  The International Pulsar Timing Array 2nd data release is the combination of
datasets from worldwide collaborations. In this study, we search for continuous
waves: gravitational wave signals produced by individual supermassive black
hole binaries in the local universe. We consider binaries on circular orbits
and neglect the evolution of orbital frequency over the observational span. We
find no evidence for such signals and set sky averaged 95% upper limits on
their amplitude h 95 . The most sensitive frequency is 10nHz with h 95 = 9.1
10-15 . We achieved the best upper limit to date at low and high frequencies of
the PTA band thanks to improved effective cadence of observations. In our
analysis, we have taken into account the recently discovered common red noise
process, which has an impact at low frequencies. We also find that the peculiar
noise features present in some pulsars data must be taken into account to
reduce the false alarm. We show that using custom noise models is essential in
searching for continuous gravitational wave signals and setting the upper
limit.


  We reflect on our experiences as designers of COVID-19 data visualizations
working in a distributed synchronous design space during the pandemic. This is
especially relevant as the pandemic posed new challenges to distributed
collaboration amidst civic lockdown measures and an increased dependency on
spatially distributed teamwork across almost all sectors. Working from home
being 'the new normal', we explored potential solutions for collaborating and
prototyping remotely from our own homes using the existing tools at our
disposal. Since members of our cross-disciplinary team had different technical
skills, we used a range of synchronous remote design tools and methods. We
aimed to preserve the richness of co-located collaboration such as face-to-face
physical presence, body gestures, facial expressions, and the making and
sharing of physical artifacts. While meeting over Zoom, we sketched on paper
and used digital collaboration tools, such as Miro and Google Docs. Using an
auto-ethnographic approach, we articulate our challenges and strategies
throughout the process, providing useful insights about synchronous distributed
collaboration.


  An induced subposet $(P_2,\le_2)$ of a poset $(P_1,\le_1)$ is a subset of
$P_1$ such that for every two $X,Y\in P_2$, $X\le_2 Y$ if and only if $X\le_1
Y$. The Boolean lattice $Q_n$ of dimension $n$ is the poset consisting of all
subsets of $\{1,\dots,n\}$ ordered by inclusion. Given two posets $P_1$ and
$P_2$ the poset Ramsey number $R(P_1,P_2)$ is the smallest integer $N$ such
that in any blue/red coloring of the elements of $Q_N$ there is either a
monochromatically blue induced subposet isomorphic to $P_1$ or a
monochromatically red induced subposet isomorphic to $P_2$.
  We provide upper bounds on $R(P,Q_n)$ for two classes of $P$: parallel
compositions of chains, i.e.\ posets consisting of disjoint chains which are
pairwise element-wise incomparable, as well as subdivided $Q_2$, which are
posets obtained from two parallel chains by adding a common minimal and a
common maximal element. This completes the determination of $R(P,Q_n)$ for
posets $P$ with at most $4$ elements. If $P$ is an antichain $A_t$ on $t$
elements, we show that $R(A_t,Q_n)=n+3$ for $3\le t\le \log \log n$.
Additionally, we briefly survey proof techniques in the poset Ramsey setting
$P$ versus $Q_n$.


  We consider the problem of computing approximate solution of Poisson equation
in the low-parametric tensor formats. We propose a new algorithm to compute the
solution based on the cross approximation algorithm in the frequency space, and
it has better complexity with respect to ranks in comparison with standard
algorithms, which are based on the exponential sums approximation. To
illustrate the effectiveness of our solver, we incorporate into a Uzawa solver
for the Stokes problem on semi-staggered grid as a subsolver. The resulting
solver outperforms the standard method for $n \geq 256$.


  We construct several stable finite element pairs for the Stokes problem on
barycentric refinements in arbitrary dimensions. A key feature of the spaces is
that the divergence maps the discrete velocity space onto the the discrete
pressure space; thus, when applied to models of incompressible flows, the pairs
yield divergence-free velocity approximations. The key result is a local
inf-sup stability that holds for any dimension and for any polynomial degree.
With this result, we construct global divergence-free and stable pairs in
arbitrary dimension and for any polynomial degree.


  The quantum phases of superconductivity and superfluidity are characterised
mathematically by the Yang concept of Off-Diagonal Long Range Order (ODLRO),
related to the existence of a large eigenvalue of the density matrix. We
analyse how the Yang criterion applies for various Hamiltonians commonly
employed to describe superfluid-type correlations in atomic nuclei. For
like-particle pairing Hamiltonians the behaviour of the largest eigenvalue of
two-body density matrix shows a clear evidence for a transition between a
normal to a paired phase. However, this is not the case for the isoscalar
proton-neutron pairing interactions acting in self-conjugate nuclei.


  Pre-training general-purpose visual features with convolutional neural
networks without relying on annotations is a challenging and important task.
Most recent efforts in unsupervised feature learning have focused on either
small or highly curated datasets like ImageNet, whereas using uncurated raw
datasets was found to decrease the feature quality when evaluated on a transfer
task. Our goal is to bridge the performance gap between unsupervised methods
trained on curated data, which are costly to obtain, and massive raw datasets
that are easily available. To that effect, we propose a new unsupervised
approach which leverages self-supervision and clustering to capture
complementary statistics from large-scale data. We validate our approach on 96
million images from YFCC100M, achieving state-of-the-art results among
unsupervised methods on standard benchmarks, which confirms the potential of
unsupervised learning when only uncurated data are available. We also show that
pre-training a supervised VGG-16 with our method achieves 74.9% top-1
classification accuracy on the validation set of ImageNet, which is an
improvement of +0.8% over the same network trained from scratch. Our code is
available at https://github.com/facebookresearch/DeeperCluster.


  To investigate whether $f(R)$ gravity can relieve current $H_0$ and
$\sigma_8$ tensions, we constrain the Hu-Sawicki $f(R)$ gravity with
Planck-2018 cosmic microwave background and redshift space distortions
observations. We find that this model fails to relieve both $H_0$ and
$\sigma_8$ tensions, and that its two typical parameters $\log_{10}f_{R0}$ and
$n$ are insensitive to other cosmological parameters. Combining the cosmic
microwave background, baryon acoustic oscillations, Type Ia supernovae, cosmic
chronometers with redshift space distortions observations, we give our best
constraint $\log_{10}f_{R0}<-6.75$ at the $2\sigma$ confidence level.


  Recent observations have shown that the scatter in opacities among coeval
segments of the Lyman-alpha forest increases rapidly at z > 5. In this paper,
we assess whether the large scatter can be explained by fluctuations in the
ionizing background in the post-reionization intergalactic medium. We find that
matching the observed scatter at z ~ 5.5 requires a short spatially averaged
mean free path of < 15 comoving Mpc/h, a factor of > 3 shorter than direct
measurements at z ~ 5.2. We argue that such rapid evolution in the mean free
path is difficult to reconcile with our measurements of the global H I
photoionization rate, which stay approximately constant over the interval z ~
4.8 - 5.5. However, we also show that measurements of the mean free path at z >
5 are likely biased towards higher values by the quasar proximity effect. This
bias can reconcile the short values of the mean free path that are required to
explain the large scatter in opacities. We discuss the implications of this
scenario for cosmological reionization. Finally, we investigate whether other
statistics applied to the z > 5 Lyman-alpha forest can shed light on the origin
of the scatter. Compared to a model with a uniform ionizing background, models
that successfully account for the scatter lead to enhanced power in the
line-of-sight flux power spectrum on scales k < 0.1 h/Mpc. We find tentative
evidence for this enhancement in observations of the high-redshift Lyman-alpha
forest.


  Game semantics has provided adequate models for a variety of programming
languages, in which types are interpreted as two-player games and programs as
strategies. Melli\`es (2018) suggested that such categories of games and
strategies may be obtained as instances of a simple abstract construction on
weak double categories. However, in the particular case of simple games, his
construction slightly differs from the standard category. We refine the
abstract construction using factorisation systems, and show that the new
construction yields the standard category of simple games and strategies.
Another perhaps surprising instance is Day's convolution monoidal structure on
the category of presheaves over a strict monoidal category.


  A new class of quantum channels called Grassmann channels is introduced and
their classical and quantum capacity is calculated. The channel class appears
in a study of the two-mode squeezing operator constructed from operators
satisfying the fermionic algebra. We compare Grassmann channels with the
channels induced by the bosonic two-mode squeezing operator. Among other
results, we challenge the relevance of calculating entanglement measures to
assess or compare the ability of bosonic and fermionic states to send quantum
information to uniformly accelerated frames.


  In this highly speculative note we conjecture that it may be possible to
understand features of coincident D-branes, such as the appearance of enhanced
non-abelian gauge symmetry, in a purely geometric fashion, using a form of
geometry known as scheme theory. We give a very brief introduction to some
relevant ideas from scheme theory, and point out how these ideas work in
special cases.


  In this paper, we first generalize a theorem for counting the number of faces
of an oriented embedding of a graph that passing through a given cut-edge set
[S. Stahl, Trans. Amer. Math. Soc. 259 (1980), 129--145] to all surfaces. Then
we extend Stahl's bounds for the average genus of the vertex-amalgamation of
graphs [S. Stahl, Discrete Math. 142 (1995), 235--245] to signed graphs.


  Supermassive stars (SMSs) forming via very rapid mass accretion (Mdot >~ 0.1
Msun/yr) could be precursors of supermassive black holes observed beyond
redshift of about 6. Extending our previous work, we here study the evolution
of primordial stars growing under such rapid mass accretion until the stellar
mass reaches 10^{4 - 5} Msun. Our stellar evolution calculations show that a
star becomes supermassive while passing through the "supergiant protostar"
stage, whereby the star has a very bloated envelope and a contracting inner
core. The stellar radius increases monotonically with the stellar mass, until
=~ 100 AU for M_* >~ 10^4 Msun, after which the star begins to slowly contract.
Because of the large radius the effective temperature is always less than 10^4
K during rapid accretion. The accreting material is thus almost completely
transparent to the stellar radiation. Only for M_* >~ 10^5 Msun can stellar UV
feedback operate and disturb the mass accretion flow. We also examine the
pulsation stability of accreting SMSs, showing that the pulsation-driven mass
loss does not prevent stellar mass growth. Observational signatures of bloated
SMSs should be detectable with future observational facilities such as the
James Webb Space Telescope. Our results predict that an inner core of the
accreting SMS should suffer from the general relativistic instability soon
after the stellar mass exceeds 10^5 Msun. An extremely massive black hole
should form after the collapse of the inner core.


