpenFlow protocol, the XML-based NETCONF
protocol is also an important means for exchanging configuration information
from a management platform and is nowadays even part of OpenFlow. In
combination with NETCONF, YANG is the corresponding protocol that defines the
associated data structures supporting virtually all network configuration
protocols. YANG itself is a semantically rich language, which -- in order to
facilitate familiarization with the relevant subject -- is often visualized to
involve other experts or developers and to support them by their daily work
(writing applications which make use of YANG). In order to support this
process, this paper presents an novel approach to optimize and simplify YANG
data models to assist further discussions with the management and
implementations (especially of interfaces) to reduce complexity. Therefore, we
have defined a bidirectional mapping of YANG to UML and developed a tool that
renders the created UML diagrams. This combines the benefits to use the formal
language YANG with automatically maintained UML diagrams to involve other
experts or developers, closing the gap between technically improved data models
and their human readability.


  We present an elaborate framework for formally modelling pathways in chemical
reaction networks on a mechanistic level. Networks are modelled mathematically
as directed multi-hypergraphs, with vertices corresponding to molecules and
hyperedges to reactions. Pathways are modelled as integer hyperflows and we
expand the network model by detailed routing constraints. In contrast to the
more traditional approaches like Flux Balance Analysis or Elementary Mode
analysis we insist on integer-valued flows. While this choice makes it
necessary to solve possibly hard integer linear programs, it has the advantage
that more detailed mechanistic questions can be formulated. It is thus possible
to query networks for general transformation motifs, and to automatically
enumerate optimal and near-optimal pathways. Similarities and differences
between our work and traditional approaches in metabolic network analysis are
discussed in detail. To demonstrate the applicability of the mathematical
framework to real-life problems we first explore the design space of possible
non-oxidative glycolysis pathways and show that recent manually designed
pathways can be further optimised. We then use a model of sugar chemistry to
investigate pathways in the autocatalytic formose process. A graph
transformation-based approach is used to automatically generate the reaction
networks of interest.


  We propose a transform theory for calculating a density profile of small
colloids around a large colloid from a force curve between the two-large
colloids. In the colloid solution, there are many small colloids and two or
several large colloids. The force curve between the two-large colloids can be
measured by laser tweezers. In this letter, the transform theory is derived in
detail, where a superposition approximation of the radial distributions of the
density profiles and rigid-body approximation are introduced. In our opinion,
if the experimental condition is satisfied, the transform theory can be used
not only for the laser tweezers, but also for surface force apparatus and
colloid probe atomic force microscopy. Furthermore, the transform theory is to
calculate a density profile of micelles around a large spherical surface.


  In this paper, we solve in the negative the following problem : Is there any
complex structure on the sphere S^6?


  Quantum information is a useful resource to set up information processing.
Despite physical components are normally two-level systems, their combination
with entangling interactions becomes in a complex dynamics. Studied for
piecewise field pulses, this work analyzes the modeling for quantum information
operations with fields affordable technologically towards a universal quantum
computation model.


  We present extensive comparisons of the composite fermion theory with exact
results in the filling factor range $2/5>\nu>1/3$, which affirm that the
composite fermion theory correctly describes the qualitative reorganization of
the low energy Hilbert space of the strongly correlated electrons, and predicts
eigenenergies with an accuracy of $\sim 0.1$ %. These facts establish the basic
validity of the composite fermion description in this filling factor region.


  We define a new interactive differentially private mechanism -- the median
mechanism -- for answering arbitrary predicate queries that arrive online.
Relative to fixed accuracy and privacy constraints, this mechanism can answer
exponentially more queries than the previously best known interactive privacy
mechanism (the Laplace mechanism, which independently perturbs each query
result). Our guarantee is almost the best possible, even for non-interactive
privacy mechanisms. Conceptually, the median mechanism is the first privacy
mechanism capable of identifying and exploiting correlations among queries in
an interactive setting.
  We also give an efficient implementation of the median mechanism, with
running time polynomial in the number of queries, the database size, and the
domain size. This efficient implementation guarantees privacy for all input
databases, and accurate query results for almost all input databases. The
dependence of the privacy on the number of queries in this mechanism improves
over that of the best previously known efficient mechanism by a
super-polynomial factor, even in the non-interactive setting.


  Feature selection is a data mining task with the potential of speeding up
classification algorithms, enhancing model comprehensibility, and improving
learning accuracy. However, finding a subset of features that is optimal in
terms of predictive accuracy is usually computationally intractable. Out of
several heuristic approaches to dealing with this problem, the Recursive
Feature Elimination (RFE) algorithm has received considerable interest from
data mining practitioners. In this paper, we propose two novel algorithms
inspired by RFE, called Fibonacci- and k-Subsecting Recursive Feature
Elimination, which remove features in logarithmic steps, probing the wrapped
classifier more densely for the more promising feature subsets. The proposed
algorithms are experimentally compared against RFE on 28 highly
multidimensional datasets and evaluated in a practical case study involving 3D
electron density maps from the Protein Data Bank. The results show that
Fibonacci and k-Subsecting Recursive Feature Elimination are capable of
selecting a smaller subset of features much faster than standard RFE, while
achieving comparable predictive performance.


  Traditional online map tiles, widely used on the Internet such as Google Map
and Baidu Map, are rendered from vector data. Timely updating online map tiles
from vector data, of which the generating is time-consuming, is a difficult
mission. It is a shortcut to generate map tiles in time from remote sensing
images, which can be acquired timely without vector data. However, this mission
used to be challenging or even impossible. Inspired by image-to-image
translation (img2img) techniques based on generative adversarial networks
(GAN), we proposed a semi-supervised Generation of styled map Tiles based on
Generative Adversarial Network (SMAPGAN) model to generate styled map tiles
directly from remote sensing images. In this model, we designed a
semi-supervised learning strategy to pre-train SMAPGAN on rich unpaired samples
and fine-tune it on limited paired samples in reality. We also designed image
gradient L1 loss and image gradient structure loss to generate a styled map
tile with global topological relationships and detailed edge curves of objects,
which are important in cartography. Moreover, we proposed edge structural
similarity index (ESSI) as a metric to evaluate the quality of topological
consistency between generated map tiles and ground truths. Experimental results
present that SMAPGAN outperforms state-of-the-art (SOTA) works according to
mean squared error, structural similarity index, and ESSI. Also, SMAPGAN won
more approval than SOTA in the human perceptual test on the visual realism of
cartography. Our work shows that SMAPGAN is potentially a new paradigm to
produce styled map tiles. Our implementation of the SMAPGAN is available at
https://github.com/imcsq/SMAPGAN.


  In this paper, we propose a two-stage heterogeneous lightweight network for
monaural speech enhancement. Specifically, we design a novel two-stage
framework consisting of a coarse-grained full-band mask estimation stage and a
fine-grained low-frequency refinement stage. Instead of using a hand-designed
real-valued filter, we use a novel learnable complex-valued rectangular
bandwidth (LCRB) filter bank as an extractor of compact features. Furthermore,
considering the respective characteristics of the proposed two-stage task, we
used a heterogeneous structure, i.e., a U-shaped subnetwork as the backbone of
CoarseNet and a single-scale subnetwork as the backbone of FineNet. We
conducted experiments on the VoiceBank + DEMAND and DNS datasets to evaluate
the proposed approach. The experimental results show that the proposed method
outperforms the current state-of-the-art methods, while maintaining relatively
small model size and low computational complexity.


