  In the certification procedure aircraft builders carry out so-called icing
tests flights, where the zero degree Celsius altitude is deliberately sought
and crossed in or under thunderstorms. Airbus also used these flights to test
ILDAS, a system aimed to determine lightning severity and attachment points
during flight from high speed data on the electric and magnetic field at the
aircraft surface. We used this unique opportunity to enhance the ILDAS systems
with two x-ray detectors coupled to high speed data recorders in an attempt to
determine the x-rays produced by lightning in-situ, with synchronous
determination of the lightning current distribution and electric field at the
aircraft. Such data are of interest in a study of lightning physics. In
addition, the data may provide clues to the x-ray dose for personnel and
equipment during flights. The icing campaign ran in April 2014; in six flights
we collected data of 61 lightning strikes on an Airbus test aircraft. In this
communication we briefly describe ILDAS and present selected results on three
strikes, two aircraft initiated and one intercepted. Most of the x-rays have
been observed synchronous with initiating negative leader steps, and as bursts
immediately preceding the current of the recoil process. Those processes
include the return stroke. The bursts last one to four micro-second and attain
x-ray energies up to 10 MeV. Intensity and spectral distribution of the x-rays
and the association with the current distribution are discussed. ILDAS also
continuously records x-rays at low resolution in time and amplitude.


  This paper is withdrawn due to copyright restrictions. The final version will
become available at this url: http://pubs.acs.org/journals/jpcbfh/


  Recent breakthroughs of pretrained language models have shown the
effectiveness of self-supervised learning for a wide range of natural language
processing (NLP) tasks. In addition to standard syntactic and semantic NLP
tasks, pretrained models achieve strong improvements on tasks that involve
real-world knowledge, suggesting that large-scale language modeling could be an
implicit method to capture knowledge. In this work, we further investigate the
extent to which pretrained models such as BERT capture knowledge using a
zero-shot fact completion task. Moreover, we propose a simple yet effective
weakly supervised pretraining objective, which explicitly forces the model to
incorporate knowledge about real-world entities. Models trained with our new
objective yield significant improvements on the fact completion task. When
applied to downstream tasks, our model consistently outperforms BERT on four
entity-related question answering datasets (i.e., WebQuestions, TriviaQA,
SearchQA and Quasar-T) with an average 2.7 F1 improvements and a standard
fine-grained entity typing dataset (i.e., FIGER) with 5.7 accuracy gains.


  We extend some algebraic properties of the classical modular group SL_2(Z) to
equivalent groups in the theory of Drinfeld modules, in particular properties
which are important in the theory of modular curves.
  We study cusp amplitudes and the level of a (congruence) subgroup of SL_2(D)
for any Dedekind domain D, as ideals of D. In particular, we extend a
remarkable result of Larcher.
  We introduce finer notions of quasi-amplitude and quasi-level, which are not
required to be ideals and encode more information about the subgroup.
  Our results also provide several new necessary conditions for a subgroup of
SL_2(D) to be a congruence subgroup.


  Quantum telepathy, or pseudotelepathy, is the phenomenon where two
non-communicating parties can exhibit correlated behaviors that are impossible
to achieve using classical mechanics. This is also known as Bell inequality
violation and is made possible by quantum entanglement. In this work, we
present a conceptual framework for applying quantum telepathy to real-world
problems. In general, the problems involve coordinating decisions given a set
of observations without being able to communicate. We argue this inability is
actually quite prevalent in the modern era where the decision-making timescales
of computer processors are so short that speed of light delay is actually quite
appreciable in comparison. We highlight the example of high-frequency trading
(HFT), where trades are made at microsecond timescales, but the speed of light
delay between different exchanges can range from the order of 10 microseconds
to 10 milliseconds. Due to the maturity of Bell inequality violation
experiments, experimental realization of quantum telepathy schemes that can
attain a quantum advantage for real-world problems $\textit{is already almost
immediately possible}$. We demonstrate this by conducting a case study for a
concrete HFT scenario that gives rise to a generalization of the CHSH game and
evaluate different possible physical implementations for achieving a quantum
advantage. It is well known that Bell inequality violation is a rigorous
mathematical proof of a quantum advantage over any classical strategy and does
not need any complexity-theoretic assumptions such as
$\text{BQP}\neq\text{BPP}$. Moreover, fault tolerance is not necessary to
realize a quantum advantage: for example, violating the CHSH inequality only
requires single-qubit gates applied on two entangled qubits.


  Cosmic rays with energies up to a few PeV are known to be accelerated within
the Milky Way. Traditionally, it has been presumed that supernova remnants were
the main source of very-high-energy cosmic rays but theoretically it is
difficult to get protons to PeV energies and observationally there simply is no
evidence to support the remnants as sources of hadrons with energies above a
few tens of TeV. One possible source of protons with those energies is the
Galactic Center region. Here we report observations of 1-100 TeV gamma rays
coming from the 'Cygnus Cocoon', which is a superbubble surrounding a region of
OB2 massive star formation. These gamma rays are likely produced by 10-1000 TeV
freshly accelerated CRs originating from the enclosed star forming region
Cygnus OB2. Hitherto it was not known that such regions could accelerate
particles to these energies. The measured flux is likely originated by hadronic
interactions. The spectral shape and the emission profile of the Cocoon changes
from GeV to TeV energies, which reveals the transport of cosmic particles and
historical activity in the superbubble.


  Reconfigurable intelligent surface (RIS) is a new and revolutionary
technology to achieve spectrum-, energy- and cost-efficient wireless networks.
This paper studies the resource allocation for RIS-empowered device-to-device
(D2D) communication underlaying a cellular network, in which an RIS is employed
to enhance desired signals and suppress interference between paired D2D and
cellular links. We maximize the overall network's spectrum efficiency (SE)
(i.e., sum rate of D2D users and cellular users) and energy efficiency (EE),
respectively, by jointly optimizing the resource reuse indicators, the transmit
power and the RIS's passive beamforming, under the
signal-to-interference-plus-noise ratio constraints and other practical
constraints. To solve the non-convex problems, we first propose an efficient
user-pairing scheme based on relative channel strength to determine the
resource reuse indicators. Then, the transmit power and the RIS's passive
beamforming are jointly optimized to maximize the SE by a proposed iterative
algorithm, based on the techniques of alternating optimization, successive
convex approximation, Lagrangian dual transform and quadratic transform.
Moreover, the EE-maximization problem is solved by an alternating algorithm
integrated with Dinkelbach's method. Also, the convergence and complexity of
both algorithms are analyzed. Numerical results show that the proposed design
achieves significant SE and EE enhancements compared to traditional underlaying
D2D network without RIS and other benchmarks.


  The eternally non-Markovian Pauli channel is an example of a unital channel
characterized by a negative decay rate for all time $t>0$. Here we consider the
problem of constructing an analogous non-unital channel, and show in particular
that a $d$-dimensional generalized amplitude damping (GAD) channel cannot be
eternally non-Markovian when the non-Markovianity originates solely from the
non-unital part of the channel. We study specific ramifications of this result
for qubit GAD. Specifically, we construct a quasi-eternally non-Markovian qubit
GAD channel, characterized by a time $t^\ast > 0$, such that the channel is
non-Markovian only and for all time $t > t^\ast$. We further point out that our
negative result for the qudit GAD channel, namely the impossibility of the
eternal non-Markovian property, does not hold for a general qubit or
higher-dimensional non-unital channel.


  We study the problem of distribution shift generally arising in
machine-learning augmented hybrid simulation, where parts of simulation
algorithms are replaced by data-driven surrogates. We first establish a
mathematical framework to understand the structure of machine-learning
augmented hybrid simulation problems, and the cause and effect of the
associated distribution shift. We show correlations between distribution shift
and simulation error both numerically and theoretically. Then, we propose a
simple methodology based on tangent-space regularized estimator to control the
distribution shift, thereby improving the long-term accuracy of the simulation
results. In the linear dynamics case, we provide a thorough theoretical
analysis to quantify the effectiveness of the proposed method. Moreover, we
conduct several numerical experiments, including simulating a partially known
reaction-diffusion equation and solving Navier-Stokes equations using the
projection method with a data-driven pressure solver. In all cases, we observe
marked improvements in simulation accuracy under the proposed method,
especially for systems with high degrees of distribution shift, such as those
with relatively strong non-linear reaction mechanisms, or flows at large
Reynolds numbers.


  Several effective methods have been developed recently to demonstrate
simultaneous high energy and high power density in Li - carbon fluoride
(CF$_x$) batteries. These methods can achieve as high as 1000 Wh/kg energy
density at 60-70 kW/kg power density (40-50 C rate) in coin cells and 750 Wh/kg
energy density at 12.5 kW/kg power density (20 C rate) in pouch cells. This
performance is made possible by ingenious nano-architecture design, controlled
porosity, boron doping and electrolyte additives. In the present study, we show
that a similarly great performance, 931 Wh/kg energy density at 59 kW/kg power
density, can be achieved by using a polyacrylonitrile binder and a LiBF4
electrolyte in Li - graphite fluoride coin cells. We also demonstrate that the
observed effect is the result of the right combination of the binder and the
electrolyte. We propose that the mechanistic origin of the observed phenomena
is an electro-catalytic effect by the polyacrylonitrile binder. While our
proposed method has a competitive performance, it also offers a simple
implementation and a scalable production of high energy and high power primary
Li-CF$_x$ cells.


  We present a controller tuning strategy for first-order plus time delay
(FOPTD) processes, where the time delay in the model is approximated using the
Pad\'e function. Using Routh-Hurwitz stability analysis, we derive the gain
that gives rise to desirable PID controller settings. The resulting PID
controller, now correctly tuned, produces satisfactory closed-loop behavior and
stabilizes the first-order plant. Our proposed technique eliminates the
dead-time component in the model and results in a minimum-phase system with all
of its poles and zeros in the left-half $s$-plane. To demonstrate the
effectiveness of our approach, we present control simulation results from an
in-depth performance comparison between our technique and other established
model-based strategies used for the control of time-delayed systems. These
results prove that, for the FOPTD model, Pad\'e approximation eliminates the
undesirable effects of the time delay and promises a faster tracking
performance superior to conventional model-based controllers.


  For building the super-massive black hole population within a Hubble time,
only sub-populations with more than 10^52 erg/s / L objects on the sky are
relevant, where L is the sample-averaged bolometric luminosity.


