  Most combinations of NLP tasks and language varieties lack in-domain examples
for supervised training because of the paucity of annotated data. How can
neural models make sample-efficient generalizations from task-language
combinations with available data to low-resource ones? In this work, we propose
a Bayesian generative model for the space of neural parameters. We assume that
this space can be factorized into latent variables for each language and each
task. We infer the posteriors over such latent variables based on data from
seen task-language combinations through variational inference. This enables
zero-shot classification on unseen combinations at prediction time. For
instance, given training data for named entity recognition (NER) in Vietnamese
and for part-of-speech (POS) tagging in Wolof, our model can perform accurate
predictions for NER in Wolof. In particular, we experiment with a typologically
diverse sample of 33 languages from 4 continents and 11 families, and show that
our model yields comparable or better results than state-of-the-art, zero-shot
cross-lingual transfer methods. Moreover, we demonstrate that approximate
Bayesian model averaging results in smoother predictive distributions, whose
entropy inversely correlates with accuracy. Hence, the proposed framework also
offers robust estimates of prediction uncertainty. Our code is located at
github.com/cambridgeltl/parameter-factorization


  We analyze non-Fermi liquid (NFL) properties along a line of critical points
in a two-channel Anderson model with phonon-assisted hybridizations. We succeed
in identifying hidden nonmagnetic SO(5) degrees of freedom for
valence-fluctuation regime and analyze the model on the basis of boundary
conformal field theory. We find that the NFL spectra along the critical line,
which is the same as those in the two-channel Kondo model, can be alternatively
derived by a fusion in the nonmagnetic SO(5) sector. The leading irrelevant
operators near the NFL fixed points vary as a function of Coulomb repulsion U;
operators in the spin sector dominate for large U, while those in the SO(5)
sector do for small U, and we confirm this variation in our numerical
renormalization group calculations. As a result, the thermodynamic singularity
for small U differs from that of the conventional two-channel Kondo problem.
Especially, the impurity contribution to specific heat is proportional to
temperature and bipolaron fluctuations, which are coupled electron-phonon
fluctuations, diverge logarithmically at low temperatures for small U.


  It is widely believed that the curvature singularities are an artifact of
general relativity and may not exist in the Universe, and likely to be defined
by the quantum gravity. In the absence of successful quantum gravity,
significant attention has been shifted towards the regular models. We present a
five-dimensional ($5D$) rotating regular Myers-Perry like black hole metric
with two rotation parameters ($a,b$) and a free parameter ($k$). Our $5D$
rotating regular metric encompasses $5D$ Myers-Perry black hole which can be
recovered as a special case when $k=0$. The size of the shadow cast by the
black holes is significantly smaller and more distorted when the parameter $k$
is increased.


  The notion of standard positive probability distribution function (tomogram)
which describes the quantum state of universe alternatively to wave function or
to density matrix is introduced. Connection of the tomographic probability
distribution with the Wigner function of the universe and with the star-product
(deformation) quantization procedure is established.
  Using the Radon transform the Wheeler-De Witt generic equation for the
probability function is written in tomographic form. Some examples of the
Wheeler-DeWitt equation in the minisuperspace are elaborated explicitly for a
homogeneous isotropic cosmological models. Some interpretational aspects of the
probability description of the quantum state are discussed.


  We analyze the sensitivity of gravitational-wave antenna with stable double
optical spring created by two independent pumps. We investigate regime of three
close eigen frequencies (roots of characteristic equation) which appears to
provide more wide frequency band in which sensitivity of antenna can beat
Standard Quantum Limit (SQL) than previously considered regime with two close
eigen frequencies. We take into account optical losses and show that they do
not degrade sensitivity significantly. We also demonstrate possible application
of considered regime to Einstein Telescope.


  We review the basic properties of the Haldane phase in spin-1 Heisenberg
antiferromagnetic chains, including its persistence in quasi-one-dimensional
geometries. Using large-scale numerical simulations, we map out the phase
diagram for a realistic model applicable to experimental Haldane compounds. We
also investigate the effect of different chain coupling geometries and confirm
a general mean field universality of the critical coupling times the
coordination number of the lattice. Inspired by the recent development of
characterization of symmetry protected topological states, of which the Haldane
phase of spin-1 Heisenberg antiferromagnetic chain is a preeminent example, we
provide direct evidence that the quasi-one-dimensional Haldane phase is indeed
a non-trivial symmetry protected topological state.


  We present the invited lectures given at the second Asia-Europe-Pacific
School of High-Energy Physics (AEPSHEP), which took place in Puri, India in
November 2014. The series of lectures aimed at graduate students in particle
experiment/theory, covering the very basics of flavor physics and CP violation,
some useful theoretical methods such as OPE and effective field theories, and
some selected topics of flavour physics in the era of LHC.


  The development and spread of information and communication technologies have
increased and diversified information. However, the increase in the volume and
the selection of information does not necessarily promote understanding. In
addition, conventional evaluations of information transfer have focused only on
the arrival of information to the receivers. They need to sufficiently take
into account the receivers' understanding of the information after it has been
acquired, which is the original purpose of the evaluation. In this study, we
propose the concept of "information digestion," which refers to the receivers'
correct understanding of the acquired information, its contents, and its
purpose. In the experiment, we proposed an evaluation model of information
digestibility using hierarchical factor analysis and extracted factors that
constitute digestibility by four types of media.


  Graph neural networks (GNNs) have been used to tackle the few-shot learning
(FSL) problem and shown great potentials under the transductive setting.
However under the inductive setting, existing GNN based methods are less
competitive. This is because they use an instance GNN as a label
propagation/classification module, which is jointly meta-learned with a feature
embedding network. This design is problematic because the classifier needs to
adapt quickly to new tasks while the embedding does not. To overcome this
problem, in this paper we propose a novel hybrid GNN (HGNN) model consisting of
two GNNs, an instance GNN and a prototype GNN. Instead of label propagation,
they act as feature embedding adaptation modules for quick adaptation of the
meta-learned feature embedding to new tasks. Importantly they are designed to
deal with a fundamental yet often neglected challenge in FSL, that is, with
only a handful of shots per class, any few-shot classifier would be sensitive
to badly sampled shots which are either outliers or can cause inter-class
distribution overlapping. %Our two GNNs are designed to address these two types
of poorly sampled few-shots respectively and their complementarity is exploited
in the hybrid GNN model. Extensive experiments show that our HGNN obtains new
state-of-the-art on three FSL benchmarks.


  We investigate the applicability of the formalism of quantum mechanics to
everyday life. It seems to be directly relevant for situations in which the
very act of coming to a conclusion or decision on one issue affects one's
confidence about conclusions or decisions on another issue. Lie algebra theory
is argued to be a very useful tool in guiding the construction of quantum
descriptions of such situations. Tests, extensions and speculative applications
and implications, including for the encoding of thoughts in neural networks,
are discussed. It is suggested that the recognition and incorporation of such
mathematical structure into machine learning and artificial intelligence might
lead to significant efficiency and generality gains in addition to ensuring
probabilistic reasoning at a fundamental level.


  Lijiang 2.4-meter Telescope(LJT), the largest common-purpose optical
telescope in China, has been applied to the world-wide astronomers since 2008.
It is located at Gaomeigu site, Lijiang Observatory(LJO), the southwest of
China. The site has very good observational conditions. Since 10-year
operation, several instruments have been equipped on the LJT. Astronomers can
perform both photometric and spectral observations. The main scientific goals
of LJT include photometric and spectral evolution of supernova, reverberation
mapping of active galactic nucleus, physical properties of binary star and
near-earth object(comet and asteroid), identification of exoplanet, and all
kinds of transients. Until now, the masses of 41 high accretion rate black
holes have been measured, and more than 168 supernova have been identified by
the LJT. More than 190 papers related to the LJT have been published. In this
paper, the general observation condition of the Gaomeigu site is introduced at
first. Then, the LJT structure is described in detail, including the optical,
mechanical, motion and control system. The specification of all the
instruments, and some detailed parameters of the YFOSC is also presented.
Finally, some important scientific results and future expectations are
summarized.


  We study particle motion in rotating saddle-shaped potentials. It is known
that such rotating potentials can generate bounded motion for particles with a
parabolic dispersion law through the combination of potential, centrifugal and
Coriolis forces in the rotating frame. When applied to massless Dirac
particles, for example electrons in graphene, such a potential is shown to lead
to eigenstates that are spatially localized near the center of the saddle at
certain energies. Although other states also exist at these energies, they have
non-overlapping support in the oscillator basis, which tend to give the
localized states a substantial life-time also when imperfections are present.


  Experimental results on c- and b-quark fragmentation are reviewed. The
discussion is concentrated on measurements of heavy-quark fragmentation
functions and fragmentation fractions. Measurements of various heavy-quark
fragmentation ratios are also discussed. The experimental results are compared
with theoretical expectations and model predictions.


  The single spike is a rigidly rotating classical string configuration closely
related to the giant magnon. We calculate bosonic and fermionic modes of this
solution, from which we see that it is not supersymmetric. It can be viewed as
an excitation above a hoop of string wound around the equator, in the same
sense that the magnon is an excitation above an orbiting point particle. We
find the operator which plays the role of the Hamiltonian for this sector,
which compared to the magnon's E-J has the angular momentum replaced by a
winding charge. The single spike solution is unstable, and we use the modes to
attempt a semi-classical computation of its lifetime.


  We give in this paper a convergence result concerning parallel synchronous
algorithm for nonlinear fixed point problems with respect to the euclidian norm
in $\Rn$. We then apply this result to some problems related to convex analysis
like minimization of functionals, calculus of saddle point, convex
programming...


  High-dimensional probability density estimation for inference suffers from
the "curse of dimensionality". For many physical inference problems, the full
posterior distribution is unwieldy and seldom used in practice. Instead, we
propose direct estimation of lower-dimensional marginal distributions,
bypassing high-dimensional density estimation or high-dimensional Markov chain
Monte Carlo (MCMC) sampling. By evaluating the two-dimensional marginal
posteriors we can unveil the full-dimensional parameter covariance structure.
We additionally propose constructing a simple hierarchy of fast neural
regression models, called Moment Networks, that compute increasing moments of
any desired lower-dimensional marginal posterior density; these reproduce exact
results from analytic posteriors and those obtained from Masked Autoregressive
Flows. We demonstrate marginal posterior density estimation using
high-dimensional LIGO-like gravitational wave time series and describe
applications for problems of fundamental cosmology.


