  The observed correlation between outer giant planets and inner super-Earths
is emerging as an important constraint on planet formation theories. In this
study we focus on Kepler-167, which is currently the only system known to
contain both inner transiting super-Earths and a confirmed outer transiting gas
giant companion beyond 1 au. Using long term radial velocity monitoring, we
measure the mass of the gas giant Kepler-167e ($P=1071$ days) to be
$1.01^{+0.16}_{-0.15}$ M$_{\rm J}$, thus confirming it as a Jupiter analog. We
re-fit the $Kepler$ photometry to obtain updated radii for all four planets.
Using a planetary structure model, we estimate that Kepler-167e contains
$66\pm19$ M$_{\oplus}$ of solids and is significantly enriched in metals
relative to its solar-metallicity host star. We use these new constraints to
explore the broader question of how systems like Kepler-167 form in the pebble
accretion framework for giant planet core formation. We utilize simple disk
evolution models to demonstrate that more massive and metal-rich disks, which
are the most favorable sites for giant planet formation, can also deliver
enough solids to the inner disk to form systems of super-Earths. We use these
same models to constrain the nature of Kepler-167's protoplanetary disk, and
find that it likely contained $\gtrsim 300$ M$_{\oplus}$ of dust and was
$\gtrsim 40$ au in size. These values overlap with the upper end of the
observed dust mass and size distributions of Class 0 and I disks, and are also
consistent with the observed occurrence rate of Jupiter analogs around sun-like
stars.


  The European Spallation Source (ESS) is intended to become the most powerful
spallation neutron source in the world and the flagship of neutron science in
the upcoming decades. The exceptionally high neutron flux will provide unique
opportunities for scientific experiments, but also set high requirements for
the detectors. One of the most challenging aspects is the rate capability and
in particular the peak instantaneous rate capability, i.e. the number of
neutrons hitting the detector per channel or cm$^2$ at the peak of the neutron
pulse. The primary purpose of this paper is to estimate the incident rates that
are anticipated for the BIFROST instrument planned for ESS, and also to
demonstrate the use of powerful simulation tools for the correct interpretation
of neutron transport in crystalline materials. A full simulation model of the
instrument from source to detector position, implemented with the use of
multiple simulation software packages is presented. For a single detector tube
instantaneous incident rates with a maximum of 1.7 GHz for a Bragg peak from a
single crystal, and 0.3 MHz for a vanadium sample are found. This paper also
includes the first application of a new pyrolytic graphite model, and a
comparison of different simulation tools to highlight their strengths and
weaknesses.


  Machine Learning seeks to identify and encode bodies of knowledge within
provided datasets. However, data encodes subjective content, which determines
the possible outcomes of the models trained on it. Because such subjectivity
enables marginalisation of parts of society, it is termed (social) `bias' and
sought to be removed. In this paper, we contextualise this discourse of bias in
the ML community against the subjective choices in the development process.
Through a consideration of how choices in data and model development construct
subjectivity, or biases that are represented in a model, we argue that
addressing and mitigating biases is near-impossible. This is because both data
and ML models are objects for which meaning is made in each step of the
development pipeline, from data selection over annotation to model training and
analysis. Accordingly, we find the prevalent discourse of bias limiting in its
ability to address social marginalisation. We recommend to be conscientious of
this, and to accept that de-biasing methods only correct for a fraction of
biases.


  A prey animal surveying its environment must decide whether there is a
dangerous predator present or not. If there is, it may flee. Flight has an
associated cost, so the animal should not flee if there is no danger. However,
the prey animal cannot know the state of its environment with certainty, and is
thus bound to make some errors. We formulate a probabilistic automaton model of
a prey animal's life and use it to compute the optimal escape decision
strategy, subject to the animal's uncertainty. The uncertainty is a major
factor in determining the decision strategy: only in the presence of
uncertainty do economic factors (like mating opportunities lost due to flight)
influence the decision. We performed computer simulations and found that
\emph{in silico} populations of animals subject to predation evolve to display
the strategies predicted by our model, confirming our choice of objective
function for our analytic calculations. To the best of our knowledge, this is
the first theoretical study of escape decisions to incorporate the effects of
uncertainty, and to demonstrate the correctness of the objective function used
in the model.


  We describe a protocol for the universal control of non-ideal $\pi$-periodic
superconducting qubits. Our proposal relies on a $\pi$-SQUID: a superconducting
loop formed by two $\pi$-periodic circuit elements, with an external magnetic
flux threading the circuit. The system exhibits an extensive sweet spot around
half-flux where residual $2\pi$-periodic Cooper pair tunneling is highly
suppressed. We demonstrate that universal single-qubit operations can be
realised by tuning the flux adiabatically and diabatically within this broad
sweet spot. We also assess how residual $2\pi$-periodicity in $\pi$-SQUIDs
impacts holonomic phase gates.


  In this paper, we study various cosmological parameters and quantities in
scalar-tensor gravity from inflation and swampland conjecture. Therefore, by
selecting different models such as power-law, exponential, and logarithmic in
the framework of scalar-tensor theory, we obtain potential, tensor-to-scalar
ratio, and the scalar spectral index. Next, we examine new constraints and
compare the corresponding results with the latest observable data. Here, we
take advantage of the obtained results and determine the compatibility or
incompatibility of the corresponding model with the swampland conjectures


  A possible inconsistency arising when a radiative cooling term is
incorporated in a finite resolution self-gravitating hydrodynamic code is
discussed. The inconsistency appears when the heating-cooling balance within
the cooling and collapsing gas cloud is broken near the resolution limit of a
numerical code. As the result, the cooling time of a fluid element increases
enormously leading to the unphysical conclusion that the fluid element does not
cool and is therefore stable against the collapse. A special cooling
consistency condition is introduced which approximately restores the
heating-cooling balance and leads to a numerical solution that closely mimics
the exact (infinite resolution) solution.


  Legacy encryption systems depend on sharing a key (public or private) among
the peers involved in exchanging an encrypted message. However, this approach
poses privacy concerns. Especially with popular cloud services, the control
over the privacy of the sensitive data is lost. Even when the keys are not
shared, the encrypted material is shared with a third party that does not
necessarily need to access the content. Moreover, untrusted servers, providers,
and cloud operators can keep identifying elements of users long after users end
the relationship with the services. Indeed, Homomorphic Encryption (HE), a
special kind of encryption scheme, can address these concerns as it allows any
third party to operate on the encrypted data without decrypting it in advance.
Although this extremely useful feature of the HE scheme has been known for over
30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE)
scheme, which allows any computable function to perform on the encrypted data,
was introduced by Craig Gentry in 2009. Even though this was a major
achievement, different implementations so far demonstrated that FHE still needs
to be improved significantly to be practical on every platform. First, we
present the basics of HE and the details of the well-known Partially
Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which
are important pillars of achieving FHE. Then, the main FHE families, which have
become the base for the other follow-up FHE schemes are presented. Furthermore,
the implementations and recent improvements in Gentry-type FHE schemes are also
surveyed. Finally, further research directions are discussed. This survey is
intended to give a clear knowledge and foundation to researchers and
practitioners interested in knowing, applying, as well as extending the state
of the art HE, PHE, SWHE, and FHE systems.


  As a fundraising method, Initial Coin Offering (ICO) has raised billions of
dollars for thousands of startups in the past two years. Existing ICO
mechanisms place more emphasis on the short-term benefits of maximal
fundraising while ignoring the problem of unbalanced token allocation, which
negatively impacts subsequent fundraising and has bad effects on introducing
new investors and resources. We propose a new ICO mechanism which uses the
concept of Gini index for the very first time as a mechanism design constraint
to control allocation inequality. Our mechanism maintains an elegant and
straightforward structure. It allows the agents to modify their bids as a price
discovery process, while limiting the bids of whales. We analyze the agents'
equilibrium behaviors under our mechanism. Under natural technical assumptions,
we show that most agents have simple dominant strategies and the equilibrium
revenue approaches the optimal revenue asymptotically in the number of agents.
We verify our mechanism using real ICO dataset we collected, and confirm that
our mechanism performs well in terms of both allocation fairness and revenue.


  Many call centres are subject to service level agreements that stipulate that
they must achieve targets in terms of the proportion of calls that are answered
within a specified time. In order to manage a centre so that targets like these
are met, we need to have a method of calculating the waiting time distributions
experienced by customers. In this paper, we provide such a method for small
call centres that employ skills-based routing. We first build the methodology
for the single-skill case and then extend it to a multi-skill case. We model
the call centre system as a continuous-time Markov chain and then make use of
the Laplace transform to calculate the relevant quantities. We later
demonstrate the use of this method to find the optimal routing policy in a
certain class of policies.


  Flux-dominated solar dynamo models, which have demonstrated to be quite
successful in reproducing most of the observed features of the large scale
solar magnetic cycle, generally produce an inappropriate latitudinal
distribution of the toroidal magnetic fields, showing fields of large magnitude
in polar regions where the radial shear has a maximum amplitude. Employing a
kinematic solar dynamo model, we here explore the contribution of both the
radial and the latitudinal shear in the generation of the toroidal magnetic
fields by varying the shape and the thickness of the solar tachocline. We also
explore the effects of the diffusivity profile of the convective zone. We find
that the latitudinal component is always dominant over the radial component at
producing toroidal field amplification. These results are very sensitive to the
adopted diffusivity profile, specially in the inner convection zone. A diagram
of the toroidal field at a latitude of 60 degrees versus the diffusivity at the
convection layer for different values of the tachocline width has revealed that
these fields are mainly eliminated for tachoclines with width d_1>0.08 Ro, for
a restric range of diffusivites; or for d_1<0.02 and almost any value of eta_c
in the appropriate solar range. For intermediate values of d_1=0.04Ro-0.06Ro,
strong toroidal fields should survive at high latitudes in the butterfly
diagram and those values are therefore not suitable.


