  Most of the metals (elements heavier than helium) ever produced by stars in
the member galaxies of galaxy clusters currently reside within the hot, X-ray
emitting intra-cluster gas. Observations of X-ray line emission from this
intergalactic medium have suggested a relatively small cluster-to-cluster
scatter outside of the cluster centers and enrichment with iron out to large
radii, leading to the idea that the metal enrichment occurred early in the
history of the Universe. Models with early enrichment predict a uniform metal
distribution at large radii in clusters, while late-time enrichment, favored by
some previous studies, is expected to introduce significant spatial variations
of the metallicity. To discriminate clearly between these competing models, it
is essential to test for potential inhomogeneities by measuring the abundances
out to large radii along multiple directions in clusters, which has not
hitherto been done. Here we report a remarkably uniform measured iron
abundance, as a function of radius and azimuth, that is statistically
consistent with a constant value of 0.306+/-0.012 Solar out to the edge of the
nearby Perseus Cluster. This homogeneous distribution requires that most of the
metal enrichment of the intergalactic medium occurred before the cluster
formed, likely over 10 billion years ago, during the period of maximal star
formation and black hole activity.


  Causal inference for testing clinical hypotheses from observational data
presents many difficulties because the underlying data-generating model and the
associated causal graph are not usually available. Furthermore, observational
data may contain missing values, which impact the recovery of the causal graph
by causal discovery algorithms: a crucial issue often ignored in clinical
studies. In this work, we use data from a multi-centric study on endometrial
cancer to analyze the impact of different missingness mechanisms on the
recovered causal graph. This is achieved by extending state-of-the-art causal
discovery algorithms to exploit expert knowledge without sacrificing
theoretical soundness. We validate the recovered graph with expert physicians,
showing that our approach finds clinically-relevant solutions. Finally, we
discuss the goodness of fit of our graph and its consistency from a clinical
decision-making perspective using graphical separation to validate causal
pathways.


  Recent observations from the Spitzer Space Telescope enable us to study the
mid-infrared dust excess of Asymptotic giant branch (AGB) stars in the Large
Magellanic Cloud (LMC). Using mid-infrared spectra, together with photometric
data from the SAGE programme, we establish a colour selection of carbon-rich
AGB stars with intermediate and high mass-loss rates. We also established
mass-loss rate versus colour relations for carbon-rich AGB stars. The
integrated mass-loss rate over all intermediate and high mass-loss rate
carbon-rich AGB candidates in the LMC is 8.5x10^-3 solar mass per year. This
number could be almost doubled if oxygen-rich stars are included. Gas mass-loss
rate from these stars is 4-5x10^-4 solar mass per year per kpc2 in the bar
and1x10^-4 solar mass per year per kpc^2 outside of the bar. AGB stars are one
of the most important dust sources in the LMC, and the dominant gas source
outside of the bar. As a consequence of recent increases in the star-formation
rate, supernovae are the most important gas source in the LMC bar and around 30
Dor. These differences in dust and gas sources impact on the gas-to-dust ratio
and dust properties of the local ISM, because the injection from SNe could have
a higher gas-to-dust ratio, resulting in a higher gas-to-dust ratio for the ISM
in certain regions of the LMC.


  Deep neural networks often exhibit sub-optimal performance under covariate
and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising
solution to this dilemma, yet most SFDA approaches are restricted to closed-set
scenarios. In this paper, we explore Source-Free Universal Domain Adaptation
(SF-UniDA) aiming to accurately classify "known" data belonging to common
categories and segregate them from target-private "unknown" data. We propose a
novel Global and Local Clustering (GLC) technique, which comprises an adaptive
one-vs-all global clustering algorithm to discern between target classes,
complemented by a local k-NN clustering strategy to mitigate negative transfer.
Despite the effectiveness, the inherent closed-set source architecture leads to
uniform treatment of "unknown" data, impeding the identification of distinct
"unknown" categories. To address this, we evolve GLC to GLC++, integrating a
contrastive affinity learning strategy. We examine the superiority of GLC and
GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in
the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by
16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel
category clustering accuracy of GLC by 4.3% in open-set scenarios on
Office-Home. Furthermore, the introduced contrastive learning strategy not only
enhances GLC but also significantly facilitates existing methodologies.


  Motivated by attempts to quantum simulate lattice models with continuous
Abelian symmetries using discrete approximations, we study an extended-O(2)
model in two dimensions that differs from the ordinary O(2) model by the
addition of an explicit symmetry breaking term $-h_q\cos(q\varphi)$. Its
coupling $h_q$ allows to smoothly interpolate between the O(2) model ($h_q=0$)
and a $q$-state clock model ($h_q\rightarrow\infty$). In the latter case, a
$q$-state clock model can also be defined for noninteger values of $q$. Thus,
such a limit can also be considered as an analytic continuation of an ordinary
$q$-state clock model to noninteger $q$. In previous work, we established the
phase diagram for noninteger $q$ in the infinite coupling limit
($h_q\rightarrow\infty$). We showed that there is a second-order phase
transition at low temperature and a crossover at high temperature. In this
work, we seek to establish the phase diagram at finite values of the coupling
using Monte Carlo and tensor methods. We show that for noninteger $q$, the
second-order phase transition at low temperature and crossover at high
temperature persist to finite coupling. For integer $q=2,3,4$, we know there is
a second-order phase transition at infinite coupling (i.e. the well-known clock
models). At finite coupling, we find that the critical exponents for $q=3,4$
vary with the coupling, and for $q=4$ the transition may turn into a BKT
transition at small coupling. We comment on the similarities and differences of
the phase diagrams with those of quantum simulators of the Abelian-Higgs model
based on ladder-shaped arrays of Rydberg atoms.


  We investigate the containment of epidemic spreading in networks from a
normative point of view. We consider a susceptible/infected model in which
agents can invest in order to reduce the contagiousness of network links. In
this setting, we study the relationships between social efficiency, individual
behaviours and network structure. First, we exhibit an upper bound on the Price
of Anarchy and prove that the level of inefficiency can scale up to linearly
with the number of agents. Second, we prove that policies of uniform reduction
of interactions satisfy some optimality conditions in a vast range of networks.
In setting where no central authority can enforce such stringent policies, we
consider as a type of second-best policy the shift from a local to a global
game by allowing agents to subsidise investments in contagiousness reduction in
the global rather than in the local network. We then characterise the scope for
Pareto improvement opened by such policies through a notion of Price of
Autarky, measuring the ratio between social welfare at a global and a local
equilibrium. Overall, our results show that individual behaviours can be
extremely inefficient in the face of epidemic propagation but that policy can
take advantage of the network structure to design efficient containment
policies.


  These are the notes from Asger T\"ornquist's Appalachian Set Theory lectures
at Carnegie Mellon University. They form a chapter in the LMS lecture notes
series 406.


  We discuss the concept of Direct Chaotic Communication (DCC). The scheme is
based on the following ideas: (1) the chaotic source generates chaotic
oscillations directly in the specified microwave band; (2) information
component is input by means of formation of the appropriate stream of chaotic
radio pulses; (3) envelope detection is used. The principle of communication
scheme is confirmed experimentally in microwave band. The transmission rates of
3.34, 10.0 and up to 70.0 Mbps are demonstrated. his only looks like the
abstract.


  We propose a novel adaptive test of goodness-of-fit, with computational cost
linear in the number of samples. We learn the test features that best indicate
the differences between observed samples and a reference model, by minimizing
the false negative rate. These features are constructed via Stein's method,
meaning that it is not necessary to compute the normalising constant of the
model. We analyse the asymptotic Bahadur efficiency of the new test, and prove
that under a mean-shift alternative, our test always has greater relative
efficiency than a previous linear-time kernel test, regardless of the choice of
parameters for that test. In experiments, the performance of our method exceeds
that of the earlier linear-time test, and matches or exceeds the power of a
quadratic-time kernel test. In high dimensions and where model structure may be
exploited, our goodness of fit test performs far better than a quadratic-time
two-sample test based on the Maximum Mean Discrepancy, with samples drawn from
the model.


  In 1951, Kuroda defined an embedding of classical first-order logic into
intuitionistic logic, such that a formula and its translation are equivalent in
classical logic. Recently, Brown and Rizkallah extended this translation to
higher-order logic, but did not prove the classical equivalence, and showed
that the embedding fails in the presence of functional extensionality. We prove
that functional extensionality and propositional extensionality are sufficient
to derive the classical equivalence between a higher-order formula and its
translation. We emphasize a condition under which Kuroda's translation works
with functional extensionality.


  We report results of NMR experiments on the ruthenium oxide Hg2Ru2O7 with the
pyrochlore structure, which exhibits a metal-insulator transition at TMI = 107
K. In the metallic phase above TMI, the nuclear spin-lattice relaxation rate
1/T1 and the Knight shift at the Hg sites follow the Korringa relation,
indicating the absence of substantial spatial spin correlation. At low
temperatures in the insulating phase, 99,101Ru-NMR signals are observed at zero
magnetic field, providing evidence for a commensurate antiferromagnetic order.
The estimated ordered moment is about 1 muB per Ru, much smaller than 3 muB
expected for the ionic (4d)3plus configuration of Ru5plus. Thus the localized
spin models are not appropriate for the insulating phase of Hg2Ru2O7. We also
discuss possible antiferromagnetic spin structures.


  We propose a fixed-point property for group actions on cones in topological
vector spaces. In the special case of equicontinuous actions, we prove that
this property always holds; this statement extends the classical
Ryll-Nardzewski theorem for Banach spaces. When restricting to cones that are
locally compact in the weak topology, we prove that the property holds for all
distal actions, thus extending the general Ryll-Nardzewski theorem for all
locally convex spaces.
  Returning to arbitrary actions, the proposed fixed-point property becomes a
group property, considerably stronger than amenability. Equivalent formulations
are established and a number of closure properties are proved for the class of
groups with the fixed-point property for cones.


