  Flood extent mapping plays a crucial role in disaster management and national
water forecasting. In recent years, high-resolution optical imagery becomes
increasingly available with the deployment of numerous small satellites and
drones. However, analyzing such imagery data to extract flood extent poses
unique challenges due to the rich noise and shadows, obstacles (e.g., tree
canopies, clouds), and spectral confusion between pixel classes (flood, dry)
due to spatial heterogeneity. Existing machine learning techniques often focus
on spectral and spatial features from raster images without fully incorporating
the geographic terrain within classification models. In contrast, we recently
proposed a novel machine learning model called geographical hidden Markov tree
that integrates spectral features of pixels and topographic constraints from
Digital Elevation Model (DEM) data (i.e., water flow directions) in a holistic
manner. This paper evaluates the model through case studies on high-resolution
aerial imagery from the National Oceanic and Atmospheric Administration (NOAA)
National Geodetic Survey together with DEM. Three scenes are selected in
heavily vegetated floodplains near the cities of Grimesland and Kinston in
North Carolina during Hurricane Matthew floods in 2016. Results show that the
proposed hidden Markov tree model outperforms several state of the art machine
learning algorithms (e.g., random forests, gradient boosted model) by an
improvement of F-score (the harmonic mean of the user's accuracy and producer's
accuracy) from around 70% to 80% to over 95% on our datasets.


  Fact-centric information needs are rarely one-shot; users typically ask
follow-up questions to explore a topic. In such a conversational setting, the
user's inputs are often incomplete, with entities or predicates left out, and
ungrammatical phrases. This poses a huge challenge to question answering (QA)
systems that typically rely on cues in full-fledged interrogative sentences. As
a solution, we develop CONVEX: an unsupervised method that can answer
incomplete questions over a knowledge graph (KG) by maintaining conversation
context using entities and predicates seen so far and automatically inferring
missing or ambiguous pieces for follow-up questions. The core of our method is
a graph exploration algorithm that judiciously expands a frontier to find
candidate answers for the current question. To evaluate CONVEX, we release
ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from
five different domains. We show that CONVEX: (i) adds conversational support to
any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and
question completion strategies.


  This paper considers a joint communication and sensing technique for
enhancing situational awareness in practical battlefield scenarios. In
particular, we propose an aerial reconfigurable intelligent surface
(ARIS)-assisted integrated sensing and communication (ISAC) system consisting
of a single access point (AP), an ARIS, multiple users, and a sensing target.
With deep reinforcement learning (DRL), we jointly optimize the transmit
beamforming of the AP, the RIS phase shifts, and the trajectory of the ARIS
under signal-to-interference-noise ratio (SINR) constraints. Numerical results
demonstrate that the proposed technique outperforms the conventional benchmark
schemes by suppressing the self-interference and clutter echo signals or
optimizing the RIS phase shifts.


  Recently, ultra diffuse galaxy (UDG) of Dragonfly 44 in the Coma Cluster was
observed and observations of the rotational speed suggest that its mass is
almost same as the mass of the Milky Way. On the other hand, interestingly, the
galaxy emits only 1 \% of the light emitted by the Milky Way. Then, astronomers
reported that Dragonfly 44 may be made almost entirely of dark matter. In this
study we try to show that the dark matter that constitutes Dragonfly 44 can
form the wormhole or not. Two possible dark matter profiles are used, namely,
ultra diffuse galaxy King's model and generalized Navarro-Frenk-White (NFW)
dark matter profile. We have shown that King's model dark matter profile does
not manage to provide wormhole whereas generalized Navarro-Frenk-White (NFW)
dark matter profile is managed to find wormholes.


  Every day the number of traffic cameras in cities rapidly increase and huge
amount of video data are generated. Parallel processing infrastruture, such as
Hadoop, and programming models, such as MapReduce, are being used to promptly
process that amount of data. The common approach for video processing by using
Hadoop MapReduce is to process an entire video on only one node, however, in
order to avoid parallelization problems, such as load imbalance, we propose to
process videos by splitting it into equal parts and processing each resulting
chunk on a different node. We used some machine learning techniques to detect
and track the vehicles. However, video division may produce inaccurate results.
To solve this problem we proposed a heuristic algorithm to avoid process a
vehicle in more than one chunk.


  We consider moving-brane-solutions in AdS type back ground. In the first
Randall-Sundrum configuration, there are two branes at fixed points of the
orbifold symmetry. We point out that if one brane is fixed and the other brane
is moving, the configuration is still a solution provided the moving brane has
a specific velocity determined by its tension and bulk cosmological constant.
In the absence of the $\bf Z_2$ symmetry, we can construct multi-brane
configurations by patching AdS-Schwarzshild solutions. In this case, we show
that the 4-dimensional effective cosmological constant on the brane world is
not well defined. We find a condition for a brane to be stationary. Using the
brane scattering, we suggest a scenario of inflation on the brane universe
during a finite time, i.e, a scenario of a graceful exit of inflation.


  We present global Schauder type estimates in all variables and unique
solvability results in kinetic H\"older spaces for kinetic
Kolmogorov-Fokker-Planck (KFP) equations. The leading coefficients are H\"older
continuous in the $x, v$ variables and are merely measurable in the temporal
variable. Our proof is inspired by Campanato's approach to Schauder estimates
and does not rely on the estimates of the fundamental solution of the KFP
operator.


  Predicting a new Dirac semimetal (DSM), as well as other topological
materials, is quite challenging, since the relationship between crystal
structure, composing atoms and the band topology is complex and elusive. Here,
we demonstrate an approach to design DSMs via exploring the chemical degree of
freedom. Based on the understanding of the well-known DSM Na$_3$Bi, three
compounds in one family, namely Na$_2$MgSn, Na$_2$MgPb and Na$_2$CdSn, have
been exactly located. Further hybrid-functional calculations with improved
estimation of band inversion show that two of them, Na$_2$MgPb and Na$_2$CdSn,
have band topology of DSMs. The nontrivial surface states with Fermi arcs on
the (010) and (100) side surfaces are shown to connect the projection of bulk
Dirac nodes. Most importantly, the candidate compounds are dynamically stable
and have been experimentally synthesized. The ideas in this work would
stimulate more designs on locating topological materials based on the
understanding of existing ones.


  In this paper we study weak solutions for the following type of stochastic
differential equation \[ dX_{t}=dW_{t}+b(t, X_{t})dt, \quad t\ge s, \quad
X_{s}=x, \] where $b: [0,\infty) \times \mathbb{R}^{d} \to \mathbb{R}^{d}$ is a
measurable drift, $W=(W_{t})_{t \ge 0}$ is a $d$-dimensional Brownian motion
and $(s,x)\in [0,\infty) \times \mathbb{R}^{d}$ is the starting point. A
solution $X=(X_t)_{t \ge s}$ for the above SDE is called a Brownian motion with
time-dependent drift $b$ starting from $(s,x)$. Under the assumption that $|b|$
belongs to the forward-Kato class $\mathcal{F} \mathcal{K}_{d-1}^{\alpha}$ for
some $\alpha \in (0,1/2)$, we prove that the above SDE has a unique weak
solution for every starting point $(s,x)\in [0,\infty) \times \mathbb{R}^{d}$.


  This paper is devoted to exploring the relationship between the $[1,n)\ni
p$-capacity and the surface-area in $\mathbb R^{n\ge 2}$ which especially
shows: if $\Omega\subset\mathbb R^n$ is a convex, compact, smooth set with its
interior $\Omega^\circ\not=\emptyset$ and the mean curvature
$H(\partial\Omega,\cdot)>0$ of its boundary $\partial\Omega$ then $$
\left(\frac{n(p-1)}{p(n-1)}\right)^{p-1}\le\frac{\left(\frac{\hbox{cap}_p(\Omega)}{\big(\frac{p-1}{n-p}\big)^{1-p}\sigma_{n-1}}\right)}{\left(\frac{\hbox{area}(\partial\Omega)}{\sigma_{n-1}}\right)^\frac{n-p}{n-1}}\le\left(\sqrt[n-1]{\int_{\partial\Omega}\big(H(\partial\Omega,\cdot)\big)^{n-1}\frac{d\sigma(\cdot)}{\sigma_{n-1}}}\right)^{p-1}\quad\forall\quad
p\in (1,n) $$ whose limits $1\leftarrow p\ \&\ p\rightarrow n$ imply $$
1=\frac{cap_1(\Omega)}{\hbox{area}(\partial\Omega)}\ \ \& \
\int_{\partial\Omega}\big(H(\partial\Omega,\cdot)\big)^{n-1}\frac{d\sigma(\cdot)}{\sigma_{n-1}}\ge
1, $$ thereby not only discovering that the new best known constant is roughly
half as far from the one conjectured by P\'olya-Szeg\"o in \cite[(2)]{P} but
also extending the P\'olya-Szeg\"o inequality in \cite[(5)]{P}, with both the
conjecture and the inequality being stated for the electrostatic capacity of a
convex solid in $\mathbb R^3$.


  We have investigated the electronic states and spin polarization of
half-metallic ferromagnet CrO$_2$ (100) epitaxial films by bulk-sensitive
spin-resolved photoemission spectroscopy with a focus on non-quasiparticle
(NQP) states derived from electron-magnon interactions. We found that the
averaged values of the spin polarization are approximately 100% and 40% at 40 K
and 300 K, respectively. This is consistent with the previously reported result
[H. Fujiwara et al., Appl. Phys. Lett. 106, 202404 (2015).]. At 100 K, peculiar
spin depolarization was observed at the Fermi level ($E_{F}$), which is
supported by theoretical calculations predicting NQP states. This suggests the
possible appearance of NQP states in CrO$_2$. We also compare the temperature
dependence of our spin polarizations with that of the magnetization.