  Modeling responses on the nodes of a large-scale network is an important task
that arises commonly in practice. This paper proposes a community network
vector autoregressive (CNAR) model, which utilizes the network structure to
characterize the dependence and intra-community homogeneity of the high
dimensional time series. The CNAR model greatly increases the flexibility and
generality of the network vector autoregressive (Zhu et al, 2017, NAR) model by
allowing heterogeneous network effects across different network communities. In
addition, the non-community-related latent factors are included to account for
unknown cross-sectional dependence. The number of network communities can
diverge as the network expands, which leads to estimating a diverging number of
model parameters. We obtain a set of stationary conditions and develop an
efficient two-step weighted least-squares estimator. The consistency and
asymptotic normality properties of the estimators are established. The
theoretical results show that the two-step estimator improves the one-step
estimator by an order of magnitude when the error admits a factor structure.
The advantages of the CNAR model are further illustrated on a variety of
synthetic and real datasets.


  Repulsive gravity is not very popular in physics. However, one comes across
it in at least two main occurrences in general relativity: in the negative-$r$
region of Kerr spacetime, and as the result of the gravitational interaction
between matter and antimatter, when the latter is assumed to be CPT-transformed
matter. Here we show how these two independent developments of general
relativity are perfectly consistent in predicting gravitational repulsion and
how the above Kerr negative-$r$ region can be interpreted as the habitat of
antimatter. As a consequence, matter particles traveling along vortical
geodesics can pass through the throat of a rotating black hole and emerge as
antimatter particles (and vice versa). An experimental definitive answer on the
gravitational behavior of antimatter is awaited in the next few years.


  We consider a loop mixing of two fermion fields of opposite parities whereas
the parity is conserved in a Lagrangian. Such kind of mixing is specific for
fermions and has no analogy in boson case. Possible applications of this effect
may be related with physics of baryon resonances. The obtained matrix
propagator defines a pair of unitary partial amplitudes which describe the
production of resonances of spin $J$ and different parity ${1/2}^{\pm}$ or
${3/2}^{\pm}$. The use of our amplitudes for joint description of $\pi N$
partial waves $P_{13}$ and $D_{13}$ shows that the discussed effect is clearly
seen in these partial waves as the specific form of interference between
resonance and background. Another interesting application of this effect may be
a pair of partial waves $S_{11}$ and $P_{11}$ where the picture is more
complicated due to presence of several resonance states.


  The mechanical and electronic properties of a graphene membrane placed on top
of a superlattice of nanopillars are investigated. We use molecular dynamics
(MD) simulations to access the deformation fields and the tight-binding
approaches to calculate the electronic properties. The system of interest
consists of a triangular lattice of nanopillars with a period of a=750 nm over
which the graphene layer is deposited. Ripples form in the graphene layer that
span across the unit cell, connecting neighbouring pillars, in agreement with
recent experiments. We find that the resulting pseudo-magnetic field (PMF)
varies strongly across the unit cell. We investigate the dependence of PMF on
unit cell boundary conditions, height of the pillars, and the strength of the
van der Waals interaction between graphene and the substrate. We find direct
correspondence with typical experiments on pillars, showing intrinsic "slack"
in the graphene membrane. PMF values are confirmed by the local density of
states calculations performed at different positions of the unit cell showing
pseudo-Landau levels at varying spacings. Our findings regarding the relaxed
membrane configuration and the induced strains are transferable to other
flexible 2D membranes.


  Consider the following cascading process on a simple undirected graph
$G(V,E)$ with diameter $\Delta$. In round zero, a set $S\subseteq V$ of
vertices, called the seeds, are active. In round $i+1,$ $i\in\mathbb{N},$ a
non-isolated vertex is activated if at least a $\rho\in(\,0,1\,]$ fraction of
its neighbors are active in round $i$; it is deactivated otherwise. For
$k\in\mathbb{N},$ let $\text{min-seed}^{(k)}(G,\rho)$ be the minimum number of
seeds needed to activate all vertices in or before round $k$. This paper
derives upper bounds on $\text{min-seed}^{(k)}(G,\rho)$. In particular, if $G$
is connected and there exist constants $C>0$ and $\gamma>2$ such that the
fraction of degree-$k$ vertices in $G$ is at most $C/k^\gamma$ for all
$k\in\mathbb{Z}^+,$ then
$\text{min-seed}^{(\Delta)}(G,\rho)=O(\lceil\rho^{\gamma-1}\,|\,V\,|\rceil)$.
Furthermore, for $n\in\mathbb{Z}^+,$ $p=\Omega((\ln{(e/\rho)})/(\rho n))$ and
with probability $1-\exp{(-n^{\Omega(1)})}$ over the Erd\H{o}s-R\'enyi random
graphs $G(n,p),$ $\text{min-seed}^{(1)}(G(n,p),\rho)=O(\rho n)$.


  We present a statistical analysis of the X-ray bursts observed from the 2002
June 18 outburst of the Anomalous X-ray Pulsar (AXP) 1E 2259+586, observed with
the Proportional Counter Array (PCA) aboard the Rossi X-ray Timing Explorer. We
show that the properties of these bursts are similar to those of Soft
Gamma-Repeaters (SGRs). The similarities we find are the burst durations follow
a log-normal distribution which peaks at 99 ms, the differential burst fluence
distribution is well described by a power law of index -1.7, the burst fluences
are positively correlated with the burst durations, the distribution of waiting
times is well described by a log-normal distribution of mean 47 s, and the
bursts are generally asymmetric with faster rise than fall times. However, we
find several quantitative differences between the AXP and SGR bursts.
Specifically, the AXP bursts we observed exhibit a wider range of durations,
the correlation between burst fluence and duration is flatter than for SGRs,
the observed AXP bursts are on average less energetic than observed SGR bursts,
and the more energetic AXP bursts have the hardest spectra -- the opposite of
what is seen for SGRs. We conclude that the bursts are sufficiently similar
that AXPs and SGRs can be considered united as a source class yet there are
some interesting differences that may help determine what physically
differentiates the two closely related manifestations of neutron stars.


  This study proposes an innovative explainable predictive quality analytics
solution to facilitate data-driven decision-making for process planning in
manufacturing by combining process mining, machine learning, and explainable
artificial intelligence (XAI) methods. For this purpose, after integrating the
top-floor and shop-floor data obtained from various enterprise information
systems, a deep learning model was applied to predict the process outcomes.
Since this study aims to operationalize the delivered predictive insights by
embedding them into decision-making processes, it is essential to generate
relevant explanations for domain experts. To this end, two complementary local
post-hoc explanation approaches, Shapley values and Individual Conditional
Expectation (ICE) plots are adopted, which are expected to enhance the
decision-making capabilities by enabling experts to examine explanations from
different perspectives. After assessing the predictive strength of the applied
deep neural network with relevant binary classification evaluation measures, a
discussion of the generated explanations is provided.


  We investigate vertical isomorphisms of Fedosov dg manifolds associated with
a Lie pair $(L,A)$, i.e. a pair of a Lie algebroid $L$ and a Lie subalgebroid
$A$ of $L$. The construction of Fedosov dg manifolds involves a choice of a
splitting and a connection. We prove that, given any two choices of a splitting
and a connection, there exists a unique vertical isomorphism, determined by an
iteration formula, between the two associated Fedosov dg manifolds. As an
application, we provide an explicit formula for the map
$\mathrm{pbw}_2^{-1}\circ \mathrm{pbw}_1$ associated with two
Poincar\'{e}--Birkhoff--Witt isomorphisms that arise from two choices of a
splitting and a connection.


  Individual-intelligence research, from a neurological perspective, discusses
the hierarchical layers of the cortex as a structure that performs conceptual
abstraction and specification. This theory has been used to explain how
motor-cortex regions responsible for different behavioral modalities such as
writing and speaking can be utilized to express the same general concept
represented higher in the cortical hierarchy. For example, the concept of a
dog, represented across a region of high-level cortical-neurons, can either be
written or spoken about depending on the individual's context. The higher-layer
cortical areas project down the hierarchy, sending abstract information to
specific regions of the motor-cortex for contextual implementation. In this
paper, this idea is expanded to incorporate collective-intelligence within a
hyper-cortical construct. This hyper-cortex is a multi-layered network used to
represent abstract collective concepts. These ideas play an important role in
understanding how collective-intelligence systems can be engineered to handle
problem abstraction and solution specification. Finally, a collection of common
problems in the scientific community are solved using an artificial
hyper-cortex generated from digital-library metadata.


  Data from e+e- annihilation into hadrons collected by the JADE experiment at
centre-of-mass energies between 14 and 44 GeV were used to study the 4-jet rate
using the Durham algorithm as well as the first five moments of event shape
observables. The data were compared with NLO QCD predictions, augmented by
resummed NLLA calculations for the 4-jet rate, in order to extract values of
the strong coupling constant alpha_S. The preliminary results are alpha_S(MZ) =
0.1169 +/- 0.0026 (4-jet rate) and alpha_S(MZ) = 0.1286 +/- 0.0072 (moments)
consistent with the world average value. For some of the higher moments
systematic deficiencies of the QCD predictions are observed.


  Quantum corrections to the conductivity allow experimental assessment of
electronic coherence in metals. We consider whether independent measurements of
different corrections are quantitatively consistent, particularly in systems
with spin-orbit or magnetic impurity scattering. We report weak localization
and time-dependent universal conductance fluctuation data in quasi-one- and
two-dimensional AuPd wires between 2 K and 20 K. The data inferred from both
methods are in excellent quantitative agreement, implying that precisely the
same coherence length is relevant to both corrections.


  Midterm stock price prediction is crucial for value investments in the stock
market. However, most deep learning models are essentially short-term and
applying them to midterm predictions encounters large cumulative errors because
they cannot avoid anomalies. In this paper, we propose a novel deep neural
network Mid-LSTM for midterm stock prediction, which incorporates the market
trend as hidden states. First, based on the autoregressive moving average model
(ARMA), a midterm ARMA is formulated by taking into consideration both hidden
states and the capital asset pricing model. Then, a midterm LSTM-based deep
neural network is designed, which consists of three components: LSTM, hidden
Markov model and linear regression networks. The proposed Mid-LSTM can avoid
anomalies to reduce large prediction errors, and has good explanatory effects
on the factors affecting stock prices. Extensive experiments on S&P 500 stocks
show that (i) the proposed Mid-LSTM achieves 2-4% improvement in prediction
accuracy, and (ii) in portfolio allocation investment, we achieve up to 120.16%
annual return and 2.99 average Sharpe ratio.