  By coupling pairs of superconducting qubits through a small Josephson
junction with a time-dependent flux bias, we show that arbitrary interactions
involving any combination of Pauli matrices can be generated with a small
number of drive tones applied through the flux bias of the coupling junction.
We then demonstrate that similar (though not fully universal) results can be
achieved in capacitively coupled qubits by exploiting the higher energy states
of the devices through multi-photon drive signals applied to the qubits' flux
degrees of freedom. By using this mechanism to couple a qubit to a detuned
resonator, the qubit's rotating frame state can be non-destructively measured
along any direction on the Bloch sphere. Finally, we describe how the
frequency-converting nature of the couplings can be used to engineer a
mechanism analogous to dynamic nuclear polarization in NMR systems, capable of
cooling an array of qubits well below the ambient temperature, and outline how
higher order interactions, such as local 3-body terms, can be engineered
through the same couplings. Our results demonstrate that a programmable quantum
simulator for large classes of interacting spin models could be engineered with
the same physical hardware.


  Magnetic switching of a single molecular magnet (SMM) due to spin-polarized
current flowing between ferromagnetic metallic electrodes is investigated
theoretically. Magnetic moments of the electrodes are assumed to be collinear
and parallel to the magnetic easy axis of the molecule. Electrons tunneling
through a barrier between magnetic leads are coupled to the SMM via exchange
interaction. The current flowing through the system as well as the spin
relaxation times of the SMM are calculated from the Fermi golden rule. It is
shown that spin of the SMM can be reversed by applying a voltage between the
two magnetic electrodes. Moreover, the switching is reflected in the
corresponding current-voltage characteristics.


  We theoretically investigate a scattering configuration in Compton
scattering, in which the orientation of the electron spin is reversed and
simultaneously, the photon polarization changes from linear polarization into
circular polarization. The intrinsic angular momentum of electron and photon
are computed along the coincident propagation direction of the incoming and
outgoing photon. We find that this intrinsic angular momentum is not conserved
in the considered scattering process. We also discuss the generation of
entanglement for the considered scattering setup and present an angle dependent
investigation of the corresponding differential cross section, Stokes
parameters and spin expectation.


  Classical systems can be entangled. Entanglement is defined by coincidence
correlations. Quantum entanglement experiments can be mimicked by a mechanical
system with a single conserved variable and 77.8% conditional efficiency.
Experiments are replicated for four particle entanglement swapping and GHZ
entanglement.


  While in every organization corporate culture and history change over time,
intentional efforts to identify performance problems are of particular interest
when trying to understand the current state of an organization. The results of
past improvement initiatives can shed light on the evolution of an organization
and represent, with the advantage of perfect hindsight, a learning opportunity
for future process improvements. The opportunity to test this premise occurred
in an applied research collaboration with the Swedish Transport Administration,
the government agency responsible for the planning, implementation, and
maintenance of long-term rail, road, shipping, and aviation infrastructure in
Sweden. This article is part of a theme issue on Process Improvement.


  The ultimate aim of image restoration like denoising is to find an exact
correlation between the noisy and clear image domains. But the optimization of
end-to-end denoising learning like pixel-wise losses is performed in a
sample-to-sample manner, which ignores the intrinsic correlation of images,
especially semantics. In this paper, we introduce the Deep Semantic Statistics
Matching (D2SM) Denoising Network. It exploits semantic features of pretrained
classification networks, then it implicitly matches the probabilistic
distribution of clear images at the semantic feature space. By learning to
preserve the semantic distribution of denoised images, we empirically find our
method significantly improves the denoising capabilities of networks, and the
denoised results can be better understood by high-level vision tasks.
Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate
the superiority of our method on both the denoising performance and semantic
segmentation accuracy. Moreover, the performance improvement observed on our
extended tasks including super-resolution and dehazing experiments shows its
potentiality as a new general plug-and-play component.


  We present a Perfect Sampling algorithm that can be applied to the Master
Equation of Gene Regulatory Networks (GRNs). The method recasts Gillespie's
Stochastic Simulation Algorithm (SSA) in the light of Markov Chain Monte Carlo
methods and combines it with the Dominated Coupling From The Past (DCFTP)
algorithm to provide guaranteed sampling from the stationary distribution. We
show how the DCFTP-SSA can be generically applied to genetic networks with
feedback formed by the interconnection of linear enzymatic reactions and
nonlinear Monod- and Hill-type elements. We establish rigorous bounds on the
error and convergence of the DCFTP-SSA, as compared to the standard SSA,
through a set of increasingly complex examples. Once the building blocks for
GRNs have been introduced, the algorithm is applied to study properly averaged
dynamic properties of two experimentally relevant genetic networks: the toggle
switch, a two-dimensional bistable system, and the repressilator, a
six-dimensional genetic oscillator.


  We show from a weak comparison principle (the Ultrapower Axiom) that the
Mitchell order is linear on certain kinds of ultrafilters: normal ultrafilters,
Dodd solid ultrafilters, and assuming GCH, generalized normal ultrafilters. In
the process we prove a generalization of Solovay's lemma to singular cardinals.


  Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival.


  In reinforcement learning an agent interacts with the environment by taking
actions and observing the next state and reward. When sampled
probabilistically, these state transitions, rewards, and actions can all induce
randomness in the observed long-term return. Traditionally, reinforcement
learning algorithms average over this randomness to estimate the value
function. In this paper, we build on recent work advocating a distributional
approach to reinforcement learning in which the distribution over returns is
modeled explicitly instead of only estimating the mean. That is, we examine
methods of learning the value distribution instead of the value function. We
give results that close a number of gaps between the theoretical and
algorithmic results given by Bellemare, Dabney, and Munos (2017). First, we
extend existing results to the approximate distribution setting. Second, we
present a novel distributional reinforcement learning algorithm consistent with
our theoretical formulation. Finally, we evaluate this new algorithm on the
Atari 2600 games, observing that it significantly outperforms many of the
recent improvements on DQN, including the related distributional algorithm C51.


  LANL Memorandum of potentially broad interest. A direct comparison is
performed between transport coefficients for weakly coupled plasmas obtained
with different formalisms to demonstrate that these formalisms give identical
predictions. This is what one would expect since for weakly coupled plasmas all
the formalisms rely on the same physical assumptions and mathematical
approximations.


  As is well known, multivariate Rogers-Szeg\"o polynomials are closely
connected with the partition functions of the $A_{N-1}$ type of Polychronakos
spin chains having long-range interactions. Applying the `freezing trick', here
we derive the partition functions for a class of $BC_N$ type of Polychronakos
spin chains containing supersymmetric analogues of polarized spin reversal
operators and subsequently use those partition functions to obtain novel
multivariate super Rogers-Szeg\"o (SRS) polynomials depending on four types of
variables. We construct the generating functions for such SRS polynomials and
show that these polynomials can be written as some bilinear combinations of the
$A_{N-1}$ type of SRS polynomials. We also use the above mentioned generating
functions to derive a set of recursion relations for the partition functions of
the $BC_N$ type of Polychronakos spin chains involving different numbers of
lattice sites and internal degrees of freedom.


  Typically, graph structures are represented by one of three different
matrices: the adjacency matrix, the unnormalised and the normalised graph
Laplacian matrices. The spectral (eigenvalue) properties of these different
matrices are compared. For each pair, the comparison is made by applying an
affine transformation to one of them, which enables comparison whilst
preserving certain key properties such as normalised eigengaps. Bounds are
given on the eigenvalue differences thus found, which depend on the minimum and
maximum degree of the graph. The monotonicity of the bounds and the structure
of the graphs are related. The bounds on a real social network graph, and on
three model graphs, are illustrated and analysed. The methodology is extended
to provide bounds on normalised eigengap differences which again turn out to be
in terms of the graph's degree extremes. It is found that if the degree extreme
difference is large, different choices of representation matrix may give rise
to disparate inference drawn from graph signal processing algorithms; smaller
degree extreme differences result in consistent inference, whatever the choice
of representation matrix. The different inference drawn from signal processing
algorithms is visualised using the spectral clustering algorithm on the three
representation matrices corresponding to a model graph and a real social
network graph.


