title: Selling to a principal and a budget-constrained agent

abstract:
  We analyze a model of selling a single object to a principal-agent pair who
want to acquire the object for a firm. The principal and the agent have
different assessments of the object's value to the firm. The agent is
budget-constrained while the principal is not. The agent participates in the
mechanism, but she can (strategically) delegate decision-making to the
principal. We derive the revenue-maximizing mechanism in a two-dimensional type
space (values of the agent and the principal). We show that below a threshold
budget, a mechanism involving two posted prices and three outcomes (one of
which involves randomization) is the optimal mechanism for the seller.
Otherwise, a single posted price mechanism is optimal.


title: (Dis)assortative Partitions on Random Regular Graphs

abstract:
  We study the problem of assortative and disassortative partitions on random
$d$-regular graphs. Nodes in the graph are partitioned into two non-empty
groups. In the assortative partition every node requires at least $H$ of their
neighbors to be in their own group. In the disassortative partition they
require less than $H$ neighbors to be in their own group. Using the cavity
method based on analysis of the Belief Propagation algorithm we establish for
which combinations of parameters $(d,H)$ these partitions exist with high
probability and for which they do not. For $H>\lceil \frac{d}{2} \rceil $ we
establish that the structure of solutions to the assortative partition problems
corresponds to the so-called frozen-1RSB. This entails a conjecture of
algorithmic hardness of finding these partitions efficiently. For $H \le \lceil
\frac{d}{2} \rceil $ we argue that the assortative partition problem is
algorithmically easy on average for all $d$. Further we provide arguments about
asymptotic equivalence between the assortative partition problem and the
disassortative one, going trough a close relation to the problem of
single-spin-flip-stable states in spin glasses. In the context of spin glasses,
our results on algorithmic hardness imply a conjecture that gapped single spin
flip stable states are hard to find which may be a universal reason behind the
observation that physical dynamics in glassy systems display convergence to
marginal stability.

categories: cond-mat.dis-nn cond-mat.stat-mech cs.DM math.PR


title:
Investigating full-heavy tetraquarks composed of $cc\bar{c}\bar{b}$ and
  $bb\bar{b}\bar{c}$

abstract:
  The full-heavy tetraquarks $cc\bar{c}\bar{b}$ and $bb\bar{b}\bar{c}$ are
systematically investigated within the chiral quark model. The meson-meson
structure, diquark-antidiquark structure and K-structure are considered in this
work. There is no bound state for $cc\bar{c}\bar{b}$ and $bb\bar{b}\bar{c}$
systems in $ IJ^{P}=00^{+},01^{+}$ and $ 02^{+}$ channels. However, for
$cc\bar{c}\bar{b}$ system, two possible resonance states with energy of $
9897{\rm MeV} $ and $ 9903{\rm MeV} $ are found in $ IJ^{P}=01^{+} $, and the
decay width of these resonance states are $ \Gamma$ = $7.2{\rm MeV} $ and $
\Gamma$ = $7.8{\rm MeV} $, while resonance state is not found in
$bb\bar{b}\bar{c}$ system. $bc\bar{c}\bar{c}$ and $cb\bar{b}\bar{b}$ systems
will have the same results as $cc\bar{c}\bar{b}$ and $bb\bar{b}\bar{c}$,
respectively. These full-heavy resonance states are worthy to be searched in
the future experiments.


title: Rule Mining over Knowledge Graphs via Reinforcement Learning

abstract:
  Knowledge graphs (KGs) are an important source repository for a wide range of
applications and rule mining from KGs recently attracts wide research interest
in the KG-related research community. Many solutions have been proposed for the
rule mining from large-scale KGs, which however are limited in the inefficiency
of rule generation and ineffectiveness of rule evaluation. To solve these
problems, in this paper we propose a generation-then-evaluation rule mining
approach guided by reinforcement learning. Specifically, a two-phased framework
is designed. The first phase aims to train a reinforcement learning agent for
rule generation from KGs, and the second is to utilize the value function of
the agent to guide the step-by-step rule generation. We conduct extensive
experiments on several datasets and the results prove that our rule mining
solution achieves state-of-the-art performance in terms of efficiency and
effectiveness.


title: Delegated Pandora's box

abstract:
  In delegation problems, a principal does not have the resources necessary to
complete a particular task, so they delegate the task to an untrusted agent
whose interests may differ from their own. Given any family of such problems
and space of mechanisms for the principal to choose from, the delegation gap is
the worst-case ratio of the principal's optimal utility when they delegate
versus their optimal utility when solving the problem on their own. In this
work, we consider the delegation gap of the generalized Pandora's box problem,
a search problem in which searching for solutions incurs known costs and
solutions are restricted by some downward-closed constraint. First, we show
that there is a special case when all random variables have binary support for
which there exist constant-factor delegation gaps for matroid constraints.
However, there is no constant-factor delegation gap for even simple non-binary
instances of the problem. Getting around this impossibility, we consider two
variants: the free-agent model, in which the agent doesn't pay the cost of
probing elements, and discounted-cost approximations, in which we discount all
costs and aim for a bicriteria approximation of the discount factor and
delegation gap. We show that there are constant-factor delegation gaps in the
free-agent model with discounted-cost approximations for certain downward
closed constraints and constant discount factors. However, constant delegation
gaps can not be achieved under either variant alone. Finally, we consider
another variant called the shared-cost model, in which the principal can choose
how costs will be shared between them and the agent before delegating the
search problem. We show that the shared-cost model exhibits a constant-factor
delegation gap for certain downward closed constraints.


title:
Independence questions in a finite axiom-schematization of first-order
  logic

abstract:
  We review some independence results in a finite axiom-schematization of
classical first-order logic introduced by Norman Megill. We also prove that a
certain axiom scheme of this system is independent although all of its
instances are provable from the other axiom schemes.


title: Generating Hard Problems of Cellular Automata

abstract:
  We propose two hard problems in cellular automata. In particular the problems
are:
  [DDP$^M_{n,p}$] Given two \emph{randomly} chosen configurations $t$ and $s$
of a cellular automata of length $n$, find the number of transitions $\tau$
between $s$ and $t$.
  [SDDP$^\delta_{k,n}$] Given two \emph{randomly} chosen configurations $s$ of
a cellular automata of length $n$ and $x$ of length $k<n$, find the
configuration $t$ such that $k$ number of cells of $t$ is fixed to $x$ and $t$
is reachable from $s$ within $\delta$ transitions.
  We show that the discrete logarithm problem over the finite field reduces to
DDP$^M_{n,p}$ and the short integer solution problem over lattices reduces to
SDDP$^\delta_{k,n}$. The advantage of using such problems as the hardness
assumptions in cryptographic protocols is that proving the security of the
protocols requires only the reduction from these problems to the designed
protocols. We design one such protocol namely a proof-of-work out of
SDDP$^\delta_{k,n}$.


title: Calibration of BAS-TR image plate response to GeV gold ions

abstract:
  The response of the BAS-TR image plate (IP) was absolutely calibrated using
CR-39 track detector for high linear energy transfer (LET) Au ions up to
$\sim$1.6 GeV (8.2 MeV/nucleon), accelerated by high-power lasers. The
calibration was carried out by employing a high-resolution Thomson parabola
spectrometer, which allowed resolving Au ions with closely spaced ionization
states up to 58$^+$. A response function was obtained by fitting the
photo-stimulated luminescence (PSL) per Au ion for different ion energies,
which is broadly in agreement with that expected from ion stopping in the
active layer of the IP. This calibration would allow quantifying the ion energy
spectra for high energy Au ions, which is important for further investigation
of the laser-based acceleration of heavy ion beams.


title: Recent developments of the SDHCAL prototype

abstract:
  After the construction and successful operation of the first technological
prototype of the Semi-Digital Hadronic CALorimeter (SDHCAL), developed within
the CALICE collaboration, new R{\&}D efforts have been initiated to fully
validate the SDHCAL option for future experiments proposed for the ILC and CEPC
colliders. The SDHCAL is a sampling hadronic calorimeter using large Glass
Resistive Plate Chamber (GRPC) as active medium with embedded readout
electronics. The GRPC prototype size is 1~m$^2$ while future detectors require
GRPC detectors with scalable length up to 3~m long (0.9$\times$3~m$^2$). The
readout Printed Circuit Board (PCB) consists of 1~cm$^2$ copper pads on one
side and 64-channel HARDROC readout chips on the other side.
  The design of such large size scalable detectors has been addressed (...) A
first fully assembled prototype of 2~m$^2$ with 4 GRPCs is expected to be ready
in year 2022.
  In addition, new developpements to replace single gap GRPC by multigap GRPC
coupled with fast timing electronics are being pursued. A time resolution
better than 50 ps is achievable. This will allow to follow the temporal
evolution of the hadronic showers developing in the calorimeter. In parallel,
the first SDHCAL prototype has been extensively tested in beam test facilities.
Refined analysis techniques are being developed to improve the energy and
shower reconstruction. The latest analysis developments cover techniques to
improve the spatial uniformity of the response and a better treatment of the
particle incidence angle in the energy reconstruction.


title:
Improving Radioactive Material Localization by Leveraging Cyber-Security
  Model Optimizations

abstract:
  One of the principal uses of physical-space sensors in public safety
applications is the detection of unsafe conditions (e.g., release of poisonous
gases, weapons in airports, tainted food). However, current detection methods
in these applications are often costly, slow to use, and can be inaccurate in
complex, changing, or new environments. In this paper, we explore how machine
learning methods used successfully in cyber domains, such as malware detection,
can be leveraged to substantially enhance physical space detection. We focus on
one important exemplar application--the detection and localization of
radioactive materials. We show that the ML-based approaches can significantly
exceed traditional table-based approaches in predicting angular direction.
Moreover, the developed models can be expanded to include approximations of the
distance to radioactive material (a critical dimension that reference tables
used in practice do not capture). With four and eight detector arrays, we
collect counts of gamma-rays as features for a suite of machine learning models
to localize radioactive material. We explore seven unique scenarios via
simulation frameworks frequently used for radiation detection and with physical
experiments using radioactive material in laboratory environments. We observe
that our approach can outperform the standard table-based method, reducing the
angular error by 37% and reliably predicting distance within 2.4%. In this way,
we show that advances in cyber-detection provide substantial opportunities for
enhancing detection in public safety applications and beyond.