  Data-driven approaches to modeling physical systems fail to generalize to
unseen systems that share the same general dynamics with the learning domain,
but correspond to different physical contexts. We propose a new framework for
this key problem, context-informed dynamics adaptation (CoDA), which takes into
account the distributional shift across systems for fast and efficient
adaptation to new dynamics. CoDA leverages multiple environments, each
associated to a different dynamic, and learns to condition the dynamics model
on contextual parameters, specific to each environment. The conditioning is
performed via a hypernetwork, learned jointly with a context vector from
observed data. The proposed formulation constrains the search hypothesis space
to foster fast adaptation and better generalization across environments. We
theoretically motivate our approach and show state-of-the-art generalization
results on a set of nonlinear dynamics, representative of a variety of
application domains. We also show, on these systems, that new system parameters
can be inferred from context vectors with minimal supervision. Code is
available at https://github.com/yuan-yin/CoDA .


  The fifth-generation (5G) network is likely to bring in high data rates, more
reliability, and low delays for mobile, personal and local area networks.
Alongside the rapid growth of smart wireless sensing and communication
technologies, data traffic has significantly risen, and existing 5G networks
are not fully capable of supporting future massive data traffic in terms of
services, storage, and processing. To meet the forthcoming challenges, the
research community is investigating the Terahertz-based sixth-generation (6G)
wireless network which is supposed to be offered for industrial usage in around
10 years. This is the right time to explore and learn about various 6G aspects
that will play a key role in the successful execution and implementation of 6G
networks in the future. This survey provides a review of specifications,
requirements, applications, enabling technologies including disruptive and
innovative, integration of 6G with advanced architectures and networks like
software-defined networks (SDN), network functions virtualization (NFV),
cloud/fog computing, etc, artificial intelligence (AI) oriented technologies,
privacy and security issues and solutions, and potential futuristic use cases:
virtual reality, smart healthcare and Industry 5.0. Furthermore, based on the
conducted review, challenges and future research directions are highlighted to
aid the deployment of 6G networks.


  Resonant inelastic x-ray scattering (RIXS) tuned for the Cu L edge is a
possible tool to detect charge excitations in cuprate superconductors. We
theoretically investigate the possibility for observing a collective charge
excitation by the RIXS. The RIXS process via the intermediate state inevitably
makes the spectral weight of charge excitation stronger in electron doping than
in hole doping. Electron-hole asymmetry also appears in the dynamical charge
structure factor, showing a new enhanced small-momentum low-energy mode in
electron doping. These facts indicate a possibility of detecting the new charge
mode by RIXS in electron-doped systems.


  Determinantal Point Processes (DPPs) were introduced by Macchi as a model for
repulsive (fermionic) particle distributions. But their recent popularization
is largely due to their usefulness for encouraging diversity in the final stage
of a recommender system.
  The standard sampling scheme for finite DPPs is a spectral decomposition
followed by an equivalent of a randomly diagonally-pivoted Cholesky
factorization of an orthogonal projection, which is only applicable to
Hermitian kernels and has an expensive setup cost. Researchers have begun to
connect DPP sampling to $LDL^H$ factorizations as a means of avoiding the
initial spectral decomposition, but existing approaches have only outperformed
the spectral decomposition approach in special circumstances, where the number
of kept modes is a small percentage of the ground set size.
  This article proves that trivial modifications of $LU$ and $LDL^H$
factorizations yield efficient direct sampling schemes for non-Hermitian and
Hermitian DPP kernels, respectively. Further, it is experimentally shown that
even dynamically-scheduled, shared-memory parallelizations of high-performance
dense and sparse-direct factorizations can be trivially modified to yield DPP
sampling schemes with essentially identical performance.
  The software developed as part of this research, Catamari,
https://hodgestar.com/catamari, is released under the Mozilla Public License
v2.0. It contains header-only, C++14 plus OpenMP 4.0 implementations of dense
and sparse-direct, Hermitian and non-Hermitian DPP samplers.


  Aims. We study whether rotational excitation makes a difference to the
abundances of the $\rm H_3^+$ isotopologs, including spin states, in physical
conditions corresponding to starless cores and protostellar envelopes.
  Methods. We developed a new rate coefficient set for the $\rm H_3^+$
isotopologs, allowing for rotational excitation, using the state-to-state rate
coefficients from Hugo et al. These new so-called species-to-species rate
coefficients are compared with previously-used ground state-to-species rate
coefficients.
  Results. The species-to-species and ground state-to-species model results
differ at high density and toward increasing temperatures ($T > 10$ K). The
species-to-species model predicts a lower $\rm H_3^+$ deuteration degree at
high density owing to an increase of the rate coefficients of endothermic
reactions that decrease deuteration. At 20 K the ground state-to-species model
overestimates the abundance of $\rm H_2D^+$ by a factor of about two while the
abundance of $\rm D_3^+$ can differ by an order of magnitude between the
models. Spin-state abundance ratios are also affected, and the new model better
reproduces recent observations of ortho and para $\rm H_2D^+$ and $\rm D_2H^+$.
The applicability regime of the new rate coefficients depends on the critical
densities of the various rotational transitions.
  Conclusions. The difference in the abundances of the $\rm H_3^+$ isotopologs
predicted by the two models is negligible at 10 K but excited states are very
important in studies of deuteration at higher temperatures, for example in
protostellar envelopes. The species-to-species rate coefficients provide a more
realistic approach to the chemistry of the $\rm H_3^+$ isotopologs than the
ground state-to-species rate coefficients do, and so the former should be
adopted in chemical models describing the chemistry of the $\rm H_3^+ + H_2$
reacting system.


  In the last two decades, theories explaining the same experiments as well as
special relativity does, were developed by using different synchronization
procedures. All of them are ether-like theories. Most authors believe these
theories to be equivalent to special relativity, but no general proof was ever
brought. By means of a Gedankenexperiment on light aberration, we produce
strong evidence that this is the case for experiments made in inertial systems.


  A quantum-limited amplifier enables the amplification of weak signals while
introducing minimal noise dictated by the principles of quantum mechanics.
These amplifiers serve a broad spectrum of applications in quantum computing,
including fast and accurate readout of superconducting qubits and spins, as
well as various uses in quantum sensing and metrology. Parametric
amplification, primarily developed using Josephson junctions, has evolved into
the leading technology for highly effective microwave measurements within
quantum circuits. Despite their significant contributions, these amplifiers
face fundamental limitations, such as their inability to handle high powers,
sensitivity to parasitic magnetic fields, and particularly their limitation to
operate only at millikelvin temperatures. To tackle these challenges, here we
experimentally develop a novel quantum-limited amplifier based on
superconducting kinetic inductance and present an extensive theoretical model
to describe this nonlinear coupled-mode system. Our device surpasses the
conventional constraints associated with Josephson junction amplifiers by
operating at much higher temperatures up to 4.5 K. With two distinct spectral
modes and tunability through bias current, this amplifier can operate
selectively in both single and double-mode amplification regimes near the
quantum noise limit. Utilizing a nonlinear thin film exhibiting kinetic
inductance, our device attains gain exceeding 50 dB in a single-mode and 32 dB
in a double-mode configuration while adding 0.35 input-referred quanta of
noise. Importantly, this amplifier eliminates the need for Josephson junctions,
resulting in significantly higher power handling capabilities than
Josephson-based amplifiers. It also demonstrates resilience in the presence of
magnetic fields, offers a straightforward design, and enhances reliability.


  Dimensionality reduction is a crucial step for pattern recognition and data
mining tasks to overcome the curse of dimensionality. Principal component
analysis (PCA) is a traditional technique for unsupervised dimensionality
reduction, which is often employed to seek a projection to best represent the
data in a least-squares sense, but if the original data is nonlinear structure,
the performance of PCA will quickly drop. An supervised dimensionality
reduction algorithm called Linear discriminant analysis (LDA) seeks for an
embedding transformation, which can work well with Gaussian distribution data
or single-modal data, but for non-Gaussian distribution data or multimodal
data, it gives undesired results. What is worse, the dimension of LDA cannot be
more than the number of classes. In order to solve these issues, Local shrunk
discriminant analysis (LSDA) is proposed in this work to process the
non-Gaussian distribution data or multimodal data, which not only incorporate
both the linear and nonlinear structures of original data, but also learn the
pattern shrinking to make the data more flexible to fit the manifold structure.
Further, LSDA has more strong generalization performance, whose objective
function will become local LDA and traditional LDA when different extreme
parameters are utilized respectively. What is more, a new efficient
optimization algorithm is introduced to solve the non-convex objective function
with low computational cost. Compared with other related approaches, such as
PCA, LDA and local LDA, the proposed method can derive a subspace which is more
suitable for non-Gaussian distribution and real data. Promising experimental
results on different kinds of data sets demonstrate the effectiveness of the
proposed approach.


  We characterise higher order Riesz transforms on the Heisenberg group and
also show that they satisfy dimension-free bounds under some assumptions on the
multipliers. Using transfer- ence theorems, we deduce boundedness theorems for
Riesz trans- forms on the reduced Heisenberg group and hence also for the Riesz
transforms associated to multiple Hermite and Laguerre ex- pansions.


  Most learners fail to develop deep text comprehension when reading textbooks
passively. Posing questions about what learners have read is a well-established
way of fostering their text comprehension. However, many textbooks lack
self-assessment questions because authoring them is timeconsuming and
expensive. Automatic question generators may alleviate this scarcity by
generating sound pedagogical questions. However, generating questions
automatically poses linguistic and pedagogical challenges. What should we ask?
And, how do we phrase the question automatically? We address those challenges
with an automatic question generator grounded in learning theory. The paper
introduces a novel pedagogically meaningful content selection mechanism to find
question-worthy sentences and answers in arbitrary textbook contents. We
conducted an empirical evaluation study with educational experts, annotating
150 generated questions in six different domains. Results indicate a high
linguistic quality of the generated questions. Furthermore, the evaluation
results imply that the majority of the generated questions inquire central
information related to the given text and may foster text comprehension in
specific learning scenarios.


  Reinforcement learning (RL) tasks are challenging to implement, execute and
test due to algorithmic instability, hyper-parameter sensitivity, and
heterogeneous distributed communication patterns. We argue for the separation
of logical component composition, backend graph definition, and distributed
execution. To this end, we introduce RLgraph, a library for designing and
executing reinforcement learning tasks in both static graph and define-by-run
paradigms. The resulting implementations are robust, incrementally testable,
and yield high performance across different deep learning frameworks and
distributed backends.


