  Labeling training examples at scale is a perennial challenge in machine
learning. Self-supervision methods compensate for the lack of direct
supervision by leveraging prior knowledge to automatically generate noisy
labeled examples. Deep probabilistic logic (DPL) is a unifying framework for
self-supervised learning that represents unknown labels as latent variables and
incorporates diverse self-supervision using probabilistic logic to train a deep
neural network end-to-end using variational EM. While DPL is successful at
combining pre-specified self-supervision, manually crafting self-supervision to
attain high accuracy may still be tedious and challenging. In this paper, we
propose Self-Supervised Self-Supervision (S4), which adds to DPL the capability
to learn new self-supervision automatically. Starting from an initial "seed,"
S4 iteratively uses the deep neural network to propose new self supervision.
These are either added directly (a form of structured self-training) or
verified by a human expert (as in feature-based active learning). Experiments
show that S4 is able to automatically propose accurate self-supervision and can
often nearly match the accuracy of supervised methods with a tiny fraction of
the human effort.


  We study the scaled variances of multiplicity fluctuations in nucleus-nucleus
collisions at SPS and RHIC energies within the HSD transport model. The HSD
results are compared with proton-proton data and with predictions of the
hadron-resonance gas statistical model. We find that the HSD scaled variances
$\omega_i$ for negatively, positively, and all charged hadrons in central
nucleus-nucleus collisions remain close to the $\omega_i$ values in
proton-proton collisions and increase with collision energy as the
corresponding multiplicities per participating nucleon. The statistical model
predicts very different behavior of $\omega_i$. However, a comparison with
preliminary NA49 data for the most central Pb+Pb collisions at SPS energies
does not permit to distinguish the HSD and statistical model results. New
measurements of the multiplicity fluctuations in nucleus-nucleus collisions in
a wide energy region with large acceptance are needed to allow for a proper
determination of the underlying dynamics.


  We examine Bose-Einstein condensation (BEC) for particles trapped in a
harmonic potential by considering it as a transition in the length of
permutation cycles that arise from wave-function symmetry. This ``loop-gas''
approach was originally developed by Feynman in his path-integral study of BEC
for an homogeneous gas in a box. For the harmonic oscillator potential it is
possible to treat the ideal gas exactly so that one can easily see how standard
approximations become more accurate in the thermodynamic limit (TDL). One
clearly sees that the condensate is made up of very long permutation loops
whose length fluctuates ever more wildly as the number of particles increases.
In the TDL, the WKB approximation, equivalent to the standard approach to BEC,
becomes precise for the non-condensate; however, this approximation neglects
completely the long cycles that make up the condensate. We examine the exact
form for the density matrix for the system and show how it describes the
condensate and behaves in the TDL.


  Narrow-band invisibility in an otherwise opaque medium has been achieved by
electromagnetically induced transparency (EIT) in atomic systems. The quantum
EIT behaviour can be classically mimicked by specially engineered metamaterials
via carefully controlled interference with a "dark mode". However, the narrow
transparency window limits the potential applications that require a tunable
wide-band transparent performance. Here, we present a macroscopic quantum
superconducting metamaterial with manipulative self-induced broadband
transparency due to a qualitatively novel nonlinear mechanism that is different
from conventional EIT or its classical analogs. A near complete disappearance
of resonant absorption under a range of applied rf flux is observed
experimentally and explained theoretically. The transparency comes from the
intrinsic bi-stability and can be tuned on/ off easily by altering rf and dc
magnetic fields, temperature and history. Hysteretic in situ 100% tunability of
transparency paves the way for auto-cloaking metamaterials, intensity dependent
filters, and fast-tunable power limiters.


  Mathematicians had little idea whether the easy-to-state union-closed
conjecture was true or false even after $40$ years. However, last winter saw a
surge of interest in the conjecture and its variants, initiated by the
contribution of a researcher at Google. Justin Gilmer [arXiv:2211.09055] made a
significant breakthrough by discovering a first constant lower bound for the
proportion of the most common element in a union-closed family.


  We compute the perturbative corrections to the HQET sum rules for the matrix
element of the \Delta B=2 operator that determines the mass shift of $B^0$,
$\bar{B}^0$ states. Technically, we obtain analytically the non-factorizable
contributions at order $\alpha_s$ to the bag parameter that first appear at the
three-loop level. Together with the known non-perturbative corrections due to
vacuum condensates and $1/m_b$ corrections, the full next-to-leading order
result is now available. We present a numerical value for the renormalization
group invariant bag parameter that is phenomenologically relevant and discuss
comparison with recent lattice determinations.


  In most current research, large language models (LLMs) are able to perform
reasoning tasks by generating chains of thought through the guidance of
specific prompts. However, there still exists a significant discrepancy between
their capability in solving complex reasoning problems and that of humans. At
present, most approaches focus on chains of thought (COT) and tool use, without
considering the adoption and application of human cognitive frameworks. It is
well-known that when confronting complex reasoning challenges, humans typically
employ various cognitive abilities, and necessitate interaction with all
aspects of tools, knowledge, and the external environment information to
accomplish intricate tasks. This paper introduces a novel intelligent
framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive
architecture framework, and propose to simulate certain aspects of human
cognition. The framework involves approximating different cognitive modules,
including attention, memory, reasoning, learning, and corresponding scheduling
and decision-making mechanisms. Inspired by the active learning mechanism of
human beings, it proposes a learning unit to record previous mistakes and
expert opinions, and dynamically refer to them to strengthen their ability to
solve similar problems. The paper also outlines common effective reasoning
frameworks for human problem-solving and designs Chain-of-Thought (COT)
templates accordingly. A comprehensive decision-making mechanism is also
proposed to maximize model accuracy. The efficacy of OlaGPT has been
stringently evaluated on multiple reasoning datasets, and the experimental
outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks,
demonstrating its superior performance. Our implementation of OlaGPT is
available on GitHub: \url{https://github.com/oladata-team/OlaGPT}.


  This paper is intended to give a review of the recent developments on black
holes with Skyrme hair. The Einstein-Skyrme system is known to possess black
hole solutions with Skyrme hair. The spherically symmetric black hole skyrmion
with B=1 was the first discovered counter example of the no-hair conjecture for
black holes. Recently we found the B=2 axially symmetric black hole skyrmion.
In this system, the black hole at the center of the skyrmion absorbs the baryon
number partially, leaving fractional charge outside the horizon. Therefore the
baryon number is no longer conserved. We examine the B=1, 2 black hole
solutions in detail in this paper. The model has a natural extension to the
gauged version which can describe monopole black hole skyrmions. Callan and
Witten discussed the monopole catalysis of proton decay within the Skyrme
model. We apply the idea to the Einstein-Maxwell-Skyrme system and obtain
monopole black hole skyrmions. Remarkably there exist multi-black hole skyrmion
solutions in which the gravitational, electromagnetic, and strong forces
between the monopoles are all in balance. The solutions turn out to be stable
under spherically symmetric linear perturbations.


  One deals with r-regular bipartite graphs with 2n vertices. In a previous
paper Butera, Pernici, and the author have introduced a quantity d(i), a
function of the number of i-matchings, and conjectured that as n goes to
infinity the fraction of graphs that satisfy Delta^k d(i) for all k and i,
approaches 1. Here Delta is the finite difference operator. This conjecture we
called the 'graph positivity conjecture'.
  In this paper it is formally shown that for each i and k the probability that
Delta^k d(i) goes to 1 with n going to infinity.
  We call this weaker result the 'weak graph positivity conjecture ( theorem
)'.
  A formalism of Wanless as systematized by Pernici is central to this effort.
  Our result falls short of being a rigorous proof since we make a sweeping
conjecture ( computer tested ), of which we so far have only a portion of the
proof.


  In this paper we continue our study about the existence of Kaehler metrics of
constant scalar curvature (Kcsc) on blow ups at points of compact manifolds
with Kcsc metrics started in math.DG/0411522. In this second part we deal with
the case of base manifolds with holomorphic vector fields and we give
sufficient conditions for the position of points to be blown up to preserve the
Kcsc property. These results are obtained by a gluing procedure. We give
explicit examples of our construction, in particular getting optimal results in
the cases of P^n and P^n x M, where M is a Kcsc compact manifold with discrete
automorphism group.


  Traditional planning and control methods could fail to find a feasible
trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
This is because the obstacle-free volume in spacetime is very small in these
scenarios for the vehicle to drive through. However, that does not mean the
task is infeasible since human drivers are known to be able to drive amongst
dense traffic by leveraging the cooperativeness of other drivers to open a gap.
The traditional methods fail to take into account the fact that the actions
taken by an agent affect the behaviour of other vehicles on the road. In this
work, we rely on the ability of deep reinforcement learning to implicitly model
such interactions and learn a continuous control policy over the action space
of an autonomous vehicle. The application we consider requires our agent to
negotiate and open a gap in the road in order to successfully merge or change
lanes. Our policy learns to repeatedly probe into the target road lane while
trying to find a safe spot to move in to. We compare against two
model-predictive control-based algorithms and show that our policy outperforms
them in simulation.


  The NA49 experiment at CERN SPS has acquired a huge data set of Pb+Pb events
over a broad range of energy and centrality during the last several years. This
high statistics data set, coupled with a state-of-the-art analysis technique,
allows for the first model-independent extraction and energy scan of 3D
emission sources for pion pairs at SPS energies. These 3D pion emission sources
provide new insights into the nature of a long-range source previously reported
by PHENIX at RHIC. The new results indicate that the pion source displays
significant non-Gaussian tails in the longitudinal direction at 40 and 158 AGeV
and in the outward direction at 158 AGeV.


  We investigate the nonlinear refraction induced by Rydberg excitons in
$\mathrm{Cu_2O}$. Using a high-precision interferometry imaging technique that
spatially resolves the nonlinear phase shift, we observe significant shifts at
extremely low laser intensity near each exciton resonance. From this, we derive
the nonlinear index $\mathrm{n_2}$, present the $\mathrm{n_2}$ spectrum for
$n\geq 5$ and report large $\mathrm{n_2}$ values of order $10^{-3}$ mm$^2$/mW.
Moreover, we observe a rapid saturation of the Kerr nonlinearity and find that
the saturation intensity $\mathrm{I_{sat}}$ decreases as $n^{-7}$. We explain
this with the Rydberg blockade mechanism, whereby giant Rydberg interactions
limit the exciton density, resulting in a maximum phase shift of 0.5 rad in our
setup.