  An unprecedented positron average current is required to fit the luminosity
demands of future $e^+e^-$ high energy physics colliders. In addition, in order
to access precision-frontier physics, these machines require positron
polarization to enable exploring the polarization dependence in many HEP
processes cross sections, reducing backgrounds and extending the reach of
chiral physics studies beyond the standard model. The ILC has a mature plan for
the polarized positron source based on conversion in a thin target of
circularly polarized gammas generated by passing the main high energy e-beam in
a long superconducting helical undulator. Compact colliders (CLIC, C3 and
advanced accelerator-based concepts) adopt a simplified approach and currently
do not plan to use polarized positrons in their baseline design, but could
greatly benefit from the development of compact alternative solutions to
polarized positron production. Increasing the positron current, the
polarization purity and simplifying the engineering design are all
opportunities where advances in accelerator technology have the potential to
make a significant impact. This white-paper describes the current status of the
field and provides R\&D short-term and long-term pathways for polarized
positron sources.


  Face manipulation detection has been receiving a lot of attention for the
reliability and security of the face images. Recent studies focus on using
auxiliary information or prior knowledge to capture robust manipulation traces,
which are shown to be promising. As one of the important face features, the
face depth map, which has shown to be effective in other areas such as the face
recognition or face detection, is unfortunately paid little attention to in
literature for detecting the manipulated face images. In this paper, we explore
the possibility of incorporating the face depth map as auxiliary information to
tackle the problem of face manipulation detection in real world applications.
To this end, we first propose a Face Depth Map Transformer (FDMT) to estimate
the face depth map patch by patch from a RGB face image, which is able to
capture the local depth anomaly created due to manipulation. The estimated face
depth map is then considered as auxiliary information to be integrated with the
backbone features using a Multi-head Depth Attention (MDA) mechanism that is
newly designed. Various experiments demonstrate the advantage of our proposed
method for face manipulation detection.


  A search for resonances decaying to top quark-antiquark pairs is performed
using a dilepton+jets data sample recorded by the CMS experiment at the LHC in
pp collisions at sqrt(s) = 7 TeV corresponding to an integrated luminosity of
5.0 inverse femtobarns. No significant deviations from the standard model
background are observed. Upper limits are presented for the production cross
section times branching fraction of top quark-antiquark resonances for masses
from 750 to 3000 GeV. In particular, the existence of a leptophobic topcolor
particle Z' is excluded at the 95% confidence level for resonance masses M[Z']
< 1.3 TeV for Gamma[Z'] = 0.012 M[Z'], and M[Z'] < 1.9 TeV for Gamma[Z'] = 0.10
M[Z'].


  Renormalization Group (RG) theory provides the theoretical framework to
define Effective Theories (ETs), i.e. systematic low-resolution approximations
of arbitrary microscopic models. Markov State Models (MSMs) are shown to be
rigorous ETs for Molecular Dynamics (MD). Based on this fact, we use Real Space
RG to vary the resolution of a MSM and define an algorithm for clustering
microstates into macrostates. The result is a lower dimensional stochastic
model which, by construction, provides the optimal coarse-grained Markovian
representation of the system's relaxation kinetics. To illustrate and validate
our theory, we analyze a number of test systems of increasing complexity,
ranging from synthetic toy models to two realistic applications, built form
all-atom MD simulations. The computational cost of computing the
low-dimensional model remains affordable on a desktop computer even for
thousands of microstates.


  Simulated Patients (SPs) play a crucial role in clinical medical education by
providing realistic scenarios for student practice. However, the high cost of
training and hiring qualified SPs, along with the heavy workload and potential
risks they face in consistently portraying actual patients, limit students'
access to this type of clinical training. Consequently, the integration of
computer program-based simulated patients has emerged as a valuable educational
tool in recent years. With the rapid development of Large Language Models
(LLMs), their exceptional capabilities in conversational artificial
intelligence and role-playing have been demonstrated, making them a feasible
option for implementing Virtual Simulated Patient (VSP). In this paper, we
present an integrated model-agnostic framework called CureFun that harnesses
the potential of LLMs in clinical medical education. This framework facilitates
natural conversations between students and simulated patients, evaluates their
dialogue, and provides suggestions to enhance students' clinical inquiry
skills. Through comprehensive evaluations, our approach demonstrates more
authentic and professional SP-scenario dialogue flows compared to other
LLM-based chatbots, thus proving its proficiency in simulating patients.
Additionally, leveraging CureFun's evaluation ability, we assess several
medical LLMs and discuss the possibilities and limitations of using LLMs as
virtual doctors from the perspective of their diagnostic abilities.


  Knowledge transfer is a promising concept to achieve real-time
decision-making for autonomous vehicles. This paper constructs a transfer deep
reinforcement learning framework to transform the driving tasks in
inter-section environments. The driving missions at the un-signalized
intersection are cast into a left turn, right turn, and running straight for
automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive
through the intersection situation efficiently and safely. This objective
promotes the studied vehicle to increase its speed and avoid crashing other
vehicles. The decision-making pol-icy learned from one driving task is
transferred and evaluated in another driving mission. Simulation results reveal
that the decision-making strategies related to similar tasks are transferable.
It indicates that the presented control framework could reduce the time
consumption and realize online implementation.


  Deep learning has been shown to be an effective tool in solving partial
differential equations (PDEs) through physics-informed neural networks (PINNs).
PINNs embed the PDE residual into the loss function of the neural network, and
have been successfully employed to solve diverse forward and inverse PDE
problems. However, one disadvantage of the first generation of PINNs is that
they usually have limited accuracy even with many training points. Here, we
propose a new method, gradient-enhanced physics-informed neural networks
(gPINNs), for improving the accuracy and training efficiency of PINNs. gPINNs
leverage gradient information of the PDE residual and embed the gradient into
the loss function. We tested gPINNs extensively and demonstrated the
effectiveness of gPINNs in both forward and inverse PDE problems. Our numerical
results show that gPINN performs better than PINN with fewer training points.
Furthermore, we combined gPINN with the method of residual-based adaptive
refinement (RAR), a method for improving the distribution of training points
adaptively during training, to further improve the performance of gPINN,
especially in PDEs with solutions that have steep gradients.


  A tree is called double starlike if it has exactly two vertices of degree
greater than two. Let $H(p,n,q)$ denote the double starlike tree obtained by
attaching $p$ pendant vertices to one pendant vertex of the path $P_n$ and $q$
pendant vertices to the other pendant vertex of $P_n$. In this paper, we prove
that $H(p,n,q)$ is determined by its Laplacian spectrum.


  The work of Oh and Park ([OP]) on the deformation problem of coisotropic
submanifolds opened the possibility of studying a large and interesting class
of foliations with some explicit geometric tools. These tools assemble into the
structure of an L-infinity algebra on the shifted foliation complex
(\Omega^*[1](\fol), d_\fol), which allows a concise description of deformations
in terms of a Maurer-Cartan equation. Infinitesimal deformations are given by
d_\fol-closed forms, and the relation between infinitesimal deformations and
full deformations can be studied in terms of obstruction classes lying in the
foliation cohomology H^*_\fol. Closely related to the foliation cohomology is
Haefliger's group \Omega^*_c(T/H), an under-appreciated model for the leaf
space of a foliation. We make integral use of this group in showing solvability
and unsolvability of the obstruction equations. We also show the L-infinity
apparatus to be capable of detecting the Liouville/diophantine distinction of
KAM theory, and argue for the greater significance of Haefliger's
integration-over-leaves map in passing this fine structure to a geometric model
for the leaf space.


  For any unbranched double covering of compact Riemann surfaces, we study the
associated character varieties that are unitary in the global sense, which we
call $\text{GL}_n\rtimes\!<\!\sigma\!>\!~$-character varieties. We introduce
$k>0$ punctures on the surface, and restrict the monodromies around the
punctures to generic semi-simple conjugacy classes in $\text{GL}_n$, and
compute the E-polynomials of these character varieties using the character
table of $\text{GL}_n(q)$. The result is expressed as the inner product of
certain symmetric functions. We are then led to a conjectural formula for the
mixed Hodge polynomial, which is built out of (modified) Macdonald polynomials,
their self-pairings, and self-pairings of wreath Macdonald polynomials.


  Diffuse X-rays from the Galactic center (GC) region were found to exhibit
many K-shell lines from iron and nickel atoms in the 6--9 keV band. The strong
emission lines seen in the spectrum are neutral iron K$\alpha$ at 6.4~keV,
He-like iron K$\alpha$ at 6.7~keV, H-like iron Ly$\alpha$ at 6.9~keV, and
He-like iron K$\beta$ at 7.8~keV. Among them, the 6.4~keV emission line is a
probe of non-thermal phenomena. We have detected strong 6.4~keV emission in
several giant molecular clouds, some of which were newly discovered by Suzaku.
All the spectra exhibit large equivalent widths of 1-2~keV and absorption
columns of $2-10\times 10^{23}\{\rm H\ cm}^{-2}$. We found time variability of
diffuse 6.4~keV emission in the Sgr B2 region comparing the maps and spectra
obtained from 1994 to 2005 with ASCA, Chandra, XMM-Newton and Suzaku. We also
report discovery of K$\alpha$ lines of neutral argon, calcium, chrome, and
manganese atoms in the Sgr~A region. We show that the equivalent width of the
6.4~keV emission line detected in X-ray faint region against the 6.4
keV-associated continuum (power-law component) is $\sim 800\ {\rm eV}$. These
features are naturally explained by the X-ray reflection nebula scenario rather
than the low energy cosmic-ray electrons scenario. On the other hand, a 6.4~keV
clump, G~0.162$-$0.217, discovered at the south end of the Radio Arc has a
small equivalent width of 6.4~keV emission line of $\sim200\ {\rm eV}$. The
Radio Arc is a site of relativistic electrons. Thus, it is conceivable that the
X-rays of G~0.162$-$0.217 are due to low energy cosmic-ray electrons


  The longest common subsequence problem is a long studied prototype of pattern
matching problems. In spite of the effort dedicated to it, the numerical value
of its central quantity, the Chvatal-Sankoff constant, is not yet known.
Numerical estimations of this constant are very difficult due to finite size
effects. We propose a numerical method to estimate the Chvatal-Sankoff constant
which combines the advantages of an analytically known functional form of the
finite size effects with an efficient multi-spin coding scheme. This method
yields very high precision estimates of the Chvatal-Sankoff constant. Our
results correct earlier estimates for small alphabet size while they are
consistent with (albeit more precise than) earlier results for larger alphabet
size.


