title: The Unexplored Treasure Trove of Phabricator Code Review

abstract:
  Phabricator is a modern code collaboration tool used by popular projects like
FreeBSD and Mozilla. However, unlike the other well-known code review
environments, such as Gerrit or GitHub, there is no readily accessible public
code review dataset for Phabricator. This paper describes our experience mining
code reviews from five different projects that use Phabricator (Blender,
FreeBSD, KDE, LLVM, and Mozilla). We discuss the challenges associated with the
data retrieval process and our solutions, resulting in a dataset with details
regarding 317,476 Phabricator code reviews. Our dataset is available in both
JSON and MySQL database dump formats. The dataset enables analyses of the
history of code reviews at a more granular level than other platforms. In
addition, given that the projects we mined are publicly accessible via the
Conduit API, our dataset can be used as a foundation to fetch additional
details and insights.


title:
Distributed On-Sensor Compute System for AR/VR Devices: A
  Semi-Analytical Simulation Framework for Power Estimation

abstract:
  Augmented Reality/Virtual Reality (AR/VR) glasses are widely foreseen as the
next generation computing platform. AR/VR glasses are a complex "system of
systems" which must satisfy stringent form factor, computing-, power- and
thermal- requirements. In this paper, we will show that a novel distributed
on-sensor compute architecture, coupled with new semiconductor technologies
(such as dense 3D-IC interconnects and Spin-Transfer Torque Magneto Random
Access Memory, STT-MRAM) and, most importantly, a full hardware-software
co-optimization are the solutions to achieve attractive and socially acceptable
AR/VR glasses. To this end, we developed a semi-analytical simulation framework
to estimate the power consumption of novel AR/VR distributed on-sensor
computing architectures. The model allows the optimization of the main
technological features of the system modules, as well as the computer-vision
algorithm partition strategy across the distributed compute architecture. We
show that, in the case of the compute-intensive machine learning based Hand
Tracking algorithm, the distributed on-sensor compute architecture can reduce
the system power consumption compared to a centralized system, with the
additional benefits in terms of latency and privacy.


title:
Invariance in Policy Optimisation and Partial Identifiability in Reward
  Learning

abstract:
  It's challenging to design reward functions for complex, real-world tasks.
Reward learning lets one instead infer reward functions from data. However,
multiple reward functions often fit the data equally well, even in the
infinite-data limit. Prior work often considers reward functions to be uniquely
recoverable, by imposing additional assumptions on data sources. By contrast,
we formally characterise the partial identifiability of popular data sources,
including demonstrations and trajectory preferences, under multiple common sets
of assumptions. We analyse the impact of this partial identifiability on
downstream tasks such as policy optimisation, including under changes in
environment dynamics. We unify our results in a framework for comparing data
sources and downstream tasks by their invariances, with implications for the
design and selection of data sources for reward learning.


title:
IMPACTS -- A program to calculate the effects of a hypervelocity impact
  into a Solar System body

abstract:
  IMPACTS! is a free web based Javascript application that can be opened and
executed in any standard browser. It can be downloaded from
https://www.dropbox.com/sh/2oanw5vxf09qpl1/AAD8uD-CsWir6slPlWndyUQAa?dl=0. This
program calculates the many characteristics of the craters, ejecta and
disruptions that are created from hypervelocity impacts into the objects of the
Solar System. The code cam also be obtained from the author.


title: Interaction of quantum systems with single pulses of quantized radiation

abstract:
  The interaction of a propagating pulse of quantum radiation with a localized
quantum system can be described by a cascaded master equation with a distinct
initially populated input and a finally populated output field mode [Phys. Rev.
Lett. 123, 123604 (2019), arXiv:1902.09833v3]. By transformation to an
appropriate interaction picture, we identify the usual Jaynes-Cummings
Hamiltonian between the scatterer and a superposition of the initial and final
mode, with a strength given by the travelling pulse mode amplitude. The
transformation also identifies a coupling of the scatterer with an orthogonal
combination of the two modes. The transformed master equation offers important
insights into the system dynamics and it permits numerically efficient
solutions.


title:
Synergistic Scheduling of Learning and Allocation of Tasks in
  Human-Robot Teams

abstract:
  We consider the problem of completing a set of $n$ tasks with a human-robot
team using minimum effort. In many domains, teaching a robot to be fully
autonomous can be counterproductive if there are finitely many tasks to be
done. Rather, the optimal strategy is to weigh the cost of teaching a robot and
its benefit -- how many new tasks it allows the robot to solve autonomously. We
formulate this as a planning problem where the goal is to decide what tasks the
robot should do autonomously (act), what tasks should be delegated to a human
(delegate) and what tasks the robot should be taught (learn) so as to complete
all the given tasks with minimum effort. This planning problem results in a
search tree that grows exponentially with $n$ -- making standard graph search
algorithms intractable. We address this by converting the problem into a mixed
integer program that can be solved efficiently using off-the-shelf solvers with
bounds on solution quality. To predict the benefit of learning, we use an
approximate simulation model of the tasks to train a precondition model that is
parameterized by the training task. Finally, we evaluate our approach on peg
insertion and Lego stacking tasks -- both in simulation and real-world, showing
substantial savings in human effort.


title:
Future Advances in Photon-Based Neutrino Detectors: A SNOWMASS White
  Paper

abstract:
  We discuss here new, enabling technologies for future photon-based neutrino
detectors. These technologies touch nearly every aspect of such detectors: new
scintillating materials, new methods of loading isotopes, new photon sensors
and collectors, new approaches to simulation and analysis, and new front-end
electronics and DAQ ideas. Of particular interest are technologies that enable
broad physics programs in hybrid Cherenkov/scintillation detectors, such as
slow fluors, water-based liquid scintillator, and spectral sorting of photons.
Several new large-scale detector ideas are also discussed, including hybrid
detectors like Theia, ArTEMIS, and generic slow-fluor detectors, as well as the
very different SLIPs and LiquidO approaches to instrumenting photon-based
detectors. A program of demonstrators for future detectors, including ANNIE,
Eos, and NuDOT are also discussed.


title:
Systematic Investigation of Dust and Gaseous CO in 12 Nearby Molecular
  Clouds

abstract:
  We report the first uniform and systematic study of dust and molecular gas in
nearby molecular clouds. We use surveys of dust extinction and emission to
determine the opacity and map the distribution of the dust within a dozen local
clouds in order to derive a uniform set of basic cloud properties. We find: 1)
the average dust opacity $\langle\kappa_{d,353}\rangle = 0.8\ {\rm cm^{2}\,
g^{-1}}$ with variations of a factor of $\sim$ 2 between clouds, 2) cloud PDFs
are exquisitely described by steeply falling power-laws with a narrow range of
slope, and 3) a tight $M_{\rm GMC} \sim R_{\rm GMC}^2$ scaling relation for the
cloud sample, indicative of a cloud population with an exactingly constant
average surface density above a common fixed boundary. We compare these results
to uniformly analyzed CO surveys. We measure the CO mass conversion factors and
assess the efficacy of CO for tracing the physical properties of molecular
clouds. We find $\langle \alpha_{\rm CO}\rangle = 4.31 \pm 0.67$ M$_\odot$ (K
km s$^{-1}$ pc$^2$)$^{-1}$ (corresponding to $X_{\rm CO}$ = 1.97 $\times$
10$^{20}$ cm$^{-2}$(K km s$^{-1}$)$^{-1}$). We demonstrate that CO observations
are a poor tracer of column density and structure on sub-cloud spatial scales.
On cloud scales, CO observations can provide measurements consistent with those
of the dust, provided data are analyzed in a similar, self-consistent fashion.
Measurements of average GMC surface density are sensitive to choice of cloud
boundary. Care must be exercised to adopt common fixed boundaries when
comparing surface densities for cloud populations within and between galaxies.


title:
Constraining Tidal Quality Factor using Spin Period in Eclipsing
  Binaries

abstract:
  Evolution of binary objects under the influence of tides drastically affects
the expected observational properties of the system. With the discovery of a
large number of close-in hot Jupiter systems and eclipsing binaries from
missions such as Kepler and TESS, it has become imperative to understand the
extent of tidal influence on their formation and observed properties. In the
case of binary systems, an efficient tidal dissipation can lead to either spin
up or spin down of the stars and/or spin-orbit synchronization, depending upon
the exchange of angular momentum between the star and the orbit. We combine the
eclipsing binary systems from the Kepler mission with stellar and orbital
parameters available in the literature to create a catalog of 41 eclipsing
binaries suitable for analysis of tidal dissipation. Empirically, the
efficiency of tidal dissipation is parameterized using a modified Tidal Quality
Factor($Q_{\star}^{'}$). We find constraints on $Q_{\star}^{'}$ using the
observed rotation period of the primary star in the eclipsing binary systems.
We calculate detailed evolutions of binary systems under the combined influence
of tides, stellar evolution, and loss of stellar angular momentum to magnetic
winds, and perform Markov Chain Monte Carlo simulations to account for the
uncertainties in the observed data. Our analysis shows that
$\log_{10}{Q^{'}_{\star}}=7.818\pm0.035$ can reproduce the observed primary
star spin in almost all systems in our sample.


title: Audiovisual Affect Assessment and Autonomous Automobiles: Applications

abstract:
  Emotion and a broader range of affective driver states can be a life decisive
factor on the road. While this aspect has been investigated repeatedly, the
advent of autonomous automobiles puts a new perspective on the role of
computer-based emotion recognition in the car -- the passenger's one. This
includes amongst others the monitoring of wellbeing during the commute such as
to adjust the driving style or to adapt the info- and entertainment. This
contribution aims to foresee according challenges and provide potential avenues
towards affect modelling in a multimodal "audiovisual plus x" on the road
context. From the technical end, this concerns holistic passenger modelling and
reliable diarisation of the individuals in a vehicle. In conclusion, automated
affect analysis has just matured to the point of applicability in autonomous
vehicles in first selected use-cases, which will be discussed towards the end.