  Antibodies comprise the most versatile class of binding molecules, with
numerous applications in biomedicine. Computational design of antibodies
involves generating novel and diverse sequences, while maintaining structural
consistency. Unique to antibodies, designing the complementarity-determining
region (CDR), which determines the antigen binding affinity and specificity,
creates its own unique challenges. Recent deep learning models have shown
impressive results, however the limited number of known antibody
sequence/structure pairs frequently leads to degraded performance, particularly
lacking diversity in the generated sequences. In our work we address this
challenge by leveraging Model Reprogramming (MR), which repurposes pretrained
models on a source language to adapt to the tasks that are in a different
language and have scarce data - where it may be difficult to train a
high-performing model from scratch or effectively fine-tune an existing
pre-trained model on the specific task. Specifically, we introduce ReprogBert
in which a pretrained English language model is repurposed for protein sequence
infilling - thus considers cross-language adaptation using less data. Results
on antibody design benchmarks show that our model on low-resourced antibody
sequence dataset provides highly diverse CDR sequences, up to more than a
two-fold increase of diversity over the baselines, without losing structural
integrity and naturalness. The generated sequences also demonstrate enhanced
antigen binding specificity and virus neutralization ability. Code is available
at https://github.com/IBM/ReprogBERT


  Recent approaches to verifying programs in separation logics for concurrency
have used state transition systems (STSs) to specify the atomic operations of
programs. A key challenge in the setting has been to compose such STSs into
larger ones, while enabling programs specified under one STS to be linked to a
larger one, without reverification. This paper develops a notion of morphism
between two STSs which permits such lifting. The morphisms are a constructive
form of simulation between the STSs, and lead to a general and concise proof
system. We illustrate the concept and its generality on several disparate
examples, including staged construction of a readers/writers lock and its
proof, and of proofs about quiescence when concurrent programs are executed
without external interference.


  It is a well known result that for $\beta\in(1,\frac{1+\sqrt{5}}{2})$ and
$x\in(0,\frac{1}{\beta-1})$ there exists uncountably many
$(\epsilon_{i})_{i=1}^{\infty}\in {0,1}^{\mathbb{N}}$ such that
$x=\sum_{i=1}^{\infty}\epsilon_{i}\beta^{-i}.$ When
$\beta\in(\frac{1+\sqrt{5}}{2},2]$ there exists $x\in (0,\frac{1}{\beta-1})$
for which there exists a unique $(\epsilon_{i})_{i=1}^{\infty}\in
{0,1}^{\mathbb{N}}$ such that $x=\sum_{i=1}^{\infty}\epsilon_{i}\beta^{-i}.$ In
this paper we consider the more general case when our sequences are elements of
${0,...,m}^{\mathbb{N}}.$ We show that an analogue of the golden ratio exists
and give an explicit formula for it.


  We present an analysis of 7Li/6Li isotope ratios along four sight lines that
probe diffuse molecular gas near the supernova remnant IC 443. Recent gamma-ray
observations have revealed the presence of shock-accelerated cosmic rays
interacting with the molecular cloud surrounding the remnant. Our results
indicate that the 7Li/6Li ratio is lower in regions more strongly affected by
these interactions, a sign of recent Li production by cosmic rays. We find that
7Li/6Li ~ 7 toward HD 254755, which is located just outside the visible edge of
IC 443, while 7Li/6Li ~ 3 along the line of sight to HD 43582, which probes the
interior region of the supernova remnant. No evidence of 7Li synthesis by
neutrino-induced spallation is found in material presumably contaminated by the
ejecta of a core-collapse supernova. The lack of a neutrino signature in the
7Li/6Li ratios near IC 443 is consistent with recent models of Galactic
chemical evolution, which suggest that the nu-process plays only a minor role
in Li production.


  We consider a chain of spinful fermions with nearest neighbor hopping in the
presence of a $XY$ antiferromagnetic interaction. The $XY$ term is mapped onto
a Kitaev chain at half-filling such that displays a bosonic zero mode
topologically protected and long-range order. As the strength of the hopping
amplitude is changed, the system undergoes a quantum phase transition from the
topological non-trivial to the trivial phase. We apply the finite-size scaling
method to determinate the phase diagram of the model.


  Tensor train (TT) decomposition is a powerful representation for high-order
tensors, which has been successfully applied to various machine learning tasks
in recent years. However, since the tensor product is not commutative,
permutation of data dimensions makes solutions and TT-ranks of TT decomposition
inconsistent. To alleviate this problem, we propose a permutation symmetric
network structure by employing circular multilinear products over a sequence of
low-order core tensors. This network structure can be graphically interpreted
as a cyclic interconnection of tensors, and thus we call it tensor ring (TR)
representation. We develop several efficient algorithms to learn TR
representation with adaptive TR-ranks by employing low-rank approximations.
Furthermore, mathematical properties are investigated, which enables us to
perform basic operations in a computationally efficiently way by using TR
representations. Experimental results on synthetic signals and real-world
datasets demonstrate that the proposed TR network is more expressive and
consistently informative than existing TT networks.


  We study the existence, the stability and the non-degeneracy of normalized
standing-waves solutions to a one dimensional non-linear Schr\"odinger
equation. The non-linearity belongs to a class of algebraic functions
appropriately defined. We can show that for some of these non-linearities one
can observe the existence of degenerate minima, and the multiplicity of
positive, radially symmetric minima having the same mass and the same energy.
We also prove the stability of the ground-state and the stability of normalized
standing-waves whose profile is a minimum of the energy constrained to the
mass.


  We define a family of polynomial ring homomorphisms generalising the Nagata
automorphism, and relate the condition of non-vanishing of their Jacobian
determinant with a certain linear partial differential equation. We also give a
condition on that family to get wild automorphisms.


  We investigate the phase behaviour of 2D mixtures of bi-functional and
three-functional patchy particles and 3D mixtures of bi-functional and
tetra-functional patchy particles by means of Monte Carlo simulations and
Wertheim theory. We start by computing the critical points of the pure systems
and then we investigate how the critical parameters change upon lowering the
temperature. We extend the Successive Umbrella Sampling method to mixtures to
make it possible to extract information about the phase behaviour of the system
at a fixed temperature for the whole range of densities and compositions of
interest.


  It is shown in the framework of the operator product expansion and the
renormalization group method that the twist-3 part of flavour nonsinglet spin
structure function g_2(x,Q^2) obeys a simple Dokshitzer-Gribov-
Lipatov-Altarelli-Parisi (DGLAP) equation in the large N_C limit even in the
case of massive quarks (N_C is the number of colours). There are four different
types of twist-3 operators which contribute to g_2, including
quark-mass-dependent operators and the ones proportional to the equation of
motion. They are not all independent but are constrained by one relation. A new
choice of the independent operator bases leads to a simple form of the
evolution equation for g_2 at large N_C.


  Early-stage bedforms develop into mature dunes through complex interactions
between wind, sand transport and surface topography. Depending on varying
environmental and wind conditions, the mechanism driving dune formation and,
ultimately, the shape of nascent dunes may differ markedly. In cases where sand
availability is plentiful one mechanism suggested for triggering dune growth is
linear stability theory. Until now, this theory has only been tested using
field evidence in unidirectional winds. However, in many areas of the world and
on other planets where aeolian processes act, wind regimes are often bimodal or
multimodal. Here, we investigate field evidence of protodune formation under
bimodal winds by applying linear stability analysis to a developing protodune
field. Employing recent theoretical and experimental research, combined with
in-situ wind, sediment transport, and topographic measurements during a month
long field campaign at Great Sand Dunes National Park, Colorado, USA, we use a
development of the linear stability theory to predict the spatial
characteristics (orientation and wavelength) and temporal evolution (growth
rate and migration velocity) of a protodune field. We find that the theoretical
predictions compare well with measured dunefield attributes as characterized by
high-resolution Digital Elevation Models measured using repeat terres trial
laser scanning. Our findings suggest that linear stability theory is a
quantitative predictor of protodune development on sandy surfaces with a
bimodal wind regime. Our findings are significant as they offer critical
validation of the linear stability theory for explaining the initiation and
development of dunes towards maturity in a complex natural environment.


  We consider coherent electromagnetic processes for colliders with short
bunches, in particular the coherent bremsstrahlung (CBS). CBS is the radiation
of one bunch particles in the collective field of the oncoming bunch. It can be
a potential tool for optimizing collisions and for measuring beam parameters. A
new simple and transparent method to calculate CBS is presented based on the
equivalent photon approximation for this collective field. The results are
applied to the $\phi$--factory $DA\Phi NE$. For this collider about $ 5 \cdot
10^{14} d E_\gamma / E_\gamma$ photons per second are expected in the photon
energy $E_\gamma$ range from the visible light up to 25 eV.


  Can the latent spaces of modern generative neural rendering models serve as
representations for 3D-aware discriminative visual understanding tasks? We use
retrieval as a proxy for measuring the metric learning properties of the latent
spaces of Shap-E, including capturing view-independence and enabling the
aggregation of scene representations from the representations of individual
image views, and find that Shap-E representations outperform those of the
classical EfficientNet baseline representations zero-shot, and is still
competitive when both methods are trained using a contrative loss. These
findings give preliminary indication that 3D-based rendering and generative
models can yield useful representations for discriminative tasks in our
innately 3D-native world. Our code is available at
\url{https://github.com/michaelwilliamtang/golden-retriever}.


  According to Anderson's orthogonality catastrophe, the overlap of the
$N$-particle ground states of a free Fermi gas with and without an (electric)
potential decays in the thermodynamic limit. For the finite one-dimensional
system various boundary conditions are employed. Unlike the usual setup the
perturbation is introduced by a magnetic (vector) potential. Although such a
magnetic field can be gauged away in one spatial dimension there is a
significant and interesting effect on the overlap caused by the phases. We
study the leading asymptotics of the overlap of the two ground states and the
two-term asymptotics of the difference of the ground-state energies. In the
case of periodic boundary conditions our main result on the overlap is based
upon a well-known asymptotic expansion by Fisher and Hartwig on Toeplitz
determinants with a discontinuous symbol. In the case of Dirichlet boundary
conditions no such result is known to us and we only provide an upper bound on
the overlap, presumably of the right asymptotic order.