  Magnetic Bloch points (BPs) are highly confined magnetization configurations,
that often occur in transient spin dynamics processes. However, opposing
chiralities of adjacent layers for instance in a FeGe bilayer stack can
stabilize such magnetic BPs at the layer interface. These BPs configurations
are metastable and consist of two coupled vortices (one in each layer) with
same circularity and opposite polarity. Each vortex is stabilized by opposite
sign Dzyaloshinskii-Moriya interactions. This stabilization mechanism
potentially opens the door towards BP-based spintronic applications. An open
question, from a methodological point of view, is whether the Heisenberg (HB)
model approach (atomistic model) as to be used to study such systems or if the
-- computationally more efficient -- micromagnetic (MM) models can be used and
still obtain robust results. We are modelling and comparing the energetics and
dynamics of a stable BP obtained using both HB and MM approaches. We find that
an MM description of a stable BP leads qualitatively to the same results as the
HB description, and that an appropriate mesh discretization plays a more
important role than the chosen model. Further, we study the dynamics by
shifting the BP with an applied in-plane field and investigating the relaxation
after switching the filed off abruptly. The precessional motion of coupled
vortices in a BP state can be drastically reduced compared to a classical
vortex, which may be also an interesting feature for fast and efficient
devices. A recent study has shown that a bilayer stack hosting BPs can be used
to retain information [1].


  Consider a wireless Gaussian network where a source wishes to communicate
with a destination with the help of N full-duplex relay nodes. Most practical
systems today route information from the source to the destination using the
best path that connects them. In this paper, we show that routing can in the
worst case result in an unbounded gap from the network capacity - or reversely,
physical layer cooperation can offer unbounded gains over routing. More
specifically, we show that for $N$-relay Gaussian networks with an arbitrary
topology, routing can in the worst case guarantee an approximate fraction
$\frac{1}{\left\lfloor N/2 \right\rfloor + 1}$ of the capacity of the full
network, independently of the SNR regime. We prove that this guarantee is
fundamental, i.e., it is the highest worst-case guarantee that we can provide
for routing in relay networks. Next, we consider how these guarantees are
refined for Gaussian layered relay networks with $L$ layers and $N_L$ relays
per layer. We prove that for arbitrary $L$ and $N_L$, there always exists a
route in the network that approximately achieves at least $\frac{2}{(L-1)N_L +
4}$ $\left(\mbox{resp.}\frac{2}{LN_L+2}\right)$ of the network capacity for odd
$L$ (resp. even $L$), and there exist networks where the best routes exactly
achieve these fractions. These results are formulated within the network
simplification framework, that asks what fraction of the capacity we can
achieve by using a subnetwork (in our case, a single path). A fundamental step
in our proof is a simplification result for MIMO antenna selection that may
also be of independent interest. To the best of our knowledge, this is the
first result that characterizes, for general wireless network topologies, what
is the performance of routing with respect to physical layer cooperation
techniques that approximately achieve the network capacity.


  This work deals with the problem of estimating a photovoltaic generation
forecasting model in scenarios where measurements of meteorological variables
(i.e. solar irradiance and temperature) at the plant site are not available. A
novel algorithm for the estimation of the parameters of the well-known PVUSA
model of a photovoltaic plant is proposed. Such a method is characterized by a
low computational complexity, and efficiently exploits only power generation
measurements, a theoretical clear-sky irradiance model, and temperature
forecasts provided by a meteorological service. An extensive experimental
validation of the proposed method on real data is also presented.


  In this work we extend classical structure and duality results in Gabor
analysis on the euclidean space to the setting of second countable locally
compact abelian (LCA) groups. We formulate the concept of rationally
oversampling of Gabor systems in an LCA group and prove corresponding
characterization results via the Zak transform. From these results we derive
non-existence results for critically sampled continuous Gabor frames. We obtain
general characterizations in time and in frequency domain of when two Gabor
generators yield dual frames. Moreover, we prove the Walnut and Janssen
representation of the Gabor frame operator and consider the Wexler-Raz
biorthogonality relations for dual generators. Finally, we prove the duality
principle for Gabor frames. Unlike most duality results on Gabor systems, we do
not rely on the fact that the translation and modulation groups are discrete
and co-compact subgroups. Our results only rely on the assumption that either
one of the translation and modulation group (in some cases both) are co-compact
subgroups of the time and frequency domain. This presentation offers a unified
approach to the study of continuous and the discrete Gabor frames.


  The task of efficient automatic music classification is of vital importance
and forms the basis for various advanced applications of AI in the musical
domain. Musical instrument recognition is the task of instrument identification
by virtue of its audio. This audio, also termed as the sound vibrations are
leveraged by the model to match with the instrument classes. In this paper, we
use an artificial neural network (ANN) model that was trained to perform
classification on twenty different classes of musical instruments. Here we use
use only the mel-frequency cepstral coefficients (MFCCs) of the audio data. Our
proposed model trains on the full London philharmonic orchestra dataset which
contains twenty classes of instruments belonging to the four families viz.
woodwinds, brass, percussion, and strings. Based on experimental results our
model achieves state-of-the-art accuracy on the same.


  Theedhum Nandrum is a sentiment polarity detection system using two
approaches--a Stochastic Gradient Descent (SGD) based classifier and a Long
Short-term Memory (LSTM) based Classifier. Our approach utilises language
features like use of emoji, choice of scripts and code mixing which appeared
quite marked in the datasets specified for the Dravidian Codemix - FIRE 2020
task. The hyperparameters for the SGD were tuned using GridSearchCV. Our system
was ranked 4th in Tamil-English with a weighted average F1 score of 0.62 and
9th in Malayalam-English with a score of 0.65. We achieved a weighted average
F1 score of 0.77 for Tamil-English using a Logistic Regression based model
after the task deadline. This performance betters the top ranked classifier on
this dataset by a wide margin. Our use of language-specific Soundex to
harmonise the spelling variants in code-mixed data appears to be a novel
application of Soundex. Our complete code is published in github at
https://github.com/oligoglot/theedhum-nandrum.


  We present initial results of very high resolution Atacama Large
Millimeter/submillimeter Array (ALMA) observations of the $z$=3.042
gravitationally lensed galaxy HATLAS J090311.6+003906 (SDP.81). These
observations were carried out using a very extended configuration as part of
Science Verification for the 2014 ALMA Long Baseline Campaign, with baselines
of up to 15 km. We present continuum imaging at 151, 236 and 290 GHz, at
unprecedented angular resolutions as fine as 23 milliarcseconds (mas),
corresponding to an un-magnified spatial scale of ~180 pc at z=3.042. The ALMA
images clearly show two main gravitational arc components of an Einstein ring,
with emission tracing a radius of ~1.5". We also present imaging of CO(10-9),
CO(8-7), CO(5-4) and H2O line emission. The CO emission, at an angular
resolution of ~170 mas, is found to broadly trace the gravitational arc
structures but with differing morphologies between the CO transitions and
compared to the dust continuum. Our detection of H2O line emission, using only
the shortest baselines, provides the most resolved detection to date of thermal
H2O emission in an extragalactic source. The ALMA continuum and spectral line
fluxes are consistent with previous Plateau de Bure Interferometer and
Submillimeter Array observations despite the impressive increase in angular
resolution. Finally, we detect weak unresolved continuum emission from a
position that is spatially coincident with the center of the lens, with a
spectral index that is consistent with emission from the core of the foreground
lensing galaxy.


  Pre-trained Natural Language Processing (NLP) models can be easily adapted to
a variety of downstream language tasks. This significantly accelerates the
development of language models. However, NLP models have been shown to be
vulnerable to backdoor attacks, where a pre-defined trigger word in the input
text causes model misprediction. Previous NLP backdoor attacks mainly focus on
some specific tasks. This makes those attacks less general and applicable to
other kinds of NLP models and tasks. In this work, we propose \Name, the first
task-agnostic backdoor attack against the pre-trained NLP models. The key
feature of our attack is that the adversary does not need prior information
about the downstream tasks when implanting the backdoor to the pre-trained
model. When this malicious model is released, any downstream models transferred
from it will also inherit the backdoor, even after the extensive transfer
learning process. We further design a simple yet effective strategy to bypass a
state-of-the-art defense. Experimental results indicate that our approach can
compromise a wide range of downstream NLP tasks in an effective and stealthy
way.


  LOFT, the Large Observatory For X-ray Timing, was one of the ESA M3 mission
candidates that completed their assessment phase at the end of 2013. LOFT is
equipped with two instruments, the Large Area Detector (LAD) and the Wide Field
Monitor (WFM). The LAD performs pointed observations of several targets per
orbit (~90 minutes), providing roughly ~80 GB of proprietary data per day (the
proprietary period will be 12 months). The WFM continuously monitors about 1/3
of the sky at a time and provides data for about ~100 sources a day, resulting
in a total of ~20 GB of additional telemetry. The LOFT Burst alert System
additionally identifies on-board bright impulsive events (e.g., Gamma-ray
Bursts, GRBs) and broadcasts the corresponding position and trigger time to the
ground using a dedicated system of ~15 VHF receivers. All WFM data are planned
to be made public immediately. In this contribution we summarize the planned
organization of the LOFT ground segment (GS), as established in the mission
Yellow Book 1 . We describe the expected GS contributions from ESA and the LOFT
consortium. A review is provided of the planned LOFT data products and the
details of the data flow, archiving and distribution. Despite LOFT was not
selected for launch within the M3 call, its long assessment phase (> 2 years)
led to a very solid mission design and an efficient planning of its ground
operations.


  Cosmological dark energy is a natural source of variation of the fine
structure constant. Using a model-independent approach we show that once
general assumptions about the alpha-varying interactions are made, astronomical
probes of its variation constrain the dark energy equation of state today to
satisfy -1 < w_f < -0.96 at 3-sigma and significantly disfavour late-time
changes in the equation of state. We show how dark-energy-induced spatial
perturbations of alpha are linked to violations of the Equivalence Principle
and are thus negligible at low-redshift, in stark contrast to the BSBM
theories. This provides a new test of dark energy as the source of alpha
variation.