  In this paper we operate under the assumption that no tensors from inflation
will be measured in the future by the dedicated experiments and argue that,
while for single-field slow-roll models of inflation the running of the
spectral index will be hard to be detected, in multi-field models the running
can be large due to its strong correlation with non-Gaussianity. A detection of
the running might therefore be related to the presence of more than one active
scalar degree of freedom during inflation.


  Noncommutative geometry is one of the quantum gravity theories, which various
researchers have been using to describe different physical and astrophysical
systems. However, so far, no direct observations can justify its existence, and
this theory remains a hypothesis. On the other hand, over the past two decades,
more than a dozen over-luminous type Ia supernovae have been observed, which
indirectly predict that they originate from white dwarfs with
super-Chandrasekhar masses $2.1-2.8 \rm\,M_\odot$. In this article, we discuss
that considering white dwarfs as squashed fuzzy spheres, a class of
noncommutative geometry, helps in accumulating more mass than the Chandrasekhar
mass-limit. The length-scale beyond which the effect of noncommutativity
becomes prominent is an emergent phenomenon, which depends only on the
inter-electron separations in the white dwarf.


  The extreme value statistics of active matter offer significant insight into
their unique properties. A phase transition has recently been reported in a
model of branching run-and-tumble particles, describing the spatial spreading
of an evolving colony of active matter in one-dimension. In a "persistent"
phase, the particles form macroscopic robust clusters that ballistically
propagate as a whole while in an "intermittent" phase, particles are isolated
instead. We focus our study on the fluctuations of the rightmost position
$x_{\max}(t)$ reached by time $t$ for this model. At long time, as the colony
progressively invades the unexplored region, the cumulative probability of
$x_{\max}(t)$ is described by a travelling front. The transition has a
remarkable impact on this front. In the intermittent phase it is qualitatively
similar to the front satisfying the Fisher-KPP equation, which famously
describes the extreme value statistics of the non-active branching Brownian
motion. A dramatically different behaviour appears in the persistent phase,
where activity imparts the front with unexpected and unusual features which we
compute exactly.


  Full-duplex small-cell relays with multiple antennas constitute a core
element of the envisioned 5G network architecture. In this paper, we use
stochastic geometry to analyze the performance of wireless networks with
full-duplex multiple-antenna small cells, with particular emphasis on the
probability of successful transmission. To achieve this goal, we additionally
characterize the distribution of the self-interference power of the full-duplex
nodes. The proposed framework reveals useful insights on the benefits of
full-duplex with respect to half-duplex in terms of network throughput.


  This paper presents a novel latent variable recurrent neural network
architecture for jointly modeling sequences of words and (possibly latent)
discourse relations between adjacent sentences. A recurrent neural network
generates individual words, thus reaping the benefits of
discriminatively-trained vector representations. The discourse relations are
represented with a latent variable, which can be predicted or marginalized,
depending on the task. The resulting model can therefore employ a training
objective that includes not only discourse relation classification, but also
word prediction. As a result, it outperforms state-of-the-art alternatives for
two tasks: implicit discourse relation classification in the Penn Discourse
Treebank, and dialog act classification in the Switchboard corpus. Furthermore,
by marginalizing over latent discourse relations at test time, we obtain a
discourse informed language model, which improves over a strong LSTM baseline.


  In this Research Note we report on an open-source compiler for the Bluespec
hardware description language.


  We calculate nuclear matrix elements (NME) of neutrinoless double beta decay
in four different candidate nuclei (Ge-76, Se-82, Mo-100, Te-130) within the
quasiparticle random phase approximation (QRPA) and its uncertainties. We
assume (up to) four coexisting mechanisms for neutrinoless double beta decay,
mediated by light Majorana neutrino exchange, heavy Majorana neutrino exchange,
R-parity breaking supersymmetry, and squark-neutrino, interfering either
constructively or destructively with each other. We find that, unfortunately,
current NME uncertainties appear to prevent a robust determination of the
relative contribution of each mechanism to the decay amplitude, even assuming
accurate measurements of decay lifetimes. The near-degeneracy of the decay
mechanisms is analyzed with simple algebraic techniques, which do not involve
assumptions about the statistical distribution of errors. We discuss
implications of such degeneracy on prospective searches for absolute neutrino
masses.


  We extend the constrained maximum likelihood estimation theory for parameters
of a completely identified model, proposed by Aitchison and Silvey (1958), to
parameters arising from a partially identified model. With a partially
identified model, some parameters of the model may only be identified through
constraints imposed by additional assumptions. We show that, under certain
conditions, the constrained maximum likelihood estimator exists and locally
maximize the likelihood function subject to constraints. We then study the
asymptotic distribution of the estimator and propose a numerical algorithm for
estimating parameters. We also discuss a special situation where exploiting
additional assumptions does not improve estimation efficiency.


  We show explicitly that four neutrino models of the 2+2 variety still provide
an acceptable global fit to the solar, atmospheric and LSND neutrino data. The
goodness of fit, defined in the usual way, is found to be 0.26 for the simplest
such model. That is, we find that there is a 26% probability of obtaining a
worse global fit to the neutrino data We also make some specific comments on
the paper, ``Ruling out four-neutrino oscillation interpretations of the LSND
anomaly'' [hep-ph/0207157], and explain why they reached drastically different
conclusions.


  The Einstein Equivalence Principle (EEP) can be probed with astrophysical
sources emitting simultaneously different types of neutral particles, or
particles with varying energies, by testing their time of flight through the
same gravitational field. Here we use the time delays between correlated
photons from cosmological transients to constrain the accuracy of the EEP. We
take data from two gamma-ray bursts as an example, and use, as a lower limit to
the theoretical time delays between different energies, delays arising from
only the gravitational field of our own galaxy. We then show that the
parameterized post-Newtonian parameter $\gamma$ is the same for photons over
energy ranges between eV and MeV and between MeV and GeV to a part in
$10^{-7}$, which is at least one order of magnitude better than previous
limits. Combining this bound on the wavelength dependence of $\gamma$ with the
absolute bound $|\gamma-1|<0.3\%$ from light-deflection measurements at optical
(eV) wavelengths, we thus extend this absolute bound on $\gamma$ to GeV
energies.


  Cabibbo-allowed two-body hadronic weak decays of bottom baryons are analyzed.
Contrary to the charmed baryon sector, many channels of bottom baryon decays
proceed only through the external or internal W-emission diagrams. Moreover,
W-exchange is likely to be suppressed in the bottom baryon sector.
Consequently, the factorization approach suffices to describe most of the
Cabibbo-allowed bottom baryon decays. We use the nonrelativistic quark model to
evaluate heavy-to-heavy and heavy-to-light baryon form factors at zero recoil.
When applied to the heavy quark limit, the quark model results do satisfy all
the constraints imposed by heavy quark symmetry. The decay rates and up-down
asymmetries for bottom baryons decaying into $(1/2)^++P(V)$ and $(3/2)^++P(V)$
are calculated. It is found that the up-down asymmetry is negative except for
$\Omega_b \to (1/2)^++P(V)$ decay and for decay modes with $\psi'$ in the final
state. The prediction $B(\Lambda_b \to J/\psi\Lambda)=1.6 \times 10^{-4}$ for
$|V_{cb}|=0.038$ is consistent with the recent CDF measurement. We also present
estimates for $\Omega_c \to (3/2)^++P(V)$ decays and compare with various model
calculations.


  Using density-functional theory, we calculate the electronic bandstructure of
single-layer graphene on top of hexagonal In_2Te_2 monolayers. The geometric
configuration with In and Te atoms at centers of carbon hexagons leads to a
Kekule' texture with an ensuing bandgap of 20 meV. The alternative structure,
nearly degenerate in energy, with the In and Te atoms on top of carbon sites is
characterized instead by gapless spectrum with the original Dirac cones of
graphene reshaped, depending on the graphene-indium chalcogenide distance,
either in the form of an undoubled pseudo-spin one Dirac cone or in a quadratic
band crossing point at the Fermi level. These electronic phases harbor charge
fractionalization and topological Mott insulating states of matter.


  Let $X=G/H$ be a reductive homogeneous space with $H$ noncompact, endowed
with a $G$-invariant pseudo-Riemannian structure. Let $L$ be a reductive
subgroup of $G$ acting properly on $X$ and $\Gamma$ a torsion-free discrete
subgroup of $L$. Under the assumption that the complexification $X_{\mathbb C}$
is $L_{\mathbb C}$-spherical, we prove an explicit correspondence between
spectral analysis on the standard locally homogeneous space
$X_{\Gamma}=\Gamma\backslash X$ and on $\Gamma\backslash L$ via branching laws
for the restriction to $L$ of irreducible representations of $G$. In
particular, we prove that the pseudo-Riemannian Laplacian on $X_{\Gamma}$ is
essentially self-adjoint, and that it admits an infinite point spectrum when
$X_{\Gamma}$ is compact or $\Gamma\subset L$ is arithmetic. The proof builds on
structural results for invariant differential operators on spherical
homogeneous spaces with overgroups.


  In recent future, vehicles will establish a spontaneous connection over a
wireless radio channel, coordinating actions and information. Vehicles will
exchange warning messages over the wireless radio channel through Dedicated
Short Range Communication (IEEE 1609) over the Wireless Access in Vehicular
Environment (802.11p). Unfortunately, the wireless communication among vehicles
is vulnerable to security threats that may lead to very serious safety hazards.
Therefore, the warning messages being exchanged must incorporate an authentic
factor such that recipient is willing to verify and accept the message in a
timely manner


  A general class of solutions of string background equations is studied and
its physical interpretations are presented. These solutions correspond to
generalizations of the standard black p-brane solutions to surfaces with
curvature k=-1,0. The relation with the recently introduced S-branes is
provided. The mass, charge, entropy and Hawking temperature are computed,
illustrating the interpretation in terms of negative tension branes. Their
cosmological interpretation is discussed as well as their potential instability
under small perturbations.


