  Personalization of the amplification function of hearing aids has been shown
to be of benefit to hearing aid users in previous studies. Several machine
learning-based personalization approaches have been introduced in the
literature. This paper presents a machine learning personalization approach
with the advantage of being efficient in its training based on paired
comparisons which makes it practical and field deployable. The training
efficiency of this approach is the result of treating frequency bands
independent of one another and by simultaneously carrying out Bayesian machine
learning in each band across all of the frequency bands. Simulation results
indicate that this approach leads to an estimated hearing preference function
close to the true hearing preference function in fewer number of paired
comparisons relative to the previous machine learning approaches. In addition,
a clinical experiment conducted on eight subjects with hearing impairment
indicate that this training efficient personalization approach provides
personalized gain settings which are on average six times more preferred over
the standard prescriptive gain settings.


  We study the implications of time-varying wave mechanics, and show how the
standard wave equation is modified if the speed of a wave is not constant in
time. In particular, waves which experience longitudinal acceleration are shown
to have clear relativistic properties when a constant reference speed exists.
Moreover, the accelerating wave equation admits only solutions propagating
forward in time, which are continuous across material interfaces. We then
consider the special case of electromagnetic waves, finding that the
Abraham-Minkowski controversy is caused by relativistic effects, and the
momentum of light is in fact conserved between different media. Furthermore, we
show that the accelerating waves conserve energy when the wave is moving along
a geodesic and demonstrate two example solutions. We conclude with some remarks
on the role of the accelerating wave equation in the context of the arrow of
time.


  This paper analyzes thoroughly the dispersion and filtering features of
periodic holey waveguides in the millimeter-wave frequency range. Two
structures are mainly studied depending on the glide and mirror symmetries of
the holes. A parametric study of the dispersion characteristics of their unit
cells is carried out. Glide-symmetric holey waveguides provide a higher
propagation constant and a low dispersion over a wide frequency range regarding
hollow waveguides. This property is particularly useful for the design of
low-loss and low-dispersive phase shifters. We also demonstrate that
glide-symmetric holey waveguides are less dispersive than waveguides loaded
with glide-symmetric pins. Furthermore, we perform a Bloch analysis to compute
the attenuation constants in holey waveguides with mirror and broken glide
symmetries. Both configurations are demonstrated to be suitable for filter
design. Finally, the simulation results are validated with two prototypes in
gap-waveguide technology. The first one is a 180$^{o}$ phase shifter based on a
glide-symmetric holey configuration that achieves a flat phase shift response
over a wide frequency range (27.5\% frequency bandwidth). The second one is a
filter based on a mirror-symmetric holey structure with 20-dB rejection from 63
GHz to 75 GHz.


  In this paper we study categories $(F,\mathbf{C},\mathbf{D})$ and
$(\mathbb{F},\mathbf{C},\mathbf{Set})$ and prove them to be fibred on
$\mathbf{C}$. Then we examine Grothendieck construction in the context of an
ordinary functor $F: \mathbf{C} \rightarrow \mathbf{D}$ through the concept of
trivial categorification, using an appropriate functor $\mathbf{F}: \mathbf{C}
\xrightarrow{F} \mathbf{D} \xrightarrow{I} \mathbf{Cat}$ to construct
$\int_{\mathbf{C}^{op}} \bar{\mathbf{F}}$. This category characterizes a
functor as an abstract right category action while its dual $\mathcal{X}
\rtimes_{\mathbf{F}} \mathbf{C}$ or $(\int_{\mathbf{C}^{op}}
\bar{\mathbf{F}})^{op}$ characterizes a functor as an abstract left category
action. Similarly using $\mathbb{F}: \mathbf{C} \xrightarrow{F} \mathbf{D}
\xrightarrow{U} \mathbf{Set}$ we define $\mathcal{X} \rtimes_{\mathbb{F}}
\mathbf{C}$ and ${\int_{\mathbf{C}^{op}} \bar{\mathbb{F}}}$ as categories
denoting concrete left and right actions of $\mathbf{C}$ respectively.
Collectively referred to as `base structured categories', these are proven
abstractly isomorphic to the base category $\mathbf{C}$ but concretely
isomorphic to each other or $(\mathbb{F},\mathbf{C},\mathbf{Set}) \cong
\int_{\mathbf{C}} \bar{\mathbb{F}} \cong \mathcal{X} \rtimes_{\mathbb{F}}
\mathbf{C}$. These are special instances of fibred categories where the base
category is $\mathbf{C}$ and fibres are $\mathbf{D}$ objects being treated as
trivial categories. The perspective of making only base structure explicit
through category theory concealing the vertical structure using identity
morphisms enables one to combine intuitions of Grothendieck's relative and
Leyton's generative theory. As explored further, it facilitates the application
of functors in certain fundamental applications which hitherto have been
treating objects of category $\mathbf{D}$ purely in a set theoretic way.


  The coupling of optical and electronic degrees of freedom together with
quantum confinement in low-dimensional electron systems is particularly
interesting for achieving exotic functionalities in strongly correlated oxide
electronics. Recently, high room-temperature mobility has been achieved for a
large bandgap transparent oxide - BaSnO$_3$ upon extrinsic La or Sb doping,
which has excited significant research attention. In this work, we report the
observation of room-temperature ferromagnetism in BaSnO$_3$ thin films and the
realization of a two-dimensional electron gas (2DEG) on the surface of
transparent BaSnO$_3$ via oxygen vacancy creation, which exhibits a high
carrier density of $\sim 7.72*10^{14} /{\rm cm}^2$ and a high room-temperature
mobility of ~18 cm$^2$/V/s. Such a 2DEG is rather sensitive to strain and a
less than 0.1% in-plane biaxial compressive strain leads to a giant resistance
enhancement of 350% (more than 540 kOhm/Square) at room temperature. Thus, this
work creates a new path to exploring the physics of low-dimensional oxide
electronics and devices applicable at room temperature.


  "Attractor" solutions for the pre-hydrodynamic, far-from-equilibrium,
evolution of the matter produced in relativistic heavy ion collisions have
emerged as crucial descriptors of the rapid hydrodynamization of quark-gluon
plasma (QGP). Adiabatic Hydrodynamization (AH) has been proposed as a framework
with which to describe, explain, and predict attractor behavior that draws upon
an analogy to the adiabatic approximation in quantum mechanics. In this work,
we systematize the description of pre-hydrodynamic attractors in kinetic theory
by showing how to use the AH framework to identify these long-lived solutions
to which varied initial conditions rapidly evolve, demonstrating the robustness
of this framework. In a simplified QCD kinetic theory in the small-angle
scattering limit, we use AH to explain both the early- and late-time scaling
behavior of a longitudinally expanding gluon gas in a unified framework. In
this context, we show that AH provides a unified description of, and intuition
for, all the stages of what in QCD would be bottom-up thermalization, starting
from a pre-hydrodynamic attractor and ending with hydrodynamization. We
additionally discuss the connection between the notions of scaling behavior and
adiabaticity and the crucial role of time-dependent coordinate redefinitions in
identifying the degrees of freedom of kinetic theories that give rise to
attractor solutions. The tools we present open a path to the intuitive
explanation of how attractor behavior arises and how the attractor evolves in
all stages of the hydrodynamization of QGP in heavy ion collisions.


  I provide a straightforward proof that a simple harmonic oscillator perturbed
by an (almost) arbitrary positive interaction has a perturbative expansion for
any finite-time Euclidian transition amplitude which obeys the following
result: the difference of the sum of the first N terms of the series and the
exact result is bounded in absolute value by the next term in the series. The
proof makes no assumptions about either the strength of the interactions or the
convergence of the perturbation series. I then argue that the result
generalizes immediately to a much broader class of quantum mechanical systems,
including bare perturbation theory in quantum field theory. The case of
renormalized perturbation theory is more subtle and remains open, as does the
generalization to energy levels and connected S-matrix elements.


  Computer simulation of the time evolution in a classical system is a standard
numerical method, used in numerous scientific articles in Natural Science.
Almost all the simulations are performed by discrete Molecular Dynamics (MD).
The algorithm used in MD was originally formulated by I. Newton at the
beginning of his book $Principia$. Newton's discrete dynamics is exact in the
same sense as Newton's analytic counterpart Classical Mechanics. Both dynamics
are time-reversible, symplectic, and have the same dynamic invariances. There
is no qualitative difference between the two kinds of dynamics. This is due to
the fact, that there exists a ''shadow Hamiltonian'' nearby the Hamiltonian
$H(\textbf{q},\textbf{p})$ for the analytic dynamics, and where its dynamics
can be obtained by an asymptotic expansion from $H(\textbf{q},\textbf{p})$, and
where the positions generated by MD are located on the analytic trajectories
for the shadow Hamiltonian.
  It is only possible to obtain the solution of Newton's classical differential
equations for a few simple systems, but the exact discrete Newtonian dynamics
can be obtained for almost all complex classical systems. Some examples are
given here: The emergence and evolution of a planetary system. The emergence
and evolution of planetary systems with inverse forces. The emergence and
evolution of galaxies in the expanding Universe.


  We report the discovery of nearly a dozen collimated outflows from young
stellar objects embedded in the molecular filament that extends north of the
Orion Nebula towards the H II region NGC 1977. The large number of nearly
co-eval outflows and embedded class-0 young stellar objects indicates that the
OMC-2/3 region is one of the most active sites of on-going low to intermediate
mass star formation known. These outflows were identified in the 2.12 microns
v=1-0 S(1) H_2 line during a survey of a 6 arcmin X 16 arcmin region containing
the OMC-2 and OMC-3 cloud cores and over a dozen recently discovered class-0
protostars. We also observe filamentary emission that is likely to trace
possible fluorescent H_2 in photo-dissociation regions associated with M 43 and
NGC 1977. Neither the suspected outflows nor the fluorescent emission are seen
at the continuum wavelength of 2.14 microns which confirms their emission line
nature. Several of the new H_2 flows are associated with recently discovered
bipolar molecular outflows. However, the most prominent bipolar CO outflow from
the region (the MMS 8 flow) has no clear H_2 counterpart. Several H_2 flows
consist of chains of knots and compact bow shocks that likely trace highly
collimated protostellar jets. Our discovery of more than 80 individual H_2
emitting shocks demonstrate that outflows from young stars are churning this
molecular cloud.