  We report the discovery of a Compton-thick Active Galactic Nucleus (AGN) at
z=4.76 in the 4 Ms Chandra Deep Field South. This object was selected as a
V-band dropout in HST/ACS images and previously recognized as an AGN from
optical spectroscopy. The 4 Ms Chandra observations show a significant
(~4.2sigma) X-ray detection at the V-band dropout position. The X-ray source
displays a hardness ratio of HR=0.23+-0.24, which, for a source at z~5, is
highly suggestive of Compton-thick absorption. The source X-ray spectrum is
seen above the background level in the energy range of ~0.9-4 keV, i.e., in the
rest-frame energy range of ~5-23 keV. When fixing the photon index to
Gamma=1.8, the measured column density is N_H=1.4^{+0.9}_{-0.5} x 10^{24}
cm^{-2}, which is Compton-thick. To our knowledge, this is the most distant
heavily obscured AGN, confirmed by X-ray spectral analysis, discovered so far.
The intrinsic (de-absorbed), rest-frame luminosity in the 2-10 keV band is ~2.5
x 10^{44} erg s^{-1}, which places this object among type-2 quasars. The
Spectral Energy Distribution shows that massive star formation is associated
with obscured black hole accretion. This system may have then been caught
during a major co-eval episode of black hole and stellar mass assembly at early
times. The measure of the number density of heavily obscured AGN at high
redshifts will be crucial to reconstruct the BH/galaxy evolution history from
the beginning.


  Young tidal dwarf galaxies (TDGs) are observed in the tidal debris of
gas-rich interacting galaxies. In contrast to what is generally assumed to be
the case for isolated dwarf galaxies, TDGs are not embedded in their own dark
matter (DM) sub-halo. Hence, they are more sensitive to stellar feedback and
could be disrupted on a short time-scale. Detailed numerical and observational
studies demonstrate that isolated DM-dominated dwarf galaxies can have
lifetimes of more than 10 Gyr. For TDGs that evolve in a tidal field with
compressing accelerations equal to the gravitational acceleration within a DM
sub-halo typical of an isolated dwarf galaxy, a similar survival time is
expected. The tidal acceleration profile depends on the virial mass of the host
galaxy and the distance between the TDG and its host. We analytically compare
the tidal compression to the gravitational acceleration due to either cuspy or
cored DM sub-halos of various virial masses. For example, the tidal field at a
distance of 100 kpc to a host halo of 10^13 Msol can be as stabilizing as a
10^9 Msol DM sub-halo. By linking the tidal field to the equivalent
gravitational field of a DM sub-halo, we can use existing models of isolated
dwarfs to estimate the survivability of TDGs. We show that part of the
unexpectedly high dynamical masses inferred from observations of some TDGs can
be explained by tidal compression and hence TDGs require to contain less
unobservable matter to understand their rotation curves.


  This letter studies how the imperfect resolution of near-field beamforming,
the key feature of near-field communications, can be used to improve the
throughput and connectivity of wireless networks. In particular, a hybrid
non-orthogonal multiple access (NOMA) transmission strategy is developed to use
preconfigured near-field beams for serving additional users. An energy
consumption minimization problem is first formulated and then solved by using
different successive interference cancellation strategies. Both analytical and
simulation results are presented to illustrate the impact of the resolution of
near-field beamforming on the design of hybrid NOMA transmission.


  Emulating chip functionality before silicon production is crucial, especially
with the increasing prevalence of RISC-V-based designs. FPGAs are promising
candidates for such purposes due to their high-speed and reconfigurable
architecture. In this paper, we introduce our Makinote, an FPGA-based Cluster
platform, hosted at Barcelona Supercomputing Center (BSC-CNS), which is
composed of a large number of FPGAs (in total 96 AMD/Xilinx Alveo U55c) to
emulate massive size RTL designs (up to 750M ASIC cells). In addition, we
introduce our FPGA shell as a powerful tool to facilitate the utilization of
such a large FPGA cluster with minimal effort needed by the designers. The
proposed FPGA shell provides an easy-to-use interface for the RTL developers to
rapidly port such design into several FPGAs by automatically connecting to the
necessary ports, e.g., PCIe Gen4, DRAM (DDR4 and HBM), ETH10g/100g. Moreover,
specific drivers for exploiting RISC-V based architectures are provided within
the set of tools associated with the FPGA shell. We release the tool online for
further extensions.
  We validate the efficiency of our hardware platform (i.e., FPGA cluster) and
the software tool (i.e., FPGA Shell) by emulating a RISC-V processor and
experimenting HPC Challenge application running on 32 FPGAs. Our results
demonstrate that the performance improves by 8 times over the single-FPGA case.


  This paper is devoted to the study of Mean-field Games (MFG) systems in the
mass critical exponent case. We firstly establish the optimal
Gagliardo-Nirenberg type inequality associated with the potential-free MFG
system. Then, under some mild assumptions on the potential function, we show
that there exists a critical mass $M^*$ such that the MFG system admits a least
energy solution if and only if the total mass of population density $M$
satisfies $M<M^*$. Moreover, the blow-up behavior of energy minimizers are
captured as $M\nearrow M^*$. In particular, given the precise asymptotic
expansions of the potential, we establish the refined blow-up behavior of
ground states as $M\nearrow M^*.$ While studying the existence of least energy
solutions, we establish new local $W^{2,p}$ estimates of solutions to
Hamilton-Jacobi equations with superlinear gradient terms.


  The purpose of this paper is to assess the feasibility of axion detection by
X-ray spectroscopy of the sun. We review the theory of axion-photon mode
conversion with special attention to axions emitted in the 14.4 keV M1 decay of
57Fe at the solar center. These then mode convert to photons in the outer
layers of the solar envelope, and may in principle be detected subsequently as
X-rays. For axion masses above about 10^-4 eV, resonant mode conversion at a
layer where the axion mass matches the local electron plasma frequency is
necessary. For axion masses above about 10^-2 eV, this mode conversion occurs
too deep in the solar atmosphere for the resulting photon to escape the solar
surface and be detected before Compton scattering obscures the line. At the
(detectable) axion masses below this, the flux of mode converted photons
predicted by axion models appears to be too low for detection to be feasible
with current instrumentation. Nonresonant mode conversion for axion masses
below 10^-4 eV is also plausible, but with still lower predicted fluxes, since
the axion coupling constant is related to it mass. Prospects for meaningful
constraints on massive axion parameters from X-ray observations of this
transition from the Sun do not appear to be promising. However parameters for
massless counterparts (e.g. the "arion") may still result from such
observations. It may mode convert in the outer layers of the solar atmosphere,
but is not restricted by this to have a small coupling constant.


  We show that the squared maximal height of the top path among $N$
non-intersecting Brownian bridges starting and ending at the origin is
distributed as the top eigenvalue of a random matrix drawn from the Laguerre
Orthogonal Ensemble. This result can be thought of as a discrete version of K.
Johansson's result that the supremum of the Airy$_2$ process minus a parabola
has the Tracy-Widom GOE distribution, and as such it provides an explanation
for how this distribution arises in models belonging to the KPZ universality
class with flat initial data. The result can be recast in terms of the
probability that the top curve of the stationary Dyson Brownian motion hits an
hyperbolic cosine barrier.


  We study the problem of space and time efficient evaluation of a
nonparametric estimator that approximates an unknown density. In the regime
where consistent estimation is possible, we use a piecewise multivariate
polynomial interpolation scheme to give a computationally efficient
construction that converts the original estimator to a new estimator that can
be queried efficiently and has low space requirements, all without adversely
deteriorating the original approximation quality. Our result gives a new
statistical perspective on the problem of fast evaluation of kernel density
estimators in the presence of underlying smoothness. As a corollary, we give a
succinct derivation of a classical result of Kolmogorov---Tikhomirov on the
metric entropy of H\"{o}lder classes of smooth functions.


  We show that in all dimensions >7 there are closed aspherical manifolds whose
fundamental groups have nontrivial center but do not possess any topological
circle actions. This disproves a conjectured converse (proposed by Conner and
Raymond) to a classical theorem of Borel.


  We describe a realistic scheme for coupling atoms or other quantum emitters
with an array of coupled optical cavities. We consider open Fabry-Perot
microcavities coupled to the emitters. Our central innovation is to connect the
microcavities to waveguide resonators, which are in turn evanescently coupled
to each other on a photonic chip to form a coupled cavity chain. In this paper,
we describe the components, their technical limitations and the factors that
need to be determined experimentally. This provides the basis for a detailed
theoretical analysis of two possible experiments to realize quantum squeezing
and controlled quantum dynamics. We close with an outline of more advanced
applications.


  We have measured the neutron electric dipole moment using spin rotation in a
non-centrosymmetric crystal. Our result is d_n = (2.5 +- 6.5(stat) +-
5.5(syst)) 10^{-24} e cm. The dominating contribution to the systematic
uncertainty is statistical in nature and will reduce with improved statistics.
The statistical sensitivity can be increased to 2 10^{-26} e cm in 100 days
data taking with an improved setup. We state technical requirements for a
systematic uncertainty at the same level.


  The paper focuses on numerical solution of parametrized diffusion equations
with scalar parameter-dependent coefficient function by the stochastic
(spectral) Galerkin method. We study preconditioning of the related discretized
problems using preconditioners obtained by modifying the stochastic part of the
partial differential equation. We present a simple but general approach for
obtaining two-sided bounds to the spectrum of the resulting matrices, based on
a particular splitting of the discretized operator. Using this tool and
considering the stochastic approximation space formed by classical orthogonal
polynomials, we obtain new spectral bounds depending solely on the properties
of the coefficient function and the type of the approximation polynomials for
several classes of block-diagonal preconditioners. These bounds are guaranteed
and applicable to various distributions of parameters. Moreover, the conditions
on the parameter-dependent coefficient function are only local, and therefore
less restrictive than those usually assumed in the literature.