  LiDAR-Inertial Odometry (LIO) is typically implemented using an
optimization-based approach, with the factor graph often being employed due to
its capability to seamlessly integrate residuals from both LiDAR and IMU
measurements. Conversely, a recent study has demonstrated that accurate LIO can
also be achieved using a loosely-coupled method. Inspired by this advancements,
we present a LIO method that leverages the recursive Bayes filter, solved via
the Extended Kalman Filter (EKF) - herein referred to as KLIO. Within KLIO,
prior and likelihood distributions are computed using IMU preintegration and
scan matching between LiDAR and local map point clouds, and the pose, velocity,
and IMU biases are updated through the EKF process. Through experiments with
the Newer College dataset, we demonstrate that KLIO achieves precise trajectory
tracking and mapping. Its accuracy is comparable to that of the
state-of-the-art methods in both tightly- and loosely-coupled methods.


  In this paper, we investigate the wellposedness of the non-isentropic
compressible Navier-Stokes/Allen-Cahn system with the heat-conductivity
proportional to a positive power of the temperature. This system describes the
flow of a two-phase immiscible heat-conducting viscous compressible mixture.
The phases are allowed to shrink or grow due to changes of density in the fluid
and incorporates their transport with the current. We established the global
existence and uniqueness of strong solutions for this system in 1-D, which
means no phase separation, vacuum, shock wave, mass or heat or phase
concentration will be developed in finite time, although the motion of the
two-phase immiscible flow has large oscillations and the interaction between
the hydrodynamic and phase-field effects is complex. Our result can be regarded
as a natural generalization of the Kazhikhov-Shelukhin's result
([Kazhikhov-Shelukhin. J. Appl. Math. Mech. 41 (1977)]) for the compressible
single-phase flow with constant heat conductivity to the non-isentropic
compressible immiscible two-phase flow with degenerate and nonlinear heat
conductivity.


  Nowadays, micromagnetic simulations are a common tool for studying a wide
range of different magnetic phenomena, including the ferromagnetic resonance. A
technique for evaluating reliability and validity of different micromagnetic
simulation tools is the simulation of proposed standard problems. We propose a
new standard problem by providing a detailed specification and analysis of a
sufficiently simple problem. By analyzing the magnetization dynamics in a thin
permalloy square sample, triggered by a well defined excitation, we obtain the
ferromagnetic resonance spectrum and identify the resonance modes via Fourier
transform. Simulations are performed using both finite difference and finite
element numerical methods, with \textsf{OOMMF} and \textsf{Nmag} simulators,
respectively. We report the effects of initial conditions and simulation
parameters on the character of the observed resonance modes for this standard
problem. We provide detailed instructions and code to assist in using the
results for evaluation of new simulator tools, and to help with numerical
calculation of ferromagnetic resonance spectra and modes in general.


  Some thoughts on the congruence D=0,1(mod 4) for the absolute discriminant D
of a number field


  Over a century ago, Oliver Wendell Holmes invited scholars to look at the law
through the lens of probability theory: "The prophecies of what the courts will
do in fact, and nothing more pretentious, are what I mean by the law." Yet few
legal scholars have taken up this intriguing invitation. As such, in place of
previous approaches to the study of law, this paper presents a non-normative,
mathematical approach to law and the legal process. Specifically, we present a
formal Bayesian model of civil and criminal litigation, or what we refer to as
the litigation game; that is, instead of focusing on the rules of civil or
criminal procedure or substantive legal doctrine, we ask and attempt to answer
a mathematical question: what is the posterior probability that a defendant in
a civil or criminal trial will be found liable, given that the defendant has,
in fact, committed a wrongful act?


  Here we study the behaviour of spin 0 sector of the DKP field in spaces with
torsion. First we show that in a Riemann-Cartan manifold the DKP field presents
an interaction with torsion when minimal coupling is performed, contrary to the
behaviour of the KG field, a result that breaks the usual equivalence between
the DKP and the KG fields.
  Next we analyse the case of Teleparallel Equivalent of General Relativity
Weitzenbock manifold, showing that in this case there is a perfect agreement
between KG and DKP fields. The origins of both results are also discussed.


  State-of-the-art approaches for hate-speech detection usually exhibit poor
performance in out-of-domain settings. This occurs, typically, due to
classifiers overemphasizing source-specific information that negatively impacts
its domain invariance. Prior work has attempted to penalize terms related to
hate-speech from manually curated lists using feature attribution methods,
which quantify the importance assigned to input terms by the classifier when
making a prediction. We, instead, propose a domain adaptation approach that
automatically extracts and penalizes source-specific terms using a domain
classifier, which learns to differentiate between domains, and
feature-attribution scores for hate-speech classes, yielding consistent
improvements in cross-domain evaluation.


  Robust face clustering is a vital step in enabling computational
understanding of visual character portrayal in media. Face clustering for
long-form content is challenging because of variations in appearance and lack
of supporting large-scale labeled data. Our work in this paper focuses on two
key aspects of this problem: the lack of domain-specific training or benchmark
datasets, and adapting face embeddings learned on web images to long-form
content, specifically movies. First, we present a dataset of over 169,000 face
tracks curated from 240 Hollywood movies with weak labels on whether a pair of
face tracks belong to the same or a different character. We propose an offline
algorithm based on nearest-neighbor search in the embedding space to mine
hard-examples from these tracks. We then investigate triplet-loss and multiview
correlation-based methods for adapting face embeddings to hard-examples. Our
experimental results highlight the usefulness of weakly labeled data for
domain-specific feature adaptation. Overall, we find that multiview
correlation-based adaptation yields more discriminative and robust face
embeddings. Its performance on downstream face verification and clustering
tasks is comparable to that of the state-of-the-art results in this domain. We
also present the SAIL-Movie Character Benchmark corpus developed to augment
existing benchmarks. It consists of racially diverse actors and provides
face-quality labels for subsequent error analysis. We hope that the large-scale
datasets developed in this work can further advance automatic character
labeling in videos. All resources are available freely at
https://sail.usc.edu/~ccmi/multiface.


  Optimizing pump operations is a challenging task for real-time management of
water distribution systems (WDSs). With suitable pump scheduling, pumping costs
can be significantly reduced. In this research, a novel economic model
predictive control (EMPC) framework for real-time management of WDSs is
proposed. Optimal pump operations are selected based on predicted system
behavior over a receding time horizon with the aim to minimize the total
pumping energy cost. Time-varying electricity tariffs are considered while all
the required water demands are satisfied. The novelty of this framework is to
choose the number of pumps to operate in each pump station as decision
variables in order to optimize the total pumping energy costs. By using integer
programming, the proposed EMPC is applied to a benchmark case study, the
Richmond Pruned network. The simulation with an EPANET hydraulic simulator is
implemented. Moreover, a comparison of the results obtained using the proposed
EMPC with those obtained using trigger-level control demonstrates significant
economic benefits of the proposed EMPC.


  In this paper, the tracking control problem for 5 DOF surgical robot which is
affected by tremor of surgeons' hand is considered. Mechanical modeling and
dynamic analysis of a robotic arm in slave subsystem of a telesurgery system
will be discussed and the reasons for selecting the appropriate materials for
different parts of robot will be explained. It would be required this robot,
which will do the main part of the surgery, be controlled based on the
uncertain properties of the tissues of patients body. Improved Lyapunov Based
control method with uncertainty observer is applied to improve the accuracy of
tracking procedure for a surgical manipulator to track a specified reference
signal in the presence of tremor that is modeled as constant bounded
disturbance. Based on the disturbance rejection scheme, tracking controllers
are constructed which are asymptotically stabilizing in the sense of Lyapunov.
The control strategy was implemented using a PC interface. Computer simulation
results demonstrate that accurate trajectory tracking can be achieved by using
the proposed controllers.