  Ranges of differentiated abstention are shown to reverse an "exact" poll
estimate on voting day allowing the minority candidate to win the election. In
a two-candidate competition A and B with voting intentions at $I_a$,
$I_b=1-I_a$ and respective turnout at $x$ and $y$, there exists a critical
value $I_{ac}$ for which $I_{ac}<I_a<\frac{1}{2}$ yields an actual election
outcome $v_a>\frac{1}{2}$. The reversal may occur without any change of
individual choices. Accordingly, for a set of turnouts $x$ and $y$ the minimum
voting intention $I_{ac}$ required for A to win the final vote can be
calculated. The various ranges of $x$ and $y$ for which $I_{Ac}$ is smaller
than the expected 50\% barrier are determined. The calculations are applied to
the coming 2017 French presidential election for a second round scenario
involving the National Front candidate Marine Le Pen against either the Right
candidate Fran\c{c}ois Fillon or the Center candidate Emmanuel Macron. Several
realistic conditions are found to make Marine Le Pen win the election despite
voting intentions about only 40-45\%.


  Vision transformers (ViTs) quantization offers a promising prospect to
facilitate deploying large pre-trained networks on resource-limited devices.
Fully-binarized ViTs (Bi-ViT) that pushes the quantization of ViTs to its limit
remain largely unexplored and a very challenging task yet, due to their
unacceptable performance. Through extensive empirical analyses, we identify the
severe drop in ViT binarization is caused by attention distortion in
self-attention, which technically stems from the gradient vanishing and ranking
disorder. To address these issues, we first introduce a learnable scaling
factor to reactivate the vanished gradients and illustrate its effectiveness
through theoretical and experimental analyses. We then propose a ranking-aware
distillation method to rectify the disordered ranking in a teacher-student
framework. Bi-ViT achieves significant improvements over popular DeiT and Swin
backbones in terms of Top-1 accuracy and FLOPs. For example, with DeiT-Tiny and
Swin-Tiny, our method significantly outperforms baselines by 22.1% and 21.4%
respectively, while 61.5x and 56.1x theoretical acceleration in terms of FLOPs
compared with real-valued counterparts on ImageNet.


  Three quantitative measures of the spatiotemporal behavior of the coupled map
lattices: reduced density matrix, reduced wave function, and an analog of
particle number, have been introduced. They provide a quantitative meaning to
the concept of coherence which in the context of complex systems have been used
rather intuitively. Their behavior suggests that the logistic coupled-map
lattices approach the states which resemble the condensed states of systems of
Bose particles. In addition, pattern formation in two-dimensional coupled map
lattices based on the logistic mapping has been investigated with respect to
the non-linear parameter, the diffusion constant and initial as well as
boundary conditions.


  We present a tool, simplify-defun, that transforms the definition of a given
function into a simplified definition of a new function, providing a proof
checked by ACL2 that the old and new functions are equivalent. When appropriate
it also generates termination and guard proofs for the new function. We explain
how the tool is engineered so that these proofs will succeed. Examples
illustrate its utility, in particular for program transformation in synthesis
and verification.


  We report the results of our search for disturbed (interacting) objects among
very isolated galaxies. The inspections of 1050 northern isolated galaxies from
KIG and 500 nearby, very isolated galaxies situated in the Local Supercluster
yielded five and four strongly disturbed galaxies, respectively. We suggest
that the existence of "dark" galaxies explains the observed signs of
interaction. This assumption leads to a cosmic abundance of dark galaxies (with
the typical masses for luminous galaxies) that is less than ~1/20 the
population of visible galaxies.


  We study the propagation of entanglement after quantum quenches in the
non-integrable para-magnetic quantum Ising spin chain. Tuning the parameters of
the system, we observe a sudden increase in the entanglement production rate,
which we show to be related to the appearance of new quasi-particle excitations
in the post-quench spectrum. We argue that the phenomenon is the
non-equilibrium version of the well-known Gibbs paradox related to mixing
entropy and demonstrate that its characteristics fit the expectations derived
from the quantum resolution of the paradox in systems with a non-trivial
quasi-particle spectrum.


  We study cascades under the threshold model on sparse random graphs with
community structure. In this model, individuals adopt the new behavior based on
how many neighbors have already chosen it. Specifically, we consider the
permanent adoption model wherein individuals that have adopted the new behavior
(or opinion) cannot change their state. We present a
differential-equation-based tight approximation to the stochastic process of
adoption and prove the validity of the mean-field equations. In addition, we
characterize both necessary and sufficient conditions for contagion to happen
no matter how small the set of initial adopters is. Finally, we study the
problem of optimum seeding given budget constraints and propose a
gradient-based heuristic seeding strategy. Our algorithm, numerically, dispels
commonly held beliefs in the literature that suggest the best seeding strategy
is to seed over the vertices with the highest number of neighbors.


  We discuss asymptotics of the zeros of orthogonal polynomials on the real
line and on the unit circle when the recursion coefficients are periodic. The
zeros on or near the absolutely continuous spectrum have a clock structure with
spacings inverse to the density of zeros. Zeros away from the a.c. spectrum
have limit points mod p and only finitely many of them.


  In addressing the demands of industrial high-fidelity computation, the
present study introduces a rapid and accurate customized solver developed on
the OpenFOAM platform. To enhance computational efficiency, a novel integrated
acceleration strategy is introduced. Initially, a sparse analytical Jacobian
approach utilizing the SpeedCHEM chemistry library was implemented to increase
the efficiency of the ODE solver. Subsequently, the Dynamic Load Balancing
(DLB) code was employed to uniformly distribute the computational workload for
chemistry among multiple processes. Further optimization was achieved through
the introduction of the Open Multi-Processing (OpenMP) method to enhance
parallel computing efficiency. Lastly, the Local Time Stepping (LTS) scheme was
integrated to maximize the individual time step for each computational cell,
resulting in a noteworthy minimum speed-up of over 31 times. The effectiveness
and robustness of this customized solver were systematically validated against
three distinct partially turbulent premixed flames, Sandia Flames D, E, and F.
Additionally, a comparative analysis was conducted, encompassing different
turbulence models, turbulent Prandtl numbers, and model constants, resulting in
the recommendation of optimal numerical parameters for various conditions. The
present study offers one viable solution for rapid and accurate calculations in
the OpenFOAM platform, while also providing insights into the selection of
turbulence models and parameters for industrial numerical simulation.


  Time-of-flight (TOF) cameras are sensors that can measure the depths of
scene-points, by illuminating the scene with a controlled laser or LED source,
and then analyzing the reflected light. In this paper, we will first describe
the underlying measurement principles of time-of-flight cameras, including: (i)
pulsed-light cameras, which measure directly the time taken for a light pulse
to travel from the device to the object and back again, and (ii)
continuous-wave modulated-light cameras, which measure the phase difference
between the emitted and received signals, and hence obtain the travel time
indirectly. We review the main existing designs, including prototypes as well
as commercially available devices. We also review the relevant camera
calibration principles, and how they are applied to TOF devices. Finally, we
discuss the benefits and challenges of combined TOF and color camera systems.


  We study rare events in systems of diffusive fields driven out of equilibrium
by the boundaries. We present a numerical technique and use it to calculate the
probabilities of rare events in one and two dimensions. Using this technique,
we show that the probability density of a slowly varying configuration can be
captured with a small number of long wave-length modes. For a configuration
which varies rapidly in space this description can be complemented by a local
equilibrium assumption.


  A comparison is presented of Sunyaev-Zeldovich measurements for 11 galaxy
clusters as obtained by Planck and by the ground-based interferometer, the
Arcminute Microkelvin Imager. Assuming a universal spherically-symmetric
Generalised Navarro, Frenk & White (GNFW) model for the cluster gas pressure
profile, we jointly constrain the integrated Compton-Y parameter (Y_500) and
the scale radius (theta_500) of each cluster. Our resulting constraints in the
Y_500-theta_500 2D parameter space derived from the two instruments overlap
significantly for eight of the clusters, although, overall, there is a tendency
for AMI to find the Sunyaev-Zeldovich signal to be smaller in angular size and
fainter than Planck. Significant discrepancies exist for the three remaining
clusters in the sample, namely A1413, A1914, and the newly-discovered Planck
cluster PLCKESZ G139.59+24.18. The robustness of the analysis of both the
Planck and AMI data is demonstrated through the use of detailed simulations,
which also discount confusion from residual point (radio) sources and from
diffuse astrophysical foregrounds as possible explanations for the
discrepancies found. For a subset of our cluster sample, we have investigated
the dependence of our results on the assumed pressure profile by repeating the
analysis adopting the best-fitting GNFW profile shape which best matches X-ray
observations. Adopting the best-fitting profile shape from the X-ray data does
not, in general, resolve the discrepancies found in this subset of five
clusters. Though based on a small sample, our results suggest that the adopted
GNFW model may not be sufficiently flexible to describe clusters universally.


  We propose a new hybrid symbolic-numerical approach to the center-focus
problem. The method allowed us to obtain center conditions for a
three-dimensional system of differential equations, which was previously not
possible using traditional, purely symbolic computational techniques.


  We study the uncertainties of quantum mechanical observables, quantified by
the standard deviation (square root of variance) in Haar-distributed random
pure states. We derive analytically the probability density functions (PDFs) of
the uncertainties of arbitrary qubit observables. Based on these PDFs, the
uncertainty regions of the observables are characterized by the supports of the
PDFs. The state-independent uncertainty relations are then transformed into the
optimization problems over uncertainty regions, which opens a new vista for
studying state independent uncertainty relations. Our results may be
generalized to multiple observable case in higher dimensional spaces.


  Food image segmentation is an important task that has ubiquitous
applications, such as estimating the nutritional value of a plate of food.
Although machine learning models have been used for segmentation in this
domain, food images pose several challenges. One challenge is that food items
can overlap and mix, making them difficult to distinguish. Another challenge is
the degree of inter-class similarity and intra-class variability, which is
caused by the varying preparation methods and dishes a food item may be served
in. Additionally, class imbalance is an inevitable issue in food datasets. To
address these issues, two models are trained and compared, one based on
convolutional neural networks and the other on Bidirectional Encoder
representation for Image Transformers (BEiT). The models are trained and
valuated using the FoodSeg103 dataset, which is identified as a robust
benchmark for food image segmentation. The BEiT model outperforms the previous
state-of-the-art model by achieving a mean intersection over union of 49.4 on
FoodSeg103. This study provides insights into transfering knowledge using
convolution and Transformer-based approaches in the food image domain.