  We consider the capacity of memoryless finite-state multiple access channel
(FS-MAC) with causal asymmetric noisy state information available at both
transmitters and complete state information available at the receiver. Single
letter inner and outer bounds are provided for the capacity of such channels
when the state process is independent and identically distributed. The outer
bound is attained by observing that the proposed inner bound is tight for the
sum-rate capacity.


  We calculate angle-dependent heat capacity in a low magnetic field range on
the basis of Kramer-Pesch approximation together with an electronic structure
obtained by first-principles calculations to determine a superconducting gap
function of (TMTSF)2ClO4 through its comparisons with experiments. The present
comparative studies reveal that a nodal d-wave gap function consistently
explains the experimental results for (TMTSF)2ClO4. Especially, it is
emphasized that the observed unusual axis-asymmetry of the angle-dependence
eliminates the possibility of s-wave and node-less d-wave functions. It is also
found that the directional ordering of ClO4 anions does not have any
significant effects on the Fermi surface structure contrary to the previous
modelings since the two Fermi surfaces obtained by the band calculations almost
cross within the present full accuracy in first-principles calculations.


  The electronic structure of various (001), (110), and (111)B surfaces of
n-type InSb were studied with scanning tunneling microscopy and spectroscopy.
The InSb(111)B (3x1) surface reconstruction is determined to be a disordered
(111)B (3x3) surface reconstruction. The surface Fermi-level of the In rich and
the equal In:Sb (001), (110), and (111)B surface reconstructions was observed
to be pinned near the valence band edge. This observed pinning is consistent
with a charge neutrality level lying near the valence band maximum. Sb
termination was observed to shift the surface Fermi-level position by up to
$254 \pm 35$ meV towards the conduction band on the InSb (001) surface and $60
\pm 35$ meV towards the conduction band on the InSb(111)B surface. The surface
Sb on the (001) can shift the surface from electron depletion to electron
accumulation. We propose the shift in the Fermi-level pinning is due to charge
transfer from Sb clusters on the Sb terminated surfaces. Additionally, many
sub-gap states were observed for the (111)B (3x1) surface, which are attributed
to the disordered nature of this surface. This work demonstrates the tuning of
the Fermi-level pinning position of InSb surfaces with Sb termination.


  Strategic classification regards the problem of learning in settings where
users can strategically modify their features to improve outcomes. This setting
applies broadly and has received much recent attention. But despite its
practical significance, work in this space has so far been predominantly
theoretical. In this paper we present a learning framework for strategic
classification that is practical. Our approach directly minimizes the
"strategic" empirical risk, achieved by differentiating through the strategic
response of users. This provides flexibility that allows us to extend beyond
the original problem formulation and towards more realistic learning scenarios.
A series of experiments demonstrates the effectiveness of our approach on
various learning settings.


  \pkg{multiplex} is a computer program that provides algebraic tools for the
analysis of multiple network structures within the \proglang{R} environment.
Apart from the possibility to create and manipulate multivariate data
representing multiplex, signed, and two-mode networks, this package offers a
collection of functions that deal with algebraic systems ---such as the
partially ordered semigroup, and balance or cluster semirings--- their
decomposition, and the enumeration of bundle patterns occurring at different
levels of the network. Moreover, through Galois derivations between families of
the pairs of subsets in different domains it is possible to analyze affiliation
networks with an algebraic approach. Visualization of multigraphs, different
forms of bipartite graphs, inclusion lattices, Cayley graphs is supported as
well with related packages.


  We present new ultraviolet (UV) observations of the luminous compact blue
galaxy KISSR242, obtained with the HST-COS. We identify multiple resolved
sub-arcsecond near-UV sources within the COS aperture. The far-UV spectroscopic
data show strong outflow absorption lines, consistent with feedback processes
related to an episode of massive star-formation. OI, CII, and SiII--SiIV are
observed with a mean outflow velocity v_{out} = -60 km/s. We also detect faint
fine-structure emission lines of singly ionized silicon for the first time in a
low-redshift starburst galaxy. These emissions have been seen previously in
deep Lyman break galaxy surveys at z ~ 3. The SiII* lines are at the galaxy
rest velocity, and they exhibit a quantitatively different line profile from
the absorption features. These lines have a width of ~ 75 km/s, too broad for
point-like emission sources such as the HII regions surrounding individual star
clusters. The size of the SiII* emitting region is estimated to be ~ 250 pc. We
discuss the possibility of this emission arising in overlapping super star
cluster HII regions, but find this explanation to be unlikely in light of
existing far-UV observations of local star-forming galaxies. We suggest that
the observed SiII* emission originates in a diffuse warm halo populated by
interstellar gas driven out by intense star-formation and/or accreted during a
recent interaction that may be fueling the present starburst episode in
KISSR242.


  We review some aspects of Nambu mechanics on the basis of the works
previously published separately by the present author. Main focuses are on
three themes, its various symmetry structures, their possible relevance to
string/M theory, and a Hamilton-Jacobi like reformulation. We try to elucidate
the basic ideas, most of which were rooted in more or less the same ground, and
to explain motivations behind these works from a unified and vantage viewpoint.
Various unsolved questions are mentioned. We also include some historical
account on the genesis of the Nambu mechanics, and discuss (in the Appendix)
some parallelism of various ideas behind the Nambu's paper with Dirac's old
works which are related to the description of vortical flows in terms of gauge
potentials.


  Next-to-leading order correction to the one-particle inclusive cross section
in the framework of high energy factorization is calculated. Numerical results
for midrapidity region are compared with predictions of conventional
calculations based on collinear factorization.


  We study an Eulerian walker on a square lattice, starting from an initially
randomly oriented background using Monte Carlo simulations. We present evidence
that, that, for large number of steps $N$, the asymptotic shape of the set of
sites visited by the walker is a perfect circle. The radius of the circle
increases as $N^{1/3}$, for large $N$, and the width of the boundary region
grows as $N^{\alpha / 3}$, with $\alpha = 0.40 \pm .05$. If we introduce
stochasticity in the evolution rules, the mean square displacement of the
walker, $<R_{N}^{2}> \sim N^{2\nu}$, shows a crossover from the Eulerian ($\nu
= 1/3$) to a simple random walk ($\nu=1/2$) behaviour.


  The hot Jupiter WASP-12b is a heavily irradiated exoplanet in a short period
orbit around a G0-star with twice the metallicity of the Sun. A recent
thermochemical equilibrium analysis based on Spitzer and ground-based infrared
observations suggests that the presence of $\ch4$ in its atmosphere and the
lack of $\h2o$ features can only be explained if the carbon-to-oxygen ratio in
the planet's atmosphere is much greater than the solar ratio ($\ctoo = 0.54$).
Here, we use a 1-D photochemical model to study the effect of disequilibrium
chemistry on the observed abundances of $\h2o, \com, \co2$ and $\ch4$ in the
WASP-12b atmosphere. We consider two cases: one with solar $\ctoo$ and another
with $\ctoo = 1.08$. The solar case predicts that $\h2o$ and $\com$ are more
abundant than $\co2$ and $\ch4$, as expected, whereas the high $\ctoo$ model
shows that $\com$, C$_{2}$H$_{2}$ and HCN are more abundant. This indicates
that the extra carbon from the high $\ctoo$ model is in hydrocarbon species.
$\h2o$ photolysis is the dominant disequilibrium mechanism that alters the
chemistry at higher altitudes in the solar $\ctoo$ case, whereas
photodissociation of C$_{2}$H$_{2}$ and HCN is significant in the super-solar
case. Furthermore, our analysis indicates that $\c2h2$ is the major absorber in
the atmosphere of WASP-12b and the absorption features detected near 1.6 and 8
micron may be arising from C$_{2}$H$_{2}$ rather than $\ch4$. The Hubble Space
Telescope's WFC3 can resolve this discrepancy, as $\c2h2$ has absorption
between $1.51 - 1.54$ microns, while $\ch4$ does not.


  Using properties of their Cayley graphs, specific examples of Ozawa kernels
are constructed for both free and amenable groups, thus showing that these
groups satisfy Property O. It is deduced both that these groups are exact and
satisfy Yu's Property A.


  A review of the Annual Review of Astronomy and Astrophysics Volume 52, 2014
(Ed. S.M. Faber, Ewine van Dishoeck, and John Kormendy) is given, with a
perspective of understanding the current trends in Astronomy and Astrophysics.
The impact of high volume data, high connectivity, and fast computations is
clearly seen in the various research areas discussed in this volume. This has
provided unprecedented development in the understanding of various
astrophysical phenomena. At the same time, some negative trends like
commodification of science, ignoring dissenting views are also evident.


  The rise of internet-based services and products in the late 1990's brought
about an unprecedented opportunity for online businesses to engage in large
scale data-driven decision making. Over the past two decades, organizations
such as Airbnb, Alibaba, Amazon, Baidu, Booking, Alphabet's Google, LinkedIn,
Lyft, Meta's Facebook, Microsoft, Netflix, Twitter, Uber, and Yandex have
invested tremendous resources in online controlled experiments (OCEs) to assess
the impact of innovation on their customers and businesses. Running OCEs at
scale has presented a host of challenges requiring solutions from many domains.
In this paper we review challenges that require new statistical methodologies
to address them. In particular, we discuss the practice and culture of online
experimentation, as well as its statistics literature, placing the current
methodologies within their relevant statistical lineages and providing
illustrative examples of OCE applications. Our goal is to raise academic
statisticians' awareness of these new research opportunities to increase
collaboration between academia and the online industry.


  In this communication we present a method of complete basis set (CBS)
extrapolation of correlation energies obtained with a systematic sequence of
one-electron basis sets. Instead of fitting the finite-basis results with a
certain functional form, we perform analytic re-summation of the missing
contributions coming from higher angular momenta, $l$. The assumption that they
vanish asymptotically as an inverse power of $l$ leads to an expression for the
CBS limit given in terms of the Riemann zeta function. This result is turned
into an extrapolation method that is very easy to use and requires no
"empirical" parameters to be optimized. The performance of the proposed method
is assessed by comparing the results with accurate reference data obtained with
explicitly correlated theories and with results obtained with standard
extrapolation schemes. On average, the errors of the zeta-function
extrapolation are several times smaller compared with the conventional schemes
employing the same number of points. A recipe for estimation of the residual
extrapolation error is also proposed.


  In this work, we explain `Peiffer pairings' in the Moore (bi)complex of a
bisimplicial group and give their applications for crossed modules and crossed
squares.


