title:
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for
  Profitable and Reliable Delivery Commitments by Wind Farm Operators

abstract:
  We present a specialized scenario generation method that utilizes forecast
information to generate scenarios for the particular usage in day-ahead
scheduling problems. In particular, we use normalizing flows to generate wind
power generation scenarios by sampling from a conditional distribution that
uses day-ahead wind speed forecasts to tailor the scenarios to the specific
day. We apply the generated scenarios in a simple stochastic day-ahead bidding
problem of a wind electricity producer and run a statistical analysis focusing
on whether the scenarios yield profitable and reliable decisions. Compared to
conditional scenarios generated from Gaussian copulas and
Wasserstein-generative adversarial networks, the normalizing flow scenarios
identify the daily trends more accurately and with a lower spread while
maintaining a diverse variety. In the stochastic day-ahead bidding problem, the
conditional scenarios from all methods lead to significantly more profitable
and reliable results compared to an unconditional selection of historical
scenarios. The obtained profits using the normalizing flow scenarios are
consistently closest to the perfect foresight solution, in particular, for
small sets of only five scenarios.


title:
Molecular formations and spectra due to electron correlations in
  three-electron hybrid double-well qubits

abstract:
  We show that systematic full configuration-interaction (FCI) calculations
enable prediction of the energy spectra and the intrinsic spatial and spin
structures of the many-body wave functions as a function of the detuning
parameter for the case of three-electron hybrid qubits based on GaAs asymmetric
double quantum dots. Specifically, in comparison with the case of weak
interactions and treating the entire three-electron double-dot hybrid qubit as
an integral unit, it is shown that the predicted spectroscopic patterns,
originating from strong electron correlations, manifest the formation of Wigner
molecules (WMs). Signatures of WM formation include: (1) a strong suppression
of the energy gaps relative to the non-interacting-electrons modeling, and (2)
the appearance of a pair of avoided crossings arising between states associated
with two-electron occupancies in the left and right wells. The Wigner molecule
is a physical entity associated with electron localization within each well and
it cannot be captured by the previously employed independent-particle or
two-site-Hubbard theoretical modeling of the hybrid qubits. The emergence of
strong WMs is investigated in depth through the concerted use of FCI-adapted
diagnostic tools like charge and spin densities, as well as conditional
probability distributions. Furthermore, the energy spectrum as a function of
the strength of the Coulomb repulsion (at constant detuning) is calculated in
order to complement the thorough analysis of the factors contributing to WM
emergence. We report remarkable agreement with recent experimental
measurements. The present FCI methodology for multi-well quantum dots can be
straightforwardly extended to treat valleytronic two-band Si/SiGe hybrid
qubits, where the central role of the WMs was confirmed recently.


title:
General Perturbations of Homogeneous and Orthogonal Locally Rotationally
  Symmetric Class II Cosmologies with Applications to Dissipative Fluids

abstract:
  First order perturbations of homogeneous and hypersurface orthogonal LRS
(Locally Rotationally Symmetric) class II cosmologies with a cosmological
constant are considered in the framework of the 1+1+2 covariant decomposition
of spacetime. The perturbations, which are for a general energy-momentum
tensor, include scalar, vector and tensor modes and extend some previous works
where matter was assumed to be a perfect fluid. Through a harmonic
decomposition, the system of equations is then transformed to evolution
equations in time and algebraic constraints. This result is then applied to
dissipative one-component fluids, and on using the simplified acausal Eckart
theory the system is reduced to two closed subsystems governed by four and
eight harmonic coefficients for the odd and even sectors respectively. The
system is also seen to close in a simplified causal theory. It is then
demonstrated, within the Eckart theory, how vorticity can be generated from
viscosity.


title: Simultaneous Primitive Root Values Of Polynomials

abstract:
  Let $z\ne \pm1,w^2$ be a fixed integer, and let $f(t)\ne g(t)^2$ be a fixed
polynomial over the integers. It is shown that the subset of primes $p\geq 2$
such that $z$ and $f(z)$ is a pair of simultaneous primitive roots modulo $p$
has nonzero density in the set of primes. The same analysis generalizes to
\textit{admissible} $k$-tuple of polynomials $z$, $f_1(z)$, $f_2(z), \ldots$,
$f_k(z)$, such that $f_i(z)\ne g_i(z)^2$, and $k\ll \log p$ is a small integer.