title: On Ramsey size-linear graphs and related questions

abstract:
  In this paper we prove several results on Ramsey numbers $R(H,F)$ for a fixed
graph $H$ and a large graph $F$, in particular for $F = K_n$. These results
extend earlier work of Erd\H{o}s, Faudree, Rousseau and Schelp and of Balister,
Schelp and Simonovits on so-called Ramsey size-linear graphs.


title: Droplet absorption and spreading into thin layers of polymer hydrogels

abstract:
  From biological tissues to layers of paint, macroscopic non-porous materials
with the capacity to swell when brought in contact with an appropriate solvent
are ubiquitous. Here, we study experimentally and theoretically one of the
conceptually simplest of such systems, the swelling of a thin hydrogel layer by
a single water drop. Using a bespoke experimental setup, we observe fast
absorption leading to a radially spreading axisymmetric blister. Employing a
linear poroelastic framework and thin-layer scalings, we develop a non-linear
one-dimensional diffusion equation for the evolution of the blister height
profile, which agrees well with experimental observations.


title: Optimizing Recursive Queries with Program Synthesis

abstract:
  Most work on query optimization has concentrated on loop-free queries.
However, data science and machine learning workloads today typically involve
recursive or iterative computation. In this work, we propose a novel framework
for optimizing recursive queries using methods from program synthesis. In
particular, we introduce a simple yet powerful optimization rule called the
"FGH-rule" which aims to find a faster way to evaluate a recursive program. The
solution is found by making use of powerful tools, such as a program
synthesizer, an SMT-solver, and an equality saturation system. We demonstrate
the strength of the optimization by showing that the FGH-rule can lead to
speedups up to 4 orders of magnitude on three, already optimized Datalog
systems.


title: Nonparametric Adaptive Robust Control Under Model Uncertainty

abstract:
  We consider a discrete time stochastic Markovian control problem under model
uncertainty. Such uncertainty not only comes from the fact that the true
probability law of the underlying stochastic process is unknown, but the
parametric family of probability distributions which the true law belongs to is
also unknown. We propose a nonparametric adaptive robust control methodology to
deal with such problem. Our approach hinges on the following building concepts:
first, using the adaptive robust paradigm to incorporate online learning and
uncertainty reduction into the robust control problem; second, learning the
unknown probability law through the empirical distribution, and representing
uncertainty reduction in terms of a sequence of Wasserstein balls around the
empirical distribution; third, using Lagrangian duality to convert the
optimization over Wasserstein balls to a scalar optimization problem, and
adopting a machine learning technique to achieve efficient computation of the
optimal control. We illustrate our methodology by considering a utility
maximization problem. Numerical comparisons show that the nonparametric
adaptive robust control approach is preferable to the traditional robust
frameworks.


title: Bound States in the Continuum in Multipolar Lattices

abstract:
  We develop a theory of bound states in the continuum (BICs) in multipolar
lattices -- periodic arrays of resonant multipoles. We predict that BICs are
completely robust to changes in lattice parameters remaining pinned to specific
directions in the $k$-space. The lack of radiation for BICs in such structures
is protected by the symmetry of multipoles forming the lattice. We also show
that some multipolar lattices can host BICs forming a continuous line in the
$k$-space and such BICs carry zero topological charge. The developed approach
sets a direct fundamental relation between the topological charge of BIC and
the asymptotic behavior of the Q-factor in its vicinity. We believe that our
theory is a significant step towards gaining deeper insight into the physics of
BICs and the engineering of high-Q states in all-dielectric metasurfaces.


title: Time of arrival operator in the momentum space

abstract:
  It is shown that in presence of certain external fields a well defined
self-adjoint time operator exists, satisfying the standard canonical
commutation relations with the Hamiltonian. Examples include uniform electric
and gravitational fields with nonrelativistic and relativistic Hamiltonians.
The physical intepretation of these operators is proposed in terms of time of
arrival in the momentum space.


title: On The Generalisation of Henstock-Kurzweil Fourier Transform

abstract:
  In this paper, a generalised integral called the Laplace integral is defined
on unbounded intervals, and some of its properties, including necessary and
sufficient condition for differentiating under the integral sign, are
discussed. It is also shown that this integral is more general than the
Henstock-Kurzweil integral. Finally, the Fourier transform is defined using the
Laplace integral, and its well-known properties are established.


title:
Nature of the anomalous $4/13$ fractional quantum Hall effect in
  graphene

abstract:
  Extensive fractional quantum Hall effect (FQHE) has been observed in
graphene-based materials. Some of the observed fractions are anomalous in that
FQHE has not been established at these fractions in conventional GaAs systems.
One such fraction is $4/13$, where incompressibility has recently been reported
in graphene [Kumar et al., Nat. Comm. 9, 2776 (2018)]. We propose a wave
function at $4/13$ and show it to be a viable candidate to describe the Coulomb
ground state. Using the effective edge theory, we make predictions for
experimentally measurable properties of the state.


title: MIST GAN: Modality Imputation Using Style Transfer for MRI

abstract:
  MRI entails a great amount of cost, time and effort for the generation of all
the modalities that are recommended for efficient diagnosis and treatment
planning. Recent advancements in deep learning research show that generative
models have achieved substantial improvement in the aspects of style transfer
and image synthesis. In this work, we formulate generating the missing MR
modality from existing MR modalities as an imputation problem using style
transfer. With a multiple-to-one mapping, we model a network that accommodates
domain specific styles in generating the target image. We analyse the style
diversity both within and across MR modalities. Our model is tested on the
BraTS'18 dataset and the results obtained are observed to be on par with the
state-of-the-art in terms of visual metrics, SSIM and PSNR. After being
evaluated by two expert radiologists, we show that our model is efficient,
extendable, and suitable for clinical applications.


title:
Dissipative Control and Observation of Linear Time-Delay Systems: Full
  State Feedback

abstract:
  The paper is the first step of a series dealing with the dissipative control
and observation problems of continuous-time linear systems with general delays
of finite length via the Krasovskii functional (KF) approach. Our model imposes
no limits to the delay numbers at the states, inputs, regulated outputs or
controller, where the distributed delays (DDs) can contain any number of
square-integrable functions over the delay intervals. We introduce the notion
of equivalent decompositions which can simultaneously factorize or approximate
any function in the DDs. Moreover, the decomposition approach allows the
construction of complete-type KFs whose integral kernels can contain any number
of differentiable and linearly independent functions, supported by the use of
integral inequalities based on the least square principle. The solutions to the
problem are expressed in terms of matrix inequalities summarized in several
theorems/corollaries and iterative algorithms, which can be used together as a
whole to compute controller gains without requiring nonlinear solvers. Two
numerical examples are tested to show the effectiveness of the proposed
methodologies.


title:
Wave Functions and Energy Spectra in Rational Billiards Are Determined
  Completely by Their Periods

abstract:
  The rational billiards (RB) are classically pseudointegrable, i.e. their
trajectories in the phase space lie on multi-tori. Each such a multi-torus can
be unfolded into elementary polygon pattern (EPP). A rational billiards Riemann
surface (RBRS) corresponding to each RB is then an infinite mosaic made by a
periodic distribution of EPP. Periods of RBRS are directly related to periodic
orbits of RB. It is shown that any stationary solutions (SS) to the
Schr\"odinger equation (SE) in RB can be extended on the whole RBRS. The
extended stationary wave functions (ESS) are then periodic on RBRS with its
periods. Conversely, for each system of boundary conditions (i.e. the Dirichlet
or the the Neumann ones or their mixture) consistent with EPP one can find so
called stationary pre-solutions (SPS) of the Schr\"odinger equation defined on
RBRS and respecting its periodic structure together with their energy spectra.
Using SPS one can easily construct SS of RB for most boundary conditions on it
by a trivial algebra over SPS. It proves therefore that the energy spectra
defined by the boundary conditions for SS corresponding to each RB are totally
determined by $2g$ independent periods of RBRS being homogeneous functions of
these periods. RBRS can be constructed exclusively due to the rationality of
the polygon billiards considered. Therefore the approach developed in the
present paper can be seen as a new way in obtaining SS to SE in RB. SPS can be
constructed explicitly for a class of RB which EPP can be decomposed into a set
of periodic orbit channel (POC) parallel to each other (POCDRB). For such a
class of RB the respective RBRS can be built as a standard multi-sheeted
Riemann surface with a periodic structure. For POCDRB a discussion of the
existence of the superscar states (SSS) can be done thoroughly.


title: Quantum field simulator for dynamics in curved spacetime

abstract:
  The observed large-scale structure in our Universe is seen as a result of
quantum fluctuations amplified by spacetime evolution. This, and related
problems in cosmology, asks for an understanding of the quantum fields of the
standard model and dark matter in curved spacetime. Even the reduced problem of
a scalar quantum field in an explicitly time-dependent spacetime metric is a
theoretical challenge and thus a quantum field simulator can lead to new
insights. Here, we demonstrate such a quantum field simulator in a
two-dimensional Bose-Einstein condensate with a configurable trap and
adjustable interaction strength to implement this model system. We explicitly
show the realisation of spacetimes with positive and negative spatial curvature
by wave packet propagation and confirm particle pair production in controlled
power-law expansion of space. We find quantitative agreement with new
analytical predictions for different curvatures in time and space. This
benchmarks and thereby establishes a quantum field simulator of a new class. In
the future, straightforward upgrades offer the possibility to enter new, so far
unexplored, regimes that give further insight into relativistic quantum field
dynamics.