  Meta-learning, or "learning to learn," is a subfield of machine learning
where the goal is to develop models and algorithms that can learn from various
tasks and improve their learning process over time. Unlike traditional machine
learning methods focusing on learning a specific task, meta-learning aims to
leverage experience from previous tasks to enhance future learning. This
approach is particularly beneficial in scenarios where the available data for a
new task is limited, but there exists abundant data from related tasks. By
extracting and utilizing the underlying structure and patterns across these
tasks, meta-learning algorithms can achieve faster convergence and better
performance with fewer data. The following notes are mainly inspired from
\cite{vanschoren2018meta}, \cite{baxter2019learning}, and
\cite{maurer2005algorithmic}.


  We investigate a discrete non-linear Schr\"odinger equation with dynamical,
density-difference-dependent, gauge fields. We find a ground-state transition
from a plane wave condensate to a localized soliton state as the gauge coupling
is varied. Interestingly we find a regime in which the condensate and soliton
are both stable. We identify an emergent chiral symmetry, which leads to the
existence of a symmetry protected zero energy edge mode. The emergent chiral
symmetry relates low and high energy solitons. These states indicate that the
interaction acts both repulsively and attractively.


  Using numerical simulations we show that novel transport phenomena can occur
for vortices moving in periodic pinning arrays when two external perpendicular
ac drives are applied. In particular, we find a ratchet effect where the
vortices can have a net dc drift even in the absence of a dc drive. This
ratchet effect can occur for ac drives which create orbits that break one or
more reflection symmetries.


  We study the differential branching ratio, branching ratio and the
forward-backward asymmetry for the exclusive $B_s->\phi \ell^+ \ell^-$ decay in
the two Higgs doublet model. We analyze the dependencies of these quantities on
the model parameters and show that these observables are highly sensitive to
new physics and hence may provide powerful probe of the SM and beyond.


  A Chandra spectrum of the moderately active nearby F6 V star Pi3 Ori is used
to study the coronal properties of mid-F dwarfs. We find that Pi3 Ori's coronal
emission measure distribution is very similar to those of moderately active G
and K dwarfs, with an emission measure peak near $\log T=6.6$ seeming to be
ubiquitous for such stars. In contrast to coronal temperature, coronal
abundances are known to depend on spectral type for main sequence stars. Based
on this previously known relation, we expected Pi3 Ori's corona to exhibit an
extremely strong "FIP effect," a phenomenon first identified on the Sun where
elements with low "First Ionization Potential" (FIP) are enhanced in the
corona. We instead find that Pi3 Ori's corona exhibits a FIP effect essentially
identical to that of the Sun and other early G dwarfs, perhaps indicating that
the increase in FIP bias towards earlier spectral types stops or at least slows
for F stars. We find that Pi3 Ori's coronal characteristics are significantly
different from two previously studied mid-F stars, Procyon (F5 IV-V) and Tau
Boo (F7 V). We believe Pi3 Ori is more representative of the coronal
characteristics of mid-F dwarfs, with Procyon being different because of
luminosity class, and Tau Boo being different because of the effects of one of
two close companions, one stellar (Tau Boo B: M2 V) and one planetary.


  We introduce a quantum generalization of classical kinetic Ising models,
described by a certain class of quantum many body master equations. Similarly
to kinetic Ising models with detailed balance that are equivalent to certain
Hamiltonian systems, our models reduce to a set of Hamiltonian systems
determining the dynamics of the elements of the many body density matrix. The
ground states of these Hamiltonians are well described by matrix product, or
pair entangled projected states. We discuss critical properties of such
Hamiltonians, as well as entanglement properties of their low energy states.


  Spectrum shape measurements in nuclear $\beta$ decay can be used to test
physics beyond the Standard Model with results being complementary to
high-energy collider experiments. In particular, Beyond Standard Model
sensitivity of the weak interaction is expressed through the so-called Fierz
interference term. Additionally, the $\beta$ spectrum shape is a useful tool to
probe Standard Model effects, among which the most prominent is \textit{weak
magnetism}, a higher-order recoil correction induced by nuclear pion exchange.
To study effects in the $\beta$ spectrum shape at a precision level competitive
with the LHC, a new spectrometer was designed and built. It consists of a 3D
low-pressure gas electron tracker and a plastic scintillator used for
triggering the data acquisition and recording the $\beta$ particle energy. In
this Letter, the results from $\beta$ spectrum shape measurements on the
allowed Gamow-Teller transition $^{114}\text{In} \rightarrow ^{114}\text{Sn}$
are presented, including a first extraction of the weak magnetism form factor
in the high nuclear mass range and a new estimate of the $90\%$ confidence
interval for the Fierz interference term.


  Standard practice within Reinforcement Learning from Human Feedback (RLHF)
involves optimizing against a Reward Model (RM), which itself is trained to
reflect human preferences for desirable generations. A notable subject that is
understudied is the (in-)consistency of RMs -- whether they can recognize the
semantic changes to different prompts and appropriately adapt their reward
assignments -- and their impact on the downstream RLHF model.
  In this paper, we visit a series of research questions relevant to RM
inconsistency: (1) How can we measure the consistency of reward models? (2) How
consistent are the existing RMs and how can we improve them? (3) In what ways
does reward inconsistency influence the chatbots resulting from the RLHF model
training?
  We propose Contrast Instructions -- a benchmarking strategy for the
consistency of RM. Each example in Contrast Instructions features a pair of
lexically similar instructions with different ground truth responses. A
consistent RM is expected to rank the corresponding instruction and response
higher than other combinations. We observe that current RMs trained with the
standard ranking objective fail miserably on Contrast Instructions compared to
average humans. To show that RM consistency can be improved efficiently without
using extra training budget, we propose two techniques ConvexDA and
RewardFusion, which enhance reward consistency through extrapolation during the
RM training and inference stage, respectively. We show that RLHF models trained
with a more consistent RM yield more useful responses, suggesting that reward
inconsistency exhibits a trickle-down effect on the downstream RLHF process.


  We construct data dependent bounds on the risk in function learning problems.
The bounds are based on the local norms of the Rademacher process indexed by
the underlying function class and they do not require prior knowledge about the
distribution of the training examples or any specific properties of the
function class.


  We theoretically investigate detuning-dependent properties of high-order
harmonic generation (HHG) in monolayer transition metal dichalcogenides
(TMDCs). In contrast to HHG in conventional materials, TMDCs show both parallel
and perpendicular emissions with respect to the incident electric field. We
find that such an anomalous emission can be artificially controlled by the
frequency detuning of the incident electric fields, i.e., the parallel and
perpendicular HHG can be strongly enhanced by multiphoton resonances. This
peculiar phenomenon would provide a way for controlling HHG in TMDCs and
stimulate the realization of novel optical devices.


  We observe a disconnect between the developers and the end users of linear
algebra libraries. On the one hand, the numerical linear algebra and the
high-performance communities invest significant effort in the development and
optimization of highly sophisticated numerical kernels and libraries, aiming at
the maximum exploitation of both the properties of the input matrices, and the
architectural features of the target computing platform. On the other hand, end
users are progressively less likely to go through the error-prone and time
consuming process of directly using said libraries by writing their code in C
or Fortran; instead, languages and libraries such as Matlab, Julia, Eigen and
Armadillo, which offer a higher level of abstraction, are becoming more and
more popular. Users are given the opportunity to code matrix computations with
a syntax that closely resembles the mathematical description; it is then a
compiler or an interpreter that internally maps the input program to lower
level kernels, as provided by libraries such as BLAS and LAPACK. Unfortunately,
our experience suggests that in terms of performance, this translation is
typically vastly suboptimal.
  In this paper, we first introduce the Linear Algebra Mapping Problem, and
then investigate how effectively a benchmark of test problems is solved by
popular high-level programming languages. Specifically, we consider Matlab,
Octave, Julia, R, Armadillo (C++), Eigen (C++), and NumPy (Python); the
benchmark is meant to test both standard compiler optimizations such as common
subexpression elimination and loop-invariant code motion, as well as linear
algebra specific optimizations such as optimal parenthesization of a matrix
product and kernel selection for matrices with properties. The aim of this
study is to give concrete guidelines for the development of languages and
libraries that support linear algebra computations.