  We present a novel cathodoluminescence spectroscopy technique which combines
a deep subwavelength excitation resolution with angle-resolved detection
capabilities. The cathodoluminescence emission is collected by a paraboloid
mirror (effective NA=0.96) and is projected onto a 2D CCD array. The azimuthal
and polar emission pattern is directly deduced from the image. As proof of
principle we use the technique to measure the angular distribution of
transition radiation from a single crystalline gold surface under 30 keV
electron irradiation. We find that the experiment matches very well with
theory, illustrating the potential of this new technique for the
characterization of photonic structures with deep subwavelength dimensions.


  We revisit the problem of the critical velocity of a clean one-dimensional
superconductor. {\changed At the level of mean-field theory}, we find that the
zero-temperature value of the critical velocity--the uniform velocity of the
superfluid condensate at which the superconducting state becomes unstable--is a
factor of $\sqrt{2}$ smaller than the Landau critical velocity. This is in
contrast to a prior finding, which held that the critical velocity is equal to
the Landau critical velocity. The smaller value of the critical velocity, which
our analysis yields, is the result of a pre-emptive
Clogston-Chandrasekhar--like discontinuous phase transition, and is an analog
of the threshold value of the uniform exchange-field of a superconductor,
previously investigated by Sarma and by Maki and Tsuneto. We also consider the
impact of nonzero temperature, study critical currents, and examine
metastability and its limits in the temperature versus flow-velocity phase
diagram. In addition, we comment on the effects of electron scattering by
impurities.


  We report the discovery of a galaxy at a redshift z = 6.17 identified from
deep narrow band imaging and spectroscopic follow-up in one of the CFHT-VIRMOS
deep survey fields at 0226-04. In addition to the existing deep BVRI images of
this field, we obtained a very deep narrow band image at 920 nm with the aim of
detecting Lyalpha emission at redshift ~ 6.5. Spectroscopic follow-up of some
of the candidates selected on the basis of their excess flux in the NB920
filter was performed at the VLT-UT4 with the FORS2 instrument. For one object a
strong and asymmetric emission line associated with a strong break in continuum
emission is identified as Lyalpha at z = 6.17. This galaxy was selected from
its continuum emission in the 920 nm filter rather than for its Lyalpha
emission, in effect performing a Lyman Break detection at z = 6.17. We estimate
a star formation rate of several tens of M/yr for this object, with a velocity
dispersion 400 km/s. The spectroscopic follow-up of other high $z$ galaxy
candidates is on-going.


  With the recent prevalence of remote education, academic assessments are
often conducted online, leading to further concerns surrounding assessment
misconducts. This paper investigates the potentials of online assessment
misconduct (e-cheating) and proposes practical countermeasures against them.
The mechanism for detecting the practices of online cheating is presented in
the form of an e-cheating intelligent agent, comprising of an internet protocol
(IP) detector and a behavioural monitor. The IP detector is an auxiliary
detector which assigns randomised and unique assessment sets as an early
procedure to reduce potential misconducts. The behavioural monitor scans for
irregularities in assessment responses from the candidates, further reducing
any misconduct attempts. This is highlighted through the proposal of the
DenseLSTM using a deep learning approach. Additionally, a new PT Behavioural
Database is presented and made publicly available. Experiments conducted on
this dataset confirm the effectiveness of the DenseLSTM, resulting in
classification accuracies of up to 90.7%.


  In order to calculate the power spectrum generated during a stage of
inflation, we have to specify the quantum state of the inflaton perturbations,
which is conventionally assumed to be the Bunch-Davies vacuum. We argue that
this choice is justified only if the interactions of cosmological perturbations
are strong enough to drive excited states toward the vacuum. We quantify this
efficiency by calculating the decay probabilities of excited states to leading
order in the slow-roll expansion in canonical single-field inflationary models.
These probabilities are suppressed by a slow-roll parameter and the squared
Planck mass, and enhanced by ultraviolet and infrared cut-offs. For natural
choices of these scales decays are unlikely, and, hence, the choice of the
Bunch-Davies vacuum as the state of the primordial perturbations does not
appear to be warranted.


  We consider the model of interaction between the immune system and tumor
cells including a memory function that reflect the influence of the past
states, to simulate the time needed by the latter to develop a chemical and
cell mediated response to the presence of the tumor. The memory function is
called delay kernel. The results are compared with those from other papers,
concluding that the memory function introduces new instabilities in the system
leading to an uncontrolable growth of the tumor. If the coefficient of the
memory function is used as a bifurcation parameter, it is found that Hopf
bifurcation occurs for kernel. The direction and stability of the bifurcating
periodic solutions are determined. Some numerical simulations for justifying
the theoretical analysis are also given.


  Social actors are often embedded in multiple social networks, and there is a
growing interest in studying social systems from a multiplex network
perspective. In this paper, we propose a mixed-effects model for
cross-sectional multiplex network data that assumes dyads to be conditionally
independent. Building on the uniplex $p_2$ model, we incorporate dependencies
between different network layers via cross-layer dyadic effects and actor
random effects. These cross-layer effects model the tendencies for ties between
two actors and the ties to and from the same actor to be dependent across
different relational dimensions. The model can also study the effect of actor
and dyad covariates. As simulation-based goodness-of-fit analyses are common
practice in applied network studies, we here propose goodness-of-fit measures
for multiplex network analyses. We evaluate our choice of priors and the
computational faithfulness and inferential properties of the proposed method
through simulation. We illustrate the utility of the multiplex $p_2$ model in a
replication study of a toxic chemical policy network. An original study that
reflects on gossip as perceived by gossip senders and gossip targets, and their
differences in perspectives, based on data from 34 Hungarian elementary school
classes, highlights the applicability of the proposed method.


  Underwater datacenters (UDCs) hold promise as next-generation data storage
due to their energy efficiency and environmental sustainability benefits. While
the natural cooling properties of water save power, the isolated aquatic
environment and long-range sound propagation in water create unique
vulnerabilities which differ from those of on-land data centers. Our research
discovers the unique vulnerabilities of fault-tolerant storage devices,
resource allocation software, and distributed file systems to acoustic
injection attacks in UDCs. With a realistic testbed approximating UDC server
operations, we empirically characterize the capabilities of acoustic injection
underwater and find that an attacker can reduce fault-tolerant RAID 5 storage
system throughput by 17% up to 100%. Our closed-water analyses reveal that
attackers can (i) cause unresponsiveness and automatic node removal in a
distributed filesystem with only 2.4 minutes of sustained acoustic injection,
(ii) induce a distributed database's latency to increase by up to 92.7% to
reduce system reliability, and (iii) induce load-balance managers to redirect
up to 74% of resources to a target server to cause overload or force resource
colocation. Furthermore, we perform open-water experiments in a lake and find
that an attacker can cause controlled throughput degradation at a maximum
allowable distance of 6.35 m using a commercial speaker. We also investigate
and discuss the effectiveness of standard defenses against acoustic injection
attacks. Finally, we formulate a novel machine learning-based detection system
that reaches 0% False Positive Rate and 98.2% True Positive Rate trained on our
dataset of profiled hard disk drives under 30-second FIO benchmark execution.
With this work, we aim to help manufacturers proactively protect UDCs against
acoustic injection attacks and ensure the security of subsea computing
infrastructures.


  Approximate random k-colouring of a graph G=(V,E) is a very well studied
problem in computer science and statistical physics. It amounts to constructing
a k-colouring of G which is distributed close to Gibbs distribution, i.e. the
uniform distribution over all the k-colourings of G. Here, we deal with the
problem when the underlying graph is an instance of Erdos-Renyi random graph
G(n,p), where p=d/n and d is fixed.
  We propose a novel efficient algorithm for approximate random k-colouring
with the following properties: given an instance of G(n,d/n) and for any
k>(2+\epsilon)d, it returns a k-colouring distributed within total variation
distance n^{-Omega(1)} from the Gibbs distribution, with probability
1-n^{-Omega(1)}.
  What we propose is neither a MCMC algorithm nor some algorithm inspired by
the message passing heuristics that were introduced by statistical physicist.
Our algorithm is of combinatorial nature. It is based on a rather simple
recursion which reduces the random k-colouring of G(n,d/n) to random
k-colouring simpler subgraphs first.
  The lower bound on the number of colours for our algorithm to run in
polynomial time is dramatically smaller than the corresponding bounds we have
for any previous algorithm.


  It is shown that an accelerating expansion of the present-day Universe
extracted from observed luminosity of the type Ia supernovae can be explained
by quantum theory which takes into account feedback coupling between geometry
and matter (like in Mach's principle). At the same time an accelerating
expansion of the Universe is explained by the influence of small negative
cosmological constant. A comparison with the model with positive cosmological
constant (dark energy) which also has obtained its theoretical grounds in a
structure of the developed formalism is made. Parameters of the Universe in the
states with large quantum numbers are calculated.


  Fluid-based soft actuators are an attractive option for lightweight and
human-safe robots. These actuators, combined with fluid pressure force
feedback, are in principle a form of series-elastic actuation (SEA), in which
nearly all driving-point (e.g. motor/gearbox) friction can be eliminated.
Fiber-elastomer soft actuators offer unique low-friction and low-hysteresis
mechanical properties which are particularly suited to force-control based on
internal pressure force feedback, rather than traditional external force
feedback using force/tactile sensing, since discontinuous (Coulomb) endpoint
friction is unobservable to internal fluid pressure. However, compensation of
endpoint smooth hysteresis through a model-based feedforward term is possible.
We report on internal-pressure force feedback through a disturbance observer
(DOB) and model-based feedforward compensation of endpoint friction and
nonlinear hysteresis for a 2-DOF lightweight robotic gripper driven by
rolling-diaphragm linear actuators coupled to direct-drive brushless motors,
achieving an active low-frequency endpoint impedance range ("Z-width") of 50dB.