categories: cond-mat.quant-gas gr-qc hep-th quant-ph


title:
GenStore: A High-Performance and Energy-Efficient In-Storage Computing
  System for Genome Sequence Analysis

abstract:
  Read mapping is a fundamental, yet computationally-expensive step in many
genomics applications. It is used to identify potential matches and differences
between fragments (called reads) of a sequenced genome and an already known
genome (called a reference genome). To address the computational challenges in
genome analysis, many prior works propose various approaches such as filters
that select the reads that must undergo expensive computation, efficient
heuristics, and hardware acceleration. While effective at reducing the
computation overhead, all such approaches still require the costly movement of
a large amount of data from storage to the rest of the system, which can
significantly lower the end-to-end performance of read mapping in conventional
and emerging genomics systems.
  We propose GenStore, the first in-storage processing system designed for
genome sequence analysis that greatly reduces both data movement and
computational overheads of genome sequence analysis by exploiting low-cost and
accurate in-storage filters. GenStore leverages hardware/software co-design to
address the challenges of in-storage processing, supporting reads with 1)
different read lengths and error rates, and 2) different degrees of genetic
variation. Through rigorous analysis of read mapping processes, we meticulously
design low-cost hardware accelerators and data/computation flows inside a NAND
flash-based SSD. Our evaluation using a wide range of real genomic datasets
shows that GenStore, when implemented in three modern SSDs, significantly
improves the read mapping performance of state-of-the-art software (hardware)
baselines by 2.07-6.05$\times$ (1.52-3.32$\times$) for read sets with high
similarity to the reference genome and 1.45-33.63$\times$ (2.70-19.2$\times$)
for read sets with low similarity to the reference genome.


title: Vision-Language Pre-Training with Triple Contrastive Learning

abstract:
  Vision-language representation learning largely benefits from image-text
alignment through contrastive losses (e.g., InfoNCE loss). The success of this
alignment strategy is attributed to its capability in maximizing the mutual
information (MI) between an image and its matched text. However, simply
performing cross-modal alignment (CMA) ignores data potential within each
modality, which may result in degraded representations. For instance, although
CMA-based models are able to map image-text pairs close together in the
embedding space, they fail to ensure that similar inputs from the same modality
stay close by. This problem can get even worse when the pre-training data is
noisy. In this paper, we propose triple contrastive learning (TCL) for
vision-language pre-training by leveraging both cross-modal and intra-modal
self-supervision. Besides CMA, TCL introduces an intra-modal contrastive
objective to provide complementary benefits in representation learning. To take
advantage of localized and structural information from image and text input,
TCL further maximizes the average MI between local regions of image/text and
their global summary. To the best of our knowledge, ours is the first work that
takes into account local structure information for multi-modality
representation learning. Experimental evaluations show that our approach is
competitive and achieves the new state of the art on various common down-stream
vision-language tasks such as image-text retrieval and visual question
answering.


title:
Study of final-state interactions of protons in neutrino-nucleus
  scattering with INCL and NuWro cascade models

abstract:
  The modeling of neutrino-nucleus interactions constitutes a challenging
source of systematic uncertainty for the extraction of precise values of
neutrino oscillation parameters in long-baseline accelerator neutrino
experiments. To improve such modeling and minimize the corresponding
uncertainties, a new generation of detectors is being developed, which aim to
measure the complete final state of particles resulting from neutrino
interactions. In order to fully benefit from the improved detector
capabilities, precise simulations of the nuclear effects on the final-state
nucleons are needed. This article presents the study of the in-medium
propagation of knocked-out protons, i.e., final-state interactions (FSI),
comparing the NuWro and INCL cascade models. The INCL model is used here for
the first time to predict exclusive final states of neutrino interactions. This
study of INCL in the framework of neutrino interactions features various
novelties, including the production of nuclear clusters (e.g., deuterons,
$\alpha$ particles) in the final state. The paper includes a complete
characterization of the final state after FSI, comparisons to available
measurements of single transverse variables, and an assessment of the
observability of nuclear clusters.


title: Unified approach for computing sum of sources over CQ-MAC

abstract:
  We consider the task of communicating a generic bivariate function of two
classical sources over a Classical-Quantum Multiple Access Channel (CQ-MAC).
The two sources are observed at the encoders of the CQ-MAC, and the decoder
aims at reconstructing a bivariate function from the received quantum state.
Inspired by the techniques developed for the analogous classical setting, and
employing the technique of simultaneous (joint) decoding developed for the
classical-quantum setting, we propose and analyze a coding scheme based on a
fusion of algebraic structured and unstructured codes. This coding scheme
allows exploiting both the symmetric structure common amongst the sources and
the asymmetries. We derive a new set of sufficient conditions that strictly
enlarges the largest known set of sources (capable of communicating the
bivariate function) for any given CQ-MAC. We provide these conditions in terms
of single-letter quantum information-theoretic quantities.


title:
Solving Poisson's Equation: Existence, Uniqueness, Martingale Structure,
  and CLT

abstract:
  The solution of Poisson's equation plays a key role in constructing the
martingale through which sums of Markov correlated random variables can be
analyzed. In this paper, we study two different representations for the
solution in countable state space, one based on regenerative structure and the
other based on an infinite sum of expectations. We also consider integrability
and related uniqueness issues associated with solutions to Poisson's equation,
and provide verifiable Lyapunov conditions to support our theory. Our key
results include a central limit theorem and law of the iterated logarithm for
Markov dependent sums, under Lyapunov conditions weaker than have previously
appeared in the literature.


title: Minimal Volume Entropy of RAAG's

abstract:
  Bregman and Clay recently characterized which right-angled Artin groups with
geometric dimension 2 have vanishing minimal volume entropy. In this note, we
extend this characterization to higher dimensions.


title: Limit cycles in mass-conserving deficiency-one mass-action systems

abstract:
  We present some simple mass-action systems with limit cycles that fall under
the scope of the Deficiency-One Theorem. All the constructed examples are
mass-conserving and their stoichiometric subspace is two-dimensional. Using the
continuation software MATCONT, we depict the limit cycles in all stoichiometric
classes at once. The networks are trimolecular and tetramolecular, and some
exhibit two or even three limit cycles. Finally, we show that the associated
mass-action system of a bimolecular reaction network with two-dimensional
stoichiometric subspace does not admit a limit cycle.


title: Learning Behavioral Soft Constraints from Demonstrations

abstract:
  Many real-life scenarios require humans to make difficult trade-offs: do we
always follow all the traffic rules or do we violate the speed limit in an
emergency? These scenarios force us to evaluate the trade-off between
collective rules and norms with our own personal objectives and desires. To
create effective AI-human teams, we must equip AI agents with a model of how
humans make these trade-offs in complex environments when there are implicit
and explicit rules and constraints. Agent equipped with these models will be
able to mirror human behavior and/or to draw human attention to situations
where decision making could be improved. To this end, we propose a novel
inverse reinforcement learning (IRL) method: Max Entropy Inverse Soft
Constraint IRL (MESC-IRL), for learning implicit hard and soft constraints over
states, actions, and state features from demonstrations in deterministic and
non-deterministic environments modeled as Markov Decision Processes (MDPs). Our
method enables agents implicitly learn human constraints and desires without
the need for explicit modeling by the agent designer and to transfer these
constraints between environments. Our novel method generalizes prior work which
only considered deterministic hard constraints and achieves state of the art
performance.


title:
Embarrassingly Simple Performance Prediction for Abductive Natural
  Language Inference

abstract:
  The task of abductive natural language inference (\alpha{}nli), to decide
which hypothesis is the more likely explanation for a set of observations, is a
particularly difficult type of NLI. Instead of just determining a causal
relationship, it requires common sense to also evaluate how reasonable an
explanation is. All recent competitive systems build on top of contextualized
representations and make use of transformer architectures for learning an NLI
model. When somebody is faced with a particular NLI task, they need to select
the best model that is available. This is a time-consuming and resource-intense
endeavour. To solve this practical problem, we propose a simple method for
predicting the performance without actually fine-tuning the model. We do this
by testing how well the pre-trained models perform on the \alpha{}nli task when
just comparing sentence embeddings with cosine similarity to what the
performance that is achieved when training a classifier on top of these
embeddings. We show that the accuracy of the cosine similarity approach
correlates strongly with the accuracy of the classification approach with a
Pearson correlation coefficient of 0.65. Since the similarity computation is
orders of magnitude faster to compute on a given dataset (less than a minute
vs. hours), our method can lead to significant time savings in the process of
model selection.


title:
Observation of anisotropic thermal expansion and Jahn-Teller effect in
  double perovskites Sr$_{2-x}$La$_x$CoNbO$_6$ using neutron diffraction

abstract:
  We use temperature dependent neutron powder diffraction (NPD) to investigate
the structural changes and magnetic interactions in double perovskite
Sr$_{2-x}$La$_x$CoNbO$_6$ ($x=$ 0.4 and 0.6). A structural phase transition
from tetragonal ($I4/m$) to monoclinic ($P2_1/n$) is observed between $x=$ 0.4
and 0.6 samples. Interestingly, temperature evolution of the unit cell
parameters follow the Gr\"uneisen approximation, and the analysis suggest the
isotropic thermal expansion in case of the $x=$ 0.4 sample, whereas the $x=$
0.6 sample shows the anisotropy where the thermal expansion along the $c$-axis
significantly deviates from the Gr\"uneisen function. We observe the $z$-out
Jahn-Teller distortion in the CoO$_6$ and consequently $z$-in distortion in the
adjacent NbO$_6$ octahedra. With increase in the La substitution, a decrease in
the degree of octahedral distortion is evident from the significant reduction
in the local distortion parameter $\Delta$ around the B-site atoms.


title:
Dirichlet sub-Laplacians on homogeneous Carnot groups: spectral
  properties, asymptotics, and heat content

abstract:
  We consider sub-Laplacians in open bounded sets in a homogeneous Carnot group
and study their spectral properties. We prove that these operators have a pure
point spectrum, and prove the existence of the spectral gap. In addition, we
give applications to the small ball problem for a hypoelliptic Brownian motion
and the large time behavior of the heat content in a regular domain.