  The dynamics of S=1/2 quantum spins on a 2D square lattice lie at the heart
of the mystery of the cuprates
\cite{Hayden2004,Vignolle2007,Li2010,LeTacon2011,Coldea2001,Headings2010,Braicovich2010}.
In bulk cuprates such as \LCO{}, the presence of a weak interlayer coupling
stabilizes 3D N\'{e}el order up to high temperatures. In a truly 2D system
however, thermal spin fluctuations melt long range order at any finite
temperature \cite{Mermin1966}. Further, quantum spin fluctuations transfer
magnetic spectral weight out of a well-defined magnon excitation into a
magnetic continuum, the nature of which remains controversial
\cite{Sandvik2001,Ho2001,Christensen2007,Headings2010}. Here, we measure the
spin response of \emph{isolated one-unit-cell thick layers} of \LCO{}. We show
that coherent magnons persist even in a single layer of \LCO{} despite the loss
of magnetic order, with no evidence for resonating valence bond (RVB)-like spin
correlations \cite{Anderson1987,Hsu1990,Christensen2007}. Thus these
excitations are well described by linear spin wave theory (LSWT). We also
observe a high-energy magnetic continuum in the isotropic magnetic response.
This high-energy continuum is not well described by 2 magnon LSWT, or indeed
any existing theories.


  Magneto-electronic properties of buckled monolayer GaAs is studied by the
developed generalized tight-binding model, considering the buckled structure,
multi-orbital chemical bondings, spin-orbit coupling, electric field, and
magnetic field simultaneously. Three group of spin-polarized Landau levels
(LLs) near the Fermi level are induced by the magnetic quantization, whose
initial energies, LL degeneracy, energy spacings, magnetic-field-dependence,
and spin polarization are investigated. The Landau state probabilities
describing the oscillation patterns, localization centers, and node
regularities of the dominated/minor orbitals are analyzed, and their
energy-dependent variations are discussed. The given density of states directly
reflects the main features of the LL energy spectra in the structure, height,
number, and frequency of the spin-polarized LL peaks. The electric field causes
the monotonous/nonmonotonous LL energy dispersions, LL crossing, gap
modulation, phase transition and spin splitting enhancement. The complex gap
modulations and phase transitions based on the competition between magnetic and
electric fields are explored in detail by the phase diagram. The
field-controlled gap modulations and phase transitions are helpful in designing
the top-gated and phase-change electronic devices. These predicted
magneto-electronic properties could be verified by scanning tunneling
spectroscopy measurements.


  Pearson correlation and mutual information based complex networks of the
day-to-day returns of US S&P500 stocks between 1985 and 2015 have been
constructed in order to investigate the mutual dependencies of the stocks and
their nature. We show that both networks detect qualitative differences
especially during (recent) turbulent market periods thus indicating strongly
fluctuating interconnections between the stocks of different companies in
changing economic environments. A measure for the strength of nonlinear
dependencies is derived using surrogate data and leads to interesting
observations during periods of financial market crises. In contrast to the
expectation that dependencies reduce mainly to linear correlations during
crises we show that (at least in the 2008 crisis) nonlinear effects are
significantly increasing. It turns out that the concept of centrality within a
network could potentially be used as some kind of an early warning indicator
for abnormal market behavior as we demonstrate with the example of the 2008
subprime mortgage crisis. Finally, we apply a Markowitz mean variance portfolio
optimization and integrate the measure of nonlinear dependencies to scale the
investment exposure. This leads to significant outperformance as compared to a
fully invested portfolio.


  We provide a toolbox of extension, gluing, and assembly techniques for
factorization algebras.
  Using these tools, we fill various gaps in the literature on factorization
algebras on stratified manifolds, the main one being that constructible
factorization algebras form a sheaf of symmetric monoidal $\infty$-categories.
Additionally, we explain how to assemble constructible factorization algebras
from the data on the individual strata together with module structures
associated to the relative links; thus answering a question by Ayala.
  Along the way, we give detailed proofs of the following facts which are also
of independent interest: constructibility is a local condition; the
$\infty$-category of disks is a localization of any sufficiently fine poset of
disks; constructibility implies the Weiss condition on disks. For each of
these, variants or special cases already existed, but they were either
incomplete or not general enough.


  We show that time-one maps of transitive Anosov flows of compact manifolds
are accumulated by diffeomorphisms robustly satisfying the following dichotomy:
either all of the measures of maximal entropy are non-hyperbolic, or there are
exactly two ergodic measures of maximal entropy, one with a positive central
exponent and the other with a negative central exponent.
  We establish this dichotomy for certain partially hyperbolic diffeomorphisms
isotopic to the identity whenever both of their strong foliations are minimal.
Our proof builds on the approach developed by Margulis for Anosov flows where
he constructs suitable families of measures on the dynamical foliations.


  We produce an ultracold and dense sample of rovibronic ground state Cs_2
molecules close to the regime of quantum degeneracy, in a single hyperfine
level, in the presence of an optical lattice. The molecules are individually
trapped, in the motional ground state of an optical lattice well, with a
lifetime of 8 s. For preparation, we start with a zero-temperature atomic
Mott-insulator state with optimized double-site occupancy and efficiently
associate weakly-bound dimer molecules on a Feshbach resonance. Despite
extremely weak Franck-Condon wavefunction overlap, the molecules are
subsequently transferred with >50% efficiency to the rovibronic ground state by
a stimulated four-photon process. Our results present a crucial step towards
the generation of Bose-Einstein condensates of ground-state molecules and, when
suitably generalized to polar heteronuclear molecules such as RbCs, the
realization of dipolar many-body quantum-gas phases in periodic potentials.


  Long-term X-ray variability of the low-luminosity active galactic nucleus of
M81 was studied, using 16 ASCA observations spanning 5.5 years. The object
exhibits a factor three variation over the 5.5 years. The source intensity was
relatively constant within each observation which lasted typically for one day,
but intra-day variability by 30% was detected on the 15th observation. The
power-spectral density (PSD) was estimated in ``forward'' manner, over a
frequency range of 10^(-8.2)--10^(-4.3) Hz (period range of 1/4 day-5.5 years),
by utilizing structure function and extensive Monte-Carlo simulations in order
to overcome the very sparse and uneven data samplings. When the PSD is assumed
to be white below a "break frequency" f_b and falls off as proptional to
f^(-alpha) above f_b, where "f" is frequency and alpha is a positive parameter,
the M81 light curve is well described with 1/f_b > 800 days and alpha = 1.4 +-
0.2.


  It is shown that there are anomalous bound-state solutions to the two-body
Dirac equation for an electron and positron interacting via an electromagnetic
potential. These anomalous solutions have quantized coordinates at nuclear
distances (fermi) and are orthogonal to the usual atomic positronium
bound-states as shown by a simple extension of the Bethe-Salpeter equation. It
is shown that the anomalous states have many properties which correspond to
those of neutrinos.