  Diffusion-based generative models excel in unconditional generation, as well
as on applied tasks such as image editing and restoration. The success of
diffusion models lies in the iterative nature of diffusion: diffusion breaks
down the complex process of mapping noise to data into a sequence of simple
denoising tasks. Moreover, we are able to exert fine-grained control over the
generation process by injecting guidance terms into each denoising step.
However, the iterative process is also computationally intensive, often taking
from tens up to thousands of function evaluations. Although consistency
trajectory models (CTMs) enable traversal between any time points along the
probability flow ODE (PFODE) and score inference with a single function
evaluation, CTMs only allow translation from Gaussian noise to data. Thus, this
work aims to unlock the full potential of CTMs by proposing generalized CTMs
(GCTMs), which translate between arbitrary distributions via ODEs. We discuss
the design space of GCTMs and demonstrate their efficacy in various image
manipulation tasks such as image-to-image translation, restoration, and
editing. Code: \url{https://github.com/1202kbs/GCTM}


  It is well known that, for even n, the n-soliton solution on the Minkowski
seed, constructed using the inverse-scattering method (ISM) of Belinski and
Zakharov (BZ), is the multi-Kerr-NUT solution. We show that, for odd n, the
natural seed to use is the Euclidean space with two manifest translational
symmetries, and the n-soliton solution is the accelerating multi-Kerr-NUT
solution. We thus define the n-soliton solution on flat space for any positive
integer n. It admits both Lorentzian and Euclidean sections. In the latter
section, we find that a number, say m, of solitons can be eliminated in a
non-trivial way by appropriately fixing their corresponding so-called BZ
parameters. The resulting solutions, which may split into separate classes, are
collectively denoted as [n-m]-soliton solutions on flat space. We then carry
out a systematic study of the n- and [n-m]-soliton solutions on flat space.
This includes, in particular, an explicit presentation of their ISM
construction, an analysis of their local geometries, and a classification of
all separate classes of solutions they form. We also show how even-soliton
solutions on the seeds of the collinearly centred Gibbons-Hawking and Taub-NUT
arise from these solutions.


  Recently, a new class of magnetic phenomenon, called altermagnetism, was
proposed where the underlying spin configuration resembles antiferromagnetic
structure, but the system violates \textbf{PT} (PT: Parity times Time reversal)
symmetry due to the alternation of crystalline symmetry across magnetic ions.
Although the original idea was proposed for the collinear spin structure, a
recent report by Cheong et al. has suggested that antiferromagnetic materials
with non-collinear spin structure and local alternation of crystalline
arrangement can also manifest altermagnetism. Besides breaking the \textbf{PT}
symmetry, altermagnetic compounds are also expected to exhibit anomalous Hall
effects of odd orders. Here, we discuss possible candidates in this regard. One
example is nickel monosilicide, which was recently shown to exhibit high
temperature antiferromagnetism with non-collinear spin structure. It fulfills
both criteria of breaking the \textbf{PT} symmetry and manifesting nonlinear
anomalous Hall effect. In addition to NiSi, we also discuss other potential
antiferromagnetic materials with non-collinear spin configuration for the
exploration of altermagnetic states.


  Interplanetary Coronal Mass Ejections (ICMEs) and High Speed Streams (HSSs)
are noteworthy drivers of disturbance of interplanetary space. Interaction
between them can cause several phenomena, such as; generation of waves,
enhanced geo-effectiveness, particle acceleration, etc. However, how does
thermodynamic properties vary during the ICME-HSS interaction remain an open
problem. In this study, we investigated the polytropic behavior of plasma
during an ICME-HSS interaction observed by STEREO and Wind spacecraft. We find
that the ICME observed by the STEREO-A has polytropic index $\alpha = 1.0$,
i.e., exhibiting isothermal process. Moreover, Wind spacecraft observed the HSS
region, non-interacting ICME, and ICME-HSS interaction region. During each
regions we found $\alpha$=1.8, $\alpha$=0.7, and $\alpha$=2.5, respectively. It
implies that the HSS region exhibits a nearly adiabatic behaviour, ICME region
is closely isothermal, and the ICME-HSS interaction region exhibits
super-adiabatic behaviour. The insufficient expansion of the ICME due to the
interaction with HSS triggers the system for heating and cooling mechanisms
which dependent on the degrees of freedom of plasma components.


  The optical response of a heavily doped quantum well, with two occupied
subbands, has been investigated as a function of the electronic density. It is
shown that the two optically active transitions are mutually coupled by
dipole-dipole Coulomb interaction, which strongly renormalizes their absorption
amplitude. In order to demonstrate this effect, we have measured a set of
optical spectra on a device in which the electronic density can be tuned by the
application of a gate voltage. Our results show that the absorption spectra can
be correctly described only by taking into account the Coulomb coupling between
the two transitions. As a consequence, the optical dipoles originating from
intersubband transitions are not independent, but rather coupled oscillators
with an adjustable strength.


  Despite being the main tool to visualize molecules at the atomic scale, AFM
with CO-functionalized metal tips is unable to chemically identify the observed
molecules. Here we present a strategy to address this challenging task using
deep learning techniques. Instead of identifying a finite number of molecules
following a traditional classification approach, we define the molecular
identification as an image captioning problem. We design an architecture,
composed of two multimodal recurrent neural networks, capable of identifying
the structure and composition of an unknown molecule using a 3D-AFM image stack
as input. The neural network is trained to provide the name of each molecule
according to the IUPAC nomenclature rules. To train and test this algorithm we
use the novel QUAM-AFM dataset, which contains almost 700,000 molecules and 165
million AFM images. The accuracy of the predictions is remarkable, achieving a
high score quantified by the cumulative BLEU 4-gram, a common metric in
language recognition studies.


  In this paper we will provide a representation of the penalty term of general
dynamic concave utilities (hence of dynamic convex risk measures) by applying
the theory of g-expectations.


  This paper purpose is to investigate exponential behavior conditions for the
infinite servers queue with Poisson arrivals busy period length distribution.
It is presented a general theoretical result that is the basis of this work.
The complementary analysis rely on the infinite servers queue with Poisson
arrivals busy period length distribution moments computation. In infinite
servers queue with Poisson arrivals practical applications, in economic,
management, and business areas, the management of the effective number of
servers is essential since the physical presence of infinite servers is not
viable and so it is necessary to create that condition through an adequate
management of the number of servers during the busy period.


  A class of three-dimensional models which satisfy supersymmetric intertwining
relations with the simplest - oscillator-like - variant of shape invariance is
constructed. It is proved that the models are not amenable to conventional
separation of variables for the complex potentials, but their spectra are real
and equidistant (like for isotropic harmonic oscillator). The special case of
such potential with quadratic interaction is solved completely. The Hamiltonian
of the system is non-diagonalizable, and its wave functions and associated
functions are built analytically. The symmetry properties of the model and
degeneracy of energy levels are studied.


  We consider the defocusing $\dot H^1$-critical nonlinear Schr\"odinger
equation in all dimensions ($n\geq 3$) with a quadratic potential $V(x)=\pm
\tfrac12 |x|^2$. We show global well-posedness for radial initial data obeying
$\nabla u_0(x), xu_0(x) \in L^2$. In view of the potential $V$, this is the
natural energy space. In the repulsive case, we also prove scattering.
  We follow the approach pioneered by Bourgain and Tao in the case of no
potential; indeed, we include a proof of their results that incorporates a
couple of simplifications discovered while treating the problem with quadratic
potential.


  A new technique named Generalized Borel Transform (GBT) is applied to the
generating functional of the $\Phi^{4}$ theory in zero dimensions with
degenerate minima. The analytical solution of this function, obtained in the
non perturbative regime, is compared with those estimations predicted by large
order perturbation theory. It was established that the GBT is a very efficient
technique to capture these contributions. On the other hand, renormalons
associated to the resummation of those perturbative series were not found to be
the genuine source of the non perturbative contributions of this model.


  We present results from the first generation of multi-dimensional general
relativistic neutrino hydrodynamics simulations of core-collapse supernovae. A
comparison with models computed using either the purely Newtonian approximation
or the "effective gravitational potential" approach reveals appreciable
quantitative differences in the heating conditions and the gravitational wave
spectra. Our results underscore the important role of general relativity in the
supernova problem (which appears to be on par with other important factors such
as the dimensionality and the equation of state) both for our understanding of
the explosion dynamics as well as for predictions of observable signatures.


  We evaluated the planetary radar capabilities at Arecibo, the Goldstone 70-m
DSS-14 and 34-m DSS-13 antennas, the 70-m DSS-43 antenna at Canberra, the Green
Bank Telescope, and the Parkes Radio Telescope in terms of their relative
sensitivities and the number of known near-Earth asteroids (NEAs) detectable
per year in monostatic and bistatic configurations. In the 2015 calendar year,
monostatic observations with Arecibo and DSS-14 were capable of detecting 253
and 131 NEAs respectively, with signal-to-noise ratios (SNRs) greater than
30/track. Combined, the two observatories were capable of detecting 276 NEAs.
Of these, Arecibo detected 77 and Goldstone detected 32, or 30% and 24% the
numbers that were possible. The two observatories detected an additional 18 and
7 NEAs respectively, with SNRs of less than 30/track. This indicates that a
substantial number of potential targets are not being observed. The bistatic
configuration with DSS-14 transmitting and the Green Bank Telescope receiving
was capable of detecting about 195 NEAs, or ~50% more than with monostatic
observations at DSS-14. Most of the detectable asteroids were targets of
opportunity that were discovered less than 15 days before the end of their
observing windows. About 50% of the detectable asteroids have absolute
magnitudes > 25, which corresponds diameters < ~30 m.


  As an emerging medical diagnostic technology, photoacoustic imaging has been
implemented for both preclinical and clinical applications. For clinical
convenience, a handheld free scan photoacoustic tomography (PAT) system
providing 3D imaging capability is essentially needed, which has potential for
surgical navigation and disease diagnosis. In this paper, we proposed a free
scan 3D PAT (fsPAT) system based on a handheld linear array ultrasound probe. A
global positioning system (GPS) is applied for ultrasound probes coordinate
acquisition. The proposed fsPAT can simultaneously realize real time 2D
imaging, and large field of view 3D volumetric imaging, which is reconstructed
from the multiple 2D images with coordinate information acquired by the GPS. To
form a high quality 3D image, a dedicated space transformation method and
reconstruction algorithm are used and validated by the proposed system. Both
simulation and experimental studies have been performed to prove the
feasibility of the proposed fsPAT. To explore its clinical potential, in vivo
3D imaging of human wrist vessels is also conducted, showing clear subcutaneous
vessel network with high image contrast.


  We study the running of the coupling in SU(2) gauge theory with 8 massless
fundamental representation fermion flavours, using the gradient flow method
with the Schr\"odinger functional boundary conditions. Gradient flow allows us
to measure robust continuum limit for the step scaling function. The results
show a clear indication of infrared fixed point consistent with perturbation
theory.


  MACHe3 (MAtrix of Cells of superfluid He3) is a project of a new detector for
direct Dark Matter (DM) search. A cell of superfluid He3 has been developed and
the idea of using a large number of such cells in a high granularity detector
is proposed. This paper presents, after a brief description of the superfluid
He3 cell, the simulation of the response of different matrix configurations
allowing to define an optimum design as a function of the number of cells and
the volume of each cell. The background rejection, for several configurations,
is presented both for neutrons and gamma-rays of various kinetic energies.