title:
The C-Band All-Sky Survey (C-BASS): Template Fitting of Diffuse Galactic
  Microwave Emission in the Northern Sky

abstract:
  The C-Band All-Sky Survey (C-BASS) has observed the Galaxy at 4.76GHz with an
angular resolution of $0.73^\circ$ full-width half-maximum, and detected
Galactic synchrotron emission with high signal-to-noise ratio over the entire
northern sky ($\delta > -15^{\circ}$). We present the results of a spatial
correlation analysis of Galactic foregrounds at mid-to-high ($b > 10^\circ$)
Galactic latitudes using a preliminary version of the C-BASS intensity map. We
jointly fit for synchrotron, dust, and free-free components between $20$ and
$1000$GHz and look for differences in the Galactic synchrotron spectrum, and
the emissivity of anomalous microwave emission (AME) when using either the
C-BASS map or the 408MHz all-sky map to trace synchrotron emission. We find
marginal evidence for a steepening ($\left<\Delta\beta\right> = -0.06\pm0.02$)
of the Galactic synchrotron spectrum at high frequencies resulting in a mean
spectral index of $\left<\beta\right> = -3.10\pm0.02$ over $4.76-22.8$GHz.
Further, we find that the synchrotron emission can be well modelled by a single
power-law up to a few tens of GHz. Due to this, we find that the AME emissivity
is not sensitive to changing the synchrotron tracer from the 408MHz map to the
4.76GHz map. We interpret this as strong evidence for the origin of AME being
spinning dust emission.


title: Polynomial bounds for chromatic number VI. Adding a four-vertex path

abstract:
  A class of graphs is $\chi$-bounded if there is a function $f$ such that
every graph $G$ in the class has chromatic number at most $f(\omega(G))$, where
$\omega(G)$ is the clique number of $G$; the class is polynomially
$\chi$-bounded if $f$ can be taken to be a polynomial. The Gy\'arf\'as-Sumner
conjecture asserts that, for every forest $H$, the class of $H$-free graphs
(graphs with no induced copy of $H$) is $\chi$-bounded. Let us say a forest $H$
is good if it satisfies the stronger property that the class of $H$-free graphs
is polynomially $\chi$-bounded.
  Very few forests are known to be good: for example, it is open for the
five-vertex path. Indeed, it is not even known that if every component of a
forest $H$ is good then $H$ is good, and in particular, it was not known that
the disjoint union of two four-vertex paths is good. Here we show the latter,
and more generally, that if $H$ is good then so is the disjoint union of $H$
and a four-vertex path. We also prove a more general result: if every component
of $H_1$ is good, and $H_2$ is any path (or broom) then the class of graphs
that are both $H_1$-free and $H_2$-free is polynomially $\chi$-bounded.


title:
Weak approximations and VIX option price expansions in forward variance
  curve models

abstract:
  We provide explicit approximation formulas for VIX futures and options in
forward variance models, with particular emphasis on the family of so-called
Bergomi models: the one-factor Bergomi model [Bergomi, Smile dynamics II, Risk,
2005], the rough Bergomi model [Bayer, Friz, and Gatheral, Pricing under rough
volatility, Quantitative Finance, 16(6):887-904, 2016], and an enhanced version
of the rough model that can generate realistic positive skew for VIX smiles --
introduced simultaneously by De Marco [Bachelier World Congress, 2018] and
Guyon [Bachelier World Congress, 2018] on the lines of [Bergomi, Smile dynamics
III, Risk, 2008], that we refer to as 'mixed rough Bergomi model'. Following
the methodology set up in [Gobet and Miri, Weak approximation of averaged
diffusion processes. Stochastic Process.\ Appl., 124(1):475-504, 2014], we
derive weak approximations for the law of the VIX, leading to option price
approximations under the form of explicit combinations of Black-Scholes prices
and greeks. As new contributions, we cope with the fractional integration
kernel appearing in rough models and treat the case of non-smooth payoffs, so
to encompass VIX futures, call and put options. We stress that our approach
does not rely on small-time asymptotics nor small-parameter (such as small
volatility-of-volatility) asymptotics, and can therefore be applied to any
option maturity and a wide range of parameter configurations. Our results are
illustrated by several numerical experiments and calibration tests to VIX
market data.


title:
Optimal Execution with Multiplicative Price Impact and Incomplete
  Information on the Return

abstract:
  We study an optimal liquidation problem with multiplicative price impact in
which the trend of the asset's price is an unobservable Bernoulli random
variable. The investor aims at selling over an infinite time-horizon a fixed
amount of assets in order to maximize a net expected profit functional, and
lump-sum as well as singularly continuous actions are allowed. Our mathematical
modelling leads to a singular stochastic control problem featuring a
finite-fuel constraint and partial observation. We provide the complete
analysis of an equivalent three-dimensional degenerate problem under full
information, whose state process is composed of the asset's price dynamics, the
amount of available assets in the portfolio, and the investor's belief about
the true value of the asset's trend. The optimal execution rule and the
problem's value function are expressed in terms of the solution to a truly
two-dimensional optimal stopping problem, whose associated belief-dependent
free boundary $b$ triggers the investor's optimal selling rule. The curve $b$
is uniquely determined through a nonlinear integral equation, for which we
derive a numerical solution allowing to understand the sensitivity of the
problem's solution with respect to the relevant model's parameters.


title:
Items from Psychometric Tests as Training Data for Personality Profiling
  Models of Twitter Users

abstract:
  Machine-learned models for author profiling in social media often rely on
data acquired via self-reporting-based psychometric tests (questionnaires)
filled out by social media users. This is an expensive but accurate data
collection strategy. Another, less costly alternative, which leads to
potentially more noisy and biased data, is to rely on labels inferred from
publicly available information in the profiles of the users, for instance
self-reported diagnoses or test results. In this paper, we explore a third
strategy, namely to directly use a corpus of items from validated psychometric
tests as training data. Items from psychometric tests often consist of
sentences from an I-perspective (e.g., "I make friends easily."). Such corpora
of test items constitute 'small data', but their availability for many concepts
is a rich resource. We investigate this approach for personality profiling, and
evaluate BERT classifiers fine-tuned on such psychometric test items for the
big five personality traits (openness, conscientiousness, extraversion,
agreeableness, neuroticism) and analyze various augmentation strategies
regarding their potential to address the challenges coming with such a small
corpus. Our evaluation on a publicly available Twitter corpus shows a
comparable performance to in-domain training for 4/5 personality traits with
T5-based data augmentation.


title:
The detailed chemical abundance patterns of accreted halo stars from the
  optical to infrared

abstract:
  Understanding the assembly of our Galaxy requires us to also characterize the
systems that helped build it. In this work, we accomplish this by exploring the
chemistry of accreted halo stars from the Gaia-Enceladus/Gaia-Sausage (GES)
selected in the infrared from the Apache Point Observatory Galactic Evolution
Experiment (APOGEE) Data Release 16. We use high resolution optical spectra for
62 GES stars to measure abundances in 20 elements spanning the $\alpha$,
Fe-peak, light, odd-Z, and notably, the neutron-capture groups of elements to
understand their trends in the context of and in contrast to the Milky Way and
other stellar populations. Using these derived abundances we find that the
optical and the infrared abundances agree to within 0.15 dex except for O, Co,
Na, Cu, and Ce. These stars have enhanced neutron-capture abundance trends
compared to the Milky Way, and their [Eu/Mg] and neutron-capture abundance
ratios (e.g., [Y/Eu], [Ba/Eu], [Zr/Ba], [La/Ba], and [Nd/Ba]) point to
r-process enhancement and a delay in s-process enrichment. Their [$\alpha$/Fe]
trend is lower than the Milky Way trend for [Fe/H]$>$-1.5 dex, similar to
previous studies of GES stars and consistent with the picture that these stars
formed in a system with a lower rate of star formation. This is further
supported by their depleted abundances in Ni, Na, and Cu abundances, again,
similar to previous studies of low-$\alpha$ stars with accreted origins.


title:
Efficient anisotropic polariton lasing using molecular conformation and
  orientation in organic microcavities

abstract:
  Organic exciton-photon polariton lasers have recently been shown to be
remarkable candidates for the realization of efficient sources of coherent
light operating at room temperature. While their thresholds are now comparable
with conventional organic photon lasers, tuning of molecular conformation and
orientation as a means to further enhance their performance remains largely
unexplored. Here, we first report a two-fold reduction in the threshold of a
polariton laser based on a high-Q microcavity filled with an active layer of
poly(9,9-dioctylfluorene) (PFO) when 15% {\beta}-phase conformation is
introduced. We then take advantage of the liquid crystalline properties of PFO
and use a thin transparent sulfuric dye 1 (SD1) photoalignment layer to induce
homogeneous nematic alignment of the polymer chains. The resulting transition
dipole moment orientation increases the Rabi energy, bringing the system into
the ultra-strong coupling regime where we observe anisotropic polariton lasing
with an eight-fold reduction in absorbed threshold, down to 1.14 pJ / 0.36
{\mu}Jcm-2 for the direction parallel to the orientation, with no emission
along the orthogonal direction. To our knowledge, this threshold is lower than
demonstrated with state-of-the art optically pumped organic vertical cavity
surface-emitting photon and polariton lasers. This demonstration opens new
opportunities for more efficient polaritonic devices and the observation of
fundamental effects at low polariton numbers.


title: Composite Anomaly Detection via Hierarchical Dynamic Search

abstract:
  Anomaly detection among a large number of processes arises in many
applications ranging from dynamic spectrum access to cybersecurity. In such
problems one can often obtain noisy observations aggregated from a chosen
subset of processes that conforms to a tree structure. The distribution of
these observations, based on which the presence of anomalies is detected, may
be only partially known. This gives rise to the need for a search strategy
designed to account for both the sample complexity and the detection accuracy,
as well as cope with statistical models that are known only up to some missing
parameters. In this work we propose a sequential search strategy using two
variations of the Generalized Local Likelihood Ratio statistic. Our proposed
Hierarchical Dynamic Search (HDS) strategy is shown to be order-optimal with
respect to the size of the search space and asymptotically optimal with respect
to the detection accuracy. An explicit upper bound on the error probability of
HDS is established for the finite sample regime. Extensive experiments are
conducted, demonstrating the performance gains of HDS over existing methods.