  "Spin noise spectroscopy" (SNS) is a powerful optical technique for probing
electron and hole spin dynamics that is based on detecting their intrinsic and
random fluctuations while in thermal equilibrium, an approach guaranteed by the
fluctuation-dissipation theorem. Because SNS measures fluctuation properties
rather than conventional response functions, we show that fluctuation
\emph{correlations} can be exploited in multi-probe noise studies to reveal
information that in general cannot be accessed by conventional linear optical
spectroscopy, such as the underlying homogeneous linewidths of individual
constituents within inhomogeneously-broadened systems. This is demonstrated in
an ensemble of singly-charged (In,Ga)As quantum dots using two weak probe
lasers: When the two lasers have the same wavelength, they are sensitive to the
same QDs in the ensemble and their spin fluctuation signals are correlated. In
contrast, two probe lasers that are widely detuned from each other measure
different subsets of QDs, leading to uncorrelated fluctuations. Measuring the
noise correlation versus laser detuning directly reveals the QD homogeneous
linewidth even in the presence of a strong inhomogeneous broadening. Such
noise-based correlation techniques are not limited to semiconductor spin
systems, but can be widely applied to any system in which intrinsic
fluctuations are measurable.


  A new single-dynamical-scalar-field model of dark energy is proposed, in
which either higher derivative terms nor structures of extra dimension are
needed. With the help of a fixed background vector field, the parameter for the
effective equation of state of dark energy may cross $w=-1$ in the evolution of
the universe. After suitable choice of the potential, the crossing $w=-1$ and
transition from decelerating to accelerating occur at $z\approx 0.2$ and
$z\approx 1.7$, respectively.


  We demonstrate that in ferromagnetic Ga1-xMnxP exchange is mediated by holes
localized in a Mn-derived band. For x<0.06, infrared absorption and
photoconductivity spectra indicate the presence of a Mn impurity band which is
not merged with the valence band. At temperatures above TC (<65 K) electrical
transport is dominated by excitation across this energy gap while nearest
neighbor hopping dominates below TC. Magnetization measurements reveal a moment
of 3.5 Bohr magnetons per substitutional Mn, while the large anomalous Hall
signal unambiguously demonstrates that the ferromagnetism is carrier-mediated.


  We have derived the Galactic bulge initial mass function of the SWEEPS field
in the mass range 0.15 $< M/M_{\odot}<$ 1.0, using deep photometry collected
with the Advanced Camera for Surveys on the Hubble Space Telescope.
Observations at several epochs, spread over 9 years, allowed us to separate the
disk and bulge stars down to very faint magnitudes, F814W $\sim$ 26 mag, with a
proper-motion accuracy better than 0.5 mas/yr. This allowed us to determine the
initial mass function of the pure bulge component uncontaminated by disk stars
for this low-reddening field in the Sagittarius window. In deriving the mass
function, we took into account the presence of unresolved binaries, errors in
photometry, distance modulus and reddening, as well as the metallicity
dispersion and the uncertainties caused by adopting different theoretical
color-temperature relations. We found that the Galactic bulge initial mass
function can be fitted with two power laws with a break at M $\sim$ 0.56
$M_{\odot}$, the slope being steeper ($\alpha$ = -2.41$\pm$0.50) for the higher
masses, and shallower ($\alpha$ = -1.25$\pm$0.20) for the lower masses. In the
high-mass range, our derived mass function agrees well with the mass function
derived for other regions of the bulge. In the low-mass range however, our mass
function is slightly shallower, which suggests that separating the disk and
bulge components is particularly important in the low-mass range. The slope of
the bulge mass function is also similar to the slope of the mass function
derived for the disk in the high-mass regime, but the bulge mass function is
slightly steeper in the low-mass regime. We used our new mass function to
derive stellar M/L values for the Galactic bulge and we obtained 2.1
$<M/L_{F814W}<$ 2.4 and 3.1 $< M/L_{F606W}<$ 3.6 according to different
assumptions on the slope of the IMF for masses larger than 1 $M_{\odot}$.


  We use the celebrated circle method of Hardy and Ramanujan to develop
convergent formulae for counting a restricted class of partitions that arise
from the G\"ollnitz--Gordon identities.


  Hyper-redundant Robotic Manipulators (HRMs) offer great dexterity and
flexibility of operation, but solving Inverse Kinematics (IK) is challenging.
In this work, we introduce VO-FABRIK, an algorithm combining Forward and
Backward Reaching Inverse Kinematics (FABRIK) for repeatable deterministic IK
computation, and an approach inspired from velocity obstacles to perform path
planning under collision and joint limits constraints. We show preliminary
results on an industrial HRM with 19 actuated joints. Our algorithm achieves
good performance where a state-of-the-art IK solver fails.


  In an easy-plane antiferromagnet with the Dzyaloshinskii-Moriya interaction
(DMI), magnons are subject to an effective spin-momentum locking. An in-plane
temperature gradient can generate interfacial accumulation of magnons with a
specified polarization, realizing the magnon thermal Edelstein effect. We
theoretically investigate the injection and detection of this thermally-driven
spin polarization in an adjacent heavy metal with strong spin Hall effect. We
find that the inverse spin Hall voltage depends monotonically on both
temperature and the DMI but non-monotonically on the hard-axis anisotropy.
Counterintuitively, the magnon thermal Edelstein effect is an even function of
a magnetic field applied along the N\'eel vector.


  We present the design and performance of an ultra-high vacuum (UHV) scanning
tunneling microscope (STM) that uses adiabatic demagnetization of electron
magnetic moments for controlling its operating temperature in the range between
30 mK and 1 K with the accuracy of up to 7 $\mu$K. The time available for STM
experiments at 50 mK is longer than 20 h, at 100 mK about 40 h. The single-shot
adiabatic demagnetization refrigerator (ADR) can be regenerated automatically
within 7 hours while keeping the STM temperature below 5 K. The whole setup is
located in a vibrationally isolated, electromagnetically shielded laboratory
with no mechanical pumping lines penetrating through its isolation walls. The
1K pot of the ADR cryostat can be operated silently for more than 20 days in a
single-shot mode using a custom-built high-capacity cryopump. A high degree of
vibrational decoupling together with the use of a specially-designed
minimalistic STM head provides an outstanding mechanical stability,
demonstrated by the tunneling current noise, STM imaging, and scanning
tunneling spectroscopy measurements all performed on atomically clean Al(100)
surface.


  We demonstrate a prototype light scattering instrument combining a frequency
domain approach to the intermediate scattering function from Super-Heterodyning
Doppler Velocimetry with the versatility of a standard homodyne Dynamic Light
Scattering goniometer setup for investigations over a large range of scattering
vectors. Comparing to reference experiments in correlation-time domain, we show
that the novel approach can determine diffusion constants and hence
hydrodynamic radii with high precision and accuracy. Possible future
applications are discussed shortly.


  Scientific studies of consciousness rely on objects whose existence is
assumed to be independent of any consciousness. On the contrary, we assume
consciousness to be fundamental, and that one of the main features of
consciousness is characterized as being other-dependent. We set up a framework
which naturally subsumes this feature by defining a compact closed category
where morphisms represent conscious processes. These morphisms are a
composition of a set of generators, each being specified by their relations
with other generators, and therefore co-dependent. The framework is general
enough and fits well into a compositional model of consciousness.
Interestingly, we also show how our proposal may become a step towards avoiding
the hard problem of consciousness, and thereby address the combination problem
of conscious experiences.


  Permanently deformed objects in binary systems can experience complex
rotation evolution, arising from the extensively studied effect of spin-orbit
coupling as well as more nuanced dynamics arising from spin-spin interactions.
The ability of an object to sustain an aspheroidal shape largely determines
whether or not it will exhibit non-trivial rotational behavior. In this work,
we adopt a simplified model of a gravitationally interacting primary and
satellite pair, where each body's quadrupole moment is approximated by two
diametrically opposed point masses. After calculating the net gravitational
torque on the satellite from the primary, and the associated equations of
motion, we employ a Hamiltonian formalism which allows for a perturbative
treatment of the spin-orbit and retrograde and prograde spin-spin coupling
states. By analyzing the resonances individually and collectively, we determine
the criteria for resonance overlap and the onset of chaos, as a function of
orbital and geometric properties of the binary. We extend the 2D planar
geometry to calculate the obliquity evolution, and find that satellites in
spin-spin resonances undergo precession when inclined out of the plane, but do
not tumble. We apply our resonance overlap criteria to the contact binary
system (216) Kleopatra, and find that its satellites, Cleoselene and
Alexhelios, may plausibly be exhibiting chaotic rotational dynamics from the
overlap of the spin-orbit and retrograde spin-spin resonances. While this model
is by construction generalizable to any binary system, it will be particularly
useful to study small bodies in the solar system, whose irregular shapes make
them ideal candidates for exotic rotational states.


  This review is based on the lectures given by the author at the Les Houches
Summer School 2016. It describes the recently developed Quantum Spectral Curve
(QSC) for a non-perturbative planar spectrum of N=4 Super Yang-Mills theory in
a pedagogical way starting from the harmonic oscillator and avoiding a long
historical path. We give many examples and provide exercises. At the end we
give a list of the recent and possible future applications of the QSC.


  We study the dynamical complexity of an open quantum driven double-well
oscillator, mapping its dependence on effective Planck's constant
$\hbar_{eff}\equiv\beta$ and coupling to the environment, $\Gamma$. We study
this using stochastic Schrodinger equations, semiclassical equations, and the
classical limit equation. We show that (i) the dynamical complexity initially
increases with effective Hilbert space size (as $\beta$ decreases) such that
the most quantum systems are the least dynamically complex. (ii) If the
classical limit is chaotic, that is the most dynamically complex (iii) if the
classical limit is regular, there is always a quantum system more dynamically
complex than the classical system. There are several parameter regimes where
the quantum system is chaotic even though the classical limit is not. While
some of the quantum chaotic attractors are of the same family as the classical
limiting attractors, we also find a quantum attractor with no classical
counterpart. These phenomena occur in experimentally accessible regimes.