  A key encapsulation mechanism (KEM) that takes as input an arbitrary string,
i.e., a tag, is known as tag-KEM, while a scheme that combines signature and
encryption is called signcryption. In this paper, we present a code-based
signcryption tag-KEM scheme. We utilize a code-based signature and an IND-CCA2
(adaptive chosen ciphertext attack) secure version of McEliece's encryption
scheme. The proposed scheme uses an equivalent subcode as a public code for the
receiver, making the NPcompleteness of the subcode equivalence problem to be
one of our main security assumptions. We then base the signcryption tag-KEM to
design a code-based hybrid signcryption scheme. A hybrid scheme deploys
asymmetric- as well as symmetric-key encryption. We give security analyses of
both our schemes in the standard model and prove that they are secure against
IND-CCA2 (indistinguishability under adaptive chosen ciphertext attack) and
SUF-CMA (strong existential unforgeability under chosen message attack).


  In recent years, the biggest advances in major Computer Vision tasks, such as
object recognition, handwritten-digit identification, facial recognition, and
many others., have all come through the use of Convolutional Neural Networks
(CNNs). Similarly, in the domain of Natural Language Processing, Recurrent
Neural Networks (RNNs), and Long Short Term Memory networks (LSTMs) in
particular, have been crucial to some of the biggest breakthroughs in
performance for tasks such as machine translation, part-of-speech tagging,
sentiment analysis, and many others. These individual advances have greatly
benefited tasks even at the intersection of NLP and Computer Vision, and
inspired by this success, we studied some existing neural image captioning
models that have proven to work well. In this work, we study some existing
captioning models that provide near state-of-the-art performances, and try to
enhance one such model. We also present a simple image captioning model that
makes use of a CNN, an LSTM, and the beam search1 algorithm, and study its
performance based on various qualitative and quantitative metrics.


  Cooperation information sharing is important to theories of human learning
and has potential implications for machine learning. Prior work derived
conditions for achieving optimal Cooperative Inference given strong, relatively
restrictive assumptions. We relax these assumptions by demonstrating
convergence for any discrete joint distribution, robustness through equivalence
classes and stability under perturbation, and effectiveness by deriving bounds
from structural properties of the original joint distribution. We provide
geometric interpretations, connections to and implications for optimal
transport, and connections to importance sampling, and conclude by outlining
open questions and challenges to realizing the promise of Cooperative
Inference.


  Though generative dialogue modeling is widely seen as a language modeling
task, the task demands an agent to have a complex natural language
understanding of its input text to carry a meaningful interaction with an user.
The automatic metrics used evaluate the quality of the generated text as a
proxy to the holistic interaction of the agent. Such metrics were earlier shown
to not correlate with the human judgement. In this work, we observe that human
evaluation of dialogue agents can be inconclusive due to the lack of sufficient
information for appropriate evaluation. The automatic metrics are deterministic
yet shallow and human evaluation can be relevant yet inconclusive. To bridge
this gap in evaluation, we propose designing a set of probing tasks to evaluate
dialogue models. The hand-crafted tasks are aimed at quantitatively evaluating
a generative dialogue model's understanding beyond the token-level evaluation
on the generated text. The probing tasks are deterministic like automatic
metrics and requires human judgement in their designing; benefiting from the
best of both worlds. With experiments on probe tasks we observe that, unlike
RNN based architectures, transformer model may not be learning to comprehend
the input text despite its generated text having higher overlap with the target
text.


  We give an efficient quantum algorithm for the Moebius function $\mu(n)$ from
the natural numbers to $\{-1,0,1\}$. The cost of the algorithm is
asymptotically quadratic in $\log n$ and does not require the computation of
the prime factorization of $n$ as an intermediate step.


  The speed of convergence of the R-linear GMRES is bounded in terms of a
polynomial approximation problem on a finite subset of the spectrum. This
result resembles the classical GMRES convergence estimate except that the
matrix involved is assumed to be condiagonalizable. The bounds obtained are
applicable to the CSYM method, in which case they are sharp. Then a three term
recurrence for generating a family of orthogonal polynomials is shown to exist,
yielding a natural link with complex symmetric Jacobi matrices. This shows that
a mathematical framework analogous to the one appearing with the Hermitian
Lanczos method exists in the complex symmetric case. The probability of being
condiagonalizable is estimated with random matrices.


  SN 1997ef has been recognized as a peculiar supernova from its light curve
and spectral properties. The object was classified as a Type Ic supernova (SN
Ic) because its spectra are dominated by broad absorption lines of oxygen and
iron, lacking any clear signs of hydrogen or helium line features. The light
curve is very different from that of previously known SNe Ic, showing a very
broad peak and a slow tail. The strikingly broad line features in the spectra
of SN 1997ef, which were also seen in the hypernova SN 1998bw, suggest the
interesting possibility that SN 1997ef may also be a hypernova. The light curve
and spectra of SN 1997ef were modeled first with a standard SN~Ic model
assuming an ordinary kinetic energy of explosion $E_{\rm K} = 10^{51}$ erg. The
explosion of a CO star of mass $M_{\rm CO} \approx 6 M_\odot$ gives a
reasonably good fit to the light curve but clearly fails to reproduce the broad
spectral features. Then, models with larger masses and energies were explored.
Both the light curve and the spectra of SN 1997ef are much better reproduced by
a C+O star model with $E_{\rm K} =$ 8 \e{51} erg and $M_{\rm CO} = 10 M_\odot$.
Therefore, we conclude that SN 1997ef is very likely a hypernova on the basis
of its kinetic energy of explosion. Finally, implications for the deviation
from spherical symmetry are discussed in an effort to improve the light curve
and spectral fits.


  We report on analysis of timing and spectroscopy of the Vela pulsar using
eleven months of observations with the Large Area Telescope on the Fermi
Gamma-Ray Space Telescope. The intrinsic brightness of Vela at GeV energies
combined with the angular resolution and sensitivity of the LAT allow us to
make the most detailed study to date of the energy-dependent light curves and
phase-resolved spectra, using a LAT-derived timing model. The light curve
consists of two peaks (P1 and P2) connected by bridge emission containing a
third peak (P3). We have confirmed the strong decrease of the P1/P2 ratio with
increasing energy seen with EGRET and previous Fermi LAT data, and observe that
P1 disappears above 20 GeV. The increase with energy of the mean phase of the
P3 component can be followed with much greater detail, showing that P3 and P2
are present up to the highest energies of pulsation. We find significant pulsed
emission at phases outside the main profile, indicating that magnetospheric
emission exists over 80% of the pulsar period. With increased high-energy
counts the phase-averaged spectrum is seen to depart from a power- law with
simple exponential cutoff, and is better fit with a more gradual cutoff. The
spectra in fixed-count phase bins are well fit with power-laws with exponential
cutoffs, revealing a strong and complex phase dependence of the cutoff energy,
especially in the peaks. By combining these results with predictions of the
outer magnetosphere models that map emission characteristics to phase, it will
be possible to probe the particle acceleration and the structure of the pulsar
magnetosphere with unprecedented detail.


  The effectiveness of measurement-based feedback control (MBFC) protocols is
hampered by the presence of measurement noise, which affects the ability to
accurately infer the underlying dynamics of a quantum system from noisy
continuous measurement records to determine an accurate control strategy. To
circumvent such limitations, this work explores a real-time stochastic state
estimation approach that enables noise-free monitoring of the conditional
dynamics including the full density matrix of the quantum system using noisy
measurement records within a single quantum trajectory -- a method we name as
`conditional state tomography'. This, in turn, enables the development of
precise MBFC strategies that lead to effective control of quantum systems by
essentially mitigating the constraints imposed by measurement noise and has
potential applications in various feedback quantum control scenarios. This
approach is particularly useful for reinforcement-learning (RL)-based control,
where the RL-agent can be trained with arbitrary conditional averages of
observables, and/or the full density matrix as input (observation), to quickly
and accurately learn control strategies.


  The field of analyzing performance is very important and sensitive in
particular when it is related to the performance of lecturers in academic
institutions. Locating the weak points of lecturers through a system that
provides an early warning to notify or reward the lecturers with warned or
punished notices will help them to improve their weaknesses, leads to a better
quality in the institutions. The current system has major issues in the higher
education at Salahaddin University-Erbil (SUE) in Kurdistan-Iraq. These issues
are: first, the assessment of lecturers' activities is conducted traditionally
via the Quality Assurance Teams at different departments and colleges at the
university, second, the outcomes in some cases of lecturers' performance
provoke a low level of acceptance among lectures, as these cases are reflected
and viewed by some academic communities as unfair cases, and finally, the
current system is not accurate and vigorous. In this paper, Particle Swarm
Optimization Neural Network is used to assess performance of lecturers in more
fruitful way and also to enhance the accuracy of recognition system. Different
real and novel data sets are collected from SUE. The prepared datasets
preprocessed and important features are then fed as input source to the
training and testing phases. Particle Swarm Optimization is used to find the
best weights and biases in the training phase of the neural network. The best
accuracy rate obtained in the test phase is 98.28 %.


  Leaf-Removal process has been widely researched and applied in many
mathematical and physical fields to help understand the complex systems, and a
lot of problems including the minimal vertex-cover are deeply related to this
process and the Leaf-Removal cores. In this paper, based on the structural
features of the Leaf-Removal cores, a method named Core Influence is proposed
to break the graphs into No-Leaf-Removal-Core ones, which takes advantages of
identifying some significant nodes by localized and greedy strategy. By
decomposing the minimal vertex-cover problem into the Leaf-Removal cores
breaking process and maximal matching of the remained graphs, it is proved that
any minimal vertex-covers of the whole graph can be located into these two
processes, of which the latter one is a P problem, and the best boundary is
achieved at the transition point. Compared with other node importance indices,
the Core Influence method could break down the Leaf-Removal cores much faster
and get the no-core graphs by removing fewer nodes from the graphs. Also, the
vertex-cover numbers resulted from this method are lower than existing node
importance measurements, and compared with the exact minimal vertex-cover
numbers, this method performs appropriate accuracy and stability at different
scales. This research provides a new localized greedy strategy to break the
hard Leaf-Removal Cores efficiently and heuristic methods could be constructed
to help understand some NP problems.