title: Bilinear Systems Induced by Proper Lie Group Actions

abstract:
  In the study of induced bilinear systems, the classical Lie algebra rank
condition (LARC) is known to be impractical since it requires computing the
rank everywhere. On the other hand, the transitive Lie algebra condition, while
more commonly used, relies on the classification of transitive Lie algebras,
which is elusive except for few simple geometric objects such as spheres. We
prove in this note that for bilinear systems induced by proper Lie group
actions, the underlying Lie algebra is closely related to the orbits of the
group action. Knowing the pattern of the Lie algebra rank over the manifold, we
show that the LARC can be relaxed so that it suffices to check the rank at an
arbitrary single point. Moreover, it removes the necessity for classifying
transitive Lie algebras. Finally, this relaxed rank condition also leads to a
characterization of controllable submanifolds by orbits.


title:
Gemini NIFS survey of feeding and feedback processes in nearby Active
  Galaxies: VI -- Stellar Populations

abstract:
  We use Gemini Near-Infrared Integral Field Spectrograph (NIFS) adaptive
optics assisted data-cubes to map the stellar population of the inner few
hundred parsecs of a sample of 18 nearby Seyfert galaxies. The near-infrared
light is dominated by the contribution of young to intermediate old stellar
populations, with light-weighted mean ages $<t>_L\ \lesssim $ 1.5\,Gyr. Hot
dust ($HD$) emission is centrally peaked (in the unresolved nucleus), but it is
also needed to reproduce the continuum beyond the nucleus in nearly half of the
sample. We have analysed the stellar population properties of the nuclear
region and their relation with more global properties of the galaxies. We find
a correlation between the X-ray luminosity and the contributions from the $HD$,
featureless continuum $FC$ and reddening $A_V$. We attribute these correlations
to the fact that all these properties are linked to the mass accretion rate to
the active galactic nuclei (AGN). We also find a correlation of the bolometric
luminosity $log(LBol_{obs})$ with the mass-weighted mean age of the stellar
population, interpreted as due a delay between the formation of new stars and
the triggering/feeding of the AGN. The gas reaching the supermassive black hole
is probably originated from mass loss from the already somewhat evolved
intermediate-age stellar population ($<t>_L \lesssim $ 1.5\,Gyr). In summary,
our results show that there is a significant fraction of young to intermediate
age stellar populations in the inner few 100\,pc of active galaxies, suggesting
that this region is facing a rejuvenation process in which the AGN, once
triggered, precludes further star formation, in the sense that it can be
associated with the lack of new star formation in the nuclear region.


title: Simplicial Attention Neural Networks

abstract:
  The aim of this work is to introduce simplicial attention networks (SANs),
i.e., novel neural architectures that operate on data defined on simplicial
complexes leveraging masked self-attentional layers. Hinging on formal
arguments from topological signal processing, we introduce a proper
self-attention mechanism able to process data components at different layers
(e.g., nodes, edges, triangles, and so on), while learning how to weight both
upper and lower neighborhoods of the given topological domain in a totally
task-oriented fashion. The proposed SANs generalize most of the current
architectures available for processing data defined on simplicial complexes.
The proposed approach compares favorably with other methods when applied to
different (inductive and transductive) tasks such as trajectory prediction and
missing data imputations in citation complexes.


title: Constrained Precision Tuning

abstract:
  Precision tuning or customized precision number representations is emerging,
in these recent years, as one of the most promising techniques that has a
positive impact on the footprint of programs concerning energy consumption,
bandwidth usage and computation time of numerical programs. In contrast to the
uniform precision, mixed precision tuning assigns different finite-precision
types to each variable and arithmetic operation of a program and offers many
additional optimization opportunities. However, this technique introduces new
challenge related to the cost of operations or type conversions which can
overload the program execution after tuning. In this article, we extend our
tool POP (Precision OPtimizer), with efficient ways to limit the number of
drawbacks of mixed precision and to achieve best compromise between performance
and memory consumption. On a popular set of tests from the FPBench suite, we
discuss the results obtained by POP.


title: Broad chemical transferability in structure-based coarse-graining

abstract:
  Compared to top-down coarse-grained (CG) models, bottom-up approaches are
capable of offering higher structural fidelity. This fidelity results from the
tight link to a higher-resolution reference, making the CG model chemically
specific. Unfortunately, chemical specificity can be at odds with
compound-screening strategies, which call for transferable parametrizations.
Here we present an approach to reconcile bottom-up, structure-preserving CG
models with chemical transferability. We consider the bottom-up CG
parametrization of 3,441 C$_7$O$_2$ small-molecule isomers. Our approach
combines atomic representations, unsupervised learning, and a large-scale
extended-ensemble force-matching parametrization. We first identify a subset of
19 representative molecules, which maximally encode the local environment of
all gas-phase conformers. Reference interactions between the 19 representative
molecules were obtained from both homogeneous bulk liquids and various binary
mixtures. An extended-ensemble parametrization over all 703 state points leads
to a CG model that is both structure-based and chemically transferable.
Remarkably, the resulting force field is on average more structurally accurate
than single-state-point equivalents. Averaging over the extended ensemble acts
as a mean-force regularizer, smoothing out both force and structural
correlations that are overly specific to a single state point. Our approach
aims at transferability through a set of CG bead types that can be used to
easily construct new molecules, while retaining the benefits of a
structure-based parametrization.


title:
Tweets in Time of Conflict: A Public Dataset Tracking the Twitter
  Discourse on the War Between Ukraine and Russia

abstract:
  On February 24, 2022, Russia invaded Ukraine. In the days that followed,
reports kept flooding in from layman to news anchors of a conflict quickly
escalating into war. Russia faced immediate backlash and condemnation from the
world at large. While the war continues to contribute to an ongoing
humanitarian and refugee crisis in Ukraine, a second battlefield has emerged in
the online space, both in the use of social media to garner support for both
sides of the conflict and also in the context of information warfare. In this
paper, we present a collection of over 63 million tweets, from February 22,
2022 through March 8, 2022 that we are publishing for the wider research
community to use. This dataset can be found at
https://github.com/echen102/ukraine-russia and will be maintained and regularly
updated as the war continues to unfold. Our preliminary analysis already shows
evidence of public engagement with Russian state sponsored media and other
domains that are known to push unreliable information; the former saw a spike
in activity on the day of the Russian invasion. Our hope is that this public
dataset can help the research community to further understand the ever evolving
role that social media plays in information dissemination, influence campaigns,
grassroots mobilization, and much more, during a time of conflict.


title:
Limits on the hard X-ray emission from the periodic fast radio burst FRB
  180916.J0158+65

abstract:
  FRB 180916.J0158+65 is one of the nearest, periodically repeating, and
actively bursting fast radio burst (FRB) which has been localized to the
outskirts of a spiral galaxy. In this work we study the FRB with the hard X-ray
$14-195$ keV data from the Burst Alert Telescope (BAT) on board The Neil
Gehrels Swift Observatory. BAT uses coded mask technology giving a localization
of $\lesssim 3$ arc-minute in the hard X-ray band, along with an accurate
background estimation. BAT has been observing the source location in survey
mode since February 2020. The survey mode observations involves background
subtracted spectra, integrated over a time span ranging $300-2000$ seconds, at
the source location (from Feb 2020-Jan 2022). We analyzed all the $\sim 230$
survey mode observations from BAT and checked for any signal in any of the
observations. We did not detect any signal at $>5\sigma$ confidence level in
any of the observations. We could estimate a $5\sigma$ upper limit on the
$14-195$ keV flux, which ranged between $4.5\times 10^{-10} - 7.6\times
10^{-9}\, \rm erg\, cm^{-2}\, s^{-1}$. At the source distance this relates to a
$5\sigma$ upper limit on luminosity of $5.08\times 10^{44}- 8.5\times 10^{45}
\rm erg\, s^{-1}$. With this estimate, we could rule out any persistent X-ray
emission, at the source location for these snapshots of BAT observations.


title: Achieving Downstream Fairness with Geometric Repair

abstract:
  Consider a scenario where some upstream model developer must train a fair
model, but is unaware of the fairness requirements of a downstream model user
or stakeholder. In the context of fair classification, we present a technique
that specifically addresses this setting, by post-processing a regressor's
scores such they yield fair classifications for any downstream choice in
decision threshold. To begin, we leverage ideas from optimal transport to show
how this can be achieved for binary protected groups across a broad class of
fairness metrics. Then, we extend our approach to address the setting where a
protected attribute takes on multiple values, by re-recasting our technique as
a convex optimization problem that leverages lexicographic fairness.


title:
Snowmass2021 Cosmic Frontier White Paper: Prospects for obtaining Dark
  Matter Constraints with DESI

abstract:
  Despite efforts over several decades, direct-detection experiments have not
yet led to the discovery of the dark matter (DM) particle. This has led to
increasing interest in alternatives to the Lambda CDM (LCDM) paradigm and
alternative DM scenarios (including fuzzy DM, warm DM, self-interacting DM,
etc.). In many of these scenarios, DM particles cannot be detected directly and
constraints on their properties can ONLY be arrived at using astrophysical
observations. The Dark Energy Spectroscopic Instrument (DESI) is currently one
of the most powerful instruments for wide-field surveys. The synergy of DESI
with ESA's Gaia satellite and future observing facilities will yield datasets
of unprecedented size and coverage that will enable constraints on DM over a
wide range of physical and mass scales and across redshifts. DESI will obtain
spectra of the Lyman-alpha forest out to z~5 by detecting about 1 million QSO
spectra that will put constraints on clustering of the low-density
intergalactic gas and DM halos at high redshift. DESI will obtain radial
velocities of 10 million stars in the Milky Way (MW) and Local Group satellites
enabling us to constrain their global DM distributions, as well as the DM
distribution on smaller scales. The paradigm of cosmological structure
formation has been extensively tested with simulations. However, the majority
of simulations to date have focused on collisionless CDM. Simulations with
alternatives to CDM have recently been gaining ground but are still in their
infancy. While there are numerous publicly available large-box and zoom-in
simulations in the LCDM framework, there are no comparable publicly available
WDM, SIDM, FDM simulations. DOE support for a public simulation suite will
enable a more cohesive community effort to compare observations from DESI (and
other surveys) with numerical predictions and will greatly impact DM science.


title:
Snowmass White Paper: Light Dark Matter Direct Detection at the
  Interface With Condensed Matter Physics

abstract:
  Direct detection experiments for light (sub-GeV) dark matter are making
enormous leaps in reaching previously unexplored theory space. The need for
accurate characterizations of target responses has led to a growing interplay
between particle and condensed matter physics. This white paper summarizes
recent progress on direct detection calculations that utilize state-of-the-art
numerical tools in condensed matter physics and effective field theory
techniques. These new results provide the theoretical framework for
interpreting ongoing and planned experiments using electronic and collective
excitations, and for optimizing future searches.