  A general theoretical approach to study the quantum kinetics in a system
coupled to a bath is proposed. Starting with the microscopic interaction, a
Lindblad master equation is established, which goes beyond the common secular
approximation. This allows for the treatment of systems, where coherences are
generated by the bath couplings while avoiding the negative occupations
occurring in the Bloch-Wangsness-Redfield kinetic equations. The versatility
and accuracy of the approach is verified by its application to three entirely
different physical systems: (i) electric transport through a double-dot system
coupled to electronic reservoirs, (ii) exciton kinetics in coupled chromophores
in the presence of a heat bath, and (iii) the simulation of quantum cascade
lasers, where the coherent electron transport is established by scattering with
phonons and impurities.


  Despite proportionality being one of the tenets of data protection laws, we
currently lack a robust analytical framework to evaluate the reach of modern
data collections and the network effects at play. We here propose a
graph-theoretic model and notions of node- and edge-observability to quantify
the reach of networked data collections. We first prove closed-form expressions
for our metrics and quantify the impact of the graph's structure on
observability. Second, using our model, we quantify how (1) from 270,000
compromised accounts, Cambridge Analytica collected 68.0M Facebook profiles;
(2) from surveilling 0.01\% the nodes in a mobile phone network, a
law-enforcement agency could observe 18.6\% of all communications; and (3) an
app installed on 1\% of smartphones could monitor the location of half of the
London population through close proximity tracing. Better quantifying the reach
of data collection mechanisms is essential to evaluate their proportionality.


  We measure the radio luminosity function (RLF) of steep-spectrum radio
sources using three redshift surveys of flux-limited samples selected at low
(151 & 178 MHz) radio frequency, low-frequency source counts and the local RLF.
The redshift surveys used are the new 7C Redshift Survey (7CRS) and the
brighter 3CRR and 6CE surveys totalling 356 sources with virtually complete
redshift information. This yields unprecedented coverage of the radio
luminosity versus z plane for steep-spectrum sources, and hence the most
accurate measurements of the steep-spectrum RLF yet made. We find that a simple
dual-population model for the RLF fits the data well, requiring differential
density evolution (with z) for the two populations. The low-luminosity
population can be associated with radio galaxies with weak emission lines, and
includes sources with both FRI and FRII radio structures; its comoving space
density $\rho$ rises by about one dex between z~0 and z~1 but cannot yet be
meaningfully constrained at higher redshifts. The high-luminosity population
can be associated with FRII radio galaxies and quasars with strong emission
lines; its $\rho$ rises by nearly three dex between z~0 and z~2. These results
mirror the situation seen in X-ray and optically-selected AGN. The integrated
radio luminosity density of the combination of the two populations is
controlled by the value of $\rho$ at the low-luminosity end of the RLF of the
high-luminosity population, a quantity which has been directly measured at z~1
by the 7CRS. We argue that robust determination of this quantity at higher
redshifts requires a new redshift survey based on a large (~1000 source) sample
about five times fainter than the 7CRS.


  A number of researchers have recently questioned the necessity of
increasingly complex neural network (NN) architectures. In particular, several
recent papers have shown that simpler, properly tuned models are at least
competitive across several NLP tasks. In this work, we show that this is also
the case for text generation from structured and unstructured data. We consider
neural table-to-text generation and neural question generation (NQG) tasks for
text generation from structured and unstructured data, respectively.
Table-to-text generation aims to generate a description based on a given table,
and NQG is the task of generating a question from a given passage where the
generated question can be answered by a certain sub-span of the passage using
NN models. Experimental results demonstrate that a basic attention-based
seq2seq model trained with the exponential moving average technique achieves
the state of the art in both tasks. Code is available at
https://github.com/h-shahidi/2birds-gen.


  Coplanarity of subcores of $\gamma$-ray--hadron families found in cosmic-ray
experiments at $E_0 \gtrsim 10^{16}$ eV requires appearance of a coplanar
generation of most energetic particles. Long-range near-side "ridge" effect in
$ pp$ collisions at $\sqrt{s} = 7$ TeV was found by the CMS Collaboration in
two-particle $\Delta\eta - \Delta\varphi$ correlation functions at
$|\Delta\eta| \gtrsim 3$ and $|\Delta\varphi| \approx 0$. The FANSY 2.0 model
reproduces both the hadron coplanarity and "ridge" effect.


  Usually, the measurements of electronic and magnetic properties of
superconducting samples are carried out under a constant temperature bath. On
the other hand, thermal gradients induce local variation of the superconducting
order parameter, and the vortex dynamics can present interesting behaviors. In
this work, we solved the time-dependent Ginzburg-Landau equations simulating
samples under two different thermal gradients, and considering two values of
the Ginzburg-Landau parameter, \k{appa}. We find out that both parameters,
i.e., \k{appa} and thermal gradients, play an important role on the vortex
dynamics and on the magnetization behavior of the samples.


  We consider a notion of exact sequences in any -not necessarily exact-
pointed category relative to a given (E;M)-factorization structure. We apply
this notion to introduce and investigate a new notion of exact sequences of
semimodules over semirings relative to the canonical image factorization.
Several homological results are proved using the new notion of exactness
including some restricted versions of the Short Five Lemma and the Snake Lemma
opening the door for introducing and investigating homology objects in such
categories. Our results apply in particular to the variety of commutative
monoids extending results in homological varieties to relative homological
varieties.


  Heat enhancement at the target in a High Intensity Focused Ultrasound (HIFU)
field is investigated by considering the effects of the injection of
microbubbles in the vicinity of the tumor to be ablated. The interaction
between the bubble cloud and the HIFU field is investigated using a
three-dimensional numerical model. The propagation of non-linear ultrasonic
waves in the tissue or in a phantom medium is modeled using the compressible
Navier-Stokes equations on a fixed grid, while the microbubbles dynamics and
motion are modeled as discrete singularities, which are tracked in a Lagrangian
framework. These two models are coupled to each other such that both the
acoustic field and the bubbles influence each other. The temperature rise in
the field is calculated by solving a heat transfer equation applied over a much
longer time scale. The compressible continuum part of the model is validated by
conducting HIFU simulation without microbubbles and comparing the pressure and
temperature fields against available experiments. The coupled
Eulerian-Lagrangian approach is then validated against existing experiments
with a phantom tissue. The various mechanisms through which microbubbles
enhance heat deposition are then examined in detail. The effects of the initial
void fraction in the cloud are then sought by considering the changes in the
attenuation of the primary ultrasonic wave and the modifications of the
enhanced heat deposition in the focal region. Finally, the effects of the
microbubble cloud size and its localization in the focal region are shown and
the effects of these parameters in altering the temperature rise and the
location of the temperature peak are discussed.


  The search for charged particle tracks in detectors with linear sensing
elements, such as wire chambers, strip silicon detectors etc., starts with
identification of straight track segments. The latter are deduced by
constructing all possible combinations of activated detector elements. In the
high multiplicity environment, with many activated detector elements, this
causes large combinatorics and significantly reduces the performance of the
track finding algorithms. In this report we trace this problem back to
determination of the parameters of a straight line build from a number of skew
lines (sensing elements of a detector) in space. Based on the intersection
points of two hyperbolas, we demonstrate an analytic solution in the case of
four skew detector elements.The procedure can also be applied as a first
approximation to the more general case of curved track finding.


  We investigated whether a combination of k-space undersampling and variable
density averaging enhances image quality for low-SNR MRI acquisitions. We
implemented 3D Cartesian k-space prospective undersampling with a variable
number of averages for each k-line. The performance of this compressed sensing
with variable-density averaging (CS-VDA) method was evaluated in retrospective
analysis of fully sampled phantom MRI measurements, as well as for
prospectively accelerated in vivo 3D brain and knee MRI scans. Both phantom and
in vivo results showed that acquisitions using the CS-VDA approach resulted in
better image quality as compared to full sampling of k-space in the same scan
time. Specifically, CS-VDA with a higher number of averages in the center of
k-space resulted in the best image quality, apparent from increased anatomical
detail with preserved soft-tissue contrast. This novel approach will facilitate
improved image quality of inherently low SNR data, such as those with
high-resolution or specific contrast-weightings with low SNR efficiency.


  In this work a symmetry of universal finite-size scaling functions under a
certain anisotropic scale transformation is postulated. This transformation
connects the properties of a finite two-dimensional system at criticality with
generalized aspect ratio $\rho > 1$ to a system with $\rho < 1$. The symmetry
is formulated within a finite-size scaling theory, and expressions for several
universal amplitude ratios are derived. The predictions are confirmed within
the exactly solvable weakly anisotropic two-dimensional Ising model and are
checked within the two-dimensional dipolar in-plane Ising model using Monte
Carlo simulations. This model shows a strongly anisotropic phase transition
with different correlation length exponents $\nu_{||} \neq \nu_\perp$ parallel
and perpendicular to the spin axis.


  The effective interaction between a quark and an anti-quark as obtained
previously with by the method of iterated resolvents is replaced by the so
called up-down-model and applied to flavor off-diagonal mesons including the
pion. The only free parameters are those of canonical quantum chromo-dynamics
(QCD), particularly the coupling constant and the masses of the quarks. The so
obtained light-cone wave function can be used to calculate the pion's form
factor, particularly its mean-square radius can be computed analytically. The
results allow for the exciting conclusion that the pion is built by highly
relativistic constituents, in strong contrast to composite systems like atoms
or nuclei with non-relativistic constituents.


  We experimentally and numerically study the atomic response and pulse
propagation effects of high-order harmonics generated in Xe, Kr, and Ar driven
by a 2.1-\mu m infrared femtosecond light source. The light source is an
optical parametric chirped-pulse amplifier, and a modified strong-field
approximation and 3-dimensional pulse propagation code are used for the
numerical simulations. The extended cutoff in the long-wavelength driven
high-harmonic generation has revealed the spectral shaping of high-order
harmonics due to the atomic structure (or photo-recombination cross-section)
and the macroscopic effects, which are the main factors of determining the
conversion efficiency besides the driving wavelength. Using precise numerical
simulations to determine the macroscopic electron wavepacket, we are able to
extract the photo-recombination cross-sections from experimental high-order
harmonic spectra in the presence of macroscopic effects. We have experimentally
observed that the macroscopic effects shift the observed Cooper minimum of Kr
from 80 eV to 60-70 eV and wash out the Cooper minimum of Ar. Measured
high-harmonic conversion efficiencies per harmonic near the cutoff are ~10^{-9}
for all three gases.