  Fluent and safe interactions of humans and robots require both partners to
anticipate the others' actions. A common approach to human intention inference
is to model specific trajectories towards known goals with supervised
classifiers. However, these approaches do not take possible future movements
into account nor do they make use of kinematic cues, such as legible and
predictable motion. The bottleneck of these methods is the lack of an accurate
model of general human motion. In this work, we present a conditional
variational autoencoder that is trained to predict a window of future human
motion given a window of past frames. Using skeletal data obtained from RGB
depth images, we show how this unsupervised approach can be used for online
motion prediction for up to 1660 ms. Additionally, we demonstrate online target
prediction within the first 300-500 ms after motion onset without the use of
target specific training data. The advantage of our probabilistic approach is
the possibility to draw samples of possible future motions. Finally, we
investigate how movements and kinematic cues are represented on the learned low
dimensional manifold.


  The paper studies a class of multidimensional optimal stopping problems with
infinite horizon for linear switching diffusions. There are two main novelties
in the optimal problems considered: the underlying stochastic process has
discontinuous paths and the cost function is not necessarily integrable on the
entire time horizon, where the latter is often a key assumption in classical
optimal stopping theory for diffusions, cf. [22, Corollary 2.9]. Under
relatively mild conditions, we show, for the class of multidimensional optimal
stopping problems under consideration, that the first entry time of the
stopping region is an optimal stopping time. Further, we prove that the
corresponding optimal stopping boundaries can be represented as the unique
solution to a nonlinear integral equation. We conclude with an application of
our results to the problem of quickest real-time detection of a Markovian
drift.


  Low-rank methods for semidefinite programming (SDP) have gained a lot of
interest recently, especially in machine learning applications. Their analysis
often involves determinant-based or Schatten-norm penalties, which are hard to
implement in practice due to high computational efforts. In this paper, we
propose Entropy Penalized Semi-definite programming (EP-SDP) which provides a
unified framework for a wide class of penalty functions used in practice to
promote a low-rank solution. We show that EP-SDP problems admit efficient
numerical algorithm having (almost) linear time complexity of the gradient
iteration which makes it useful for many machine learning and optimization
problems. We illustrate the practical efficiency of our approach on several
combinatorial optimization and machine learning problems.


  We prove the existence of global minimizers to the double minimization
problem \[
  \inf\Big\{ P(E) + \lambda W_p(\mathcal{L}^n \lfloor \, E,\mathcal{L}^n
\lfloor\, F) \colon |E \cap F| = 0, \, |E| = |F| = 1\Big\}, \] where $P(E)$
denotes the perimeter of the set $E$, $W_p$ is the $p$-Wasserstein distance
between Borel probability measures, and $\lambda > 0$ is arbitrary. The result
holds in all space dimensions, for all $p \in [1,\infty),$ and for all positive
$\lambda $. This answers a question of Buttazzo, Carlier, and Laborde.


  We investigate the electrostatic contribution to the persistence length of a
semiflexible polymer chain whose segments interact via a screened Debye-H\"
uckel dipolar interaction potential. We derive the expressions for the
renormalized persistence length on the level of a 1/D-expansion method already
successfully used in other contexts of polyelectrolye physics. We investigate
different limiting forms of the renormalized persistence length of the dipolar
chain and show that in general it depends less strongly on the screening length
than in the context of a monopolar chain. We show that for a dipolar chain the
electrostatic persistence length in the same regime of the parameter phase
space as the original Odijk-Skolnick-Fixman (OSF) form for a monopolar chain
depends logarithmically on the screening length rather than quadratically. This
can be understood solely on the basis of a swifter decay of the dipolar
interactions with separation compared to the monopolar electrostatic
interactions. We comment also on the general contribution of higher multipoles
on the electrostatic renormalization of the bending rigidity.


  We develop a package of monopole Floer-theoretic invariants for webs and
foams using real Seiberg-Witten theory with orbifold singularities, based on
Kronheimer and Mrowka's framework of monopole Floer homology.


  We present a detailed analysis of a recently introduced version of Causal
Dynamical Triangulations (CDT) that does not rely on a distinguished time
slicing. Focussing on the case of 2+1 spacetime dimensions, we analyze its
geometric and causal properties, present details of the numerical set-up and
explain how to extract "volume profiles". Extensive Monte Carlo measurements of
the system show the emergence of a de Sitter universe on large scales from the
underlying quantum ensemble, similar to what was observed previously in
standard CDT quantum gravity. This provides evidence that the distinguished
time slicing of the latter is not an essential part of its kinematical set-up.


  We consider properties of a fully superconducting variant of the
superconducting quantum interference proximity transistor magnetic flux sensor.
We study the density of states in a finite-size superconducting metal wire in
the diffusive limit, and how it depends on the phase gradient of the order
parameter. We describe the dependence on the junction length and interface
transparency, and discuss properties relevant for using the structure in
magnetic flux detection applications.


  The restricted Boltzmann machine (RBM) is a representative generative model
based on the concept of statistical mechanics. In spite of the strong merit of
interpretability, unavailability of backpropagation makes it less competitive
than other generative models. Here we derive differentiable loss functions for
both binary and multinary RBMs. Then we demonstrate their learnability and
performance by generating colored face images.


  A new cryogenic noise calibration source for radio astronomy receivers is
presented. Dissipated power is only 4.2 mW, allowing it to be integrated with
the cold part of the receiver. Measured long-term stability, sensitivity to
bias voltages, and noise power output versus frequency are presented. The
measured noise output versus frequency is compared to a warm noise diode
injected into cryogenic K-band receiver and shows the integrated noise module
to have less frequency structure, which will result in more accurate
astronomical flux calibrations. It is currently in operation on the new
7-element K-band focal plane array receiver on the NRAO Robert C. Byrd Green
Bank Telescope (GBT).


  We propose an energy-efficient network architecture that consists of ad hoc
(mobile) cognitive radios (CRs) and infrastructure wireless sensor nodes. The
sensor nodes within communications range of each CR are grouped into a cluster
and the clusters of CRs are regularly updated according to the random mobility
of the CRs. We reduce the energy consumption and the end-to-end delay of the
sensor network by dividing each cluster into disjoint subsets with overlapped
sensing coverage of primary user (PU) activity. Respective subset of a CR
provides target detection and false alarm probabilities. Substantial energy
efficiency is achieved by activating only one subset of the cluster, while
putting the rest of the subsets in the cluster into sleep mode.


  Computational quantum mechanics based molecular and materials design
campaigns consume increasingly more high-performance compute resources, making
improved job scheduling efficiency desirable in order to reduce carbon
footprint or wasteful spending. We introduce quantum machine learning (QML)
models of the computational cost of common quantum chemistry tasks. For 2D
non-linear toy systems, single point, geometry optimization, and transition
state calculations the out of sample prediction error of QML models of wall
times decays systematically with training set size. We present numerical
evidence for a toy system containing two functions and three commonly used
optimizer and for thousands of organic molecular systems including closed and
open shell equilibrium structures, as well as transition states. Levels of
electronic structure theory considered include B3LYP/def2-TZVP, MP2/6-311G(d),
local CCSD(T)/VTZ-F12, CASSCF/VDZ-F12, and MRCISD+Q-F12/VDZ-F12. In comparison
to conventional indiscriminate job treatment, QML based wall time predictions
significantly improve job scheduling efficiency for all tasks after training on
just thousands of molecules. Resulting reductions in CPU time overhead range
from 10% to 90%.


  We establish various general results concerning static and spherically
symmetric black hole solutions of general higher-derivative extensions of
Einstein gravity. We prove that the only theories susceptible of admitting
solutions with $g_{tt}g_{rr}=-1$ and representing the exterior field of a
spherically symmetric distribution of mass are those that only propagate a
massless and traceless graviton on the vacuum. Then, we provide a simple (and
computationally powerful) sufficient condition for a theory to admit solutions
of that kind, as well as a systematic way for constructing them for a given
theory. We conjecture (and provide strong evidence) that all black holes
constructed according to our criteria are completely determined by their mass
(non-hairy), and such that their thermodynamic properties can be obtained by
solving a system of algebraic equations without free parameters. Our results
can be straightforwardly extended to planar and hyperbolic horizons. We
illustrate this by obtaining new planar asymptotically $AdS_5$ black hole
solutions of the recently constructed Generalized quasitopological gravity
[arXiv:1703.01631], which belongs to the class of theories selected by our
results.


  In this work, we present Fairness Aware Counterfactuals for Subgroups
(FACTS), a framework for auditing subgroup fairness through counterfactual
explanations. We start with revisiting (and generalizing) existing notions and
introducing new, more refined notions of subgroup fairness. We aim to (a)
formulate different aspects of the difficulty of individuals in certain
subgroups to achieve recourse, i.e. receive the desired outcome, either at the
micro level, considering members of the subgroup individually, or at the macro
level, considering the subgroup as a whole, and (b) introduce notions of
subgroup fairness that are robust, if not totally oblivious, to the cost of
achieving recourse. We accompany these notions with an efficient,
model-agnostic, highly parameterizable, and explainable framework for
evaluating subgroup fairness. We demonstrate the advantages, the wide
applicability, and the efficiency of our approach through a thorough
experimental evaluation of different benchmark datasets.


  Recently, magnetorheological elastomers have become an interesting smart
material with many new designs for robotics. A variety of applications have
been built with magnetorheological elastomers, such as vibration absorbers,
actuators, or grippers, showing that this material is promising for soft
robotics. In this work, the novel concept of a gripper is proposed, exploring
the features of a magnetorheological elastomer and permanent magnet. The
gripper uses the energy of a permanent magnet to provide a self-closing
gripping mechanism. The usage of flexible material enables one to hold delicate
objects of various shapes. This paper presents the rolling effect of
magnetorheological elastomer and permanent magnet, the design process, and the
features of the soft gripper. The effectiveness of the soft gripper was
validated in a series of experiments that involved lifting different objects.


  Breast cancer (BC) is the most common cancer among women world-wide,
approximately 20-25% of BCs are HER-2 positive. Analysis of HER-2 is
fundamental to defining the appropriate therapy for patients with breast
cancer. Inter-pathologist variability in the test results can affect diagnostic
accuracy. The present study intends to propose an automatic scoring HER-2
algorithm. Based on color features, the technique is fully-automated and avoids
segmentation, showing a concordance higher than 90% with a pathologist in the
experiments realized.