title:
Approaching Massive MIMO Performance with Reconfigurable Intelligent
  Surfaces: We Do Not Need Many Antennas

abstract:
  This paper considers an antenna structure where a (non-large) array of
radiating elements is placed at short distance in front of a reconfigurable
intelligent surface (RIS). This structure is analyzed as a possible emulator of
a traditional MIMO antenna with a large number of active antenna elements and
RF chains. Focusing on both the cases of active and passive RIS, we tackle the
issues of channel estimation, downlink signal processing, power control, and
RIS configuration optimization. With regard to the last point, an optimization
problem is formulated and solved, both for the cases of active and passive RIS,
aimed at minimizing the channel signatures cross-correlations and thereby
reducing the interference. Downlink spectral efficiency (SE) formulas are also
derived by using the popular hardening lower-bound. Numerical results,
represented with reference to max-fairness power control, show that the
proposed structure is capable of outperforming conventional non-RIS aided MIMO
systems even when the MIMO system has a considerably larger number of antennas
and RF chains. The proposed antenna structure is thus shown to be able to
approach massive MIMO performance levels in a cost-effective way with reduced
hardware resources.


title: Explainability and Graph Learning from Social Interactions

abstract:
  Social learning algorithms provide models for the formation of opinions over
social networks resulting from local reasoning and peer-to-peer exchanges.
Interactions occur over an underlying graph topology, which describes the flow
of information among the agents. In this work, we propose a technique that
addresses questions of explainability and interpretability when the graph is
hidden. Given observations of the evolution of the belief over time, we aim to
infer the underlying graph topology, discover pairwise influences between the
agents, and identify significant trajectories in the network. The proposed
framework is online in nature and can adapt dynamically to changes in the graph
topology or the true hypothesis.


title:
Snowmass White Paper: Probing New Physics with $\mu^+ \mu^- \to bs$ at a
  Muon Collider

abstract:
  In this white paper for the Snowmass process, we discuss the prospects of
probing new physics explanations of the persistent rare $B$ decay anomalies
with a muon collider. If the anomalies are indirect signs of heavy new physics,
non-standard rates for $\mu^+ \mu^- \to b s$ production should be observed with
high significance at a muon collider with center of mass energy of $\sqrt{s} =
10$ TeV. The forward-backward asymmetry of the $b$-jet provides diagnostics of
the chirality structure of the new physics couplings. In the absence of a
signal, $\mu^+ \mu^- \to b s$ can indirectly probe new physics scales as large
as $86$ TeV. Beam polarization would have an important impact on the new
physics sensitivity.


title: Studies on Au colloid-embedded active-carbon based supercapacitors

abstract:
  Supercapacitors (S-C) are short-term energy storage elements that find many
applications, e.g., electronic charging devices and suppressors of power
fluctuations in grids that are interfaced with sustainable sources. For a
double-layer S-C, one takes advantage of the very narrow interface at an
electrolyte and the large-surface of the porous conductors. Capacitance of an
ordinary capacitor is increased when dispersing metallic colloids in its
dielectric. It is not clear, though, if the same strategy works for S-C since
its double layer region is so narrow. Here we explore the addition of
functionalized Au nano-particles (AuNPs) to active-carbon electrodes in S-C to
find a large specific capacitance increase.


title:
A hackable, multi-functional, and modular extrusion 3D printer for soft
  materials

abstract:
  Three-dimensional (3D) printing has emerged as a powerful tool for material,
food, and life science research and development, where the technology
democratization necessitates the advancement of open-source platforms. Herein,
we developed a hackable, multi-functional, and modular extrusion 3D printer for
soft materials, nicknamed Printer.HM. Multi-printhead modules are established
based on a robotic arm for heterogeneous construct creation, where ink
printability can be tuned by accessories such as heating and UV modules.
Software associated with Printer.HM were designed to accept geometry inputs
including computer-aided design models, coordinates, equations, and pictures,
to create prints of distinct characteristics. Printer.HM could further perform
versatile operations, such as liquid dispensing, non-planar printing, and
pick-and-place of meso-objects. By mix-and-match software and hardware
settings, Printer.HM demonstrated printing of pH-responsive soft actuators,
plant-based functional hydrogels, and organ macro-anatomical models.
Integrating affordability and open design, we envisage the Printer.HM concept
to widen open innovations for soft, biological, and sustainable material
architectures.


title:
Numerical study of airborne particle dynamics in vortices subject to
  electric field

abstract:
  Capture, selective collection and flight manipulation of airborne particulate
are three important functional requirements in various actively growing aerosol
technology applications. Aerodynamic drag, particle inertia and
dielectrophoretic (DEP) force due to externally applied electrostatic forces
influence the behavior of micron sized particles significantly, in such
situations. In this work, we numerically study how a combination of these
forces uniquely influences the behavior of uncharged or mildly charged airborne
particles, distinct from that with their individual influences. Uncharged
particle movements in a numerically fabricated well structured steady vortical
flow between two curved electrode surfaces are analyzed. Vortical air
circulation towards and away from the electrode tip enhances and deteriorates
electrostatic particulate capture on the electrodes, termed as co and counter
directions with respect to the electrostatic force on particles respectively.
Particles in counter vortices under an electric field reveals a rich variety of
unique behaviors due to the interplay of drag, inertia and DEP forces.
Distortion of the vortex structure due to convexity of electrode surfaces
results in an inverse inertial limit cycle trajectory trapping of particles;
with the airborne particles spatially segregated, trapping larger particles
further inside the vortex than the smaller particles.Value of a dimensionless
number $\xi_v$, the ratio of DEP force and particle inertia, represents the
combination of the operating flow and electric field strengths. Inertial
cut-off of particle capture in this configuration abruptly shift to DEP capture
at a critical value of $\xi_v\approx 0.2$, as its value increases. Selective
deposition of particles within a closed range of size and density, emerges due
to the interplay of vortex trapping, inertial expulsion and electrostatic
capture.


title:
An Approximate Reinforcement Learning Algorithm for Controlled Diffusion
  Processes

abstract:
  We study a reinforcement learning algorithm for continuous time stochastic
control problems. The proposed algorithm uses the sampled state process by
discretizing the state and control action spaces under piece-wise constant
control processes. We show that the algorithm converges to the optimality
equation of a finite Markov decision process (MDP). Using this MDP model, we
provide an upper bound for the approximation error for the optimal value
function of the continuous time control problem. Furthermore, we give
upper-bounds for the performance loss of the learned control process compared
to the optimal admissible control process of the original problem. The provided
error upper-bounds are functions of the time and space discretization
parameters, and they reveal the effect of different levels of the
approximation: (i) approximation of the continuous time control problem by an
MDP, (ii) use of piece-wise constant control processes, (iii) space
discretization. Finally, we state a time complexity bound for the proposed
algorithm as a function of the time and space discretization parameters.


title:
Reinforcement Learning for Optimal Control of a District Cooling Energy
  Plant

abstract:
  District cooling energy plants (DCEPs) consisting of chillers, cooling
towers, and thermal energy storage (TES) systems consume a considerable amount
of electricity. Optimizing the scheduling of the TES and chillers to take
advantage of time-varying electricity price is a challenging optimal control
problem. The classical method, model predictive control (MPC), requires solving
a high dimensional mixed-integer nonlinear program (MINLP) because of the
on/off actuation of the chillers and charging/discharging of TES, which are
computationally challenging. RL is an attractive alternative to MPC: the real
time control computation is a low-dimensional optimization problem that can be
easily solved. However, the performance of an RL controller depends on many
design choices. In this paper, we propose a Q-learning based reinforcement
learning (RL) controller for this problem. Numerical simulation results show
that the proposed RL controller is able to reduce energy cost over a rule-based
baseline controller by approximately 8%, comparable to savings reported in the
literature with MPC for similar DCEPs. We describe the design choices in the RL
controller, including basis functions, reward function shaping, and learning
algorithm parameters. Compared to existing work on RL for DCEPs, the proposed
controller is designed for continuous state and actions spaces.


title: SoLAr: Solar Neutrinos in Liquid Argon

abstract:
  SoLAr is a new concept for a liquid-argon neutrino detector technology to
extend the sensitivities of these devices to the MeV energy range - expanding
the physics reach of these next-generation detectors to include solar
neutrinos.
  We propose this novel concept to significantly improve the precision on solar
neutrino mixing parameters and to observe the "hep branch" of the proton-proton
fusion chain. The SoLAr detector will achieve flavour-tagging of solar
neutrinos in liquid argon. The SoLAr technology will be based on the concept of
monolithic light-charge pixel-based readout which addresses the main
requirements for such a detector: a low energy threshold with excellent energy
resolution (approximately 7%) and background rejection through pulse-shape
discrimination.
  The SoLAr concept is also timely as a possible technology choice for the DUNE
"Module of Opportunity", which could serve as a next-generation multi-purpose
observatory for neutrinos from the MeV to the GeV range. The goal of SoLAr is
to observe solar neutrinos in a 10 ton-scale detector and to demonstrate that
the required background suppression and energy resolution can be achieved.
SoLAr will pave the way for a precise measurement of the 8-B flux, an improved
precision on solar neutrino mixing parameters, and ultimately lead to the first
observation of hep neutrinos in the DUNE Module of Opportunity.


title: Inverted analemmatic sundial of the Bronze Age

abstract:
  This article describes the study of a Bronze Age limestone slab with cup
marks, discovered during the archaeological excavations of kurgan 1 of the
kurgan grave field Prolom II, located in the Belogorsk region in Crimea.
According to the results of the study, it is concluded that the Belogorsk slab
is a sundial of about the XV - XII centuries BC and belongs to the Srubnaya
culture. By type of sundial, it is closest to the analemmatic sundial. However,
the principle of the hourly markings of the Belogorsk slab is so unique that it
is proposed to separate this type of sundial into a new type - inverted
analemmatic sundial. This type is characterized by the fact that, unlike
typical analemmatic sundials, the gnomon remains motionless throughout the
year, and in accordance with the analemma, the "dial" "moves" - an ellipse of
hour markers from cup marks, i.e. gnomon and hour markers (cup marks) change
places in terms of mobility. The movement of hour markers on the Belogorsk slab
is not literal, but is imitated by several rows of cup marks, which are
fragments of hour marker ellipses for different months of the year. The idea
behind this type of sundial is so revolutionary that we can talk about the
discovery of a completely new type of sundial, the analogue of which has not
yet been discovered. Keywords: cup marks, sundial, inverted, analemma, true
solar time, mean solar time, hour markers, Srubnaya culture, Bronze Age, kurgan
grave field, slab.