  The flux of ultra-high energy cosmic rays reaching Earth above the ankle
energy (5 EeV) can be described as a mixture of nuclei injected by
extragalactic sources with very hard spectra and a low rigidity cutoff.
Extragalactic magnetic fields existing between the Earth and the closest
sources can affect the observed CR spectrum by reducing the flux of
low-rigidity particles reaching Earth. We perform a combined fit of the
spectrum and distributions of depth of shower maximum measured with the Pierre
Auger Observatory including the effect of this magnetic horizon in the
propagation of UHECRs in the intergalactic space. We find that, within a
specific range of the various experimental and phenomenological systematics,
the magnetic horizon effect can be relevant for turbulent magnetic field
strengths in the local neighbourhood of order $B_{\rm rms}\simeq (50-100)\,{\rm
nG}\,(20\rm{Mpc}/{d_{\rm s})( 100\,\rm{kpc}/L_{\rm coh}})^{1/2}$, with $d_{\rm
s}$ the typical intersource separation and $L_{\rm coh}$ the magnetic field
coherence length. When this is the case, the inferred slope of the source
spectrum becomes softer and can be closer to the expectations of diffusive
shock acceleration, i.e., $\propto E^{-2}$. An additional cosmic-ray population
with higher source density and softer spectra, presumably also extragalactic
and dominating the cosmic-ray flux at EeV energies, is also required to
reproduce the overall spectrum and composition results for all energies down to
0.6~EeV.


  We present a NIR polarimetric map of the 1deg by 1deg region toward the
Galactic center. Comparing Stokes parameters between highly reddened stars and
less reddened ones, we have obtained a polarization originating from
magnetically aligned dust grains at the central region of our Galaxy. The
distribution of position angles shows a peak at the parallel direction to the
Galactic plane, suggesting a toroidal magnetic field configuration. However, at
high Galactic latitudes, the peak of the position angles departs from the
direction of the Galactic plane. This may be a transition of a large-scale
magnetic field configuration from toroidal to poloidal.


  The kinetic energy variation of emitted light clusters has been employed as a
clock to explore the time evolution of the temperature for thermalizing
composite systems produced in the reactions of 26A, 35A and 47A MeV $^{64}$Zn
with $^{58}$Ni, $^{92}$Mo and $^{197}$Au. For each system investigated, the
double isotope ratio temperature curve exhibits a high maximum apparent
temperature, in the range of 10-25 MeV, at high ejectile velocity. These
maximum values increase with increasing projectile energy and decrease with
increasing target mass. The time at which the maximum in the temperature curve
is reached ranges from 80 to 130 fm/c after contact. For each different target,
the subsequent cooling curves for all three projectile energies are quite
similar. Temperatures comparable to those of limiting temperature systematics
are reached 30 to 40 fm/c after the times corresponding to the maxima, at a
time when AMD-V transport model calculations predict entry into the final
evaporative or fragmentation stage of de-excitation of the hot composite
systems. Evidence for the establishment of thermal and chemical equilibrium is
discussed.


  Text style transfer rephrases a text from a source style (e.g., informal) to
a target style (e.g., formal) while keeping its original meaning. Despite the
success existing works have achieved using a parallel corpus for the two
styles, transferring text style has proven significantly more challenging when
there is no parallel training corpus. In this paper, we address this challenge
by using a reinforcement-learning-based generator-evaluator architecture. Our
generator employs an attention-based encoder-decoder to transfer a sentence
from the source style to the target style. Our evaluator is an adversarially
trained style discriminator with semantic and syntactic constraints that score
the generated sentence for style, meaning preservation, and fluency.
Experimental results on two different style transfer tasks (sentiment transfer
and formality transfer) show that our model outperforms state-of-the-art
approaches. Furthermore, we perform a manual evaluation that demonstrates the
effectiveness of the proposed method using subjective metrics of generated text
quality.


  In this paper, we present a new model, hollow Gaussian-Schell model beams
(HGSMBs), to describe the practical dark hollow beams. An analytical
propagation formula for HGSMBs passing through a paraxial first-order optical
system is derived based on the theory of coherence. Based on the derived
formula, an application example showing the influence of spatial coherence on
the propagation of beams is illustrated. It is found that the beam propagating
properties of HGSMBs will be greatly affected by their spatial coherence. Our
model provides a very convenient way for analyzing the propagation properties
of partially coherent dark hollow beams.


  Unified description on the long-time tail of velocity autocorrelation
function and the long-range correlation for the equal-time spatial correlation
functions is developed based on the generalized fluctuating hydrodynamics. The
cross-over of the long-time tail from $t^{-3/2}$ to $t^{-5/2}$ is predicted
independent of the density, and the equal-time spatial density correlation
function and the equal-time spatial velocity correlation function respectively
satisfy $r^{-11/3}$ and $r^{-5/3}$ for large $r$ limit.


  DG CVn is a binary system in which one of the components is an M type dwarf
ultra fast rotator, only three of which are known in the solar neighborhood.
Observations of DG CVn by the Swift satellite and several ground-based
observatories during its super-flare event on 2014 allowed us to perform a
complete hard X-ray - optical follow-up of a super-flare from the red-dwarf
star. The observations support the fact that the super-flare can be explained
by the presence of (a) large active region(s) on the surface of the star. Such
activity is similar to the most extreme solar flaring events. This points
towards a plausible extrapolation between the behaviour from the most active
red-dwarf stars and the processes occurring in the Sun.


  Concluding remarks at the international workshop ``Tracing Cosmic Evolution
with Galaxy Clusters", held at Sesto Pusteria, Italy, July 3-6, 2001.


  We discuss in detail the spatial distribution of angular momentum inside the
nucleon. We show that the discrepancies between different definitions originate
from terms that integrate to zero. Even though these terms can safely be
dropped at the integrated level, they have to be taken into account at the
density level. Using the scalar diquark model, we illustrate our results and,
for the first time, check explicitly that the equivalence between kinetic and
canonical orbital angular momentum persists at the density level, as expected
in a system without gauge degrees of freedom.


  The maximal symmetry of a quantum system with Heisenberg commutation
relations is given by the projective representations of the automorphism group
of the Weyl-Heisenberg algebra. The automorphism group is the central extension
of the inhomogeneous symplectic group with a conformal scaling that acts on
extended phase space. We determine the subgroup that also leaves invariant a
degenerate orthogonal Minkowski line element. This defines noninertial
relativistic symmetry transformations that have the expected classical limit as
c becomes infinite.


  For fixed $r\geq 3$ and $n$ divisible by $r$, let ${\mathcal H}={\mathcal
H}^r_{n,M}$ be the random $M$-edge $r$-graph on $V=\{1,\ldots ,n\}$; that is,
${\mathcal H}$ is chosen uniformly from the $M$-subsets of ${\mathcal K}:={V
\choose r}$ ($:= \{\mbox{$r$-subsets of $V$}\}$). Shamir's Problem (circa 1980)
asks, roughly, for what $M=M(n)$ is ${\mathcal H}$ likely to contain a perfect
matching (that is, $n/r$ disjoint $r$-sets)?
  In 2008 Johansson, Vu and the author showed that this is true for $M>C_rn\log
n$. More recently the author proved the asymptotically correct version of that
result: for fixed $C> 1/r$ and $M> Cn\log n$, $P({\mathcal H} ~\mbox{contains a
perfect matching})\rightarrow 1 \,\,\, \mbox{as $n\rightarrow\infty$}.$
  The present work completes a proof, begun in that recent paper, of the
definitive "hitting time" statement:
  $\mbox{Theorem.}$ If $A_1, \ldots ~$ is a uniform permutation of ${\mathcal
K}$, ${\mathcal H}_t=\{A_1\dots A_t\}$, and \[ T=\min\{t:A_1\cup \cdots\cup
A_t=V\}, \] then $P({\mathcal H}_T ~\mbox{contains a perfect
matching})\rightarrow 1 \,\,\, \mbox{as $n\rightarrow\infty$}$.


  We study the coarse-grained selection module in retrieval-based chatbot.
Coarse-grained selection is a basic module in a retrieval-based chatbot, which
constructs a rough candidate set from the whole database to speed up the
interaction with customers. So far, there are two kinds of approaches for
coarse-grained selection module: (1) sparse representation; (2) dense
representation. To the best of our knowledge, there is no systematic comparison
between these two approaches in retrieval-based chatbots, and which kind of
method is better in real scenarios is still an open question. In this paper, we
first systematically compare these two methods from four aspects: (1)
effectiveness; (2) index stoarge; (3) search time cost; (4) human evaluation.
Extensive experiment results demonstrate that dense representation method
significantly outperforms the sparse representation, but costs more time and
storage occupation. In order to overcome these fatal weaknesses of dense
representation method, we propose an ultra-fast, low-storage, and highly
effective Deep Semantic Hashing Coarse-grained selection method, called DSHC
model. Specifically, in our proposed DSHC model, a hashing optimizing module
that consists of two autoencoder models is stacked on a trained dense
representation model, and three loss functions are designed to optimize it. The
hash codes provided by hashing optimizing module effectively preserve the rich
semantic and similarity information in dense vectors. Extensive experiment
results prove that, our proposed DSHC model can achieve much faster speed and
lower storage than sparse representation, with limited performance loss
compared with dense representation. Besides, our source codes have been
publicly released for future research.


  The distortion-rate function of output-constrained lossy source coding with
limited common randomness is analyzed for the special case of squared error
distortion measure. An explicit expression is obtained when both source and
reconstruction distributions are Gaussian. This further leads to a partial
characterization of the information-theoretic limit of quadratic Gaussian
rate-distortion-perception coding with the perception measure given by
Kullback-Leibler divergence or squared quadratic Wasserstein distance.


  Given a set of sequences, the distance between pairs of them helps us to find
their similarity and derive structural relationship amongst them. For genomic
sequences such measures make it possible to construct the evolution tree of
organisms. In this paper we compare several distance measures and examine a
method that involves circular shifting one sequence against the other for
finding good alignment to minimize Hamming distance. We also use run-length
encoding together with LZ77 to characterize information in a binary sequence.


  We study the production cross section of the $t$-channel single top quark at
the LHC in the noncommutative space-time. It is shown that the deviation of the
$t$-channel single top cross section from the Standard Model value because of
noncommutativity is significant when $|\vec{\theta}| \gtrsim 10^{-4}$
GeV$^{-2}$. Using the present experimental precision in measurement of the
$t$-channel cross section, we apply upper limit on the noncommutative
parameter. When a single top quark decays, there is a significant amount of
angular correlation, in the top quark rest frame between the top spin direction
and the direction of the charged lepton momentum from its decay. We study the
effect of noncommutativity on the spin correlation and we find that depending
on the noncommutative scale, the angular correlation can enhance considerably.
Then, we provide limits on the noncommutative scale for various possible
relative uncertainties on the spin correlation measurement.