  We study a well known noisy model of the graph isomorphism problem. In this
model, the goal is to perfectly recover the vertex correspondence between two
edge-correlated Erd\H{o}s-R\'{e}nyi random graphs, with an initial seed set of
correctly matched vertex pairs revealed as side information. For seeded
problems, our result provides a significant improvement over previously known
results. We show that it is possible to achieve the information-theoretic limit
of graph sparsity in time polynomial in the number of vertices $n$. Moreover,
we show the number of seeds needed for exact recovery in polynomial-time can be
as low as $n^{3\epsilon}$ in the sparse graph regime (with the average degree
smaller than $n^{\epsilon}$) and $\Omega(\log n)$ in the dense graph regime.
  Our results also shed light on the unseeded problem. In particular, we give
sub-exponential time algorithms for sparse models and an $n^{O(\log n)}$
algorithm for dense models for some parameters, including some that are not
covered by recent results of Barak et al.


  To distinguish between the effects of strain and magnetism on the
superconductivity in $c$-axis
La$_{2/3}$Ca$_{1/3}$MnO$_3$/YBa$_2$Cu$_3$O$_{7-\delta}$ (LCMO/YBCO)
heterostructures, we study perovskite/YBCO/perovskite thin films using either
ferromagnetic LCMO or paramagnetic LaNiO$_3$ (LNO) as perovskite. For a
lattice-symmetry matched comparison, we also use orthorhombic
PrBa$_2$Cu$_3$O$_{7-\delta}$ (PBCO) in place of the pseudocubic perovskites.
Unlike PBCO/YBCO/PBCO, both LCMO/YBCO/LCMO and LNO/YBCO/LNO trilayers show
strong attenuation of the superconducting $T_c$ as YBCO layer thickness is
reduced from 21.4 to 5.4 nm. Our results indicate that heteroepitaxial strain,
rather than long-range proximity effect, is responsible for the long length
scales of $T_c$ attenuation observed in $c$-axis LCMO/YBCO heterostructures.


  Purpose: This study proposes an adaptive bandwidth management system which
can be explicitly used by educational institutions. The primary goal of the
system is to increase the bandwidth of the users who access more on educational
websites. Through this proposed bandwidth management, the users of the campus
networks is encouraged to utilize the internet for educational purposes.
  Method: The weblog from a university's pfSense proxy server was utilized and
undergo Web Usage Mining (WUM) to determine the number of educational and
non-educational websites accessed by the users. Certain formulas were used in
the computation of the bandwidth which was dynamically assigned to the users. A
prototyping technique was applied in developing adaptive bandwidth management
system. The prototype was simulated and evaluated by experts in compliance with
ISO/IEC 14598-6 and ISO/IEC 9126-1 standards.
  Results: This study found that the prototype is capable of adjusting the
bandwidth of the network users dynamically. The users who browsed more on
educational websites or contents were assigned with higher bandwidth compared
to those who are not. Further, the evaluated prototype met the software
standards of ISO.
  Conclusion: The proposed adaptive bandwidth management can contribute to the
continuous development in the area of computer networking, especially in
designing and managing campus networks. It also helps the network
administrators or IT managers in allocating bandwidth with minimal effort.


  In this work, we investigate the dynamics of vortex-induced vibration of an
elastically mounted cylinder with very low values of mass and damping. We use
two methods to investigate this canonical problem: first we calculate the
instantaneous phase between the cylinder motion and the fluid forcing; second
we decompose the total hydrodynamic force into drag and lift components that
act along and normal to, respectively, the instantaneous effective angle of
attack. We focus on the phase dynamics in the large-amplitude-response range,
consisting of the initial, upper and lower branches of response. The
instantaneous phase between the transverse force and displacement shows
repeated phase slips separating periods of constant, or continuous-drifting,
phase in the second half of the upper branch. The phase between the lift
component and displacement shows strong phase locking throughout the
large-amplitude range - the average phase varies linearly with the primary
frequency - however the modulation of this phase is largest in the second half
of the upper branch. These observations suggest that the
large-amplitude-response dynamics is driven by two distinct limit cycles - one
that is stable over a very small range of reduced velocity at the beginning of
the upper branch, and another that consists of the lower branch. The chaotic
oscillation between them - the majority of the upper branch - occurs when
neither limit cycle is stable. The transition between the upper and lower
branches is marked by intermittent switching with epochs of time where
different states exist at a constant reduced velocity. These different states
are clearly apparent in the phase between the lift and displacement,
illustrating the utility of the force decomposition employed.


  We investigate a new paradigm that uses differentiable SLAM architectures in
a self-supervised manner to train end-to-end deep learning models in various
LiDAR based applications. To the best of our knowledge there does not exist any
work that leverages SLAM as a training signal for deep learning based models.
We explore new ways to improve the efficiency, robustness, and adaptability of
LiDAR systems with deep learning techniques. We focus on the potential benefits
of differentiable SLAM architectures for improving performance of deep learning
tasks such as classification, regression as well as SLAM. Our experimental
results demonstrate a non-trivial increase in the performance of two deep
learning applications - Ground Level Estimation and Dynamic to Static LiDAR
Translation, when used with differentiable SLAM architectures. Overall, our
findings provide important insights that enhance the performance of LiDAR based
navigation systems. We demonstrate that this new paradigm of using SLAM Loss
signal while training LiDAR based models can be easily adopted by the
community.


  The 1$_{11}$-1$_{01}$ lines of ortho and para--NH$_2$D (o/p-NH$_2$D),
respectively at 86 and 110 GHz, are commonly observed to provide constraints on
the deuterium fractionation in the interstellar medium. In cold regions, the
hyperfine structure due to the nitrogen ($^{14}$N) nucleus is resolved. To
date, this splitting is the only one which is taken into account in the NH$_2$D
column density estimates. We investigate how the inclusion of the hyperfine
splitting caused by the deuterium (D) nucleus affects the analysis of the
rotational lines of NH$_2$D. We present 30m IRAM observations of the above
mentioned lines, as well as APEX o/p-NH$_2$D observations of the
1$_{01}$-0$_{00}$ lines at 333 GHz. The hyperfine spectra are first analyzed
with a line list that only includes the hyperfine splitting due to the $^{14}$N
nucleus. We find inconsistencies between the line widths of the
1$_{01}$-0$_{00}$ and 1$_{11}$-1$_{01}$ lines, the latter being larger by a
factor of $\sim$1.6$\pm0.3$. Such a large difference is unexpected given the
two sets of lines are likely to originate from the same region. We next employ
a newly computed line list for the o/p-NH$_2$D transitions, where the hyperfine
structure induced by both nitrogen and deuterium nuclei is included. With this
new line list, the analysis of the previous spectra leads to linewidths which
are compatible. Neglecting the hyperfine structure owing to D leads to
overestimate the linewidths of the o/p-NH$_2$D lines at 3 mm. The error for a
cold molecular core is about 50\%. This error propagates directly to the column
density estimate. It is therefore recommended to take into account the
hyperfine splittings caused by both the $^{14}$N and D nuclei in any analysis
relying on these lines.


  In this paper, we generalize the results about generalized derivations of Lie
algebras to the case of BiHom-Lie algebras. In particular we give the
classification of generalized derivations of Heisenberg BiHom-Lie algebras. The
definition of the generalized derivation depends on some parameters $
(\lambda,\mu,\gamma)\in \mathbb{C}^{3}. $ In particular for
$(\lambda,\mu,\gamma)=(1,1,1)$, we obtain classical concept of derivation of
BiHom-Lie algebra and for $(\lambda,\mu,\gamma)=(1,1,0) $ we obtain the
centroid of BiHom-Lie algebra. We give classifications of $ 2 $-dimensional
BiHom-Lie algebra, centroides and derivations of $ 2 $-dimensional BiHom-Lie
algebras.


  Lyman-alpha emitter (LAE) surveys have successfully used the excess in a
narrow-band filter compared to a nearby broad-band image to find candidates.
However, the odd spectral energy distribution (SED) of LAEs combined with the
instrumental profile have important effects on the properties of the candidate
samples extracted from these surveys. We investigate the effect of the bandpass
width and the transmission profile of the narrow-band filters used for
extracting LAE candidates at redshifts z ~ 6.5 through Monte Carlo simulations,
and we present pilot observations to test the performance of tunable filters to
find LAEs and other emission-line candidates. We compare the samples obtained
using a narrow ideal-rectangular-filter, the Subaru NB921 narrow-band filter,
and sweeping across a wavelength range using the ultra-narrow-band tunable
filters of the instrument OSIRIS, installed at the 10.4m Gran Telescopio
Canarias. We use this instrument for extracting LAE candidates from a small set
of real observations. Broad-band data from the Subaru, HST and Spitzer
databases were used for fiting SEDs to calculate photometric redshifts and to
identify interlopers. Narrow-band surveys are very efficient to find LAEs in
large sky areas, but the samples obtained are not evenly distributed in
redshift along the filter bandpass, and the number of LAEs with equivalent
widths < 60 angstroms can be underestimated. These biased results do not appear
in samples obtained using ultra-narrow-band tunable filters. However, the field
size of tunable filters is restricted because of the variation of the effective
wavelength across the image. Thus narrow-band and ultra-narrow-band surveys are
complementary strategies to investigate high-redshift LAEs.


  In this paper we study the enumeration of number (denoted as ${D_I}$) of spin
$I$ states for fermions in a single-$j$ shell and bosons with spin $l$. We show
that $D_I$ can be enumerated by the reduction from SU$(n+1)$ to SO(3). New
regularities of $D_I$ are discerned.


  Psychological models are increasingly being used to explain online behavioral
traces. Aside from the commonly used personality traits as a general user
model, more domain dependent models are gaining attention. The use of domain
dependent psychological models allows for more fine-grained identification of
behaviors and provide a deeper understanding behind the occurrence of those
behaviors. Understanding behaviors based on psychological models can provide an
advantage over data-driven approaches. For example, relying on psychological
models allow for ways to personalize when data is scarce. In this preliminary
work we look at the relation between users' musical sophistication and their
online music listening behaviors and to what extent we can successfully predict
musical sophistication. An analysis of data from a study with 61 participants
shows that listening behaviors can successfully be used to infer users' musical
sophistication.