title: BR2 discontinuous Galerkin methods for finite hyperelastic deformations

abstract:
  In this work we introduce a dG framework for nonlinear elasticity based on a
Bassi-Rebay (BR2) formulation. The framework encompasses compressible and
incompressible hyperelastic materials and is capable of dealing with large
deformations. In order to achieve stability, we combine higher-order lifting
operators for the BR2 stabilization term with an adaptive stabilization
strategy which relies on the BR2 Laplace operator stabilization and a penalty
parameter based on the spectrum of the fourth-order elasticity tensor.
Dirichlet boundary conditions for the displacement can be imposed by means of
Lagrange multipliers and Nitsche method. Efficiency of the solution strategy is
achieved by means of state-of-the-art agglomeration based $h$-multigrid
preconditioners and the code implementation supports distributed memory
execution on modern parallel architectures. Several benchmark test cases are
proposed in order to investigate some relevant computational aspects, namely
the performance of the $h$-multigrid iterative solver varying the stabilization
parameters and the influence of Dirichlet boundary conditions on Newton's
method globalisation strategy.


title:
VAST: The Valence-Assessing Semantics Test for Contextualizing Language
  Models

abstract:
  VAST, the Valence-Assessing Semantics Test, is a novel intrinsic evaluation
task for contextualized word embeddings (CWEs). VAST uses valence, the
association of a word with pleasantness, to measure the correspondence of
word-level LM semantics with widely used human judgments, and examines the
effects of contextualization, tokenization, and LM-specific geometry. Because
prior research has found that CWEs from GPT-2 perform poorly on other intrinsic
evaluations, we select GPT-2 as our primary subject, and include results
showing that VAST is useful for 7 other LMs, and can be used in 7 languages.
GPT-2 results show that the semantics of a word incorporate the semantics of
context in layers closer to model output, such that VAST scores diverge between
our contextual settings, ranging from Pearson's rho of .55 to .77 in layer 11.
We also show that multiply tokenized words are not semantically encoded until
layer 8, where they achieve Pearson's rho of .46, indicating the presence of an
encoding process for multiply tokenized words which differs from that of singly
tokenized words, for which rho is highest in layer 0. We find that a few
neurons with values having greater magnitude than the rest mask word-level
semantics in GPT-2's top layer, but that word-level semantics can be recovered
by nullifying non-semantic principal components: Pearson's rho in the top layer
improves from .32 to .76. After isolating semantics, we show the utility of
VAST for understanding LM semantics via improvements over related work on four
word similarity tasks, with a score of .50 on SimLex-999, better than the
previous best of .45 for GPT-2. Finally, we show that 8 of 10 WEAT bias tests,
which compare differences in word embedding associations between groups of
words, exhibit more stereotype-congruent biases after isolating semantics,
indicating that non-semantic structures in LMs also mask biases.


title:
Closing the Loop: A Framework for Trustworthy Machine Learning in Power
  Systems

abstract:
  Deep decarbonization of the energy sector will require massive penetration of
stochastic renewable energy resources and an enormous amount of grid asset
coordination; this represents a challenging paradigm for the power system
operators who are tasked with maintaining grid stability and security in the
face of such changes. With its ability to learn from complex datasets and
provide predictive solutions on fast timescales, machine learning (ML) is
well-posed to help overcome these challenges as power systems transform in the
coming decades. In this work, we outline five key challenges (dataset
generation, data pre-processing, model training, model assessment, and model
embedding) associated with building trustworthy ML models which learn from
physics-based simulation data. We then demonstrate how linking together
individual modules, each of which overcomes a respective challenge, at
sequential stages in the machine learning pipeline can help enhance the overall
performance of the training process. In particular, we implement methods that
connect different elements of the learning pipeline through feedback, thus
"closing the loop" between model training, performance assessments, and
re-training. We demonstrate the effectiveness of this framework, its
constituent modules, and its feedback connections by learning the N-1
small-signal stability margin associated with a detailed model of a proposed
North Sea Wind Power Hub system.


title:
Snowmass2021 Cosmic Frontier White Paper: Cosmology and Fundamental
  Physics from the three-dimensional Large Scale Structure

abstract:
  Advances in experimental techniques make it possible to map the high redshift
Universe in three dimensions at high fidelity in the near future. This will
increase the observed volume by many-fold, while providing unprecedented access
to very large scales, which hold key information about primordial physics.
Recently developed theoretical techniques, together with the smaller size of
non-linearities at high redshift, allow the reconstruction of an order of
magnitude more "primordial modes", and should improve our understanding of the
early Universe through measurements of primordial non-Gaussianity and features
in the primordial power spectrum. In addition to probing the first epoch of
accelerated expansion, such measurements can probe the Dark Energy density in
the dark matter domination era, tightly constraining broad classes of dynamical
Dark Energy models. The shape of the matter power spectrum itself has the
potential to detect sub-percent fractional amounts of Early Dark Energy to $z
\sim 10^5$, probing Dark Energy all the way to when the Universe was only a few
years old. The precision of these measurements, combined with CMB observations,
also has the promise of greatly improving our constraints on the effective
number of relativistic species, the masses of neutrinos, the amount of spatial
curvature and the gravitational slip. Studies of linear or quasi-linear
large-scale structure with redshift surveys and the CMB currently provide our
tightest constraints on cosmology and fundamental physics. Pushing the redshift
and volume frontier will provide guaranteed, significant improvements in the
state-of-the-art in a manner that is easy to forecast and optimize.


title: Conformance Checking Over Stochastically Known Logs

abstract:
  With the growing number of devices, sensors and digital systems, data logs
may become uncertain due to, e.g., sensor reading inaccuracies or incorrect
interpretation of readings by processing programs. At times, such uncertainties
can be captured stochastically, especially when using probabilistic data
classification models. In this work we focus on conformance checking, which
compares a process model with an event log, when event logs are stochastically
known. Building on existing alignment-based conformance checking fundamentals,
we mathematically define a stochastic trace model, a stochastic synchronous
product, and a cost function that reflects the uncertainty of events in a log.
Then, we search for an optimal alignment over the reachability graph of the
stochastic synchronous product for finding an optimal alignment between a model
and a stochastic process observation. Via structured experiments with two
well-known process mining benchmarks, we explore the behavior of the suggested
stochastic conformance checking approach and compare it to a standard
alignment-based approach as well as to an approach that creates a lower bound
on performance. We envision the proposed stochastic conformance checking
approach as a viable process mining component for future analysis of stochastic
event logs.


title:
Smoothed Phase-Coded FMCW: Waveform Properties and Transceiver
  Architecture

abstract:
  Smoothed phase-coded frequency modulated continuous waveform (SPC-FMCW),
which is aimed to improve the coexistence of multiple radars operating within
the same frequency bandwidth, is studied and the receiving strategy with a low
ADC sampling requirement is investigated. The Gaussian filter is applied to
obtain smooth phase transition, and the phase lag compensation is performed on
transmitted phase code to enhance decoding. The proposed waveform is examined
in different domains, and its waveform properties are analysed theoretically
and demonstrated experimentally. Both the simulation and experimental results
show that the introduced waveform with the investigated processing steps helps
to combine all advantages of the FMCW waveform including hardware simplicity
and small operational bandwidth of the receiver with advantages of the phase
coding.


title:
Rank Two Approximations of $2 \times 2 \times 2$ Tensors over
  $\mathbb{R}$

abstract:
  We provide a coordinate-free proof that real $2 \times 2 \times 2$ rank three
tensors do not have optimal rank two approximations with respect to the
Frobenius norm. This result was first proved in by considering the
${\text{GL}(V^1) \times \text{GL}(V^2) \times \text{GL}(V^3)}$ orbit classes of
${V^1 \otimes V^2 \otimes V^3}$ and the $2 \times 2 \times 2$ hyperdeterminant.
Our coordinate-free proof expands on this known result by developing a proof
method that can be generalized more readily to higher dimensional $n_1 \times
n_2 \times n_3$ tensor spaces.


title:
Measurement induced entanglement transition in two dimensional shallow
  circuit

abstract:
  We prepare two dimensional states generated by shallow circuits composed of
(1) one layer of two-qubit CZ gate or (2) a few layers of two-qubit random
Clifford gate. After measuring all of the bulk qubits, we study the
entanglement structure of the remaining qubits on the one dimensional boundary.
In the first model, we observe that the competition between the bulk X and Z
measurements can lead to an entanglement phase transition between an entangled
volume law phase and a disentangled area law phase. We numerically evaluate the
critical exponents and generalize this idea to other qudit systems with local
Hilbert space dimension larger than 2. In the second model, we observe the
entanglement transition by varying the density of the two-qubit gate in each
layer. We give an interpretation of this transition in terms of random bond
Ising model in a similar shallow circuit composed of random Haar gates.


title:
Contrastive Visual Semantic Pretraining Magnifies the Semantics of
  Natural Language Representations

abstract:
  We examine the effects of contrastive visual semantic pretraining by
comparing the geometry and semantic properties of contextualized English
language representations formed by GPT-2 and CLIP, a zero-shot multimodal image
classifier which adapts the GPT-2 architecture to encode image captions. We
find that contrastive visual semantic pretraining significantly mitigates the
anisotropy found in contextualized word embeddings from GPT-2, such that the
intra-layer self-similarity (mean pairwise cosine similarity) of CLIP word
embeddings is under .25 in all layers, compared to greater than .95 in the top
layer of GPT-2. CLIP word embeddings outperform GPT-2 on word-level semantic
intrinsic evaluation tasks, and achieve a new corpus-based state of the art for
the RG65 evaluation, at .88. CLIP also forms fine-grained semantic
representations of sentences, and obtains Spearman's rho = .73 on the
SemEval-2017 Semantic Textual Similarity Benchmark with no fine-tuning,
compared to no greater than rho = .45 in any layer of GPT-2. Finally,
intra-layer self-similarity of CLIP sentence embeddings decreases as the layer
index increases, finishing at .25 in the top layer, while the self-similarity
of GPT-2 sentence embeddings formed using the EOS token increases
layer-over-layer and never falls below .97. Our results indicate that high
anisotropy is not an inevitable consequence of contextualization, and that
visual semantic pretraining is beneficial not only for ordering visual
representations, but also for encoding useful semantic representations of
language, both on the word level and the sentence level.