  The Bidirectional Encoder Representations from Transformers (BERT) model has
been radically improving the performance of many Natural Language Processing
(NLP) tasks such as Text Classification and Named Entity Recognition (NER)
applications. However, it is challenging to scale BERT for low-latency and
high-throughput industrial use cases due to its enormous size. We successfully
optimize a Query-Title Relevance (QTR) classifier for deployment via a compact
model, which we name BERT Bidirectional Long Short-Term Memory (BertBiLSTM).
The model is capable of inferring an input in at most 0.2ms on CPU. BertBiLSTM
exceeds the off-the-shelf BERT model's performance in terms of accuracy and
efficiency for the aforementioned real-world production task. We achieve this
result in two phases. First, we create a pre-trained model, called eBERT, which
is the original BERT architecture trained with our unique item title corpus. We
then fine-tune eBERT for the QTR task. Second, we train the BertBiLSTM model to
mimic the eBERT model's performance through a process called Knowledge
Distillation (KD) and show the effect of data augmentation to achieve the
resembling goal. Experimental results show that the proposed model outperforms
other compact and production-ready models.


  We develop a theory of fluctuations for Brownian systems with weak long-range
interactions. For these systems, there exists a critical point separating a
homogeneous phase from an inhomogeneous phase. Starting from the stochastic
Smoluchowski equation governing the evolution of the fluctuating density field,
we determine the expression of the correlation function of the density
fluctuations around a spatially homogeneous equilibrium distribution. In the
stable regime, we find that the temporal correlation function of the Fourier
components of the density fluctuations decays exponentially rapidly with the
same rate as the one characterizing the damping of a perturbation governed by
the mean field Smoluchowski equation (without noise). On the other hand, the
amplitude of the spatial correlation function in Fourier space diverges at the
critical point $T=T_{c}$ (or at the instability threshold $k=k_{m}$) implying
that the mean field approximation breaks down close to the critical point and
that the phase transition from the homogeneous phase to the inhomogeneous phase
occurs sooner. By contrast, the correlations of the velocity fluctuations
remain finite at the critical point (or at the instability threshold). We give
explicit examples for the Brownian Mean Field (BMF) model and for Brownian
particles interacting via the gravitational potential and via the attractive
Yukawa potential. We also introduce a stochastic model of chemotaxis for
bacterial populations generalizing the mean field Keller-Segel model by taking
into account fluctuations.


  Despite the successes of large language models (LLMs), they exhibit
significant drawbacks, particularly when processing long contexts. Their
inference cost scales quadratically with respect to sequence length, making it
expensive for deployment in some real-world text processing applications, such
as retrieval-augmented generation (RAG). Additionally, LLMs also exhibit the
"distraction phenomenon", where irrelevant context in the prompt degrades
output quality. To address these drawbacks, we propose a novel RAG prompting
methodology, *superposition prompting*, which can be directly applied to
pre-trained transformer-based LLMs *without the need for fine-tuning*. At a
high level, superposition prompting allows the LLM to process input documents
in parallel *prompt paths*, discarding paths once they are deemed irrelevant.
We demonstrate the capability of our method to simultaneously enhance time
efficiency across a variety of question-answering benchmarks using multiple
pre-trained LLMs. Furthermore, our technique significantly improves accuracy
when the retrieved context is large relative the context the model was trained
on. For example, our approach facilitates a 93x reduction in compute time while
*improving* accuracy by 43% on the NaturalQuestions-Open dataset with the
MPT-7B instruction-tuned model over naive RAG.


  Real-world networks typically exhibit several aspects, or layers, of
interactions among their nodes. By permuting the role of the nodes and the
layers, we establish a new criterion to construct the dual of a network. This
approach allows to examine information from either a node-centric or
layer-centric viewpoint. Through rigorous analytical methods and extensive
simulations, we demonstrate that nodewise and layerwise connectivity measure
different but related aspects of the same system. Leveraging node-layer duality
provides complementary insights, enabling a deeper comprehension of diverse
networks across social science, technology and biology. Taken together, these
findings reveal previously unappreciated features of complex systems and
provide a fresh tool for delving into their structure and dynamics.


  We examine the Wheeler-DeWitt equaton for a static, eternal Schwarzschild
black hole in Kucha\v r-Brown variables and obtain its energy eigenstates.
Consistent solutions vanish in the exterior of the Kruskal manifold and are
non-vanishing only in the interior. The system is reminiscent of a particle in
a box. States of definite parity avoid the singular geometry by vanishing at
the origin. These definite parity states admit a discrete energy spectrum,
depending on one quantum number which determines the Arnowitt-Deser-Misner
(ADM) mass of the black hole according to a relation conjectured long ago by
Bekenstein, $M \sim \sqrt{n}M_p$. If attention is restricted only to these
quantized energy states, a black hole is described not only by its mass but
also by its parity. States of indefinite parity do not admit a quantized mass
spectrum.


  We construct a central extension of the smooth Deligne cohomology group of a
compact oriented odd dimensional smooth manifold, generalizing that of the loop
group of the circle. While the central extension turns out to be trivial for a
manifold of dimension 3, 7, 11,..., it is non-trivial for 1, 5, 9,.... In the
case where the central extension is non-trivial, we show an analogue of the
Segal-Witten reciprocity law.


  Nanocomposites made of polymer films embedding silver nanoparticles were
prepared by thermal annealing of poly-(vinyl) alcohol films containing AgNO3.
Low (2.5% w:w) and high (25% w:w) doping concentration of silver nitrate were
considered as well as their effect on the optical properties of thin (30 nm)
and thick (300 nm and more) films. The topography and the optical properties
(refractive index $n$ and extinction coefficient $k$) of such films were
studied by atomic force microscopy and spectroscopic ellipsometry. For a given
doping level, the parameters of the surface plasmon-polariton resonance
(amplitude, position and width) were shown to be thickness-dependent.
Multivariate statistical analysis techniques (principal component analysis and
support vector machines) were used to explain the differences in the optical
behavior of the thick and thin films.


  A new technique permits high fidelity measurement of the tunneling density of
states (TDOS) of the two-dimensional electron gas. The obtained TDOS contains
no distortions arising from low 2D in-plane conductivity and includes the
contribution from localized tunneling sites. In a perpendicular magnetic field,
a pseudogap develops in the TDOS at the Fermi level. Improved sensitivity
enables resolution of a linear dependence of the TDOS on energy near the Fermi
energy. The slopes of this linear gap are strongly field dependent. The data
are suggestive of a new model of the gap at low energies.


  One of the long sought-after goals in manipulation of light through
light-matter interactions is the realization of magnetic-field-tuneable
colouration, so-called magneto-chromatic effect, which holds great promise for
optical, biochemical and medical applications due to its contactless and
non-invasive nature. This goal can be achieved by magnetic-field controlled
birefringence, where colours are produced by the interference between
phase-retarded components of transmitted polarised light. Thus far
birefringence-tuneable coloration has been demonstrated using electric field,
material chirality and mechanical strain but magnetic field control remained
elusive due to either weak magneto-optical response of transparent media or low
transmittance to visible light of magnetically responsive media, such as
ferrofluids. Here we demonstrate magnetically tuneable colouration of aqueous
suspensions of two-dimensional cobalt-doped titanium oxide which exhibit an
anomalously large magneto-birefringence effect. The colour of the suspensions
can be tuned over more than two wavelength cycles in the visible range by
moderate magnetic fields below 0.8 T. We show that such giant magneto-chromatic
response is due to particularly large phase retardation (>3 pi) of the
polarised light, which in its turn is a combined result of a large
Cotton-Mouton coefficient (three orders of magnitude larger than for known
liquid crystals), relatively high saturation birefringence (delta n = 2 x
10^-4) and high transparency of our suspensions to visible light. The work
opens a new avenue to achieve tuneable colouration through engineered magnetic
birefringence and can readily be extended to other magnetic 2D nanocrystals.
The demonstrated effect can be used in a variety of magneto-optical
applications, including magnetic field sensors, wavelength-tuneable optical
filters and see-through printing.


  This paper presents a renormalization approach to many-particle systems. By
starting from a bare Hamiltonian ${\cal H}= {\cal H}_0 +{\cal H}_1$ with an
unperturbed part ${\cal H}_0$ and a perturbation ${\cal H}_1$,we define an
effective Hamiltonian which has a band-diagonal shape with respect to the
eigenbasis of ${\cal H}_0$. This means that all transition matrix elements are
suppressed which have energy differences larger than a given cutoff $\lambda$
that is smaller than the cutoff $\Lambda$ of the original Hamiltonian. This
property resembles a recent flow equation approach on the basis of continuous
unitary transformations. For demonstration of the method we discuss an exact
solvable model, as well as the Anderson-lattice model where the well-known
quasiparticle behavior of heavy fermions is derived.


  Molecular chirality frustrates the two-dimensional assembly of filamentous
molecules, a fact that reflects the generic impossibility of imposing a global
twisting of layered materials. We explore the consequences of this frustration
for hexagonally-ordered assemblies of chiral filaments that are {\it finite} in
lateral dimension. Specifically, we employ a continuum-elastic description of
cylindrical bundles of filaments, allowing us to consider the most general
resistance to and preference for chiral ordering of the assembly. We explore
two distinct mechanisms by which chirality at the molecular scale of the
filament frustrates the assembly into aggregates. In the first, chiral
interactions between filaments impart an overall twisting of filaments around
the central axis of the bundle. In the second, we consider filaments that are
inherently helical in structure, imparting a writhing geometry to the central
axis. For both mechanisms, we find that a thermodynamically-stable state of
dispersed bundles of {\it finite} width appears close to, but below, the point
of bulk filament condensation. The range of thermodynamic stability of
dispersed bundles is sensitive only to the elastic cost and preference for
chiral filament packing. The self-limited assembly of chiral filaments has
particular implications for a large class of biological molecules -- DNA,
filamentous proteins, viruses, bacterial flagella -- which are universally
chiral and are observed to form compact bundles under a broad range of
conditions.