  I will give a short review the physics of strangeness enhancement in
quark-gluon plasma, and argue that it is currently the best candidate of a
signature of deconfinement. I will also discuss what strangeness abundance can
tell us about the bulk properties of the system created in heavy ion
collisions.


  There is growing interest in designing recommender systems that aim at being
fair towards item producers or their least satisfied users. Inspired by the
domain of inequality measurement in economics, this paper explores the use of
generalized Gini welfare functions (GGFs) as a means to specify the normative
criterion that recommender systems should optimize for. GGFs weight individuals
depending on their ranks in the population, giving more weight to worse-off
individuals to promote equality. Depending on these weights, GGFs minimize the
Gini index of item exposure to promote equality between items, or focus on the
performance on specific quantiles of least satisfied users. GGFs for ranking
are challenging to optimize because they are non-differentiable. We resolve
this challenge by leveraging tools from non-smooth optimization and projection
operators used in differentiable sorting. We present experiments using real
datasets with up to 15k users and items, which show that our approach obtains
better trade-offs than the baselines on a variety of recommendation tasks and
fairness criteria.


  We report the experimental investigations on the mixing of a ferrofluid
droplet with a non-magnetic fluid in the presence of a time-dependent magnetic
field on an open surface microfluidic platform. The bright field visualization
technique, in combination with the micro-PIV analysis, is carried out to
explore the internal hydrodynamics of the ferrofluid droplet. Also, using the
Laser-induced fluorescence (micro-LIF) technique, we quantify the mass transfer
occurring between the two droplets, which in effect, determines the underlying
mixing performance under the modulation of the frequency of the applied
magnetic field. We show that the magnetic nanoparticles exhibit complex
spatio-temporal movement inside the ferrofluid droplet domain under the
influence of a time-dependent magnetic field, which, in turn, promotes the
mixing efficiency in the convective mixing regime. Our analysis establishes
that the movement of magnetic nanoparticles in presence of the time-periodic
field strengthens the interfacial instability, which acts like a sparking agent
to initiate an augmented mixing in the present scenario. By performing
numerical simulations, we also review the onset of interfacial instability,
mainly stemming from the susceptibility mismatch between the magnetic and
non-magnetic fluids. Inferences of the present analysis, which focuses on the
simple, wireless, robust, and low-cost open surface micromixing mechanism, will
provide a potential solution for rapid droplets mixing without requiring pH
level or ion concentration dependency of the fluids.


  We study the performance of cognitive Underlay System (US) that employ power
control mechanism at the Secondary Transmitter (ST) from a deployment
perspective. Existing baseline models considered for performance analysis
either assume the knowledge of involved channels at the ST or retrieve this
information by means of a band manager or a feedback channel, however, such
situations rarely exist in practice. Motivated by this fact, we propose a novel
approach that incorporates estimation of the involved channels at the ST, in
order to characterize the performance of the US in terms of interference power
received at the primary receiver and throughput at the secondary receiver (or
\textit{secondary throughput}). Moreover, we apply an outage constraint that
captures the impact of imperfect channel knowledge, particularly on the
uncertain interference. Besides this, we employ a transmit power constraint at
the ST to classify the operation of the US in terms of an interference-limited
regime and a power-limited regime. In addition, we characterize the expressions
of the uncertain interference and the secondary throughput for the case where
the involved channels encounter Nakagami-$m$ fading. Finally, we investigate a
fundamental tradeoff between the estimation time and the secondary throughput
depicting an optimized performance of the US.


  We show that empirical risk minimization procedures and regularized empirical
risk minimization procedures satisfy nonexact oracle inequalities in an
unbounded framework, under the assumption that the class has a subexponential
envelope function. The main novelty, in addition to the boundedness assumption
free setup, is that those inequalities can yield fast rates even in situations
in which exact oracle inequalities only hold with slower rates. We apply these
results to show that procedures based on $\ell_1$ and nuclear norms
regularization functions satisfy oracle inequalities with a residual term that
decreases like $1/n$ for every $L_q$-loss functions ($q\geq2$), while only
assuming that the tail behavior of the input and output variables are well
behaved. In particular, no RIP type of assumption or "incoherence condition"
are needed to obtain fast residual terms in those setups. We also apply these
results to the problems of convex aggregation and model selection.


  The kinematic analysis of dark matter and hydrodynamical simulations suggests
that the vorticity in large-scale structure is mostly confined to, and
predominantly aligned with their filaments, with an excess of probability of 20
per cent to have the angle between vorticity and filaments direction lower than
60 degrees relative to random orientations. The cross sections of these
filaments are typically partitioned into four quadrants with opposite vorticity
sign, arising from multiple flows, originating from neighbouring walls. The
spins of halos embedded within these filaments are consistently aligned with
this vorticity for any halo mass, with a stronger alignment for the most
massive structures up to an excess of probability of 165 per cent. On large
scales, adiabatic/cooling hydrodynamical simulations display the same vorticity
in the gas as in the dark matter. The global geometry of the flow within the
cosmic web is therefore qualitatively consistent with a spin acquisition for
smaller halos induced by this large-scale coherence, as argued in Codis et al.
(2012). In effect, secondary anisotropic infall (originating from the
vortex-rich filament within which these lower-mass halos form) dominates the
angular momentum budget of these halos. The transition mass from alignment to
orthogonality is related to the size of a given multi-flow region with a given
polarity. This transition may be reconciled with the standard tidal torque
theory if the latter is augmented so as to account for the larger scale
anisotropic environment of walls and filaments.


  The vector vortex is a coronagraphic imaging mode of the recently
commissioned Subaru Coronagraphic Extreme-Adaptive Optics (SCExAO) platform on
the 8-m Subaru Telescope. This multi-purpose high-contrast visible and
near-infrared (R- to K-band) instrument is not only intended to serve as a
VLT-class "planet-imager" instrument in the Northern hemisphere, but also to
operate as a technology demonstration testbed ahead of the ELTs-era, with a
particular emphasis on small inner-working angle (IWA) coronagraphic
capabilities. The given priority to small-IWA imaging led to the early design
choice to incorporate focal-plane phase-mask coronagraphs. In this context, a
test H-band vector vortex liquid crystal polymer waveplate was provided to
SCExAO, to allow a one-to-one comparison of different small-IWA techniques on
the same telescope instrument, before considering further steps. Here we
present a detailed overview of the vector vortex coronagraph, from its
installation and performances on the SCExAO optical bench, to the on-sky
results in the extreme AO regime, as of late 2016/early 2017. To this purpose,
we also provide a few recent on-sky imaging examples, notably high-contrast ADI
detection of the planetary-mass companion \kappa Andromedae b, with a
signal-to-noise ratio above 100 reached in less than 10 mn exposure time.


  In this letter we report the direct perturbative QCD evaluation of twist-4
effects in diffractive DIS. They are large and have a strong impact on the
$Q^2$ dependence of diffractive structure functions at large $\beta$. Based on
the AGK rules, we comment on the possible contribution from diffractive higher
twists to $\propto {1 \over Q^{2}}$ corrections to proton structure function at
small $x$. These corrections to the longitudinal structure function $F_{L}$ may
be particularly large.


  We study the faithfulness of an explanation system to the underlying
prediction model. We show that this can be captured by two properties,
consistency and sufficiency, and introduce quantitative measures of the extent
to which these hold. Interestingly, these measures depend on the test-time data
distribution. For a variety of existing explanation systems, such as anchors,
we analytically study these quantities. We also provide estimators and sample
complexity bounds for empirically determining the faithfulness of black-box
explanation systems. Finally, we experimentally validate the new properties and
estimators.


  The most common reason for students' tardiness is due to waking up late every
morning. The causes may vary from lack of rest and sleep, to failure of
ascertaining an alarm clock's noise or sometimes, falling back to sleep again
after snoozing an alarm. This paper's purpose is to address the problem
concerning student's inability to continue to stay awake after turning off
their alarms using Arduino-based pulse rate triggered alarm interrupt (APRTAI).
The proposed system will enable students to make use of a higher pulse rate
from their thumb to turn off an alarm which can be achieved by engaging
themselves in intensive physical activities that will eventually eradicate
drowsiness and therefore prevent them from falling back to sleep again.


  When a gravitational wave (GW) from a distant source propagates through the
universe, its amplitude and phase change due to gravitational lensing by the
inhomogeneous mass distribution. We derive the amplitude and phase
fluctuations, and calculate these variances in the limit of a weak
gravitational field of density perturbation. If the scale of the perturbation
is smaller than the Fresnel scale $\sim 100 {pc} (f/{mHz})^{-1/2}$ ($f$ is the
GW frequency), the GW is not magnified due to the diffraction effect. The rms
amplitude fluctuation is $1-10 %$ for $f > 10^{-10}$ Hz, but it is reduced less
than 5% for a very low frequency of $f < 10^{-12}$ Hz. The rms phase
fluctuation in the chirp signal is $\sim 10^{-3}$ radian at LISA frequency band
($10^{-5} - 10^{-1}$ Hz). Measurements of these fluctuations will provide
information about the matter power spectrum on the Fresnel scale $\sim 100$ pc.


  With the rapid growth of knowledge bases (KBs), question answering over
knowledge base, a.k.a. KBQA has drawn huge attention in recent years. Most of
the existing KBQA methods follow so called encoder-compare framework. They map
the question and the KB facts to a common embedding space, in which the
similarity between the question vector and the fact vectors can be conveniently
computed. This, however, inevitably loses original words interaction
information. To preserve more original information, we propose an attentive
recurrent neural network with similarity matrix based convolutional neural
network (AR-SMCNN) model, which is able to capture comprehensive hierarchical
information utilizing the advantages of both RNN and CNN. We use RNN to capture
semantic-level correlation by its sequential modeling nature, and use an
attention mechanism to keep track of the entities and relations simultaneously.
Meanwhile, we use a similarity matrix based CNN with two-directions pooling to
extract literal-level words interaction matching utilizing CNNs strength of
modeling spatial correlation among data. Moreover, we have developed a new
heuristic extension method for entity detection, which significantly decreases
the effect of noise. Our method has outperformed the state-of-the-arts on
SimpleQuestion benchmark in both accuracy and efficiency.