title:
Continuously Discovering Novel Strategies via Reward-Switching Policy
  Optimization

abstract:
  We present Reward-Switching Policy Optimization (RSPO), a paradigm to
discover diverse strategies in complex RL environments by iteratively finding
novel policies that are both locally optimal and sufficiently different from
existing ones. To encourage the learning policy to consistently converge
towards a previously undiscovered local optimum, RSPO switches between
extrinsic and intrinsic rewards via a trajectory-based novelty measurement
during the optimization process. When a sampled trajectory is sufficiently
distinct, RSPO performs standard policy optimization with extrinsic rewards.
For trajectories with high likelihood under existing policies, RSPO utilizes an
intrinsic diversity reward to promote exploration. Experiments show that RSPO
is able to discover a wide spectrum of strategies in a variety of domains,
ranging from single-agent particle-world tasks and MuJoCo continuous control to
multi-agent stag-hunt games and StarCraftII challenges.


title:
Linear power corrections to $e^+e^-$ shape variables in the three-jet
  region

abstract:
  We use an abelian model to study linear power corrections which arise from
infrared renormalons and affect event shapes in $e^+e^-$ annihilation into
hadrons. While previous studies explored power corrections in the two-jet
region, in this paper we focus on the three-jet region, which is the most
relevant one for the determination of the strong coupling constant. We show
that for a broad class of shape variables, linear power corrections can be
written in a factorised form, that involves an analytically-calculable
function, that characterises changes in the shape variable when a soft parton
is emitted, and a constant universal factor. This universal factor is
proportional to the so-called Milan factor, introduced in earlier literature to
describe linear power corrections in the two-jet region. We find that the power
corrections in the two-jet and in the three-jet regions are different, a result
which is bound to have important consequences for the determination of the
strong coupling constant from event shapes. As a further illustration of the
power of the approach developed in this paper, we provide explicit analytic
expressions for the leading power corrections to the $C$-parameter and the
thrust distributions in the $N$-jet region for arbitrary $N$, albeit in the
abelian model.


title:
A first-principles study of superconductivity on RbH by doping without
  applied pressure

abstract:
  The structural, electronic, lattice dynamics, electron-phonon coupling, and
superconducting properties of the alkali-metal hydride RbH, metalized through
electron-doping by the construction of the solid-solution Rb$_{1-x}$Sr$_x$H,
are systematically analyzed as a function of Sr-content within the framework of
density functional perturbation and Migdal-Eliashberg theories, taking into
account the effect of zero-point energy contribution by the quasi-harmonic
approximation. For the entire studied range of Sr-content, steady increments of
the electron-phonon coupling constant and the superconducting critical
temperature are found with progressive alkaline-earth metal content through
electron-doping, reaching the values of $\lambda=1.92$ and $T_c=51.3(66.1)$~K
with $\mu^*$=0.1(0). The steady rise of such quantities as a function of
Sr-content is consequence of the metallization of the hydride as an increase of
density of states at the Fermi level is observed, as well as the softening of
the phonon spectrum, mainly coming from H-optical modes. Our results indicate
that electron-doping on metal-hydrides is an encouraging alternative to look
for superconductivity without applied pressure.


title:
A Comparison of Deep Learning MOS Predictors for Speech Synthesis
  Quality

abstract:
  This paper introduces a comparison of deep learning-based techniques for the
MOS prediction task of synthesised speech in the Interspeech VoiceMOS
challenge. Using the data from the main track of the VoiceMOS challenge we
explore both existing predictors and propose new ones. We evaluate two groups
of models: NISQA-based models and techniques based on fine-tuning the
self-supervised learning (SSL) model wav2vec2_base. Our findings show that a
simplified version of NISQA with 40% fewer parameters achieves results close to
the original NISQA architecture on both utterance-level and system-level
performances. Pre-training NISQA with the NISQA corpus improves utterance-level
performance but shows no benefit on the system-level performance. Also, the
NISQA-based models perform close to LDNet and MOSANet, 2 out of 3 baselines of
the challenge. Fine-tuning wav2vec2_base shows superior performance than the
NISQA-based models. We explore the mismatch between natural and synthetic
speech and discovered that the performance of the SSL model drops consistently
when fine-tuned on natural speech samples. We show that adding CNN features
with the SSL model does not improve the baseline performance. Finally, we show
that the system type has an impact on the predictions of the non-SSL models.