  We study the geometrical optics generated by a refractive index of the form
$n(x,y)=1/y$ $(y>0)$, where $y$ is the coordinate of the vertical axis in an
orthogonal reference frame in $\R^2$. We thus obtain what we call "hyperbolic
geometrical optics" since the ray trajectories are geodesics in the
Poincar\'e-Lobachevsky half--plane $\HH^2$. Then we prove that the constant
phase surface are horocycles and obtain the \emph{horocyclic waves}, which are
closely related to the classical Poisson kernel and are the analogs of the
Euclidean plane waves. By studying the transport equation in the Beltrami
pseudosphere, we prove(i) the conservation of the flow in the entire strip
$0<y\leqslant 1$ in $\HH^2$, which is the limited region of physical interest
where the ray trajectories lie; (ii) the nonuniform distribution of the density
of trajectories: the rays are indeed focused toward the horizontal x axis,
which is the boundary of $\HH^2$. Finally the process of ray focusing and
defocusing is analyzed in detail by means of the sine--Gordon equation.


  Thermomechanical processing such as annealing is one of the main methods to
tailor the mechanical properties of materials, however, much is unknown about
the reorganization of dislocation structures deep inside macroscopic crystals
that give rise to those changes. Here, we demonstrate the self-organization of
dislocation structures upon high-temperature annealing in a mm-sized single
crystal of aluminum. We map a large embedded 3D volume ($100\times300\times300$
$\mu $m$^3$) of dislocation structures using dark field x-ray microscopy
(DFXM), a diffraction-based imaging technique. Over the wide field of view,
DFXM's high angular resolution allows us to identify subgrains, separated by
dislocation boundaries, which we identify and characterize down to the
single-dislocation level using computer-vision methods. We demonstrate how even
after long annealing times at high temperatures, the remaining low density of
dislocations still pack into well-defined, straight dislocation boundaries
(DBs) that lie on specific crystallographic planes. In contrast to conventional
grain growth models, our results show that the dihedral angles at the triple
junctions are not the predicted 120$\degree$, suggesting additional
complexities in the boundary stabilization mechanisms. Mapping the local
misorientation and lattice strain around these boundaries shows that the
observed strain is shear, imparting an average misorientation around the DB of
$\approx 0.003-0.006 \degree{}$


  Perceptual Extreme Super-Resolution for single image is extremely difficult,
because the texture details of different images vary greatly. To tackle this
difficulty, we develop a super resolution network with receptive field block
based on Enhanced SRGAN. We call our network RFB-ESRGAN. The key contributions
are listed as follows. First, for the purpose of extracting multi-scale
information and enhance the feature discriminability, we applied receptive
field block (RFB) to super resolution. RFB has achieved competitive results in
object detection and classification. Second, instead of using large convolution
kernels in multi-scale receptive field block, several small kernels are used in
RFB, which makes us be able to extract detailed features and reduce the
computation complexity. Third, we alternately use different upsampling methods
in the upsampling stage to reduce the high computation complexity and still
remain satisfactory performance. Fourth, we use the ensemble of 10 models of
different iteration to improve the robustness of model and reduce the noise
introduced by each individual model. Our experimental results show the superior
performance of RFB-ESRGAN. According to the preliminary results of NTIRE 2020
Perceptual Extreme Super-Resolution Challenge, our solution ranks first among
all the participants.


  We construct the Lagrange-D'Alembert Principle as a pure mathematical theory
that meets the modern standards of rigor. We note several new aspects of the
Lagrange-D'Alembert principle but the article is mainly methodological by its
substance. The article is addressed to specialists in ODE who are not experts
in theoretical mechanics and to graduate students that specialize in dynamical
systems.


  We derive a cross-Kerr type effective Hamiltonian for the four-level atom
interacting with three electromagnetic fields in the N-configuration. When the
atom has relaxed into the ground state a cross-Kerr nonlinearity arises between
two weak probe fields. As a development on earlier work we show in general that
the atom will also display a linear and self-Kerr response. However, if certain
resonance conditions are satisfied then the linear and self-Kerr interactions
will vanish. The electrical susceptibilities of the probe transitions are also
explored and it is shown that a large, pure cross-Kerr nonlinearity can be
generated with vanishing absorption of both probe fields.


  Let $n$ be a positive integer, $p$ be an odd prime and integers $a,b \not= 0$
with $gcd(a,b)=1$, $p \nmid ab$, and $p|(a^n \pm b^n)$, we prove the identity
$$\nu_p(a^n \pm b^n)-\nu_p(n)=\nu_p(a^{p-1}-b^{p-1}).$$ An unintended
interesting immediate consequence is the following variant of Wieferich's
criterion for FLT : Let $x^n+y^n=z^n$ with $n$ prime and $x,y,z$ pairwise
relatively prime. Then every odd prime $p|y$ satisfies $\nu_p(z^{p-1}-x^{p-1})
\ge n-1$ and every odd prime $p|x$ satisfies $\nu_p(z^{p-1}-y^{p-1}) \ge n-1$,
and every odd prime $p|z$ satisfies $\nu_p(x^{p-1}-y^{p-1}) \ge n-1$, ie. every
odd prime dividing $xyz$ is a Wieferich prime of order at least $n-1$ to some
base pair. In the "first case" where $n \nmid xyz$, the lower bound for the
Wieferich order can be improved to $n$. This gives us very strong intuition why
there should not be any solution even for moderately large $n$.


  We report a quantitative analysis of the wavevector diagram emitted by
nonlinear photoluminescence generated by a tightly focused pulsed laser beam
and distributed along Au nanowire via the mediation of surface plasmon
polaritions. The nonlinear photoluminescence is locally excited at key
locations along the nanowire in order to understand the different contributions
constituting the emission pattern measured in a conjugate Fourier plane of the
microscope. Polarization-resolved measurements reveal that the nanowire
preferentially emits nonlinear photoluminescence polarized transverse to the
long axis at close to the detection limit wavevectors with a small azimuthal
spread in comparison to the signal polarized along the long axis. We utilize
finite element method to simulate the observed directional scattering by using
localized incoherent sources placed on the nanowire. Simulation results
faithfully mimic the directional emission of the nonlinear signal emitted by
the different portions of the nanowire.


  GJ758 B is a brown dwarf companion to a nearby (15.76 pc) solar-type,
metal-rich (M/H = +0.2 dex) main-sequence star (G9V) that was discovered with
Subaru/HiCIAO in 2009. From previous studies, it has drawn attention as being
the coldest (~600K) companion ever directly imaged around a neighboring star.
We present new high-contrast data obtained during the commissioning of the
SPHERE instrument at the VLT. The data was obtained in Y-, J-, H-, and Ks-bands
with the dual-band imaging (DBI) mode of IRDIS, providing a broad coverage of
the full near-infrared (near-IR) range at higher contrast and better spectral
sampling than previously reported. In this new set of high-quality data, we
report the re-detection of the companion, as well as the first detection of a
new candidate closer-in to the star. We use the new 8 photometric points for an
extended comparison of GJ758 B with empirical objects and 4 families of
atmospheric models. From comparison to empirical object, we estimate a T8
spectral type, but none of the comparison object can accurately represent the
observed near-IR fluxes of GJ758 B. From comparison to atmospheric models, we
attribute a Teff = 600K $\pm$ 100K, but we find that no atmospheric model can
adequately fit all the fluxes of GJ758 B. The photometry of the new candidate
companion is broadly consistent with L-type objects, but a second epoch with
improved photometry is necessary to clarify its status. The new astrometry of
GJ758 B shows a significant proper motion since the last epoch. We use this
result to improve the determination of the orbital characteristics using two
fitting approaches, Least-Square Monte Carlo and Markov Chain Monte Carlo.
Finally, we analyze the sensitivity of our data to additional closer-in
companions and reject the possibility of other massive brown dwarf companions
down to 4-5 AU. [abridged]


  Although memristive devices with threshold voltages are the norm rather than
the exception in experimentally realizable systems, their SPICE programming is
not yet common. Here, we show how to implement such systems in the SPICE
environment. Specifically, we present SPICE models of a popular
voltage-controlled memristive system specified by five different parameters for
PSPICE and NGSPICE circuit simulators. We expect this implementation to find
widespread use in circuits design and testing.


  We investigate systematically the asymptotic dynamics and symmetries of all
three-dimensional extended AdS supergravity models. First, starting from the
Chern-Simons formulation, we show explicitly that the (super)anti-de Sitter
boundary conditions imply that the asymptotic symmetry algebra is the extended
superconformal algebra with quadratic nonlinearies in the currents. We then
derive the super-Liouville action by solving the Chern-Simons theory and obtain
a realization of the superconformal algebras in terms of super-Liouville
fields. Finally, we discuss the possible periodic conditions that can be
imposed on the generators of the algebra and generalize the spectral flow
analysed previously in the context of the $N$-extended linear superconformal
algebras with $N \leq 4$. The $(2+1)$-AdS/2-CFT correspondence sheds a new
light on the properties of the nonlinear superconformal algebras. It also
provides a general and natural interpretation of the spectral flow.


  We consider a recent proposal by Horv\'ath {\em et al.} to address the
question whether topological charge fluctuations in QCD are instanton dominated
via the response of fermions using lattice fermions with exact chiral symmetry,
the overlap fermions. Considering several volumes and lattice spacings we find
strong evidence for chirality of a finite density of low-lying eigenvectors of
the overlap-Dirac operator in the regions where these modes are peaked. This
result suggests instanton dominance of topological charge fluctuations in
quenched QCD.


  One of the first and foremost non-verbal interactions that humans perform is
a handshake. It has an impact on first impressions as touch can convey complex
emotions. This makes handshaking an important skill for the repertoire of a
social robot. In this paper, we present a novel framework for learning reaching
behaviours for human-robot handshaking behaviours for humanoid robots solely
using third-person human-human interaction data. This is especially useful for
non-backdrivable robots that cannot be taught by demonstrations via kinesthetic
teaching. Our approach can be easily executed on different humanoid robots.
This removes the need for re-training, which is especially tedious when
training with human-interaction partners. We show this by applying the learnt
behaviours on two different humanoid robots with similar degrees of freedom but
different shapes and control limits.