  The recent research in semi-supervised learning (SSL) is mostly dominated by
consistency regularization based methods which achieve strong performance.
However, they heavily rely on domain-specific data augmentations, which are not
easy to generate for all data modalities. Pseudo-labeling (PL) is a general SSL
approach that does not have this constraint but performs relatively poorly in
its original formulation. We argue that PL underperforms due to the erroneous
high confidence predictions from poorly calibrated models; these predictions
generate many incorrect pseudo-labels, leading to noisy training. We propose an
uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo
labeling accuracy by drastically reducing the amount of noise encountered in
the training process. Furthermore, UPS generalizes the pseudo-labeling process,
allowing for the creation of negative pseudo-labels; these negative
pseudo-labels can be used for multi-label classification as well as negative
learning to improve the single-label classification. We achieve strong
performance when compared to recent SSL methods on the CIFAR-10 and CIFAR-100
datasets. Also, we demonstrate the versatility of our method on the video
dataset UCF-101 and the multi-label dataset Pascal VOC.


  A homologically trivial part of any Turaev-Viro invariant odd order $r$ is a
Turaev-Viro type invariant order $\frac{r + 1}{2}$. In this paper we find an
explicit formulas for this Turaev -- Viro type invariant, corresponding to the
invariant order $r = 7$. Our formulas express $6j$-symbols and colour weights
in terms of $\gamma$, where $\gamma$ is a root of the polynomial
$\mathcal{T}(x) = x^3 - 2x^2 - x + 1$.


  We investigated the influence of different Ag additions (up to 10 wt %) on
the superconducting properties of FeSe0.94. The structural investigations (XRD
and SEM) indicated that Ag is present in three different forms. Ag at grain
boundaries supports the excellent intergrain connections and reduces
superconducting transition width to values smaller than 1K at B=0 and smaller
than 2.74 K at B=14 T. Ag insertion in the crystal lattice unit cell provides
additional carriers and changes the electron hole imbalance in FeSe0.94. This
results in an increase in the magnetoresistive effect (MR) and critical
temperature (Tc). Reacted Ag forms a small amount (~1%) of Ag2Se impurity
phase, which may increase the pinning energy in comparison with that of the
undoped sample. The enhanced upper critical field (Bc2) is also a result of the
increased impurity scattering. Thus, unlike cuprates Ag addition enhances the
Tc, Bc2, pinning energy and MR making the properties of polycrystalline
FeSe0.94 similar to those of single crystals.


  ATLAS and CMS collaborations have reported the results on the Higgs search
analyzing $\sim 36$ fb$^{-1}$ data from Run-II of LHC at 13 TeV. In this work,
we study the Higgs sector of the phenomenological Minimal Supersymmetric
Standard Model, in light of the recent Higgs data, by studying separately the
impact of Run-I and Run-II data. One of the major impacts of the new data on
the parameter space comes from the direct searches of neutral CP-even and
CP-odd heavy Higgses ($H$ and $A$, respectively) in the $H/A \to \tau^{+}
\tau^{-}$ channel which disfavours high $\tan\beta$ regions more efficiently
than Run-I data. Secondly, we show that the latest result of the rare radiative
decay of $B$ meson imposes a slightly stronger constraint on low $\tan \beta$
and low $M_A$ region of the parameter space, as compared to its previous
measurement. Further, we find that in a global fit Run-II light Higgs signal
strength data is almost comparable in strength with the corresponding Run-I
data. Finally, we discuss scenarios with the Heavy Higgs boson decaying into
electroweakinos and third generation squarks and sleptons.


  We show that the radiative corrections containing terms up to order $\alpha q
/ \pi M_1$ for unpolarized semileptonic decays of baryons with positron
emission can be obtained by simply reversing the sign of the axial-vector form
factors in the corresponding final expressions of such decays with electron
emission. This rule is valid regardless of the final kinematical variables
chosen and of the particular Lorentz frame in which the final results are
required.


  To account for pulsar frequency glitches, it is necessary to use a neutron
star crust model allowing not only for neutron superfluidity but also for
elastic solidity. These features have been treated separarately in previous
treatments of crust matter, but are combined here in a unified treatment that
is based on the use of a Lagrangian master functon, so that the coherence the
system is ensured by the relevant Noether identities. As well as the model
obtained directly from the variation principle, the same master function can
provide other conservative alternatives, allowing in particular for the effect
of perfect vortex pinning. It is also shown how such models can be generalised
to allow for dissipative effects, including that of imperfect pinning, meaning
vortex drag or creep.


  Buckminsterfullerene compounds exibit remarkable physics at low temperatures,
e.g. high temperature superconductivity in alkali-fullerenes, and
ferromagnetism in TDAE -C60. Here we review recent theoretical studies of
electron correlations in these compounds. In particular, we discuss models of
electron-vibron interactions, electron-electron interactions, and
intermolecular hopping. We show that the origin of novel electronic phases lies
in {\em local} degeneracies of C60; a direct consequence of the high molecular
symmetry.


  I investigate the properties of forces on bodies in theories governed by the
generalized Poisson equation div[mu(abs(grad_phi))grad_phi]=G rho, for the
potential phi produced by a distribution of sources rho. This equation
describes, inter alia, media with a response coefficient, mu, that depends on
the field strength, such as in nonlinear, dielectric, or diamagnetic, media;
nonlinear transport problems with field-strength dependent conductivity or
diffusion coefficient; nonlinear electrostatics, as in the Born-Infeld theory;
certain stationary potential flows in compressible fluids, in which case the
forces act on sources or obstacles in the flow. The expressions for the force
on a point charge is derived exactly for the limits of very low and very high
charge. The force on an arbitrary body in an external field of asymptotically
constant gradient, E, is shown to be F=QE, where Q is the total effective
charge of the body. The corollary Q=0 implies F=0 is a generalization of
d'Aembert's paradox. I show that for G>0 (as in Newtonian gravity) two point
charges of the same (opposite) sign still attract (repel) each other. The
opposite is true for G<0. I discuss the generalization of this to extended
bodies, and derive virial relations.


  The investigation of the penetration depth \lambda_{ab}(T,p) in
YBa_2Cu_3O_{7-x} crystals allowed to observe the following features of the
superfluid density n_s(T,p)\propto \lambda_{ab}^{-2}(T,p) as a function of
temperature T<Tc/2 and carrier concentration 0.078\le p\le 0.16 in CuO_2
planes: (i) n_s(0,p) depends linearly on p, (ii) the derivative
|dn_s(T,p)/dT|_{T\to 0} depends on p slightly in the optimally and moderately
doped regions (0.10<p\le 0.16); however, it rapidly increases with p further
lowering and (iii) the latter finding is accompanied by the linear
low-temperature dependence [-\Delta n_s(T)]\propto T changing to [-\Delta
n_s(T)]\propto \sqrt{T}. All these peculiarities can be treated in the
framework of d-density wave scenario of electronic processes in underdoped
high-Tc materials.


  We describe all real points of the parameter space of two-generator Kleinian
groups with a parabolic generator, that is, we describe a certain
two-dimensional slice through this space. In order to do this we gather
together known discreteness criteria for two-generator groups and present them
in the form of conditions on parameters. We complete the description by giving
discreteness criteria for groups generated by a parabolic and a
$\pi$-loxodromic elements whose commutator has real trace and present all
orbifolds uniformized by such groups.


  This paper introduces the model, numerical methods, algorithms and parallel
implementation of a thermal reservoir simulator that designed for numerical
simulations of thermal reservoir with multiple components in three dimensional
domain using distributed-memory parallel computers. Its full mathematical model
is introduced with correlations for important properties and well modeling.
Various well constraints, such as fixed bottom hole pressure, fixed oil, water,
gas and liquid rates, constant heat transfer model, convective heat transfer
model, heater model (temperature control, rate control, dual rate/temperature
control), and subcool (steam trap), are introduced in details, including their
mathematical models and methods. Efficient numerical methods and parallel
computing technologies are presented. The simulator is designed for giant
models with billions or even trillions of grid blocks using hundreds of
thousands of CPUs. Numerical experiments show that our results match commercial
simulators, which confirms the correctness of our methods and implementations.
SAGD simulation with 15106 well pairs is also presented to study the
effectiveness of our numerical methods. Scalability testings demonstrate that
our simulator can handle giant models with over 200 billion grid blocks using
100,800 CPU cores and the simulator has good scalability.