  This paper extends the model reduction method by the operator projection to
the three-dimensional special relativistic Boltzmann equation. The derivation
of arbitrary order moment system is built on our careful study of infinite
families of the complicate Grad type orthogonal polynomials depending on a
parameter and the real spherical harmonics. We derive the recurrence relations
of the polynomials, calculate their derivatives with respect to the independent
variable and parameter respectively, and study their zeros. The recurrence
relations and partial derivatives of the real spherical harmonics are also
given. It is proved that our moment system is globally hyperbolic, and linearly
stable. Moreover, the Lorentz covariance is also studied in the 1D space.


  This paper critically examines a recently developed proposal for a border
control system called iBorderCtrl, designed to detect deception based on facial
recognition technology and the measurement of micro-expressions, termed
'biomarkers of deceit'. Funded under the European Commission's Horizon 2020
programme, we situate our analysis in the wider political economy of 'emotional
AI' and the history of deception detection technologies. We then move on to
interrogate the design of iBorderCtrl using publicly available documents and
assess the assumptions and scientific validation underpinning the project
design. Finally, drawing on a Bayesian analysis we outline statistical
fallacies in the foundational premise of mass screening and argue that it is
very unlikely that the model that iBorderCtrl provides for deception detection
would work in practice. By interrogating actual systems in this way, we argue
that we can begin to question the very premise of the development of
data-driven systems, and emotional AI and deception detection in particular,
pushing back on the assumption that these systems are fulfilling the tasks they
claim to be attending to and instead ask what function such projects carry out
in the creation of subjects and management of populations. This function is not
merely technical but, rather, we argue, distinctly political and forms part of
a mode of governance increasingly shaping life opportunities and fundamental
rights.


  Equilibrium topological phases are robust against weak static disorder but
may break down in the strong disorder regime. Here we explore the stability of
the quench-induced emergent dynamical topology in the presence of dynamical
noise. We develop an analytic theory and show that for weak noise, the quantum
dynamics induced by quenching an initial trivial phase to Chern insulating
regime exhibits robust emergent topology on certain momentum subspaces called
band inversion surfaces (BISs). The dynamical topology is protected by the
minimal oscillation frequency over the BISs, mimicking a bulk gap of the
dynamical phase. Singularities emerge in the quench dynamics, with the minimal
oscillation frequency vanishing on the BISs if increasing noise to critical
strength, manifesting a dynamical topological transition, beyond which the
emergent topology breaks down. Two types of dynamical transitions are
predicted. Interestingly, we predict a sweet spot in the critical transition
when noise couples to all three spin components in the same strength, in which
case the dynamical topology survives at arbitrarily strong noise regime. This
work unveils novel features of the dynamical topology under dynamical noise,
which can be probed with control in experiment.


  We present a method to design distributed generation and demand control
schemes for primary frequency regulation in power networks that guarantee
asymptotic stability and ensure fairness of allocation. We impose a passivity
condition on net power supply variables and provide explicit steady state
conditions on a general class of generation and demand control dynamics that
ensure convergence of solutions to equilibria that solve an appropriately
constructed network optimization problem. We also show that the inclusion of
controllable demand results in a drop in steady state frequency deviations. We
discuss how various classes of dynamics used in recent studies fit within our
framework and show that this allows for less conservative stability and
optimality conditions. We illustrate our results with simulations on the IEEE
68 bus system and observe that both static and dynamic demand response schemes
that fit within our framework offer improved transient and steady state
behavior compared with control of generation alone. The dynamic scheme is also
seen to enhance the robustness of the system to time-delays.


  The study of dynamical phase transitions has been attracting considerable
research efforts in the last decade. One theme of present interest is to search
for exotic scenarios beyond the framework of equilibrium phase transitions.
Here, we establish a duality between many-body dynamics and static Hamiltonian
ground states for both classical and quantum systems. We construct frustration
free Hamiltonians whose ground state phase transitions have rigorous duality to
chaotic transitions in dynamical systems. By this duality, we show the
corresponding ground state phase transitions are characterized by
bulk-to-surface response, which are then dubbed "peratic" meaning defined by
response to the boundary. For the classical system, we show how the time-like
dimension emerges in the static ground states. For the quantum system, the
ground state is a superposition of geometrical lines on a two dimensional
array, which encode the dynamical Floquet evolution history of one dimensional
disordered spin chains. Our prediction of peratic phase transition has direct
consequences in quantum simulation platforms such as Rydberg atoms and
superconducting qubits, as well as anisotropic spin glass materials. The
discovery would shed light on the unification of dynamical phase transitions
with equilibrium systems.


  We show that the introduction of frustration into the spin-1/2
two-dimensional (2D) antiferromagnetic Heisenberg model on a square lattice via
a next-nearest neighbor exchange interaction can lead to a Peierls-like
transition, from a tetragonal to an orthorhombic phase, when the spins are
coupled to adiabatic phonons. The two different orthorhombic ground states
define an Ising order parameter, which is expected to lead to a finite
temperature transition. Implications for ${\rm Li_2VOSiO_4}$, the first
realization of that model, will be discussed.


  Basic ideas of muon tracker technique for the solution of primary cosmic ray
(PCR) composition problem in the energy range $10^{17}-10^{18}$ eV are
presented. The approach uses MC simulation data made with CORSIKA6.990 for
"Pamir-XXI" site conditions. Similar technology can certainly be developed for
other observation levels and interaction models. One can probably extend it to
much higher primary energies.


  We describe here the higher rank numerical range, as defined by Choi, Kribs
and Zyczkowski, of a normal operator on an infinite dimensional Hilbert space
in terms of its spectral measure. This generalizes a result of Avendano for
self-adjoint operators. An analogous description of the numerical range of a
normal operator by Durszt is derived for the higher rank numerical range as an
immediate consequence. It has several interesting applications. We show using
Durszt's example that there exists a normal contraction $T$ for which the
intersection of the higher rank numerical ranges of all unitary dilations of
$T$ contains the higher rank numerical range of $T$ as a proper subset.
Finally, we strengthen and generalize a result of Wu by providing a necessary
and sufficient condition for the higher rank numerical range of a normal
contraction being equal to the intersection of the higher rank numerical ranges
of all possible unitary dilations of it.


  Recently, A. Jerez, P. Vitushinsky and M. Lavagna [Phys. Rev. Lett. 95,
127203 (2005)] claimed that the transmission phase through a quantum fot, as
measured via the Aharonov-Bohm interferometer, differs from the phase which
determines the corresponding conductance. Here we show that this claim is wrong
for the single level Anderson model, which is usually used to describe the
quantum dot. So far, there exists no derivation of this claim from any explicit
theoretical model.


  Majorana fermions were originally proposed as elementary particles acting as
their own antiparticles. In recent years, it has become clear that Majorana
fermions can instead be realized in condensed-matter systems as emergent
quasiparticles, a situation often accompanied by topological order. Here we
propose a physical system which realizes Landau levels - highly degenerate
single-particle states usually resulting from an orbital magnetic field acting
on charged particles - for Majorana fermions. This is achieved in a variant of
a quantum spin system due to Kitaev which is distorted by triaxial strain. This
strained Kitaev model displays a spin-liquid phase with charge-neutral
Majorana-fermion excitations whose spectrum corresponds to that of Landau
levels, here arising from a tailored pseudo-magnetic field. We show that
measuring the dynamic spin susceptibility reveals the Landau-level structure by
a remarkable mechanism of probe-induced bound-state formation.


  Much of the present day qualitative phenomenology of the fractional quantum
Hall effect can be understood by neglecting the interactions between composite
fermions altogether. For example the fractional quantum Hall effect at
$\nu=n/(2pn\pm 1)$ corresponds to filled composite-fermion Landau levels,and
the compressible state at $\nu=1/2p$ to the Fermi sea of composite fermions.
Away from these filling factors, the residual interactions between composite
fermions will determine the nature of the ground state. In this article, a
model is constructed for the residual interaction between composite fermions,
and various possible states are considered in a variational approach. Our study
suggests formation of composite-fermion stripes, bubble crystals, as well as
fractional quantum Hall states for appropriate situations.


  Researchers often misinterpret and misrepresent statistical outputs. This
abuse has led to a large literature on modification or replacement of testing
thresholds and $P$-values with confidence intervals, Bayes factors, and other
devices. Because the core problems appear cognitive rather than statistical, we
review simple aids to statistical interpretations. These aids emphasize logical
and information concepts over probability, and thus may be more robust to
common misinterpretations than are traditional descriptions. We use the Shannon
transform of the $P$-value $p$, also known as the binary surprisal or $S$-value
$s=-\log_{2}(p)$, to measure the information supplied by the testing procedure,
and to help calibrate intuitions against simple physical experiments like coin
tossing. We also use tables or graphs of test statistics for alternative
hypotheses, and interval estimates for different percentile levels, to thwart
fallacies arising from arbitrary dichotomies. Finally, we reinterpret
$P$-values and interval estimates in unconditional terms, which describe
compatibility of data with the entire set of analysis assumptions. We
illustrate these methods with a reanalysis of data from an existing
record-based cohort study. In line with other recent recommendations, we advise
that teaching materials and research reports discuss $P$-values as measures of
compatibility rather than significance, compute $P$-values for alternative
hypotheses whenever they are computed for null hypotheses, and interpret
interval estimates as showing values of high compatibility with data, rather
than regions of confidence. Our recommendations emphasize cognitive devices for
displaying the compatibility of the observed data with various hypotheses of
interest, rather than focusing on single hypothesis tests or interval
estimates. We believe these simple reforms are well worth the minor effort they
require.


  Frame semantics-based approaches have been widely used in semantic parsing
tasks and have become mainstream. It remains challenging to disambiguate frame
representations evoked by target lexical units under different contexts.
Pre-trained Language Models (PLMs) have been used in semantic parsing and
significantly improve the accuracy of neural parsers. However, the PLMs-based
approaches tend to favor collocated patterns presented in the training data,
leading to inaccurate outcomes. The intuition here is to design a mechanism to
optimally use knowledge captured in semantic frames in conjunction with PLMs to
disambiguate frames. We propose a novel Knowledge-Augmented Frame Semantic
Parsing Architecture (KAF-SPA) to enhance semantic representation by
incorporating accurate frame knowledge into PLMs during frame semantic parsing.
Specifically, a Memory-based Knowledge Extraction Module (MKEM) is devised to
select accurate frame knowledge and construct the continuous templates in the
high dimensional vector space. Moreover, we design a Task-oriented Knowledge
Probing Module (TKPM) using hybrid prompts (in terms of continuous and discrete
prompts) to incorporate the selected knowledge into the PLMs and adapt PLMs to
the tasks of frame and argument identification. Experimental results on two
public FrameNet datasets demonstrate that our method significantly outperforms
strong baselines (by more than +3$\%$ in F1), achieving state-of-art results on
the current benchmark. Ablation studies verify the effectiveness of KAF-SPA.