  Whether or not perfect imaging is obtained in the mirrored version of
Maxwell's fisheye lens is debated in the comment/reply sequence
[Blaikie-2010njp, Leonhardt-2010njp] discussing Leonhardt's original paper
[Leonhardt-2009njp]. Here we show that causal solutions can be obtained without
the need for an "active localized drain", contrary to the claims in
[Leonhardt-2010njp].


  Inverse bicontinuous cubic phases are ubiquitous in lipid-water mixtures and
consist of a lipid bilayer forming a cubic minimal surface, thereby dividing
space into two cubic networks of water channels. For small hydrocarbon chain
lengths, the monolayers can be modeled as parallel surfaces to a minimal
midsurface. The bending energy of the cubic phases is determined by the
distribution of Gaussian curvature over the minimal midsurfaces which we
calculate for seven different structures (G, D, P, I-WP, C(P), S and F-RD). We
show that the free-energy densities of the structures G, D and P are
considerably lower than those of the other investigated structures due to their
narrow distribution of Gaussian curvature. The Bonnet transformation between G,
D, and P implies that these phases coexist along a triple line, which also
includes an excess water phase. Our model includes thermal membrane
undulations. Our qualitative predictions remain unchanged when higher order
terms in the curvature energy are included. Calculated phase diagrams agree
well with the experimental results for 2:1 lauric acid/dilauroyl
phosphatidylcholine and water.


  A scaling hypothesis for the n-particle spectral densities of the O(3)
nonlinear sigma-model is described. It states that for large particle numbers
the n-particle spectral densities are ``self-similar'' in being basically
rescaled copies of a universal shape function. This can be viewed as a
2-dimensional, but non-perturbative analogue of the KNO scaling in QCD.
Promoted to a working hypothesis, it allows one to compute the two point
functions at ``all'' energy or length scales. In addition, the values of two
non-perturbative constants (needed for a parameter-free matching of the
perturbative and the non-perturbative regime) are determined exactly.


  We present a study of dynamical scaling and domain growth in a non potential
system that models Rayleigh-Benard convection in a rotating cell. In d=1,
dynamical scaling holds, but the non potential terms modify the characteristic
growth law with a crossover from logarithmic to linear in time. In d=2 the non
potential terms prevent coarsening for any value of the angular rotation speed.


  In the Josephson effect, coherent Cooper pair tunneling is driven by the
phase difference between the superconducting order parameters on opposite sides
of the junction. By analogy, differences in order parameters across a junction
should lead to coherent tunneling of the condensed objects that exist in the
broken symmetry state. To exhibit the generality of this phenomenon and make
predictions from a realistic model, we study the case of a tunnel junction
between two itinerant antiferromagnets. At the mean-field level, we find an
equilibrium current of the staggered magnetization through the junction that is
proportional the normal state conductance and to $\mathbf{S}_{L}\times
\mathbf{S}_{R}$ where $\mathbf{S}_{L}$ and $\mathbf{S}_{R}$ are the staggered
magnetizations on either sides. Microscopically, this effect comes from
coherent tunneling of spin-one charge-zero particle-hole pairs that have a net
wave vector equal to the antiferromagnetic one. We explain similarities and
differences with the standard DC and AC Josephson effects.


  We introduce the framework of performative reinforcement learning where the
policy chosen by the learner affects the underlying reward and transition
dynamics of the environment. Following the recent literature on performative
prediction~\cite{Perdomo et. al., 2020}, we introduce the concept of
performatively stable policy. We then consider a regularized version of the
reinforcement learning problem and show that repeatedly optimizing this
objective converges to a performatively stable policy under reasonable
assumptions on the transition dynamics. Our proof utilizes the dual perspective
of the reinforcement learning problem and may be of independent interest in
analyzing the convergence of other algorithms with decision-dependent
environments. We then extend our results for the setting where the learner just
performs gradient ascent steps instead of fully optimizing the objective, and
for the setting where the learner has access to a finite number of trajectories
from the changed environment. For both settings, we leverage the dual
formulation of performative reinforcement learning and establish convergence to
a stable solution. Finally, through extensive experiments on a grid-world
environment, we demonstrate the dependence of convergence on various parameters
e.g. regularization, smoothness, and the number of samples.


  Behaviour selection has been an active research topic for robotics, in
particular in the field of human-robot interaction. For a robot to interact
effectively and autonomously with humans, the coupling between techniques for
human activity recognition, based on sensing information, and robot behaviour
selection, based on decision-making mechanisms, is of paramount importance.
However, most approaches to date consist of deterministic associations between
the recognised activities and the robot behaviours, neglecting the uncertainty
inherent to sequential predictions in real-time applications. In this paper, we
address this gap by presenting a neurorobotics approach based on computational
models that resemble neurophysiological aspects of living beings. This
neurorobotics approach was compared to a non-bioinspired, heuristics-based
approach. To evaluate both approaches, a robot simulation is developed, in
which a mobile robot has to accomplish tasks according to the activity being
performed by the inhabitant of an intelligent home. The outcomes of each
approach were evaluated according to the number of correct outcomes provided by
the robot. Results revealed that the neurorobotics approach is advantageous,
especially considering the computational models based on more complex animals.


  We present a parameterized synthetic dataset called Moving Symbols to support
the objective study of video prediction networks. Using several instantiations
of the dataset in which variation is explicitly controlled, we highlight issues
in an existing state-of-the-art approach and propose the use of a performance
metric with greater semantic meaning to improve experimental interpretability.
Our dataset provides canonical test cases that will help the community better
understand, and eventually improve, the representations learned by such
networks in the future. Code is available at
https://github.com/rszeto/moving-symbols .


  We describe recent progress in a program to understand the classification of
three-dimensional Fano varieties with $\mathbb{Q}$-factorial terminal
singularities using mirror symmetry. As part of this we give an improved and
more conceptual understanding of Laurent inversion, a technique that sometimes
allows one to construct a Fano variety $X$ directly from a Laurent polynomial
$f$ that corresponds to it under mirror symmetry.


  Electronic shot counters allow armourers to perform preventive and predictive
maintenance based on quantitative measurements, improving reliability, reducing
the frequency of accidents, and reducing maintenance costs. To answer a market
pressure for both low lead time to market and increased customisation, we aim
to solve the shot detection and shot counting problem in a generic way through
machine learning.
  In this study, we describe a method allowing one to construct a dataset with
minimal labelling effort by only requiring the total number of shots fired in a
time series. To our knowledge, this is the first study to propose a technique,
based on learning from label proportions, that is able to exploit these weak
labels to derive an instance-level classifier able to solve the counting
problem and the more general discrimination problem. We also show that this
technique can be deployed in heavily constrained microcontrollers while still
providing hard real-time (<100ms) inference. We evaluate our technique against
a state-of-the-art unsupervised algorithm and show a sizeable improvement,
suggesting that the information from the weak labels is successfully leveraged.
Finally, we evaluate our technique against human-generated state-of-the-art
algorithms and show that it provides comparable performance and significantly
outperforms them in some offline and real-world benchmarks.


  Special solutions of string theory in supercritical dimensions can
interpolate in time between theories with different numbers of spacetime
dimensions (via dimension quenching) and different amounts of worldsheet
supersymmetry (via c-duality). These solutions connect supercritical string
theories to the more familiar string duality web in ten dimensions, and provide
a precise link between supersymmetric and purely bosonic string theories.
Dimension quenching and c-duality appear to be natural concepts in string
theory, giving rise to large networks of interconnected theories. We describe
some of these networks in detail and discuss general consistency constraints on
the types of transitions that arise in this framework.


  Effects of residual C impurities and Ga vacancies on the dynamic
instabilities of AlN/AlGaN/GaN metal insulator semiconductor high electron
mobility transistors are investigated. Secondary ion mass spectroscopy,
positron annihilation spectroscopy, steady state and time-resolved
photoluminescence (PL) measurements have been performed in conjunction with
electrical characterization and current transient analyses. The correlation
between yellow luminescence (YL), C- and Ga vacancy concentration is
investigated. Time-resolved PL indicating the C$_{\mathrm{N}}$O$_{\mathrm{N}}$
complex as the main source of the YL, while Ga vacancies or related complexes
with C seem not to play a major role. The device dynamic performance is found
to be significantly dependent on the C concentration close to the channel of
the transistor. Additionally, the magnitude of the YL is found to be in
agreement with the threshold voltage shift and with the on-resistance
degradation. Trap analysis of the GaN buffer shows an apparent activation
energy of $\sim$0.8eV for all samples, pointing to a common dominating trapping
process and that the growth parameters affect solely the density of trap
centres. It is inferred that the trapping process is likely to be directly
related to C based defects.


  In the research community, there exists the strong belief that a continuous
Galerkin scheme is notoriously unstable and additional stabilization terms have
to be added to guarantee stability. In the first part of the series [6], the
application of simultaneous approximation terms for linear problems is
investigated where the boundary conditions are imposed weakly. By applying this
technique, the authors demonstrate that a pure continuous Galerkin scheme is
indeed linear stable if the boundary conditions are done in the correct way. In
this work, we extend this investigation to the non-linear case and focusing on
entropy conservation. Switching to entropy variables, we will provide an
estimation on the boundary operators also for non-linear problems to guarantee
conservation. In numerical simulations, we verify our theoretical analysis.


  We present solutions of the coupled, non-relativistic $^3S_1$-$^3D_1$ gap
equations for neutron-proton pairing in symmetric nuclear matter, and estimate
relativistic effects by solving the same gap equations modified according to
minimal relativity and using single-particle energies from a
Dirac-Brueckner-Hartree-Fock calculation. As a main result we find that
relativistic effects decrease the value of the gap at the saturation density
$k_F=1.36 fm^{-1}$ considerably, in conformity with the lack of evidence for
strong neutron-proton pairing in finite nuclei.


  This is a slightly edited version of the transparencies for a seminar at UCL,
May 7, 2003. It is intended to give a quick view of background, ideas, and some
calculations, in the applicatioon of some non commutative methods to algebraic
topology.