  We explore the potential of current and future liquid scintillator neutrino
detectors of O (10) kt mass to localize a pre-supernova neutrino signal in the
sky. In the hours preceding the core collapse of a nearby star (at distance D <
1 kpc), tens to hundreds of inverse beta decay events will be recorded, and
their reconstructed topology in the detector can be used to estimate the
direction to the star. Although the directionality of inverse beta decay is
weak (~8% forward-backward asymmetry for currently available liquid
scintillators), we find that for a fiducial signal of 200 events (which is
realistic for Betelgeuse), a positional error of ~60 degrees can be achieved,
resulting in the possibility to narrow the list of potential stellar candidates
to less than ten, typically. For a configuration with improved forward-backward
asymmetry (~40%, as expected for a lithium-loaded liquid scintillator), the
angular sensitivity improves to ~15 degrees, and - when a distance upper limit
is obtained from the overall event rate - it is in principle possible to
uniquely identify the progenitor star. Any localization information
accompanying an early supernova alert will be useful to multi-messenger
observations and to particle physics tests using collapsing stars.


  We use the Chern-Simons quantum field theory in order to prove a recently
conjectured limitation on the 1/K expansion of the Jones polynomial of a knot
and its relation to the Alexander polynomial. This limitation allows us to
derive a surgery formula for the loop corrections to the contribution of the
trivial connection to Witten's invariant. The 2-loop part of this formula
coincides with Walker's surgery formula for Casson-Walker invariant. This
proves a conjecture that Casson-Walker invariant is a 2-loop correction to the
trivial connection contribution to Witten's invariant of a rational homology
sphere. A contribution of the trivial connection to Witten's invariant of a
manifold with nontrivial rational homology is calculated for the case of
Seifert manifolds.


  Generating natural human motion from a story has the potential to transform
the landscape of animation, gaming, and film industries. A new and challenging
task, Story-to-Motion, arises when characters are required to move to various
locations and perform specific motions based on a long text description. This
task demands a fusion of low-level control (trajectories) and high-level
control (motion semantics). Previous works in character control and
text-to-motion have addressed related aspects, yet a comprehensive solution
remains elusive: character control methods do not handle text description,
whereas text-to-motion methods lack position constraints and often produce
unstable motions. In light of these limitations, we propose a novel system that
generates controllable, infinitely long motions and trajectories aligned with
the input text. (1) We leverage contemporary Large Language Models to act as a
text-driven motion scheduler to extract a series of (text, position, duration)
pairs from long text. (2) We develop a text-driven motion retrieval scheme that
incorporates motion matching with motion semantic and trajectory constraints.
(3) We design a progressive mask transformer that addresses common artifacts in
the transition motion such as unnatural pose and foot sliding. Beyond its
pioneering role as the first comprehensive solution for Story-to-Motion, our
system undergoes evaluation across three distinct sub-tasks: trajectory
following, temporal action composition, and motion blending, where it
outperforms previous state-of-the-art motion synthesis methods across the
board. Homepage: https://story2motion.github.io/.


  A conjecture of Fuglede states that a bounded measurable set D, of measure 1,
can tile space by translations if and only if the Hilbert space L^2(D) has an
orthonormal basis consisting of exponentials exp(i 2 pi lambda x). If D has the
latter property it is called spectral. Let D be a polytope with the following
property: there is a direction u such that, of all the polytope faces
perpendicular to u, the total area of the faces pointing in the positive u
direction is more than the total area of the faces pointing in the negative u
direction. It is almost obvious that such a polytope D cannot tile space by
translation. We prove in this paper that such a domain is also not spectral,
which agrees with Fuglede's conjecture.


  We investigate theoretical properties of beams of light with non-uniform
polarization patterns. Specifically, we determine all possible configurations
of cylindrically polarized modes (CPMs) of the electro-magnetic field,
calculate their total angular momentum and highlight the subtleties of their
structure. Furthermore, a hybrid spatio-polarization description for such modes
is introduced and developed. In particular, two independent Poincar\'e spheres
have been introduced to represent simultaneously the polarization and spatial
degree of freedom of CPMs. Possible mode-to-mode transformations accomplishable
with the help of conventional polarization and spatial phase retarders are
shown within this representation. Moreover, the importance of these CPMs in the
quantum optics domain due to their classical features is highlighted.


  With the rapid development of deep learning techniques, the generation and
counterfeiting of multimedia material are becoming increasingly straightforward
to perform. At the same time, sharing fake content on the web has become so
simple that malicious users can create unpleasant situations with minimal
effort. Also, forged media are getting more and more complex, with manipulated
videos that are taking the scene over still images. The multimedia forensic
community has addressed the possible threats that this situation could imply by
developing detectors that verify the authenticity of multimedia objects.
However, the vast majority of these tools only analyze one modality at a time.
This was not a problem as long as still images were considered the most widely
edited media, but now, since manipulated videos are becoming customary,
performing monomodal analyses could be reductive. Nonetheless, there is a lack
in the literature regarding multimodal detectors, mainly due to the scarsity of
datasets containing forged multimodal data to train and test the designed
algorithms. In this paper we focus on the generation of an audio-visual
deepfake dataset. First, we present a general pipeline for synthesizing speech
deepfake content from a given real or fake video, facilitating the creation of
counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS)
and Dynamic Time Warping techniques to achieve realistic speech tracks. Then,
we use the pipeline to generate and release TIMIT-TTS, a synthetic speech
dataset containing the most cutting-edge methods in the TTS field. This can be
used as a standalone audio dataset, or combined with other state-of-the-art
sets to perform multimodal research. Finally, we present numerous experiments
to benchmark the proposed dataset in both mono and multimodal conditions,
showing the need for multimodal forensic detectors and more suitable data.


  Glioblastoma, a highly aggressive primary brain tumor, is associated with
poor patient outcomes. Although magnetic resonance imaging (MRI) plays a
critical role in diagnosing, characterizing, and forecasting glioblastoma
progression, public MRI repositories present significant drawbacks, including
insufficient postoperative and follow-up studies as well as expert tumor
segmentations. To address these issues, we present the "R\'io Hortega
University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of
multiparametric MRI images, volumetric assessments, molecular data, and
survival details for glioblastoma patients who underwent total or near-total
enhancing tumor resection. The dataset features expert-corrected segmentations
of tumor subregions, offering valuable ground truth data for developing
algorithms for postoperative and follow-up MRI scans. The public release of the
RHUH-GBM dataset significantly contributes to glioblastoma research, enabling
the scientific community to study recurrence patterns and develop new
diagnostic and prognostic models. This may result in more personalized,
effective treatments and ultimately improved patient outcomes.


  In this paper, adapted from the author's PhD thesis, we present otherwise
unpublished results relating to global control schemes, culminating in the
calculation of a fault-tolerant threshold for one such scheme. As with early
fault-tolerant threshold results, the aim is to calculate a positive number,
not to optimise it. We also discuss how the results might affect other related
schemes, such as those based on cellular automata.


  The discovery of a flux excess in the far-ultraviolet (UV) spectrum of
elliptical galaxies was a major surprise in 1969. While it is now clear that
this UV excess is caused by an old population of hot helium-burning stars
without large hydrogen-rich envelopes, rather than young stars, their origin
has remained a mystery. Here we show that these stars most likely lost their
envelopes because of binary interactions, similar to the hot subdwarf
population in our own Galaxy. We have developed an evolutionary population
synthesis model for the far-UV excess of elliptical galaxies based on the
binary model developed by Han et al (2002, 2003) for the formation of hot
subdwarfs in our Galaxy. Despite its simplicity, it successfully reproduces
most of the properties of elliptical galaxies with a UV excess: the range of
observed UV excesses, both in $(1550-V)$ and $(2000-V)$, and their evolution
with redshift. We also present colour-colour diagrams for use as diagnostic
tools in the study of elliptical galaxies. The model has major implications for
understanding the evolution of the UV excess and of elliptical galaxies in
general. In particular, it implies that the UV excess is not a sign of age, as
had been postulated previously, and predicts that it should not be strongly
dependent on the metallicity of the population, but exists universally from
dwarf ellipticals to giant ellipticals.


  Is string theory relevant to the black hole information problem? This is an
attempt to clarify some of the issues involved. Presented at Strings '95.


  The paper presents a novel analysis of Floquet-Bloch flexural waves in a
periodic lattice-like structure consisting of flexural beam ligaments. A
special feature of this structure is in the presence of the rotational inertia,
which is commonly neglected in conventional models of the Euler-Bernoulli type.
The dispersion properties of the Rayleigh beam structure with rotational
inertia include degeneracies linked to Dirac cones on the dispersion diagrams
as well as directional anisotropy and special refraction properties. Steering
of Dirac cones is described for rectangular flexural structures with a
rotational inertia. Numerical examples for a forced network of Rayleigh and
Euler-Bernoulli beams illustrate directional localisation, negative refraction,
localisation at an interface and neutrality for propagating plane waves across
a structured interface for a frequency range corresponding to a Dirac cone.


  Contrary to its initial idea, Belnap's (1992) theory of Branching Space-Times
(BST) has models in which histories do not resemble relativistic space-times or
any other physical space-times. The aim of this paper is to define a certain
class of BST models, called "Minkowskian Branching Structures" (MBS), in which
histories are isomorphic to Minkowski space-time. By focusing on these models
rather than on general BST models, we hope that one may be able to improve on
earlier BST analyzes of physical phenomena. Also, introducing MBS' sets the
stage for recent discussions about whether or not `branching is a bad idea',
physically speaking.


  Automated evaluation of text generation systems has recently seen increasing
attention, particularly checking whether generated text stays truthful to input
sources. Existing methods frequently rely on an evaluation using task-specific
language models, which in turn allows for little interpretability of generated
scores. We introduce SRLScore, a reference-free evaluation metric designed with
text summarization in mind. Our approach generates fact tuples constructed from
Semantic Role Labels, applied to both input and summary texts. A final
factuality score is computed by an adjustable scoring mechanism, which allows
for easy adaption of the method across domains. Correlation with human
judgments on English summarization datasets shows that SRLScore is competitive
with state-of-the-art methods and exhibits stable generalization across
datasets without requiring further training or hyperparameter tuning. We
experiment with an optional co-reference resolution step, but find that the
performance boost is mostly outweighed by the additional compute required. Our
metric is available online at https://github.com/heyjing/SRLScore.