  Double porosity models for the liquid filtration in an absolutely rigid body
is derived from homogenization theory. The governing equations of the fluid
dynamics on the microscopic level consist of the Stokes system for a slightly
compressible viscous fluid, occupying a crack -- pore space. In turn, this
domain is a union of two independent systems of cracks (fissures) and pores. We
suppose that the dimensionless size $\delta$ of pores depends on the
dimensionless size $\varepsilon$ of cracks: $\delta=\varepsilon^{r}$ with
$r>1$. The rigorous justification is fulfilled for homogenization procedure as
the dimensionless size of the cracks tends to zero, while the solid body is
geometrically periodic. As a result, for the long-time process we derive the
usual Darcy equations of filtration for the liquid in cracks, while the liquid
in pores is blocked and unmoved. For the short-time processes the homogenized
system consists of acoustic equations, describing a two-velocity continuum with
three independent parameters: the liquid velocity in pores, the liquid velocity
in cracks and the common pressure. The proofs are based on the method of
reiterated homogenization, suggested by G. Allaire and M. Briane.


  Iodine is distinguished from other elements used in dark matter direct
detection experiments both by its large mass as well as its large magnetic
moment. Inelastic dark matter utilizes the large mass of iodine to allay
tensions between the DAMA annual modulation signature and the null results from
other experiments. We explore models of inelastic dark matter that also take
advantage of the second distinct property of iodine, namely its large magnetic
moment. In such models the couplings are augmented by magnetic, rather than
merely electric, interactions. These models provide simple examples where the
DAMA signal is compatible with all existing limits. We consider dipole moments
for the WIMP, through conventional magnetism as well as "dark" magnetism,
including both magnetic-magnetic and magnetic-electric scattering. We find
XENON100 and CRESST should generically see a signal, although suppressed
compared with electric inelastic dark matter models, while KIMS should see a
modulated signal comparable to or larger than that of DAMA. In a large portion
of parameter space, de-excitation occurs promptly, producing a ~ 100 keV photon
inside large xenon experiments alongside the nuclear recoil. This effect could
be searched for, but if not properly considered may cause nuclear recoil events
to fail standard cuts.


  The hyperdeteminants considered here are the simplest analogues of
determinants for higher rank tensors which have been defined by Cayley, and
apply only to tensors with an even number of indices. We have shown in a
previous article that the calculation of certain multidimensional integrals
could be reduced to the evaluation of hyperdeterminants of Hankel type. Here,
we carry out this computation by purely algebraic means in the cases of
Selberg's and Aomoto's integrals.


  We consider an active learning setting where a learner is presented with a
pool S of n unlabeled examples belonging to a domain X and asks queries to find
the underlying labeling that agrees with a target concept h^* \in H.
  In contrast to traditional active learning that queries a single example for
its label, we study more general region queries that allow the learner to pick
a subset of the domain T \subset X and a target label y and ask a labeler
whether h^*(x) = y for every example in the set T \cap S.
  Such more powerful queries allow us to bypass the limitations of traditional
active learning and use significantly fewer rounds of interactions to learn but
can potentially lead to a significantly more complex query language. Our main
contribution is quantifying the trade-off between the number of queries and the
complexity of the query language used by the learner.
  We measure the complexity of the region queries via the VC dimension of the
family of regions. We show that given any hypothesis class H with VC dimension
d, one can design a region query family Q with VC dimension O(d) such that for
every set of n examples S \subset X and every h^* \in H, a learner can submit
O(d log n) queries from Q to a labeler and perfectly label S. We show a
matching lower bound by designing a hypothesis class H with VC dimension d and
a dataset S \subset X of size n such that any learning algorithm using any
query class with VC dimension less than O(d) must make poly(n) queries to label
S perfectly.
  Finally, we focus on well-studied hypothesis classes including unions of
intervals, high-dimensional boxes, and d-dimensional halfspaces, and obtain
stronger results. In particular, we design learning algorithms that (i) are
computationally efficient and (ii) work even when the queries are not answered
based on the learner's pool of examples S but on some unknown superset L of S


  We prove a rank-one theorem \`a la G. Alberti for the derivatives of
vector-valued maps with bounded variation in a class of Carnot groups that
includes Heisenberg groups $\mathbb H^n$ for $n\geq 2$. The main tools are
properties relating the horizontal derivatives of a real-valued function with
bounded variation and its subgraph.


  An algorithm for computing the nonlinearity of a Boolean function from its
algebraic normal form (ANF) is proposed. By generalizing the expression of the
weight of a Boolean function in terms of its ANF coefficients, a formulation of
the distances to linear functions is obtained. The special structure of these
distances can be exploited to reduce the task of nonlinearity computation to
solving an associated binary integer programming problem. The proposed
algorithm can be used in cases where applying the Fast Walsh transform is
infeasible, typically when the number of input variables exceeds 40.


  The propagator of the spinless Aharonov-Bohm-Coulomb system is derived by
following the Duru-Kleinert method. We use this propagator to explore the
spin-1/2 Aharonov-Bohm-Coulomb system which contains a point interaction as a
Zeeman term. Incorporation of the self-adjoint extension method into the
Green's function formalism properly allows us to derive the finite propagator
of the spin-1/2 Aharonov-Bohm-Coulomb system. As a by-product, the relation
between the self-adjoint extension parameter and the bare coupling constant is
obtained. Bound-state energy spectra of both spinless and spin-1/2
Aharonov-Bohm-Coulomb systems are examined.


  It is shown that if A is an AF algebra then a crossed product of A by the
integers can be embedded into an AF algebra if and only if the crossed product
is stably finite. This equivalence follows from a simple K-theoretic
characterization of AF embeddability. It is then shown that if a crossed
product of an AF algebra by the integers is AF embeddable then the AF embedding
can be chosen in such a way as to induce a rationally injective map on K_0 of
the crossed product.


  We construct the consistent supersymmetric extensions of the operators
describing the recoil of a D-brane and show that they realize an N=1
logarithmic superconformal algebra. The corresponding supersymmetric vertex
operator is related to the action of a twisted superparticle with twist field
determined by the angular momentum of the recoiling D-brane and with explicitly
broken kappa-symmetry. We show that the superconformal completion removes the
logarithmic modular divergences that are present in the bosonic string loop
scattering amplitudes. These features are all consequences of the relationship
that exists in these models between worldsheet rescaling and the time evolution
of the D-brane in target space.


  Image segmentation is a primary task in many medical applications. Recently,
many deep networks derived from U-Net have been extensively used in various
medical image segmentation tasks. However, in most of the cases, networks
similar to U-net produce coarse and non-smooth segmentations with lots of
discontinuities. To improve and refine the performance of U-Net like networks,
we propose the use of parallel decoders which along with performing the mask
predictions also perform contour prediction and distance map estimation. The
contour and distance map aid in ensuring smoothness in the segmentation
predictions. To facilitate joint training of three tasks, we propose a novel
architecture called Psi-Net with a single encoder and three parallel decoders
(thus having a shape of $\Psi$), one decoder to learns the segmentation mask
prediction and other two decoders to learn the auxiliary tasks of contour
detection and distance map estimation. The learning of these auxiliary tasks
helps in capturing the shape and the boundary information. We also propose a
new joint loss function for the proposed architecture. The loss function
consists of a weighted combination of Negative Log likelihood and Mean Square
Error loss. We have used two publicly available datasets: 1) Origa dataset for
the task of optic cup and disc segmentation and 2) Endovis segment dataset for
the task of polyp segmentation to evaluate our model. We have conducted
extensive experiments using our network to show our model gives better results
in terms of segmentation, boundary and shape metrics.


  We extend calculus from smooth manifolds to topological manifolds making use
of a theory of generalized functions developed for this aim. Actually such
extension fits into a boarder context: the universal construction of a site
containing all continuous maps between topological spaces and whose arrows are
smooth in the generalized sense. As an application we prove the existence of
non singular generalized tangent vector fields on spheres of any dimension,
showing how this result is coherent with the non existence of smooth vector
fields on spheres of even dimension. Many other applications are outlined or
suggested, some of which are under development by the author.


  In a recent experiment the superconducting gap of a single unit cell thick
FeSe film on SrTiO$_3$ substrate is observed by scanning tunneling spectroscopy
and angle-resolved photoemission spectroscopy. The value of the superconducting
gap is much larger than that of the bulk FeSe under ambient pressure. In this
paper we study the effects of screening due to the ferroelectric phonons on
Cooper pairing. We conclude it can significantly enhance the energy scale of
Cooper pairing and even change the pairing symmetry. Our results also raise
some concerns on whether phonons can be completely ignored for bulk iron-based
superconductors.


  We report the detection of a dispersed Fast Radio Burst (FRB) in archival
intermediate-latitude Parkes Radio Telescope data. The burst appears to be of
the same physical origin as the four purported extragalactic FRBs reported by
Thornton et al. (2013). This burst's arrival time precedes the Thornton et
al.~bursts by ten years. We consider that this survey, and many other archival
low-latitude (|gb|<30deg) pulsar surveys, have been searched for FRBs but
produced fewer detections than the comparatively brief Thornton et al.~search.
Such a rate dependence on Galactic position could provide critical supporting
evidence for an extragalactic origin for FRBs. To test this, we form an
analytic expression to account for Galactic position and survey setup in FRB
rate predictions. Employing a sky temperature, scattering, and dispersion model
of the Milky Way, we compute the expected number of FRBs if they are
isotropically distributed on the sky w.r.t. Galactic position (i.e. local), and
if they are of extragalactic origin. We demonstrate that the relative detection
rates reject a local origin with a confidence of 99.96% (~3.6 sigma). The
extragalactic predictions provide a better agreement, however are still strong
discrepancies with the low-latitude detection rate at a confidence of 99.69%
(~2.9 sigma). However, for the extragalactic population, the differences in
predicted vs.~detected population may be accounted for by a number of factors,
which we discuss.