  We report on the multi-wavelength observations of PKS 1510-089 (a flat
spectrum radio quasar at z=0.361) during its high activity period between 2008
September and 2009 June. During this 11 months period, the source was
characterized by a complex variability at optical, UV and gamma-ray bands, on
time scales down to 6-12 hours. The brightest gamma-ray isotropic luminosity,
recorded on 2009 March 26, was ~ 2x10^48erg s^-1. The spectrum in the Fermi-LAT
energy range shows a mild curvature well described by a log-parabolic law, and
can be understood as due to the Klein-Nishina effect. The gamma-ray flux has a
complex correlation with the other wavelengths. There is no correlation at all
with the X-ray band, a weak correlation with the UV, and a significant
correlation with the optical flux. The gamma-ray flux seems to lead the optical
one by about 13 days. From the UV photometry we estimated a black hole mass of
~ 5.4x10^8 solar masses, and an accretion rate of ~ 0.5 solar masses/year.
Although the power in the thermal and non-thermal outputs is smaller compared
to the very luminous and distant flat spectrum radio quasars, PKS 1510-089
exhibits a quite large Compton dominance and a prominent big blue bump (BBB) as
observed in the most powerful gamma-ray quasars. The BBB was still prominent
during the historical maximum optical state in 2009 May, but the optical/UV
spectral index was softer than in the quiescent state. This seems to indicate
that the BBB was not completely dominated by the synchrotron emission during
the highest optical state. We model the broadband spectrum assuming a leptonic
scenario in which the inverse Compton emission is dominated by the scattering
of soft photons produced externally to the jet. The resulting model-dependent
jet energetic content is compatible with the accretion disk powering the jet,
with a total efficiency within the Kerr black hole limit.


  The key challenge of multi-domain translation lies in simultaneously encoding
both the general knowledge shared across domains and the particular knowledge
distinctive to each domain in a unified model. Previous work shows that the
standard neural machine translation (NMT) model, trained on mixed-domain data,
generally captures the general knowledge, but misses the domain-specific
knowledge. In response to this problem, we augment NMT model with additional
domain transformation networks to transform the general representations to
domain-specific representations, which are subsequently fed to the NMT decoder.
To guarantee the knowledge transformation, we also propose two complementary
supervision signals by leveraging the power of knowledge distillation and
adversarial learning. Experimental results on several language pairs, covering
both balanced and unbalanced multi-domain translation, demonstrate the
effectiveness and universality of the proposed approach. Encouragingly, the
proposed unified model achieves comparable results with the fine-tuning
approach that requires multiple models to preserve the particular knowledge.
Further analyses reveal that the domain transformation networks successfully
capture the domain-specific knowledge as expected.


  The kicked rotor system is a textbook example of how classical and quantum
dynamics can drastically differ. The energy of a classical particle confined to
a ring and kicked periodically will increase linearly in time whereas in the
quantum version the energy saturates after a finite number of kicks. The
quantum system undergoes Anderson localization in the angular-momentum space.
Conventional wisdom says that in a many-particle system with short-range
interactions the localization will be destroyed due to the coupling of widely
separated momentum states. Here we provide evidence that for an interacting
one-dimensional Bose gas, the Lieb-Linger model, the dynamical localization can
persist.


  The analytical expressions for both diagonal and off-diagonal ac and dc
conductivities of graphene placed in an external magnetic field are derived.
These conductivities exhibit rather unusual behavior as functions of frequency,
chemical potential and applied field which is caused by the fact that the
quasiparticle excitations in graphene are Dirac-like. One of the most striking
effects observed in graphene is the odd integer quantum Hall effect. We argue
that it is caused by the anomalous properties of the Dirac quasiparticles from
the lowest Landau level. Other quantities such as Hall angle and Nernst signal
also exhibit rather unusual behavior, in particular when there is an excitonic
gap in the spectrum of the Dirac quasiparticle excitations.


  Random walks are one of the best investigated dynamical processes on graphs.
A particularly fascinating phenomenon is the scaling relationship of
fluctuations $\sigma $ with the average flux $\langle f \rangle $. Here we
analyze how network topology and nodes with finite capacity lead to deviations
from a simple scaling law $\sigma \sim \langle f \rangle ^\alpha$. Sources of
randomness are the random walk itself (internal noise) and the fluctuation of
the number of walkers (external noise). We obtained exact results for the
extreme case of a star network which are indicative of the behavior of large
scale systems with a broad degree distribution.The latter are subsequently
studied using Monte Carlo simulations. We find that the network heterogeneity
amplifies the effects of external noise. By computing the `effective' scaling
of each node we show that multiple scaling relationships can coexist in a graph
with a heterogeneous degree distribution at an intermediate level of external
noise. Finally, we analyze the effect of a finite capacity of nodes for random
walkers and find that this also can lead to a heterogeneous scaling of
fluctuations.


  Since the introduction of the quantum neural network model, it has been
widely studied due to its strong expressive power and robustness to
overfitting. To date, the model has been evaluated primarily in classification
tasks, but its performance in practical multivariate regression problems has
not been thoroughly examined. In this study, the Auto-MPG data set (392 valid
data points, excluding missing data, on fuel efficiency for various vehicles)
was used to construct QNN models and investigate the effect of the size of the
training data on generalization performance. The results indicate that QNN is
particularly effective when the size of training data is small, suggesting that
it is especially suitable for small-data problems such as those encountered in
Materials Informatics.


  We say that a function $f:[0,1]\rightarrow \R$ is \emph{nowhere $L^q$} if,
for each nonvoid open subset $U$ of $[0,1]$, the restriction $f|_U$ is not in
$L^q(U)$. For a fixed $1 \leq p <\infty$, we will show that the set $$
S_p\doteq {f \in L^p[0,1]: f is nowhere $L^q$, for each p<q \leq \infty}, $$
united with ${0}$, contains an isometric and complemented copy of $\ell_p$. In
particular, this improves a result from G. Botelho, V. F\'avaro, D. Pellegrino,
and J. B. Seoane-Sep\'ulveda, $L_p[0,1]\setminus \cup_{q>p} L_q[0,1]$ is
spaceable for every $p>0$, preprint, 2011., since $S_p$ turns out to be
spaceable. In addition, our result is a generalization of one of the main
results from S. G{\l}\c{a}b, P. L. Kaufmann, and L. Pellegrini, Spaceability
and algebrability of sets of nowhere integrable functions, preprint, 2011.


  This paper continues the author's previous studies on continued fractions and
Heron's algorithm, as from his former JMM2017 presentation (see
\cite{CF.HA}).\par\medskip Extending the notion of continued fraction to the
$p$-adic fields, one can find continued fractions which converge in both real
and $p$-adic topologies to the `same' quadratic irrational number, some of
which are given by the Heron's algorithm using a generalized version of an
author's theorem from the cited JMM presentation. The definition can be
possibly generalized to other global fields, as left as an open question. We
will end the part on hybrid convergence with many numerical examples. After
that, we will recall the basic algorithms on the $p$-adic fields studied by the
author and see some applications of theirs to computer science: applying
Heron's algorithm to quickly compute $p$-adic square roots, finding new
elementary cryptography procedures and some methods to get pseudo-random
numbers, calculate last digits of some peculiar very big numbers.