  Ultraluminous X-ray sources (ULXs) are bright extragalactic sources with
X-ray luminosities above 10^39 erg/s powered by accretion onto compact objects.
According to the first studies performed with XMM-Newton ULXs seemed to be
excellent candidates to host intermediate-mass black holes (10^2-4 solar
masses). However, in the last years the interpretation of super-Eddington
accretion onto stellar-mass black holes or neutron stars for most ULXs has
gained a strong consensus. One critical missing piece to confirm the
super-Eddington scenario was the direct detection of the massive,
radiatively-driven winds expected as atomic emission/absorption lines in ULX
spectra. The first evidence for winds was found as residuals in the soft X-ray
spectra of ULXs. Most recently we have been able to resolve these residuals
into rest-frame emission and blueshifted (~0.2c) absorption lines arising from
highly ionized gas in the deep high-resolution XMM-Newton spectra of two
ultraluminous X-ray sources. The compact object is therefore surrounded by
powerful ultrafast winds as predicted by models of hyper-Eddington accretion.
Here we discuss the relevance of these discoveries and the importance of
further, deep, XMM-Newton observations of powerful winds in many other
ultraluminous X-ray sources to estimate the energetics of the wind, the
geometry of the system, and the masses of the central accretors.


  In this paper we obtain the extended genus field of a finite abelian
extension of a global rational function field. We first study the case of a
cyclic extension of prime power degree. Next, we use that the extended genus
fields of a composite of two cyclotomic extensions of a global rational
function field is equal to the composite of their respective extended genus
fields, to obtain our main result. This result is that the extended genus field
of a general finite abelian extension of a global rational function field, is
given explicitly in terms of the field and of the extended genus field of its
"cyclotomic projection".


  In PRL 85, 3773 (2000) it was suggested to use random polynomials to analyze
and understand the properties of two-body random ensembles. In this comment we
point out that for the vibron model the random polynomial is not quadratic, but
has a more general form. We make a comparison for the percentage of ground
states with L=0 in the vibron model with random interactions.


  A heterostructure consisting of a magnetic Weyl semimetal and a conventional
superconductor exhibits an equilibrium current parallel to the superconductor
interface and perpendicular to the magnetization. Analyzing a minimal model,
which as a function of parameters may be in a trivial magnetic insulator phase,
a Weyl semimetal phase, or a three-dimensional weak Chern insulator phase, we
find that the equilibrium current is sensitive to the presence of surface
states, such as the topological Fermi-arc states of the Weyl semimetal or the
chiral surface states of the weak Chern insulator. While there is a nonzero
equilibrium current in all three phases, the appearance of the surface states
in the topological regime leads to a reversal of the direction of the current,
compared to the current direction for the trivial magnetic insulator phase. We
discuss the interpretation of the surface-state contribution to the equilibrium
current as a real-space realization of the superconductivity-enabled
equilibrium chiral magnetic effect of a single chirality, predicted to occur in
bulk Weyl superconductors.


  The SU(3)-Casson invariant for integral homology 3-spheres as studied by
Boden-Herald possesses a 'spectral flow obstruction' to being an integer valued
invariant which depends only on the non-degenerate (perturbed) moduli space of
flat SU(3)-connections. This obstruction is the non-trivial spectral flow of a
family of twisted signature operators in 3-dimensions. The parallel
U(2)-Seiberg-Witten construction also has an obstruction but from the
non-trivial spectral flow of a family of twisted Dirac operators. By taking the
SU(3)-flat and U(2)-Seiberg-Witten equations simultaneously the obstructions
can be made to cancel and an integer invariant is obtained.


  We have developed a vectorial type of measurement for the spin Seebeck effect
(SSE) in epitaxial NiFe$_2$O$_4$ thin films which have been grown by pulsed
laser deposition on MgGa$_2$O$_4$ (MGO) with (001) and (011) orientation as
well as CoGa$_2$O$_4$ (011) (CGO), thus varying the lattice mismatch and
crystal orientation. We confirm that a large lattice mismatch leads to strain
anisotropy in addition to the magnetocrystalline anisotropy in the thin films
using vibrating sample magnetometry and ferromagnetic resonance measurements.
Moreover, we show that the existence of a magnetic strain anisotropy in
NiFe$_2$O$_4$ thin films significantly impacts the shape and magnitude of the
magnetic-field-dependent SSE voltage loops. We further demonstrate that
bidirectional field-dependent SSE voltage curves can be utilized to reveal the
complete magnetization reversal process, which establishes a vectorial
magnetometry technique based on a spin caloric effect.


  When algorithms for sorting and searching are applied to keys that are
represented as bit strings, we can quantify the performance of the algorithms
not only in terms of the number of key comparisons required by the algorithms
but also in terms of the number of bit comparisons. Some of the standard
sorting and searching algorithms have been analyzed with respect to key
comparisons but not with respect to bit comparisons. In this paper, we
investigate the expected number of bit comparisons required by Quickselect
(also known as Find). We develop exact and asymptotic formulae for the expected
number of bit comparisons required to find the smallest or largest key by
Quickselect and show that the expectation is asymptotically linear with respect
to the number of keys. Similar results are obtained for the average case. For
finding keys of arbitrary rank, we derive an exact formula for the expected
number of bit comparisons that (using rational arithmetic) requires only finite
summation (rather than such operations as numerical integration) and use it to
compute the expectation for each target rank.


  Magnon Bose condensation (BC)in the symmetry breaking magnetic field is a
result of unusual form of the Zeeman energy, which has terms linear in the
spin-wave operators and terms mixing excitations differ in the Wave-vector of
the magnetic structure. The following examples are considered: simple
easy-plane tetragonal antiferromagnets (AF), frustrated AF family$R_2Cu O_4$
where $R=Pr,Nd$ etc. and cubic magnets with the Dzyaloshinskii-Moriya
interaction ($Mn Si$ etc.). In all cases the BC becomes important when the
magnetic field becomes comparable with the spin-wave gap. The theory is
illustrated by existing experimental results.


  The dynamical mechanism of channel coupling with the decay channels is
applied to the case of coupled charmonium - $DD^*$ states with $J^{PC}=1^{++}$.
A pole analysis is done and the $DD^*$ production cross section is calculated
in qualitative agreement with experiment. The sharp peak at the $D_0D^*_0$
threshold and flat background are shown to be due to Breit-Wigner resonance,
shifted by channel coupling from the original position of 3954 MeV for the
$2^3P_1$, $Q\bar Q$ state. A similar analysis, applied to the $n=2$, $^3P_2$,
$^1P_1$, $^3P_0$, allows us to associate the first one with the observed
$Z(3930)$ J=2 and explains the destiny of $^3P_0$.


  Driven by low-cost, resource abundance and distinct material properties, the
use of paper in electronics, optics and fluidics is under investigation.
Considering sensor systems based on magneto-resistance principles (anisotropic,
giant, tunnel) that are conventionally manufactured onto inorganic
semiconductor materials, we propose the use of paper substrates for cost
reduction purposes primarily. In particular, we studied the magneto-resistance
sensitivity of permalloy (Py:Ni81Fe19) onto paper substrates. In this work, we
report on our findings with clean room paper (80 g/m2, Rrms = 2.877 {\mu}m, 23%
surface porosity, latex impregnation, no embossed macro-structure). Here, the
Py:Ni81Fe19 coating was manufactured by means of a dry process, sputter
deposition, and spans an area of 10x10 mm2 and a thickness of 70 nm. Employing
a four-point-probe DC resistivity measurement setup, we investigated the change
of electrical resistance of Py:Ni81Fe19 under the presence of an oriented
external magnetic field. In particular, we investigate the magneto-resistive
change at two configurations: (1) the direction of the magnetic field is
parallel to the nominal induced electric current and (2) the direction of the
magnetic field is perpendicular to the electric current. Due to the stochastic
orientation of the fibers interplaying with the Py:Ni81Fe19 coating, the change
in magneto-resistance of the overall system at both measurement configurations
closely corresponds to the classical response of Py:Ni81Fe19 at a +/-45{\deg}
angle between the direction of electrical current and magnetic field. Using the
magneto-optic kerr effect, we observed the formation of domain walls at the
fiber bending locations. Future work will focus on the impact of layer
thickness, fiber dimensions and structure of magnetic coating on the
performance of the paper-based Py:Ni81Fe19 magneto-resistors.


  We geometrically engineer d=4 N=1 supersymmetric Yang-Mills theories by
considering M theory on various Joyce orbifolds. We argue that the
superpotential of these models is generated by fractional membrane instantons.
The relation of this superpotential to membrane anomalies is also discussed.


  We construct the Feynman integrands for a class of exponentially growing
time-dependent potentials as white noise functionals. We show that they solve
the Schroedinger equation. The Morse potential is considered as a special case.


  The Deep learning (DL) models for diagnosing breast cancer from mammographic
images often operate as "black boxes", making it difficult for healthcare
professionals to trust and understand their decision-making processes. The
study presents an integrated framework combining Convolutional Neural Networks
(CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis
of breast cancer using the CBIS-DDSM dataset. The methodology encompasses an
elaborate data preprocessing pipeline and advanced data augmentation techniques
to counteract dataset limitations and transfer learning using pre-trained
networks such as VGG-16, Inception-V3 and ResNet was employed. A focal point of
our study is the evaluation of XAI's effectiveness in interpreting model
predictions, highlighted by utilizing the Hausdorff measure to assess the
alignment between AI-generated explanations and expert annotations
quantitatively. This approach is critical for XAI in promoting trustworthiness
and ethical fairness in AI-assisted diagnostics. The findings from our research
illustrate the effective collaboration between CNNs and XAI in advancing
diagnostic methods for breast cancer, thereby facilitating a more seamless
integration of advanced AI technologies within clinical settings. By enhancing
the interpretability of AI driven decisions, this work lays the groundwork for
improved collaboration between AI systems and medical practitioners, ultimately
enriching patient care. Furthermore, the implications of our research extended
well beyond the current methodologies. It encourages further research into how
to combine multimodal data and improve AI explanations to meet the needs of
clinical practice.


  White-light superflares from ultra cool stars are thought to be resulted from
magnetic reconnection, but the magnetic dynamics in a fully convective star is
not clear yet. In this paper, we report a stellar superflare detected with the
Ground Wide Angle Camera (GWAC), along with rapid follow-ups with the F60A,
Xinglong 2.16m and LCOGT telescopes. The effective temperature of the
counterpart is estimated to be $2200\pm50$K by the BT-Settl model,
corresponding to a spectral type of L0. The $R-$band light curve can be modeled
as a sum of three exponential decay components, where the impulsive component
contributes a fraction of 23\% of the total energy, while the gradual and the
shallower decay phases emit 42\% and 35\% of the total energy, respectively.
The strong and variable Balmer narrow emission lines indicate the large
amplitude flare is resulted from magnetic activity. The bolometric energy
released is about $6.4\times10^{33}$ ergs, equivalent to an energy release in a
duration of 143.7 hours at its quiescent level. The amplitude of $\Delta R=-8.6
$mag ( or $\Delta V=-11.2$ mag), placing it one of the highest amplitudes of
any ultra cool star recorded with excellent temporal resolution. We argue that
a stellar flare with such rapidly decaying and huge amplitude at distances
greater than 1 kpc may be false positive in searching for counterparts of
catastrophic events such as gravitational wave events or gamma-ray bursts,
which are valuable in time-domain astronomy and should be given more attention.