  We study the causal bandit problem that entails identifying a near-optimal
intervention from a specified set $A$ of (possibly non-atomic) interventions
over a given causal graph. Here, an optimal intervention in ${A}$ is one that
maximizes the expected value for a designated reward variable in the graph, and
we use the standard notion of simple regret to quantify near optimality.
Considering Bernoulli random variables and for causal graphs on $N$ vertices
with constant in-degree, prior work has achieved a worst case guarantee of
$\widetilde{O} (N/\sqrt{T})$ for simple regret. The current work utilizes the
idea of covering interventions (which are not necessarily contained within
${A}$) and establishes a simple regret guarantee of
$\widetilde{O}(\sqrt{N/T})$. Notably, and in contrast to prior work, our simple
regret bound depends only on explicit parameters of the problem instance. We
also go beyond prior work and achieve a simple regret guarantee for causal
graphs with unobserved variables. Further, we perform experiments to show
improvements over baselines in this setting.


  Deep learning model inference is a key service in many businesses and
scientific discovery processes. This paper introduces RIBBON, a novel deep
learning inference serving system that meets two competing objectives:
quality-of-service (QoS) target and cost-effectiveness. The key idea behind
RIBBON is to intelligently employ a diverse set of cloud computing instances
(heterogeneous instances) to meet the QoS target and maximize cost savings.
RIBBON devises a Bayesian Optimization-driven strategy that helps users build
the optimal set of heterogeneous instances for their model inference service
needs on cloud computing platforms -- and, RIBBON demonstrates its superiority
over existing approaches of inference serving systems using homogeneous
instance pools. RIBBON saves up to 16% of the inference service cost for
different learning models including emerging deep learning recommender system
models and drug-discovery enabling models.


  Residuals in regression models are often spatially correlated. Prominent
examples include studies in environmental epidemiology to understand the
chronic health effects of pollutants. I consider the effects of residual
spatial structure on the bias and precision of regression coefficients,
developing a simple framework in which to understand the key issues and derive
informative analytic results. When unmeasured confounding introduces spatial
structure into the residuals, regression models with spatial random effects and
closely-related models such as kriging and penalized splines are biased, even
when the residual variance components are known. Analytic and simulation
results show how the bias depends on the spatial scales of the covariate and
the residual: one can reduce bias by fitting a spatial model only when there is
variation in the covariate at a scale smaller than the scale of the unmeasured
confounding. I also discuss how the scales of the residual and the covariate
affect efficiency and uncertainty estimation when the residuals are independent
of the covariate. In an application on the association between black carbon
particulate matter air pollution and birth weight, controlling for large-scale
spatial variation appears to reduce bias from unmeasured confounders, while
increasing uncertainty in the estimated pollution effect.


  Motivated by the problem of Many-Body Localization and the recent numerical
results for the level and eigenfunction statistics on the random regular
graphs, a generalization of the Rosenzweig-Porter random matrix model is
suggested that possesses two localization transitions as the parameter $\gamma$
of the model varies from 0 to $\infty$. One of them is the Anderson transition
from the localized to the extended states that happens at $\gamma=2$. The other
one at $\gamma=1$ is the transition from the extended non-ergodic
(multifractal) states to the extended ergodic states similar to the eigenstates
of the Gaussian Orthogonal Ensemble. We computed the two-level spectral
correlation function, the spectrum of multifractality $f(\alpha)$ and the wave
function overlap which all show the transitions at $\gamma=1$ and $\gamma=2$.


  We consider the following problem that we call the Shortest Two Disjoint
Paths problem: given an undirected graph $G=(V,E)$ with edge weights $w:E
\rightarrow \mathbb{R}$, two terminals $s$ and $t$ in $G$, find two internally
vertex-disjoint paths between $s$ and $t$ with minimum total weight. As shown
recently by Schlotter and Seb\H{o} (2022), this problem becomes NP-hard if
edges can have negative weights, even if the weight function is conservative,
there are no cycles in $G$ with negative total weight. We propose a
polynomial-time algorithm that solves the Shortest Two Disjoint Paths problem
for conservative weights in the case when the negative-weight edges form a
constant number of trees in $G$.


  Mars shares many similarities and characteristics to Earth including various
geological features and planetary structure. The remarkable bimodal
distribution of elevations in both planets is one of the most striking global
features suggesting similar geodynamic processes of crustal differentiation on
Earth and Mars. There also exist several evidences, based on geographic
features resembling ancient shorelines, for existence of an ancient martian
ocean in the northern hemisphere which covers nearly one third of the planet's
surface. However, the interpretation of some features as ancient shorelines has
been thoroughly challenged that left the existence of a primordial martian
ocean controversial. Moreover, if oceans were formerly present on Mars, there
is still a big ambiguity about the volume of water with the estimations ranging
over $4$ orders of magnitude. Here we map the martian sea level problem onto a
percolation model that provides strong evidence that the longest iso-height
line on Mars that separates the northern and southern hemispheres, acts as a
critical level height with divergent correlation length and plays the same role
as the present mean sea level does on Earth. Our results unravel remarkable
similarities between Mars and Earth, posing a testable hypothesis about the
level of the ancient ocean on Mars that can be answered experimentally by the
future investigations and spacecraft exploration.


  Machine-based brain tumor segmentation can help doctors make better
diagnoses. However, the complex structure of brain tumors and expensive
pixel-level annotations present challenges for automatic tumor segmentation. In
this paper, we propose a counterfactual generation framework that not only
achieves exceptional brain tumor segmentation performance without the need for
pixel-level annotations, but also provides explainability. Our framework
effectively separates class-related features from class-unrelated features of
the samples, and generate new samples that preserve identity features while
altering class attributes by embedding different class-related features. We
perform topological data analysis on the extracted class-related features and
obtain a globally explainable manifold, and for each abnormal sample to be
segmented, a meaningful normal sample could be effectively generated with the
guidance of the rule-based paths designed within the manifold for comparison
for identifying the tumor regions. We evaluate our proposed method on two
datasets, which demonstrates superior performance of brain tumor segmentation.
The code is available at https://github.com/xrt11/tumor-segmentation.


  Deepfakes are AI-generated media in which an image or video has been
digitally modified. The advancements made in deepfake technology have led to
privacy and security issues. Most deepfake detection techniques rely on the
detection of a single modality. Existing methods for audio-visual detection do
not always surpass that of the analysis based on single modalities. Therefore,
this paper proposes an audio-visual-based method for deepfake detection, which
integrates fine-grained deepfake identification with binary classification. We
categorize the samples into four types by combining labels specific to each
single modality. This method enhances the detection under intra-domain and
cross-domain testing.


  Traditional radio telescopes use large, steel dishes to observe radio
sources. The LOFAR radio telescope is different, and uses tens of thousands of
fixed, non-movable antennas instead, a novel design that promises
ground-breaking research in astronomy. The antennas observe omnidirectionally,
and sky sources are observed by signal-processing techniques that combine the
data from all antennas.
  Another new feature of LOFAR is the elaborate use of software to do signal
processing in real time, where traditional telescopes use custom-built
hardware. The use of software leads to an instrument that is inherently more
flexible. However, the enormous data rate (198 Gb/s of input data) and
processing requirements compel the use of a supercomputer: we use an IBM Blue
Gene/P.
  This paper presents a collection of new processing pipelines, collectively
called the beam-forming pipelines, that greatly enhance the functionality of
the telescope. Where our first pipeline could only correlate data to create sky
images, the new pipelines allow the discovery of unknown pulsars, observations
of known pulsars, and (in the future), to observe cosmic rays and study
transient events. Unlike traditional telescopes, we can observe in hundreds of
directions simultaneously. This is useful, for example, to search the sky for
new pulsars. The use of software allows us to quickly add new functionality and
to adapt to new insights that fully exploit the novel features and the power of
our unique instrument. We also describe our optimisations to use the Blue
Gene/P at very high efficiencies, maximising the effectiveness of the entire
telescope. A thorough performance study identifies the limits of our system.


  We analyse the LIGO-Virgo data, including the recently released GWTC-2
dataset, to test a hypothesis that the data contains more than one population
of black holes. We perform a maximum likelihood analysis including a population
of astrophysical black holes with a truncated power-law mass function whose
merger rate follows from star formation rate, and a population of primordial
black holes for which we consider log-normal and critical collapse mass
functions. We find that primordial black holes alone are strongly disfavoured
by the data, while the best fit is obtained for the template combining
astrophysical and primordial merger rates. Alternatively, the data may hint
towards two different astrophysical black hole populations. We also update the
constraints on primordial black hole abundance from LIGO-Virgo observations
finding that in the $2-400M_\odot$ mass range they must comprise less than 0.2%
of dark matter.


  Contrastive learning is a model pre-training technique by first creating
similar views of the original data, and then encouraging the data and its
corresponding views to be close in the embedding space. Contrastive learning
has witnessed success in image and natural language data, thanks to the
domain-specific augmentation techniques that are both intuitive and effective.
Nonetheless, in tabular domain, the predominant augmentation technique for
creating views is through corrupting tabular entries via swapping values, which
is not as sound or effective. We propose a simple yet powerful improvement to
this augmentation technique: corrupting tabular data conditioned on class
identity. Specifically, when corrupting a specific tabular entry from an anchor
row, instead of randomly sampling a value in the same feature column from the
entire table uniformly, we only sample from rows that are identified to be
within the same class as the anchor row. We assume the semi-supervised learning
setting, and adopt the pseudo labeling technique for obtaining class identities
over all table rows. We also explore the novel idea of selecting features to be
corrupted based on feature correlation structures. Extensive experiments show
that the proposed approach consistently outperforms the conventional corruption
method for tabular data classification tasks. Our code is available at
https://github.com/willtop/Tabular-Class-Conditioned-SSL.