  We present the final nine-year maps and basic results from the WMAP mission.
We provide new nine-year full sky temperature maps that were processed to
reduce the asymmetry of the effective beams. Temperature and polarization sky
maps are examined to separate CMB anisotropy from foreground emission, and both
types of signals are analyzed in detail. The WMAP mission has resulted in a
highly constrained LCDM cosmological model with precise and accurate parameters
in agreement with a host of other cosmological measurements. When WMAP data are
combined with finer scale CMB, baryon acoustic oscillation, and Hubble constant
measurements, we find that Big Bang nucleosynthesis is well supported and there
is no compelling evidence for a non-standard number of neutrino species
(3.84+/-0.40). The model fit also implies that the age of the universe is
13.772+/-0.059 Gyr, and the fit Hubble constant is H0 = 69.32+/-0.80 km/s/Mpc.
Inflation is also supported: the fluctuations are adiabatic, with Gaussian
random phases; the detection of a deviation of the scalar spectral index from
unity reported earlier by WMAP now has high statistical significance (n_s =
0.9608+/-0.0080); and the universe is close to flat/Euclidean, Omega_k =
-0.0027 (+0.0039/-0.0038). Overall, the WMAP mission has resulted in a
reduction of the cosmological parameter volume by a factor of 68,000 for the
standard six-parameter LCDM model, based on CMB data alone. For a model
including tensors, the allowed seven-parameter volume has been reduced by a
factor 117,000. Other cosmological observations are in accord with the CMB
predictions, and the combined data reduces the cosmological parameter volume
even further. With no significant anomalies and an adequate goodness-of-fit,
the inflationary flat LCDM model and its precise and accurate parameters rooted
in WMAP data stands as the standard model of cosmology.


  Let $X$ be a nonsingular variety defined over an algebraically closed field
of characteristic $0$, and $D$ be a free divisor. We study the motivic Chern
class of $D$ in the Grothendieck group of coherent sheaves $G_0(X)$, and
another class defined by the sheaf of logarithmic differentials along $D$. We
give explicit calculations of the difference of these two classes when: $D$ is
a divisor on a nonsingular surface; $D$ is a hyperplane arrangement whose
affine cone is free.


  Neutron scattering is used to study magnetic field induced ordering in the
quasi-1D quantum spin-tube compound Sul--Cu$_2$Cl$_4$ that in zero field has a
non-magnetic spin-liquid ground state. The experiments reveal an incommensurate
chiral high-field phase stabilized by a geometric frustration of the magnetic
interactions. The measured critical exponents $\beta\approx0.235$ and
$\nu\approx0.34$ at $H_c\approx3.7$ T point to an unusual sub-critical scaling
regime and may reflect the chiral nature of the quantum critical point.


  In biomedical data analysis, Multiple Instance Learning (MIL) models have
emerged as a powerful tool to classify patients' microscopy samples. However,
the data-intensive requirement of these models poses a significant challenge in
scenarios with scarce data availability, e.g., in rare diseases. We introduce a
topological regularization term to MIL to mitigate this challenge. It provides
a shape-preserving inductive bias that compels the encoder to maintain the
essential geometrical-topological structure of input bags during projection
into latent space. This enhances the performance and generalization of the MIL
classifier regardless of the aggregation function, particularly for scarce
training data. The effectiveness of our method is confirmed through experiments
across a range of datasets, showing an average enhancement of 2.8% for MIL
benchmarks, 15.3% for synthetic MIL datasets, and 5.5% for real-world
biomedical datasets over the current state-of-the-art.


  Do LMs infer the semantics of text from co-occurrence patterns in their
training data? Merrill et al. (2022) argue that, in theory, sentence
co-occurrence probabilities predicted by an optimal LM should reflect the
entailment relationship of the constituent sentences, but it is unclear whether
probabilities predicted by neural LMs encode entailment in this way because of
strong assumptions made by Merrill et al. (namely, that humans always avoid
redundancy). In this work, we investigate whether their theory can be used to
decode entailment relations from neural LMs. We find that a test similar to
theirs can decode entailment relations between natural sentences, well above
random chance, though not perfectly, across many datasets and LMs. This
suggests LMs implicitly model aspects of semantics to predict semantic effects
on sentence co-occurrence patterns. However, we find the test that predicts
entailment in practice works in the opposite direction to the theoretical test.
We thus revisit the assumptions underlying the original test, finding its
derivation did not adequately account for redundancy in human-written text. We
argue that better accounting for redundancy related to explanations might
derive the observed flipped test and, more generally, improve computational
models of speakers in linguistics.


  We revisit the S-procedure for general functions with "geometrical glasses".
We thus delineate a necessary condition, and almost a sufficient condition, to
have the S-procedure valid. Everything is expressed in terms of convexity of
augmented sets (convex hulls, conical hulls) of images built from the data
functions.


  Arbitrary-oriented objects exist widely in natural scenes, and thus the
oriented object detection has received extensive attention in recent years. The
mainstream rotation detectors use oriented bounding boxes (OBB) or
quadrilateral bounding boxes (QBB) to represent the rotating objects. However,
these methods suffer from the representation ambiguity for oriented object
definition, which leads to suboptimal regression optimization and the
inconsistency between the loss metric and the localization accuracy of the
predictions. In this paper, we propose a Representation Invariance Loss (RIL)
to optimize the bounding box regression for the rotating objects. Specifically,
RIL treats multiple representations of an oriented object as multiple
equivalent local minima, and hence transforms bounding box regression into an
adaptive matching process with these local minima. Then, the Hungarian matching
algorithm is adopted to obtain the optimal regression strategy. We also propose
a normalized rotation loss to alleviate the weak correlation between different
variables and their unbalanced loss contribution in OBB representation.
Extensive experiments on remote sensing datasets and scene text datasets show
that our method achieves consistent and substantial improvement. The source
code and trained models are available at https://github.com/ming71/RIDet.


  In recent years, deep learning has shown itself to be an incredibly valuable
tool in cybersecurity as it helps network intrusion detection systems to
classify attacks and detect new ones. Adversarial learning is the process of
utilizing machine learning to generate a perturbed set of inputs to then feed
to the neural network to misclassify it. Much of the current work in the field
of adversarial learning has been conducted in image processing and natural
language processing with a wide variety of algorithms. Two algorithms of
interest are the Elastic-Net Attack on Deep Neural Networks and TextAttack. In
our experiment the EAD and TextAttack algorithms are applied to a Domain Name
System amplification classifier. The algorithms are used to generate malicious
Distributed Denial of Service adversarial examples to then feed as inputs to
the network intrusion detection systems neural network to classify as valid
traffic. We show in this work that both image processing and natural language
processing adversarial learning algorithms can be applied against a network
intrusion detection neural network.


  Mean field games (MFG) and mean field control (MFC) are critical classes of
multi-agent models for efficient analysis of massive populations of interacting
agents. Their areas of application span topics in economics, finance, game
theory, industrial engineering, crowd motion, and more. In this paper, we
provide a flexible machine learning framework for the numerical solution of
potential MFG and MFC models. State-of-the-art numerical methods for solving
such problems utilize spatial discretization that leads to a
curse-of-dimensionality. We approximately solve high-dimensional problems by
combining Lagrangian and Eulerian viewpoints and leveraging recent advances
from machine learning. More precisely, we work with a Lagrangian formulation of
the problem and enforce the underlying Hamilton-Jacobi-Bellman (HJB) equation
that is derived from the Eulerian formulation. Finally, a tailored neural
network parameterization of the MFG/MFC solution helps us avoid any spatial
discretization. Our numerical results include the approximate solution of
100-dimensional instances of optimal transport and crowd motion problems on a
standard work station and a validation using an Eulerian solver in two
dimensions. These results open the door to much-anticipated applications of MFG
and MFC models that were beyond reach with existing numerical methods.