  Let $\Omega$ be a smooth bounded domain in $\mathbb{R}^2$. For $\epsilon>0$
small, we construct non-constant solutions to the Ginzburg-Landau equations
$-\Delta u=\frac{1}{\epsilon^2}(1-|u|^2)u$ in $\Omega$ such that on $\partial
\Omega$ u satisfies $|u|=1$ and $u\wedge \partial_\nu u=0$. These boundary
conditions are called semi-stiff and are intermediate between the Dirichlet and
the homogeneous Neumann boundary conditions. In order to construct such
solutions we use a variational reduction method very similar to the one used by
del Pino-Kowalczyk-Musso to find solutions of the Ginzburg-Landau equations
with Dirichlet and homogeneous Neumann boundary conditions. We obtain the exact
same result as the authors of the aforementioned article obtained for the
Neumann problem. This is because the renormalized energy for the Neumann
problem and for the semi-stiff problem are the same. In particular if $\Omega$
is simply connected a solution with degree one on the boundary always exists
and if $\Omega$ is not simply connected then for any $k\geq 1$ a solution with
$k$ vortices of degree one exists.


  We consider the Parity Nonconserving (PNC) mixing in the ground state of
exotic (halo) nuclei caused by the PNC weak interaction between outer neutron
and nucleons within nuclear interior. For the nucleus $^{11}Be$ as an example
of typical nucleus with neutron halo, we use analytical model for the external
neutron wave functions to estimate the scale of the PNC mixing. The amplitude
of the PNC mixing in halo state is found to be an order of magnitude bigger
than that of typical PNC mixing between the ``normal'' nuclear states in nearby
nuclei. The enhanced PNC mixing in halo cloud is proportional to the neutron
weak PNC potential constant $g^W_n$ only.


  We consider a quantum two-particle system on a d-dimensional lattice with
interaction and in presence of an IID external potential. We establish
Wegner-typer estimates for such a model. The main tool used is Stollmann's
lemma.


  A few experiments have reported that the time development of shear stress
under fast start-up shear deformations exhibits double peaks before reaching
the steady state for bimodal blends of entangled linear polymers in specific
conditions. To analyze the molecular origin of this phenomenon, multi-chain
slip-link simulations based on the primitive chain network model were conducted
for the data of a bimodal polystyrene solution reported by Osaki et al. [J Pol
Sci B Pol Phys, 38, 2043 (2000)] Owing to the reasonable agreement with the
data and the simulation results, the stress was decomposed into contributions
from long and short-chain components and decoupled into segment number,
stretch, and orientation. The analysis revealed that the first and second peaks
correspond to the short-chain orientation and the long-chain stretch,
respectively. The results also imply that the peak positions are not affected
by the mixing of short and long chains, whereas the second peak intensity
depends on the mixing conditions in a complicated manner, dominating the
emergence of double peaks.


  In 1996, J. Pendry, an English theoretical physicist put forward an idea
about the dependence of the effective electron mass on the magnetic field,
while interpreting the dielectric response of metal wire mesh structures. The
idea was based on the well-known relation between the kinematic and canonical
momenta of a charged particle moving in the magnetic field. In this paper,
proceeding from the universal character of that relation, the applicability of
Pendry's effective electron mass $m_{\text{eff}}$ to the problem of electrons
in metal mesh structures, as well as to a wide class of problems for charges
moving in the magnetic field, has been examined. The general properties of
$m_{\text{eff}}$ following directly from its definition were found, and an
analogy between the effective electron mass $m_{\text{eff}}$ and $m^{\ast}$
known in the solid-state theory was established. A physical interpretation of
$m_{\text{eff}}$ was proposed. It was demonstrated in several examples that,
despite the generality of the defining relation for the effective mass
$m_{\text{eff}}$, the use of $m_{\text{eff}}$ beyond the problem of the
dielectric response of metal wire mesh structures leads to incorrect results.


  Our aim in this note is to present four remarkable facts about quotient sets.
These observations seem to have been overlooked by the Monthly, despite its
intense coverage of quotient sets over the years.


  We have identified the four most significant features in the UV velocity
distribution of solar neighborhood stars: H1, H2 in the Hercules stream and W1,
W2 in the Wolf 630 stream. We have formulated the problem of determining
several characteristics of the central Galactic bar independently from each of
the identified features by assuming that the Hercules and Wolf 630 streams are
of a bar-induced dynamical nature. The problem has been solved by constructing
2:1 resonant orbits in the rotating bar frame for each star in these streams.
Analysis of the resonant orbits found has shown that the bar pattern speed is
45-55 km/s/kpc, while the bar angle lies within the range 40-60 degrees. The
results obtained are consistent with the view that the Hercules and Wolf 630
streams could be formed by a long-term influence of the Galactic bar leading to
a characteristic bimodal splitting of the UV velocity plane.


  The four essential chambers of one's heart that lie in the thoracic cavity
are crucial for one's survival, yet ironically prove to be the most vulnerable.
Cardiovascular disease (CVD) also commonly referred to as heart disease has
steadily grown to the leading cause of death amongst humans over the past few
decades. Taking this concerning statistic into consideration, it is evident
that patients suffering from CVDs need a quick and correct diagnosis in order
to facilitate early treatment to lessen the chances of fatality. This paper
attempts to utilize the data provided to train classification models such as
Logistic Regression, K Nearest Neighbors, Support Vector Machine, Decision
Tree, Gaussian Naive Bayes, Random Forest, and Multi-Layer Perceptron
(Artificial Neural Network) and eventually using a soft voting ensemble
technique in order to attain as many correct diagnoses as possible.


  We calculate (q-deformed) Clebsch-Gordan and 6j-coefficients for rank two
quantum groups. We explain in detail how such calculations are done, which
should allow the reader to perform similar calculations in other cases.
Moreover, we tabulate the q-Clebsch-Gordan and 6j-coefficients explicitly, as
well as some other topological data associated with theories corresponding to
rank-two quantum groups. Finally, we collect some useful properties of the
fusion rules of particular conformal field theories.


  Representation of molecular vibrational degrees of freedom by independent
harmonic oscillators, when describing electronic spectra or electronic
excitation energy transport, raises unfavourable effects as vibrational energy
relaxation becomes inaccessible. A standard theoretical description is extended
in this paper by including both electronic-phonon and vibrational-phonon
couplings. Using this approach we have simulated a model pigment-protein system
and have shown that intermode coupling leads to the quenching of pigment
vibrational modes, and to the redistribution of fluctuation spectral density
with respect to the electronic excitations. Moreover, new energy relaxation
pathways, opened by the vibrational-phonon interaction, allow to reach the
electronic excited state equilibrium quicker in the naturally occurring water
soluble chlorophyll binding protein (WSCP) aggregate, demonstrating the
significance that the damping of molecular vibrations has for the
intrarmolecular energy relaxation process rate.


  We discuss the variation of the nuclear matrix element (NME) for the
neutrinoless double beta ($0\nu\beta\beta$) decay of $^{76}$Ge when the wave
functions are constrained to reproduce the experimental occupancies of the two
nuclei involved in the transition. In the Interacting Shell Model description
the value of the NME is enhanced about 15% compared to previous calculations,
whereas in the QRPA the NME's are reduced by 20%-30%. This diminishes the
discrepancies between both approaches. In addition, we discuss effect of the
short range correlations on the NME in the light of the recently proposed
parametrizations based on a consistent renormalization of the $0\nu\beta\beta$
transition operator.


  This paper studies the problem of aligning a set of face images of the same
individual into a normalized image while removing the outliers like partial
occlusion, extreme facial expression as well as significant illumination
variation. Our model seeks an optimal image domain transformation such that the
matrix of misaligned images can be decomposed as the sum of a sparse matrix of
noise and a rank-one matrix of aligned images. The image transformation is
learned in an unsupervised manner, which means that ground-truth aligned images
are unnecessary for our model. Specifically, we make use of the remarkable
non-linear transforming ability of generative adversarial network(GAN) and
guide it with low-rank generation as well as sparse noise constraint to achieve
the face alignment. We verify the efficacy of the proposed model with extensive
experiments on real-world face databases, demonstrating higher accuracy and
efficiency than existing methods.


  Gamma-positivity is an elementary property that polynomials with symmetric
coefficients may have, which directly implies their unimodality. The idea
behind it stems from work of Foata, Sch\"utzenberger and Strehl on the Eulerian
polynomials; it was revived independently by Br\"and\'en and Gal in the course
of their study of poset Eulerian polynomials and face enumeration of flag
simplicial spheres, respectively, and has found numerous applications since
then. This paper surveys some of the main results and open problems on
gamma-positivity, appearing in various combinatorial or geometric contexts, as
well as some of the diverse methods that have been used to prove it.


  A simple theory of the covariant derivatives, deformed derivatives and
relative covariant derivatives of extensor fields is present using algebraic
and analytical tools developed in previous papers. Several important formulas
are derived.


  In this paper, evolution of the high energy area of universe, through the
scenario of 5 dimensional (5D) universe, has been studied. For this purpose, we
solve Einstein equations for 5D metric and 5D perfect fuid to derive
Friedmann-like equations. Then we obtain the evolution of scale factor and
energy density with respect to both space-like and time-like extra dimensions.
We obtain the novel equations for the space-like extra dimension and show that
the matter with zero pressure cannot exist in the bulk. Also, for dark energy
fuid and vacuum fluid, we have both accelerated expansion and contraction in
the bulk.