  Motivated by studies suggesting that the patterns exhibited by the
collectively expanding fronts of thin cells during the closing of a wound [Mark
et al., Biophys. J., 98:361-370, 2010] and the shapes of single cells crawling
on surfaces [Callan-Jones et al., Phys. Rev. Lett., 100:258106, 2008] are due
to fingering instabilities, we investigate the stability of actively driven
interfaces under Hele-Shaw confinement. An initially radial interface between a
pair of viscous fluids is driven by active agents. Surface tension and bending
rigidity resist deformation of the interface. A point source at the origin and
a distributed source are also included to model the effects of injection or
suction, and growth or depletion, respectively. Linear stability analysis
reveals that for any given initial radius of the interface, there are two key
dimensionless driving rates that determine interfacial stability. We discuss
stability regimes in a state space of these parameters and their implications
for biological systems. An interesting finding is that an actively mobile
interface is susceptible to fingering instability irrespective of viscosity
contrast.


  Limited sensitivity and sensing range are arguably the greatest challenges in
microwave sensor design. Recent attempts to improve these properties have
relied on metamaterial- (MTM-) inspired open-loop resonators (OLRs) coupled to
transmission lines (TLs). Although the strongly resonant properties of the OLR
sensitively reflect small changes in the environment through a shift in its
resonance frequency, the resulting sensitivities remain ultimately limited by
the level of coupling between the OLR and the TL. This work introduces a novel
solution to this problem that employs negative-refractiveindex TL (NRI-TL) MTMs
to substantially improve this coupling so as to fully exploit its resonant
properties. A MTM-infused planar microwave sensor is designed for operation at
2.5 GHz, and is shown to exhibit a significant improvement in sensitivity and
linearity. A rigorous signal-flow analysis (SFA) of the sensor is proposed and
shown to provide a fully analytical description of all salient features of both
the conventional and MTM-infused sensors. Full-wave simulations confirm the
analytical predictions, and all data demonstrate excellent agreement with
measurements of a fabricated prototype. The proposed device is shown to be
especially useful in the characterization of commonly-available
high-permittivity liquids as well as in sensitively distinguishing
concentrations of ethanol/methanol in water.


  XSS J12270-4859 (henceforth J12270) is the first low-mass X-ray binary to
exhibit a transition, taking place at the end of 2012, from an X-ray active
state to a radio pulsar state. The X-ray emission based on archival XMM-Newton
observations is highly variable, showing rapid variations (10 s) from a high
X-ray luminosity mode to a low mode and back. A flaring mode has also been
observed. X-ray pulsations have been detected during the high mode only. In
this work we present two possible interpretations for the rapid swings between
the high and low modes. In the first scenario, this phenomenon can be explained
by a rapid oscillation between a propeller state and a radio-ejection pulsar
state, during which the pulsar wind prevents matter from falling onto the
neutron star surface. In the second scenario, a radio pulsar is always active,
the intra-binary shock is located just outside the light cylinder in the high
mode, while it expands during the low mode. At variance with other transitional
pulsars, J12270 shows two instances of the low mode: a low-soft and low-hard
mode. Performing an X-ray spectral analysis, we show that the harder component,
present in the low-hard spectra, is probably related to the tail of the flare
emission. This supports the understanding that the flare mechanism is
independent of the high-to-low mode transitions.


  It has always been a great challenge for clustering algorithms to
automatically determine the cluster numbers according to the distribution of
datasets. Several approaches have been proposed to address this issue,
including the recent promising work which incorporate Bayesian Nonparametrics
into the $k$-means clustering procedure. This approach shows simplicity in
implementation and solidity in theory, while it also provides a feasible way to
inference in large scale datasets. However, several problems remains unsolved
in this pioneering work, including the power-law data applicability, mechanism
to merge centers to avoid the over-fitting problem, clustering order problem,
e.t.c.. To address these issues, the Pitman-Yor Process based k-means (namely
\emph{pyp-means}) is proposed in this paper. Taking advantage of the Pitman-Yor
Process, \emph{pyp-means} treats clusters differently by dynamically and
adaptively changing the threshold to guarantee the generation of power-law
clustering results. Also, one center agglomeration procedure is integrated into
the implementation to be able to merge small but close clusters and then
adaptively determine the cluster number. With more discussion on the clustering
order, the convergence proof, complexity analysis and extension to spectral
clustering, our approach is compared with traditional clustering algorithm and
variational inference methods. The advantages and properties of pyp-means are
validated by experiments on both synthetic datasets and real world datasets.


  We derive all-order expressions for perturbations of the Einstein-Hilbert
action and the Einstein equation with the general $n$-th order terms. To this
end, we employ Cheung and Remmen's perturbation conventions both in tensor
density and the usual metric tensor formalisms, including the Einstein-dilaton
theory. Remarkably, we find minimal building blocks that generate the entire
perturbations for each of our formulations. We show that the number of terms of
perturbations grows linearly as the order of perturbations increases. We regard
our results as the reference and discuss how to derive perturbations in other
conventions from the reference. As a consistency check, we compute graviton
scattering amplitudes using the perturbiner method based on the perturbative
Einstein equation. Finally we discuss how to generalise the results to curved
backgrounds and incorporate additional matter.


  A worldsheet approach to the study of non-abelian D-particle dynamics is
presented based on viewing matrix-valued D-brane coordinate fields as coupling
constants of a deformed sigma-model which defines a logarithmic conformal field
theory. The short-distance structure of spacetime is shown to be naturally
captured by the Zamolodchikov metric on the corresponding moduli space which
encodes the geometry of the string interactions between D-particles. Spacetime
quantization is induced directly by the string genus expansion and leads to new
forms of uncertainty relations which imply that general relativity at very
short-distance scales is intrinsically described by a non-commutative geometry.
The indeterminancies exhibit decoherence effects suggesting the natural
incorporation of quantum gravity by short-distance D-particle probes. Some
potential experimental tests are briefly described.


  This paper proposes a new deep learning approach to antipodal grasp
detection, named Double-Dot Network (DD-Net). It follows the recent anchor-free
object detection framework, which does not depend on empirically pre-set
anchors and thus allows more generalized and flexible prediction on unseen
objects. Specifically, unlike the widely used 5-dimensional rectangle, the
gripper configuration is defined as a pair of fingertips. An effective CNN
architecture is introduced to localize such fingertips, and with the help of
auxiliary centers for refinement, it accurately and robustly infers grasp
candidates. Additionally, we design a specialized loss function to measure the
quality of grasps, and in contrast to the IoU scores of bounding boxes adopted
in object detection, it is more consistent to the grasp detection task. Both
the simulation and robotic experiments are executed and state of the art
accuracies are achieved, showing that DD-Net is superior to the counterparts in
handling unseen objects.


  We report $^{75}$As Nuclear Magnetic Resonance data in a single crystal of
CaFe$_2$As$_2$. The Knight shift, electric field gradient, and spin-lattice
relaxation rate are strongly temperature dependent in the paramagnetic state,
and change discontinuously at the structural transition temperature,
$T_S=T_N=167$ K. Immediately below, the NMR spectra reveal an internal field at
the As site associated with the presence of a commensurate magnetic order.
These results indicate that the structural and magnetic transitions in
CaFe$_2$As$_2$ are first order and strongly coupled, and that the electron
density in the FeAs plane is highly sensitive to the out-of-plane structure.


  On a doubling metric measure space $(M,d,\mu)$ endowed with a "carr\'e du
champ", let $\mathcal{L}$ be the associated Markov generator and $\dot
L^{p}_\alpha(M,\mathcal{L},\mu)$ the corresponding homogeneous Sobolev space of
order $0<\alpha<1$ in $L^p$, $1<p<+\infty$, with norm
$\left\|\mathcal{L}^{\alpha/2}f\right\|_p$. We give sufficient conditions on
the heat semigroup $(e^{-t\mathcal{L}})_{t>0}$ for the spaces $\dot
L^{p}_\alpha(M,\mathcal{L},\mu) \cap L^\infty(M,\mu)$ to be algebras for the
pointwise product. Two approaches are developed, one using paraproducts
(relying on extrapolation to prove their boundedness) and a second one through
geometrical square functionals (relying on sharp estimates involving
oscillations). A chain rule and a paralinearisation result are also given. In
comparison with previous results ([29,11]), the main improvements consist in
the fact that we neither require any Poincar\'e inequalities nor
$L^p$-boundedness of Riesz transforms, but only $L^p$-boundedness of the
gradient of the semigroup. As a consequence, in the range $p\in(1,2]$, the
Sobolev algebra property is shown under Gaussian upper estimates of the heat
kernel only.


  We prove that every cubic surface in $\mathbb{C}[x,y,z,t]$ is Pfaffian. A
constructive proof is given.


  A classic problem in microbiology is that bacteria display two types of
growth behavior when cultured on a mixture of two carbon sources: the two
sources are sequentially consumed one after another (diauxie) or they are
simultaneously consumed (co-utilization). The search for the molecular
mechanism of diauxie led to the discovery of the lac operon. However, questions
remain as why microbes would bother to have different strategies of taking up
nutrients. Here we show that diauxie versus co-utilization can be understood
from the topological features of the metabolic network. A model of optimal
allocation of protein resources quantitatively explains why and how the cell
makes the choice. In case of co-utilization, the model predicts the percentage
of each carbon source in supplying the amino acid pools, which is
quantitatively verified by experiments. Our work solves a long-standing puzzle
and provides a quantitative framework for the carbon source utilization of
microbes.


  Agriculture is a key component in any country's development. Domain-specific
knowledge resources serve to gain insight into the domain. Existing knowledge
resources such as AGROVOC and NAL Thesaurus are developed and maintained by the
domain experts. Population of terms into these knowledge resources can be
automated by using automatic term extraction tools for processing unstructured
agricultural text. Automatic term extraction is also a key component in many
semantic web applications, such as ontology creation, recommendation systems,
sentiment classification, query expansion among others. The primary goal of an
automatic term extraction system is to maximize the number of valid terms and
minimize the number of invalid terms extracted from the input set of documents.
Despite its importance in various applications, the availability of online
tools for the said purpose is rather limited. Moreover, the performance of the
most popular ones among them varies significantly. As a consequence, selection
of the right term extraction tool is perceived as a serious problem for
different knowledge-based applications. This paper presents an analysis of
three commonly used term extraction tools, viz. RAKE, TerMine, TermRaider and
compares their performance in terms of precision and recall, vis-a-vis RENT, a
more recent term extractor developed by these authors for agriculture domain.