title: Interpreting Language Models with Contrastive Explanations

abstract:
  Model interpretability methods are often used to explain NLP model decisions
on tasks such as text classification, where the output space is relatively
small. However, when applied to language generation, where the output space
often consists of tens of thousands of tokens, these methods are unable to
provide informative explanations. Language models must consider various
features to predict a token, such as its part of speech, number, tense, or
semantics. Existing explanation methods conflate evidence for all these
features into a single explanation, which is less interpretable for human
understanding.
  To disentangle the different decisions in language modeling, we focus on
explaining language models contrastively: we look for salient input tokens that
explain why the model predicted one token instead of another. We demonstrate
that contrastive explanations are quantifiably better than non-contrastive
explanations in verifying major grammatical phenomena, and that they
significantly improve contrastive model simulatability for human observers. We
also identify groups of contrastive decisions where the model uses similar
evidence, and we are able to characterize what input tokens models use during
various language generation decisions.


title: Effective Hilbert's Irreducibility Theorem for global fields

abstract:
  We prove an effective form of Hilbert's irreducibility theorem for
polynomials over a global field $K$. More precisely, we give effective bounds
for the number of specializations $t\in \mathcal{O}_K$ that do not preserve the
irreducibility or the Galois group of a given irreducible polynomial $F(T,Y)\in
K[T,Y]$. The bounds are explicit in the height and degree of the polynomial
$F(T,Y)$, and are optimal in terms of the size of the parameter $t\in
\mathcal{O}_K$. Our proofs deal with the function field and number field cases
in a unified way.


title: Reorientation dynamics of microswimmers at fluid-fluid interfaces

abstract:
  We study the orientational and translational dynamics of spherical
microswimmers trapped at fluid interfaces, in terms of the force dipole and
source dipole components of their flow field. Using numerical simulations and
analytical calculations, we show that the force dipole exerts a torque,
orienting pushers parallel to the interface, and pullers in normal direction.
The source dipole results in particle rotation only for a finite viscosity
contrast between the two fluids, in agreement with previous studies. The
superposition of these two contributions leads to an rotational dynamics with a
steady-state orientation that depends on the relative magnitudes of the force
and source dipoles. In the general case, swimmers with weak force dipoles and
strong pullers are observed to align perpendicular to the interface and become
stationary, while strong pushers have a finite inclination angle towards the
lower viscosity fluid and swim along the interface.


title: Disappearing of the Fermi level pinning at semiconductor interfaces

abstract:
  We identify a universality in the Fermi level change of Van der Waals
interacting semiconductor interfaces-based Schottky junctions. We show that the
disappearing of quasi-Fermi level pinning at a certain thickness of
semiconductor films for both intrinsic (undoped) and extrinsic (doped)
semiconductors, over a wide range of bulk systems including inorganic, organic,
and even organic-inorganic hybridized semiconductors. The Fermi level (EF)
position located in the energy bandgap was dominated by not only the substrate
work function, but also the thickness of semiconductor films, in which the
final EF shall be located at the position reflecting the thermal equilibrium of
semiconductors themselves. Such universalities originate from the charge
transfer between the substrate and semiconductor films after solving
one-dimensional Poisson's equation. Our calculation resolves some of the
conflicting results from experimental results determined by using ultraviolet
photoelectron spectroscopy (UPS) and unifies the general rule on extracting EF
positions in energy bandgaps from (i) inorganic semiconductors to organic
semiconductors and (ii) intrinsic (undoped) to extrinsic (doped)
semiconductors. Our findings shall provide a simple analytical scaling for
obtaining the quantitative energy diagram regarding thickness in the real
devices, thus paving the way for a fundamental understanding of interface
physics and designing functional devices.


title:
Physics-Informed Machine Learning and Uncertainty Quantification for
  Mechanics of Heterogeneous Materials

abstract:
  In this work, a model based on the Physics - Informed Neural Networks (PINNs)
for solving elastic deformation of heterogeneous solids and associated
Uncertainty Quantification (UQ) is presented. For the present study, the PINNs
framework - Modulus developed by Nvidia is utilized, wherein we implement a
module for mechanics of heterogeneous solids. We use PINNs to approximate
momentum balance by assuming isotropic linear elastic constitutive behavior
against a loss function. Along with governing equations, the associated initial
/ boundary conditions also softly participate in the loss function. Solids
where the heterogeneity manifests as voids (low elastic modulus regions) and
fibers (high elastic modulus regions) in a matrix are analyzed, and the results
are validated against solutions obtained from a commercial Finite Element (FE)
analysis package. The present study also reveals that PINNs can capture the
stress jumps precisely at the material interfaces. Additionally, the present
study explores the advantages associated with the surrogate features in PINNs
via the variation in geometry and material properties. The presented UQ studies
suggest that the mean and standard deviation of the PINNs solution are in good
agreement with Monte Carlo FE results. The effective Young's modulus predicted
by PINNs for single representative void and single fiber composites compare
very well against the ones predicted by FE, which establishes the PINNs
formulation as an efficient homogenization tool.


title:
Fast and Scalable Memristive In-Memory Sorting with Column-Skipping
  Algorithm

abstract:
  Memristive in-memory sorting has been proposed recently to improve hardware
sorting efficiency. Using iterative in-memory min computations, data movements
between memory and external processing units can be eliminated for improved
latency and energy efficiency. However, the bit-traversal algorithm to search
the min requires a large number of column reads on memristive memory. In this
work, we propose a column-skipping algorithm with help of a near-memory
circuit. Redundant column reads can be skipped based on recorded states for
improved latency and hardware efficiency. To enhance the scalability, we
develop a multi-bank management that enables column-skipping for dataset stored
in different memristive memory banks. Prototype column-skipping sorters are
implemented with a 1T1R memristive memory in 40nm CMOS technology. Experimented
on a variety of sorting datasets, the length-1024 32-bit column-skipping sorter
with state recording of 2 demonstrates up to 4.08x speedup, 3.14x area
efficiency and 3.39x energy efficiency, respectively, over the latest
memristive in-memory sorting.


title:
Simulation-Guided Optimization of Granular Phononic Crystal Structure
  Using the Discrete Element Method

abstract:
  The paper describes a novel methodology of designing granular phononic
crystals for acoustic wave manipulations. A discrete element method is utilized
to model the dynamics of a pulse wave propagating through the densely packed
assembly of elastic spherical particles with an embedded phononic crystal - the
region consisting of a certain arrangement of particles with varying densities.
We suggest an optimization strategy that extremizes the useful properties of a
granular phononic crystal, which are described in terms of a noise-proof
functional based on frequency-wavenumber summation of spectral energy density.
Few types of efficient phononic crystals are identified. The suggested
methodology is of interest for a number of applications, in particular, for
seismic shielding and selective sound absorption.


title:
Malaria detection in Segmented Blood Cell using Convolutional Neural
  Networks and Canny Edge Detection

abstract:
  We apply convolutional neural networks to identify between malaria infected
and non-infected segmented cells from the thin blood smear slide images. We
optimize our model to find over 95% accuracy in malaria cell detection. We also
apply Canny image processing to reduce training file size while maintaining
comparable accuracy (~ 94%).


title: Dichotomous point counts over finite fields

abstract:
  We establish a near dichotomy between randomness and structure for the point
counts of arbitrary projective cubic threefolds over finite fields. Certain
"special" subvarieties, not unlike those in the Manin conjectures, play a
distinguished role. This suggests that similar (near) dichotomies may exist
more generally for other natural families of varieties.
  We also establish partial results for projective hypersurfaces in general,
using tools including "worst-case" cohomological results of Skorobogatov or
Katz, "average-case" results of Lindner, the existence theory of Lefschetz
pencils, and the theory of discriminants. This continues a line of inquiry
initiated by Hooley.


title: Homotopies of constant Cuntz class

abstract:
  Let $A$ be a unital simple separable exact C$^*$-algebra which is
approximately divisible and of real rank zero. We prove that the set of
positive elements in $A$ with a fixed Cuntz class is path connected. This
result applies in particular to irrational rotation algebras and AF algebras.


title:
Examination of computed aluminum grain boundary structures and interface
  energies that span the 5D space of crystallographic character

abstract:
  The space of possible grain boundary structures is vast, with 5 macroscopic,
crystallographic degrees of freedom that define the character of a grain
boundary. While numerous datasets of grain boundaries have examined this space
in part or in full, we present a computed dataset of over 7304 unique aluminum
grain boundaries in the 5D crystallographic space. Our sampling also includes a
range of possible microscopic, atomic configurations for each unique 5D
crystallographic structure, which total over 43 million structures. We present
an overview of the methods used to generate this dataset, an initial
examination of the energy trends that follow the Read-Shockley relationship,
hints at trends throughout the 5D space, variations in GB energy when
non-minimum energy structures are examined, and insights gained in machine
learning of grain boundary energy structure-property relationships. This
dataset, which is available for download, has great potential for insight into
GB structure-property relationships.


title:
Learning Causal Overhypotheses through Exploration in Children and
  Computational Models

abstract:
  Despite recent progress in reinforcement learning (RL), RL algorithms for
exploration still remain an active area of research. Existing methods often
focus on state-based metrics, which do not consider the underlying causal
structures of the environment, and while recent research has begun to explore
RL environments for causal learning, these environments primarily leverage
causal information through causal inference or induction rather than
exploration. In contrast, human children - some of the most proficient
explorers - have been shown to use causal information to great benefit. In this
work, we introduce a novel RL environment designed with a controllable causal
structure, which allows us to evaluate exploration strategies used by both
agents and children in a unified environment. In addition, through
experimentation on both computation models and children, we demonstrate that
there are significant differences between information-gain optimal RL
exploration in causal environments and the exploration of children in the same
environments. We conclude with a discussion of how these findings may inspire
new directions of research into efficient exploration and disambiguation of
causal structures for RL algorithms.