  The accidental ignition of combustible atmospheres by hot surfaces is of
great concern for chemical and process plant safety. In this paper, we present
our research regarding the evolution of thermal plumes originating from hot
hemispheres and discs. In particular, we focus on the effect of the orientation
of the surface on the ignition process. The auto-ignition temperatures and
ignition locations were studied experimentally. To get further insight, we
conducted detailed numerical simulations and validated them with measurements.
Three-dimensional simulations were performed on hot hemispheres and hot discs
for different orientations ranging from 0{\deg} to 180{\deg}. The solver
employs a transient, implicit scheme which is based on the coupled heat
transfer and flow equations. The mesh in the vicinity of the hot surfaces is
refined to resolve the steep temperature gradients and to capture the boundary
layer separation. The influence of the orientation on critical hot spots in the
gas mixture is analysed by examining the flow structures and the temperature
evolution of the buoyancy-driven flow. Using the obtained results, we discuss
the change of the onset and location of the ignition.


  Most active supermassive black holes (SMBH) in present-day galaxies are
underfed and consist of low-luminosity active galactic nuclei (LLAGN). They
have multiwavelength broadband spectral energy distributions (SED) dominated by
non-thermal processes which are quite different from those of the brighter,
more distant quasars. Modelling the observed SEDs of LLAGNs is currently
challenging, given the large computational expenses required. In this work, we
used machine learning (ML) methods to generate model SEDs and fit sparse
observations of LLAGNs. Our ML model consisted of a neural network and
reproduced with excellent precision the radio-to-X-rays emission from a
radiatively inefficient accretion flow around a SMBH and a relativistic jet, at
a small fraction of the computational cost. The ML method performs the fit $4
\times 10^5$ times faster than previous semianalytic models. As a
proof-of-concept, we used the ML model to reproduce the SEDs of the LLAGNs M87,
NGC 315 and NGC 4261.


  Spectroscopic amplitudes play an important role in nuclear capture reactions.
These amplitudes are shown to include both single-particle and polarization
effects: the former through their spatial dependence and the latter through
their normalization (the spectroscopic factors). Coupled-channels equations are
developed for the spectroscopic amplitudes. These equations serve as a
convenient starting point for the derivation of several approximations:
Hartree, Hartree-Fock and two different single-particle models. The
single-particle models include antisymmetry in different ways, but both miss
many-body effects. Therefore, cross sections calculated with either of these
models need to be multiplied by the spectroscopic factor.


  The null distance of Sormani and Vega encodes the manifold topology as well
as the causality structure of a (smooth) spacetime. We extend this concept to
Lorentzian length spaces, the analog of (metric) length spaces, which
generalize Lorentzian causality theory beyond the manifold level. We then study
Gromov-Hausdorff convergence based on the null distance in warped product
Lorentzian length spaces and prove first results on its compatibility with
synthetic curvature bounds.


  This paper makes no sense. It only increases the entropy of our universe. It
has been withdrawn by the author.


  We study the integrability properties of Wilson loops in the ${\cal N}=6$
three-dimensional Chern-Simons-matter (ABJM) theory. We begin with the
construction of an open spin chain that describes the anomalous dimensions of
operators inserted along the contour of a 1/2 BPS Wilson loop. Moreover, we
compute the all-loop reflection matrices that govern the interaction of
spin-chain excitations with the boundary, including their dressing factors, and
we check them against weak- and strong-coupling results. Furthermore, we
propose a $Y$-system of equations for the cusped Wilson line of ABJM, and we
use it to reproduce the one-loop cusp anomalous dimension of ABJM from a
leading-order finite-size correction. Finally, we write a set of BTBA equations
consistent with the $Y$-system proposal.


  A quantum Monte Carlo method is introduced to optimize excited state trial
wavefunctions. The method is applied in a correlation function Monte Carlo
calculation to compute ground and excited state energies of bosonic van der
Waals clusters of upto seven particles. The calculations are performed using
trial wavefunctions with general three-body correlations.


  Core-Collapse Supernovae, failed supernovae and quark novae are expected to
release an energy of few $10^{53}$ ergs through MeV neutrinos and a network of
detectors is operative to look online for these events. However, when the
source distance increases and/or the average energy of emitted neutrinos
decreases, the signal statistics drops and the identification of these low
statistic astrophysical bursts could be challenging. In a standard search,
neutrino detectors characterise the observed clusters of events with a
parameter called multiplicity, i.e. the number of collected events in a fixed
time-window. We discuss a new parameter called $\xi$ (=multiplicity/duration of
the cluster) in order to add the information on the temporal behaviour of the
expected signal with respect to background. By adding this parameter to the
multiplicity we optimise the search of astrophysical bursts and we increase
their detection horizon. Moreover, the use of the $\xi$ can be easily
implemented in an online system and can apply also to a network of detectors
like SNEWS. For these reasons this work is relevant in the multi-messengers era
when fast alerts with high significance are mandatory.


  Vision videos are established for soliciting feedback and stimulating
discussions in requirements engineering (RE) practices, such as focus groups.
Different researchers motivated the transfer of these benefits into crowd-based
RE (CrowdRE) by using vision videos on social media platforms. So far, however,
little research explored the potential of using vision videos for CrowdRE in
detail. In this paper, we analyze and assess this potential, in particular,
focusing on video comments as a source of feedback. In a case study, we
analyzed 4505 comments on a vision video from YouTube. We found that the video
solicited 2770 comments from 2660 viewers in four days. This is more than 50%
of all comments the video received in four years. Even though only a certain
fraction of these comments are relevant to RE, the relevant comments address
typical intentions and topics of user feedback, such as feature request or
problem report. Besides the typical user feedback categories, we found more
than 300 comments that address the topic safety, which has not appeared in
previous analyses of user feedback. In an automated analysis, we compared the
performance of three machine learning algorithms on classifying the video
comments. Despite certain differences, the algorithms classified the video
comments well. Based on these findings, we conclude that the use of vision
videos for CrowdRE has a large potential. Despite the preliminary nature of the
case study, we are optimistic that vision videos can motivate stakeholders to
actively participate in a crowd and solicit numerous of video comments as a
valuable source of feedback.


  Hinging on ideas from physical-layer network coding, some promising proposals
of coded random access systems seek to improve system performance (while
preserving low complexity) by means of packet repetitions and decoding of
linear combinations of colliding packets, whenever the decoding of individual
packets fails. The resulting linear combinations are then temporarily stored in
the hope of gathering enough linearly independent combinations so as to
eventually recover all individual packets through the resolution of a linear
system at the end of the contention frame. However, it is unclear which among
the numerous linear combinations---whose number grows exponentially with the
degree of collision---will have low probability of decoding error. Since no
analytical framework exists to determine which combinations are easiest to
decode, this makes the case for a machine learning algorithm to assist the
receiver in deciding which linear combinations to target. For this purpose, we
train neural networks that approximate the error probability for every possible
linear combination based on the estimated channel gains and demonstrate the
effectiveness of our approach by numerical simulations.


  The maximum likelihood estimation is computationally demanding for large
datasets, particularly when the likelihood function includes integrals.
Subsampling can reduce the computational burden, but it typically results in
efficiency loss. This paper proposes a moment-assisted subsampling (MAS) method
that can improve the estimation efficiency of existing subsampling-based
maximum likelihood estimators. The motivation behind this approach stems from
the fact that sample moments can be efficiently computed even if the sample
size of the whole data set is huge. Through the generalized method of moments,
the proposed method incorporates informative sample moments of the whole data.
The MAS estimator can be computed rapidly and is asymptotically normal with a
smaller asymptotic variance than the corresponding estimator without
incorporating sample moments of the whole data. The asymptotic variance of the
MAS estimator depends on the specific sample moments incorporated. We derive
the optimal moment that minimizes the resulting asymptotic variance in terms of
Loewner order. Simulation studies and real data analysis were conducted to
compare the proposed method with existing subsampling methods. Numerical
results demonstrate the promising performance of the MAS method across various
scenarios.


  In this paper, we characterize the second bounded characteristic classes of
foliated bundles in terms of the non-descendible quasi-morphisms on the
universal covering of the structure group. As its application, we study the
boundedness of obstruction classes for (contact) Hamiltonian fibrations and
show the non-existence of foliated structures on some Hamiltonian fibrations.
Moreover, for any closed symplectic manifold, we show the non-triviality of the
second bounded cohomology group of the Hamiltonian diffeomorphism group.


  We study the special case of $n\times n$ 1D Gaussian Hermitian random band
matrices, when the covariance of the elements is determined by the matrix
$J=(-W^2\triangle+1)^{-1}$. Assuming that $n\ge CW\log W\gg 1$, we prove that
the averaged density of states coincides with the Wigner semicircle law up to
the correction of order $W^{-1}$.


  Cartan-Hadamard manifold is a simply connected Riemannian manifold with
non-positive sectional curvature. In this article, we have proved that a
Cartan-Hadamard manifold satisfying steady gradient Ricci soliton with the
integral condition of potential function is isometric to the Euclidean space.
Next we have proved a compactness theorem for gradient shrinking Ricci soliton
satisfying some scalar curvature condition. Finally, we have showed that a
gradient expanding Ricci soliton with linear volume growth and positive
potential function is an Einstein manifold.


  We have carried out numerical simulations of strongly gravitating systems
based on the Einstein equations coupled to the relativistic hydrodynamic
equations using adaptive mesh refinement (AMR) techniques. We show AMR
simulations of NS binary inspiral and coalescence carried out on a workstation
having an accuracy equivalent to that of a $1025^3$ regular unigrid simulation,
which is, to the best of our knowledge, larger than all previous simulations of
similar NS systems on supercomputers. We believe the capability opens new
possibilities in general relativistic simulations.


  A minimal (by inclusion) generating set for the algebra of semi-invariants of
a quiver of dimension (2,...,2) is established over an infinite field of
arbitrary characteristic. The mentioned generating set consists of the
determinants of generic matrices and the traces of tree paths of pairwise
different multidegrees, where in the case of characteristic different from two
we take only admissible paths. As a consequence, we describe relations modulo
decomposable semi-invariants.


  We examine several ``landmarks'' for the effects of the tensor interaction on
the properties of light nuclei. These were usually discussed in the context of
small-space shell-model calculations. We show, using $G$ matrices derived from
a realistic nucleon-nucleon potential, that when the model space is small
(e.g., $0\hbar\omega$), these effects are overestimated, indicating that the
tensor interaction is too strong. However, when larger spaces are used, there
is a diminishing of these effects, which, in general, leads to better agreement
with experiment.