title:
Don't fear the unlabelled: safe deep semi-supervised learning via simple
  debiasing

abstract:
  Semi supervised learning (SSL) provides an effective means of leveraging
unlabelled data to improve a model's performance. Even though the domain has
received a considerable amount of attention in the past years, most methods
present the common drawback of being unsafe. By safeness we mean the quality of
not degrading a fully supervised model when including unlabelled data. Our
starting point is to notice that the estimate of the risk that most
discriminative SSL methods minimise is biased, even asymptotically. This bias
makes these techniques untrustable without a proper validation set, but we
propose a simple way of removing the bias. Our debiasing approach is
straightforward to implement, and applicable to most deep SSL methods. We
provide simple theoretical guarantees on the safeness of these modified
methods, without having to rely on the strong assumptions on the data
distribution that SSL theory usually requires. We evaluate debiased versions of
different existing SSL methods and show that debiasing can compete with classic
deep SSL techniques in various classic settings and even performs well when
traditional SSL fails.


title:
Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a
  Pre-Existing Pipeline

abstract:
  Consider an actor making selection decisions using a series of classifiers,
which we term a sequential screening process. The early stages filter out some
applicants, and in the final stage an expensive but accurate test is applied to
the individuals that make it to the final stage. Since the final stage is
expensive, if there are multiple groups with different fractions of positives
at the penultimate stage (even if a slight gap), then the firm may naturally
only choose to the apply the final (interview) stage solely to the highest
precision group which would be clearly unfair to the other groups. Even if the
firm is required to interview all of those who pass the final round, the tests
themselves could have the property that qualified individuals from some groups
pass more easily than qualified individuals from others. Thus, we consider
requiring Equality of Opportunity (qualified individuals from each each group
have the same chance of reaching the final stage and being interviewed). We
then examine the goal of maximizing quantities of interest to the decision
maker subject to this constraint, via modification of the probabilities of
promotion through the screening process at each stage based on performance at
the previous stage. We exhibit algorithms for satisfying Equal Opportunity over
the selection process and maximizing precision (the fraction of interview that
yield qualified candidates) as well as linear combinations of precision and
recall (recall determines the number of applicants needed per hire) at the end
of the final stage. We also present examples showing that the solution space is
non-convex, which motivate our exact and (FPTAS) approximation algorithms for
maximizing the linear combination of precision and recall. Finally, we discuss
the `price of' adding additional restrictions, such as not allowing the
decision maker to use group membership in its decision process.


title: Tile Multiple-Readout Compensated Calorimetry

abstract:
  We propose extending parallel fiber dual readout calorimetry to tiles, more
applicable to many future experimental requirements, with superior energy
resolution. Monte Carlo (MC) studies indicate that a tile dual calorimeter
including an integral Cerenkov-compensated e-m front end and further
longitudinal segmentation, not possible with parallel fibers, has equivalent or
better resolution. Besides comparison and tuning with dual tile calorimeter
data, a MC can be extended to study other dual and then multiple tile sensors
including tiles with higher contrast to em-hadron shower fluctuations with low
refractive indices (much lower than quartz or plastic), transition radiation,
secondary emission, hydrogenous/non-hydrogenous ionization-sensing, and neutron
and ion-fragment sensing tiles for improving dual readout not available with
fibers, and beyond to triple or more readout. For example, secondary emission
tiles (like dynodes) are very sensitive to ion fragments and low energy
neutrons. We suggest MC studies for adding Cerenkov and other tiles to Particle
Flow/High Granularity tile calorimeters such CALICE and planned in CMS & ATLAS
Phase III upgrades, groups studying future machine(ee,pp,ep) detectors,
b-physics, tagged neutrino experiments, and space-based calorimeters.

categories: physics.ins-det astro-ph.HE hep-ex nucl-ex


title:
Three-Loop Corrections to Lamb Shift in Positronium: Electron Factor and
  Polarization

abstract:
  Hard spin-independent three-loop radiative contributions to energy levels in
positronium generated by one-loop radiative insertions in the fermion lines and
exchanged photons are calculated. This is a next step in calculations of all
corrections of order $m\alpha^7$ inspired by the new generation of precise
$1S-2S$ and $2S-2P$ measurements in positronium.


title:
Skydiver: A Spiking Neural Network Accelerator Exploiting
  Spatio-Temporal Workload Balance

abstract:
  Spiking Neural Networks (SNNs) are developed as a promising alternative to
Artificial Neural networks (ANNs) due to their more realistic brain-inspired
computing models. SNNs have sparse neuron firing over time, i.e.,
spatio-temporal sparsity; thus, they are useful to enable energy-efficient
hardware inference. However, exploiting spatio-temporal sparsity of SNNs in
hardware leads to unpredictable and unbalanced workloads, degrading the energy
efficiency. In this work, we propose an FPGA-based convolutional SNN
accelerator called Skydiver that exploits spatio-temporal workload balance. We
propose the Approximate Proportional Relation Construction (APRC) method that
can predict the relative workload channel-wisely and a Channel-Balanced
Workload Schedule (CBWS) method to increase the hardware workload balance ratio
to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on
image segmentation and MNIST classification tasks. Results show improved
throughput by 1.4X and 1.2X for the two tasks. Skydiver achieved 22.6 KFPS
throughput, and 42.4 uJ/Image prediction energy on the classification task with
98.5% accuracy.


title:
Generalized fast quasi-adiabatic population transfer for improved qubit
  readout, shuttling, and noise mitigation

abstract:
  Population-transfer schemes are commonly used to convert information robustly
stored in some quantum system for manipulation and memory into more macroscopic
degrees of freedom for measurement. These schemes may include, e.g.,
spin-to-charge conversion for spins in quantum dots, detuning of charge qubits
between a noise-insensitive operating point and a measurement point, spatial
shuttling of qubits encoded in spins or ions, and parity-to-charge conversion
schemes for qubits based on Majorana zero modes. A common strategy is to use a
slow (adiabatic) conversion. However, in an adiabatic scheme, the adiabaticity
conditions on the one hand, and accumulation of errors through dephasing,
leakage, and energy relaxation processes on the other hand, limit the fidelity
that can be achieved. Here, we give explicit fast quasi-adiabatic (fast-QUAD)
conversion strategies (pulse shapes) beyond the adiabatic approximation that
allow for optimal state conversion. In contrast with many other approaches,
here we account for a general source of noise in combination with pulse
shaping. Inspired by analytic methods that have been developed for dynamical
decoupling theory, we provide a general framework for unique noise mitigation
strategies that can be tailored to the system and environment of interest.


title: Erd\H{o}s--Szekeres-type problems in the real projective plane

abstract:
  We consider point sets in the real projective plane $\mathbb{R}\mathcal{P}^2$
and explore variants of classical extremal problems about planar point sets in
this setting, with a main focus on Erd\H{o}s--Szekeres-type problems.
  We provide asymptotically tight bounds for a variant of the
Erd\H{o}s--Szekeres theorem about point sets in convex position in
$\mathbb{R}\mathcal{P}^2$, which was initiated by Harborth and M\"oller in
1994. The notion of convex position in $\mathbb{R}\mathcal{P}^2$ agrees with
the definition of convex sets introduced by Steinitz in 1913.
  For $k \geq 3$, an (affine) $k$-hole in a finite set $S \subseteq
\mathbb{R}^2$ is a set of $k$ points from $S$ in convex position with no point
of $S$ in the interior of their convex hull. After introducing a new notion of
$k$-holes for points sets from $\mathbb{R}\mathcal{P}^2$, called projective
$k$-holes, we find arbitrarily large finite sets of points from
$\mathbb{R}\mathcal{P}^2$ with no projective 8-holes, providing an analogue of
a classical result by Horton from 1983. We also prove that they contain only
quadratically many projective $k$-holes for $k \leq 7$. On the other hand, we
show that the number of $k$-holes can be substantially larger in
$\mathbb{R}\mathcal{P}^2$ than in $\mathbb{R}^2$ by constructing, for every $k
\in \{3,\dots,6\}$, sets of $n$ points from $\mathbb{R}^2 \subset
\mathbb{R}\mathcal{P}^2$ with $\Omega(n^{3-3/5k})$ projective $k$-holes and
only $O(n^2)$ affine $k$-holes. Last but not least, we prove several other
results, for example about projective holes in random point sets in
$\mathbb{R}\mathcal{P}^2$ and about some algorithmic aspects.
  The study of extremal problems about point sets in $\mathbb{R}\mathcal{P}^2$
opens a new area of research, which we support by posing several open problems.


title:
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on
  Intermediate Pre-training for Cross-modal Knowledge Transfer

abstract:
  Pre-trained language models are still far from human performance in tasks
that need understanding of properties (e.g. appearance, measurable quantity)
and affordances of everyday objects in the real world since the text lacks such
information due to reporting bias. In this work, we study whether integrating
visual knowledge into a language model can fill the gap. We investigate two
types of knowledge transfer: (1) text knowledge transfer using image captions
that may contain enriched visual knowledge and (2) cross-modal knowledge
transfer using both images and captions with vision-language training
objectives. On 5 downstream tasks that may need visual knowledge to solve the
problem, we perform extensive empirical comparisons over the presented
objectives. Our experiments show that visual knowledge transfer can improve
performance in both low-resource and fully supervised settings.