  Structure factors obtained from diffraction experiments are one of the most
important quantities for characterizing the electronic and structural
properties of materials. Methods for calculating this quantity from plane-wave
density functional theory (DFT) codes are typically prohibitively expensive to
perform, requiring the electron density to be constructed and evaluated on
dense real-space grids. Making use of the projector functions found in both the
Vanderbilt ultrasoft pseudopotential and projector augmented wave methods, we
implement an approach to calculate structure factors which avoids the use of a
dense grid by separating the rapidly changing contributions to the electron
density and treating them on logarithmic radial grids. This approach is
successfully validated against structure factors obtained from all-electron DFT
and experiments for three prototype systems, allowing structure factors to be
obtained at all-electron accuracy at a fraction of the cost of previous
approaches for plane-wave DFT.


  A measured laminations on the universal hyperbolic solenoid $\S$ is, by our
definition, a leafwise measured lamination with appropriate continuity for the
transverse variations. An earthquakes on theuniversal hyperbolic solenoid $\S$
is uniquely determined by a measured lamination on $\S$; it is a leafwise
earthquake with the leafwise earthquake measure equal to the leafwise measured
lamination. Leafwise earthquakes fit together to produce a new hyperbolic
metric on $\S$ which is transversely continuous and we show that any two
hyperbolic metrics on $\S$ are connected by an earthquake. We also establish
the space of projective measured lamination $PML(\S)$ as a natural
Thurston-type boundary to the Teichm\"uller space $T(\S)$ of the universal
hyperbolic solenoid $\S$. The (baseleaf preserving) mapping class group
$MCG_{BLP}(\S)$ acts continuously on the closure $T(\S)\cup PML(\S)$ of
$T(\S)$. Moreover, the set of transversely locally constant measured
laminations on $\S$ is dense in $ML(\S)$.


  In clinical trials, an experimental treatment is sometimes added on to a
standard of care or control therapy in multiple treatment phases (e.g.,
concomitant and maintenance phases) to improve patient outcomes. When the new
regimen provides meaningful benefit over the control therapy in such cases, it
proves difficult to separately assess the contribution of each phase to the
overall effect observed. This article provides an approach for assessing the
importance of a specific treatment phase in such a situation through tipping
point analyses of a time-to-event endpoint using
rank-preserving-structural-failure-time (RPSFT) modeling. A tipping-point
analysis is commonly used in situations where it is suspected that a
statistically significant difference between treatment arms could be a result
of missing or unobserved data instead of a real treatment effect.
Rank-preserving-structural-failure-time modeling is an approach for causal
inference that is typically used to adjust for treatment switching in clinical
trials with time to event endpoints. The methodology proposed in this article
is an amalgamation of these two ideas to investigate the contribution of a
treatment phase of interest to the effect of a regimen comprising multiple
treatment phases. We provide two different variants of the method corresponding
to two different effects of interest. We provide two different tipping point
thresholds depending on inferential goals. The proposed approaches are
motivated and illustrated with data from a recently concluded, real-life phase
3 cancer clinical trial. We then conclude with several considerations and
recommendations.


  We report progress in the development of a model-based hybrid probabilistic
approach to an on-board IVHM for solid rocket boosters (SRBs) that can
accommodate the abrupt changes of the model parameters in various nonlinear
dynamical off-nominal regimes. The work is related to the ORION mission
program. Specifically, a case breach fault for SRBs is considered that takes
into account burning a hole through the rocket case, as well as ablation of the
nozzle throat under the action of hot gas flow. A high-fidelity model (HFM) of
the fault is developed in FLUENT in cylindrical symmetry. The results of the
FLUENT simulations are shown to be in good agreement with quasi-stationary
approximation and analytical solution of a system of one-dimensional partial
differential equations (PDEs) for the gas flow in the combustion chamber and in
the hole through the rocket case.


  Initiated by Abramsky [1994], the Proofs as Processes agenda is to establish
a solid foundation for the study of concurrent languages, by researching the
connection between linear logic and the $\pi$-calculus.
  To date, Proofs as Processes is still a partial success. Caires and Pfenning
[2010] showed that linear propositions correspond to session types, which
prescribe the observable behaviour of processes. Further, Carbone et al. [2018]
demonstrated that adopting devices from hypersequents [Avron 1991] shapes
proofs such that they correspond to the expected syntactic structure of
processes in the $\pi$-calculus. What remains is reconstructing the expected
metatheory of session types and the $\pi$-calculus. In particular, the hallmark
of session types, session fidelity, still has to be reconstructed: a
correspondence between the observable behaviours of processes and their session
types, in terms of labelled transitions.
  In this article, we present $\pi$LL, a new process calculus rooted in linear
logic. The key novelty of $\pi$LL is that it comes with a carefully formulated
design recipe, based on a dialgebraic view of labelled transition systems.
Thanks to our recipe, $\pi$LL offers the expected transition systems of session
types, which we use to establish session fidelity. We use $\pi$LL to carry out
the first thorough investigation of the metatheoretical properties enforced by
linear logic on the observable behaviour of processes, uncovering connections
with similarity and bisimilarity. We also prove that $\pi$LL and our recipe
form a robust basis for the further exploration of Proofs as Processes, by
considering different features: polymorphism, process mobility, and recursion.


  A multiwavelength investigation of the southern infrared dust bubble CS51 is
presented in this paper. We probe the associated ionized, cold dust, molecular
and stellar components. Radio continuum emission mapped at 610 and 1300 MHz,
using the Giant Metrewave Radio Telescope, India, reveal the presence of three
compact emission components (A, B, and C) apart from large-scale diffuse
emission within the bubble interior. Radio spectral index map show the
coexistence of thermal and non-thermal emission components. Modified blackbody
fits to the thermal dust emission using Herschel PACS and SPIRE data is
performed to generate dust temperature and column density maps. We identify
five dust clumps associated with CS51 with masses and radius in the range 810 -
4600 M{\sun} and 1.0 - 1.9 pc, respectively. We further construct the column
density probability distribution functions of the surrounding cold dust which
display the impact of ionization feedback from high-mass stars. The estimated
dynamical and fragmentation timescales indicate the possibility of collect and
collapse mechanism in play at the bubble border. Molecular line emission from
the MALT90 survey is used to understand the nature of two clumps which show
signatures of expansion of CS51.


  A system of electrons in a local or nonlocal external potential can be
studied with 1-matrix functional theory (1MFT), which is similar to density
functional theory (DFT) but takes the one-particle reduced density matrix
(1-matrix) instead of the density as its basic variable. Within 1MFT, Gilbert
derived [PRB 12, 2111 (1975)] effective single-particle equations analogous to
the Kohn-Sham (KS) equations in DFT. The self-consistent solution of these
1MFT-KS equations reproduces not only the density of the original electron
system but also its 1-matrix. While in DFT it is usually possible to reproduce
the density using KS orbitals with integer (0 or 1) occupancy, in 1MFT
reproducing the 1-matrix requires in general fractional occupancies. The
variational principle implies that the KS eigenvalues of all fractionally
occupied orbitals must collapse at self-consistency to a single level, equal to
the chemical potential. We show that as a consequence of the degeneracy the
iteration of the KS equations is intrinsically divergent. Fortunately, the
level shifting method, commonly introduced in Hartree-Fock calculations, is
always able to force convergence. We introduce an alternative derivation of the
1MFT-KS equations that allows control of the eigenvalue collapse by
constraining the occupancies. As an explicit example, we apply the 1MFT-KS
scheme to calculate the ground state 1-matrix of an exactly solvable two-site
Hubbard model.


  We prove a conjecture due to Stephen Harrap on inhomogeneous linear
Diophantine approximation related to ${\rm BAD}(\alpha,\beta)$ sets.


  We investigate the possibility that the geometry dual to a typical AdS black
hole microstate corresponds to the extended AdS-Schwarzschild geometry,
including a region spacelike to the exterior. We argue that this region can be
described by the mirror operators, a set of state-dependent operators in the
dual CFT. We probe the geometry of a typical state by considering
state-dependent deformations of the CFT Hamiltonian, which have an
interpretation as a one-sided analogue of the Gao-Jafferis-Wall traversable
wormhole protocol for typical states. We argue that the validity of the
conjectured bulk geometry requires that out-of-time-order correlators of simple
CFT operators on typical pure states must exhibit the same chaotic effects as
thermal correlators at scrambling time. This condition is related to the
question of whether the product of operators separated by scrambling time obey
the Eigenstate Thermalization Hypothesis. We investigate some of these
statements in the SYK model and discuss similarities with state-dependent
perturbations of pure states in the SYK model previously considered by
Kourkoulou and Maldacena. Finally, we discuss how the mirror operators can be
used to implement an analogue of the Hayden-Preskill protocol.


  In this work we present an X-ray Absorption Spectroscopy characterization of
the local environment of the impurity in room temperature ferromagnetic anatase
TiO2 thin films doped with Co, Ni, Cu, or Zn, deposited on LaAlO3 substrate by
Pulsed Laser Deposition. It was found that there is a considerable amount of
impurity atoms substituting Ti in TiO2 anatase, although the presence of metal
transition monoxide clusters can not be discarded. From our results we infer
that the observed room temperature ferromagnetism of the samples could be
assigned to the metal transition atoms replacing Ti in TiO2 anatase.


  We study the representation theory of the algebraic Toeplitz algebra
$R={\mathbb K}\langle x,y\rangle/\langle xy-1\rangle$, give a few new structure
and homological theorems, completely determine one-sided ideals and survey and
re-obtain results from the existing literature as well. We discuss its
connection to Kaplansky's direct finiteness conjecture, and a possible approach
to it based on the module theory of $R$. In addition, we discuss the
conjecture's connections to several central problems in mathematics, including
Connes' embedding conjecture.


  An "experimental" study on the randomness of the fractional digits of $\pi$,
$e$ and $\phi$ irrational numbers are presented. This is done by exploiting the
$1D$ Poisson-Voronoi tessellation. We employed two approaches and in both
cases, within the numerical error, no differences have been detected between
the irrational fractional digits and an equivalent random sequence of digits.
The number of tested digits is $1.6 \times 10^7$ and $4 \times 10^7$ for the
first and second approach, respectively. Although not shown here, we
investigated several irrational numbers and all of them have displayed a
similar behavior.