  Injection of spins into semiconductors is essential for the integration of
the spin functionality into conventional electronics. Insulating layers are
often inserted between ferromagnetic metals and semiconductors for obtaining an
efficient spin injection, and it is therefore crucial to distinguish between
signatures of electrical spin injection and impurity-driven effects in the
tunnel barrier. Here we demonstrate an impurity-assisted tunneling
magnetoresistance effect in nonmagnetic-insulator-nonmagnetic and
ferromagnetic-insulator-nonmagnetic tunnel barriers. In both cases, the effect
reflects on/off switching of the tunneling current through impurity channels by
the external magnetic field. The reported effect, which is universal for any
impurity-assisted tunneling process, finally clarifies the controversy of a
widely used technique that employs the same ferromagnetic electrode to inject
and detect spin accumulation.


  Macroscopic systems are described most completely by local densities
(particle number, momentum and energy) yet the superposition states of such
physical variables, indicated by the Everett interpretation, are not observed.
In order to explain this, it is argued that histories of local number, momentum
and energy density are approximately decoherent when coarse-grained over
sufficiently large volumes. Decoherence arises directly from the proximity of
these variables to exactly conserved quantities (which are exactly decoherent),
and not from environmentally-induced decoherence. We discuss the approach to
local equilibrium and the subsequent emergence of hydrodynamic equations for
the local densities. The results are general but we focus on a chain of
oscillators as a specific example in which explicit calculations may be carried
out. We discuss the relationships between environmentally-induced and
conservation-induced decoherence and present a unified view of these two
mechanisms.


  A review of methods for finding general expressions for matrix elements
(non-diagonal with respect to configurations included) of any one- and
two-particle operator for an arbitrary number of shells in an atomic
configuration is given. These methods are compared in various aspects, and the
advantages or shortcomings of each particular method are discussed. Efficient
method to find the abovementioned quantities in LS coupling is presented, based
on the use of symmetry properties of operators and matrix elements in three
spaces (orbital, spin and quasispin), second quantization in coupled tensorial
form, graphical technique and Wick's theorem. This allows to efficiently
account for correlation effects practically for any atom and ion of periodical
table.


  Tau-based finite-energy sum-rule (FESR) analyses often assume that scales
$s_0\sim m_\tau^2$ are large enough that (i) integrated duality violations
(DVs) can be neglected, and (ii) contributions from non-perturbative OPE
condensates of dimension $D$ scale as $(\Lambda_{\rm QCD}/m_\tau )^D$, allowing
the OPE series to be truncated at low dimension. The latter assumption is not
necessarily valid since the OPE series is not convergent, while the former is
open to question given experimental results for the electromagnetic, $I=1$
vector ($V$), $I=1$ axial vector ($A$) and $I=1$ $V+A$ current spectral
functions, which show DV oscillations with amplitudes comparable in size to the
corresponding $\alpha_s$-dependent perturbative contributions at $s\sim2-3$
GeV$^2$. Here, we discuss recently introduced new tools for assessing the
numerical relevance of omitted higher-$D$ OPE contributions. Applying these to
the ``truncated OPE'' strategy used in Refs.[1,2] and earlier work by the same
authors, we find that this strategy fails to yield reliable results for the
strong coupling from hadronic $\tau$ decays.


  In this paper, I propose a project of enlisting quantum information science
as a source of task-oriented axioms for use in the investigation of operational
theories in a general framework capable of encompassing quantum mechanics,
classical theory, and more. Whatever else they may be, quantum states of
systems are compendia of probabilities for the outcomes of possible operations
we may perform on the systems: ``operational theories.'' I discuss appropriate
general frameworks for such theories, in which convexity plays a key role. Such
frameworks are appropriate for investigating what things look like from an
``inside view,'' i.e. for describing perspectival information that one
subsystem of the world can have about another. Understanding how such views can
combine, and whether an overall ``geometric'' picture (``outside view'')
coordinating them all can be had, even if this picture is very different in
nature from the structure of the perspectives within it, is the key to
understanding whether we may be able to achieve a unified, ``objective''
physical view in which quantum mechanics is the appropriate description for
certain perspectives, or whether quantum mechanics is truly telling us we must
go beyond this ``geometric'' conception of physics. The nature of information,
its flow and processing, as seen from various operational persepectives, is
likely to be key to understanding whether and how such coordination and
unification can be achieved.


  This paper investigates a novel problem of generating images from visual
attributes. We model the image as a composite of foreground and background and
develop a layered generative model with disentangled latent variables that can
be learned end-to-end using a variational auto-encoder. We experiment with
natural images of faces and birds and demonstrate that the proposed models are
capable of generating realistic and diverse samples with disentangled latent
representations. We use a general energy minimization algorithm for posterior
inference of latent variables given novel images. Therefore, the learned
generative models show excellent quantitative and visual results in the tasks
of attribute-conditioned image reconstruction and completion.


  Not only a voltage controlled oscillator (VCO) is one of the most significant
component of every telecommunication system, but also it has been widely used
in many other high-speed systems. In fact, a VCO has an important role in
system operation, in other word, VCO is the heart of a system which gives
existence to it. Nowadays, designing a high performance VCO for different
applications is a challenging task for engineers. Moreover, up to now many
designs and solutions have been proposed by the scientists to improve the
performance of VCOs to be exploited in different cutting-edge applications. In
this paper I will give a brief overview of the new techniques in designing high
performance VCOs. Furthermore, the results of the proposed solutions have been
compared with each other to see which method has advantages over the others.


  In the paper, we consider several types of queries for classical and new
problems of learning and testing read-once functions. In several cases, the
border between polynomial and exponential complexities is obtained.


  We present black hole solutions in $2+1-$dimensional Einstein's theory of
gravity coupled with Born-Infeld nonlinear electrodynamic and a massless
self-interacting scalar field. The model has five free parameters: mass $M$,
cosmological constant $\ell $, electric $q$ and scalar $r_{0}$ charges and
Born-Infeld parameter $\beta $. To attain exact solution for such a highly
non-linear system we adjust, i.e. finely tune, the parameters of the theory
with the integration constants. In the limit $\beta \rightarrow 0$ we recover
the results of Einstein-Maxwell-Scalar theory, obtained before. The self
interacting potential admits finite minima apt for the vacuum contribution.
Hawking temperature of the model is investigated versus properly tuned
parameters. By employing this tuned-solution as basis, we obtain also a dynamic
solution which in the proper limit admits the known solution in Einstein
gravity coupled with self-interacting scalar field. Finally we establish the
equations of a general scalar-tensor field coupled to nonlinear electrodynamics
field\ in $2+1-$dimensions without searching for exact solutions.


  We revisit the study of a 2D quantum field theory in the hydrodynamic regime
and develop a formalism based on Euclidean one-loop partition functions that is
suitable to analyze transport properties due to gauge and gravitational
anomalies. To do so, we generalize the method of a modified Dirac operator
developed for zero-temperature anomalies to finite temperature, chemical
potentials and rotations.


  Occupational exoskeletons promise to alleviate musculoskeletal injuries among
industrial workers. Knowledge of the usability of the exoskeleton designs with
respect to the user device interaction points, and the problems in design
features, functions and parts, evaluated and rated using design principles is
still limited. Further, the usability of exoskeletons when assembling, donning,
doffing and disassembling them, tasks that can be considered pre and post use
tasks are also critical to evaluate, especially from a device design
standpoint. We conducted a heuristic evaluation of the usability of three
popular exoskeletons, a back support device, a shoulder support device, and a
sit stand exoskeleton when assembling, donning, doffing and disassembling them.
Seven evaluators used Nielsen and Shneiderman usability heuristics to evaluate
the devices. Results indicate that none of the three exoskeletons had any
catastrophic usability problems, but all three had major usability problems
including accommodating diverse users, the assembly, donning and doffing being
a two person operation, poor documentation, a lack of sequence indicators
during assembly of the devices, presence of safety hazards while donning and
doffing the devices, and manual strength requirements. Further, the assembly
task is the most difficult task resulting in the most violations of usability
heuristics. The exoskeleton human factors research community should include
diverse users in their evaluations and conduct usability, accessibility, and
safety evaluations of these devices to provide design feedback to device
designers.


  This paper is concerned with the integrodifferential equation $$\partial_t
u-\Delta u -\int_0^\infty \kappa(s)\Delta u(t-s)\,\d s + \varphi(u)=f$$ arising
in the Coleman-Gurtin's theory of heat conduction with hereditary memory, in
presence of a nonlinearity $\varphi$ of critical growth. Rephrasing the
equation within the history space framework, we prove the existence of global
and exponential attractors of optimal regularity and finite fractal dimension
for the related solution semigroup, acting both on the basic weak-energy space
and on a more regular phase space.


  We generalize QCD at asymptotically large isospin chemical potential to an
arbitrary even number of flavors. We also allow for small quark chemical
potentials, which stress the coincident Fermi surfaces of the paired quarks and
lead to a sign problem in Monte Carlo simulations. We derive the corresponding
low-energy effective theory in both $p$- and $\epsilon$-expansion and quantify
the severity of the sign problem. We construct the random matrix theory
describing our physical situation and show that it can be mapped to a known
random matrix theory at low baryon density so that new insights can be gained
without additional calculations. In particular, we explain the Silver Blaze
phenomenon at high isospin density. We also introduce stressed singular values
of the Dirac operator and relate them to the pionic condensate. Finally we
comment on extensions of our work to two-color QCD.