  The analysis of high dimensional survival data is challenging, primarily due
to the problem of overfitting which occurs when spurious relationships are
inferred from data that subsequently fail to exist in test data. Here we
propose a novel method of extracting a low dimensional representation of
covariates in survival data by combining the popular Gaussian Process Latent
Variable Model (GPLVM) with a Weibull Proportional Hazards Model (WPHM). The
combined model offers a flexible non-linear probabilistic method of detecting
and extracting any intrinsic low dimensional structure from high dimensional
data. By reducing the covariate dimension we aim to diminish the risk of
overfitting and increase the robustness and accuracy with which we infer
relationships between covariates and survival outcomes. In addition, we can
simultaneously combine information from multiple data sources by expressing
multiple datasets in terms of the same low dimensional space. We present
results from several simulation studies that illustrate a reduction in
overfitting and an increase in predictive performance, as well as successful
detection of intrinsic dimensionality. We provide evidence that it is
advantageous to combine dimensionality reduction with survival outcomes rather
than performing unsupervised dimensionality reduction on its own. Finally, we
use our model to analyse experimental gene expression data and detect and
extract a low dimensional representation that allows us to distinguish high and
low risk groups with superior accuracy compared to doing regression on the
original high dimensional data.


  We define the fibre-restricted Gottlieb group with respect to a fibration
$\xi :X\to E\to Y$ in CW complexes. It is a subgroup of the Gottlieb group of
$X$. When $X$ and $E$ are finite simply connected, its rationalized model is
given by the arguments of derivations of Sullivan models based on F\'{e}lix,
Lupton and Smith \cite{FLS}. We consider the realization problem of groups in a
Gottlieb group as fibre-restricted Gottlieb groups in rational homotoy theory.
Especially we define an invariant named as (Gottlieb) depth of $X$ over $Y$. In
particular, when $Y=BS^1$, it is related to the rational toral rank of $X$.


  Real-time 3D fluorescence microscopy is crucial for the spatiotemporal
analysis of live organisms, such as neural activity monitoring. The eXtended
field-of-view light field microscope (XLFM), also known as Fourier light field
microscope, is a straightforward, single snapshot solution to achieve this. The
XLFM acquires spatial-angular information in a single camera exposure. In a
subsequent step, a 3D volume can be algorithmically reconstructed, making it
exceptionally well-suited for real-time 3D acquisition and potential analysis.
Unfortunately, traditional reconstruction methods (like deconvolution) require
lengthy processing times (0.0220 Hz), hampering the speed advantages of the
XLFM. Neural network architectures can overcome the speed constraints at the
expense of lacking certainty metrics, which renders them untrustworthy for the
biomedical realm. This work proposes a novel architecture to perform fast 3D
reconstructions of live immobilized zebrafish neural activity based on a
conditional normalizing flow. It reconstructs volumes at 8 Hz spanning
512x512x96 voxels, and it can be trained in under two hours due to the small
dataset requirements (10 image-volume pairs). Furthermore, normalizing flows
allow for exact Likelihood computation, enabling distribution monitoring,
followed by out-of-distribution detection and retraining of the system when a
novel sample is detected. We evaluate the proposed method on a cross-validation
approach involving multiple in-distribution samples (genetically identical
zebrafish) and various out-of-distribution ones.


  The well-known game of Rock--Paper--Scissors can be used as a simple model of
competition between three species. When modelled in continuous time using
differential equations, the resulting system contains a heteroclinic cycle
between the three equilibrium solutions representing the existence of only a
single species. The game can be extended in a symmetric fashion by the addition
of two further strategies (`Lizard' and `Spock'): now each strategy is dominant
over two of the remaining four strategies, and is dominated by the remaining
two. The differential equation model contains a set of coupled heteroclinic
cycles forming a heteroclinic network. In this paper we carefully consider the
dynamics near this heteroclinic network. We are able to identify regions of
parameter space in which arbitrarily long periodic sequences of visits are made
to the neighbourhoods of the equilibria, which form a complicated pattern in
parameter space.


  The edge physics of the $\nu=5/2$ fractional quantum Hall state is of
relevance to several recent experiments that use it as a probe to gain insight
into the nature of the bulk state. We perform calculations in a semi-realistic
setup with positive background charge at a distance $d$, by exact
diagonalization both in the full Hilbert space (neglecting Landau level mixing)
and in the restricted Pfaffian basis of edge excitations. Our principal finding
is that the 5/2 edge is unstable to a reconstruction except for very small $d$.
In addition, the interactions between the electrons in the second Landau level
and the lowest Landau level enhance the tendency toward edge reconstruction. We
identify the bosonic and fermionic modes of edge excitations and obtain their
dispersions by back-calculating from the energy spectra as well as directly
from appropriate trial wave functions. We find that the edge reconstruction is
driven by an instability in the fermionic sector for setback distances close to
the critical ones. We also study the edge of the $\nu=7/3$ state and find that
edge reconstruction occurs here more readily than for the $\nu=1/3$ state. Our
study indicates that the $\nu=5/2$ and 7/3 edge states are reconstructed for
all experimental systems investigated so far and thus must be taken into
account when analyzing experimental results. We also consider an effective
field theory to gain insight into how edge reconstruction might influence
various observable quantities.


  Recent works have proposed to craft adversarial clothes for evading person
detectors, while they are either only effective at limited viewing angles or
very conspicuous to humans. We aim to craft adversarial texture for clothes
based on 3D modeling, an idea that has been used to craft rigid adversarial
objects such as a 3D-printed turtle. Unlike rigid objects, humans and clothes
are non-rigid, leading to difficulties in physical realization. In order to
craft natural-looking adversarial clothes that can evade person detectors at
multiple viewing angles, we propose adversarial camouflage textures (AdvCaT)
that resemble one kind of the typical textures of daily clothes, camouflage
textures. We leverage the Voronoi diagram and Gumbel-softmax trick to
parameterize the camouflage textures and optimize the parameters via 3D
modeling. Moreover, we propose an efficient augmentation pipeline on 3D meshes
combining topologically plausible projection (TopoProj) and Thin Plate Spline
(TPS) to narrow the gap between digital and real-world objects. We printed the
developed 3D texture pieces on fabric materials and tailored them into T-shirts
and trousers. Experiments show high attack success rates of these clothes
against multiple detectors.


  We consider two dimensional systems in which edge states coexist with a
gapless bulk. Such systems may be constructed, for example, by coupling a
gapped two dimensional state of matter that carries edge states to a gapless
two dimensional system in which the spectrum is composed of a number of Dirac
cones. We find that in the absence of disorder the edge states could be
protected even when the two systems are coupled, due to momentum and energy
conservation. We distinguish between weak and strong edge states by the level
of their mixing with the bulk. In the presence of disorder, the edge states may
be stabilized when the bulk is localized or destabilized when the bulk is
metallic. We analyze the conditions under which these two cases occur. Finally,
we propose a concrete physical realization for one of our models on the basis
of bilayer Hg(Cd)Te quantum wells.


  We consider matrix completion for recommender systems from the point of view
of link prediction on graphs. Interaction data such as movie ratings can be
represented by a bipartite user-item graph with labeled edges denoting observed
ratings. Building on recent progress in deep learning on graph-structured data,
we propose a graph auto-encoder framework based on differentiable message
passing on the bipartite interaction graph. Our model shows competitive
performance on standard collaborative filtering benchmarks. In settings where
complimentary feature information or structured data such as a social network
is available, our framework outperforms recent state-of-the-art methods.


  We study the effect of a pure spin current on the Kondo singlet in a diluted
magnetic alloy using non-local lateral spin valve structures with highly spin
polarized Co2FeSi electrodes. Temperature dependence of the non-local spin
signals shows a sharp reduction with decreasing temperature, followed by a
plateau corresponding to the low temperature Fermi liquid regime below the
Kondo temperature (TK). The spin diffusion length of the Kondo alloy is found
to increase with the evolution of spin accumulation. The results are in
agreement with the intuitive description that the Kondo singlet cannot survive
any more in sufficiently large spin accumulation even below TK.


  In this paper we continue to study a special class of Passarino-Veltman
functions J arising at the reduction of infrared divergent box diagrams. We
describe a procedure of separation of two types of singularities, infrared and
mass singularities, which are absorbed in simple C0 functions. The infrared
divergences of C0's can be regularized then by any method: photon mass,
dimensionally or by the width of an unstable particle. Functions J, in turn,
are represented as certain linear combinations of the standard D0 and C0
Passarino-Veltman functions. The former are free of both types of singularities
and are expressed as explicit and compact linear combinations of logarithms and
dilogarithm functions. We present extensive comparisons of numerical results
with those obtained with the aid of the LoopTools package.


  We give an elementary approach to proving the birationality of multiple
Berglund-H\"ubsch-Krawitz (BHK) mirrors by using Shioda maps. We do this by
creating a birational picture of the BHK correspondence in general. Although a
similar result has been obtained in recent months by Shoemaker, our proof is
new in that it sidesteps using toric geometry and drops an unnecessary
hypothesis. We give an explicit quotient of a Fermat variety to which the
mirrors are birational.


  We prove that the functions constructed by the kernel-based regressions with
Wendland kernels under $\ell_1$-norm constraints converge to unique viscosity
solutions of the corresponding fully nonlinear parabolic equations. A key
ingredient in our proof is the max-min representations of the nonlinearities of
the equations.


  Let $X$ be a finite set and $\mathfrak p\subseteq 2^X$, the power set of $X$,
satisfying three conditions: (a) $\mathfrak p$ is an ideal in $2^X$, that is,
if $A\in \mathfrak p$ and $B\subset A$, then $B\in \mathfrak p$; (b) For $A\in
2^X$ with $|A|\geq 2$, $A\in \mathfrak p$ if $\{x,y\}\in \mathfrak p$ for any
$x,y\in A$ with $x\neq y$; (c) $\{x\}\in \mathfrak p$ for every $x\in X$. The
pair $(X,\mathfrak p)$ is called a symmetric system if there is a group
$\Gamma$ transitively acting on $X$ and preserving the ideal $\mathfrak p$. A
family $\{A_1,A_2,\ldots,A_m\}\subseteq 2^X$ is said to be a
cross-$\mathfrak{p}$-family of $X$ if $\{a, b\}\in \mathfrak{p}$ for any $a\in
A_i$ and $b\in A_j$ with $i\neq j$. We prove that if $(X,\mathfrak p)$ is a
symmetric system and $\{A_1,A_2,\ldots,A_m\}\subseteq 2^X$ is a
cross-$\mathfrak{p}$-family of $X$, then \[\sum_{i=1}^m|{A}_i|\leq\left\{
\begin{array}{cl} |X| & \hbox{if $m\leq \frac{|X|}{\alpha(X,\, \mathfrak p)}$,}
\\ m\, \alpha(X,\, \mathfrak p) & \hbox{if $m\geq \frac{|X|}{\alpha{(X,\,
\mathfrak p)}}$,} \end{array}\right.\] where $\alpha(X,\, \mathfrak
p)=\max\{|A|:A\in\mathfrak p\}$. This generalizes Hilton's theorem on
cross-intersecting families of finite sets, and provides analogs for
cross-$t$-intersecting families of finite sets, finite vector spaces and
permutations, etc.
  Moreover, the primitivity of symmetric systems is introduced to characterize
the optimal families.