  Of concern is the study of the space of curves in homogeneous spaces.
Motivated by applications in shape analysis we identify two curves if they only
differ by their parametrization and/or a rigid motion. For curves in Euclidean
space the Square-Root-Velocity-Function (SRVF) allows to define and efficiently
compute a distance on this infinite dimensional quotient space. In this article
we present a generalization of the SRVF to curves in homogeneous spaces. We
prove that, under mild conditions on the curves, there always exist optimal
reparametrizations realizing the quotient distance and demonstrate the
efficiency of our framework in selected numerical examples.


  Sophisticated statistical mechanics approaches and human intuition have
demonstrated the possibility to self-assemble complex lattices or finite size
constructs, but have mostly only been successful in silico. The proposed
strategies quite often fail in experiment due to unpredicted traps associated
to kinetic slowing down (gelation, glass transition), as well as to competing
ordered structures. An additional challenge that theoretical predictions face
is the difficulty to encode the desired inter-particle interaction potential
with the currently available library of nano- and micron-sized particles. To
overcome these issues, we conjugate here SAT-assembly -- a patchy-particle
interaction design algorithm based on constrained optimization solvers -- with
coarse-grained simulations of DNA nanotechnology to experimentally realize
trap-free self-assembly pathways. As a proof of concept we investigate the
assembly of the pyrochlore (also known as tetrastack) lattice, a highly coveted
3D crystal lattice due to its promise in construction of optical metamaterials.
We confirm the successful assembly with two different patchy DNA origami
designs via SAXS as well as SEM visualization of the silica-coated lattice. Our
approach offers a versatile modeling pipeline that starts from patchy particles
designed in silico and ends with wireframe DNA origami that self-assemble into
the desired structure.


  Scanning tunneling microscopy images of hydrogen-terminated Si(110) surfaces
are studied using first-principles calculations. Our results show that the
calculated filled-state images and local density of states are consistent with
recent experimental results, and the empty-state images appear significantly
different from the filled-state ones. To elucidate the origin of this
difference, we examined in detail the local density of states, which affects
the images, and found that the bonding and antibonding states of surface
silicon atoms largely affect the difference between the filled- and empty-state
images.


  In a multilevel database, cover stories are usually managed using the
ambiguous technique of polyinstantiation. In this paper, we define a new
technique to manage cover stories and propose a formal representation of a
multilevel database containing cover stories. Our model aims to be a generic
model, that is, it can be interpreted for any kind of database (e.g.
relational, object- oriented etc). We then consider the problem of updating a
multilevel database containing cover stories managed with our technique.


  Influence Maximization (IM), that seeks a small set of key users who spread
the influence widely into the network, is a core problem in multiple domains.
It finds applications in viral marketing, epidemic control, and assessing
cascading failures within complex systems. Despite the huge amount of effort,
IM in billion-scale networks such as Facebook, Twitter, and World Wide Web has
not been satisfactorily solved. Even the state-of-the-art methods such as TIM+
and IMM may take days on those networks.
  In this paper, we propose SSA and D-SSA, two novel sampling frameworks for
IM-based viral marketing problems. SSA and D-SSA are up to 1200 times faster
than the SIGMOD'15 best method, IMM, while providing the same
$(1-1/e-\epsilon)$ approximation guarantee. Underlying our frameworks is an
innovative Stop-and-Stare strategy in which they stop at exponential check
points to verify (stare) if there is adequate statistical evidence on the
solution quality. Theoretically, we prove that SSA and D-SSA are the first
approximation algorithms that use (asymptotically) minimum numbers of samples,
meeting strict theoretical thresholds characterized for IM. The absolute
superiority of SSA and D-SSA are confirmed through extensive experiments on
real network data for IM and another topic-aware viral marketing problem, named
TVM. The source code is available at https://github.com/hungnt55/Stop-and-Stare


  Few-shot visual recognition refers to recognize novel visual concepts from a
few labeled instances. Many few-shot visual recognition methods adopt the
metric-based meta-learning paradigm by comparing the query representation with
class representations to predict the category of query instance. However,
current metric-based methods generally treat all instances equally and
consequently often obtain biased class representation, considering not all
instances are equally significant when summarizing the instance-level
representations for the class-level representation. For example, some instances
may contain unrepresentative information, such as too much background and
information of unrelated concepts, which skew the results. To address the above
issues, we propose a novel metric-based meta-learning framework termed
instance-adaptive class representation learning network (ICRL-Net) for few-shot
visual recognition. Specifically, we develop an adaptive instance revaluing
network with the capability to address the biased representation issue when
generating the class representation, by learning and assigning adaptive weights
for different instances according to their relative significance in the support
set of corresponding class. Additionally, we design an improved bilinear
instance representation and incorporate two novel structural losses, i.e.,
intra-class instance clustering loss and inter-class representation
distinguishing loss, to further regulate the instance revaluation process and
refine the class representation. We conduct extensive experiments on four
commonly adopted few-shot benchmarks: miniImageNet, tieredImageNet, CIFAR-FS,
and FC100 datasets. The experimental results compared with the state-of-the-art
approaches demonstrate the superiority of our ICRL-Net.


  Controlling peak side-lobe level (PSL) is of great importance in
high-resolution applications of multiple-input multiple-output (MIMO) radars.
In this paper, designing sequences with good autocorrelation properties are
studied. The PSL of the autocorrelation is regarded as the main merit and is
optimized through newly introduced cyclic algorithms, namely; PSL Minimization
Quadratic Approach (PMQA), PSL Minimization Algorithm, the smallest Rectangular
(PMAR), and PSL Optimization Cyclic Algorithm (POCA). It is revealed that
minimizing PSL results in better sequences in terms of autocorrelation
side-lobes when compared with traditional integrated side-lobe level (ISL)
minimization. In order to improve the performance of these algorithms,
fast-randomized Singular Value Decomposition (SVD) is utilized. To achieve
waveform design for MIMO radars, this algorithm is applied to the waveform
generated from a modified Bernoulli chaotic system. The numerical experiments
confirm the superiority of the newly developed algorithms compared to
high-performance algorithms in mono-static and MIMO radars.


  A physical system is in local equilibrium if it cannot be distinguished from
a global equilibrium by ``infinitesimally localized measurements''. This should
be a natural characterization of local equilibrium, but the problem is to give
a precise meaning to the qualitative phrase ``infinitesimally localized
measurements''. A solution is suggested in form of a Local Equilibrium
Condition, which can be applied to linear relativistic quantum field theories
but not directly to selfinteracting quantum fields. The concept of local
temperature resulting from LEC is compared to an old approach to local
temperature based on the principle of maximal entropy. It is shown that the
principle of maximal entropy does not always lead to physical states if it is
applied to relativistic quantum field theories.


  We show that any surface of infinite type admits an ideal triangulation.
Furthermore, we show that a set of disjoint arcs can be completed into a
triangulation if and only if, as a set, they intersect every simple closed
curve a finite number of times.


  The properties of the two-flavored Gross-Neveu model in the (1+1)-dimensional
spacetime are investigated in the presence of the isospin $\mu_I$ as well as
quark number $\mu$ chemical potentials both at zero and nonzero temperatures.
The consideration is performed in the limit $N_c\to\infty$, i.e. in the case
with an infinite number of colored quarks. In the plane of parameters
$\mu_I,\mu$ a rather rich phase structure is found, which contains phases with
and without pion condensation. Moreover, we have found a great variety of
one-quark excitations of these phases, including gapless and gapped
quasiparticles.


  In this paper we obtain, by the method of Loewner chains, some sufficient
conditions for the analyticity and the univalence of the functions defined by
an integral operator. These conditions in- volves Ruscheweyh and Salagean
derivative operator in the open unit disk. In particular cases, we find the
well-known conditions for univalency established by Becker [3], Ahlfors [2],
Kanas and Srivastava [8] and others for analytic mappings f : U ! C: Also, we
obtain the corresponding new, useful and simpler conditions for this integral
operator.


  Recent times have seen data analytics software applications become an
integral part of the decision-making process of analysts. The users of these
software applications generate a vast amount of unstructured log data. These
logs contain clues to the user's goals, which traditional recommender systems
may find difficult to model implicitly from the log data. With this assumption,
we would like to assist the analytics process of a user through command
recommendations. We categorize the commands into software and data categories
based on their purpose to fulfill the task at hand. On the premise that the
sequence of commands leading up to a data command is a good predictor of the
latter, we design, develop, and validate various sequence modeling techniques.
In this paper, we propose a framework to provide goal-driven data command
recommendations to the user by leveraging unstructured logs. We use the log
data of a web-based analytics software to train our neural network models and
quantify their performance, in comparison to relevant and competitive
baselines. We propose a custom loss function to tailor the recommended data
commands according to the goal information provided exogenously. We also
propose an evaluation metric that captures the degree of goal orientation of
the recommendations. We demonstrate the promise of our approach by evaluating
the models with the proposed metric and showcasing the robustness of our models
in the case of adversarial examples, where the user activity is misaligned with
selected goal, through offline evaluation.


  We propose using a Gaussian Mixture Model (GMM) as reverse transition
operator (kernel) within the Denoising Diffusion Implicit Models (DDIM)
framework, which is one of the most widely used approaches for accelerated
sampling from pre-trained Denoising Diffusion Probabilistic Models (DDPM).
Specifically we match the first and second order central moments of the DDPM
forward marginals by constraining the parameters of the GMM. We see that moment
matching is sufficient to obtain samples with equal or better quality than the
original DDIM with Gaussian kernels. We provide experimental results with
unconditional models trained on CelebAHQ and FFHQ and class-conditional models
trained on ImageNet datasets respectively. Our results suggest that using the
GMM kernel leads to significant improvements in the quality of the generated
samples when the number of sampling steps is small, as measured by FID and IS
metrics. For example on ImageNet 256x256, using 10 sampling steps, we achieve a
FID of 6.94 and IS of 207.85 with a GMM kernel compared to 10.15 and 196.73
respectively with a Gaussian kernel.