  A novel realization of the classical SU(2) algebra is introduced for the
Dirac relativistic hydrogen atom defining a set of operators that, besides,
allow the factorization of the problem. An extra phase is needed as a new
variable in order to define the algebra. We take advantage of the operators to
solve the Dirac equation using algebraic methods. To acomplish this, a similar
path to the one used in the angular momentum case is employed; hence, the
radial eigenfuntions calculated comprise non unitary representations of the
algebra. One of the interesting properties of such non unitary representations
is that they are not labeled by integer nor by half-integer numbers as happens
in the usual angular momentum representation.


  We employ general parabolic recursion methods to demonstrate the recently
devised hypercube formula for Kazhdan-Lusztig polynomials of $S_n$, and
establish its generalization to the full setting of a finite Coxeter system
through algebraic proof. We introduce procedures for positive decompositions of
$q$-derived Kazhdan-Lusztig polynomials within this setting, that utilize
classical Hecke algebra positivity phenomena of Dyer-Lehrer and
Grojnowski-Haiman. This leads to a distinct algorithmic approach to the
subject, based on induction from a parabolic subgroup. We propose suitable weak
variants of the combinatorial invariance conjecture and verify their validity
for permutation groups.


  In this paper we establish an equivalence between the category of graded
D-branes of type B in Landau-Ginzburg models with homogeneous superpotential W
and the triangulated category of singularities of the fiber of W over zero. The
main result is a theorem that shows that the graded triangulated category of
singularities of the cone over a projective variety is connected via a fully
faithful functor to the bounded derived category of coherent sheaves on the
base of the cone. This implies that the category of graded D-branes of type B
in Landau-Ginzburg models with homogeneous superpotential W is connected via a
fully faithful functor to the derived category of coherent sheaves on the
projective variety defined by the equation W=0.


  Vortices in topological superconductors host Majorana zero modes (MZMs),
which are proposed to be building blocks of fault-tolerant topological quantum
computers. Recently, a new single-material platform for realizing MZM has been
discovered in iron-based superconductors, without involving hybrid
semiconductor-superconductor structures. Here we report on a detailed scanning
tunneling spectroscopy study of a FeTe0.55Se0.45 single crystal, revealing two
distinct classes of vortices present in this system which differ by a
half-integer level shift in the energy spectra of the vortex bound states. This
level shift is directly tied with the presence or absence of zero-bias peak and
also alters the ratios of higher energy levels from integer to
half-odd-integer. Our model calculations fully reproduce the spectra of these
two types of vortex bound states, suggesting the presence of topological and
conventional superconducting regions that coexist within the same crystal. Our
findings provide strong evidence for the topological nature of
superconductivity in FeTe0.55Se0.45 and establish it as an excellent platform
for further studies on MZMs.


  The components of the graphs $D(n, q)$ provide the best-known general lower
bound for the number of edges in a graph with $n$ vertices and no cycles of
length less than $g$. In this paper, we give a new, short, and simpler proof of
the fact that the length of the shortest cycle appearing in $D(n, q)$ is $n +
5$ when $n$ is odd, and $n + 4$ when $n$ is even.


  For video frame interpolation (VFI), existing deep-learning-based approaches
strongly rely on the ground-truth (GT) intermediate frames, which sometimes
ignore the non-unique nature of motion judging from the given adjacent frames.
As a result, these methods tend to produce averaged solutions that are not
clear enough. To alleviate this issue, we propose to relax the requirement of
reconstructing an intermediate frame as close to the GT as possible. Towards
this end, we develop a texture consistency loss (TCL) upon the assumption that
the interpolated content should maintain similar structures with their
counterparts in the given frames. Predictions satisfying this constraint are
encouraged, though they may differ from the pre-defined GT. Without the bells
and whistles, our plug-and-play TCL is capable of improving the performance of
existing VFI frameworks. On the other hand, previous methods usually adopt the
cost volume or correlation map to achieve more accurate image/feature warping.
However, the O(N^2) ({N refers to the pixel count}) computational complexity
makes it infeasible for high-resolution cases. In this work, we design a
simple, efficient (O(N)) yet powerful cross-scale pyramid alignment (CSPA)
module, where multi-scale information is highly exploited. Extensive
experiments justify the efficiency and effectiveness of the proposed strategy.


  In octilinear drawings of planar graphs, every edge is drawn as an
alternating sequence of horizontal, vertical and diagonal ($45^\circ$)
line-segments. In this paper, we study octilinear drawings of low edge
complexity, i.e., with few bends per edge. A $k$-planar graph is a planar graph
in which each vertex has degree less or equal to $k$. In particular, we prove
that every 4-planar graph admits a planar octilinear drawing with at most one
bend per edge on an integer grid of size $O(n^2) \times O(n)$. For 5-planar
graphs, we prove that one bend per edge still suffices in order to construct
planar octilinear drawings, but in super-polynomial area. However, for 6-planar
graphs we give a class of graphs whose planar octilinear drawings require at
least two bends per edge.


  In this note we present an approach using both constructive and Hopf
algebraic methods to contribute to the not yet fully satisfactory definition of
an integral on kappa-deformed spacetime. The integral presented here is based
on the inner product of differential forms and it is shown that this integral
is explicitly invariant under the deformed symmetry structure.


  The Schubert polynomials lift the Schur basis of symmetric polynomials into a
basis for Z[x1,x2,...]. We suggest the "prism tableau model" for these
polynomials. A novel aspect of this alternative to earlier results is that it
directly invokes semistandard tableaux; it does so as part of a colored tableau
amalgam. In the Grassmannian case, a prism tableau with colors ignored is a
semistandard Young tableau. Our arguments are developed from the Groebner
geometry of matrix Schubert varieties.


  In our derivation of the second law of thermodynamics from the relation of
adiabatic accessibility of equilibrium states we stressed the importance of
being able to scale a system's size without changing its intrinsic properties.
This leaves open the question of defining the entropy of macroscopic, but
unscalable systems, such as gravitating bodies or systems where surface effects
are important. We show here how the problem can be overcome, in principle, with
the aid of an `entropy meter'. An entropy meter can also be used to determine
entropy functions for non-equilibrium states and mesoscopic systems.


  The TOTEM collaboration has measured the elastic proton-proton differential
cross section ${\rm d}\sigma/{\rm d}t$ at $\sqrt{s}=13$ TeV LHC energy using
dedicated $\beta^{*}=90$ m beam optics. The Roman Pot detectors were inserted
to 10$\sigma$ distance from the LHC beam, which allowed the measurement of the
range $[0.04$ GeV$^{2}$$; 4 $GeV$^{2}$$]$ in four-momentum transfer squared
$|t|$. The efficient data acquisition allowed to collect about 10$^{9}$ elastic
events to precisely measure the differential cross-section including the
diffractive minimum (dip), the subsequent maximum (bump) and the large-$|t|$
tail. The average nuclear slope has been found to be $B=(20.40 \pm 0.002^{\rm
stat} \pm 0.01^{\rm syst})~$GeV$^{-2}$ in the $|t|$-range $0.04~$GeV$^{2}$ to
$0.2~$GeV$^{2}$. The dip position is $|t_{\rm dip}|=(0.47 \pm 0.004^{\rm stat}
\pm 0.01^{\rm syst})~$GeV$^{2}$. The differential cross section ratio at the
bump vs. at the dip $R=1.77\pm0.01^{\rm stat}$ has been measured with high
precision. The series of TOTEM elastic pp measurements show that the dip is a
permanent feature of the pp differential cross-section at the TeV scale.


  Quantum theory (QT) has been confirmed by numerous experiments, yet we still
cannot fully grasp the meaning of the theory. As a consequence, the quantum
world appears to us paradoxical. Here we shed new light on QT by having it
follow from two main postulates (i) the theory should be logically consistent;
(ii) inferences in the theory should be computable in polynomial time. The
first postulate is what we require to each well-founded mathematical theory.
The computation postulate defines the physical component of the theory. We show
that the computation postulate is the only true divide between QT, seen as a
generalised theory of probability, and classical probability. All quantum
paradoxes, and entanglement in particular, arise from the clash of trying to
reconcile a computationally intractable, somewhat idealised, theory (classical
physics) with a computationally tractable theory (QT) or, in other words, from
regarding physics as fundamental rather than computation.


  This work aims at providing some (further) perspective into the analysis of
two well-known datasets in terms of the methods of linear regression. The first
set has been taken from Hubble's 1929 investigation of the relation between the
distances of galaxies and their recessional velocities. The second set relates
to Galton's family data on human stature, collected for the purposes of his
1889 book on natural inheritance.


  Gravitational-wave observations have the potential of allowing the
identification of a population of merging primordial black-hole binaries. We
provide an overview of the capabilities of present and future GW detectors,
with a special emphasis on the perspective for observing quantities that are
signatures of a primordial origin.


  A multiple-antenna amplify-and-forward two-hop interference network with
multiple links and multiple relays is considered. We optimize transmit
precoders, receive decoders and relay AF matrices to maximize the achievable
sum rate. Under per user and total relay sum power constraints, we propose an
efficient algorithm to maximize the total signal to total interference plus
noise ratio (TSTINR). Computational complexity analysis shows that our proposed
algorithm for TSTINR has lower complexity than the existing weighted minimum
mean square error (WMMSE) algorithm. We analyze and confirm by simulations that
the TSTINR, WMMSE and the total leakage interference plus noise (TLIN)
minimization models with per user and total relay sum power constraints can
only transmit a single data stream for each user. Thus we propose a novel
multiple stream TSTINR model with requirement of orthogonal columns for
precoders, in order to support multiple data streams and thus utilize higher
Degrees of Freedom. Multiple data streams and larger multiplexing gains are
guaranteed. Simulation results show that for single stream models, our TSTINR
algorithm outperforms the TLIN algorithm generally and outperforms WMMSE in
medium to high Signal-to-Noise-Ratio scenarios; the system sum rate
significantly benefits from multiple data streams in medium to high SNR
scenarios.


  We propose a mechanism for engineering chiral interactions in Rydberg atoms
via a directional antiblockade condition, where an atom can change its state
only if an atom to its right (or left) is excited. The scalability of our
scheme enables us to explore the many-body dynamics of kinetically constrained
models with unidirectional character. We observe non-ergodic behavior via
either scars, confinement, or localization, upon simply tuning the strength of
two driving fields acting on the atoms. We discuss how our mechanism persists
in the presence of classical noise and how the degree of chirality in the
interactions can be tuned, opening towards the frontier of directional,
strongly correlated, quantum mechanics using neutral atoms arrays.