title: Collective states of active particles with elastic dipolar interactions

abstract:
  Many types of mammalian cells exert active contractile forces and
mechanically deform their elastic substrate, to accomplish biological functions
such as cell migration. These substrate deformations provide a mechanism by
which cells can sense other cells, leading to long range mechanical intercell
interactions and possible self organization. Here, we treat cells as noisy
motile particles that exert contractile dipolar stresses on elastic substrates
as they move. By combining this minimal model for the motility of individual
cells with a linear elastic model that accounts for substrate mediated cell
cell interactions, we examine emergent collective states that result from the
interplay of cell motility and long range elastic dipolar interactions. In
particular, we show that particles self assemble into flexible, motile chains
which can cluster to form diverse larger scale compact structures with polar
order. By computing key structural and dynamical metrics, we distinguish
between the collective states at weak and strong elastic interactions, as well
as at low and high motility. We also show how these states are affected by
confinement, an important characteristic of the complex mechanical
microenvironment inhabited by cells. Our model predictions are generally
applicable to active matter with dipolar interactions ranging from biological
cells to synthetic colloids endowed with electric or magnetic dipole moments.


title:
Reasoning with Scene Graphs for Robot Planning under Partial
  Observability

abstract:
  Robot planning in partially observable domains is difficult, because a robot
needs to estimate the current state and plan actions at the same time. When the
domain includes many objects, reasoning about the objects and their
relationships makes robot planning even more difficult. In this paper, we
develop an algorithm called scene analysis for robot planning (SARP) that
enables robots to reason with visual contextual information toward achieving
long-term goals under uncertainty. SARP constructs scene graphs, a factored
representation of objects and their relations, using images captured from
different positions, and reasons with them to enable context-aware robot
planning under partial observability. Experiments have been conducted using
multiple 3D environments in simulation, and a dataset collected by a real
robot. In comparison to standard robot planning and scene analysis methods, in
a target search domain, SARP improves both efficiency and accuracy in task
completion. Supplementary material can be found at https://tinyurl.com/sarp22


title: The Role of Heterogeneity in Autonomous Perimeter Defense Problems

abstract:
  When is heterogeneity in the composition of an autonomous robotic team
beneficial and when is it detrimental? We investigate and answer this question
in the context of a minimally viable model that examines the role of
heterogeneous speeds in perimeter defense problems, where defenders share a
total allocated speed budget. We consider two distinct problem settings and
develop strategies based on dynamic programming and on local interaction rules.
We present a theoretical analysis of both approaches and our results are
extensively validated using simulations. Interestingly, our results demonstrate
that the viability of heterogeneous teams depends on the amount of information
available to the defenders. Moreover, our results suggest a universality
property: across a wide range of problem parameters the optimal ratio of the
speeds of the defenders remains nearly constant.


title:
Empirical Optimal Transport between Different Measures Adapts to Lower
  Complexity

abstract:
  The empirical optimal transport (OT) cost between two probability measures
from random data is a fundamental quantity in transport based data analysis. In
this work, we derive novel guarantees for its convergence rate when the
involved measures are different, possibly supported on different spaces. Our
central observation is that the statistical performance of the empirical OT
cost is determined by the less complex measure, a phenomenon we refer to as
lower complexity adaptation of empirical OT. For instance, under Lipschitz
ground costs, we find that the empirical OT cost based on $n$ observations
converges at least with rate $n^{-1/d}$ to the population quantity if one of
the two measures is concentrated on a $d$-dimensional manifold, while the other
can be arbitrary. For semi-concave ground costs, we show that the upper bound
for the rate improves to $n^{-2/d}$. Similarly, our theory establishes the
general convergence rate $n^{-1/2}$ for semi-discrete OT. All of these results
are valid in the two-sample case as well, meaning that the convergence rate is
still governed by the simpler of the two measures. On a conceptual level, our
findings therefore suggest that the curse of dimensionality only affects the
estimation of the OT cost when both measures exhibit a high intrinsic
dimension. Our proofs are based on the dual formulation of OT as a maximization
over a suitable function class $\mathcal{F}_c$ and the observation that the
$c$-transform of $\mathcal{F}_c$ under bounded costs has the same uniform
metric entropy as $\mathcal{F}_c$ itself.


title: Survey on Large Scale Neural Network Training

abstract:
  Modern Deep Neural Networks (DNNs) require significant memory to store
weight, activations, and other intermediate tensors during training. Hence,
many models do not fit one GPU device or can be trained using only a small
per-GPU batch size. This survey provides a systematic overview of the
approaches that enable more efficient DNNs training. We analyze techniques that
save memory and make good use of computation and communication resources on
architectures with a single or several GPUs. We summarize the main categories
of strategies and compare strategies within and across categories. Along with
approaches proposed in the literature, we discuss available implementations.


title: Functional Renormalisation for $f(R_{\mu\nu\rho\sigma})$ Quantum Gravity

abstract:
  We derive new functional renormalisation group flows for quantum gravity, in
any dimension. The key new achievement is that the equations apply for any
theory of gravity whose underlying Lagrangian $\sim f(R_{\mu\nu\rho\sigma})$ is
a function of the Riemann tensor and the inverse metric. The results centrally
exploit the benefits of maximally symmetric spaces for the evaluation of
operator traces. The framework is highly versatile and offers a wide range of
new applications to study quantum gravitational effects in extensions of
Einstein gravity, many of which have hitherto been out of reach. The phase
diagram and sample flows for Einstein-Hilbert gravity, Gauss-Bonnet, and
selected higher-order theories of gravity are given. We also provide an
algorithm to find the flow for general polynomial Riemann curvature
interactions. The setup vastly enhances the reach of fixed point searches,
enabling novel types of search strategies including across the operator space
spanned by polynomial curvature invariants, and in extensions of general
relativity relevant for cosmology. Further implications, and links with
unimodular versions of gravity are indicated.


title: Discovering Affinity Relationships between Personality Types

abstract:
  Psychology research findings suggest that personality is related to
differences in friendship characteristics and that some personality traits
correlate with linguistic behavior. In this paper, we investigate the influence
that personality may have on affinity formation. To this end, we derive
affinity relationships from social media interactions, examine personality
based on language use to discover the emotional stability of affinity
relationships, and measure semantic similarity at the personality type level to
understand the logic behind the development of affinity. Specifically, we
conduct extensive experiments using a publicly available dataset containing
information on individuals who self-identified with a Myers-Briggs personality
type. Our results identify certain influential personality types that weigh
more heavily on affinity relationships and show that personality can be
predicted from spontaneous language with an F-1 score superior to 0.76. Future
research avenues are proposed.


title:
Wireless Connectivity and Localization for Advanced Air Mobility
  Services

abstract:
  By serving as an analog to traffic signal lights, communication signaling for
drone to drone communications holds the key to the success of advanced air
mobility (AAM) in both urban and rural settings. Deployment of AAM applications
such as air taxis and air ambulances, especially at large-scale, requires a
reliable channel for a point-to-point and broadcast communication between two
or more aircraft. Achieving such high reliability, in a highly mobile
environment, requires communication systems designed for agility and
efficiency. This paper presents the foundations for establishing and
maintaining a reliable communication channel among multiple aircraft in unique
AAM settings. Subsequently, it presents concepts and results on wireless
coverage and mobility for AAM services using cellular networks as a ground
network infrastructure. Finally, we analyze the wireless localization
performance at 3D AAM corridors when cellular networks are utilized,
considering different corridor heights and base station densities. We highlight
future research directions and open problems to improve wireless coverage and
localization throughout the manuscript.


title: Pion-induced radiative corrections to neutron beta-decay

abstract:
  We compute the electromagnetic corrections to neutron beta decay using a
low-energy hadronic effective field theory. We identify and compute new
radiative corrections arising from virtual pions that were missed in previous
studies. The largest correction is a percent-level shift in the axial charge of
the nucleon proportional to the electromagnetic part of the pion-mass
splitting. Smaller corrections, comparable to anticipated experimental
precision, impact the $\beta$-$\nu$ angular correlations and the
$\beta$-asymmetry. We comment on implications of our results for the comparison
of the experimentally measured axial charge with first-principle computations
using lattice QCD and on the potential of $\beta$-decay experiments to
constrain beyond-the-Standard-Model interactions.


title: Scalar quantum fields in cosmologies with 2+1 spacetime dimensions

abstract:
  Motivated by the possibility to use Bose-Einstein condensates as quantum
simulators for spacetime curvature, we study a massless relativistic scalar
quantum field in spatially curved Friedmann-Lema\^itre-Robertson-Walker
universes with $d=2+1$ spacetime dimensions. In particular, we investigate
particle production caused by a time-dependent background geometry, by means of
the spectrum of fluctuations and several two-point field correlation functions.
We derive new analytical results for several expansion scenarios.


title: Curved and expanding spacetime geometries in Bose-Einstein condensates

abstract:
  Phonons have the characteristic linear dispersion relation of massless
relativistic particles. They arise as low energy excitations of Bose-Einstein
condensates and, in nonhomogeneous situations, are governed by a space- and
time-dependent acoustic metric. We discuss how this metric can be
experimentally designed to realize curved spacetime geometries, in particular,
expanding Friedmann-Lema\^itre-Robertson-Walker cosmologies, with negative,
vanishing, or positive spatial curvature. A nonvanishing Hubble rate can be
obtained through a time-dependent scattering length of the background
condensate. For relativistic quantum fields this leads to the phenomenon of
particle production, which we describe in detail. We explain how particle
production and other interesting features of quantum field theory in curved
spacetime can be tested in terms of experimentally accessible correlation
functions.