  Spacecraft potential has often been used to infer electron density with much
higher time resolution than is typically possible with plasma instruments.
However, recently two studies by Torkar et al. 2017 and Graham et al. 2018 have
shown that external electric fields can also have an effect on the spacecraft
potential by enhancing photoelectron escape from the surface. Consequently,
should the electron density derived from the spacecraft potential be used
during an event with a large electric field, the estimation would be
contaminated and the user would see the effects of the electric field rather
than density perturbations. The goal of this paper is to propose a method to
remove the electric field effects to allow the density derived from spacecraft
potential to be used even during large amplitude wave events such as Langmuir
waves or upper hybrid waves.


  We study the equilibrium state of a superconducting film covered with a
regular square array of perpendicularly magnetized magnetic dots. The dots
induce vortices in the film directly under them and antivortices between the
dots. We show that the symmetry of the dot array is spontaneously violated by
the vortices. The positions of the vortices and the antivortices depend on the
magnetization and the size of the dots.


  Two dimensional (2D) materials have received a surge in research interest due
to their exciting range of properties. Here we show that 2D cobalt telluride
(Co2Te3), successfully synthesized via liquid-phase exfoliation in an organic
solvent, exhibits weak ferromagnetism and semiconducting behavior at room
temperature. The magnetic field-dependent piezoelectric properties of 2D Co2Te3
sample show magneto-electric response of the material and a linear relationship
between the output voltage and applied magnetic field. First-principles density
functional theory (DFT) and ab initio molecular dynamics are used to explain
these experimental results. Our work could pave the way for the development of
2D materials with coupled magnetism and piezoelectricity, leading to new
applications in electromagnetics.


  The temporal pseudoscalar meson correlation function in a QCD plasma is
investigated in a range of temperatures exceeding $T_c$ and yet of experimental
interest. Only the flavour-singlet channel is considered and the imaginary time
formalism is employed for the finite temperature calculations. The behaviour of
the meson spectral function and of the temporal correlator is first studied in
the HTL approximation, where one replaces the free thermal quark propagators
with the HTL resummed ones. This procedure satisfactory describes the soft
fermionic modes, but its application to the propagation of hard quarks is not
reliable. An improved version of the so-called NLA scheme, which allows a
better treatment of the hard fermionic modes, is then proposed. The impact of
the improved NLA on the pseudoscalar temporal correlator is investigated.


  For any odd prime $p$, we give an example of a locally finite $p$-group $G$
containing a left 3-Engel element $x$ where $\langle x \rangle^G$ is not
nilpotent.


  We investigate the possibility of using icosahedral symmetry as a family
symmetry group in the lepton sector. The rotational icosahedral group, which is
isomorphic to A5, the alternating group of five elements, provides a natural
context in which to explore (among other possibilities) the intriguing
hypothesis that the solar neutrino mixing angle is governed by the golden
ratio. We present a basic toolbox for model-building using icosahedral
symmetry, including explicit representation matrices and tensor product rules.
As a simple application, we construct a minimal model at tree level in which
the solar angle is related to the golden ratio, the atmospheric angle is
maximal, and the reactor angle vanishes to leading order. The approach provides
a rich setting in which to investigate the flavor puzzle of the Standard Model.


  This paper concerns a fully nonlinear version of the Yamabe problem on
manifolds with boundary. We establish some existence results and estimates of
solutions.


  Lapses of transmission phase in transport through quantum dots are ubiquitous
already in the absence of interaction, in which case their precise location is
determined by the signs and magnitudes of the tunnelling matrix elements.
However, actual measurements for a quantum dot embedded in an Aharonov-Bohm
interferometer show systematic sequences of phase lapses separated by Coulomb
peaks -- an issue that attracted much attention and generated controversy.
Using a two-level quantum dot as an example we show that this phenomenon can be
accounted for by the combined effect of asymmetric dot-lead couplings (left
lead/right lead asymmetry as well as different level broadening for different
levels) and interaction-induced "population switching" of the levels, rendering
this behaviour generic. We construct and analyse a mean field scheme for an
interacting quantum dot, and investigate the properties of the mean field
solution, paying special attention to the character of its dependence
(continuous vs. discontinuous) on the chemical potential or gate voltage.


  We measured the work of separation of single and few-layer MoS2 membranes
from a SiOx substrate using a mechanical blister test, and found a value of 220
+- 35 mJ/m^2. Our measurements were also used to determine the 2D Young's
modulus of a single MoS2 layer to be 160 +- 40 N/m. We then studied the
delamination mechanics of pressurized MoS2 bubles, demonstrating both stable
and unstable transitions between the bubbles' laminated and delaminated states
as the bubbles were inflated. When they were deflated, we observed edge pinning
and a snap-in transition which are not accounted for by the previously reported
models. We attribute this result to adhesion hysteresis and use our results to
estimate the work of adhesion of our membranes to be 42 +- 20 mJ/m^2.


  Monogamy of quantum correlations is a vibrant area of research because of its
potential applications in several areas in quantum information ranging from
quantum cryptography to co-operative phenomena in many-body physics. In this
paper, we investigate conditions under which monogamy is preserved for
functions of quantum correlation measures. We prove that a monogamous measure
remains monogamous on raising its power, and a non-monogamous measure remains
non-monogamous on lowering its power. We also prove that monogamy of a convex
quantum correlation measure for arbitrary multipartite pure quantum state leads
to its monogamy for mixed states in the same Hilbert space. Monogamy of squared
negativity for mixed states and that of entanglement of formation follow as
corollaries of our results.


  We develop a theory of equivariant factorization algebras on varieties with
an action of a connected algebraic group $G$, extending the definitions of
Francis-Gaitsgory [FG] and Beilinson-Drinfeld [BD1] to the equivariant setting.
We define an equivariant analogue of factorization homology, valued in modules
over $\text{H}^\bullet_G(\text{pt})$, and in the case $G=(\mathbb{C}^\times)^n$
we prove an equivariant localization theorem for factorization homology,
analogous to the classical localization theorem [AtB]. We establish a
relationship between $\mathbb{C}^\times$ equivariant factorization algebras and
filtered quantizations of their restrictions to the fixed point subvariety.
These results provide a model for predictions from the physics literature about
the $\Omega$-background construction introduced in [Nek1], interpreting
factorization $\mathbb{E}_n$ algebras as observables in mixed
holomorphic-topological quantum field theories.
  In the companion paper [Bu2], we develop tools to give geometric
constructions of factorization $\mathbb{E}_n$ algebras, and apply them to
define those corresponding to holomorphic-topological twists of supersymmetric
gauge theories in low dimensions. Further, we apply our above results in these
examples to give an account of the predictions of [CosG] as well as [Beem4],
and explain the relation between these constructions from this perspective.


  The potentialities of the production of the scalar $K\bar K$ molecules in the
$\phi$ radiative decays are considered beyond the narrow resonance width
approximation. It is shown that $BR(\phi\rightarrow\gamma
f_0(a_0)\rightarrow\gamma\pi\pi(\pi\eta))\approx (1\div 2)\times 10^{-5}\
,\BR(\phi\rightarrow\gamma (f_0+a_0)\rightarrow\gamma K^+K^-)\alt 10^{-6}$ and
$BR(\phi\rightarrow\gamma (f_0+a_0) \to \gamma K^0\bar K^0)\alt 10^{-8}$. The
mass spectra in the $\pi\pi\ ,\ \pi\eta\ ,\ K^+K^-\ ,\ K^0\bar K^0$ channels
are calculated. The imaginary part of the amplitude $\phi\rightarrow\gamma
f_0(a_0)$ is calculated analytically. It is obtained the phase of the scalar
resonance production amplitude that causes the interference patterns in the
reaction $e^+e^-\rightarrow\gamma \pi^+\pi^-$ in the $\phi$ meson mass region.


  This paper introduces a robust, learning-based method for diagnosing the
state of distribution network switchgear, which is crucial for maintaining the
power quality for end users. Traditional diagnostic models often rely heavily
on expert knowledge and lack robustness. To address this, our method
incorporates an expanded feature vector that includes environmental data,
temperature readings, switch position, motor operation, insulation conditions,
and local discharge information. We tackle the issue of high dimensionality
through feature mapping. The method introduces a decision radius to categorize
unlabeled samples and updates the model parameters using a combination of
supervised and unsupervised loss, along with a consistency regularization
function. This approach ensures robust learning even with a limited number of
labeled samples. Comparative analysis demonstrates that this method
significantly outperforms existing models in both accuracy and robustness.


  Software testing is vital to improve software quality. However, software
tester role is stigmatized, partly due to misperception and partly due to the
treatment of the testing process within the software industry. The present
study analyses this situation aiming to explore what might inhibit an
individual from taking up a software testing career. In order to investigate
this issue, we surveyed 132 senior students pursuing degrees in information
systems, information and communication technology, computer science, computer
engineering, software engineering, and other closely-related disciplines at
three universities in the United Arab Emirates: two publicly funded and one
top-notch private university. The students were asked to describe the PROs and
CONs of taking up a career in software testing and to ponder the likelihood
that they would take up the career themselves. The study identified 7 main PROs
and 9 main CONSs for pursuing a testing career, and indicated that the role of
software tester is perceived as a social role, which may require as many soft
skills as technical prowess. The results also show that UAE software-related
students have a stronger negative attitude towards software testing compared to
their counterparts in other countries where similar investigations have been
carried out in the past three years.


  Achieving a precise understanding of galaxy formation in a cosmological
context is one of the great challenges in theoretical astrophysics, due to the
vast range of spatial scales involved in the relevant physical processes.
Observations in the millimeter bands, particularly those using the cosmic
microwave background (CMB) radiation as a "backlight", provide a unique probe
of the thermodynamics of these processes, with the capability to directly
measure the density, pressure, and temperature of ionized gas. Moreover, these
observations have uniquely high sensitivity into the outskirts of the halos of
galaxies and clusters, including systems at high redshift. In the next decade,
the combination of large spectroscopic and photometric optical galaxy surveys
and wide-field, low-noise CMB surveys will transform our understanding of
galaxy formation via these probes.


  We present a dispersive method which allows to investigate the low-energy
couplings of chiral perturbation theory at the next-to-leading order (NLO) in
the 1/N(C) expansion, keeping full control of their renormalization scale
dependence. Using the resonance chiral theory Lagrangian, we perform a NLO
calculation of the scalar and pseudoscalar two-point functions, within the
single-resonance approximation. Imposing the correct QCD short-distance
constraints, one determines their difference Pi(t)=Pi_S(t)-Pi_P(t) in terms of
the pion decay constant and resonance masses. Its low momentum expansion fixes
then the low-energy chiral couplings L_8 and C_38. At mu_0=0.77 GeV, we obtain
L_8(mu_0)^{SU(3)} = (0.6+-0.4)10^{-3} and C_{38}(mu_0)^{SU(3)}=(2+-6)10^{-6}.