  We calculate the energy and hydrostatic pressure densities of a hot
quark-gluon plasma in thermal equilibrium through diagrammatic analyses of the
statistical average, $\langle \Theta_{\mu \nu} \rangle$, of the
energy-momentum-tensor operator $\Theta_{\mu \nu}$. To leading order at high
temperature, the energy density of the long wave length modes is consistently
extracted by applying the hard-thermal-loop resummation scheme to the
operator-inserted no-leg thermal amplitudes $\langle \Theta_{\mu \nu} \rangle$.
We find that, for the long wave length gluons, the energy density, being
positive, is tremendously enhanced as compared to the noninteracting case,
while, for the quarks, no noticeable deviation from the noninteracting case is
found.


  The identification of primal variables and adjoint variables is usually done
via indices in operator overloading algorithmic differentiation tools. One
approach is a linear management scheme, which is easy to implement and supports
memory optimization for copy statements. An alternative approach performs a
reuse of indices, which requires more implementation effort but results in much
smaller adjoint vectors. Therefore, the vector mode of algorithmic
differentiation scales better with the reuse management scheme. In this paper,
we present a novel approach that reuses the indices and allows the copy
optimization, thus combining the advantages of the two aforementioned schemes.
The new approach is compared to the known approaches on a simple synthetic test
case and a real-world example using the computational fluid dynamics solver
SU2.


  Quasi-one-dimensional quantum antiferromagnets formed by a d-dimensional
hypercubic lattice of weakly coupled spin-1/2 antiferromagnetic Heisenberg
chains are studied by combining exact results in one-dimension and
renormalization group analyses of the interchain correlations. It is shown that
d-dimensional magnetic long-range order develops at zero-temperature for
infinitesimal antiferromagnetic or ferromagnetic interchain couplings. In the
presence of weak bond alternations, the order-disorder transition occurs at a
finite interchain coupling. Relevances to the lightly doped quantum
antiferromagnets and multi-layer quantum Hall systems are discussed.


  Harvesting energy from ambient environment is a new promising solution to
free electronic devices from electric wire or limited-lifetime battery, which
may find very significant applications in sensor networks and body-area
networks. This paper mainly investigate the fundamental limits of information
transmission in wireless communication system with RF-based energy harvesting,
in which a master node acts not only as an information source but also an
energy source for child node while only information is transmitted back from
child to master node. Three typical structures: optimum receiver, orthogonal
receiver and power splitting receiver are considered where two way information
transmission between two nodes under an unique external power supply constraint
at master node are jointly investigated in the viewpoint of systemic level. We
explicitly characterize the achievable capacity-rate region and also discuss
the effect of signal processing power consumption at child node. The optimal
transmission strategy corresponding to the most energy-efficient status, namely
the point on the boundary of achievable capacity-rate region, is derived with
help of conditional capacity function. Simulation confirms the substantial
gains of employing optimal transmission strategy and optimum receiver
structure. Besides, a typical application on minimizing required transmit power
to green system is presented.


  A new exactly solvable alternative to the Calogero three-particle model is
proposed. Sharing its confining long-range part, it contains the mere
zero-range two-particle barriers. Their penetrability gives rise to a
tunneling, tunable via their three independent strengths. Their variability can
control the removal of the degeneracy of the energy levels in an innovative,
non-perturbative manner.


  The Universal Transformer (UT) is a variant of the Transformer that shares
parameters across its layers. Empirical evidence shows that UTs have better
compositional generalization than Vanilla Transformers (VTs) in formal language
tasks. The parameter-sharing also affords it better parameter efficiency than
VTs. Despite its many advantages, scaling UT parameters is much more compute
and memory intensive than scaling up a VT. This paper proposes the Sparse
Universal Transformer (SUT), which leverages Sparse Mixture of Experts (SMoE)
and a new stick-breaking-based dynamic halting mechanism to reduce UT's
computation complexity while retaining its parameter efficiency and
generalization ability. Experiments show that SUT achieves the same performance
as strong baseline models while only using half computation and parameters on
WMT'14 and strong generalization results on formal language tasks (Logical
inference and CFQ). The new halting mechanism also enables around 50\%
reduction in computation during inference with very little performance decrease
on formal language tasks.


  We report on recent results and a new line of research at the crossroad of
two major theories in the analysis of partial differential equations. The
celebrated De Giorgi-Nash-Moser theory shows H{\"o}lder estimates and the
Harnack inequality for uniformly elliptic or parabolic equations with rough
coefficients in divergence form. The theory of hypoellipticity of H{\"o}rmander
shows, under general "bracket" conditions, the regularity of solutions to
partial differential equations combining first and second order derivative
operators when ellipticity fails in some directions. We discuss recent
extensions of the De Giorgi-Nash-Moser theory to hypoelliptic equations of
Kolmogorov (kinetic) type with rough coefficients. These equations combine a
first-order skew-symmetric operator with a second-order elliptic operator
involving derivatives in only certain variables, and with rough coefficients.
We then discuss applications to the Boltzmann and Landau equations in kinetic
theory and present a program of research with some open questions.


  One possible origin for polarization structures across jets of Active
Galactic Nuclei (AGNs) with a central "spine" of orthogonal magnetic field and
a "sheath" of longitudinal magnetic field along one or both edges of the jet is
the presence of a helical jet magnetic field. Simultaneous Very Long Baseline
Array (VLBA) polarization observations of AGN displaying partial or full
spine--sheath polarization structures were obtained at 4.6, 5.0, 7.9, 8.9, 12.9
and 15.4 GHz, in order to search for additional evidence for helical jet
magnetic fields, such as transverse Faraday rotation gradients (due to the
systematic change in the line-of-sight magnetic-field component across the
jet). Results for eight sources displaying monotonic transverse Faraday
rotation gradients with significances $\geq 3\sigma$ are presented here.
Reversals in the directions of the transverse RM gradients with distance from
the core or with time are detected in three of these AGNs. These can be
interpreted as evidence for a nested helical magnetic field structure, with
different directions for the azimuthal field component in the inner and outer
regions of helical field. The results presented here support the idea that many
spine--sheath polarization structures reflect the presence of helical magnetic
fields being carried by these jets.


  Though entropy production is forbidden in standard FRW Cosmology, Berman and
Som presented a simple inflationary model where entropy production by bulk
viscosity, during standard inflation without ad hoc pressure terms can be
accommodated with Robertson-Walker's metric, so the requirement that the early
Universe be anisotropic is not essential in order to have entropy growth during
inflationary phase, as we show. Entropy also grows due to shear viscosity, for
the anisotropic case. The intrinsically inflationary metric that we propose can
be thought of as defining a polarized vacuum, and leads directly to the desired
effects without the need of introducing extra pressure terms.


  In this paper we study the asymptotic behavior (in the sense of meromorphic
functions) of the zeta function of a Laplace-type operator on a closed manifold
when the underlying manifold is stretched in the direction normal to a dividing
hypersurface, separating the manifold into two manifolds with infinite
cylindrical ends. We also study the related problem on a manifold with boundary
as the manifold is stretched in the direction normal to its boundary, forming a
manifold with an infinite cylindrical end. Such singular deformations fall
under the category of "analytic surgery", developed originally by Hassell,
Mazzeo and Melrose \cite{mazz95-5-14,hass95-3-115,hass98-6-255} in the context
of eta invariants and determinants.


  Recent years have seen a great deal of progress in our understanding of
bootstrap percolation models, a particular class of monotone cellular automata.
In the two dimensional lattice there is now a quite satisfactory understanding
of their evolution starting from a random initial condition, with a strikingly
beautiful universality picture for their critical behaviour. Much less is known
for their non-monotone stochastic counterpart, namely kinetically constrained
models (KCM). In KCM each vertex is resampled (independently) at rate one by
tossing a p-coin iff it can be infected in the next step by the bootstrap
model. In particular infection can also heal, hence the non-monotonicity.
Besides the connection with bootstrap percolation, KCM have an interest in
their own as they feature some of the most striking features of the
liquid/glass transition, a major and still largely open problem in condensed
matter physics. In this paper we pave the way towards proving universality
results for KCM similar to those for bootstrap percolation. Our novel and
general approach establishes a close connection between the critical scaling of
characteristic time scales for KCM and the scaling of the critical length in
critical bootstrap models. Although the full proof of universality for KCM is
deferred to a forthcoming paper, here we apply our general method to the
Friedrickson-Andersen k-facilitated models, amongst the most studied KCM, and
to the Gravner-Griffeath model. In both cases our results are close to optimal.


  In this paper we study the existence of homomorphisms $G\to H$ using
semidefinite programming. Specifically, we use the vector chromatic number of a
graph, defined as the smallest real number $t \ge 2$ for which there exists an
assignment of unit vectors $i\mapsto p_i$ to its vertices such that $\langle
p_i, p_j\rangle\le -1/(t-1),$ when $i\sim j$. Our approach allows to reprove,
without using the Erd\H{o}s-Ko-Rado Theorem, that for $n>2r$ the Kneser graph
$K_{n:r}$ and the $q$-Kneser graph $qK_{n:r}$ are cores, and furthermore, that
for $n/r = n'/r'$ there exists a homomorphism $K_{n:r}\to K_{n':r'}$ if and
only if $n$ divides $n'$. In terms of new applications, we show that the
even-weight component of the distance $k$-graph of the $n$-cube $H_{n,k}$ is a
core and also, that non-bipartite Taylor graphs are cores. Additionally, we
give a necessary and sufficient condition for the existence of homomorphisms
$H_{n,k}\to H_{n',k'}$ when $n/k = n'/k'$. Lastly, we show that if a
2-walk-regular graph (which is non-bipartite and not complete multipartite) has
a unique optimal vector coloring, it is a core. Based on this sufficient
condition we conducted a computational study on Ted Spence's list of strongly
regular graphs and found that at least 84% are cores.