  Most existing trackers based on discriminative correlation filters (DCF) try
to introduce predefined regularization term to improve the learning of target
objects, e.g., by suppressing background learning or by restricting change rate
of correlation filters. However, predefined parameters introduce much effort in
tuning them and they still fail to adapt to new situations that the designer
did not think of. In this work, a novel approach is proposed to online
automatically and adaptively learn spatio-temporal regularization term.
Spatially local response map variation is introduced as spatial regularization
to make DCF focus on the learning of trust-worthy parts of the object, and
global response map variation determines the updating rate of the filter.
Extensive experiments on four UAV benchmarks have proven the superiority of our
method compared to the state-of-the-art CPU- and GPU-based trackers, with a
speed of ~60 frames per second running on a single CPU.
  Our tracker is additionally proposed to be applied in UAV localization.
Considerable tests in the indoor practical scenarios have proven the
effectiveness and versatility of our localization method. The code is available
at https://github.com/vision4robotics/AutoTrack.


  In this letter, we present anti-cancer drug delivery strategies using
knowledges obtained from our recent studies. We have conducted inverse analyses
of "density distributions of colloidal particles near a focused surface" and
"pair potentials between the surface and the colloidal particle" using data
measured by optical tweezers (OT) and atomic force microscopy (AFM).
Non-additive Asakura-Oosawa (NAO) theory and a lattice theory in statistical
mechanics of simple polymers have been also our research topics. Summarizing
the knowledges, we propose two strategies to increase the delivery rate of
capsule shaped anti-cancer drugs to cancer cells. We consider that enhanced
repulsion between the normal cell and the drug accelerates the attraction
between the cancer cell and the drug, which can be named enhanced repulsion and
accelerated adsorption (ERAA) effect. To realize the ERAA effect, we propose a
supporting method for measuring the interactions using OT and AFM. In the
second strategy, dose of water-soluble polymers is considered to realize
adsorptions of the drugs and cancer cell derived exosomes onto the cancer
cells, which we call non-specific and selective adsorption (NSSA) effect. In
the main text, we explain the NSSA effect using the NAO theory. Moreover, we
explain that structural stabilities of normal dispersed proteins around the
cancer cells are not largely destroyed by the dosed polymers from the viewpoint
of the lattice theory.


  We discuss the effect of site dilution on both the magnetization and the
density of states of quantum spins in the honeycomb lattice, described by the
antiferromagnetic Heisenberg spin-S model. For this purpose a real-space
Bogoliubov-Valatin transformation is used. In this work we show that for the
S>1/2 the system can be analyzed in terms of linear spin wave theory. For spin
S=1/2, however, the linear spin wave approximation breaks down. In this case,
we have studied the effect of dilution on the staggered magnetization using the
Stochastic Series Expansion Monte Carlo method. Two main results are to be
stressed from the Monte Carlo method: (i) a better value for the staggered
magnetization of the undiluted system, m=0.2677(6); (ii) a finite value of the
staggered magnetization of the percolating cluster at the classical percolation
threshold, showing that there is no quantum critical transition driven by
dilution in the Heisenberg model. In the solution of the problem using linear
the spin wave method we pay special attention to the presence of zero energy
modes. Using a combination of linear spin wave analysis and the recursion
method we were able to obtain the thermodynamic limit behavior of the density
of states for both the square and the honeycomb lattices. We have used both the
staggered magnetization and the density of states to analyze neutron scattering
experiments and Neel temperature measurements on quasi-two- -dimensional
honeycomb systems. Our results are in quantitative agreement with experimental
results on Mn_pZn_{1-p}PS_3 and on the Ba(Ni_pMg_{1-p})_2V_2O_8.


  We summarize some recent results from our observational campaign to study the
central regions of spiral galaxies of late Hubble type (Scd - Sm). These
disk-dominated, bulgeless galaxies have an apparently uneventful merger
history. The evolution of their nuclei thus holds important constraints on the
mechanisms that are responsible for bulge formation and nuclear activity in
spiral galaxies. We discuss the structural properties, stellar populations, and
dynamical masses of the compact, luminous star cluster that is found in the
nuclei of most late-type spiral galaxies. Although preliminary, our results
strongly indicate that many galaxies of our sample experience repeated periods
of nuclear star formation. While the exact mechanism that leads to the required
high gas densities in the galaxy nucleus remains unclear, results from our
recent CO survey of late-type spirals demonstrate that in most cases, the
central kpc contains enough molecular gas to support repetitive nuclear
starbursts.


  The results of the experimental searches for Higgs particles at LEP, using
the data collected at centre-of-mass energies up to 189 GeV, are reviewed and
the prospects for the near future outlined.


  Neutrino physics is a forefront topic of today's research. Large detectors
installed underground study neutrino properties using neutrino beams from muons
decaying in flight. DAEdALUS looks at neutrinos from stopped muons, "decay at
rest" (DAR) neutrinos. The DAR neutrino spectrum has effectively no electron
antineutrinos (essentially all pi- are absorbed), so a detector with free
protons is sensitive to appearance of nu-e-bar oscillating from nu-mu-bar via
inverse-beta-decay (IBD). Oscillations are studied using sources relatively
near the detector, but which explore the same physics as the high-energy
neutrino beams from Long Baseline experiments. As the DAR spectrum is fixed,
the baseline is varied: plans call for 3 accelerator-based neutrino sources at
1.5, 8 and 20 km with staggered beam-on times. Compact, cost-effective
superconducting ring cyclotrons accelerating molecular hydrogen ions (H2+) to
800 MeV/n with stripping extraction are being designed by L. Calabretta and his
group. This revolutionary design could find application in many ADS-related
fields.


  Evidence is now compelling that most externally-polluted white dwarfs derive
their heavy atoms by accretion from asteroids - the building blocks of rocky
planets. Optical and ultraviolet spectroscopy of a small sample of suitable
white dwarf stars shows that to zeroth order, the accreted extrasolar parent
bodies compositionally resemble bulk Earth. (1) Extrasolar planetesimals are at
least 85% by mass composed of O, Mg, Si and Fe. (2) Compared to the Sun, C is
often deficient, usually by at least a factor of 10 and therefore comprises
less than 1% of an extrasolar planetesimal's mass. At least to-date, C has
never been found to be enhanced as would be expected if carbon-rich
planetesimals have formed. (3) While there may be individual exceptions,
considered as a whole, the population of extrasolar asteroids accreted onto a
well-defined sample of local white dwarf stars is less than 1% water by mass.


  The main purpose of this paper is to obtain strong laws of large numbers for
arrays or weighted sums of random variables under a scenario of dependence.
Namely, for triangular arrays $\{X_{n,k}, \, 1 \leqslant k \leqslant n, \, n
\geqslant 1 \}$ of row-wise extended negatively dependent random variables
weakly mean dominated by a random variable $X \in \mathscr{L}_{1}$ and
sequences $\{b_{n} \}$ of positive constants, conditions are given to ensure
$\sum_{k=1}^{n} \left(X_{n,k} - \mathbb{E} \, X_{n,k} \right)/b_{n}
\overset{\textnormal{a.s.}}{\longrightarrow} 0$. Our statements also allow us
to improve recent results about complete convergence.


  If a dynamical system is long-lived and non-resonant (that is, if there is a
set of tracers that have evolved independently through many orbital times), and
if the system is observed at any non-special time, it is possible to infer the
dynamical properties of the system (such as the gravitational force or
acceleration law) from a snapshot of the positions and velocities of the tracer
population at a single moment in time. In this paper we describe a general
inference technique that solves this problem while allowing (1) the unknown
distribution function of the tracer population to be simultaneously inferred
and marginalized over, and (2) prior information about the gravitational field
and distribution function to be taken into account. As an example, we consider
the simplest problem of this kind: We infer the force law in the Solar System
using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for
the eight major planets. We consider purely radial acceleration laws of the
form a_r = -A [r/r_0]^{-\alpha}, where r is the distance from the Sun. Using a
probabilistic inference technique, we infer 1.989 < \alpha < 2.052 (95 percent
interval), largely independent of any assumptions about the distribution of
energies and eccentricities in the system beyond the assumption that the system
is phase-mixed. Generalizations of the methods used here will permit, among
other things, inference of Milky Way dynamics from Gaia-like observations.


  Quasars provide our farthest-reaching view of the Universe. The Sloan Survey
now contains over 100,000 quasar candidates. A careful look at the angular
distribution of quasar magnitudes shows a surprising intensity enhancement with
a "bulls eye" pattern toward (alpha,delta) ~ (195{\deg}, 0{\deg}) for all
wavelengths from UV through infrared. The angular pattern and size of the
enhancement is very similar for all wavelengths, which is inconsistent with a
Doppler shift due to a large peculiar velocity toward that direction. The shift
is also too large to explain as a systematic error in the quasar magnitudes.
The general features of the anomaly can be explained by the gravitational
lensing of a massive bubble with Mlens ~ 10^21 M\odot, a lens radius ~350 Mpc,
and with the lens subtending an angle of \pm15{\deg} on the sky. It is
remarkable that the presence of such a massive bubble universe can explain not
only the anomalies in the angular distribution of quasar intensities, but also
anomalies in the distribution of luminous red galaxies, anomalies in the CMB,
and bulk flow discrepancies, all of which appear in roughly the same direction.


  Recent work shows Large Language Models (LLMs) struggle to understand natural
language constraints for various text generation tasks in zero- and few-shot
settings. While, in the code domain, there is wide usage of constraints in code
format to maintain the integrity of code written in Domain-Specific Languages
(DSLs), yet there has been no work evaluating LLMs with these constraints. We
propose two novel tasks to assess the controllability of LLMs using hard and
soft constraints represented as code across five representations. Our findings
suggest that LLMs struggle to comprehend constraints in all representations
irrespective of their portions in the pre-training data. While models are
better at comprehending constraints in JSON, YAML, and natural language
representations, they struggle with constraints represented in XML and the
resource-rich language Python.


  The EM algorithm is a widely used methodology for penalized likelihood
estimation. Provable monotonicity and convergence are the hallmarks of the EM
algorithm and these properties are well established for smooth likelihood and
smooth penalty functions. However, many relaxed versions of variable selection
penalties are not smooth. The goal of this paper is to introduce a new class of
Space Alternating Penalized Kullback Proximal extensions of the EM algorithm
for nonsmooth likelihood inference. We show that the cluster points of the new
method are stationary points even when on the boundary of the parameter set.
Special attention has been paid to the construction of component-wise version
of the method in order to ease the implementation for complicated models.
Illustration for the problems of model selection for finite mixtures of
regression and to sparse image reconstruction is presented.