title:
Astronomy at the University of Salamanca at the end of the 15th century.
  What "El Cielo de Salamanca" tells us

abstract:
  "El Cielo de Salamanca" ("The Sky of Salamanca") is a quarter-sphere-shaped
vault 8.70 metres in diameter. It was painted sometime between 1480 and 1493
and shows five zodiacal constellations, three boreal and six austral. The Sun
and Mercury are also represented. It formed part of a three times larger vault
depicting the 48 Ptolemaic constellations and the rest of the planets known at
the time. This was a splendid work of art that covered the ceiling of the first
library of the University of Salamanca, one of the oldest in Europe having
obtained its royal charter in 1218. But it was also a pioneering scientific
work: a planetarium used to teach astronomy, the first of its kind in the
history of Astronomy that has come down to us in the preserved part that we now
call "The Sky of Salamanca". We describe the scientific context surrounding the
chair of Astrology founded around 1460 at the University of Salamanca, which
led to the production of this unique scientific work of art and to the
flourishing of Astronomy in Salamanca. We analyse the possible dates compatible
with it, showing that they are extremely infrequent. In the period of 1100
years from 1200 to 2300 that we studied there are only 23 years that have
feasible days. We conclude that the information contained in "El Cielo de
Salamanca" is not sufficient to assign it to a specific date but rather to an
interval of several days that circumstantial evidence seems to place in August
1475. The same configuration of the sky will be observable, for the first time
in 141 years, from the 22nd to the 25th of August 2022. The next occasion to
observe it live will be in 2060.


title:
Performance of a one-parameter correlation factor for transcorrelation:
  the Li-Ne total energies and ionization potentials

abstract:
  In this work we investigate the performance of a recently proposed
transcorrelated (TC) approach based on a single-parameter correlation factor
[JCP, 154, 8, 2021] for systems involving more than two electrons. The benefit
of such an approach relies on its simplicity as efficient numerical-analytical
schemes can be set up to compute the two- and three-body integrals occuring in
the effective TC Hamiltonian. To obtain accurate ground state energies within a
given basis set, the present TC scheme is coupled to the recently proposed
TC-full configuration interaction quantum Monte Carlo method [JCP, 151, 6,
2019]. We report ground state total energies on the Li-Ne series, together with
their first cations, computed in increasing large basis sets and compare to
more elaborate correlation factors involving electron-electron-nucleus
coordinates. Numerical results on the Li-Ne ionization potentials show that the
use of the single-parameter correlation factor brings on average only a
slightly lower accuracy (1.2 mH) in a triple-zeta quality basis set with
respect to a more sophisticated correlation factor. However, already using a
quadruple-zeta quality basis set yields results within chemical accuracy to
complete basis set limit results when using this novel single-parameter
correlation factor.


title: A Pricing Rule for Third-Party Platoon Coordination Service Provider

abstract:
  We model a platooning system including trucks and a third-party service
provider that performs platoon coordination, distributes the platooning profit
within platoons, and charges the trucks in exchange for its services. This
paper studies one class of pricing rules, where the third-party service
provider keeps part of the platooning profit each time a platoon is formed.
Furthermore, we propose a platoon coordination solution based on distributed
model predictive control in which the pricing rule is integrated. To evaluate
the effect of the pricing on the platooning system, we perform a simulation
over the Swedish road network. The simulation shows that the platooning rate
and profit highly depend on the pricing. This suggests that pricing needs to be
set carefully to obtain a satisfactory platooning system in the future.


title:
A Grounded Theory of Coordination in Remote-First and Hybrid Software
  Teams

abstract:
  While the long-term effects of the COVID-19 pandemic on software
professionals and organizations are difficult to predict, it seems likely that
working from home, remote-first teams, distributed teams, and hybrid
(part-remote/part-office) teams will be more common. It is therefore important
to investigate the challenges that software teams and organizations face with
new remote and hybrid work. Consequently, this paper reports a year-long,
participant-observation, constructivist grounded theory study investigating the
impact of working from home on software development. This study resulted in a
theory of software team coordination. Briefly, shifting from in-office to
at-home work fundamentally altered coordination within software teams. While
group cohesion and more effective communication appear protective, coordination
is undermined by distrust, parenting and communication bricolage. Poor
coordination leads to numerous problems including misunderstandings, help
requests, lower job satisfaction among team members, and more ill-defined
tasks. These problems, in turn, reduce overall project success and prompt
professionals to alter their software development processes (in this case, from
Scrum to Kanban). Our findings suggest that software organizations with many
remote employees can improve performance by encouraging greater engagement
within teams and supporting employees with family and childcare
responsibilities.


title: EINNs: Epidemiologically-Informed Neural Networks

abstract:
  We introduce a new class of physics-informed neural networks-EINN-crafted for
epidemic forecasting. We investigate how to leverage both the theoretical
flexibility provided by mechanistic models as well as the data-driven
expressability afforded by AI models, to ingest heterogeneous information.
Although neural forecasting models has been successful in multiple tasks,
long-term predictions and anticipating trend changes remain open challenges.
Epidemiological ODE models contain mechanisms that can guide us in these two
tasks; however, they have limited capability of ingesting data sources and
modeling composite signals. Thus we propose to supervise neural networks with
epidemic mechanistic models while simultaneously learning their hidden
dynamics. Our method EINN allows neural models have the flexibility to learn
the disease spread dynamics and use auxiliary features in a general framework.
In contrast with previous work, we not assume the observability of complete
dynamics and do not need to numerically solve the ODE equations during
training. Our thorough experiments showcase the clear benefits of our approach
with other non-trivial alternatives.


title: Transformer Quality in Linear Time

abstract:
  We revisit the design choices in Transformers, and propose methods to address
their weaknesses in handling long sequences. First, we propose a simple layer
named gated attention unit, which allows the use of a weaker single-head
attention with minimal quality loss. We then propose a linear approximation
method complementary to this new layer, which is accelerator-friendly and
highly competitive in quality. The resulting model, named FLASH, matches the
perplexity of improved Transformers over both short (512) and long (8K) context
lengths, achieving training speedups of up to 4.9$\times$ on Wiki-40B and
12.1$\times$ on PG-19 for auto-regressive language modeling, and 4.8$\times$ on
C4 for masked language modeling.


title:
Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans
  on Youtube

abstract:
  We build a system that enables any human to control a robot hand and arm,
simply by demonstrating motions with their own hand. The robot observes the
human operator via a single RGB camera and imitates their actions in real-time.
Human hands and robot hands differ in shape, size, and joint structure, and
performing this translation from a single uncalibrated camera is a highly
underconstrained problem. Moreover, the retargeted trajectories must
effectively execute tasks on a physical robot, which requires them to be
temporally smooth and free of self-collisions. Our key insight is that while
paired human-robot correspondence data is expensive to collect, the internet
contains a massive corpus of rich and diverse human hand videos. We leverage
this data to train a system that understands human hands and retargets a human
video stream into a robot hand-arm trajectory that is smooth, swift, safe, and
semantically similar to the guiding demonstration. We demonstrate that it
enables previously untrained people to teleoperate a robot on various dexterous
manipulation tasks. Our low-cost, glove-free, marker-free remote teleoperation
system makes robot teaching more accessible and we hope that it can aid robots
that learn to act autonomously in the real world. Videos at
https://robotic-telekinesis.github.io/


title:
Optimal Multi-Agent Path Finding for Precedence Constrained Planning
  Tasks

abstract:
  Multi-Agent Path Finding (MAPF) is the problem of finding collision-free
paths for multiple agents from their start locations to end locations. We
consider an extension to this problem, Precedence Constrained Multi-Agent Path
Finding (PC-MAPF), wherein agents are assigned a sequence of planning tasks
that contain precedence constraints between them. PC-MAPF has various
applications, for example in multi-agent pickup and delivery problems where
some objects might require multiple agents to collaboratively pickup and move
them in unison. Precedence constraints also arise in warehouse assembly
problems where before a manufacturing task can begin, its input resources must
be manufactured and delivered. We propose a novel algorithm, Precedence
Constrained Conflict Based Search (PC-CBS), which finds makespan-optimal
solutions for this class of problems. PC-CBS utilizes a Precedence-Constrained
Task-Graph to define valid intervals for each planning task and updates them
when precedence conflicts are encountered. We benchmark the performance of this
algorithm over various warehouse assembly, and multi-agent pickup and delivery
tasks, and use it to evaluate the sub-optimality of a recently proposed
efficient baseline.


title: A Survey of Ad Hoc Teamwork: Definitions, Methods, and Open Problems

abstract:
  Ad hoc teamwork is the well-established research problem of designing agents
that can collaborate with new teammates without prior coordination. This survey
makes a two-fold contribution. First, it provides a structured description of
the different facets of the ad hoc teamwork problem. Second, it discusses the
progress that has been made in the field so far, and identifies the immediate
and long-term open problems that need to be addressed in the field of ad hoc
teamwork.


title:
SapientML: Synthesizing Machine Learning Pipelines by Learning from
  Human-Written Solutions

abstract:
  Automatic machine learning, or AutoML, holds the promise of truly
democratizing the use of machine learning (ML), by substantially automating the
work of data scientists. However, the huge combinatorial search space of
candidate pipelines means that current AutoML techniques, generate sub-optimal
pipelines, or none at all, especially on large, complex datasets. In this work
we propose an AutoML technique SapientML, that can learn from a corpus of
existing datasets and their human-written pipelines, and efficiently generate a
high-quality pipeline for a predictive task on a new dataset. To combat the
search space explosion of AutoML, SapientML employs a novel divide-and-conquer
strategy realized as a three-stage program synthesis approach, that reasons on
successively smaller search spaces. The first stage uses a machine-learned
model to predict a set of plausible ML components to constitute a pipeline. In
the second stage, this is then refined into a small pool of viable concrete
pipelines using syntactic constraints derived from the corpus and the
machine-learned model. Dynamically evaluating these few pipelines, in the third
stage, provides the best solution. We instantiate SapientML as part of a fully
automated tool-chain that creates a cleaned, labeled learning corpus by mining
Kaggle, learns from it, and uses the learned models to then synthesize
pipelines for new predictive tasks. We have created a training corpus of 1094
pipelines spanning 170 datasets, and evaluated SapientML on a set of 41
benchmark datasets, including 10 new, large, real-world datasets from Kaggle,
and against 3 state-of-the-art AutoML tools and 2 baselines. Our evaluation
shows that SapientML produces the best or comparable accuracy on 27 of the
benchmarks while the second best tool fails to even produce a pipeline on 9 of
the instances.