  We report the results of a combined experimental and theoretical study on
nonstoichiometric CrN1+d thin films grown by reactive magnetron sputtering on
c-plane sapphire, MgO (100) and LaAlO3 (100) substrates in a Ar/N2 gas mixture
using different percentage of N2. There is a transition from n-type to p-type
behavior in the layers as a function of nitrogen concentration varying from 48
at. % to 52 at. % in CrN films. The compositional change follows a similar
trend for all substrates, with a N/Cr ratio increasing from approximately 0.7
to 1.06-1.10 by increasing percentage of N2 in the gas flow ratio. As a result
of the change in stoichiometry, the lattice parameter and the Seebeck
coefficient increase together with the increase of N in CrN1+d; in particular,
the Seebeck value coefficient transitions from -50 uV.K-1 for CrN0.97 to +75
uV.K-1 for CrN1.1. Density functional theory calculations show that Cr
vacancies can account for the change in Seebeck coefficient, since they push
the Fermi level down in the valence band, whereas N interstitial defects in the
form of N2 dumbbells are needed to explain the increasing lattice parameter.
Calculations including both types of defects, which have a strong tendency to
bind together, reveal a slight increase in the lattice parameter and a
simultaneous formation of holes in the valence band. To explain the
experimental trends, we argue that both Cr vacancies and N2 dumbbells, possibly
in combined configurations, are present in the films. We demonstrate the
possibility of controlling the semiconducting behavior of CrN with intrinsic
defects from n- to p-type, opening possibilities to integrate this compound in
energy-harvesting thermoelectric devices.


  The magnetic, thermodynamic and electrical/thermal transport properties of
the caged-structure quasi-skutterudite Gd$_3$Ir$_4$Sn$_{13}$ are
re-investigated. The magnetization $M(T)$, specific heat $C_p(T)$ and the
resistivity $\rho(T)$ reveal a double-phase transition -- at $T_{N1}\sim$ 10~K
and at $T_{N2}\sim$ 8.8~K -- which was not observed in the previous report on
this compound. The antiferromagnetic transition is also visible in the thermal
transport data, thereby suggesting a close connection between the electronic
and lattice degrees of freedom in this Sn-based quasi-skutterudite. The
temperature dependence of $\rho(T)$ is analyzed in terms of a power-law for
resistivity pertinent to Fermi liquid picture. Giant, positive
magnetoresistance (MR) $\approx$ 80$\%$ is observed in Gd$_3$Ir$_4$Sn$_{13}$ at
2~K with the application of 9~T. The giant MR and the double magnetic
transition can be attributed to the quasi-cages and layered antiferromagnetic
structure of Gd$_3$Ir$_4$Sn$_{13}$ vulnerable to structural distortions and/or
dipolar or spin-reorientation effects. The giant value of MR observed in this
class of 3:4:13 type alloys, especially in a Gd-compound, is the highlight of
this work.


  This paper presents VulnSense framework, a comprehensive approach to
efficiently detect vulnerabilities in Ethereum smart contracts using a
multimodal learning approach on graph-based and natural language processing
(NLP) models. Our proposed framework combines three types of features from
smart contracts comprising source code, opcode sequences, and control flow
graph (CFG) extracted from bytecode. We employ Bidirectional Encoder
Representations from Transformers (BERT), Bidirectional Long Short-Term Memory
(BiLSTM) and Graph Neural Network (GNN) models to extract and analyze these
features. The final layer of our multimodal approach consists of a fully
connected layer used to predict vulnerabilities in Ethereum smart contracts.
Addressing limitations of existing vulnerability detection methods relying on
single-feature or single-model deep learning techniques, our method surpasses
accuracy and effectiveness constraints. We assess VulnSense using a collection
of 1.769 smart contracts derived from the combination of three datasets:
Curated, SolidiFI-Benchmark, and Smartbugs Wild. We then make a comparison with
various unimodal and multimodal learning techniques contributed by GNN, BiLSTM
and BERT architectures. The experimental outcomes demonstrate the superior
performance of our proposed approach, achieving an average accuracy of 77.96\%
across all three categories of vulnerable smart contracts.


  Adapting to the changes in transition dynamics is essential in robotic
applications. By learning a conditional policy with a compact context,
context-aware meta-reinforcement learning provides a flexible way to adjust
behavior according to dynamics changes. However, in real-world applications,
the agent may encounter complex dynamics changes. Multiple confounders can
influence the transition dynamics, making it challenging to infer accurate
context for decision-making. This paper addresses such a challenge by
Decomposed Mutual INformation Optimization (DOMINO) for context learning, which
explicitly learns a disentangled context to maximize the mutual information
between the context and historical trajectories, while minimizing the state
transition prediction error. Our theoretical analysis shows that DOMINO can
overcome the underestimation of the mutual information caused by
multi-confounded challenges via learning disentangled context and reduce the
demand for the number of samples collected in various environments. Extensive
experiments show that the context learned by DOMINO benefits both model-based
and model-free reinforcement learning algorithms for dynamics generalization in
terms of sample efficiency and performance in unseen environments.


  This note is an extended version of the slides for my talk with the same
title at the {\it Arithmetic, geometry, and modular forms: a conference in
honour of Bill Duke} in June 2019 at the ETH in Z"urich. The results presented
concern three geometric criteria for the integrality of factorial ratios,
numbers such as (30n)!n!/(6n)!(10n)!(15n)!, which are integral in a
non-immediate way for all n. This work is an offshoot of an ongoing project on
hypergeometric motives joint with D. Roberts and M. Watkins.


  Diagrammatic techniques are invented to implement QCD gauge transformations.
These techniques can be used to discover how gauge-dependent terms are
cancelled among diagrams to yield gauge-invariant results in the sum. In this
way a multiloop pinching technique can be developed to change ordinary vertices
into background-gauge vertices. The techniques can also be used to design new
gauges to simplify calculations by reducing the number of gauge-dependent terms
present in the intermediate steps. Two examples are discussed to illustrate
this aspect of the applications.


  In this article we describe relations of the topology of closed 1-forms to
the group theoretic invariants of Bieri-Neumann-Strebel-Renz. Starting with a
survey, we extend these Sigma invariants to finite CW- complexes and show that
many properties of the group theoretic version have analogous statements. In
particular we show the relation between Sigma invariants and finiteness
properties of certain infinite covering spaces. We also discuss applications of
these invariants to the Lusternik- Schnirelmann category of a closed 1-form and
to the existence of a non- singular closed 1-form in a given cohomology class
on a high-dimensional closed manifold.


  Reinforcement learning has been increasingly applied in monitoring
applications because of its ability to learn from previous experiences and can
make adaptive decisions. However, existing machine learning-based health
monitoring applications are mostly supervised learning algorithms, trained on
labels and they cannot make adaptive decisions in an uncertain complex
environment. This study proposes a novel and generic system, predictive deep
reinforcement learning (PDRL) with multiple RL agents in a time series
forecasting environment. The proposed generic framework accommodates virtual
Deep Q Network (DQN) agents to monitor predicted future states of a complex
environment with a well-defined reward policy so that the agent learns existing
knowledge while maximizing their rewards. In the evaluation process of the
proposed framework, three DRL agents were deployed to monitor a subject's
future heart rate, respiration, and temperature predicted using a BiLSTM model.
With each iteration, the three agents were able to learn the associated
patterns and their cumulative rewards gradually increased. It outperformed the
baseline models for all three monitoring agents. The proposed PDRL framework is
able to achieve state-of-the-art performance in the time series forecasting
process. The proposed DRL agents and deep learning model in the PDRL framework
are customized to implement the transfer learning in other forecasting
applications like traffic and weather and monitor their states. The PDRL
framework is able to learn the future states of the traffic and weather
forecasting and the cumulative rewards are gradually increasing over each
episode.


  In this work we investigate the merger rate of primordial black hole-neutron
star (PBH-NS) binaries in two widely-studied modified gravity (MG) models:
Hu-Sawicki $f(R)$ gravity and the normal branch of Dvali-Gabadadze-Porrati
(nDGP) gravity. In our analysis, we take into account the effects of MG on the
halo properties including halo mass function, halo concentration parameter,
halo density profile, and velocity dispersion of dark matter particles. We find
that these MG models, due to their stronger gravitational field induced by an
effective fifth force, predict enhanced merger rates compared to general
relativity. This enhancement is found to be redshift-dependent and sensitive to
model parameters, PBH mass and fraction. Assuming PBH mass range of $5-50
M_{\odot}$, we compare the predicted merger rate of PBH-NS binaries with those
inferred from LIGO-Virgo-KAGRA observations of gravitational waves (GWs). We
find that the merger rates obtained from MG models will be consistent with the
GW observations, if the abundance of PBHs is relatively large, with the exact
amount depending on the MG model and its parameter values, as well as PBH mass.
We also establish upper limits on the abundance of PBHs in these MG frameworks
while comparing with the existing non-GW constraints, which can potentially
impose even more stringent constraints.


  We characterise the spontaneous emission time and direction from small
numbers of dipole-coupled two-level atoms (2LAs) in the presence of incident
fields. We show how to use adiabatic passage to admit population transfer
between states in the one-quantum subspace for two and three 2LAs. Our method
is a multi-atom generalisation of stimulated-Raman-adiabatic-passage (STIRAP)
for a single multi-level atom. We use numerical results to justify an ansatz
that enables us to give analytical expressions for the directional emission
which depends on the incident fields. Our results admit a characterisation of
the efficacy of population transfer in small numbers of dipole-coupled 2LAs,
and are applicable to proof-of-principle experiments involving dipole-coupled
2LAs.


  In the frame of the Kerr-Schild approach, we obtain a generalization of the
Kerr solution to a nonstationary case corresponding to a rotating source moving
with arbitrary acceleration. Similar to the Kerr solution, the solutions
obtained have the geodesic and shear free principal null congruence. The
current parameters of the solutions are determined by a complex retarded-time
construction via a given complex worldline of source. The real part of the
complex worldline defines the values of the boost and acceleration while the
imaginary part controls the rotation. The acceleration of the source is
accompanied by a lightlike radiation along the principal null congruence. The
solutions obtained generalize to the rotating case the known Kinnersley class
of the "photon rocket" solutions.