  A new hybrid inflationary scenario in the context of $f(R,{\Box}R)$-gravity
is proposed. Demanding the waterfall field to 'support the potential from
below' [unlike the original proposal by Stewart in Phys. Lett. B345, 414
(1995)], we demonstrate that the scalar potential is similar to that of the
large-field chaotic inflation model proposed by Linde in Phys. Lett. B129, 177
(1983). Inflationary observables are used to constrain the parameter space of
our model; in the process, an interesting limit on the number of e-folds N is
found.


  Researchers in the field of materials science, chemistry, and computational
physics are regularly posed with the challenge of managing large and
heterogeneous data spaces. The amount of data increases in lockstep with
computational efficiency multiplied by the amount of available computational
resources, which shifts the bottleneck in the scientific process from data
acquisition to data processing and analysis. We present a framework designed to
aid in the integration of various specialized data formats, tools and
workflows. The signac framework provides all basic components required to
create a well-defined and thus collectively accessible and searchable data
space, simplifying data access and modification through a homogeneous data
interface that is largely agnostic to the data source, i.e., computation or
experiment. The framework's data model is designed to not require absolute
commitment to the presented implementation, simplifying adaption into existing
data sets and workflows. This approach not only increases the efficiency with
which scientific results can be produced, but also significantly lowers
barriers for collaborations requiring shared data access.


  A canonical formalism for Lagrangians of maximal nonlocality is established.
The method is based on the familiar Legendre transformation to a new function
which can be derived from the maximally nonlocal Lagrangian. The corresponding
canonical equations are derived through the standard procedure in local theory
and appear much like those local ones, though the implication of the equations
is largely expanded.


  The assembly and architecture of planetary systems strongly depend on the
physical processes governing the evolution and dispersal of protoplanetary
disks. Since Protostars and Planets VI, new observations and theoretical
insights favor disk winds as being one of those key processes. This chapter
provides a comprehensive review of recent observations probing outflowing gas
launched over a range of disk radii for a wide range of evolutionary stages,
enabling an empirical understanding of how winds evolve. In parallel, we review
theoretical advancements in both magnetohydrodynamic and photoevaporative disk
wind models and identify predictions that can be confronted with observations.
By linking theory and observations we critically assess the role of disk winds
in the evolution and dispersal of protoplanetary disks. Finally, we explore the
impact of disk winds on planet formation and evolution and highlight
theoretical work, observations, and critical tests for future progress.


  Nd0.05Ce0.95CoIn5 features a magnetic field-driven quantum phase transition
that separates two antiferromagnetic phases with an identical magnetic
structure inside the superconducting condensate. Using neutron diffraction we
demonstrate that the population of the two magnetic domains in the two phases
is affected differently by the rotation of the magnetic field in the tetragonal
basal plane. In the low-field SDW-phase the domain population is only weakly
affected while in the high-field Q-phase they undergo a sharp switch for fields
around the a-axis. Our results provide evidence that the anisotropic spin
susceptibility in both phases arises ultimately from spin-orbit interactions
but are qualitatively different in the two phases. This provides evidence that
the electronic structure is changed at the quantum phase transition, which
yields a modified coupling between magnetism and superconductivity in the
Q-phase.


  The critical exponents for a class of one-dimensional models of interface
depinning in disordered media can be calculated through a mapping onto directed
percolation (DP). In higher dimensions these models give rise to directed
surfaces, which do not belong to the directed percolation universality class.
We formulate a scaling theory of directed surfaces, and calculate critical
exponents numerically, using a cellular automaton that locates the directed
surfaces without making reference to the dynamics of the underlying interface
growth models.


  In this work, deep learning models are applied to a segment of a robust
hand-washing dataset that has been created with the help of 30 volunteers. This
work demonstrates the classification of presence of one hand, two hands and no
hand in the scene based on transfer learning. The pre-trained model; simplest
NN from Keras library is utilized to train the network with 704 images of hand
gestures and the predictions are carried out for the input image. Due to the
controlled and restricted dataset, 100% accuracy is achieved during the
training with correct predictions for the input image. Complete handwashing
dataset with dense models such as AlexNet for video classification for hand
hygiene stages will be used in the future work.


  Trust in robots is widely believed to be imperative for the adoption of
robots into people's daily lives. It is, therefore, understandable that the
literature of the last few decades focuses on measuring how much people trust
robots -- and more generally, any agent - to foster such trust in these
technologies. Researchers have been exploring how people trust robot in
different ways, such as measuring trust on human-robot interactions (HRI) based
on textual descriptions or images without any physical contact, during and
after interacting with the technology. Nevertheless, trust is a complex
behaviour, and it is affected and depends on several factors, including those
related to the interacting agents (e.g. humans, robots, pets), itself (e.g.
capabilities, reliability), the context (e.g. task), and the environment (e.g.
public spaces vs private spaces vs working spaces). In general, most
roboticists agree that insufficient levels of trust lead to a risk of
disengagement while over-trust in technology can cause over-reliance and
inherit dangers, for example, in emergency situations. It is, therefore, very
important that the research community has access to reliable methods to measure
people's trust in robots and technology. In this position paper, we outline
current methods and their strengths, identify (some) weakly covered aspects and
discuss the potential for covering a more comprehensive amount of factors
influencing trust in HRI.


  We show that using the cyclic group the transpose of an R-cyclic matrix can
be decomposed along diagonal parts into a sum of parts which are freely
independent over diagonal scalar matrices. Moreover, if the R-cyclic matrix is
self-adjoint then the off-diagonal parts are R-diagonal.


  The isospin-breaking decay $X(3872)\to(D^*\bar D+ \bar D^*D) \to\pi^0D\bar
D\to\pi^0\pi^+\pi^-$ is discussed. In its amplitude there is a triangle
logarithmic singularity, due to which the dominant contribution to
$BR(X(3872)\to\pi^0\pi^+\pi^-)$ comes from the production of the $\pi^+\pi^-$
system in a narrow interval of the invariant mass $m_{\pi^+\pi^-}$ near the
value of $2m_{D^0} \approx 3.73$ GeV. The analysis shows that
$BR(X(3872)\to\pi^0 \pi^+\pi^-)$ can be expected at the level of
$10^{-3}$--$10^{-4}$. This estimate includes, in particular, the assumption
that the $S$-wave inelastic scattering length $|\alpha''_{D^0\bar D^0
\to\pi^+\pi^-}|\approx1/(2m_{D^{*+}})\approx0.25\ \mbox{GeV}^{-1}$.


  We have measured the magnetic susceptibility of single crystal samples of
non-hydrated NaxCoO2 (x ~ 0.75, 0.67, 0.5, and 0.3) and hydrated Na0.3CoO2-yH2O
(y ~ 0, 0.6, 1.3). Our measurements reveal considerable anisotropy between the
susceptibilities with H||c and H||ab. The derived anisotropic g-factor ratio
(g_ab/g_c) decreases significantly as the composition is changed from the
Curie-Weiss metal with x = 0.75 to the paramagnetic metal with x = 0.3. Fully
hydrated Na0.3CoO2-1.3H2O samples have a larger susceptibility than
non-hydrated Na0.3CoO2 samples, as well as a higher degree of anisotropy. In
addition, the fully hydrated compound contains a small additional fraction of
anisotropic localized spins.


  Recent advances in remote heart rate measurement, motivated by data-driven
approaches, have notably enhanced accuracy. However, these improvements
primarily focus on recovering the rPPG signal, overlooking the implicit
challenges of estimating the heart rate (HR) from the derived signal. While
many methods employ the Fast Fourier Transform (FFT) for HR estimation, the
performance of the FFT is inherently affected by a limited frequency
resolution. In contrast, the Chirp-Z Transform (CZT), a generalization form of
FFT, can refine the spectrum to the narrow-band range of interest for heart
rate, providing improved frequential resolution and, consequently, more
accurate estimation. This paper presents the advantages of employing the CZT
for remote HR estimation and introduces a novel data-driven adaptive CZT
estimator. The objective of our proposed model is to tailor the CZT to match
the characteristics of each specific dataset sensor, facilitating a more
optimal and accurate estimation of HR from the rPPG signal without compromising
generalization across diverse datasets. This is achieved through a Sparse
Matrix Optimization (SMO). We validate the effectiveness of our model through
exhaustive evaluations on three publicly available datasets UCLA-rPPG, PURE,
and UBFC-rPPG employing both intra- and cross-database performance metrics. The
results reveal outstanding heart rate estimation capabilities, establishing the
proposed approach as a robust and versatile estimator for any rPPG method.


  In 1991, Yannakakis (J. Comput. System Sci., 1991) proved that no symmetric
extended formulation for the matching polytope of the complete graph K_n with n
nodes has a number of variables and constraints that is bounded
subexponentially in n. Here, symmetric means that the formulation remains
invariant under all permutations of the nodes of K_n. It was also conjectured
in the paper mentioned above that "asymmetry does not help much," but no
corresponding result for general extended formulations has been found so far.
In this paper we show that for the polytopes associated with the matchings in
K_n with log(n) (rounded down) edges there are non-symmetric extended
formulations of polynomial size, while nevertheless no symmetric extended
formulations of polynomial size exist. We furthermore prove similar statements
for the polytopes associated with cycles of length log(n) (rounded down). Thus,
with respect to the question for smallest possible extended formulations, in
general symmetry requirements may matter a lot. Compared to the extended
abtract that has appeared in the Proceedings of IPCO XIV at Lausanne, this
paper does not only contain proofs that had been ommitted there, but it also
presents slightly generalized and sharpened lower bounds.


  Analyzing IO performance anomalies is a crucial task in various computing
environments, ranging from large-scale cloud applications to desktop
applications. However, the IO stack of modern operating systems is complicated,
making it hard to understand the performance anomalies with existing tools.
Kernel IO executions are frequently interrupted by internal kernel activities,
requiring a sophisticated IO profile tool to deal with the noises. Furthermore,
complicated interactions of concurrent IO requests cause different sources of
tail latencies in kernel IO stack. As a consequence, developers want to know
fine-grained latency profile across IO layers, which may differ in each IO
requests. To meet the requirements, this paper suggests ReLayTracer, a
per-request, per-layer IO profiler. ReLayTracer enables a detailed analysis to
identify root causes of IO performance anomalies by providing per-layer latency
distributions of each IO request, hardware performance behavior, and time spent
by kernel activities such as an interrupt.