  Understanding the processes that give rise to quantitative measurements
associated with molecular sequence data remains an important issue in
statistical phylogenetics. Examples of such measurements include geographic
coordinates in the context of phylogeography and phenotypic traits in the
context of comparative studies. A popular approach is to model the evolution of
continuously varying traits as a Brownian diffusion process. However, standard
Brownian diffusion is quite restrictive and may not accurately characterize
certain trait evolutionary processes. Here, we relax one of the major
restrictions of standard Brownian diffusion by incorporating a nontrivial
estimable drift into the process. We introduce a relaxed drift diffusion model
for the evolution of multivariate continuously varying traits along a
phylogenetic tree via Brownian diffusion with drift. Notably, the relaxed drift
model accommodates branch-specific variation of drift rates while preserving
model identifiability. We implement the relaxed drift model in a Bayesian
inference framework to simultaneously reconstruct the evolutionary histories of
molecular sequence data and associated multivariate continuous trait data, and
provide tools to visualize evolutionary reconstructions. We illustrate our
approach in three viral examples. In the first two, we examine the
spatiotemporal spread of HIV-1 in central Africa and West Nile virus in North
America and show that a relaxed drift approach uncovers a clearer, more
detailed picture of the dynamics of viral dispersal than standard Brownian
diffusion. Finally, we study antigenic evolution in the context of HIV-1
resistance to three broadly neutralizing antibodies. Our analysis reveals
evidence of a continuous drift at the HIV-1 population level towards enhanced
resistance to neutralization by the VRC01 monoclonal antibody over the course
of the epidemic.


  We continue to investigate properties of the strongly coupled inflaton in a
setup introduced in arXiv:0807.3191 through the AdS/CFT correspondence. These
properties are qualitatively different from those in conventional inflationary
models. For example, in slow-roll inflation, the inflaton velocity is not
determined by the shape of potential; the fine-tuning problem concerns the dual
infrared geometry instead of the potential; the non-Gaussianities such as the
local form can naturally become large.


  Visual contents, such as illustrations and images, play a big role in product
manual understanding. Existing Product Manual Question Answering (PMQA)
datasets tend to ignore visual contents and only retain textual parts. In this
work, to emphasize the importance of multimodal contents, we propose a
Multimodal Product Manual Question Answering (MPMQA) task. For each question,
MPMQA requires the model not only to process multimodal contents but also to
provide multimodal answers. To support MPMQA, a large-scale dataset PM209 is
constructed with human annotations, which contains 209 product manuals from 27
well-known consumer electronic brands. Human annotations include 6 types of
semantic regions for manual contents and 22,021 pairs of question and answer.
Especially, each answer consists of a textual sentence and related visual
regions from manuals. Taking into account the length of product manuals and the
fact that a question is always related to a small number of pages, MPMQA can be
naturally split into two subtasks: retrieving most related pages and then
generating multimodal answers. We further propose a unified model that can
perform these two subtasks all together and achieve comparable performance with
multiple task-specific models. The PM209 dataset is available at
https://github.com/AIM3-RUC/MPMQA.


  This article settles the convergence question for multivariate barycentric
subdivision schemes with nonnegative masks on complete metric spaces of
nonpositive Alexandrov curvature, also known as Hadamard spaces. We establish a
link between these types of refinement algorithms and the theory of Markov
chains by characterizing barycentric subdivision schemes as nonlinear Markov
semigroups. Exploiting this connection, we subsequently prove that any such
scheme converges on arbitrary Hadamard spaces if and only if it converges for
real valued input data. Moreover, we generalize a characterization of
convergence from the linear theory, and consider approximation qualities of
barycentric subdivision schemes. A concluding section addresses the
relationship between the convergence properties of a scheme and its so-called
characteristic Markov chain.


  We describe the design, operation, and first results of a photometric
calibration project, called DICE (Direct Illumination Calibration Experiment),
aiming at achieving precise instrumental calibration of optical telescopes. The
heart of DICE is an illumination device composed of 24 narrow-spectrum,
high-intensity, light-emitting diodes (LED) chosen to cover the
ultraviolet-to-near-infrared spectral range. It implements a point-like source
placed at a finite distance from the telescope entrance pupil, yielding a flat
field illumination that covers the entire field of view of the imager. The
purpose of this system is to perform a lightweight routine monitoring of the
imager passbands with a precision better than 5 per-mil on the relative
passband normalisations and about 3{\AA} on the filter cutoff positions. The
light source is calibrated on a spectrophotometric bench. As our fundamental
metrology standard, we use a photodiode calibrated at NIST. The radiant
intensity of each beam is mapped, and spectra are measured for each LED. All
measurements are conducted at temperatures ranging from 0{\deg}C to 25{\deg}C
in order to study the temperature dependence of the system. The photometric and
spectroscopic measurements are combined into a model that predicts the spectral
intensity of the source as a function of temperature. We find that the
calibration beams are stable at the $10^{-4}$ level -- after taking the slight
temperature dependence of the LED emission properties into account. We show
that the spectral intensity of the source can be characterised with a precision
of 3{\AA} in wavelength. In flux, we reach an accuracy of about 0.2-0.5%
depending on how we understand the off-diagonal terms of the error budget
affecting the calibration of the NIST photodiode. With a routine 60-mn
calibration program, the apparatus is able to constrain the passbands at the
targeted precision levels.


  Given a closed flat 3-torus $N$, for each $H>0$ and each non-negative integer
$g$, we obtain area estimates for closed surfaces with genus $g$ and constant
mean curvature $H$ embedded in $N$. This result contrasts with the theorem of
Traizet [33], who proved that every flat 3-torus admits for every positive
integer $g$ with $g\neq 2$, connected closed embedded minimal surfaces of genus
$g$ with arbitrarily large area.


  We derive sub-Gaussian bounds for the annealed transition density of the
simple random walk on a high-dimensional loop-erased random walk. The walk
dimension that appears in these is the exponent governing the space-time
scaling of the process with respect to the extrinsic Euclidean distance, which
contrasts with the exponent given by the intrinsic graph distance that appears
in the corresponding quenched bounds. We prove our main result using novel
pointwise Gaussian estimates on the distribution of the high-dimensional
loop-erased random walk.


  Understanding the evolution of galaxies from the starforming blue cloud to
the quiescent red sequence has been revolutionized by observations taken with
Herschel Space Observatory, and the onset of the era of sensitive millimeter
interferometers, allowing astronomers to probe both cold dust as well as the
cool interstellar medium in a large set of galaxies with unprecedented
sensitivity. Recent Herschel observations of of H2-bright Hickson Compact
Groups of galaxies (HCGs) has shown that [CII] may be boosted in diffuse
shocked gas. CARMA CO(1-0) observations of these [CII]-bright HCGs has shown
that these turbulent systems also can show suppression of SF. Here we present
preliminary results from observations of HCGs with Herschel and CARMA, and
their [CII] and CO(1-0) properties to discuss how shocks influence galaxy
transitions and star formation.


  We introduce two hierarchies of clause-sets, SLUR_k and UC_k, based on the
classes SLUR (Single Lookahead Unit Refutation), introduced in 1995, and UC
(Unit refutation Complete), introduced in 1994.
  The class SLUR, introduced in [Annexstein et al, 1995], is the class of
clause-sets for which unit-clause-propagation (denoted by r_1) detects
unsatisfiability, or where otherwise iterative assignment, avoiding obviously
false assignments by look-ahead, always yields a satisfying assignment. It is
natural to consider how to form a hierarchy based on SLUR. Such investigations
were started in [Cepek et al, 2012] and [Balyo et al, 2012]. We present what we
consider the "limit hierarchy" SLUR_k, based on generalising r_1 by r_k, that
is, using generalised unit-clause-propagation introduced in [Kullmann, 1999,
2004].
  The class UC, studied in [Del Val, 1994], is the class of Unit refutation
Complete clause-sets, that is, those clause-sets for which unsatisfiability is
decidable by r_1 under any falsifying assignment. For unsatisfiable clause-sets
F, the minimum k such that r_k determines unsatisfiability of F is exactly the
"hardness" of F, as introduced in [Ku 99, 04]. For satisfiable F we use now an
extension mentioned in [Ansotegui et al, 2008]: The hardness is the minimum k
such that after application of any falsifying partial assignments, r_k
determines unsatisfiability. The class UC_k is given by the clause-sets which
have hardness <= k. We observe that UC_1 is exactly UC.
  UC_k has a proof-theoretic character, due to the relations between hardness
and tree-resolution, while SLUR_k has an algorithmic character. The
correspondence between r_k and k-times nested input resolution (or tree
resolution using clause-space k+1) means that r_k has a dual nature: both
algorithmic and proof theoretic. This corresponds to a basic result of this
paper, namely SLUR_k = UC_k.


  We study the stochastic formalism of inflation beyond the usual slow-roll
approximation. We verify that the assumptions on which the stochastic formalism
relies still hold even far from the slow-roll attractor. This includes
demonstrating the validity of the separate universe approach to evolving
long-wavelength scalar field perturbations beyond slow roll. We also explain
that, in general, there is a gauge correction to the amplitude of the
stochastic noise. This is because the amplitude is usually calculated in the
spatially-flat gauge, while the number of e-folds is used as the time variable
(hence one works in the uniform-$N$ gauge) in the Langevin equations. We show
that these corrections vanish in the slow-roll limit, but we also explain how
to calculate them in general. We compute them in difference cases, including
ultra-slow roll and the Starobinsky model that interpolates between slow roll
and ultra-slow roll, and find the corrections to be negligible in practice.
This confirms the validity of the stochastic formalism for studying quantum
backreaction effects in the very early universe beyond slow roll.


  In this paper, we consider a mobility system of travelers and providers, and
propose a "mobility game" to study when a traveler is matched to a provider.
Each traveler seeks to travel using the services of only one provider, who
manages one specific mode of transportation (e.g., car, bus, train, bike). The
services of each provider are capacitated and can serve up to a fixed number of
travelers at any instant of time. Thus, our problem falls under the category of
many-to-one assignment problems, where the goal is to find the conditions that
guarantee the stability of assignments. We formulate a linear program of
maximizing the social welfare of travelers and providers and show how it is
equivalent to the original problem and relate its solutions to stable
assignments. We also investigate our results under informational asymmetry and
provide a "mechanism" that elicits the information of travelers and providers.
Finally, we investigate and validate the advantages of our method by providing
a numerical simulation example.