title:
WD 1145+017: Alternative models of the atmosphere, dust clouds, and gas
  rings

abstract:
  WD 1145+017 is the first white dwarf known to be orbited by disintegrating
exoasteroids. It is a DBZ-type white dwarf with strongly variable broad
circumstellar lines and variable shallow UV transits. Various models of the
dust clouds and gaseous rings have been proposed as an explanation for this
behavior. Here we propose alternative models.The simple radiative transfer code
Shellspec was modified for this purpose and used for testing the new dust cloud
and gas disk models. We used modified TLUSTY and SYNSPEC codes to calculate
atmosphere models assuming the LTE or NLTE, and to calculate the intrinsic
spectrum of the star. We then used these atmosphere models to estimate the mass
of the radiative and convective zones and NLTE spectrum synthesis to estimate
their chemical composition. We offer an alternative explanation of some (not
all) shallow UV transits. These may be naturally caused by the optical
properties of the dust grains: opacities and mainly phase functions as a result
of the forward scattering. The latter is much stronger in UV compared to the
optical region, leaving more UV photons in the original direction during the
transit. We also developed an alternative model of the gaseous disk, consisting
of an inner, hotter, and almost circular disk and an outer, cooler, and
eccentric disk. The structure precesses with a period of $3.83 \pm 0.12$ yr. We
demonstrate that it fits the observed circumstellar lines reasonably well.
These alternative models solve a few drawbacks that might be associated with
the previous models, but they also have their own disadvantages. We confirm
that the chemical composition of the atmosphere is similar to that of CI
chondrites but C, N, and S are significantly underabundant and much closer to
the bulk Earth composition. This is a strong argument that the star has
recently encountered and accreted material from a body of Earth-like
composition.


title:
Automatic lane change scenario extraction and generation of scenarios in
  OpenX format from real-world data

abstract:
  Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite
many safety challenges yet to be resolved. The modern autonomous vehicles will
undoubtedly include machine learning and probabilistic techniques that add
significant complexity to the traditional verification and validation methods.
Road testing is essential before the deployment, but scenarios are repeatable,
and it's hard to collect challenging events. Exploring numerous, diverse and
crucial scenarios is a time-consuming and expensive approach. The research
community and industry have widely accepted scenario-based testing in the last
few years. As it is focused directly on the relevant critical road situations,
it can reduce the effort required in testing. The scenario-based testing in
simulation requires the realistic behaviour of the traffic participants to
assess the System Under Test (SUT). It is essential to capture the scenarios
from the real world to encode the behaviour of actual traffic participants.
This paper proposes a novel scenario extraction method to capture the lane
change scenarios using point-cloud data and object tracking information. This
method enables fully automatic scenario extraction compared to similar
approaches in this area. The generated scenarios are represented in OpenX
format to reuse them in the SUT evaluation easily. The motivation of this
framework is to build a validation dataset to generate many critical concrete
scenarios. The code is available online at
https://github.com/dkarunakaran/scenario_extraction_framework.


title:
Choose Your QA Model Wisely: A Systematic Study of Generative and
  Extractive Readers for Question Answering

abstract:
  While both extractive and generative readers have been successfully applied
to the Question Answering (QA) task, little attention has been paid toward the
systematic comparison of them. Characterizing the strengths and weaknesses of
the two readers is crucial not only for making a more informed reader selection
in practice but also for developing a deeper understanding to foster further
research on improving readers in a principled manner. Motivated by this goal,
we make the first attempt to systematically study the comparison of extractive
and generative readers for question answering. To be aligned with the
state-of-the-art, we explore nine transformer-based large pre-trained language
models (PrLMs) as backbone architectures. Furthermore, we organize our findings
under two main categories: (1) keeping the architecture invariant, and (2)
varying the underlying PrLMs. Among several interesting findings, it is
important to highlight that (1) the generative readers perform better in long
context QA, (2) the extractive readers perform better in short context while
also showing better out-of-domain generalization, and (3) the encoder of
encoder-decoder PrLMs (e.g., T5) turns out to be a strong extractive reader and
outperforms the standard choice of encoder-only PrLMs (e.g., RoBERTa). We also
study the effect of multi-task learning on the two types of readers varying the
underlying PrLMs and perform qualitative and quantitative diagnosis to provide
further insights into future directions in modeling better readers.


title:
Sense Embeddings are also Biased--Evaluating Social Biases in Static and
  Contextualised Sense Embeddings

abstract:
  Sense embedding learning methods learn different embeddings for the different
senses of an ambiguous word. One sense of an ambiguous word might be socially
biased while its other senses remain unbiased. In comparison to the numerous
prior work evaluating the social biases in pretrained word embeddings, the
biases in sense embeddings have been relatively understudied. We create a
benchmark dataset for evaluating the social biases in sense embeddings and
propose novel sense-specific bias evaluation measures. We conduct an extensive
evaluation of multiple static and contextualised sense embeddings for various
types of social biases using the proposed measures. Our experimental results
show that even in cases where no biases are found at word-level, there still
exist worrying levels of social biases at sense-level, which are often ignored
by the word-level bias evaluation measures.


title:
Convolutional-Recurrent Neural Network Proxy for Robust Optimization and
  Closed-Loop Reservoir Management

abstract:
  Production optimization under geological uncertainty is computationally
expensive, as a large number of well control schedules must be evaluated over
multiple geological realizations. In this work, a convolutional-recurrent
neural network (CNN-RNN) proxy model is developed to predict well-by-well oil
and water rates, for given time-varying well bottom-hole pressure (BHP)
schedules, for each realization in an ensemble. This capability enables the
estimation of the objective function and nonlinear constraint values required
for robust optimization. The proxy model represents an extension of a recently
developed long short-term memory (LSTM) RNN proxy designed to predict well
rates for a single geomodel. A CNN is introduced here to processes permeability
realizations, and this provides the initial states for the RNN. The CNN-RNN
proxy is trained using simulation results for 300 different sets of BHP
schedules and permeability realizations. We demonstrate proxy accuracy for
oil-water flow through multiple realizations of 3D multi-Gaussian permeability
models. The proxy is then incorporated into a closed-loop reservoir management
(CLRM) workflow, where it is used with particle swarm optimization and a
filter-based method for nonlinear constraint satisfaction. History matching is
achieved using an adjoint-gradient-based procedure. The proxy model is shown to
perform well in this setting for five different (synthetic) `true' models.
Improved net present value along with constraint satisfaction and uncertainty
reduction are observed with CLRM. For the robust production optimization steps,
the proxy provides O(100) runtime speedup over simulation-based optimization.


title: Epsilon-Near-Zero (ENZ)-based Optomechanics

abstract:
  Optomechanics deals with the control and applications of mechanical effects
of light that stems from the redistribution of photon momenta in light
scattering. Here, we investigate, analytically and numerically, optical forces
on polarizable particles in proximity of epsilon-near-zero (ENZ) metamaterials.
We look at the general features of the repulsive-attractive optomechanics from
the nano to the microscale exploiting different theoretical methods (dipole
approximation, finite elements calculations, transition (T-)matrix). We discuss
the role of realistic layered materials, as our ENZ substrate, on optical
forces and analyze the influence of composition and shape by studying a range
of complex particles (dielectric, core-shell, plasmonic ellipsoids). Physical
insights into the results are discussed and future research directions are
forecasted. Our results provide new possibilities in exploiting engineered
materials and surfaces for the manipulation and tailoring of light-induced
forces in optomechanics.


title:
How do galaxies populate halos in extreme density environments? An
  analysis of the Halo Occupation Distribution in SDSS

abstract:
  Recent works have shown that the properties of galaxy populations in dark
matter halos vary with large-scale environments. These results suggest a
variation in the halo occupation distribution (HOD) in extreme density
environments. To analyse these effects, we identify cosmic voids and future
virialised structures (FVS) in the SDSS-DR12 and estimate the HOD within these
superstructures using group catalogues as dark matter halo proxies.
  Our goal is to use observational galaxy data to characterise the HOD within
voids and FVS, explore the different properties of these galaxies populations
and compare them with the general results outside these superstructures.
  Using a galaxy group catalogue we compute the HOD within both types of
superstructures. We also study the dependence on the results on the main void
and FVS properties. We also analysed the mean stellar age of the galaxies
inside these regions. In all cases, we compare the results with those derived
from the Field sample.
  Inside voids, we find a strong decrease in HOD concerning the Field results.
The mean number of satellites fall to 50%. Inside FVS, the HOD shows a
significant increase to the Field, with a 40% excess in the mean number of
satellites. In both regions, the differences with respect to the Field
increases for the extreme values of the density environments. We obtain no
signs of variations related to intrinsic characteristics of voids and FVS. We
find that the cumulative distribution of the mean age of stars of the central
galaxy also varies in these regions. Finally, we explore the HOD for the 25%
youngest (oldest) galaxies. We find that for the low-mass groups the youngest
galaxies are only present inside voids. On the other hand, for the high-mass
groups the FVS environments show the same increase in the HOD concerning the
Field. We find that cosmic voids lack of oldest galaxies.


title:
A Linearly Convergent Douglas-Rachford Splitting Solver for Markovian
  Information-Theoretic Optimization Problems

abstract:
  In this work, we propose solving the Information bottleneck (IB) and Privacy
Funnel (PF) problems with Douglas-Rachford Splitting methods (DRS). We study a
general Markovian information-theoretic Lagrangian that formulates the IB and
PF problems into a convex-weakly convex pair of functions in a unified
framework. Exploiting the recent non-convex convergence analysis for splitting
methods, we prove the linear convergence of the proposed algorithms using the
Kurdyka-{\L}ojasiewicz inequality. Moreover, our analysis is beyond IB and PF
and applies to any convex-weakly convex pair objectives. Based on the results,
we develop two types of IB solvers, with one improves the performance of
convergence over existing solvers while the other is linearly convergent
independent to the relevance-compression trade-off, and a class of PF solvers
that can handle both random and deterministic mappings. Empirically, we
evaluate the proposed DRS solvers for both the IB and PF problems with our
gradient-descent-based implementation. For IB, the proposed solvers result in
solutions that are comparable to those obtained through the
Blahut-Arimoto-based benchmark and is convergent for a wider range of the
penalty coefficient than existing solvers. For PF, our non-greedy solvers can
explore the information plane better than the clustering-based greedy solvers.


title: Energy and Matter Supply for Active Droplets

abstract:
  Chemically active droplets provide simple models for cell-like systems that
can grow and divide. Such active droplet systems are driven away from
thermodynamic equilibrium and turn over chemically, which corresponds to a
simple metabolism. We consider two scenarios of non-equilibrium driving. First,
droplets are driven via the system boundaries by external reservoirs that
supply nutrient and remove waste (boundary-driven). Second, droplets are driven
by a chemical energy provided by a fuel in the bulk (bulk-driven). For both
scenarios, we discuss the conservation of energy and matter as well as the
balance of entropy. We use conserved and non-conserved fields to analyze the
non-equilibrium steady states of active droplets. Using an effective droplet
model, we explore droplet stability and instabilities leading to droplet
division. Our work reveals that droplet division occurs quite generally in
active droplet systems. Our results suggest that life-like processes such as
metabolism and division can emerge in simple non-equilibrium systems that
combine the physics of phase separation and chemical reactions.