  An equiangular tight frame (ETF) is a set of unit vectors in a Euclidean
space whose coherence is as small as possible, equaling the Welch bound. Also
known as Welch-bound-equality sequences, such frames arise in various
applications, such as waveform design and compressed sensing. At the moment,
there are only two known flexible methods for constructing ETFs: harmonic ETFs
are formed by carefully extracting rows from a discrete Fourier transform;
Steiner ETFs arise from a tensor-like combination of a combinatorial design and
a regular simplex. These two classes seem very different: the vectors in
harmonic ETFs have constant amplitude, whereas Steiner ETFs are extremely
sparse. We show that they are actually intimately connected: a large class of
Steiner ETFs can be unitarily transformed into constant-amplitude frames,
dubbed Kirkman ETFs. Moreover, we show that an important class of harmonic ETFs
is a subset of an important class of Kirkman ETFs. This connection informs the
discussion of both types of frames: some Steiner ETFs can be transformed into
constant-amplitude waveforms making them more useful in waveform design; some
harmonic ETFs have low spark, making them less desirable for compressed
sensing. We conclude by showing that real-valued constant-amplitude ETFs are
equivalent to binary codes that achieve the Grey-Rankin bound, and then
construct such codes using Kirkman ETFs.


  We present results on rapidity and transverse momentum distributions of
inclusive charm quark production in hadronic and heavy-ion collisions at RHIC
and LHC energies, including the next-to-leading order, $O(\alpha_s^3)$,
radiative corrections and the nuclear shadowing effect. We determine the
hadronic and the {\it effective} (in-medium) K-factor for the differential and
total inclusive charm cross sections. We find the fraction of central and
inelastic events that contain at least one charm quark pair at LHC energies and
obtain the effective $A$-dependence of the inclusive charm production in
proton-nucleus and nucleus-nucleus collisions at RHIC and in nucleus-nucleus
collisions at the LHC. We discuss theoretical uncertainties inherent in our
calculation. In particular, we show how different extrapolations of gluon
density in a nucleon {\em and} in a nucleus to the low-$x$ region introduce
large theoretical uncertainty in the calculation of charm production at LHC
energies.


  Neural networks have gained importance as the machine learning models that
achieve state-of-the-art performance on large-scale image classification,
object detection and natural language processing tasks. In this paper, we
consider noisy binary neural networks, where each neuron has a non-zero
probability of producing an incorrect output. These noisy models may arise from
biological, physical and electronic contexts and constitute an important class
of models that are relevant to the physical world. Intuitively, the number of
neurons in such systems has to grow to compensate for the noise while
maintaining the same level of expressive power and computation reliability. Our
key finding is a lower bound for the required number of neurons in noisy neural
networks, which is first of its kind. To prove this lower bound, we take an
information theoretic approach and obtain a novel strong data processing
inequality (SDPI), which not only generalizes the Evans-Schulman results for
binary symmetric channels to general channels, but also improves the tightness
drastically when applied to estimate end-to-end information contraction in
networks. Our SDPI can be applied to various information processing systems,
including neural networks and cellular automata. Applying the SDPI in noisy
binary neural networks, we obtain our key lower bound and investigate its
implications on network depth-width trade-offs, our results suggest a
depth-width trade-off for noisy neural networks that is very different from the
established understanding regarding noiseless neural networks. Furthermore, we
apply the SDPI to study fault-tolerant cellular automata and obtain bounds on
the error correction overheads and the relaxation time. This paper offers new
understanding of noisy information processing systems through the lens of
information theory.


  Quantum key agreement requires all participants to recover the shared key
together, so it is crucial to resist the participant attack. In this paper, we
propose a verifiable multi-party quantum key agreement protocol based on the
six-qubit cluster states. A verifiable distributor who preserves some
subsequences of the six-qubit cluster states is introduced into this protocol,
thus the participants can not obtain the shared key in advance. Besides, the
correctness and simultaneity of the shared key are guaranteed by the trusted
design combiner and homomorphic hash function. Furthermore, the security
analysis shows that the new protocol can resist the external and internal
attacks.


  Luo et al. proposed a new method to design the maximally permissive and
efficient supervisor for enforcing linear constraints on an ordinary Petri net
with uncontrollable transitions. In order to develop this method, Theorem 3 is
given. It is claimed that 'a linear constraint can be equivalently transformed
at an uncontrollable transition into a disjunction of weakly admissible ones.'
However, this result is erroneous. In this correspondence paper, a
counterexample contradicting it is presented.


  We study the pointwise supremum of convex integral functionals
$\mathcal{I}_{f,\gamma}(\xi)= \sup_{Q} \left( \int_\Omega
f(\omega,\xi(\omega))Q(d\omega)-\gamma(Q)\right)$ on
$L^\infty(\Omega,\mathcal{F},\mathbb{P})$ where
$f:\Omega\times\mathbb{R}\rightarrow\overline{\mathbb{R}}$ is a proper normal
convex integrand, $\gamma$ is a proper convex function on the set of
probability measures absolutely continuous w.r.t. $\mathbb{P}$, and the
supremum is taken over all such measures. We give a pair of upper and lower
bounds for the conjugate of $\mathcal{I}_{f,\gamma}$ as direct sums of a common
regular part and respective singular parts; they coincide when
$\mathrm{dom}(\gamma)=\{\mathbb{P}\}$ as Rockafellar's result, while both
inequalities can generally be strict. We then investigate when the conjugate
eliminates the singular measures, which a fortiori yields the equality in
bounds, and its relation to other finer regularity properties of the original
functional and of the conjugate.


  In this paper, we generalize the notion of rational singularities for any
reflexive sheaf of rank $1$, link our notion of rational singularities with the
notion of rational singularities in [Kov11], and prove generalizations of
standard facts about rational singularities. Moreover, by using a definition of
non-rational locus, we introduce the notion of $(B_{q+1})$ as a dual notion of
well-known Serre's notion of $(S_{q+1})$, and prove a theorem about
$q$-birational morphisms.


  We consider the pricing of derivatives in a setting with trading
restrictions, but without any probabilistic assumptions on the underlying
model, in discrete and continuous time. In particular, we assume that European
put or call options are traded at certain maturities, and the forward price
implied by these option prices may be strictly decreasing in time. In discrete
time, when call options are traded, the short-selling restrictions ensure no
arbitrage, and we show that classical duality holds between the smallest
super-replication price and the supremum over expectations of the payoff over
all supermartingale measures. More surprisingly in the case where the only
vanilla options are put options, we show that there is a duality gap. Embedding
the discrete time model into a continuous time setup, we make a connection with
(strict) local-martingale models, and derive framework and results often seen
in the literature on financial bubbles. This connection suggests a certain
natural interpretation of many existing results in the literature on financial
bubbles.


  Although brain circuits presumably carry out useful perceptual algorithms,
few instances of derived biological methods have been found to compete
favorably against algorithms that have been engineered for specific
applications. We forward a novel analysis of function of cortico-striatal
loops, which constitute more than 80% of the human brain, thus likely
underlying a broad range of cognitive functions. We describe a family of
operations performed by the derived method, including a nonstandard method for
supervised classification, which may underlie some forms of
cortically-dependent associative learning. The novel supervised classifier is
compared against widely-used algorithms for classification, including support
vector machines (SVM) and k-nearest neighbor methods, achieving corresponding
classification rates --- at a fraction of the time and space costs. This
represents an instance of a biologically-derived algorithm comparing favorably
against widely used machine learning methods on well-studied tasks.


  We propose a preliminary analytical procedure in 4 steps (based on an
improved fully relativistic plane hydrodynamic model) to tailor the initial
density of a cold diluted plasma to the laser pulse profile so as to control
wave-breaking (WB) of the plasma wave and maximize the acceleration of small
bunches of electrons self-injected by the first WB at the density down-ramp.


  Results are given from a density matrix renormalization group study of
pairing on a striped $t$-$t'$-$J$ lattice in the presence of boundary magnetic
and pair fields. We find that pairing on a stripe depends sensitively on both
$J/t$ and $t'/t$. In the strong-pairing model-parameter regime the stripes are
easily coupled by the pair field, and have a uniform phase. There is a small
but measurable energy cost to create anti-phase superconducting domain walls.


  Regenerating codes for distributed storage have attracted much research
interest in the past decade. Such codes trade the bandwidth needed to repair a
failed node with the overall amount of data stored in the network. Minimum
storage regenerating (MSR) codes are an important class of optimal regenerating
codes that minimize (first) the amount of data stored per node and (then) the
repair bandwidth. Specifically, an $[n,k,d]$-$(\alpha)$ MSR code $\mathbb{C}$
over $\mathbb{F}_q$ is defined as follows. Using such a code $\mathbb{C}$, a
file $\cal{F}$ consisting of $\alpha k$ symbols over $\mathbb{F}_q$ can be
distributed among $n$ nodes, each storing $\alpha$ symbols, in such a way that:
The file $\cal{F}$ can be recovered by downloading the content of any $k$ of
the $n$ nodes; and the content of any failed node can be reconstructed by
accessing any $d$ of the remaining $n-1$ nodes and downloading $\alpha/(d-k+1)$
symbols from each of these nodes. Unfortunately, explicit constructions of
$[n,k,d]$ MSR codes are known only for certain special cases: either low rate,
namely $k/n<0.5$, or high repair connectivity, namely $d = n-1$. Although
setting $d = n-1$ minimizes the repair bandwidth, it may be impractical to
connect to all the remaining nodes in order to repair a single failed node. Our
main result in this paper is an explicit construction of systematic-repair
$[n,k,d]$ MSR codes for all possible values of parameters $n,k,d$. In
particular, we construct systematic-repair MSR codes of high rate $k/n>0.5$ and
low repair connectivity $k< d<n-1$. Such codes were not previously known to
exist. In order to construct these codes, we solve simultaneously several
repair scenarios, each of which is expressible as an interference alignment
problem. Extension of our results beyond systematic repair remains an open
problem.


  We give a new proof of the equivalence between two of the main models for
$(\infty,n)$-categories, namely the $n$-fold Segal spaces of Barwick and the
$\Theta_{n}$-spaces of Rezk, by proving that these are algebras for the same
monad on the $\infty$-category of $n$-globular spaces. The proof works for a
broad class of $\infty$-categories that includes all $\infty$-topoi.