  In sponsored search it is critical to match ads that are relevant to a query
and to accurately predict their likelihood of being clicked. Commercial search
engines typically use machine learning models for both query-ad relevance
matching and click-through-rate (CTR) prediction. However, matching models are
based on the similarity between a query and an ad, ignoring the fact that a
retrieved ad may not attract clicks, while click models rely on click history,
being of limited use for new queries and ads. We propose a deeply supervised
architecture that jointly learns the semantic embeddings of a query and an ad
as well as their corresponding CTR.We also propose a novel cohort negative
sampling technique for learning implicit negative signals. We trained the
proposed architecture using one billion query-ad pairs from a major commercial
web search engine. This architecture improves the best-performing baseline deep
neural architectures by 2\% of AUC for CTR prediction and by statistically
significant 0.5\% of NDCG for query-ad matching.


  For delay-limited communication over block-fading channels, the difference
between the ergodic capacity and the maximum achievable expected rate for
coding over a finite number of coherent blocks represents a fundamental measure
of the penalty incurred by the delay constraint. This paper introduces a notion
of worst-case expected-capacity loss. Focusing on the slow-fading scenario
(one-block delay), the worst-case additive and multiplicative expected-capacity
losses are precisely characterized for the point-to-point fading channel.
Extension to the problem of writing on fading paper is also considered, where
both the ergodic capacity and the additive expected-capacity loss over
one-block delay are characterized to within one bit per channel use.


  The presence and strength of a stellar magnetic field and activity is rooted
in a star's fundamental parameters such as mass and age. Can flares serve as an
accurate stellar "clock"?
  To explore if we can quantify an activity-age relation in the form of a
flaring-age relation, we measured trends in the flaring rates and energies for
stars with different masses and ages.
  We investigated the time-domain photometry provided by Kepler's follow-up
mission K2 and searched for flares in three solar metallicity open clusters
with well-known ages, M45 (0.125 Gyr), M44 (0.63 Gyr), and M67 (4.3 Gyr). We
updated and employed the automated flare finding and analysis pipeline
Appaloosa, originally designed for Kepler. We introduced a synthetic flare
injection and recovery subroutine to ascribe detection and energy recovery
rates for flares in a broad energy range for each light curve. We collected a
sample of 1 761 stars, mostly late-K to mid-M dwarfs and found 751 flare
candidates with energies ranging from $4\cdot10^{32}$ erg to $6\cdot10^{34}$
erg, of which 596 belong to M45, 155 to M44, and none to M67.
  We find that flaring activity depends both on $T_\mathrm{eff}$, and age. But
all flare frequency distributions have similar slopes with $\alpha
\approx2.0-2.4$, supporting a universal flare generation process. We discuss
implications for the physical conditions under which flares occur, and how the
sample's metallicity and multiplicity affect our results.


  We consider a Weyl semimetal (WSM) with finite doping and tilt within a
continuum model Hamiltonian with both broken time reversal and inversion
symmetry. We calculate the absorptive part of the anomalous AC Hall
conductivity as a function of photon energy ($\Omega$) for both type I and type
II Weyl semimetal. For a given Weyl node, changing the sign of its chirality or
of its tilt changes the sign of its contribution to the absorptive Hall
conductivity with no change in magnitude. For a noncentrosymmetric system we
find that there are ranges of photon energies for which only the positive or
only the negative chirality node contributes to the imaginary (absorptive) part
of the Hall conductivity. There are also other photon energies where both
chirality contribute and there can be other ranges of $\Omega$ where there is
no absorption associated with the AC Hall conductivity in type I and regions
where it is instead constant for type II. We comment on implications for the
absorption of circular polarized light.


  Anticipating human actions is an important task that needs to be addressed
for the development of reliable intelligent agents, such as self-driving cars
or robot assistants. While the ability to make future predictions with high
accuracy is crucial for designing the anticipation approaches, the speed at
which the inference is performed is not less important. Methods that are
accurate but not sufficiently fast would introduce a high latency into the
decision process. Thus, this will increase the reaction time of the underlying
system. This poses a problem for domains such as autonomous driving, where the
reaction time is crucial. In this work, we propose a simple and effective
multi-modal architecture based on temporal convolutions. Our approach stacks a
hierarchy of temporal convolutional layers and does not rely on recurrent
layers to ensure a fast prediction. We further introduce a multi-modal fusion
mechanism that captures the pairwise interactions between RGB, flow, and object
modalities. Results on two large-scale datasets of egocentric videos,
EPIC-Kitchens-55 and EPIC-Kitchens-100, show that our approach achieves
comparable performance to the state-of-the-art approaches while being
significantly faster.


  We study the interaction between two adjacent but electrically isolated
quantum point contacts (QPCs). At high enough source-drain bias on one QPC, the
drive QPC, we detect a finite electric current in the second, unbiased,
detector QPC. The current generated at the detector QPC always flows in the
opposite direction than the current of the drive QPC. The generated current is
maximal, if the detector QPC is tuned to a transition region between its
quantized conductance plateaus and the drive QPC is almost pinched-off. We
interpret this counterflow phenomenon in terms of an asymmetric phonon-induced
excitation of electrons in the leads of the detector QPC.


  A half-integral polygon with quasi-period collapse behaves similarly to a
lattice polygon in the sense that the number of lattice points in its integer
dilates can be calculated as values of a polynomial, its Ehrhart polynomial. As
a main result, we classify the Ehrhart polynomials of all half-integral
non-lattice polygons with quasi-period collapse. In particular, we obtain that
for any positive integer $i$, the polynomial
$\frac{4i+5}{2}t^2+\frac{2i+7}{2}t+1\in \mathbb{Q}[t]$ is an Ehrhart polynomial
of a rational polygon, which was an open question for $i>1$.
  We also study some extreme cases in detail. In particular, we show that up to
affine unimodular equivalence there exist exactly $30$ half-integral
non-lattice polygons with quasi-periodic collapse with exactly one interior
lattice point, which are the dual polygons of the $30$ LDP polygons of
Gorenstein index $2$. Furthermore, we classify all half-integral polygons with
quasi-period collapse with at most $6$ interior lattice points or with $i\geq
1$ interior lattice points and the maximum possible number $2i+7$ of boundary
lattice points.


  We present experimental evidence, which contradicts the the standard boundary
conditions used in continuum theories of non-cohesive granular flows for the
velocity normal to a boundary u.n=0, where n points into the fluid. We propose
and experimentally verify a new boundary condition for u.n, based on the
observation that the boundary cannot exert a tension force Fb on the fluid. The
new boundary condition is u.n=0 if Fb.n>=0 else n.P.n=0, where P is the
pressure tensor. This is the analog of cavitation in ordinary fluids, but due
the lack of attractive forces and dissipation it occurs frequently in granular
flows.


  We compute the leading-color (planar) three-loop four-point amplitude of N=4
supersymmetric Yang-Mills theory in 4 - 2 epsilon dimensions, as a Laurent
expansion about epsilon = 0 including the finite terms. The amplitude was
constructed previously via the unitarity method, in terms of two Feynman loop
integrals, one of which has been evaluated already. Here we use the
Mellin-Barnes integration technique to evaluate the Laurent expansion of the
second integral. Strikingly, the amplitude is expressible, through the finite
terms, in terms of the corresponding one- and two-loop amplitudes, which
provides strong evidence for a previous conjecture that higher-loop planar N =
4 amplitudes have an iterative structure. The infrared singularities of the
amplitude agree with the predictions of Sterman and Tejeda-Yeomans based on
resummation. Based on the four-point result and the exponentiation of infrared
singularities, we give an exponentiated ansatz for the maximally
helicity-violating n-point amplitudes to all loop orders. The 1/epsilon^2 pole
in the four-point amplitude determines the soft, or cusp, anomalous dimension
at three loops in N = 4 supersymmetric Yang-Mills theory. The result confirms a
prediction by Kotikov, Lipatov, Onishchenko and Velizhanin, which utilizes the
leading-twist anomalous dimensions in QCD computed by Moch, Vermaseren and
Vogt. Following similar logic, we are able to predict a term in the three-loop
quark and gluon form factors in QCD.


  Humans use environmental constraints (EC) in manipulation to compensate for
uncertainties in their world model. The same principle was recently applied to
robotics, so that soft underactuated hands improve their grasping capability by
using environmental constraints exploitation (ECE) [1]. Due to orientation of
the robotic hand for example in the EC wall grasp, the length of the robot
wrist plus the hand length gets quite important, if objects are grasp out of a
box [2] . Most of the modern cobots have quite long wrist, so we have
constructed a two degree of freedom wrist for the CLASH [3], to solve this
problem (Fig. 1).


  Glacier ice flow is shaped and defined by several properties, including the
bedrock elevation profile and the basal slip distribution. The effect of these
two basal properties can present in similar ways in the surface. For bedrock
recovery this makes distinguishing between them an interesting and complex
problem. The results of this paper show that in some synthetic test cases it is
indeed possible to distinguish and recover both bedrock elevation and basal
slip given free surface elevation and free surface velocity. The unidirectional
shallow ice approximation is used to compute steady state surface data for a
number of synthetic cases with different bedrock profiles and basal slip
distributions. A simple inversion method based on Newton's method is applied to
the known surface data to return the bedrock profile and basal slip
distribution. In each synthetic test case, the inversion was successful in
recovering both the bedrock elevation profile and the basal slip distribution
variables. These results imply that there is a unique bedrock profile and basal
slip which give rise to a unique combination of free surface velocity and free
surface elevation.


  The dynamical Jahn-Teller effect of C$_{60}^{n-}$ anions ($n = $ 1-5) is
studied using the numerical diagonalization of the linear $p^n \otimes 8d$
Jahn-Teller Hamiltonian with the currently established coupling parameters. It
is found that in all anions the Jahn-Teller effect stabilizes the low-spin
states, resulting in the violation of Hund's rule. The energy gain due to the
Jahn-Teller dynamics is found to be comparable to the static Jahn-Teller
stabilization. The Jahn-Teller dynamics influences the thermodynamic properties
via strong variation of the density of vibronic states with energy. Thus, the
large vibronic entropy in the low-spin states enhances the effective spin gap
of C$_{60}^{3-}$ quenching the spin crossover. From the calculations of the
effective spin gap in function of the Hund's rule coupling, we found that the
latter should amount 40 $\pm$ 5 meV in order to cope with the violation of
Hund's rule and to reproduce the large spin gap. With the obtained numerical
solutions the matrix elements of electronic operators for the low-lying
vibronic levels and the vibronic reduction factors are calculated for all
anions.