  In cellular networks, it can become necessary for authorities to physically
locate user devices for tracking criminals or illegal devices. While cellular
operators can provide authorities with cell information the device is camping
on, fine-grained localization is still required. Therefore, the authorized
agents trace the device by monitoring its uplink signals. However, tracking the
uplink signal source without its cooperation is challenging even for operators
and authorities. Particularly, three challenges remain for fine-grained
localization: i) localization works only if devices generate enough uplink
traffic reliably over time, ii) the target device might generate its uplink
traffic with significantly low power, and iii) cellular repeater may add too
much noise to true uplink signals. While these challenges present practical
hurdles for localization, they have been overlooked in prior works.
  In this work, we investigate the impact of these real-world challenges on
cellular localization and propose an Uncooperative Multiangulation Attack (UMA)
that addresses these challenges. UMA can 1) force a target device to transmit
traffic continuously, 2) boost the target's signal strength to the maximum, and
3) uniquely distinguish traffic from the target and the repeaters. Notably, the
UMA technique works without privilege on cellular operators or user devices,
which makes it operate on any LTE network. Our evaluations show that UMA
effectively resolves the challenges in real-world environments when devices are
not cooperative for localization. Our approach exploits the current cellular
design vulnerabilities, which we have responsibly disclosed to GSMA.


  Using the OPAL detector at LEP, the running of the effective QED coupling
alpha(t) is measured for space-like momentum transfer from the angular
distribution of small-angle Bhabha scattering. In an almost ideal QED
framework, with very favourable experimental conditions, we obtain:
  Delta alpha(-6.07GeV^2) - Delta alpha(-1.81GeV^2) = (440 pm 58 pm 43 pm 30) X
10^-5, where the first error is statistical, the second is the experimental
systematic and the third is the theoretical uncertainty. This agrees with
current evaluations of alpha(t).The null hypothesis that alpha remains constant
within the above interval of -t is excluded with a significance above 5sigma.
Similarly, our results are inconsistent at the level of 3sigma with the
hypothesis that only leptonic loops contribute to the running. This is
currently the most significant direct measurment where the running alpha(t) is
probed differentially within the measured t range.


  Carpet-2 is an air-shower array at Baksan Valley, Russia, equipped with a
large-area (175 m^2) muon detector, which makes it possible to separate primary
photons from hadrons. We report the first results of the search for primary
photons with energies E_\gamma>1 PeV, directionally associated with IceCube
high-energy neutrino events, in the data obtained in 3080 days of Carpet-2 live
time.


  A sample of 198 soft X--ray selected active galactic nuclei (AGN) from the
ROSAT International X--ray Optical Survey (RIXOS), is used to investigate the
X--ray luminosity function and its evolution. RIXOS, with a flux limit of 3E-14
erg s-1 cm-2 (0.5 to 2.0 keV), samples a broad range in redshift over 20 deg^2
of sky, and is almost completely identified; it is used in combination with the
Einstein Extended Medium Sensitivity Survey (EMSS), to give a total sample of
over 600 AGN. We find the evolution of AGN with redshift to be consistent with
pure luminosity evolution (PLE) models in which the rate of evolution slows
markedly or stops at high redshifts z>1.8. We find that this result is not
affected by the inclusion, or exclusion, of narrow emission line galaxies at
low redshift in the RIXOS and EMSS samples, and is insensitive to uncertainties
in the conversion between flux values measured with ROSAT and Einstein. We
confirm, using a model independent Ve/Va test, that our survey is consistent
with no evolution at high redshifts.


  We rigorously define the Liouville action functional for finitely generated,
purely loxodromic quasi-Fuchsian group using homology and cohomology double
complexes naturally associated with the group action. We prove that the
classical action - the critical point of the Liouville action functional,
considered as a function on the quasi-Fuchsian deformation space, is an
antiderivative of a 1-form given by the difference of Fuchsian and
quasi-Fuchsian projective connections. This result can be considered as global
quasi-Fuchsian reciprocity which implies McMullen's quasi-Fuchsian reciprocity.
We prove that the classical action is a Kahler potential of the Weil-Petersson
metric. We also prove that Liouville action functional satisfies holography
principle, i.e., it is a regularized limit of the hyperbolic volume of a
3-manifold associated with a quasi-Fuchsian group. We generalize these results
to a large class of Kleinian groups including finitely generated, purely
loxodromic Schottky and quasi-Fuchsian groups and their free combinations.


  Although various visual interfaces for digital libraries have been developed
in prototypical systems, very few of these visual approaches have been
integrated into today's digital libraries. In this position paper we argue that
this is most likely due to the fact that the evaluation results of most visual
systems lack comparability. There is no fix standard on how to evaluate visual
interactive user interfaces. Therefore it is not possible to identify which
approach is more suitable for a certain context. We feel that the comparability
of evaluation results could be improved by building a common evaluation setup
consisting of a reference system, based on a standardized corpus with fixed
tasks and a panel for possible participants.


  An analysis of charged B decay data in a quasi two-body decay into a charged
pion plus a S, P,or D state of two charged pions is performed in the QCD
factorization framework. The short distance amplitudes are calculated including
next-to-leading order corrections in the strong coupling constant for vertex
and penguin contributions. The long distance amplitudes due to the two charged
pion final state interactions are described by the pion non-strange scalar and
vector form factors for the two-pion S- and P-wave states, respectively and by
a relativistic Breit-Wigner formula for the D-wave state. We achieve a good fit
of the data with only three parameters for the S wave, one for the P wave and
one for the D wave. Our model gives a unified unitary description of the three
scalar resonances, f_0(600), f_0(980) and f_0(1400) in terms of the pion scalar
form factor. We predict for the B to f_2(1270) transition form factor a value
of 0.098+-0.007 at a momentum transfer square equal to the pion-mass square.


  A magnetoconductivity formula is presented for the surface states of a
magnetically doped topological insulator. It reveals a competing effect of weak
localization and weak antilocalization in quantum transport when an energy gap
is opened at the Dirac point by magnetic doping. It is found that, while random
magnetic scattering always drives the system from the symplectic to the unitary
class, the gap could induce a crossover from weak antilocalization to weak
localization, tunable by the Fermi energy or the gap. This crossover presents a
unique feature characterizing the surface states of a topological insulator
with the gap opened at the Dirac point in the quantum diffusion regime.


  Motivated by the possibility of further improving the thermoelectric
properties of skutterudites we investigate charge transfer and bonding in this
class of materials using density functional calculations. Results for the CoP3,
CoSb3, LaFe4P12 and the hypothetical FeP3 compounds are presented using the
procrystal as the non-binding reference. Spherical integration and Bader
analysis are performed to illustrate charge transfer differences between these
compounds. The results are in good qualitative agreement with simple
electronegativity considerations. The calculations confirm that the transition
metal-pnictogen and the pnictogen-pnictogen bonds are covalent, while the
filler atom-pnictogen bond is of a more polar and complex nature. The success
of the "rattling" cage as phonon inhibitor is explained by a unique
semi-correlated bonding scheme between lanthanum and phosphorus. Elongated
bonds along the crystal axes surrounds the lanthanum ion and generate a
dodecahedra grid. Vibrations along the crystal axes are then closely connected
to and scatter from the phosphorus rings. In the other directions, a more
uncorrelated vibration is possible. This duality widens the phonon dampening
possibilities.


  It is usually assumed that when performing a photoluminescence experiment on
a microcavity containing an inhomogeneously broadened quantum dots ensemble,
the cavity mode appears as a positive peak with a linewidth that reflects the
mode quality factor Q. We show in this article that this conclusion is in
general not true, and that the measured mode linewidth depends strongly on the
excitation power for microcavities having large Purcell factors. We analyze
theoretically this effect in the case of the micropillar cavity and we show
that the same microcavity can give rise to a large variety of photoluminescence
spectral signatures depending on the excitation power and collection set-up. We
finally give guidelines to measure the real cavity quality factor by
photoluminescence.


  Identifying the tasks a given piece of malware was designed to perform (e.g.
logging keystrokes, recording video, establishing remote access, etc.) is a
difficult and time-consuming operation that is largely human-driven in
practice. In this paper, we present an automated method to identify malware
tasks. Using two different malware collections, we explore various
circumstances for each - including cases where the training data differs
significantly from test; where the malware being evaluated employs packing to
thwart analytical techniques; and conditions with sparse training data. We find
that this approach consistently out-performs the current state-of-the art
software for malware task identification as well as standard machine learning
approaches - often achieving an unbiased F1 score of over 0.9. In the near
future, we look to deploy our approach for use by analysts in an operational
cyber-security environment.


  Deep neural networks are susceptible to poisoning attacks by purposely
polluted training data with specific triggers. As existing episodes mainly
focused on attack success rate with patch-based samples, defense algorithms can
easily detect these poisoning samples. We propose DeepPoison, a novel
adversarial network of one generator and two discriminators, to address this
problem. Specifically, the generator automatically extracts the target class'
hidden features and embeds them into benign training samples. One discriminator
controls the ratio of the poisoning perturbation. The other discriminator works
as the target model to testify the poisoning effects. The novelty of DeepPoison
lies in that the generated poisoned training samples are indistinguishable from
the benign ones by both defensive methods and manual visual inspection, and
even benign test samples can achieve the attack. Extensive experiments have
shown that DeepPoison can achieve a state-of-the-art attack success rate, as
high as 91.74%, with only 7% poisoned samples on publicly available datasets
LFW and CASIA. Furthermore, we have experimented with high-performance defense
algorithms such as autodecoder defense and DBSCAN cluster detection and showed
the resilience of DeepPoison.


  This paper collects and extends the lectures I gave at the "XXIV
International Fall Workshop on Geometry and Physics" held in Zaragoza (Spain)
August 31 - September 4, 2015. Within these lectures I review the formulation
of Quantum Mechanics, and quantum theories in general, from a mathematically
advanced viewpoint, essentially based on the orthomodular lattice of elementary
propositions, discussing some fundamental ideas, mathematical tools and
theorems also related to the representation of physical symmetries. The final
step consists of an elementary introduction the so-called (C*-) algebraic
formulation of quantum theories.


  In this paper we discuss Waldspurger's local period integral for newforms in
new cases. The main ingredient is the work \cite{HN18} on Waldspurger's period
integral using the minimal vectors, and the explicit relation between the
newforms and the minimal vectors. We use a representation theoretical trick to
simplify computations for newforms. As an example, we compute the local
integral coming from a special arithmetic setting which was used to study
3-part full BSD conjecture in \cite{HSY}.