  Heavy quarkonium continues to play a central role in the study of nuclear
matter under extremes of temperature and density in relativistic heavy-ion
collisions. In this talk I report on recent developments in the theoretical
description of quarkonium in-medium dynamics using the open-quantum systems
approach. Not only does it provide a clear interpretation of the imaginary part
of the complex heavy-quark potential but also reveals that a subtle interplay
between screening and wavefunction decoherence is responsible for the melting
of heavy quarkonium states.


  We describe todolists (top-down skiplists), a variant of skiplists (Pugh
1990) that can execute searches using at most $\log_{2-\varepsilon} n + O(1)$
binary comparisons per search and that have amortized update time
$O(\varepsilon^{-1}\log n)$. A variant of todolists, called working-todolists,
can execute a search for any element $x$ using $\log_{2-\varepsilon} w(x) +
o(\log w(x))$ binary comparisons and have amortized search time
$O(\varepsilon^{-1}\log w(w))$. Here, $w(x)$ is the "working-set number" of
$x$. No previous data structure is known to achieve a bound better than
$4\log_2 w(x)$ comparisons. We show through experiments that, if implemented
carefully, todolists are comparable to other common dictionary implementations
in terms of insertion times and outperform them in terms of search times.


  The chiral magnetic wave (CMW) is a collective mode in quark-gluon plasma
originated from the chiral magnetic effect (CME) and chiral separation effect.
Its detection in heavy-ion collisions is challenging due to significant
background contamination. In Ref.[1], we have constructed a neural network
which can accurately identify the CME-related signal from the final-state pion
spectra. In this paper, we generalize such a neural network to the case of CMW
search. We show that, after a updated training, the neural network can
effectively recognize the CMW-related signal. Additionally, we assess the
performance of the neural network compared to other known methods for CMW
search.


  We investigate the polarization dependence of optical nonlinearity
enhancement for a uniaxial anisotropic composite of metal nanocrystals in a
dielectric host. Three cases are distinguished depending on whether the
polarization is parallel, perpendicular or unpolarized with respect to the axis
of anisotropy. For the parallel polarization, the results show that the 3D
results are qualitatively similar to the 2D case reported recently. For the
perpendicular polarization, the results are markedly different from the
parallel counterpart: In contrast to the absorption, the enhancement factor
actually increases with the anisotropy. Thus the separation of the absorption
and enhancement peaks becomes even more pronounced than the parallel
polarization case. These results indicate a strong polarization dependence of
the nonlinear optical response.


  Based on the trajectories of the separation between water molecule pairs from
MD simulations, we investigate the bond breakage dynamics in bulk water. From
the spectrum of mean first-passage times, the Fokker-Planck equation allows us
to derive the diffusivity profile along the separation coordinate and thus to
unambiguously disentangle the effects of free-energy and local friction on the
separation kinetics. For tightly coordinated water the friction is six times
higher than in bulk, which can be interpreted in terms of a dominant reaction
path that involves additional orthogonal coordinates.


  Efforts to reconstruct phylogenetic trees and understand evolutionary
processes depend fundamentally on stochastic models of speciation and mutation.
The simplest continuous-time model for speciation in phylogenetic trees is the
Yule process, in which new species are "born" from existing lineages at a
constant rate. Recent work has illuminated some of the structural properties of
Yule trees, but it remains mostly unknown how these properties affect sequence
and trait patterns observed at the tips of the phylogenetic tree. Understanding
the interplay between speciation and mutation under simple models of evolution
is essential for deriving valid phylogenetic inference methods and gives
insight into the optimal design of phylogenetic studies. In this work, we
derive the probability distribution of interspecies covariance under Brownian
motion and Ornstein-Uhlenbeck models of phenotypic change on a Yule tree. We
compute the probability distribution of the number of mutations shared between
two randomly chosen taxa in a Yule tree under discrete Markov mutation models.
Our results suggest summary measures of phylogenetic information content,
illuminate the correlation between site patterns in sequences or traits of
related organisms, and provide heuristics for experimental design and
reconstruction of phylogenetic trees.


  We reported in a previous paper the discovery of large-scale structure of
Lyman Alpha emitters (LAEs) at z=4.86+-0.03 with a projected size of 20 Mpc x
50 Mpc in narrow-band data of a 25' x 45' area of the Subaru Deep Field
(Omega_0=0.3, lambda_0=0.7, H0=70 km/s/Mpc). However, the surveyed area, which
corresponds to 55 Mpc x 100 Mpc, was not large enough that we can conclude that
we are seeing a typical distribution of z~5 LAEs. In this Letter, we report the
results of follow-up imaging of the same sky area using a new narrow-band
filter (NB704, lambda_c=7046 A and FWHM=100 A) to detect LAEs at z=4.79, i.e.,
LAEs lying closer to us by 39 Mpc on average than the z=4.86 objects. We detect
51 LAEs at z=4.79+-0.04 down to NB704=25.7, and find that their sky
distribution is quite different from the z=4.86 LAEs'. The clustering of z=4.79
LAEs is very weak on any scales and there is no large-scale high- contrast
structure. The shape and the amplitude of the angular correlation function are
thus largely different between the two samples. These results demonstrate a
large cosmic variance in the clustering properties of LAEs on scales of ~ 50
Mpc.


  Convolutions or Hadamard products of analytic functions is a well explored
area of research and many nice results are available in literature. On the
other hand, very little is known in general about the convolutions of univalent
harmonic mappings. So, researchers started exploring properties of convolutions
of some specific univalent harmonic mappings and while doing so, they have
mostly used well known Cohn's rule or/and Schur-Cohn's algorithm, which
involves computations that are very cumbersome. The main objective of this
article is to present an alternative approach, which requires very less
computational efforts and allows us to prove more general results. Most of the
earlier known results follow as particular cases of the results proved herein.


  In this paper we aim to generalize results obtained in the framework of
fractional calculus by the way of reformulating them in terms of operator
theory. In its own turn, the achieved generalization allows us to spread the
obtained technique on practical problems that connected with various physical -
chemical processes.


  Although many scalable event matching algorithms have been proposed to
achieve scalability for large-scale content-based networks, content-based
publish/subscribe networks (especially for large-scale real time systems) still
suffer performance deterioration when subscription scale increases. While
subscription aggregation techniques can be useful to reduce the amount of
subscription dissemination traffic and the subscription table size by
exploiting the similarity among subscriptions, efficient subscription
aggregation is not a trivial task to accomplish. Previous research works have
proved that it is either a NP-Complete or a co-NP complete problem. In this
paper, we propose DLS (Discrete Label Set), a novel subscription representation
model, and design algorithms to achieve the mapping from traditional Boolean
predicate model to the DLS model. Based on the DLS model, we propose a
subscription aggregation algorithm with O(1) time complexity in most cases, and
an event matching algorithm with O(1) time complexity. The significant
performance improvement is at the cost of memory consumption and controllable
false positive rate. Our theoretical analysis shows that these algorithms are
inherently scalable and can achieve real time event matching in a large-scale
content-based publish/subscribe network. We discuss the tradeoff between
memory, false positive rate and partition granules of content space.
Experimental results show that proposed algorithms achieve expected
performance. With the increasing of computer memory capacity and the dropping
of memory price, more and more large-scale real time applications can benefit
from our proposed DLS model, such as stock quote distribution, earthquake
monitoring, and severe weather alert.


  The gamma-ray survey of the sky by the Fermi Gamma-ray Space Telescope offers
both opportunities and challenges for multiwavelength and multi-messenger
studies. Gamma-ray bursts, pulsars, binary sources, flaring Active Galactic
Nuclei, and Galactic transient sources are all phenomena that can best be
studied with a wide variety of instruments simultaneously or contemporaneously.
Identification of newly-discovered gamma-ray sources is largely a
multiwavelength effort. From the gamma-ray side, a principal challenge is the
latency from the time of an astrophysical event to the recognition of this
event in the data. Obtaining quick and complete multiwavelength coverage of
gamma-ray sources can be difficult both in terms of logistics and in terms of
generating scientific interest. The Fermi LAT team continues to welcome
cooperative efforts aimed at maximizing the scientific return from the mission
through multiwavelength studies.


  We present Hierarchical Deep Q-Network (HDQfD) that took first place in the
MineRL competition. HDQfD works on imperfect demonstrations and utilizes the
hierarchical structure of expert trajectories. We introduce the procedure of
extracting an effective sequence of meta-actions and subgoals from
demonstration data. We present a structured task-dependent replay buffer and
adaptive prioritizing technique that allow the HDQfD agent to gradually erase
poor-quality expert data from the buffer. In this paper, we present the details
of the HDQfD algorithm and give the experimental results in the Minecraft
domain.


  We study the large deviation function for the empirical measure of diffusing
particles at one fixed position. We find that the large deviation function
exhibits anomalous system size dependence in systems that satisfy the following
conditions: (i) there exists no macroscopic flow, and (ii) their space
dimension is one or two. We investigate this anomaly by using a contraction
principle. We also analyze the relation between this anomaly and the so-called
long-time tail behavior on the basis of phenomenological arguments.


  We extend previously known two-dimensional multiplication tiling systems that
simulate multiplication by two natural numbers $p$ and $q$ in base $pq$ to
higher dimensional multiplication tessellation systems. We develop the theory
of these systems and link different multiplication tessellation systems with
each other via macrotile operations that glue cubes in one tessellation system
into larger cubes of another tessellation system. The macrotile operations
yield topological conjugacies and factor maps between cellular automata
performing multiplication by positive numbers in various bases.


  We present a detailed electron diffraction study of individual single-walled
carbon nanotubes. A novel sample preparation procedure provides well-separated,
long and straight individual single-shell nanotubes. Diffraction experiments
are carried out at 60kV, below the threshold for knock-on damage in carbon
nanotubes. We describe experimental parameters that allow single-tube electron
diffraction experiments with widely available thermal emission transmission
electron microscopes. Further, we review the simulation of diffraction patterns
for these objects.