  Conditional random fields (CRF) for label decoding has become ubiquitous in
sequence labeling tasks. However, the local label dependencies and inefficient
Viterbi decoding have always been a problem to be solved. In this work, we
introduce a novel two-stage label decoding framework to model long-term label
dependencies, while being much more computationally efficient. A base model
first predicts draft labels, and then a novel two-stream self-attention model
makes refinements on these draft predictions based on long-range label
dependencies, which can achieve parallel decoding for a faster prediction. In
addition, in order to mitigate the side effects of incorrect draft labels,
Bayesian neural networks are used to indicate the labels with a high
probability of being wrong, which can greatly assist in preventing error
propagation. The experimental results on three sequence labeling benchmarks
demonstrated that the proposed method not only outperformed the CRF-based
methods but also greatly accelerated the inference process.


  We study second-order PDEs in 4D for which the conformal structure defined by
the characteristic variety of the equation is half-flat (self-dual or
anti-self-dual) on every solution. We prove that this requirement implies the
Monge-Ampere property. Since half-flatness of the conformal structure is
equivalent to the existence of a nontrivial dispersionless Lax pair, our result
explains the observation that all known scalar second-order integrable
dispersionless PDEs in dimensions four and higher are of Monge-Ampere type.
Some partial classification results of Monge-Ampere equations in 4D with
half-flat conformal structure are also obtained.


  N-body simulations give us a rough idea of how the shape of a simulated
object appears in three-dimensional space. From an observational point of view
this may give us a misleading picture. The faint stars may be the most common
stars in the system but the morphological information obtained by observations
of an object may be dominated by the color properties of the bright stars. Due
to dynamical effects, such as energy equipartition, different masses of stars
may populate different regions in the object. Since stars are evolving in mass
the stellar evolution may also influence the dynamics of the system. Hence, if
one is interested in simulating what the morphology will look like through a
telescope, one needs to simulate in addition evolving stars and weight them by
their luminosity. Therefore we need to combine simulations of the dynamical
evolution and a stellar population synthesis at the same time. For the
dynamical evolution part we use a parallel version of a direct N-body code,
NBODY6++. This code also includes stellar evolution. We link the parameters
from this stellar evolution routine to the BaSeL 2.0 stellar library. For
isolated clusters we found results assimilable to standard stellar population
synthesis codes such as the PEGASE code. For clusters in a tidal field we found
that the integrated colors become relatively bluer due to energy equipartition
effects. In the time shortly before dissolution of the cluster the stellar M/L
ratio becomes lower compared to isolated clusters. We compared the results of
our simulations to integrated spectra of galactic globular clusters. For the
cluster NGC 1851 we found good agreement between simulation and observation.
For extragalactic clusters in M81 and M31 we looked at medium band spectral
energy distributions and found for some clusters also a good agreement.


  We prove that an analytic quasiperiodically forced circle flow with a not
super-Liouvillean base frequency and which is close enough to some constant
rotation is $C^{\infty}$ rotations reducible, provided its fibered rotation
number is Diophantine with respect to the base frequency. As a corollary, we
obtain that among such systems, the linearizable ones and those displaying
mode-locking are locally dense for the $C^\infty$-topology.


  We report on the preliminary determination of the visual orbit of the
double-lined spectroscopic binary system BY Draconis with data obtained by the
Palomar Testbed Interferometer in 1999. BY Dra is a nearly equal-mass
double-lined binary system whose spectroscopic orbit is well known. We have
estimated the visual orbit of BY Dra from our interferometric visibility data
fit both separately and in conjunction with archival radial velocity data. Our
BY Dra orbit is in good agreement with the spectroscopic results. Due to the
orbit's face-on orientation, the physical parameters implied by a combined fit
to our visibility data and radial velocity data do not yet result in precise
component masses and a system distance, but with continued interferometric
monitoring we hope to improve the mass estimates to better than 10%
determinations.


  Molecular static simulations of 190 symmetric tilt grain boundaries in HCP
metals were used to understand the energetics of vacancy segregation, which is
important for designing stable interfaces in harsh environments. Simulation
results show that the local arrangements of grain boundaries and the resulting
structural units have a significant influence on the magnitude of vacancy
binding energies, and the site-to-site variation within each boundary is
substantial. Comparing the vacancy binding energies for each site in different
c/a ratio materials shows that the binding energy increases significantly with
an increase in c/a ratio. For example, in the [1-210] tilt axis, Ti and Zr with
c/a=1.5811 have a lower vacancy binding energy than the Mg with c/a=1.6299.
Furthermore, when the grain boundary energies of all 190 boundaries in all
three elements are plotted against the vacancy binding energies of the same
boundaries, a highly negative correlation (r = -0.7144) is revealed that has a
linear fit with a proportionality constant of -25 ang^2. This is significant
for applications where extreme environmental damage generates lattice defects
and grain boundaries act as sinks for both vacancies and interstitial atoms.


  We derive the asymptotic behavior of determinants of truncated Wiener-Hopf
operators generated by symbols having Fisher-Hartwig singularities. This task
is achieved thanks to an asymptotic resolution of the Riemann-Hilbert problem
associated to some generalized sine kernel. As a byproduct, we give yet another
derivation of the asymptotic behavior of Toeplitz determinants having
Fisher-Hartwig singularities. The Riemann-Hilbert problem approach to these
asymptotics yields a systematic although quickly cumbersome way to compute
their sub-leading asymptotics.


  Blondin et al. showed at LICS 2015 that two-dimensional vector addition
systems with states have reachability witnesses of length exponential in the
number of states and polynomial in the norm of vectors. The resulting
guess-and-verify algorithm is optimal (PSPACE), but only if the input vectors
are given in binary. We answer positively the main question left open by their
work, namely establish that reachability witnesses of pseudo-polynomial length
always exist. Hence, when the input vectors are given in unary, the improved
guess-and-verify algorithm requires only logarithmic space.


  The influence of uncorrelated (nonmagnetic) overlayers on the magnetic
properties of thin itinerant-electron films is investigated within the
single-band Hubbard model. The Coulomb correlation between the electrons in the
ferromagnetic layers is treated by using the spectral density approach (SDA).
It is found that the presence of nonmagnetic layers has a strong effect on the
magnetic properties of thin films. The Curie temperatures of very thin films
are modified by the uncorrelated overlayers. The quasiparticle density of
states is used to analyze the results. In addition, the coupling between the
ferromagnetic layers and the nonmagnetic layers is discussed in detail. The
coupling depends on the band occupation of the nonmagnetic layers, while it is
almost independent of the number of the nonmagnetic layers. The induced
polarization in the nonmagnetic layers shows a long-range decreasing
oscillatory behavior and it depends on the coupling between ferromagnetic and
nonmagnetic layers.


  This paper presents a method that improve state-of-the-art of the concave
point detection methods as a first step to segment overlapping objects on
images. It is based on the analysis of the curvature of the objects contour.
The method has three main steps. First, we pre-process the original image to
obtain the value of the curvature on each contour point. Second, we select
regions with higher curvature and we apply a recursive algorithm to refine the
previous selected regions. Finally, we obtain a concave point from each region
based on the analysis of the relative position of their neighbourhood We
experimentally demonstrated that a better concave points detection implies a
better cluster division. In order to evaluate the quality of the concave point
detection algorithm, we constructed a synthetic dataset to simulate overlapping
objects, providing the position of the concave points as a ground truth. As a
case study, the performance of a well-known application is evaluated, such as
the splitting of overlapped cells in images of peripheral blood smears samples
of patients with sickle cell anaemia. We used the proposed method to detect the
concave points in clusters of cells and then we separate this clusters by
ellipse fitting.


  We discuss two ways in which one can study two-charge supertubes as
components of generic three-charge, three-dipole charge supergravity solutions.
The first is using the Born-Infeld action of the supertubes, and the second is
via the complete supergravity solution. Even though the Born-Infeld description
is only a probe approximation, we find that it gives exactly the same essential
physics as the complete supergravity solution. Since supertubes can depend on
arbitrary functions, our analysis strengthens the evidence for the existence of
three-charge black-hole microstate geometries that depend on an infinite set of
parameters, and sets the stage for the computation of the entropy of these
backgrounds. We examine numerous other aspects of supertubes in three-charge,
three-dipole charge supergravity backgrounds, including chronology protection
during mergers, the contribution of supertubes to the charges and angular
momenta, and the enhancement of their entropy. In particular, we find that
entropy enhancement affects supertube fluctuations both along the internal and
the spacetime directions, and we prove that the charges that give the enhanced
entropy can be much larger than the asymptotic charges of the solution. We also
re-examine the embedding of five-dimensional black rings in Taub-NUT, and show
that in different coordinate patches a ring can correspond to different
four-dimensional black holes. Last, but not least, we show that all the
three-charge black hole microstate geometries constructed so far can be
embedded in AdS_3 x S^3, and hence can be related to states of the D1-D5 CFT.


  We present the results of a search for new members of the Taurus star-forming
region using the Infrared Array Camera (IRAC) aboard the Spitzer Space
Telescope}. With IRAC images of 29.7 deg^2 of Taurus at 3.6, 4.5, 5.8, and 8.0
um, we have identified sources with red mid-infrared colors indicative of
disk-bearing objects and have obtained optical and infrared spectra of 23 of
these candidate members. Through this work, we have discovered 13 new members
of Taurus, two of which have spectral types later than M6 and thus are likely
to be brown dwarfs according to the theoretical evolutionary models of Chabrier
and Baraffe. This survey indicates that the previous census of Taurus has a
completeness of ~80% for members with disks. The new members that we have found
do not significantly modify the previously measured distributions of Taurus
members as a function of position, mass, and extinction. For instance, we find
no evidence for a population of highly reddened brown dwarfs (A_K~2) that has
been missed by previous optical and near-infrared surveys, which suggests that
brown dwarf disks are not significantly more flared than disks around stars. In
addition to the new members, we also present IRAC photometry for the 149
previously known members that appear within this survey, which includes 27
objects later than M6.