  This paper instantiates a convex electric powertrain design optimization
framework, bridging the gap between high-level powertrain sizing and low-level
components design. We focus on the electric motor and transmission of electric
vehicles, using a scalable convex motor model based on surrogate modeling
techniques. Specifically, we first select relevant motor design variables and
evaluate high-fidelity samples according to a predefined sampling plan. Second,
using the sample data, we identify a convex model of the motor, which predicts
its losses as a function of the operating point and the design parameters. We
also identify models of the remaining components of the powertrain, namely a
battery and a fixed-gear transmission. Third, we frame the minimum-energy
consumption design problem over a drive cycle as a second-order conic program
that can be efficiently solved with optimality guarantees. Finally, we showcase
our framework in a case study for a compact family car and compute the optimal
motor design and transmission ratio. We validate the accuracy of our models
with a high-fidelity simulation tool and calculate the drift in battery energy
consumption. We show that our model can capture the optimal operating line and
the error in battery energy consumption is low. Overall, our framework can
provide electric motor design experts with useful starting points for further
design optimization.


  Visual homing is a lightweight approach to robot visual navigation. Based
upon stored visual information of a home location, the navigation back to this
location can be accomplished from any other location in which this location is
visible by comparing home to the current image. However, a key challenge of
visual homing is that the target home location must be within the robot's field
of view (FOV) to start homing. Therefore, this work addresses such a challenge
by integrating blockchain technology into the visual homing navigation system.
Based on the decentralized feature of blockchain, the proposed solution enables
visual homing robots to share their visual homing information and synchronously
access the stored data (visual homing information) in the decentralized ledger
to establish the navigation path. The navigation path represents a per-robot
sequence of views stored in the ledger. If the home location is not in the FOV,
the proposed solution permits a robot to find another robot that can see the
home location and travel towards that desired location. The evaluation results
demonstrate the efficiency of the proposed framework in terms of end-to-end
latency, throughput, and scalability.


  Nuclear quantum effects lead to an anomalous shift of the volume of hexagonal
ice; heavy ice has a larger volume than light ice. This anomaly in ice
increases with temperature and persists in liquid water up to the boiling
point. We study nuclear quantum effects on the density and compressibility of
several ice-like structures and crystalline ice phases. By calculating the
anisotropic contributions to the stain tensor, we analyze how the
compressibility changes along different directions in hexagonal ice, and find
that hexagonal ice is softer along the x-y plane than the z-direction.
Furthermore, by performing ab initio density functional theory calculations
with a van der Waals functional and with the quasiharmonic approximation, we
find an anomalous isotope effect in the bulk modulus of hexagonal ice: heavy
ice has a smaller bulk modulus than light ice. In agreement with the
experiments, we also obtain an anomalous isotope effect for clathrate hydrate
structure I. For the rest of the ice polymorphs, the isotope effect is: i)
anomalous for ice IX, Ih, Ic, clathrate, and low density liquid-like amorphous
ice; ii) normal at T=0 K and becomes anomalous with increasing temperature for
ice IX, II, high density liquid-like amorphous ices, and ice XV; iii) normal
for ice VIII up to the melting point. There is a transition from an anomalous
isotope effect to a normal isotope effect for both the volume and bulk modulus,
as the density (compressibility) of the structures increases (decreases). This
result can explain the anomalous isotope effect in liquid water: as the
compressibility decreases from melting point to the compressibility minimum
temperature, the difference between the volumes of the heavy and light water
rapidly decreases, but the effect stays anomalous up to the boiling temperature
as the hydrogen bond network is never completely broken by fully filling all
the interstitial sites.


  J. Andre constructed a skewaffine structure as a group space of a normally
transitive group. In the paper this construction was used to describe such an
external structure associated with a point of Laguerre plane. Necessary
conditions for ensure that the external structure is a skewaffine plane are
given.


  We develop a new Low-level, First-order Probabilistic Programming Language
(LF-PPL) suited for models containing a mix of continuous, discrete, and/or
piecewise-continuous variables. The key success of this language and its
compilation scheme is in its ability to automatically distinguish parameters
the density function is discontinuous with respect to, while further providing
runtime checks for boundary crossings. This enables the introduction of new
inference engines that are able to exploit gradient information, while
remaining efficient for models which are not everywhere differentiable. We
demonstrate this ability by incorporating a discontinuous Hamiltonian Monte
Carlo (DHMC) inference engine that is able to deliver automated and efficient
inference for non-differentiable models. Our system is backed up by a
mathematical formalism that ensures that any model expressed in this language
has a density with measure zero discontinuities to maintain the validity of the
inference engine.


  Classical and quantum machine learning are being increasingly applied to
various tasks in quantum information technologies. Here, we present an
experimental demonstration of quantum control using a physics-informed neural
network (PINN). PINN's salient feature is how it encodes the entire control
sequence in terms of its network parameters. This feature enables the control
sequence to be later adopted to any hardware with optimal time discretization,
which contrasts with conventional methods involving a priory time
discretization. Here, we discuss two important quantum information tasks: gate
synthesis and state preparation. First, we demonstrate quantum gate synthesis
by designing a two-qubit CNOT gate and experimentally implementing it on a
heteronuclear two-spin NMR register. Second, we demonstrate quantum state
preparation by designing a control sequence to efficiently transfer the thermal
state into the long-lived singlet state and experimentally implement it on a
homonuclear two-spin NMR register. We present a detailed numerical analysis of
the PINN control sequences regarding bandwidth, discretization levels, control
field errors, and external noise.


  The stellar initial mass function and the stellar lifetimes are basic
ingredients of chemical evolution models, for which different recipes can be
found in the literature. In this paper, we quantify the effects on chemical
evolution studies of the uncertainties in these two parameters. We concentrate
on chemical evolution models for the Milky Way, because of the large number of
good observational constraints. Such chemical evolution models have already
ruled out significant temporal variations for the stellar initial mass function
in our own Galaxy, with the exception perhaps of the very early phases of its
evolution. Therefore, here we assume a Galactic initial mass function constant
in time. Through an accurate comparison of model predictions for the Milky Way
with carefully selected data sets, it is shown that specific prescriptions for
the initial mass function in particular mass ranges should be rejected. As far
as the stellar lifetimes are concerned, the major differences among existing
prescriptions are found in the range of very low-mass stars. Because of this,
the model predictions widely differ for those elements which are produced
mostly by very long-lived objects, as for instance 3He and 7Li. However, it is
concluded that model predictions of several important observed quantities,
constraining the plausible Galactic formation scenarios, are fairly robust with
respect to changes in both the stellar mass spectrum and the stellar lifetimes.
For instance, the metallicity distribution of low-mass stars is nearly
unaffected by these changes, since its shape is dictated mostly by the time
scale for thin-disk formation.


  Expressive text-to-speech has shown improved performance in recent years.
However, the style control of synthetic speech is often restricted to discrete
emotion categories and requires training data recorded by the target speaker in
the target style. In many practical situations, users may not have reference
speech recorded in target emotion but still be interested in controlling speech
style just by typing text description of desired emotional style. In this
paper, we propose a text-based interface for emotional style control and
cross-speaker style transfer in multi-speaker TTS. We propose the bi-modal
style encoder which models the semantic relationship between text description
embedding and speech style embedding with a pretrained language model. To
further improve cross-speaker style transfer on disjoint, multi-style datasets,
we propose the novel style loss. The experimental results show that our model
can generate high-quality expressive speech even in unseen style.


  In this industry talk at ECIR'2022, we illustrate how to build a modern
recommender system that can serve recommendations in real-time for a diverse
set of application domains. Specifically, we present our system architecture
that utilizes popular recommendation algorithms from the literature such as
Collaborative Filtering, Content-based Filtering as well as various neural
embedding approaches (e.g., Doc2Vec, Autoencoders, etc.). We showcase the
applicability of our system architecture using two real-world use-cases, namely
providing recommendations for the domains of (i) job marketplaces, and (ii)
entrepreneurial start-up founding. We strongly believe that our experiences
from both research- and industry-oriented settings should be of interest for
practitioners in the field of real-time multi-domain recommender systems.


  A quantum many-body system which is prepared in the ground state of an
integrable Hamiltonian does not directly thermalize after a sudden small
parameter quench away from integrability. Rather, it will be trapped in a
prethermalized state and can thermalize only at a later stage. We discuss
several examples for which this prethermalized state shares some properties
with the nonthermal steady state that emerges in the corresponding integrable
system. These examples support the notion that nonthermal steady states in
integrable systems may be viewed as prethermalized states that never decay
further. Furthermore we show that prethermalization plateaus are under certain
conditions correctly predicted by generalized Gibbs ensembles, which are the
appropriate extension of standard statistical mechanics in the presence of many
constants of motion. This establishes that the relaxation behaviors of
integrable and nearly integrable systems are continuously connected and
described by the same statistical theory.


  The relative Dolbeault cohomology which naturally comes up in the theory of
Cech-Dolbeault cohomology turns out to be canonically isomorphic with the local
(relative) cohomology of A. Grothendieck and M. Sato so that it provides a
handy way of representing the latter. In this paper we use this cohomology to
give simple explicit expressions of Sato hyperfunctions, some fundamental
operations on them and related local duality theorems. This approach also
yields a new insight into the theory of hyperfunctions and leads to a number of
further results and applications. As one of such, we give an explicit embedding
morphism of Schwartz distributions into the space of hyperfunctions.


  Mice vocalize in the ultrasonic range during social interactions. These
vocalizations are used in neuroscience and clinical studies to tap into complex
behaviors and states. The analysis of these ultrasonic vocalizations (USVs) has
been traditionally a manual process, which is prone to errors and human bias,
and is not scalable to large scale analysis. We propose a new method to
automatically create a dictionary of USVs based on a two-step spectral
clustering approach, where we split the set of USVs into inlier and outlier
data sets. This approach is motivated by the known degrading performance of
sparse subspace clustering with outliers. We apply spectral clustering to the
inlier data set and later find the clusters for the outliers. We propose
quantitative and qualitative performance measures to evaluate our method in
this setting, where there is no ground truth. Our approach outperforms two
baselines based on k-means and spectral clustering in all of the proposed
performance measures, showing greater distances between clusters and more
variability between clusters.