title: Data-Directed Search for New Physics based on Symmetries of the SM

abstract:
  We propose exploiting symmetries (exact or approximate) of the Standard Model
(SM) to search for physics Beyond the Standard Model (BSM) using the
data-directed paradigm (DDP). Symmetries are very powerful because they provide
two samples that can be compared without requiring simulation. Focusing on the
data, exclusive selections which exhibit significant asymmetry can be
identified efficiently and marked for further study. Using a simple and generic
test statistic which compares two matrices already provides good sensitivity,
only slightly worse than that of the profile likelihood ratio test statistic
which relies on the exact knowledge of the signal shape. This can be exploited
for rapidly scanning large portions of the measured data, in an attempt to
identify regions of interest. Weakly supervised Neural Networks could be used
for this purpose as well.


title: Fast Active Monocular Distance Estimation from Time-to-Contact

abstract:
  Distance estimation is fundamental for a variety of robotic applications
including navigation, manipulation and planning. Inspired by the mammal's
visual system, which gazes at specific objects (active fixation), and estimates
when the object will reach it (time-to-contact), we develop a novel constraint
between time-to-contact, acceleration, and distance that we call the
$\tau$-constraint. It allows an active monocular camera to estimate depth using
time-to-contact and inertial measurements (linear accelerations and angular
velocities) within a window of time.
  Our work differs from other approaches by focusing on patches instead of
feature points. This is, because the change in the patch area determines the
time-to-contact directly. The result enables efficient estimation of distance
while using only a small portion of the image, leading to a large speedup.
  We successfully validate the proposed $\tau$-constraint in the application of
estimating camera position with a monocular grayscale camera and an Inertial
Measurement Unit (IMU). Specifically, we test our method on different
real-world planar objects over trajectories 8-40 seconds in duration and 7-35
meters long. Our method achieves 8.5 cm Average Trajectory Error (ATE) while
the popular Visual-Inertial Odometry methods VINS-Mono and ROVIO achieve 12.2
and 16.9 cm ATE respectively. Additionally, our implementation runs 27$\times$
faster than VINS-Mono's and 6.8$\times$ faster than ROVIO's. We believe these
results indicate the $\tau$-constraints potential to be the basis of robust,
sophisticated algorithms for a multitude of applications involving an active
camera and an IMU.


title: UEFA EURO 2020: a "pure game of chance"?

abstract:
  We analysed the distribution of the number of goals scored in each of the 51
football matches played in the UEFA EURO 2020 final phase as well as the
waiting times between scores (also considering censored times). We found that
the Poisson model fits the score data and the exponential distribution fits
waiting times quite well. Such a good fit could be considered somewhat
counterintuitive and unrealistic given the memoryless property of the
exponential model. However, some peculiar features of this study have to be
considered: the abilities of the teams were relatively homogeneous; the time
span was short; there was no distinction between home and away games; only the
total number of goals scored in each game was considered. Although UEFA EURO
2020 can certainly not be considered a "pure game of chance", this competition
can be seen as an intriguing example of the pervasive real-world ubiquity of
the concept of independence and its ability to encompass complex situations in
a single, parsimonious model.


title: Statistics on bargraphs of inversion sequences of permutations

abstract:
  We consider the joint distribution of the area and perimeter statistics on
the set I_n of inversion sequences of length n represented as bargraphs.
Functional equations for both the ordinary and exponential generating functions
are derived from recurrences satisfied by this distribution. Explicit formulas
are found in some special cases as are expressions for the totals of the
respective statistics on I_n. A similar treatment is provided for the joint
distribution on I_n for the statistics recording the number of levels, descents
and ascents. Some connections are made between specific cases of this latter
distribution and the Stirling numbers of the first kind and Eulerian numbers.


title:
Warped Compactifications in Particle Physics, Cosmology and Quantum
  Gravity

abstract:
  Particle physics has evolved in the past decade through evaluating the
consequences of experimental measurements as well as exploiting theoretical
tools that permit exploration of new model building and cosmological
possibilities. Particularly due to insights from the AdS/CFT correspondence,
higher-dimensional warped compactifications, in particular, have played a big
role in recent developments by allowing a study of regimes of parameters that
would otherwise be intractable. Similarly, theoretical developments in quantum
gravity benefit from the bigger range of possibilities that can be explored
using warped geometry, allowing for constructions of string vacua with positive
cosmological constant and for the exploration of entanglement and information
transfer in arbitrary dimensions. Puzzles remain in both more
phenomenologically oriented and more theoretically oriented contexts which form
the basis for a rich research program in the future as well.


title: Elliptic operators on non-compact manifolds have closed range

abstract:
  We show that a second-order elliptic differential operator $P$, on any
manifold $M$, has closed range in $C^\infty(M)$. If $M$ has no compact
components, then $P$ is surjective on $C^\infty(M)$. Applications to Helmholtz
decomposition are discussed.


title: Strange quark as a probe for new physics in the Higgs sector

abstract:
  This paper describes a novel algorithm for tagging jets originating from the
hadronisation of strange quarks (strange-tagging) with the future International
Large Detector (ILD) at the International Linear Collider (ILC). It also
presents the first application of such a strange-tagger to a Higgs to strange
($h \rightarrow s\bar{s}$) analysis with the $P(e^-,e^+) = (-80\%,+30\%)$
polarisation scenario, corresponding to 900 fb$^{-1}$ of the initial proposed
2000 fb$^{-1}$ of data which will be collected by ILD during its first 10 years
of data taking at $\sqrt{s} = 250$ GeV. Upper limits on the Standard Model
Higgs-strange coupling strength modifier, $\kappa_s$, are derived at the 95%
confidence level to be 6.74. The paper includes as well a preliminary study of
a Ring Imaging Cherenkov (RICH) system capable of discriminating between kaons
and pions at high momenta (up to 25 GeV), and thus enhancing strange-tagging
performance at future Higgs factory detectors.


title: Quantum Computation of Reactions on Surfaces Using Local Embedding

abstract:
  Quantum computational techniques have been proposed to model electronic
systems. Despite encouraging preliminary results for small molecular species,
the modeling of complex material systems using these techniques is less
understood and established. We outline a workflow for modeling chemical
reactions on surfaces with quantum algorithms, and illustrate it for water
dissociation on a magnesium surface. This is the first example, to the best of
our knowledge, of modeling a reaction on a surface using a quantum computer.
Two local embedding methods for systematic determination of active spaces are
developed and compared. These methods are automated, systematically improvable
and based on the physics of the system. To achieve this goal, they target a
spatially localized region in which electron correlation effects are more
important than in the rest of the system. To reduce finite-size effects, the
local embedding methods are designed to enable Brillouin zone integration.
Ground-state energies of the active-space projected Hamiltonians are computed
with variational quantum algorithms suitable for near-term quantum computers,
using classical simulators and quantum hardware, and the impact of various
approximations is discussed. Results indicate that the system size which can be
feasibly treated with quantum simulations is extended by using local embedding
and Brillouin zone integration methods, as only a local region is correlated by
a quantum simulation.


title:
Denoising and feature extraction in photoemission spectra with
  variational auto-encoder neural networks

abstract:
  In recent years, distinct machine learning (ML) models have been separately
used for feature extraction and noise reduction from energy-momentum dispersion
intensity maps obtained from raw angle-resolved photoemission spectroscopy
(ARPES) data. In this work, we employ a shallow variational auto-encoder (VAE)
neural network to demonstrate the prospect of using ML for both denoising of as
well as feature extraction from ARPES dispersion maps.


title: Autistic Children's Mental Model of an Humanoid Robot

abstract:
  This position paper introduces the results of an initial card sorting
experiment based on the reactions and questions of a group of children with
autism working with a humanoid robot in a therapeutic laboratory on autonomy.


title: The TAP free energy for high-dimensional linear regression

abstract:
  We derive a variational representation for the log-normalizing constant of
the posterior distribution in Bayesian linear regression with a uniform
spherical prior and an i.i.d. Gaussian design. We work under the "proportional"
asymptotic regime, where the number of observations and the number of features
grow at a proportional rate. This rigorously establishes the
Thouless-Anderson-Palmer (TAP) approximation arising from spin glass theory,
and proves a conjecture of Krzakala et. al. (2014) in the special case of the
spherical prior.


title: ScienceWorld: Is your Agent Smarter than a 5th Grader?

abstract:
  This paper presents a new benchmark, ScienceWorld, to test agents' scientific
reasoning abilities in a new interactive text environment at the level of a
standard elementary school science curriculum. Despite the recent
transformer-based progress seen in adjacent fields such as question-answering,
scientific text processing, and the wider area of natural language processing,
we find that current state-of-the-art models are unable to reason about or
explain learned science concepts in novel contexts. For instance, models can
easily answer what the conductivity of a previously seen material is but
struggle when asked how they would conduct an experiment in a grounded,
interactive environment to find the conductivity of an unknown material. This
begs the question of whether current models are simply retrieving answers by
way of seeing a large number of similar input examples or if they have learned
to reason about concepts in a reusable manner. We hypothesize that agents need
to be grounded in interactive environments to achieve such reasoning
capabilities. Our experiments provide empirical evidence supporting this
hypothesis -- showing that a 1.5 million parameter agent trained interactively
for 100k steps outperforms a 11 billion parameter model statically trained for
scientific question-answering and reasoning via millions of expert
demonstrations.


title: Crystal-based pair production for a lepton collider positron source

abstract:
  An intense positron sources is a demanding element in the design of future
lepton colliders. A crystal-based hybrid positron source could be an
alternative to a more conventional scheme based on the electron conversion into
positron in a thick amorphous target. The conceptual idea of the hybrid source
is to have two separate objects, a photon radiator and a photon-to-positron
converter target. In such a scheme an electron beam crosses a thin axially
oriented crystal with the emission of a channeling radiation, characterized by
a considerably larger amount of photons if compared to Bremsstrahlung. The net
result is an increase in the number of produced positrons at the converter
target. In this paper we present the results of a beam test conducted at the
DESY TB 21 with 5.6 GeV electron beam and a crystalline tungsten radiator.
Experimental data clearly highlight an increased production of photons and they
are critically compared with the outcomes of novel method to simulate the
number of radiated photons, showing a very good agreement. Strong of this, the
developed simulation tool has been exploited to design a simple scheme for a
positron source based on oriented crystal, demonstrating the advantages in
terms of reduction of both deposited energy and the peak energy deposition
density if compared to conventional sources. The presented work opens the way
for a realistic and detailed design of a hybrid crystal-based positron source
for future lepton colliders.