  Results of three-color photoelectric UBV observations of UU Cas performed at
Abastumani Astrophysical Observatory in 1972-1984 are presented.


  The dynamical properties of a 2D Heisenberg model with dipolar interactions
and perpendicular anisotropy are studied using Monte Carlo simulations in two
different ordered regions of the equilibrium phase diagram. We find a
temperature defining a dynamical transition below which the relaxation suddenly
slows down and the system aparts from the typical Arrhenius relaxation to a
Vogel-Fulcher-Tamann law. This anomalous behavior is observed in the scaling of
the magnetic relaxation and may eventually lead to a freezing of the system.
Through the analysis of the domain structures we explain this behavior in terms
of the domains dynamics. Moreover, we calculate the energy barriers
distribution obtained from the data of the magnetic viscosity. Its shape
supports our comprehension of both, the Vogel-Fulcher-Tamann dynamical slowing
down and the freezing mechanism.


  In this paper we generalize classical results regarding minimal realizations
of non-commutative (nc) rational functions using nc Fornasini-Marchesini
realizations which are centred at an arbitrary matrix point. We prove the
existence and uniqueness of a minimal realization for every nc rational
function, centred at an arbitrary matrix point in its domain of regularity.
Moreover, we show that using this realization we can evaluate the function on
all of its domain (of matrices of all sizes) and also with respect to any
stably finite algebra. As a corollary we obtain a new proof of the theorem by
Cohn and Amitsur, that equivalence of two rational expressions over matrices
implies the expressions are equivalent over all stably finite algebras.
Applications to the matrix valued and the symmetric cases are presented as
well.


  It is shown that using beam splitters with non-equal wave vectors results in
a new recoil diagram which is qualitatively different from the well-known
diagram associated with the Mach-Zehnder atom interferometer. We predict a new
asymmetric Mach-Zehnder atom interferometer (AMZAI) and study it when one uses
a Raman beam splitter. The main feature is that the phase of AMZAI contains a
quantum part proportional to the recoil frequency. A response sensitive only to
the quantum phase was found. A new technique to measure the recoil frequency
and fine structure constant is proposed and studied outside of the Raman-Nath
approximation.


  We investigate what is remaining of the 3x3 lemma and of the denormalized 3x3
lemma, respectively valid in a pointed protomodular and in a Maltsev category,
in the context of partial pointed protomodular and partial Maltsev categories,
relatively to a class ${\Sigma}$ of points. The results apply, among other
structures, to monoids, semirings and quandles


  We investigate the existence of solutions of constrained nonlinear
differential inclusions with nonlocal boundary conditions. Our viability
theorems are based on the assumption that the right-hand side of differential
inclusion is defined on the domain possessing a certain type of geometric
regularity, expressed in terms of locally Lipschitz functional constraints. For
solvability of the Floquet boundary value problems associated with differential
inclusions we engage the bound set technique. It relies on the usage of not
necessarily differentiable bounding functions.


  We consider the implications of some simple assumptions about the nature of
the quantum theory of gravity which are plausible for a class of possible
theories I have been attempting to construct. The simple assumptions turn out
to have surprizingly wide implications of a type one might call philosophical.
The paper is short and nontechnical. ( This builds on recent unpublished work
of Lee Smolin, but comes to opposite conclusions).


  Persistence modules and barcodes are used in symplectic topology to define
various invariants of Hamiltonian diffeomorphisms, however numerical methods
for computing these barcodes are not yet well developed. In this paper we
define one such invariant called the generating function barcode of compactly
supported Hamiltonian diffeomorphisms of $ \mathbb{R}^{2n} $ by applying Morse
theory to generating functions quadratic at infinity associated to such
Hamiltonian diffeomorphisms and provide an algorithm (i.e a finite sequence of
explicit calculation steps) that approximates it.


  Natural and man-made transport webs are frequently dominated by dense sets of
nested cycles. The architecture of these networks, as defined by the topology
and edge weights, determines how efficiently the networks perform their
function. Yet, the set of tools that can characterize such a weighted
cycle-rich architecture in a physically relevant, mathematically compact way is
sparse. In order to fill this void, we have developed a new algorithm that
rests on an abstraction of the physical `tiling' in the case of a two
dimensional network to an effective tiling of an abstract surface in space that
the network may be thought to sit in. Generically these abstract surfaces are
richer than the flat plane and as a result there are now two families of
fundamental units that may aggregate upon cutting weakest links -- the
plaquettes of the tiling and the longer `topological' cycles associated with
the abstract surface itself. Upon sequential removal of the weakest links, as
determined by the edge weight, neighboring plaquettes merge and a tree
characterizing this merging process results. The properties of this
characteristic tree can provide the physical and topological data required to
describe the architecture of the network and to build physical models. The new
algorithm can be used for automated phenotypic characterization of any weighted
network whose structure is dominated by cycles, such as mammalian vasculature
in the organs, the root networks of clonal colonies like quaking aspen, or the
force networks in jammed granular matter.


  We study the ground state of a large number N of 2D anyons in an external
magnetic field. We consider a scaling limit where the statistics parameter
$\alpha$ tends to zero when N tends to infinity which allows the statistics to
be seen as a "perturbation from the bosonic end". Our model is that of bosons
in a magnetic field and interacting through long-range magnetic potential
generated by magnetic charges carried by each particle, smeared over discs of
radius R. Our method allows to take R tends to not too fast at the same time as
N tends to infinity. We use the information theoretic version of the de Finetti
theorem of Brand{\~a}o and Harrow to justify the so-called "average field
approximation": the particles behave like independent, identically distributed
bosons interacting via a self-consistent magnetic field.


  On the topic of journalistic integrity, the current state of accurate,
impartial news reporting has garnered much debate in context to the 2016 US
Presidential Election. In pursuit of computational evaluation of news text, the
statements (attributions) ascribed by media outlets to sources provide a common
category of evidence on which to operate. In this paper, we develop an approach
to compare partisan traits of news text attributions and apply it to
characterize differences in statements ascribed to candidate, Hilary Clinton,
and incumbent President, Donald Trump. In doing so, we present a model trained
on over 600 in-house annotated attributions to identify each candidate with
accuracy > 88%. Finally, we discuss insights from its performance for future
research.


  We provide the classification of locally conformally flat gradient Yamabe
solitons with positive sectional curvature. We first show that locally
conformally flat gradient Yamabe solitons with positive sectional curvature
have to be rotationally symmetric and then give the classification and
asymptotic behavior of all radially symmetric gradient Yamabe solitons. We also
show that any eternal solutions to the Yamabe flow with positive Ricci
curvature and with the scalar curvature attaining an interior space-time
maximum must be a steady Yamabe soliton.


  The Flatness theorem states that the maximum lattice width ${\rm Flt}(d)$ of
a $d$-dimensional lattice-free convex set is finite. It is the key ingredient
for Lenstra's algorithm for integer programming in fixed dimension, and much
work has been done to obtain bounds on ${\rm Flt}(d)$. While most results have
been concerned with upper bounds, only few techniques are known to obtain lower
bounds. In fact, the previously best known lower bound ${\rm Flt}(d) \ge
1.138d$ arises from direct sums of a $3$-dimensional lattice-free simplex. In
this work, we establish the lower bound ${\rm Flt}(d) \ge 2d - O(\sqrt{d})$,
attained by a family of lattice-free simplices. Our construction is based on a
differential equation that naturally appears in this context. Additionally, we
provide the first local maximizers of the lattice width of $4$- and
$5$-dimensional lattice-free convex bodies.


  Electromyography (EMG) is a way of measuring the bioelectric activities that
take place inside the muscles. EMG is usually performed to detect abnormalities
within the nerves or muscles of a target area. The recent developments in the
field of Machine Learning allow us to use EMG signals to teach machines the
complex properties of human movements. Modern machines are capable of detecting
numerous human activities and distinguishing among them solely based on the EMG
signals produced by those activities. However, success in accomplishing this
task mostly depends on the learning technique used by the machine to analyze
EMG signals; and even the latest algorithms do not result in flawless
classification. In this study, a novel classification method has been described
employing a multichannel Convolutional Neural Network (CNN) that interprets
surface EMG signals by the properties they exhibit in the power domain. The
proposed method was tested on a well-established EMG dataset, and the result
yields very high classification accuracy. This learning model will help
researchers to develop prosthetic arms capable of detecting various hand
gestures to mimic them afterwards.


  Nonreciprocal elements are a vital building block of electrical and optical
systems. In the infrared regime, there is a particular interest in structures
that break reciprocity because their thermal absorptive (and emissive)
properties should not obey the Kirchhoff thermal radiation law. In this work,
we break time-reversal symmetry and reciprocity in n-type doped magneto-optic
InAs with a static magnetic field where light coupling is mediated by a
guided-mode-resonator (GMR) structure whose resonant frequency coincides with
the epsilon-near-zero (ENZ) resonance of the doped InAs. Using this structure,
we observe the nonreciprocal absorptive behavior as a function of magnetic
field and scattering angle in the infrared. Accounting for resonant and
nonresonant optical scattering, we reliably model experimental results that
break reciprocal absorption relations in the infrared. The ability to design
such nonreciprocal absorbers opens an avenue to explore devices with unequal
absorptivity and emissivity in specific channels.


  This work introduces DADAO: the first decentralized, accelerated,
asynchronous, primal, first-order algorithm to minimize a sum of $L$-smooth and
$\mu$-strongly convex functions distributed over a given network of size $n$.
Our key insight is based on modeling the local gradient updates and gossip
communication procedures with separate independent Poisson Point Processes.
This allows us to decouple the computation and communication steps, which can
be run in parallel, while making the whole approach completely asynchronous.
This leads to communication acceleration compared to synchronous approaches.
Our new method employs primal gradients and does not use a multi-consensus
inner loop nor other ad-hoc mechanisms such as Error Feedback, Gradient
Tracking, or a Proximal operator. By relating the inverse of the smallest
positive eigenvalue of the Laplacian matrix $\chi_1$ and the maximal resistance
$\chi_2\leq \chi_1$ of the graph to a sufficient minimal communication rate
between the nodes of the network, we show that our algorithm requires
$\mathcal{O}(n\sqrt{\frac{L}{\mu}}\log(\frac{1}{\epsilon}))$ local gradients
and only
$\mathcal{O}(n\sqrt{\chi_1\chi_2}\sqrt{\frac{L}{\mu}}\log(\frac{1}{\epsilon}))$
communications to reach a precision $\epsilon$, up to logarithmic terms. Thus,
we simultaneously obtain an accelerated rate for both computations and
communications, leading to an improvement over state-of-the-art works, our
simulations further validating the strength of our relatively unconstrained
method.