  We give a description of the growth of research on Knot Theory in Japan. We
place our report in a general historical context. In particular, we compare the
development of research on mathematical topology in Japan with that in Poland
and USA, observing several similarities. Toward the end of XIX century and at
the beginning of XX century several young mathematicians, educated in Germany,
France or England were returning to their native countries and building, almost
from scratch, schools of modern mathematics. After a general description of the
growth of topology in Japan between the World Wars, we describe the beginning
of Knot Theory in Japan. Gaisi Takeuti, later a famous logician, conducted the
first Knot Theory seminar in Japan in 1952 or 1953. Kunio Murasugi (later a
prominent knot theoretist) was the only student who attended it. In Osaka, a
Knot Theory seminar started in 1955, initiated by Hidetaka Terasaka and his
students Shin'ichi Kinoshita and Takeshi Yajima. We complete the paper by
listing 70 Japanese topologists born before 1946, and by sketching the
biography of Fox.


  A probability density function (pdf) encodes the entire stochastic knowledge
about data distribution, where data may represent stochastic observations in
robotics, transition state pairs in reinforcement learning or any other
empirically acquired modality. Inferring data pdf is of prime importance,
allowing to analyze various model hypotheses and perform smart decision making.
However, most density estimation techniques are limited in their representation
expressiveness to specific kernel type or predetermined distribution family,
and have other restrictions. For example, kernel density estimation (KDE)
methods require meticulous parameter search and are extremely slow at querying
new points. In this paper we present a novel non-parametric density estimation
approach, DeepPDF, that uses a neural network to approximate a target pdf given
samples from thereof. Such a representation provides high inference accuracy
for a wide range of target pdfs using a relatively simple network structure,
making our method highly statistically robust. This is done via a new
stochastic optimization algorithm, \emph{Probabilistic Surface Optimization}
(PSO), that turns to advantage the stochastic nature of sample points in order
to force network output to be identical to the output of a target pdf. Once
trained, query point evaluation can be efficiently done in DeepPDF by a simple
network forward pass, with linear complexity in the number of query points.
Moreover, the PSO algorithm is capable of inferring the frequency of data
samples and may also be used in other statistical tasks such as conditional
estimation and distribution transformation. We compare the derived approach
with KDE methods showing its superior performance and accuracy.


  Augmenting pretrained language models with retrievers has shown promise in
effectively solving common NLP problems, such as language modeling and question
answering. In this paper, we evaluate the strengths and weaknesses of popular
retriever-augmented language models, namely kNN-LM, REALM, DPR + FiD,
Contriever + ATLAS, and Contriever + Flan-T5, in reasoning over retrieved
statements across different tasks. Our findings indicate that the simple
similarity metric employed by retrievers is insufficient for retrieving all the
necessary statements for reasoning. Additionally, the language models do not
exhibit strong reasoning even when provided with only the required statements.
Furthermore, when combined with imperfect retrievers, the performance of the
language models becomes even worse, e.g., Flan-T5's performance drops by 28.6%
when retrieving 5 statements using Contriever. While larger language models
improve performance, there is still a substantial room for enhancement. Our
further analysis indicates that multihop retrieve-and-read is promising for
large language models like GPT-3.5, but does not generalize to other language
models like Flan-T5-xxl.


  Using the momentum constraint, the standard evolution system is written in a
fully first order form. The class of first order invariant algebraic slicing
conditions is considered. The full set of characteristic fields is explicitly
given. Characteristic speeds associated to the gauge dependent eigenfields
(gauge speeds) are related to light speed.


  In this paper we study the Sobolev inequality in the Dunkl setting using two
new approaches which provide a simpler elementary proof of the classical case
$p=2$, as well as an extension to the coefficient $p=1$ that was previously
unknown. We also find estimates of the sharp constants for the Sobolev
inequality for Dunkl gradient. Related inequalities and some improvements are
also considered (Nash inequality, Besov space embeddings).


  Urban sound classification has been achieving remarkable progress and is
still an active research area in audio pattern recognition. In particular, it
allows to monitor the noise pollution, which becomes a growing concern for
large cities. The contribution of this paper is two-fold. First, we present our
DCASE 2020 task 5 winning solution which aims at helping the monitoring of
urban noise pollution. It achieves a macro-AUPRC of 0.82 / 0.62 for the coarse
/ fine classification on validation set. Moreover, it reaches accuracies of
89.7% and 85.41% respectively on ESC-50 and US8k datasets. Second, it is not
easy to find a fair comparison and to reproduce the performance of existing
models. Sometimes authors copy-pasting the results of the original papers which
is not helping reproducibility. As a result, we provide a fair comparison by
using the same input representation, metrics and optimizer to assess
performances. We preserve data augmentation used by the original papers. We
hope this framework could help evaluate new architectures in this field. For
better reproducibility, the code is available on our GitHub repository.


  Adversarial training (AT) is one of the most effective defenses against
adversarial attacks for deep learning models. In this work, we advocate
incorporating the hypersphere embedding (HE) mechanism into the AT procedure by
regularizing the features onto compact manifolds, which constitutes a
lightweight yet effective module to blend in the strength of representation
learning. Our extensive analyses reveal that AT and HE are well coupled to
benefit the robustness of the adversarially trained models from several
aspects. We validate the effectiveness and adaptability of HE by embedding it
into the popular AT frameworks including PGD-AT, ALP, and TRADES, as well as
the FreeAT and FastAT strategies. In the experiments, we evaluate our methods
under a wide range of adversarial attacks on the CIFAR-10 and ImageNet
datasets, which verifies that integrating HE can consistently enhance the model
robustness for each AT framework with little extra computation.


  This paper addresses the semantic instance segmentation task in the open-set
conditions, where input images can contain known and unknown object classes.
The training process of existing semantic instance segmentation methods
requires annotation masks for all object instances, which is expensive to
acquire or even infeasible in some realistic scenarios, where the number of
categories may increase boundlessly. In this paper, we present a novel open-set
semantic instance segmentation approach capable of segmenting all known and
unknown object classes in images, based on the output of an object detector
trained on known object classes. We formulate the problem using a Bayesian
framework, where the posterior distribution is approximated with a simulated
annealing optimization equipped with an efficient image partition sampler. We
show empirically that our method is competitive with state-of-the-art
supervised methods on known classes, but also performs well on unknown classes
when compared with unsupervised methods.


  In this paper, a coaxial pulsed plasma thruster was designed, built, and
tested. This design confirmed PPT operation at substantially reduced discharge
voltages of 100 to 200 V across the discharge plasma at a discharge current
level of 10.4 kA similar to magnetoplasmadynamic arcs. The PPT cathode was
imaged with an ICCD camera over a wide range of pressures, and the photos
indicated "spotless" diffuse arc attachment to the cathode at higher pressures,
and the appearance of conventional cathode spots at lower pressures. Cathode
erosion rate measurements support the conclusion of "spotless" operation at
higher pressures and the presence of cathode spots at lower pressures (the
erosion rates of 16.194 ug/C and 40.785 ug/C were measured at 4 Torr and 3.5e-5
Torr, respectively).


  EUV images of solar flares provided by the Atmospheric Imaging Assembly
onboard the Solar Dynamics Observatory (SDO/AIA) are often affected by
saturation effects in their core, physically most interesting region. We
introduce an image reconstruction procedure that allows recovering information
in the primary saturation domain using the secondary images produced by the
diffraction fringes as input data. Such a procedure is based on standard
image-processing tools like correlation, convolution, and back-projection. Its
effectiveness is tested in the case of SDO/AIA observations of the July 8 2013
flaring event.


  Inspired by recent strides in empirical efficacy of implicit learning in many
robotics tasks, we seek to understand the theoretical benefits of implicit
formulations in the face of nearly discontinuous functions, common
characteristics for systems that make and break contact with the environment
such as in legged locomotion and manipulation. We present and motivate three
formulations for learning a function: one explicit and two implicit. We derive
generalization bounds for each of these three approaches, exposing where
explicit and implicit methods alike based on prediction error losses typically
fail to produce tight bounds, in contrast to other implicit methods with
violation-based loss definitions that can be fundamentally more robust to steep
slopes. Furthermore, we demonstrate that this violation implicit loss can
tightly bound graph distance, a quantity that often has physical roots and
handles noise in inputs and outputs alike, instead of prediction losses which
consider output noise only. Our insights into the generalizability and physical
relevance of violation implicit formulations match evidence from prior works
and are validated through a toy problem, inspired by rigid-contact models and
referenced throughout our theoretical analysis.


  We develop numerical multiscale methods for viscous boundary layer flow. The
goal is to derive effective boundary conditions, or wall laws, through high
resolution simulations localized to the boundary coupled to a coarser
simulation in the domain interior. The multiscale framework is analyzed in the
context of laminar flow over a rough boundary. Asymptotic analysis shows that,
up to a small perturbation, the method recovers the slip constant in the wall
law derived from periodic homogenization theory. Numerical experiments
illustrate the utility of the method for more general roughness patterns and
fair field flow conditions.


  Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) is a momentum version of
stochastic gradient descent with properly injected Gaussian noise to find a
global minimum. In this paper, non-asymptotic convergence analysis of SGHMC is
given in the context of non-convex optimization, where subsampling techniques
are used over an i.i.d dataset for gradient updates. Our results complement
those of [RRT17] and improve on those of [GGZ18].


  This paper is about reaching EPICS with the DELTA Control System and other
projects in the DELTA Control System.


  In this paper, we address the question of comparison between populations of
trees. We study an statistical test based on the distance between empirical
mean trees, as an analog of the two sample z statistic for comparing two means.
Despite its simplicity, we can report that the test is quite powerful to
separate distributions with different means but it does not distinguish between
different populations with the same mean, a more complicated test should be
applied in that setting. The performance of the test is studied via simulations
on Galton-Watson branching processes. We also show an application to a real
data problem in genomics.