  Rgtsvm provides a fast and flexible support vector machine (SVM)
implementation for the R language. The distinguishing feature of Rgtsvm is that
support vector classification and support vector regression tasks are
implemented on a graphical processing unit (GPU), allowing the libraries to
scale to millions of examples with >100-fold improvement in performance over
existing implementations. Nevertheless, Rgtsvm retains feature parity and has
an interface that is compatible with the popular e1071 SVM package in R.
Altogether, Rgtsvm enables large SVM models to be created by both experienced
and novice practitioners.


  We compare the phenomenology of two models, the so-called minimal Z' and an
effective model for a SM-like Higgs realised as a composite state of a new
strong interaction, at a multi-TeV linear collider in the hypothesis that the
new physics is at a scale beyond the direct reach of the machine.


  We propose here a first-principles, parameter free, real space method for the
study of disordered extended defects in solids. We shall illustrate the power
of the technique with an application to graphene sheets with randomly placed
Stone-Wales defects and shall examine the signature of such random defects on
the density of states as a function of their concentration. The technique is
general enough to be applied to a whole class of systems with lattice
translational symmetry broken not only locally but by extended defects and
defect clusters. The real space approach will allow us to distinguish
signatures of specific defects and defect clusters.


  To date, blind quantum computing demonstrations require clients to have weak
quantum devices. Here we implement a proof-of-principle experiment for
completely classical clients. Via classically interacting with two quantum
servers that share entanglement, the client accomplishes the task of having the
number 15 factorized by servers who are denied information about the
computation itself. This concealment is accompanied by a verification protocol
that tests servers' honesty and correctness. Our demonstration shows the
feasibility of completely classical clients and thus is a key milestone towards
secure cloud quantum computing.


  We present an approach for unsupervised learning of speech representation
disentangling contents and styles. Our model consists of: (1) a local encoder
that captures per-frame information; (2) a global encoder that captures
per-utterance information; and (3) a conditional decoder that reconstructs
speech given local and global latent variables. Our experiments show that (1)
the local latent variables encode speech contents, as reconstructed speech can
be recognized by ASR with low word error rates (WER), even with a different
global encoding; (2) the global latent variables encode speaker style, as
reconstructed speech shares speaker identity with the source utterance of the
global encoding. Additionally, we demonstrate an useful application from our
pre-trained model, where we can train a speaker recognition model from the
global latent variables and achieve high accuracy by fine-tuning with as few
data as one label per speaker.


  In neural machine translation (NMT), researchers face the challenge of
un-seen (or out-of-vocabulary OOV) words translation. To solve this, some
researchers propose the splitting of western languages such as English and
German into sub-words or compounds. In this paper, we try to address this OOV
issue and improve the NMT adequacy with a harder language Chinese whose
characters are even more sophisticated in composition. We integrate the Chinese
radicals into the NMT model with different settings to address the unseen words
challenge in Chinese to English translation. On the other hand, this also can
be considered as semantic part of the MT system since the Chinese radicals
usually carry the essential meaning of the words they are constructed in.
Meaningful radicals and new characters can be integrated into the NMT systems
with our models. We use an attention-based NMT system as a strong baseline
system. The experiments on standard Chinese-to-English NIST translation shared
task data 2006 and 2008 show that our designed models outperform the baseline
model in a wide range of state-of-the-art evaluation metrics including LEPOR,
BEER, and CharacTER, in addition to BLEU and NIST scores, especially on the
adequacy-level translation. We also have some interesting findings from the
results of our various experiment settings about the performance of words and
characters in Chinese NMT, which is different with other languages. For
instance, the fully character level NMT may perform well or the state of the
art in some other languages as researchers demonstrated recently, however, in
the Chinese NMT model, word boundary knowledge is important for the model
learning.


  Mental health problems are on the rise globally and strain national health
systems worldwide. Mental disorders are closely associated with fear of stigma,
structural barriers such as financial burden, and lack of available services
and resources which often prohibit the delivery of frequent clinical advice and
monitoring. Technologies for mental well-being exhibit a range of attractive
properties, which facilitate the delivery of state-of-the-art clinical
monitoring. This review article provides an overview of traditional techniques
followed by their technological alternatives, sensing devices, behaviour
changing tools, and feedback interfaces. The challenges presented by these
technologies are then discussed with data collection, privacy, and battery life
being some of the key issues which need to be carefully considered for the
successful deployment of mental health toolkits. Finally, the opportunities
this growing research area presents are discussed including the use of portable
tangible interfaces combining sensing and feedback technologies. Capitalising
on the data these ubiquitous devices can record, state of the art machine
learning algorithms can lead to the development of robust clinical decision
support tools towards diagnosis and improvement of mental well-being delivery
in real-time.


  We propose a model for the spectral formation of Gamma Ray Burst (GRB) prompt
emission, where the phenomenological Band's function is usually applied to
describe the GRB prompt emission. We suggest that the GRB prompt emission is
mainly a result of two upscattering processes. The first process is the
Comptonization of relatively cold soft photons of the star off electrons of a
hot shell of plasma of temperature T_e of the order of 10^{9} K (or kT_e~100
keV) that moves sub-relativistically with the bulk velocity V_b substantially
less than the speed of light c. In this phase, the Comptonization parameter Y
is high and the interaction between a blackbody-like soft seed photon
population and hot electrons leads to formation of a saturated Comptonization
spectrum modified by the sub-relativistic bulk outflow. The second process is
an upscattering of the previously Comptonized spectrum by the plasma outflow
once it becomes relativistic. This process gives rise to the high-energy
power-law component above the peak in the EF(E)-diagram where F(E) is the
energy flux. The latter process can be described by a convolution of the
Comptonized spectrum with a broken-power-law Green function. Possible physical
scenarios for this second upscattering process are discussed. In the framework
of our model, we give an interpretation of the Amati relation between the
intrinsic spectral peak photon energy and radiated energy or luminosity, and we
propose a possible explanation of the GRB temporal variability.


  Nonstandard interaction is expected to be a crucial hint to explain the
experimental data of neutrino scattering off electrons. In this context, the
nonstandard interaction vector and axial-vector couplings are needed to be
extracted from recent experiments and a few of them are now available in the
literature. With these coupling bounds, in this paper, I explore their impacts
on the neutrinos interacting with the free electron gas in dense matter. To
this end, I compute and predict the neutrino differential cross section and
mean free path in dense matter for all those experimental bounds. Interesting
behavior in the neutrino cross sections and mean free path is found for the
different nonstandard interaction couplings from different experiment
constraints. I also found that the neutrino cross-section and mean free path in
the dense matter are very sensitive to values and signs of the nonstandard
interaction couplings, leading to different prediction results to the Standard
Model cross-section and mean free path.


  This paper studies a life-cycle optimal portfolio-consumption problem when
the consumption performance is measured by a shortfall aversion preference with
an additional drawdown constraint on consumption rate. Meanwhile, the agent
also dynamically chooses her life insurance premium to maximize the expected
bequest at the death time. By using dynamic programming arguments and the dual
transform, we solve the HJB variational inequality explicitly in a piecewise
form across different regions and derive some thresholds of the wealth variable
for the piecewise optimal feedback controls. Taking advantage of our analytical
results, we are able to numerically illustrate some quantitative impacts on
optimal consumption and life insurance by model parameters and discuss their
financial implications.


  We address the problem of distributing approximation errors in large-scale
quantum programs. It has been known for some time that when compiling quantum
algorithms for a fault-tolerant architecture, some operations must be
approximated as they cannot be implemented with arbitrary accuracy by the
underlying gate set. This leads to approximation errors which often can be
grouped along subroutines that the given quantum algorithm is composed of.
Typically, choices can be made as to how to distribute approximation errors so
that the overall error is kept beneath a user- or application-defined
threshold. These choices impact the resource footprint of the fault-tolerant
implementation. We develop an automatic approximation error management module
to tackle the resulting optimization problems. The module is based on annealing
and can be integrated into any quantum software framework. Using the benchmark
of simulating an Ising model with transverse field, we provide numerical
results to quantify the benefits and trade-offs involved in our approach.


  Partial-order reduction (POR) and lazy abstraction with interpolants are two
complementary techniques that have been successfully employed to make model
checking tools for concurrent programs effective. In this work, we present
AbPress - Abstraction-based Partial-order Reduction with Source-Sets - an
algorithm that fuses a recently proposed and powerful dynamic POR technique
based on source-sets and lazy abstraction to obtain an efficient software model
checker for multi-threaded programs. It trims the inter- leaving space by
taking the abstraction and source-sets into account. We amplify the
effectiveness of AbPress with a novel solution that summarizes the accesses to
shared variables over a collection of interleavings. We have implemented
AbPress in a tool that analyzes concurrent programs using lazy abstraction,
viz., Impara. Our evaluation on the effectiveness of the presented approach has
been encouraging. AbPress compares favorably to existing state-of-the-art tools
in the landscape.


  Secondary particles produced in spallation reactions of cosmic rays with the
interstellar gas provide valuable information that allow us to investigate the
injection and transport of charged particles in the Galaxy. A good
understanding of the cross sections of production of these particles is crucial
to correctly interpret our models, although the existing experimental data is
very scarce and uncertain. We have developed a new set of cross sections, both
inelastic and inclusive, computed with the {\tt FLUKA} Monte Carlo nuclear code
and tested its compatibility with CR data. Inelastic and inclusive cross
sections have been compared to the most up-to-date data and parameterisations
finding a general good agreement. Then, these cross sections have been
implemented in the {\tt DRAGON2} code to characterize the spectra of CR nuclei
up to $Z=26$ and the secondary-to-primary ratios of B, Be and Li.
Interestingly, we find that the FLUKA cross sections allow us to predict an
energy-dependence of the B, Be and Li flux ratios which is compatible with
AMS-02 data and to reproduce simultaneously these flux ratios with a scaling
lower than $20\%$. Finally, we implement the cross sections of production of
gamma rays, calculated with {\tt FLUKA}, in the {\tt Gammasky} code and compute
diffuse gamma-ray sky maps and the local HI emissivity spectrum, finding a very
good agreement with Fermi Large Area Telescope data.


  For a given base scheme, a correspondence is established between a class of
sheaves on curves over this base scheme and certain points of infinite
Grassmannians. This equivalence extends to a characterization of commutative
algebras of ordinary differential operators with coefficients in the ring of
formal power series over a given $k$-algebra. Our construction generalizes the
approach of M.Mulase, which gives the above connection in the case that the
base scheme is one closed point.