  Fast, byte-addressable non-volatile memory (NVM) embraces both near-DRAM
latency and disk-like persistence, which has generated considerable interests
to revolutionize system software stack and programming models. However, it is
less understood how NVM can be combined with managed runtime like Java virtual
machine (JVM) to ease persistence management. This paper proposes Espresso, a
holistic extension to Java and its runtime, to enable Java programmers to
exploit NVM for persistence management with high performance. Espresso first
provides a general persistent heap design called Persistent Java Heap (PJH) to
manage persistent data as normal Java objects. The heap is then strengthened
with a recoverable mechanism to provide crash consistency for heap metadata. It
then provides a new abstraction called Persistent Java Object (PJO) to provide
an easy-to-use but safe persistent programming model for programmers to persist
application data. The evaluation confirms that Espresso significantly
outperforms state-of-art NVM support for Java (i.e., JPA and PCJ) while being
compatible to existing data structures in Java programs.


  This paper is about randomized iterative algorithms for solving a linear
system of equations $X \beta = y$ in different settings. Recent interest in the
topic was reignited when Strohmer and Vershynin (2009) proved the linear
convergence rate of a Randomized Kaczmarz (RK) algorithm that works on the rows
of $X$ (data points). Following that, Leventhal and Lewis (2010) proved the
linear convergence of a Randomized Coordinate Descent (RCD) algorithm that
works on the columns of $X$ (features). The aim of this paper is to simplify
our understanding of these two algorithms, establish the direct relationships
between them (though RK is often compared to Stochastic Gradient Descent), and
examine the algorithmic commonalities or tradeoffs involved with working on
rows or columns. We also discuss Kernel Ridge Regression and present a
Kaczmarz-style algorithm that works on data points and having the advantage of
solving the problem without ever storing or forming the Gram matrix, one of the
recognized problems encountered when scaling kernelized methods.


  Bayesian methods have been successfully applied to sparsify weights of neural
networks and to remove structure units from the networks, e. g. neurons. We
apply and further develop this approach for gated recurrent architectures.
Specifically, in addition to sparsification of individual weights and neurons,
we propose to sparsify preactivations of gates and information flow in LSTM. It
makes some gates and information flow components constant, speeds up forward
pass and improves compression. Moreover, the resulting structure of gate
sparsity is interpretable and depends on the task. Code is available on github:
https://github.com/tipt0p/SparseBayesianRNN


  In recent years online advertising has become increasingly ubiquitous and
effective. Advertisements shown to visitors fund sites and apps that publish
digital content, manage social networks, and operate e-mail services. Given
such large variety of internet resources, determining an appropriate type of
advertising for a given platform has become critical to financial success.
Native advertisements, namely ads that are similar in look and feel to content,
have had great success in news and social feeds. However, to date there has not
been a winning formula for ads in e-mail clients. In this paper we describe a
system that leverages user purchase history determined from e-mail receipts to
deliver highly personalized product ads to Yahoo Mail users. We propose to use
a novel neural language-based algorithm specifically tailored for delivering
effective product recommendations, which was evaluated against baselines that
included showing popular products and products predicted based on
co-occurrence. We conducted rigorous offline testing using a large-scale
product purchase data set, covering purchases of more than 29 million users
from 172 e-commerce websites. Ads in the form of product recommendations were
successfully tested on online traffic, where we observed a steady 9% lift in
click-through rates over other ad formats in mail, as well as comparable lift
in conversion rates. Following successful tests, the system was launched into
production during the holiday season of 2014.


  The rise of the 4H-silicon-carbide-on-insulator (SiCOI) platform marks a
promising pathway towards the realization of monolithic quantum photonic
networks. However, the challenge of establishing room-temperature entangled
registers on these integrated photonics platforms remains unresolved. Herein,
we demonstrate the first entangled processor on the SiCOI platform. We show
that both deterministic generation of single divacancy electron spins and
near-unity spin initialization of a single $^{13}$C nuclear spin can be
achieved on SiCOI at room temperature. Besides coherently manipulating the
single nuclear spin, a maximally entangled state with a fidelity of 0.89 has
been prepared on this CMOS-compatible semiconductor-integrated photonics
system. This work establishes the foundation for compact and on-chip solutions
within existing defect-based computing and sensing protocols, positioning the
SiCOI platform as the most promising candidate for integrated monolithic
quantum photonic networks.


  This paper has been withdrawn by the author due to the presented idea is
wrong.


  Two single-molecule magnets are coupled antiferromagnetically to form a
supramolecule dimer. We study the coupling effect and tunneling process by
means of the numerical exact diagonalization method, and apply them to the
recently synthesized supramoleculer dimer [Mn4]2 The model parameters are
calculated for the dimer based on the tunneling process. The absence of
tunneling at zero field and sweeping rate effect on the step height in the
hysterisis loops are understood very well in this theory.


  Voice signal classification based on human behaviours involves analyzing
various aspects of speech patterns and delivery styles. In this study, a
real-time dataset collection is performed where participants are instructed to
speak twelve psychology questions in two distinct manners: first, in a harsh
voice, which is categorized as "misbehaved"; and second, in a polite manner,
categorized as "normal". These classifications are crucial in understanding how
different vocal behaviours affect the interpretation and classification of
voice signals. This research highlights the significance of voice tone and
delivery in automated machine-learning systems for voice analysis and
recognition. This research contributes to the broader field of voice signal
analysis by elucidating the impact of human behaviour on the perception and
categorization of voice signals, thereby enhancing the development of more
accurate and context-aware voice recognition technologies.


  The Accurate fission data for nuclear safety (AlFONS) project aims at high
precision measurements of fission yields, using the renewed IGISOL mass
separator facility in combination with a new high current light ion cyclotron
at the University of Jyvaskyla. The 30 MeV proton beam will be used to create
fast and thermal neutron spectra for the study of neutron induced fission
yields. Thanks to a series of mass separating elements, culminating with the
JYFLTRAP Penning trap, it is possible to achieve a mass resolving power in the
order of a few hundred thousands.
  In this paper we present the experimental setup and the design of a neutron
converter target for IGISOL. The goal is to have a flexible design. For studies
of exotic nuclei far from stability a high neutron flux (10^12 neutrons/s) at
energies 1 - 30 MeV is desired while for reactor applications neutron spectra
that resembles those of thermal and fast nuclear reactors are preferred. It is
also desirable to be able to produce (semi-)monoenergetic neutrons for
benchmarking and to study the energy dependence of fission yields.
  The scientific program is extensive and is planed to start in 2013 with a
measurement of isomeric yield ratios of proton induced fission in uranium. This
will be followed by studies of independent yields of thermal and fast neutron
induced fission of various actinides.


  Kernel methods are used frequently in various applications of machine
learning. For large-scale high dimensional applications, the success of kernel
methods hinges on the ability to operate certain large dense kernel matrix K.
An enormous amount of literature has been devoted to the study of symmetric
positive semi-definite (SPSD) kernels, where Nystrom methods compute a low-rank
approximation to the kernel matrix via choosing landmark points. In this paper,
we study the Nystrom method for approximating both symmetric indefinite kernel
matrices as well SPSD ones. We first develop a theoretical framework for
general symmetric kernel matrices, which provides a theoretical guidance for
the selection of landmark points. We then leverage discrepancy theory to
propose the anchor net method for computing accurate Nystrom approximations
with optimal complexity. The anchor net method operates entirely on the dataset
without requiring the access to $K$ or its matrix-vector product. Results on
various types of kernels (both indefinite and SPSD ones) and machine learning
datasets demonstrate that the new method achieves better accuracy and stability
with lower computational cost compared to the state-of-the-art Nystrom methods.


  We prove that the Hilbert scheme of $k$ points on $\mathbb{C}^2$
(Hilb$^k[\mathbb{C}^2]$) is self-dual under three-dimensional mirror symmetry
using methods of geometry and integrability. Namely, we demonstrate that the
corresponding quantum equivariant K-theory is invariant upon interchanging its
K\"ahler and equivariant parameters as well as inverting the weight of the
$\mathbb{C}^\times_\hbar$-action. First, we find a two-parameter family
$X_{k,l}$ of self-mirror quiver varieties of type A and study their quantum
K-theory algebras. The desired quantum K-theory of Hilb$^k[\mathbb{C}^2]$ is
obtained via direct limit $l\to\infty$ and by imposing certain periodic
boundary conditions on the quiver data. Throughout the proof, we employ the
quantum/classical (q-Langlands) correspondence between XXZ Bethe Ansatz
equations and spaces of twisted $\hbar$-opers. In the end, we propose the 3d
mirror dual for the moduli spaces of torsion-free rank-$N$ sheaves on
$\mathbb{P}^2$ with the help of a different (three-parametric) family of type A
quiver varieties with known mirror dual.


  The paper proposes a new high spatial resolution hyperspectral (HR-HS) image
estimation method based on convex optimization. The method assumes a low
spatial resolution HS (LR-HS) image and a guide image as observations, where
both observations are contaminated by noise. Our method simultaneously
estimates an HR-HS image and a noiseless guide image, so the method can utilize
spatial information in a guide image even if it is contaminated by heavy noise.
The proposed estimation problem adopts hybrid spatio-spectral total variation
as regularization and evaluates the edge similarity between HR-HS and guide
images to effectively use apriori knowledge on an HR-HS image and spatial
detail information in a guide image. To efficiently solve the problem, we apply
a primal-dual splitting method. Experiments demonstrate the performance of our
method and the advantage over several existing methods.


  A novel compressed matrix format is proposed that combines an adaptive
hierarchical partitioning of the matrix with low-rank approximation. One
typical application is the approximation of discretized functions on
rectangular domains; the flexibility of the format makes it possible to deal
with functions that feature singularities in small, localized regions. To deal
with time evolution and relocation of singularities, the partitioning can be
dynamically adjusted based on features of the underlying data. Our format can
be leveraged to efficiently solve linear systems with Kronecker product
structure, as they arise from discretized partial differential equations
(PDEs). For this purpose, these linear systems are rephrased as linear matrix
equations and a recursive solver is derived from low-rank updates of such
equations. We demonstrate the effectiveness of our framework for stationary and
time-dependent, linear and nonlinear PDEs, including the Burgers' and
Allen-Cahn equations.