title:
Neural Network Solver for Coherent Synchrotron Radiation Wakefield
  Calculations in Accelerator-based Charged Particle Beams

abstract:
  Particle accelerators support a wide array of scientific, industrial, and
medical applications. To meet the needs of these applications, accelerator
physicists rely heavily on detailed simulations of the complicated particle
beam dynamics through the accelerator. One of the most computationally
expensive and difficult-to-model effects is the impact of Coherent Synchrotron
Radiation (CSR). As a beam travels through a curved trajectory (e.g. due to a
bending magnet), it emits radiation that in turn interacts with the rest of the
beam. At each step through the trajectory, the electromagnetic field introduced
by CSR (called the CSR wakefield) needs to computed and used when calculating
the updates to the positions and momenta of every particle in the beam. CSR is
one of the major drivers of growth in the beam emittance, which is a key metric
of beam quality that is critical in many applications. The CSR wakefield is
very computationally intensive to compute with traditional electromagnetic
solvers, and this is a major limitation in accurately simulating accelerators.
Here, we demonstrate a new approach for the CSR wakefield computation using a
neural network solver structured in a way that is readily generalizable to new
setups. We validate its performance by adding it to a standard beam tracking
test problem and show a ten-fold speedup along with high accuracy.


title: Sugar, Salt & Pepper -- Humanoid robotics for autism

abstract:
  This paper introduces an experimental trial that will take place from
February to June 2021, and which will see the use of the Pepper robot in a
therapeutic laboratory on autonomies that will promote functional acquisitions
in children diagnosed with high functioning autism/Asperger's syndrome.


title:
A Unified Framework for Rank-based Evaluation Metrics for Link
  Prediction in Knowledge Graphs

abstract:
  The link prediction task on knowledge graphs without explicit negative
triples in the training data motivates the usage of rank-based metrics. Here,
we review existing rank-based metrics and propose desiderata for improved
metrics to address lack of interpretability and comparability of existing
metrics to datasets of different sizes and properties. We introduce a simple
theoretical framework for rank-based metrics upon which we investigate two
avenues for improvements to existing metrics via alternative aggregation
functions and concepts from probability theory. We finally propose several new
rank-based metrics that are more easily interpreted and compared accompanied by
a demonstration of their usage in a benchmarking of knowledge graph embedding
models.


title: Neutrinos from Stored Muons (nuSTORM)

abstract:
  The 2020 Update of the European Strategy for Particle Physics (ESPP) (see
https://cds.cern.ch/record/2720129 ) recommended that muon beam R\&D should be
considered a high-priority future initiative and that a programme of
experimentation be developed to determine the neutrino cross-sections required
to extract the most physics from the DUNE and Hyper-K long-baseline
experiments. The ENUBET and nuSTORM collaborations have begun to work within
and alongside the CERN Physics Beyond Colliders study group and the
international Muon Collider collaboration to carry out a joint, five-year R\&D
programme to deliver a detailed plan for the implementation of an
infrastructure in which:
  (1) ENUBET and nuSTORM deliver the neutrino cross-section measurement
programme identified in the ESPP and allow sensitive searches for physics
beyond the Standard Model to be carried out; and in which \ (2) A 6D muon
ionisation cooling experiment is delivered as part of the technology
development programme defined by the international Muon Collider collaboration.
  This document summarises the status of development of the nuSTORM and 6D
cooling experiments and identifies opportunities for collaboration in the
development of the initiative outlined above.


title:
Permutation Invariant Representations with Applications to Graph Deep
  Learning

abstract:
  This paper presents primarily two Euclidean embeddings of the quotient space
generated by matrices that are identified modulo arbitrary row permutations.
The original application is in deep learning on graphs where the learning task
is invariant to node relabeling. Two embedding schemes are introduced, one
based on sorting and the other based on algebras of multivariate polynomials.
While both embeddings exhibit a computational complexity exponential in problem
size, the sorting based embedding is globally bi-Lipschitz and admits a low
dimensional target space. Additionally, an almost everywhere injective scheme
can be implemented with minimal redundancy and low computational cost. In turn,
this proves that almost any classifier can be implemented with an arbitrary
small loss of performance. Numerical experiments are carried out on two data
sets, a chemical compound data set (QM9) and a proteins data set (PROTEINS).


title:
On the Violation of Honesty in Mobile Apps: Automated Detection and
  Categories

abstract:
  Human values such as integrity, privacy, curiosity, security, and honesty are
guiding principles for what people consider important in life. Such human
values may be violated by mobile software applications (apps), and the negative
effects of such human value violations can be seen in various ways in society.
In this work, we focus on the human value of honesty. We present a model to
support the automatic identification of violations of the value of honesty from
app reviews from an end-user perspective. Beyond the automatic detection of
honesty violations by apps, we also aim to better understand different
categories of honesty violations expressed by users in their app reviews. The
result of our manual analysis of our honesty violations dataset shows that
honesty violations can be characterised into ten categories: unfair
cancellation and refund policies; false advertisements; delusive subscriptions;
cheating systems; inaccurate information; unfair fees; no service; deletion of
reviews; impersonation; and fraudulent-looking apps. Based on these results, we
argue for a conscious effort in developing more honest software artefacts
including mobile apps, and the promotion of honesty as a key value in software
development practices. Furthermore, we discuss the role of app distribution
platforms as enforcers of ethical systems supporting human values, and
highlight some proposed next steps for human values in software engineering
(SE) research.


title: Physical Neural Cellular Automata for 2D Shape Classification

abstract:
  Materials with the ability to self-classify their own shape have the
potential to advance a wide range of engineering applications and industries.
Biological systems possess the ability not only to self-reconfigure but also to
self-classify themselves to determine a general shape and function. Previous
work into modular robotics systems have only enabled self-recognition and
self-reconfiguration into a specific target shape, missing the inherent
robustness present in nature to self-classify. In this paper we therefore take
advantage of recent advances in deep learning and neural cellular automata, and
present a simple modular 2D robotic system that can infer its own class of
shape through the local communication of its components. Furthermore, we show
that our system can be successfully transferred to hardware which thus opens
opportunities for future self-classifying machines.


title:
Cell-Free Massive MIMO with OTFS Modulation: Power Control and Resource
  Allocation

abstract:
  We consider the downlink of cell-free massive multiple-input multiple-output
(MIMO) systems with orthogonal time frequency space (OTFS) modulation. Two
pilot-based channel estimation schemes, namely superimposed pilot-based
(SP-CHE) and embedded pilot-based channel estimation (EP-CHE), are applied to
estimate the channels at the access points (APs). The SP-CHE scheme
superimposes low power pilots onto the data symbols in the delay-Doppler domain
to avoid the spectral efficiency (SE) loss due to null guard intervals used in
the EP-CHE scheme. In the case of SP-CHE scheme, we consider a max-min fairness
optimization problem to jointly optimize the peruser pilot/data power
allocation coefficients and per-AP power control coefficients. The complicated
non-convex problem is then iteratively solved through two decoupled
sub-problems. Moreover, a max-min fairness problem is cast for the EP-CHE
scheme, where the optimization variables are the per-AP power control
coefficients. Numerical results show that the proposed resource allocation
approaches provide at most 42 and 5-fold increase in the 95%-likely per-user SE
for the SP-CHE and EP-CHE scheme, respectively, compared with the uniform power
control and in correlated shadowing fading channels.


title:
Phases of MANES: Multi-Asset Non-Equilibrium Skew Model of a Strongly
  Non-Linear Market with Phase Transitions

abstract:
  This paper presents an analytically tractable and practically-oriented model
of non-linear dynamics of a multi-asset market in the limit of a large number
of assets. The asset price dynamics are driven by money flows into the market
from external investors, and their price impact. This leads to a model of a
market as an ensemble of interacting non-linear oscillators with the Langevin
dynamics. In a homogeneous portfolio approximation, the mean field treatment of
the resulting Langevin dynamics produces the McKean-Vlasov equation as a
dynamic equation for market returns. Due to the strong non-linearity of the
McKean-Vlasov equation, the resulting dynamics give rise to ergodicity breaking
and first- or second-order phase transitions under variations of model
parameters. Using a tractable potential of the Non-Equilibrium Skew (NES) model
previously suggested by the author for a single-stock case, the new Multi-Asset
NES (MANES) model enables an analytically tractable framework for a multi-asset
market. The equilibrium expected market log-return is obtained as a
self-consistent mean field of the McKean-Vlasov equation, and derived in closed
form in terms of parameters that are inferred from market prices of S&P 500
index options. The model is able to accurately fit the market data for either a
benign or distressed market environments, while using only a single volatility
parameter.

categories: q-fin.CP nlin.AO physics.data-an q-fin.GN q-fin.ST


title:
Fe-based high field superconductors for cost-effective future
  accelerator magnets

abstract:
  We propose a targeted attack on making the Fe-based superconductor (FBS)
AEFe2As2 (122) (AE = alkaline earth, here Ba or Sr) into a much cheaper, higher
stability margin, round, twisted and effectively isotropic multifilament
conductor suitable for 16-20 T dipole magnet construction. This white paper
urges to greatly accelerate progress by providing a fundamental understanding
of what controls the transport critical current density, Jc, of 122
polycrystals and then in the wire forms. High grain-to-grain Jc is the one key
property not yet fully demonstrated in this new superconductor and this is the
key breakthrough that would make 122 practical. Whereas the intragrain (i.e.
vortex pinning) Jc is already high enough to replace Nb3Sn, our recent work
strongly suggests that unpredictable superconducting connectivity at grain
boundaries (GBs) is the critical problem that limits the long-range Jc. We
strongly emphasize the need to evaluate the true intrinsic nature of GBs in the
polycrystalline forms before focusing on the wire fabrication.