  We compute the frequency and magnetic field dependencies of the reflectivity
$R(\omega)$ in layered superconductors with two alternating intrinsic Josephson
junctions with different critical current densities and quasiparticle
conductivities for the electric field polarized along the c-axis. The parameter
$\alpha$ describing the electronic compressibility of the layers and the charge
coupling of neighboring junctions was extracted for the
SmLa$_{1-x}$Sr$_{x}$CuO$_{4-\delta}$ superconductor from two independent
optical measurements, the fit of the loss function $L(\omega)$ at zero magnetic
field and the magnetic field dependence of the peak positions in $L(\omega)$.
  The experiments are consistent with a free electron value for $\alpha$ near
the Josephson plasma frequencies.


  We construct Green's functions for divergence form, second order parabolic
systems in non-smooth time-varying domains whose boundaries are locally
represented as graph of functions that are Lipschitz continuous in the spatial
variables and 1/2-H\"older continuous in the time variable, under the
assumption that weak solutions of the system satisfy an interior H\"older
continuity estimate. We also derive global pointwise estimates for Green's
function in such time-varying domains under the assumption that weak solutions
of the system vanishing on a portion of the boundary satisfy a certain local
boundedness estimate and a local H\"older continuity estimate. In particular,
our results apply to complex perturbations of a single real equation.


  In this paper we investigate the behavior of the shear hydrodynamic response
functions in a simple holographic model exhibiting momentum relaxation. We
compute several stress tensor response functions in the transverse channel, and
from there derive the ratio of shear viscosity to entropy density, $\eta/s$,
using two different methods. The two values differ from each other, one which
satisfies the KSS bound $\eta/s \geq \frac{1}{4\pi}$ and one which does not,
and we discuss the causes and implications of this result.


  Pair creation of strings in time-dependent backgrounds is studied from an
effective field theory viewpoint, and some possible cosmological applications
are discussed. Simple estimates suggest that excited strings may have played a
significant role in preheating, if the string tension as measured in
four-dimensional Einstein frame falls a couple of orders of magnitude below the
four-dimensional Planck scale.


  FeNi L10 (tetrataenite) phase has great perspectives for hard magnetic
materials production. In this paper it was synthesized in chemically
co-precipitated FeNi nanopowder by means of thermal treating included cycling
of oxidation and reduction processes at 320 {\deg}C. The presence of FeNi L10
phase in the samples was confirmed by magnetic measurements and DSC analysis.


  Most observed extrasolar planets have masses similar to, but orbits very
different from, the gas giants of our solar system. Many are much closer to
their parent stars than would have been expected and their orbits are often
rather eccentric. We show that some of these planets might have formed in
systems much like our solar system, i.e. in systems where the gas giants were
originally on orbits with a semi-major axis of several au, but where the masses
of the gas giants were all rather similar. If such a system is perturbed by
another star, strong planet-planet interactions follow, causing the ejection of
several planets while leaving those remaining on much tighter and more
eccentric orbits. The eccentricity distribution of these perturbed systems is
very similar to that of the observed extrasolar planets with semi-major axis
between 1 and 6 au.


  We present $^{57}$Fe-NMR measurements of the novel normal and
superconducting-state characteristics of the iron-arsenide superconductor
Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$ ($T_c$ = 38 K). In the normal state, the
measured Knight shift and nuclear spin-lattice relaxation rate $(1/T_1)$
demonstrate the development of wave-number ($q$)-dependent spin fluctuations,
except at $q$ = 0, which may originate from the nesting across the disconnected
Fermi surfaces. In the superconducting state, the spin component in the
$^{57}$Fe-Knight shift decreases to almost zero at low temperatures, evidencing
a spin-singlet superconducting state. The $^{57}$Fe-$1/T_1$ results are totally
consistent with a $s^\pm$-wave model with multiple full gaps, regardless of
doping with either electrons or holes.


  We use measurements of the galaxy-cluster angular size versus redshift to
test and compare the standard model (LCDM) and the R_h=ct Universe. We show
that the latter fits the data with a reduced chi^2_dof=0.786 for a Hubble
constant H_0= 72.6 (-3.4+3.8) km/s/Mpc, and H_0 is the sole parameter in this
model. By comparison, the optimal flat LCDM model, with two free parameters
(including Omega_m=0.50 and H_0=73.9 (-9.5+10.6) km/s/Mpc), fits the
angular-size data with a reduced chi^2_dof=0.806. On the basis of their
chi^2_dof values alone, both models appear to account for the data very well in
spite of the fact that the R_h=ct Universe expands at a constant rate, while
LCDM does not. However, because of the different number of free parameters in
these models, selection tools, such as the Bayes Information Criterion, favour
R_h=ct over LCDM with a likelihood of ~86% versus ~14%. These results impact
the question of galaxy growth at large redshifts. Previous work suggested an
inconsistency with the underlying cosmological model unless elliptical and disk
galaxies grew in size by a surprisingly large factor ~6 from z~3 to 0. The fact
that both LCDM and R_h=ct fit the cluster-size measurements quite well casts
some doubt on the suggestion that the unexpected result with individual
galaxies may be due to the use of an incorrect expansion scenario, rather than
astrophysical causes, such as mergers and/or selection effects.


  Pre-trained language models have demonstrated impressive performance in both
natural language processing and program understanding, which represent the
input as a token sequence without explicitly modeling its structure. Some prior
works show that pre-trained language models can capture the syntactic rules of
natural languages without finetuning on syntax understanding tasks. However,
there is limited understanding of how well pre-trained models understand the
code structure so far. In this work, we perform the first thorough benchmarking
of the state-of-the-art pre-trained models for identifying the syntactic
structures of programs. Specifically, we introduce CodeSyntax, a large-scale
dataset of programs annotated with the syntactic relationships in their
corresponding abstract syntax trees. Our key observation is that existing
language models pretrained on code still lack the understanding of code syntax.
In fact, these pre-trained programming language models fail to match the
performance of simple baselines based on positional offsets and keywords. We
also present a natural language benchmark to highlight the differences between
natural languages and programming languages in terms of syntactic structure
understanding. Our findings point out key limitations of existing pre-training
methods for programming languages, and suggest the importance of modeling code
syntactic structures.


  Traditional crowd counting approaches usually use Gaussian assumption to
generate pseudo density ground truth, which suffers from problems like
inaccurate estimation of the Gaussian kernel sizes. In this paper, we propose a
new measure-based counting approach to regress the predicted density maps to
the scattered point-annotated ground truth directly. First, crowd counting is
formulated as a measure matching problem. Second, we derive a semi-balanced
form of Sinkhorn divergence, based on which a Sinkhorn counting loss is
designed for measure matching. Third, we propose a self-supervised mechanism by
devising a Sinkhorn scale consistency loss to resist scale changes. Finally, an
efficient optimization method is provided to minimize the overall loss
function. Extensive experiments on four challenging crowd counting datasets
namely ShanghaiTech, UCF-QNRF, JHU++, and NWPU have validated the proposed
method.


  C60 fullerene has been studied extensively, as it is considered to be a good
candidate for building single-molecule junctions. Here, we theoretically
demonstrate that the conductance of single-molecule junctions based on a newly
discovered molecule, borospherene (B40), is comparable to that for the
C60-based junction with its more delocalized {\pi} electrons. The charge
injection efficiency in the B40-based junction is improved, as up to 7 atoms in
direct contact with the electrode are possible in the Au-B40-Au junction.
Interestingly, a higher number of atoms in direct contact with the electrode
does not result in a higher number of conduction channels because of the unique
chemical bonding in the B40 molecule, without two-center two- electron bonds.
The transport properties of Au-B40-Au junctions can be proved by doping. With a
Ca, Sr, or Y atom encapsulated into the B40 cage, the conductance at zero bias
increases significantly. Moreover, our calculations show that the lowest
unoccupied molecular orbital dominates the low-bias transport, as the
thermopower in these junctions is negative. Our study indicates that B40 is an
attractive new platform for designing highly conductive single-molecule
junctions for future molecular circuits.


  A finite ergodic Markov chain is said to exhibit cutoff if its distance to
stationarity remains close to 1 over a certain number of iterations and then
abruptly drops to near 0 on a much shorter time scale. Discovered in the
context of card shuffling (Aldous-Diaconis, 1986), this phenomenon is now
believed to be rather typical among fast mixing Markov chains. Yet,
establishing it rigorously often requires a challengingly detailed
understanding of the underlying chain. Here we consider non-backtracking random
walks on random graphs with a given degree sequence. Under a general sparsity
condition, we establish the cutoff phenomenon, determine its precise window,
and prove that the (suitably rescaled) cutoff profile approaches a remarkably
simple, universal shape.


  We determine the relationship between the contact structure induced by a
fibered knot, K, in the three-sphere and the contact structures induced by its
various cables. Understanding this relationship allows us to classify fibered
cable knots which bound a properly embedded complex curve in the four-ball
satisfying a genus constraint. This generalizes the well-known classification
of links of plane curve singularities.


  In this paper, we propose a class of efficient, accurate, and general methods
for solving state-estimation problems with equality and inequality constraints.
The methods are based on recent developments in variable splitting and
partially observed Markov processes. We first present the generalized framework
based on variable splitting, then develop efficient methods to solve the
state-estimation subproblems arising in the framework. The solutions to these
subproblems can be made efficient by leveraging the Markovian structure of the
model as is classically done in so-called Bayesian filtering and smoothing
methods. The numerical experiments demonstrate that our methods outperform
conventional optimization methods in computation cost as well as the estimation
performance.


  In the Large High Altitude Air Shower Observatory (LHAASO), the Water
Cherenkov Detector Array (WCDA) is one of the key parts. The WCDA consists of
3600 Photomultiplier Tubes (PMTs) scattered in a 90000 m2 area, and both high
precision time and charge measurements are required over a large dynamic range
from 1 to 4000 Photo Electrons (P.E.). To achieve time measurement precision
better than 500 ps RMS, high quality clock distribution and automatic phase
compensation are needed among the 400 Front End Electronics (FEE) modules. To
simplify the readout electronics architecture, clock, data, and commands are
transferred simultaneously over 400-meter fibers, while high speed data
transfer interface is implemented based on TCP/IP protocol. Design and testing
of the readout electronics prototype for WCDA is presented in this paper. Test
results indicate that a charge resolution better than 10% RMS @ 1 P.E. and 1%
RMS @ 4000 P.E., and a time resolution better than 300 ps RMS are successfully
achieved over the whole dynamic range, beyond the application requirement.