title:
A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia
  from Chest Radiographs

abstract:
  While many quantum computing techniques for machine learning have been
proposed, their performance on real-world datasets remains to be studied. In
this paper, we explore how a variational quantum circuit could be integrated
into a classical neural network for the problem of detecting pneumonia from
chest radiographs. We substitute one layer of a classical convolutional neural
network with a variational quantum circuit to create a hybrid neural network.
We train both networks on an image dataset containing chest radiographs and
benchmark their performance. To mitigate the influence of different sources of
randomness in network training, we sample the results over multiple rounds. We
show that the hybrid network outperforms the classical network on different
performance measures, and that these improvements are statistically
significant. Our work serves as an experimental demonstration of the potential
of quantum computing to significantly improve neural network performance for
real-world, non-trivial problems relevant to society and industry.


title:
Predicting emotion from music videos: exploring the relative
  contribution of visual and auditory information to affective responses

abstract:
  Although media content is increasingly produced, distributed, and consumed in
multiple combinations of modalities, how individual modalities contribute to
the perceived emotion of a media item remains poorly understood. In this paper
we present MusicVideos (MuVi), a novel dataset for affective multimedia content
analysis to study how the auditory and visual modalities contribute to the
perceived emotion of media. The data were collected by presenting music videos
to participants in three conditions: music, visual, and audiovisual.
Participants annotated the music videos for valence and arousal over time, as
well as the overall emotion conveyed. We present detailed descriptive
statistics for key measures in the dataset and the results of feature
importance analyses for each condition. Finally, we propose a novel transfer
learning architecture to train Predictive models Augmented with Isolated
modality Ratings (PAIR) and demonstrate the potential of isolated modality
ratings for enhancing multimodal emotion recognition. Our results suggest that
perceptions of arousal are influenced primarily by auditory information, while
perceptions of valence are more subjective and can be influenced by both visual
and auditory information. The dataset is made publicly available.


title:
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a
  Dynamic Graph Neural Network

abstract:
  Anomaly detection is widely used to distinguish system anomalies by analyzing
the temporal and spatial features of wireless sensor network (WSN) data
streams; it is one of critical technique that ensures the reliability of WSNs.
Currently, graph neural networks (GNNs) have become popular state-of-the-art
methods for conducting anomaly detection on WSN data streams. However, the
existing anomaly detection methods based on GNNs do not consider the temporal
and spatial features of WSN data streams simultaneously, such as multi-node,
multi-modal and multi-time features, seriously impacting their effectiveness.
In this paper, a novel anomaly detection model is proposed for multimodal WSN
data flows, where three GNNs are used to separately extract the temporal
features of WSN data flows, the correlation features between different modes
and the spatial features between sensor node positions. Specifically, first,
the temporal features and modal correlation features extracted from each sensor
node are fused into one vector representation, which is further aggregated with
the spatial features, i.e., the spatial position relationships of the nodes;
finally, the current time-series data of WSN nodes are predicted, and abnormal
states are identified according to the fusion features. The simulation results
obtained on a public dataset show that the proposed approach is able to
significantly improve upon the existing methods in terms of its robustness, and
its F1 score reaches 0.90, which is 14.2% higher than that of the graph
convolution network (GCN) with long short-term memory (LSTM).


title: A Clustering Preserving Transformation for k-Means Algorithm Output

abstract:
  This note introduces a novel clustering preserving transformation of cluster
sets obtained from $k$-means algorithm. This transformation may be used to
generate new labeled data{}sets from existent ones. It is more flexible that
Kleinberg axiom based consistency transformation because data points in a
cluster can be moved away and datapoints between clusters may come closer
together.


title:
Feasibility Study of Multi-Site Split Learning for Privacy-Preserving
  Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and
  Cholesterol Dataset

abstract:
  It seems as though progressively more people are in the race to upload
content, data, and information online; and hospitals haven't neglected this
trend either. Hospitals are now at the forefront for multi-site medical data
sharing to provide groundbreaking advancements in the way health records are
shared and patients are diagnosed. Sharing of medical data is essential in
modern medical research. Yet, as with all data sharing technology, the
challenge is to balance improved treatment with protecting patient's personal
information. This paper provides a novel split learning algorithm coined the
term, "multi-site split learning", which enables a secure transfer of medical
data between multiple hospitals without fear of exposing personal data
contained in patient records. It also explores the effects of varying the
number of end-systems and the ratio of data-imbalance on the deep learning
performance. A guideline for the most optimal configuration of split learning
that ensures privacy of patient data whilst achieving performance is
empirically given. We argue the benefits of our multi-site split learning
algorithm, especially regarding the privacy preserving factor, using CT scans
of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.


title:
Brane world creation from flat or almost flat space in dynamical tension
  string theories

abstract:
  There is great interest in the construction of brane worlds, where matter and
gravity are forced to be effective only in a lower dimensional surface , the
brane . How these could appear as a consequence of string theory is a crucial
question and this has been widely discussed. Here we will examine a distinct
scenario that appears in dynamical tension theories and where string tension is
positive between two surfaces separated by a short distance and at the two
surfaces themselves the string tensions become infinite, therefore producing an
effective confinement of the strings and therefore of all matter and gravity to
the space between these to surfaces, which is in fact a new type of stringy
brane world scenario. The basic formulation for obtaining this scenario consist
of assuming two types of strings characterized by a different constant of
integration related to the spontaneous string tension generation. These string
tension multiplied by the embedding metric define conformally related metrics
that both satisfy Einsteins equation. The braneworlds appear very naturally
when these two metrics are both flat spaces related by a special conformal
transformation. The two types of string tensions are determined and they blow
up at two close expanding surfaces. A puzzling aspect appears then: the
construction is based on flat spaces, but then there are also strings with very
large tension near the boundaries of the braneworld,so can the back reaction
from the infinite tension strings destroy the flat space background?.
Fortunatelly that can be resolved using the mechanism Universe creation from
almost flat (or empty) spaces, which incorporates a gas of very large string
tensions in a membrane, studied before in 1+1 membranes in a 2+1 embedding
space and now is generalized for a 1+(D-2) membrane moving in a 1+(D-1) space.


title:
Quantum Squeezing of Slow-Light Dark Solitons via Electromagnetically
  Induced Transparency

abstract:
  We consider the quantum effect of slow light dark soliton (SLDS) in a cold
atomic gas with defocuing Kerr nonlinearity via electromagnetically induced
transparency (EIT). We calculate the quantum fluctuations of the SLDS by
solving the relevant non-Hermitian eigenvalue problem describing the quantum
fluctuations, and find that only one zero mode is allowed. This is different
from the quantum fluctuations of bright solitons, where two independent zero
modes occur. We rigorously prove that the eigenmodes, which consist of
continuous modes and the zero mode, are bi-orthogonal and constitute a complete
bi-orthonormalized basis, useful for the calculation on the quantum
fluctuations of the SLDS. We demonstrate that, due to the large Kerr
nonlinearity contributed from the EIT effect, a significant quantum squeezing
of the SLDS can be realized; the squeezing efficiency can be manipulated by the
Kerr nonlinearity and the soliton's amplitude, which can be much higher than
that of bright solitons. Our work contributes to efforts for developing quantum
nonlinear optics and non-Hermitian Physics, and for possible applications in
quantum information processing and precision measurements.


title:
Towards technological adaptation of advanced farming through AI, IoT,
  and Robotics: A Comprehensive overview

abstract:
  The population explosion of the 21st century has adversely affected the
natural resources with restricted availability of cultivable land, increased
average temperatures due to global warming, and carbon footprint resulting in a
drastic increase in floods as well as droughts thus making food security
significant anxiety for most countries. The traditional methods were no longer
sufficient which paved the way for technological ascents such as a substantial
rise in Artificial Intelligence (AI), Internet of Things (IoT), as well as
Robotics that provides high productivity, functional efficiency, flexibility,
cost-effectiveness in the domain of agriculture. AI, IoT, and Robotics-based
devices and methods have produced new paradigms and opportunities in
agriculture. AI's existing approaches are soil management, crop diseases
identification, weed identification, and management in collaboration with IoT
devices. IoT has utilized automatic agricultural operations and real-time
monitoring with few personnel employed in real-time. The major existing
applications of agricultural robotics are for the function of soil preparation,
planting, monitoring, harvesting, and storage. In this paper, researchers have
explored a comprehensive overview of recent implementation, scopes,
opportunities, challenges, limitations, and future research instructions of AI,
IoT, and Robotics based methodology in the agriculture sector.


title: Dispersion Characteristics of Accelerated Spacetime-Modulated Media

abstract:
  This paper opens up the field of nonuniform-velocity SpaceTime-Modulated
(STM) metamaterials, with the canonical example of an STM metamaterial of
constant proper acceleration or, equivalently, hyperbolic acceleration.
Combining tools of General Relativity and Classical Electrodynamics, it derives
the dispersion relation of this exotic medium and reports its fundamental
physics, whose most striking feature is the bending of light in the direction
opposite to the direction of the modulation.


title:
A Novel Architecture Slimming Method for Network Pruning and Knowledge
  Distillation

abstract:
  Network pruning and knowledge distillation are two widely-known model
compression methods that efficiently reduce computation cost and model size. A
common problem in both pruning and distillation is to determine compressed
architecture, i.e., the exact number of filters per layer and layer
configuration, in order to preserve most of the original model capacity. In
spite of the great advances in existing works, the determination of an
excellent architecture still requires human interference or tremendous
experimentations. In this paper, we propose an architecture slimming method
that automates the layer configuration process. We start from the perspective
that the capacity of the over-parameterized model can be largely preserved by
finding the minimum number of filters preserving the maximum parameter variance
per layer, resulting in a thin architecture. We formulate the determination of
compressed architecture as a one-step orthogonal linear transformation, and
integrate principle component analysis (PCA), where the variances of filters in
the first several projections are maximized. We demonstrate the rationality of
our analysis and the effectiveness of the proposed method through extensive
experiments. In particular, we show that under the same overall compression
rate, the compressed architecture determined by our method shows significant
performance gain over baselines after pruning and distillation. Surprisingly,
we find that the resulting layer-wise compression rates correspond to the layer
sensitivities found by existing works through tremendous experimentations.