  Amateur contributions to professional publications have increased
exponentially over the last decades in the field of Planetary Astronomy. Here
we review the different domains of the field in which collaborations between
professional and amateur astronomers are effective and regularly lead to
scientific publications. We discuss the instruments, detectors, softwares and
methodologies typically used by amateur astronomers to collect the scientific
data in the different domains of interest. Amateur contributions to the
monitoring of planets and interplanetary matter, characterization of asteroids
and comets, as well as the determination of the physical properties of Kuiper
Belt Objects and exoplanets are discussed.


  In the machine learning algorithms, the choice of the hyperparameter is often
an art more than a science, requiring labor-intensive search with expert
experience. Therefore, automation on hyperparameter optimization to exclude
human intervention is a great appeal, especially for the black-box functions.
Recently, there have been increasing demands of solving such concealed tasks
for better generalization, though the task-dependent issue is not easy to
solve. The Black-Box Optimization challenge (NeurIPS 2020) required competitors
to build a robust black-box optimizer across different domains of standard
machine learning problems. This paper describes the approach of team KAIST OSI
in a step-wise manner, which outperforms the baseline algorithms by up to
+20.39%. We first strengthen the local Bayesian search under the concept of
region reliability. Then, we design a combinatorial kernel for a Gaussian
process kernel. In a similar vein, we combine the methodology of Bayesian and
multi-armed bandit,(MAB) approach to select the values with the consideration
of the variable types; the real and integer variables are with Bayesian, while
the boolean and categorical variables are with MAB. Empirical evaluations
demonstrate that our method outperforms the existing methods across different
tasks.


  Protein binding and function often involves conformational changes. Advanced
NMR experiments indicate that these conformational changes can occur in the
absence of ligand molecules (or with bound ligands), and that the ligands may
'select' protein conformations for binding (or unbinding). In this review, we
argue that this conformational selection requires transition times for ligand
binding and unbinding that are small compared to the dwell times of proteins in
different conformations, which is plausible for small ligand molecules. Such a
separation of timescales leads to a decoupling and temporal ordering of
binding/unbinding events and conformational changes. We propose that
conformational-selection and induced-change processes (such as induced fit) are
two sides of the same coin, because the temporal ordering is reversed in
binding and unbinding direction. Conformational-selection processes can be
characterized by a conformational excitation that occurs prior to a binding or
unbinding event, while induced-change processes exhibit a characteristic
conformational relaxation that occurs after a binding or unbinding event. We
discuss how the ordering of events can be determined from relaxation rates and
effective on- and off-rates determined in mixing experiments, and from the
conformational exchange rates measured in advanced NMR or single-molecule FRET
experiments. For larger ligand molecules such as peptides, conformational
changes and binding events can be intricately coupled and exhibit aspects of
conformational-selection and induced-change processes in both binding and
unbinding direction.


  In imperfect-information games, subgame solving is significantly more
challenging than in perfect-information games, but in the last few years, such
techniques have been developed. They were the key ingredient to the milestone
of superhuman play in no-limit Texas hold'em poker. Current subgame-solving
techniques analyze the entire common-knowledge closure of the player's current
information set, that is, the smallest set of nodes within which it is common
knowledge that the current node lies. While this is acceptable in games like
poker where the common-knowledge closure is relatively small, many practical
games have more complex information structure, which renders the
common-knowledge closure impractically large to enumerate or even reasonably
approximate. We introduce an approach that overcomes this obstacle, by instead
working with only low-order knowledge. Our approach allows an agent, upon
arriving at an infoset, to basically prune any node that is no longer
reachable, thereby massively reducing the game tree size relative to the
common-knowledge subgame. We prove that, as is, our approach can increase
exploitability compared to the blueprint strategy. However, we develop three
avenues by which safety can be guaranteed. Even without the safety-guaranteeing
additions, experiments on medium-sized games show that our approach always
reduced exploitability in practical games even when applied at every infoset,
and a depth-limited version of it led to -- to our knowledge -- the first
strong AI for the challenge problem dark chess.


  A model of two oscillating pendula placed on a mobile support is studied.
Once an overall scheme of equations, under general assumptions, is formulated
via the Lagrangian equations of motion, the specific case of absence of
escapement is examined. The mechanical models consists of two coupled pendula
both oscillating on a moving board attached to a spring. The final result
performs a selection among the peculiar parameters of the physical process
(lenghts, ratio of masses, friction and damping coefficients, stiffness of the
spring) which provide a tendency to synchronization.


  We consider the problem of computing the closest stable/unstable non-negative
matrix to a given real matrix. This problem is important in the study of linear
dynamical systems, numerical methods, etc. The distance between matrices is
measured in the Frobenius norm. The problem is addressed for two types of
stability: the Schur stability (the matrix is stable if its spectral radius is
smaller than one) and the Hurwitz stability (the matrix is stable if its
spectral abscissa is negative). We show that the closest unstable matrix can
always be explicitly found. For the closest stable matrix, we present an
iterative algorithm which converges to a local minimum with a linear rate. It
is shown that the total number of local minima can be exponential in the
dimension. Numerical results and the complexity estimates are presented.


  We critically assess to what extent it makes sense to bound the Wilson
coefficients of dimension-six operators. In the context of Higgs physics, we
establish that a closely related observable, $c_H$, is well-defined and
satisfies a two-sided bound. $c_H$ is derived from the low momentum expansion
of the scattering amplitude, or the derivative of the amplitude at the origin
with respect to the Mandelstam variable $s$, expressed as $M(H_iH_i\rightarrow
H_jH_j)=c_H s +O(g_\text{SM}, s^{-2})$ where $g_\text{SM}$ represents all
Standard Model couplings. This observable is non-dispersive and, as a result,
not sign-definite. We also determine the conditions under which the bound on
$c_H$ is equivalent to a bound on the dimension-six operator $O_H=\partial|
H|^2 \partial |H|^2$.


  Sampling efficiency in a highly constrained environment has long been a major
challenge for sampling-based planners. In this work, we propose
Rapidly-exploring Random disjointed-Trees* (RRdT*), an incremental optimal
multi-query planner. RRdT* uses multiple disjointed-trees to exploit
local-connectivity of spaces via Markov Chain random sampling, which utilises
neighbourhood information derived from previous successful and failed samples.
To balance local exploitation, RRdT* actively explore unseen global spaces when
local-connectivity exploitation is unsuccessful. The active trade-off between
local exploitation and global exploration is formulated as a multi-armed bandit
problem. We argue that the active balancing of global exploration and local
exploitation is the key to improving sample efficient in sampling-based motion
planners. We provide rigorous proofs of completeness and optimal convergence
for this novel approach. Furthermore, we demonstrate experimentally the
effectiveness of RRdT*'s locally exploring trees in granting improved
visibility for planning. Consequently, RRdT* outperforms existing
state-of-the-art incremental planners, especially in highly constrained
environments.


  Consider a point set D with a measure function w : D -> R. Let A be the set
of subsets of D induced by containment in a shape from some geometric family
(e.g. axis-aligned rectangles, half planes, balls, k-oriented polygons). We say
a range space (D, A) has an eps-approximation P if max {R \in A} | w(R \cap
P)/w(P) - w(R \cap D)/w(D) | <= eps. We describe algorithms for
deterministically constructing discrete eps-approximations for continuous point
sets such as distributions or terrains. Furthermore, for certain families of
subsets A, such as those described by axis-aligned rectangles, we reduce the
size of the eps-approximations by almost a square root from O(1/eps^2 log
1/eps) to O(1/eps polylog 1/eps). This is often the first step in transforming
a continuous problem into a discrete one for which combinatorial techniques can
be applied. We describe applications of this result in geo-spatial analysis,
biosurveillance, and sensor networks.


  With the growing computer vision applications, lots of videos are transmitted
for content analysis, the way to allocate resources can affect the performance
of video content analysis. For this purpose, the traditional resource
allocation schemes for video transmission in vehicular networks, such as
qualityof-service (QoS) based or quality-of-experience (QoE) based schemes, are
no longer optimal anymore. In this paper, we propose an efficient content
driven resource allocation scheme for vehicles equipped with cameras under
bandwidth constraints in order to improve the video content analysis
performance. The proposed resource allocation scheme is based on maximizing the
quality-of-content (QoC), which is related to the content analysis performance.
A QoC based assessment model is first proposed. Then, the resource allocation
problem is converted to a solvable convex optimization problem. Finally,
simulation results show the better performance of our proposed scheme than the
existing schemes like QoE based schemes.


  The laws of thermodynamics put limits to the efficiencies of thermal
machines. Analogues of these laws are now established for quantum engines
weakly and passively coupled to the environment providing a framework to find
improvements to their performance. Systems whose interaction with the
environment is actively controlled do not fall in that framework. Here we
consider systems actively and locally coupled to the environment, evolving with
a so-called boundary-driven Lindblad equation. Starting from a unitary
description of the system plus the environment we simultaneously obtain the
Lindblad equation and the appropriate expressions for heat, work and
entropy-production of the system extending the framework for the analysis of
new, and some already proposed, quantum heat engines. We illustrate our
findings in spin 1/2 chains and explain why an XX chain coupled in this way to
a single heat bath relaxes to thermodynamic-equilibrium while and XY chain does
not. Additionally, we show that an XX chain coupled to a left and a right heat
baths behaves as a quantum engine, a heater or refrigerator depending on the
parameters, with efficiencies bounded by Carnot efficiencies.


  We investigate exclusive electroproduction of $\pi^-\Delta^{++}$ within the
handbag approach in which the helicity amplitudes factorize into generalized
parton distributions (GPDs) and hard partonic subprocesses. We define the
$p-\Delta$ transversity GPDs while the helicity non-flip GPDs are taken from
the literature. For the numerical estimates of observables we utilize
large-$N_C$ results in order to relate a few of the $p-\Delta$ GPDs to the
proton-proton ones and neglect all other GPDs. In the calculation of the
twist-2 and twist-3 subprocess amplitudes we take into account quark transverse
momenta in combination with Sudakov suppressions. The partial cross sections
for $\gamma^*p\to \pi^-\Delta^{++}$ are predicted in the large-$N_C$ limit.