  Understanding how to effectively control an epidemic spreading on a network
is a problem of paramount importance for the scientific community. The ongoing
COVID-19 pandemic has highlighted the need for policies that mitigate the
spread, without relying on pharmaceutical interventions, that is, without the
medical assurance of the recovery process. These policies typically entail
lockdowns and mobility restrictions, having thus nonnegligible socio-economic
consequences for the population. In this paper, we focus on the problem of
finding the optimum policies that "flatten the epidemic curve" while limiting
the negative consequences for the society, and formulate it as a nonlinear
control problem over a finite prediction horizon. We utilize the model
predictive control theory to design a strategy to effectively control the
disease, balancing safety and normalcy. An explicit formalization of the
control scheme is provided for the susceptible--infected--susceptible epidemic
model over a network. Its performance and flexibility are demonstrated by means
of numerical simulations.


  We present a detailed study of isospin dynamics in peripheral collisions at
Fermi energies. We consider symmetric and mixed collisions of (124,112)Sn
isotopes at 35 and 50 AMeV to study the isospin transport between the different
reaction components (residues, gas and possibly intermediate mass fragments)
and, in particular, the charge equilibration in the mixed system. We evaluate
the effects of drift terms due to asymmetry and density gradients, which are
directly related to the poorly known value and slope of the symmetry energy
below saturation density. We verify the importance of an isoscalar momentum
dependence of the mean field, which is found to influence the isospin transport
since it changes the reaction times. We finally suggest two observables
particularly sensitive to the isovector part of the nuclear equation-of-state:
the correlation between isospin equilibration and kinetic energy loss for
binary events, and the isospin content of the produced mid-rapidity fragments
for neck fragmentation events.


  The Kaczmarz method is an iterative projection scheme for solving con-sistent
system $Ax = b$. It is later extended to the inconsistent and ill-posed linear
problems. But the classical Kaczmarz method is sensitive to the correlation of
the adjacent equations. In order to reduce the impact of correlation on the
convergence rate, the randomized Kaczmarz method and randomized block Kaczmarz
method are proposed, respectively. In the current literature, the error
estimate results of these methods are established based on the error
$\|x_k-x_*\|_2$, where $x_*$ is the solution of linear system $Ax=b$. In this
paper, we extend the present error estimates of the Kaczmarz and randomized
Kaczmarz methods on the basis of the convergence theorem of Kunio Tanabe, and
obtain some general results about the error $\|x_k-P_{N(A)}x_0-x^\dagger\|_2$.


  Quantum effects in weakly disordered systems are governed by the properties
of the elementary interaction between propagating particles and impurities.
Long range mesoscopic effects due to multiple scattering are derived by
iterating the single scattering vertex, which has to be appropriately
diagonalized. In the present contribution, we present a systematic and detailed
diagonalisation of the diffuson and cooperon vertices responsible for weak
localisation effects. We obtain general expressions for eigenvalues and
projectors onto eigenmodes, for any spin and arbitrary elementary interaction
with impurities. This description provides a common frame for a unified theory
of mesoscopic spin physics for electrons, photons, and other quantum particles.
We treat in detail the case of spin-flip scattering of electrons by freely
orientable magnetic impurities and briefly review the case of photon scattering
from degenerate dipole transitions in cold atomic gases.


  In this work we present constraints on different shapes of primordial
non-Gaussianity using the Wilkinson Microwave Anisotropy Probe (WMAP) 7-year
data and the spherical Mexican hat wavelet fnl estimator including the linear
term correction. In particular we focus on the local, equilateral and
orthogonal shapes. We first analyse the main statistical properties of the
wavelet estimator and show the conditions to reach optimality. We include the
linear term correction in our estimators and compare the estimates with the
values already published using only the cubic term. The estimators are tested
with realistic WMAP simulations with anisotropic noise and the WMAP KQ75 sky
cut. The inclusion of the linear term correction shows a negligible improvement
(< 1 per cent) in the error-bar for any of the shapes considered. The results
of this analysis show that, in the particular case of the wavelet estimator,
the optimality for WMAP anisotropy levels is basically achieved with the mean
subtraction and in practical terms there is no need of including a linear term
once the mean has been subtracted. Our best estimates are now: local fnl = 39.0
+/ 21.4, equilateral fnl = -62.8 +/- 154.0 and orthogonal fnl = -159.8 +/-
115.1 (all cases 68 per cent CL). We have also computed the expected linear
term correction for simulated Planck maps with anisotropic noise at 143 GHz
following the Planck Sky Model and including a mask. The improvement achieved
in this case for the local fnl error-bar is also negligible (0.4 per cent).


  Coherent time-domain spectroscopy is used to measure the screening and
dissipation of high-frequency electromagnetic fields in a set of underdoped
Bi_2Sr_2CaCu_2O_8+d thin films. The measurements provide direct evidence for a
phase-fluctuation driven transition from the superconductor to normal state,
with dynamics described well by the Berezinskii-Kosterlitz-Thouless theory of
vortex-pair unbinding.


  The inclusion of spatial smoothing in finite-dimensional particle-based
Hamiltonian reductions of the Vlasov equation are considered. In the context of
the Vlasov-Poisson equation (and other mean-field Lie-Poisson systems),
smoothing amounts to a convolutive regularization of the Hamiltonian. This
regularization may be interpreted as a change of the inner product structure
used to identify the dual space in the Lie-Poisson Hamiltonian formulation. In
particular, the shape function used for spatial smoothing may be identified as
the kernel function of a reproducing kernel Hilbert space whose inner product
is used to define the Lie-Poisson Hamiltonian structure. It is likewise
possible to introduce smoothing in the Vlasov-Maxwell system, but in this case
the Poisson bracket must be modified rather than the Hamiltonian. The smoothing
applied to the Vlasov-Maxwell system is incorporated by inserting smoothing in
the map from canonical to kinematic coordinates. In the filtered system, the
Lorentz force law and the current, the two terms coupling the Vlasov equation
with Maxwell's equations, are spatially smoothed.


  Scale-free behavior as well as oscillations are frequently observed in the
activity of many natural systems. One important example is the cortical tissues
of mammalian brain where both phenomena are simultaneously observed. Rhythmic
oscillations as well as critical (scale-free) dynamics are thought to be
important, but theoretically incompatible, features of a healthy brain.
Motivated by the above, we study the possibility of coexistence of scale-free
avalanches along with rhythmic behavior within the framework of self-organized
criticality. In particular, we add an oscillatory perturbation to local
threshold condition of the continuous Zhang model and characterize the
subsequent activity of the system. We observe regular oscillations embedded in
well-defined avalanches which exhibit scale-free size and duration in line with
observed neuronal avalanches. The average amplitude of such oscillations are
shown to decrease with increasing frequency consistent with real brain
oscillations. Furthermore, it is shown that optimal amplification of
oscillations occur at the critical point, further providing evidence for
functional advantages of criticality.


  We review both theoretical and experimental advances in the recently emerged
physics of modulated photonic lattices. Artificial periodic dielectric media,
such as photonic crystals and photonic lattices, provide a powerful tool for
the control of the fundamental properties of light propagation in photonic
structures. Photonic lattices are arrays of coupled optical waveguides, where
the light propagation becomes effectively discretized. Such photonic structures
allow one to study many useful optical analogies with other fields, such as the
physics of solid state and electron theory. In particular, the light
propagation in periodic photonic structures resembles the motion of electrons
in a crystalline lattice of semiconductor materials. The discretized nature of
light propagation gives rise to many new phenomena which are not possible in
homogeneous bulk media, such as discrete diffraction and diffraction
management, discrete and gap solitons, and discrete surface waves. Recently, it
was discovered that applying periodic modulation to a photonic lattice by
varying its geometry or refractive index is very much similar to applying a
bias to control the motion of electrons in a crystalline lattice. An interplay
between periodicity and modulation in photonic lattices opens up unique
opportunities for tailoring diffraction and dispersion properties of light as
well as controlling nonlinear interactions.


  Termination is an important property of programs; notably required for
programs formulated in proof assistants. It is a very active subject of
research in the Turing-complete formalism of term rewriting systems, where many
methods and tools have been developed over the years to address this problem.
Ensuring reliability of those tools is therefore an important issue. In this
paper we present a library formalizing important results of the theory of
well-founded (rewrite) relations in the proof assistant Coq. We also present
its application to the automated verification of termination certificates, as
produced by termination tools.


  When implementing unfamiliar programming tasks, developers commonly search
code examples and learn usage patterns of APIs from the code examples or reuse
them by copy-pasting and modifying. For providing high-quality code examples,
previous studies present several methods to recommend code snippets mainly
based on information retrieval. In this paper, to provide better recommendation
results, we propose ROSF, Recommending cOde Snippets with multi-aspect
Features, a novel method combining both information retrieval and supervised
learning. In our method, we recommend Top-Kcode snippets for a givenfree-form
query based on two stages, i.e., coarse-grained searching and fine-grained
re-ranking. First, we generate a code snippet candidate set by searching a code
snippet corpus using an information retrieval method. Second, we predict
probability values of the code snippets for different relevance scores in the
candidate set by the learned prediction model from a training set, re-rank
these candidate code snippets according to the probability values, and
recommend the final results to developers. We conduct several experiments to
evaluate our method in a large-scale corpus containing 921,713 real-world code
snippets. The results show that ROSF is an effective method for code snippets
recommendation and outperforms the-state-of-the-art methods by 20%-41% in
Precision and 13%-33% in NDCG


  In several gamma-ray bursts (GRBs) excess emission, in addition to the
standard synchrotron afterglow spectrum, has been discovered in the early time
X-ray observations. It has been proposed that this excess comes from black body
emission, which may be related to the shock break-out of a supernova in the
GRBs progenitor star. This hypothesis is supported by the discovery of excess
emission in several GRBs with an associated supernova. Using mock spectra we
show that it is only likely to detect such a component, similar to the one
proposed in GRB 101219B, at low redshift and in low absorption environments. We
also perform a systematic search for black body components in all the GRBs
observed with the Swift satellite and find six bursts (GRB 061021, 061110A,
081109, 090814A, 100621A and 110715A) with possible black body components.
Under the assumption that their excess emission is due to a black body
component we present radii, temperatures and luminosities of the emitting
components. We also show that detection of black body components only is
possible in a fraction of the Swift bursts.


  We describe overcommutative varieties of semigroups whose lattice of
overcommutative subvarieties satisfies a non-trivial identity or quasiidentity.
These two properties turn out to be equivalent.