  The upsilon Andromedae system was the first multi-planet system discovered
orbiting a main sequence star. We describe the detection of water vapor in the
atmosphere of the innermost non-transiting gas giant ups~And~b by treating the
star-planet system as a spectroscopic binary with high-resolution, ground-based
spectroscopy. We resolve the signal of the planet's motion and break the
mass-inclination degeneracy for this non-transiting planet via deep combined
flux observations of the star and the planet. In total, seven epochs of Keck
NIRSPEC $L$ band observations, three epochs of Keck NIRSPEC short wavelength
$K$ band observations, and three epochs of Keck NIRSPEC long wavelength $K$
band observations of the ups~And~system were obtained. We perform a multi-epoch
cross correlation of the full data set with an atmospheric model. We measure
the radial projection of the Keplerian velocity ($K_P$ = 55 $\pm$ 9 km/s), true
mass ($M_b$ = 1.7 $^{+0.33}_{-0.24}$ $M_J$), and orbital inclination \big($i_b$
= 24 $\pm$ 4$^{\circ}$\big), and determine that the planet's opacity structure
is dominated by water vapor at the probed wavelengths. Dynamical simulations of
the planets in the ups~And~system with these orbital elements for ups~And~b
show that stable, long-term (100 Myr) orbital configurations exist. These
measurements will inform future studies of the stability and evolution of the
ups~And~system, as well as the atmospheric structure and composition of the hot
Jupiter.


  A population evolving in an inhomogeneous environment will adapt differently
to different regions. We study the conditions under which such a population can
maintain adaptations to a particular region when that region is not stationary,
but can move. In particular, we study a quasispecies living near a favorable
patch ("oasis") in the middle of a large "desert." The population has two
genetic states, one of which which conveys a relative advantage while in the
oasis at the cost of a disadvantage in the desert. We consider the population
dynamics when the oasis is moving, or equivalently some form of "wind" is
blowing the population away from the oasis. We find that the ratio of the two
types of individuals exhibits sharp transitions at particular oasis velocities.
We calculate an extinction velocity, and a switching velocity above which the
dominance switches from the oasis-adapted genotype to the desert-adapted one.
This switching velocity is analagous to the quasispecies mutational error
threshold. Above this velocity, the population cannot maintain adaptations to
the properties of the oasis.


  Robust explanations of machine learning models are critical to establish
human trust in the models. Due to limited cognition capability, most humans can
only interpret the top few salient features. It is critical to make top salient
features robust to adversarial attacks, especially those against the more
vulnerable gradient-based explanations. Existing defense measures robustness
using $\ell_p$-norms, which have weaker protection power. We define explanation
thickness for measuring salient features ranking stability, and derive
tractable surrogate bounds of the thickness to design the \textit{R2ET}
algorithm to efficiently maximize the thickness and anchor top salient
features. Theoretically, we prove a connection between R2ET and adversarial
training. Experiments with a wide spectrum of network architectures and data
modalities, including brain networks, demonstrate that R2ET attains higher
explanation robustness under stealthy attacks while retaining accuracy.


  In this paper, we described possible directions for deeper understanding,
helping bridge the gap between psychology / cognitive science and computational
approaches in sentiment/opinion analysis literature. We focus on the opinion
holder's underlying needs and their resultant goals, which, in a utilitarian
model of sentiment, provides the basis for explaining the reason a sentiment
valence is held. While these thoughts are still immature, scattered,
unstructured, and even imaginary, we believe that these perspectives might
suggest fruitful avenues for various kinds of future work.


  We designed a modular robotic control stack that provides a customizable and
accessible interface to the Franka Emika Panda Research robot. This framework
abstracts high-level robot control commands as skills, which are decomposed
into combinations of trajectory generators, feedback controllers, and
termination handlers. Low-level control is implemented in C++ and runs at
$1$kHz, and high-level commands are exposed in Python. In addition, external
sensor feedback, like estimated object poses, can be streamed to the low-level
controllers in real time. This modular approach allows us to quickly prototype
new control methods, which is essential for research applications. We have
applied this framework across a variety of real-world robot tasks in more than
$5$ published research papers. The framework is currently shared internally
with other robotics labs at Carnegie Mellon University, and we plan for a
public release in the near future.


  We consider a class of $0$-$1$ polynomial programming termed multiple choice
polynomial programming (MCPP) where the constraint requires exact one component
per subset of the partition to be $1$ after all the entries are partitioned.
Compared to the unconstrained counterpart, there are few polyhedral studies of
MCPP in general form. This paper serves as the first attempt to propose a
polytope associated with a hypergraph to study MCPP, which is the convex hull
of $0$-$1$ vectors satisfying multiple choice constraints and production
constraints. With the help of the decomposability property, we obtain an
explicit half-space representation of the MCPP polytope when the underlying
hypergraph is $\alpha$-acyclic by induction on the number of hyperedges, which
is an analogy of the acyclicity results on the multilinear polytope by Del Pia
and Khajavirad (SIAM J Optim 28 (2018) 1049) when the hypergraph is
$\gamma$-acyclic. We also present a necessary and sufficient condition for the
inequalities lifted from the facet-inducing ones for the multilinear polytope
to be still facet-inducing for the MCPP polytope. This result covers the
particular cases by B\"armann, Martin and Schneider (SIAM J Optim 33 (2023)
2909).


  All cells derive nutrition by absorbing some chemical and energy resources
from the environment; these resources are used by the cells to reproduce the
chemicals within them, which in turn leads to an increase in their volume. In
this study, we introduce a protocell model exhibiting catalytic reaction
dynamics, energy metabolism, and cell growth. Results of extensive simulations
of this model show the existence of four phases with regard to the rates of
both the influx of resources and the cell growth. These phases include an
active phase with high influx and high growth rates, an inefficient phase with
high influx but low growth rates, a quasi-static phase with low influx and low
growth rates, and a death phase with negative growth rate. A mean field model
well explains the transition among these phases as bifurcations. The
statistical distribution of the active phase is characterized by a power law
and that of the inefficient phase is characterized by a nearly equilibrium
distribution. We also discuss the relevance of the results of this study to
distinct states in the existing cells.


  The Faddeev equations for the atomic helium-trimer systems are solved
numerically with high accuracy both for the most sophisticated realistic
potentials available and for simple phenomenological potentials. An efficient
numerical procedure is described. The large-distance asymptotic behavior,
crucial for weakly bound three-body systems, is described almost analytically
for arbitrary potentials. The Efimov effect is especially considered. The
geometric structures of the bound states are quantitatively investigated. The
accuracy of the schematic models and previous computations is comparable, i.e.
within 20% for the spatially extended states and within 40% for the smaller
^4He-trimer ground state.


  Dynamics of a simple system, such as a two-state (dimer) model, are
dramatically changed in the presence of interactions and external driving, and
the resultant unitary dynamics show both regular and chaotic regions. We
investigate the non-unitary dynamics of such a dimer in the presence of
balanced gain and loss for the two states, i.e. a $\mathcal{PT}$ symmetric
dimer. We find that at low and high driving frequencies, the
$\mathcal{PT}$-symmetric dimer motion continues to be regular, and the system
is in the $\mathcal{PT}$-symmetric state. On that other hand, for intermediate
driving frequency, the system shows chaotic motion, and is usually in the
$\mathcal{PT}$-symmetry broken state. Our results elucidate the interplay
between the $\mathcal{PT}$-symmetry breaking transitions and regular-chaotic
transitions in an experimentally accessible toy model.


  We study a pentaquark model of the negative parity hyperon Lambda(1405). We
choose a specific quark configuration for trial, and use the perturbative
chiral quark model extended to four quarks and one anti-quark system. We
calculate a sigma-term and a scalar form factor of the hyperon and demonstrate
their usefulness to study the nature of the hyperon.


  Human-centric perceptions include a variety of vision tasks, which have
widespread industrial applications, including surveillance, autonomous driving,
and the metaverse. It is desirable to have a general pretrain model for
versatile human-centric downstream tasks. This paper forges ahead along this
path from the aspects of both benchmark and pretraining methods. Specifically,
we propose a \textbf{HumanBench} based on existing datasets to comprehensively
evaluate on the common ground the generalization abilities of different
pretraining methods on 19 datasets from 6 diverse downstream tasks, including
person ReID, pose estimation, human parsing, pedestrian attribute recognition,
pedestrian detection, and crowd counting. To learn both coarse-grained and
fine-grained knowledge in human bodies, we further propose a \textbf{P}rojector
\textbf{A}ssis\textbf{T}ed \textbf{H}ierarchical pretraining method
(\textbf{PATH}) to learn diverse knowledge at different granularity levels.
Comprehensive evaluations on HumanBench show that our PATH achieves new
state-of-the-art results on 17 downstream datasets and on-par results on the
other 2 datasets. The code will be publicly at
\href{https://github.com/OpenGVLab/HumanBench}{https://github.com/OpenGVLab/HumanBench}.


  Gaussian process is a very promising novel technology that has been applied
to both the regression problem and the classification problem. While for the
regression problem it yields simple exact solutions, this is not the case for
the classification problem, because we encounter intractable integrals. In this
paper we develop a new derivation that transforms the problem into that of
evaluating the ratio of multivariate Gaussian orthant integrals. Moreover, we
develop a new Monte Carlo procedure that evaluates these integrals. It is based
on some aspects of bootstrap sampling and acceptancerejection. The proposed
approach has beneficial properties compared to the existing Markov Chain Monte
Carlo approach, such as simplicity, reliability, and speed.


  The Hot Universe Baryon Surveyor (HUBS) mission is proposed to study
"missing" baryons in the universe. Unlike dark matter, baryonic matter is made
of elements in the periodic table, and can be directly observed through the
electromagnetic signals that it produces. Stars contain only a tiny fraction of
the baryonic matter known to be present in the universe. Additional baryons are
found to be in diffuse (gaseous) form, in or between galaxies, but a
significant fraction has not yet been seen. The latter (missing baryons) are
thought to be hiding in low-density warm-hot ionized medium (WHIM), based on
results from theoretical studies and recent observations, and be distributed in
the vicinity of galaxies (i.e., circum-galactic medium) and between galaxies
(i.e., intergalactic medium). Such gas would radiate mainly in the soft X-ray
band and the emission would be very weak, due to its very low density. HUBS is
optimized to detect the X-ray emission from the hot baryons in the
circum-galactic medium, and thus fill a void in observational astronomy. The
goal is not only to detect the missing baryons, but to characterize their
physical and chemical properties, as well as to measure their spatial
distribution. The results would establish the boundary conditions for
understanding galaxy evolution. Though highly challenging, detecting missing
baryons in the intergalactic medium could be attempted, perhaps in the
outskirts of galaxy clusters, and could shed significant light on the
large-scale structures of the universe. The current design of HUBS will be
presented, along with the status of technology development.