  We investigate the implications of a fundamental length scale on the
centripetal force on a rotating Casimir apparatus in $\kappa$-space-time. We
model the Casimir apparatus rotating with constant angular speed using
appropriate $\kappa$-deformed coordinates. We find the $\kappa$-deformed
centripetal force on a single plate, as well as for parallel plates. We show
that the Casimir energy, including the divergent part (self energies of the
plates) experiences centripetal forces like a conventional mass. We also find
centripetal force on oriented parallel plates rotating with constant angular
speed in $\kappa$-space-time. Results show that the mass-energy equivalence
principle holds in the $\kappa$-space-time.


  In this paper, we develop an expected utility model for the retirement
behavior in the decumulation phase of Australian retirees with sequential
family status subject to consumption, housing, investment, bequest and
government provided means-tested Age Pension. We account for mortality risk and
risky investment assets, and introduce a health proxy to capture the decreasing
level of consumption for older retirees. Then we find optimal housing at
retirement, and optimal consumption and optimal risky asset allocation
depending on age and wealth. The model is solved numerically as a stochastic
control problem, and is calibrated using the maximum likelihood method on
empirical data of consumption and housing from the Australian Bureau of
Statistics 2009-2010 Survey. The model fits the characteristics of the data
well to explain the behavior of Australian retirees. The key findings are the
following: First, the optimal policy is highly sensitive to means-tested Age
Pension early in retirement but this sensitivity fades with age. Secondly, the
allocation to risky assets shows a complex relationship with the means-tested
Age Pension that disappears once minimum withdrawal rules are enforced. As a
general rule, when wealth decreases the proportion allocated to risky assets
increases, due to the Age Pension working as a buffer against investment
losses. Finally, couples can be more aggressive with risky allocations due to
their longer life expectancy compared with singles.


  We present new and archival atomic hydrogen (\hi) observations of \galnum\ of
the most massive spiral galaxies in the local Universe ($M_\star>10^{11} \,
\mathrm{M}_\odot$). From 3D kinematic modeling of the datacubes, we derive
extended \hi\ rotation curves, and from these, we estimate masses of the dark
matter halos and specific angular momenta of the discs. We confirm that massive
spiral galaxies lie at the upper ends of the Tully-Fisher relation (mass vs
velocity, $M \propto V^{4}$) and Fall relation (specific angular momentum vs
mass, $j \propto M^{0.6}$), in both stellar and baryonic forms, with no
significant deviations from single power laws. We study the connections between
baryons and dark matter through the stellar (and baryon)-to-halo ratios of mass
$f_\mathrm{M} \equiv M_\star/M_\mathrm{h}$ and specific angular momentum
$f_\mathrm{j} \equiv j_\star/j_\mathrm{h}$ and $f_\mathrm{j,bar} \equiv
j_\mathrm{bar}/j_\mathrm{h}$. Combining our sample with others from the
literature for less massive disc-dominated galaxies, we find that
$f_\mathrm{M}$ rises monotonically with $M_\star$ and $M_\mathrm{h}$ (instead
of the inverted-U shaped $f_\mathrm{M}$ for spheroid-dominated galaxies), while
$f_\mathrm{j}$ and $f_\mathrm{j,bar}$ are essentially constant near unity over
four decades in mass. Our results indicate that disc galaxies constitute a
self-similar population of objects closely linked to the self-similarity of
their dark halos. This picture is reminiscent of early analytical models of
galaxy formation wherein discs grow by relatively smooth and gradual inflow,
isolated from disruptive events such as major mergers and strong AGN feedback,
in contrast to the more chaotic growth of spheroids.


  Recently, there has been a growing interest in analyzing human daily
activities from data collected by wearable cameras. Since the hands are
involved in a vast set of daily tasks, detecting hands in egocentric images is
an important step towards the recognition of a variety of egocentric actions.
However, besides extreme illumination changes in egocentric images, hand
detection is not a trivial task because of the intrinsic large variability of
hand appearance. We propose a hand detector that exploits skin modeling for
fast hand proposal generation and Convolutional Neural Networks for hand
recognition. We tested our method on UNIGE-HANDS dataset and we showed that the
proposed approach achieves competitive hand detection results.


  In this paper, we study optimal transportation problems for multifractal
random measures. Since these measures are much less regular than optimal
transportation theory requires, we introduce a new notion of transportation
which is intuitively some kind of multistep transportation. Applications are
given for construction of multifractal random changes of times and to the
existence of random metrics, the volume forms of which coincide with the
multifractal random measures. This study is motivated by recent problems in the
KPZ context.


  Using two different approaches, we perform updated and detailed calculations
of the complete one-loop (Next-to-Leading Order (NLO)) set of electroweak
radiative corrections to the parity violating e- e- -> e- e- (gamma) scattering
asymmetry. Our first approach, more classical, relies on calculations "by hand"
with reasonable approximations. Our second approach relies on program packages
FeynArts, FormCalc, LoopTools, and FORM. The detailed numerical analysis of the
various contributions is provided for a wide range of energies relevant for the
ultra-precise 11 GeV MOLLER experiment planned at the Jefferson Laboratory, as
well as future experiments at the International Linear Collider (ILC). The
numerical results obtained within the on-shell renormalization scheme using two
different sets of renormalization conditions are in excellent agreement. We
also calculate the total NLO correction in the Constrained Differential
Renormalization (CDR) scheme. Analysis of the results, along with the
increasing experimental precision, shows that it is feasible that the
corrections at the Next-to-Next-to-Leading Order (NNLO) level may be important
for the next generation of high-precision experiments.


  The hydrodynamics associated with water-entry of spheres can be highly
variable with respect to the material and kinematic properties of the sphere.
This series of five fluid dynamics videos illustrates several subtle but
interesting variations. The first series of videos contrasts the nature of
impact between a hydrophilic and hydrophobic sphere, and illustrates how
surface coating can affect whether or not an air cavity is formed. The second
video series illustrates how spin and surface treatments can alter the splash
and cavity formation following water entry. The spinning sphere causes a wedge
of fluid to be drawn into the cavity due to the no-slip condition and follows a
curved trajectory. The non-spinning sphere has two distinct surface treatments
on the left and right hemispheres: the left hemisphere is hydrophobic and the
right hemisphere is hydrophilic . Interestingly, the cavity formation for the
half-and-half sphere has many similarities to that of the spinning sphere
especially when viewed from above. The third video series compares two
millimetric nylon spheres impacting at slightly different impact speeds (Uo =
40 and 45 cm/s); the faster sphere fully penetrates the free surface, forming a
cavity, whereas the slower sphere does not. The fourth series shows the
instability of an elongated water-entry cavity formed by a millimetric steel
sphere with a hydrophobic coating impacting at Uo = 600 cm/s. The elongated
cavity forms multiple pinch-off points along its decent. Finally, a millimetric
steel sphere with a hydrophobic coating breaks the free surface with an impact
speed of Uo = 350 cm/s. The cavity pinches-off below the surface, generating a
Worthington jet that pinches into droplets owing to the Rayleigh-Plateau
instability.


  Regge trajectories of quark-antiquark mesons can be well approximated for
phenomenology purposes by a specific nonlinear form, reflecting that the flux
tubes cannot be arbitrarily large, but break due to the effect of
pair-production. If confirmed, this would imply that there is only a finite
number of bound states on each trajectory, and consequently, an existence of
``spectroscopy windows'' for each flavor. Here we present our results for these
windows.


  Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks.
However, training RNNs involves dense matrix multiplications which require
hardware that can support a large number of arithmetic operations and memory
accesses. Implementing online training of RNNs on the edge calls for optimized
algorithms for an efficient deployment on hardware. Inspired by the spiking
neuron model, the Delta RNN exploits temporal sparsity during inference by
skipping over the update of hidden states from those inactivated neurons whose
change of activation across two timesteps is below a defined threshold. This
work describes a training algorithm for Delta RNNs that exploits temporal
sparsity in the backward propagation phase to reduce computational requirements
for training on the edge. Due to the symmetric computation graphs of forward
and backward propagation during training, the gradient computation of
inactivated neurons can be skipped. Results show a reduction of $\sim$80% in
matrix operations for training a 56k parameter Delta LSTM on the Fluent Speech
Commands dataset with negligible accuracy loss. Logic simulations of a hardware
accelerator designed for the training algorithm show 2-10X speedup in matrix
computations for an activation sparsity range of 50%-90%. Additionally, we show
that the proposed Delta RNN training will be useful for online incremental
learning on edge devices with limited computing resources.


  A model of "hot"-dimer deposition in one dimension, introduced by Pereyra and
Albano, is modified to have an unbounded dissociation range. The resulting
dynamical equations are solved exactly. A related k-mer dissociation model is
also introduced and its solution obtained as a quadrature.


  We present a long Suzaku observation of 3C 382. A Swift BAT spectrum from the
58-month survey is also analyzed, together with an archival XMM-Newton EPIC
exposure. Our main result is the finding with Suzaku of a broad FeK line with a
relativistic profile consistent with emission from an accretion disk at tens of
gravitational radii from the central black hole. The XIS data indicate emission
from highly ionized iron and allow us to set tight, albeit model-dependent,
constraints on the inner and outer radii of the disk reflecting region,
r_in~10r_g and r_out~20r_g, respectively, and on the disk inclination, i~30deg.
Two ionized reflection components are possibly observed, with similar
contributions of ~10% to the total continuum. A highly ionized one, with
log_xi~3, which successfully models the relativistic line and a mildly ionized
one, with log_xi~1.5, which models the narrow Fe K line and high energy hump.
When both these components are included, there is no further requirement for an
additional black body soft excess below 2keV. The Suzaku data confirm the
presence of a warm absorber previously known from grating studies. After
accounting for all the spectral features, the intrinsic photon index of the
X-ray continuum is ~1.8 with a cutoff energy at ~200keV, consistent with
Comptonization models and excluding jet-related emission up to these energies.
Comparison of the X-ray properties of 3C 382 and other BLRGs to Seyferts
confirms the idea that the distinction between radio-loud and radio-quiet AGN
at X-rays is blurred.