  The phase space reduction of Schwarzschild black holes by Thiemann and
Kastrup and by Kucha\v{r} is reexamined from a different perspective on gauge
freedom. This perspective introduces additional gauge transformations which
correspond to asymptotically nontrivial diffeomorphisms. Various subtleties
concerning variational principles for asymptotically flat systems are addressed
which allow us to avoid the usual conclusion that treating such transformations
as gauge implies the vanishing of corresponding total charges. Instead,
superselection rules are found for the (nonvanishing) ADM mass at the
asymptotic boundaries. The addition of phenomenological clocks at each
asymptotic boundary is also studied and compared with the `parametrization
clocks' of Kucha\v{r}.


  Neural network-based driving planners have shown great promises in improving
task performance of autonomous driving. However, it is critical and yet very
challenging to ensure the safety of systems with neural network based
components, especially in dense and highly interactive traffic environments. In
this work, we propose a safety-driven interactive planning framework for neural
network-based lane changing. To prevent over conservative planning, we identify
the driving behavior of surrounding vehicles and assess their aggressiveness,
and then adapt the planned trajectory for the ego vehicle accordingly in an
interactive manner. The ego vehicle can proceed to change lanes if a safe
evasion trajectory exists even in the predicted worst case; otherwise, it can
stay around the current lateral position or return back to the original lane.
We quantitatively demonstrate the effectiveness of our planner design and its
advantage over baseline methods through extensive simulations with diverse and
comprehensive experimental settings, as well as in real-world scenarios
collected by an autonomous vehicle company.


  Many coupled evolution equations can be described via $2\times2$-block
operator matrices of the form $\mathcal{A}=\begin{bmatrix} A & B \\ C & D
\end{bmatrix}$ in a product space $X=X_1\times X_2$ with possibly unbounded
entries. Here, the case of diagonally dominant block operator matrices is
considered, that is, the case where the full operator $\mathcal{A}$ can be seen
as a relatively bounded perturbation of its diagonal part with
$\mathsf{D}(\mathcal{A})=\mathsf{D}(A)\times \mathsf{D}(D)$ though with
possibly large relative bound. For such operators the properties of
sectoriality, $\mathcal{R}$-sectoriality and the boundedness of the
$H^\infty$-calculus are studied, and for these properties perturbation results
for possibly large but structured perturbations are derived. Thereby, the time
dependent parabolic problem associated with $\mathcal{A}$ can be analyzed in
maximal $L^p_t$-regularity spaces, and this is applied to a wide range of
problems such as different theories for liquid crystals, an artificial Stokes
system, strongly damped wave and plate equations, and a Keller-Segel model.


  We study the edge-coloring problem in simple $n$-vertex $m$-edge graphs with
maximum degree $\Delta$. This is one of the most classical and fundamental
graph-algorithmic problems. Vizing's celebrated theorem provides
$(\Delta+1)$-edge-coloring in $O(m\cdot n)$ deterministic time. This running
time was improved to $O\left(m\cdot\min\left\{\Delta\cdot\log
n,\sqrt{n}\right\}\right)$, and very recently to randomized
$\tilde{O}\left(m\cdot n^{1/3}\right)$. A randomized
$(1+\varepsilon)\Delta$-edge-coloring algorithm can be computed in
$O\left(m\cdot\frac{\log^6 n}{\varepsilon^2}\right)$ time, and for large values
of $\Delta$, this task requires randomized
$O\left(\frac{m\cdot\log\varepsilon^{-1}}{\varepsilon^2}\right)$ time. It was
however open if there exists a deterministic near-linear time algorithm for
this basic problem. We devise a simple deterministic
$(1+\varepsilon)\Delta$-edge-coloring algorithm with running time
$O\left(m\cdot\frac{\log n}{\varepsilon}\right)$. A randomized variant of our
algorithm has running time
$O(m\cdot(\varepsilon^{-18}+\log(\varepsilon\cdot\Delta)))$.
  We also study edge-coloring of graphs with arboricity at most $\alpha$. A
randomized computation of $(\Delta+1)$-edge-coloring requires
$\tilde{O}\left(\min\{m\cdot\sqrt{n},m\cdot\Delta\}\cdot\frac{\alpha}{\Delta}\right)$
time. Deterministically, this task can be done in
$O\left(m\cdot\alpha^7\cdot\log n\right)$ time. However, for large values of
$\alpha$, these algorithms require super-linear time. We devise a deterministic
$(\Delta+\varepsilon\alpha)$-edge-coloring algorithm with running time
$O\left(\frac{m\cdot\log n}{\varepsilon^7}\right)$. A randomized version of our
algorithm requires $O\left(\frac{m\cdot\log n}{\varepsilon}\right)$ expected
time.
  Our algorithm is based on a novel two-way degree-splitting, which we devise
in this paper. We believe that this technique is of independent interest.


  This paper is a translation of the paper "Idealtheorie in Ringbereichen",
written by Emmy Noether in 1920, from the original German into English. It in
particular brings the language used into the modern world so that it is easily
understandable by the mathematicians of today.
  The paper itself deals with ideal theory, and was revolutionary in its field,
that is modern algebra. Topics covered include: the representation of an ideal
as the least common multiple of irreducible ideals; the representation of an
ideal as the least common multiple of maximal primary ideals; the association
of prime ideals with primary ideals; the representation of an ideal as the
least common multiple of relatively prime irreducible ideals; isolated ideals;
the representation of an ideal as the product of coprime irreducible ideals;
equivalent concepts regarding modules.


  Subseasonal forecasting -- predicting temperature and precipitation 2 to 6
weeks ahead -- is critical for effective water allocation, wildfire management,
and drought and flood mitigation. Recent international research efforts have
advanced the subseasonal capabilities of operational dynamical models, yet
temperature and precipitation prediction skills remain poor, partly due to
stubborn errors in representing atmospheric dynamics and physics inside
dynamical models. Here, to counter these errors, we introduce an adaptive bias
correction (ABC) method that combines state-of-the-art dynamical forecasts with
observations using machine learning. We show that, when applied to the leading
subseasonal model from the European Centre for Medium-Range Weather Forecasts
(ECMWF), ABC improves temperature forecasting skill by 60-90% (over baseline
skills of 0.18-0.25) and precipitation forecasting skill by 40-69% (over
baseline skills of 0.11-0.15) in the contiguous U.S. We couple these
performance improvements with a practical workflow to explain ABC skill gains
and identify higher-skill windows of opportunity based on specific climate
conditions.


  A real valued function $f$ defined on a convex $K$ is anemconvex function iff
it satisfies $$ f((x+y)/2) \le (f(x)+f(y))/2 + 1. $$ A thorough study of
approximately convex functions is made. The principal results are a sharp
universal upper bound for lower semi-continuous approximately convex functions
that vanish on the vertices of a simplex and an explicit description of the
unique largest bounded approximately convex function~$E$ vanishing on the
vertices of a simplex.
  A set $A$ in a normed space is an approximately convex set iff for all
$a,b\in A$ the distance of the midpoint $(a+b)/2$ to $A$ is $\le 1$. The bounds
on approximately convex functions are used to show that in $\R^n$ with the
Euclidean norm, for any approximately convex set $A$, any point $z$ of the
convex hull of $A$ is at a distance of at most
$[\log_2(n-1)]+1+(n-1)/2^{[\log_2(n-1)]}$ from $A$. Examples are given to show
this is the sharp bound. Bounds for general norms on $R^n$ are also given.


  Information fusion deals with the integration and merging of data and
information from multiple (heterogeneous) sources. In many cases, the
information that needs to be fused has security classification. The result of
the fusion process is then by necessity restricted with the strictest
information security classification of the inputs. This has severe drawbacks
and limits the possible dissemination of the fusion results. It leads to
decreased situational awareness: the organization knows information that would
enable a better situation picture, but since parts of the information is
restricted, it is not possible to distribute the most correct situational
information. In this paper, we take steps towards defining fusion and data
mining processes that can be used even when all the underlying data that was
used cannot be disseminated. The method we propose here could be used to
produce a classifier where all the sensitive information has been removed and
where it can be shown that an antagonist cannot even in principle obtain
knowledge about the classified information by using the classifier or situation
picture.


  We present a novel experimental approach to Bose-Einstein condensation by
increasing the particle number of the system at almost constant temperature. In
particular the emergence of a new condensate is observed in multi-component F=1
spinor condensates of 87-Rb. Furthermore we develop a simple rate-equation
model for multi-component BEC thermodynamics at finite temperature which well
reproduces the measured effects.


  We seek to provide an interpretable framework for segmenting users in a
population for personalized decision-making. We propose a general methodology,
Market Segmentation Trees (MSTs), for learning market segmentations explicitly
driven by identifying differences in user response patterns. To demonstrate the
versatility of our methodology, we design two new, specialized MST algorithms:
(i) Choice Model Trees (CMTs), which can be used to predict a user's choice
amongst multiple options and (ii) Isotonic Regression Trees (IRTs), which can
be used to solve the bid landscape forecasting problem. We provide a
theoretical analysis of the asymptotic running times of our algorithmic
methods, which validates their computational tractability on large datasets. We
also provide a customizable, open-source code base for training MSTs in Python
which employs several strategies for scalability, including parallel processing
and warm starts. Finally, we assess the practical performance of MSTs on
several synthetic and real world datasets, showing that our method reliably
finds market segmentations which accurately model response behavior.


  Pursuit-evasion differential games using the Disturbance Attenuation approach
are revisited. Under this approach, the pursuer actions are considered to be
control actions, whereas all external actions, such as target maneuvers,
measurement errors and initial position uncertainties, are considered to be
disturbances. Two open issues have been addressed, namely the effect of noise
on the control gains, and the effect of trajectory shaping on the solution.
These issues are closely related to the question of the best choice for the
disturbance attenuation ratio. Detailed analyses are performed for two simple
pursuit-evasion cases: a Simple Boat Guidance Problem and Missile Guidance
Engagement.


  A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in
the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming
energy efficient clusters in a self-organized fashion while restricting
interference to Primary User (PU) systems. The spectrum-aware clustered
structure is presented where the communications consist of intra-cluster
aggregation and inter-cluster relaying. In order to save communication power,
the optimal number of clusters is derived and the idea of groupwise constrained
clustering is introduced to minimize intra-cluster distance under
spectrum-aware constraint. In terms of practical implementation, DSAC
demonstrates preferable scalability and stability because of its low complexity
and quick convergence under dynamic PU activity. Finally, simulation results
are given to validate the proposed scheme.