title:
${\cal K}$rylov ${\cal C}$omplexity in ${\cal Q}$uantum ${\cal F}$ield
  ${\cal T}$heory

abstract:
  In this paper, we study the Krylov complexity in quantum field theory and
make a connection with the holographic "Complexity equals Volume" conjecture.
When Krylov basis matches with Fock basis, for several interesting settings, we
observe that the Krylov complexity equals the average particle number showing
that complexity scales with volume. Using similar formalism, we compute the
Krylov complexity for free scalar field theory and find surprising similarities
with holography. We also extend this framework for field theory where an
inverted oscillator appears naturally and explore its chaotic behavior.

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


title: RBGNet: Ray-based Grouping for 3D Object Detection

abstract:
  As a fundamental problem in computer vision, 3D object detection is
experiencing rapid growth. To extract the point-wise features from the
irregularly and sparsely distributed points, previous methods usually take a
feature grouping module to aggregate the point features to an object candidate.
However, these methods have not yet leveraged the surface geometry of
foreground objects to enhance grouping and 3D box generation. In this paper, we
propose the RBGNet framework, a voting-based 3D detector for accurate 3D object
detection from point clouds. In order to learn better representations of object
shape to enhance cluster features for predicting 3D boxes, we propose a
ray-based feature grouping module, which aggregates the point-wise features on
object surfaces using a group of determined rays uniformly emitted from cluster
centers. Considering the fact that foreground points are more meaningful for
box estimation, we design a novel foreground biased sampling strategy in
downsample process to sample more points on object surfaces and further boost
the detection performance. Our model achieves state-of-the-art 3D detection
performance on ScanNet V2 and SUN RGB-D with remarkable performance gains. Code
will be available at https://github.com/Haiyang-W/RBGNet.


title:
The Primitive Model in Classical Density Functional Theory: Beyond the
  Standard Mean-Field Approximation

abstract:
  The primitive model describes ions by point charges with an additional
hard-core interaction. In classical density-functional theory the mean-field
electrostatic contribution can be obtained from the first order of a functional
perturbation of the pair potential for an uncharged reference system of hard
spheres. This mean-field electrostatic term particularly contributes at
particle separations that are forbidden due to hard-core overlap. In this work
we modify the mean-field contribution such that the pair potential is constant
for distances smaller than the contact distance of the ions. We motivate our
modification by the underlying splitting of the potential, which is similar to
the splitting of the Weeks-Chandler-Andersen potential and leads to
higher-order terms in the respective expansion of the functional around the
reference system. The resulting formalism involves weighted densities similar
to the ones found in fundamental measure theory. To test our modifications, we
analyze and compare density profiles, direct and total correlation functions,
and the thermodynamic consistency of the functional via a widely established
sum rule and the virial pressure formula for our modified functional, for
established functionals, and for data from computer simulations. We found that
our modifications clearly show improvements compared to the standard mean-field
functional, especially when predicting layering effects and direct correlation
functions in high concentration scenarios; for the latter we also find improved
consistency when calculated via different thermodynamic routes. In conclusion,
we demonstrate how modifications towards higher order corrections beyond
mean-field functionals can be made and how they perform, by this providing a
basis for systematic future improvements in classical density-functional theory
for the description of electrostatic interactions.


title: Recent Trends on Nonlinear Filtering for Inverse Problems

abstract:
  Among the class of nonlinear particle filtering methods, the Ensemble Kalman
Filter (EnKF) has gained recent attention for its use in solving inverse
problems. We review the original method and discuss recent developments in
particular in view of the limit for infinitely particles and extensions towards
stability analysis and multi--objective optimization. We illustrate the
performance of the method by using test inverse problems from the literature.


title:
Degenerate Rabi spectroscopy of the Floquet engineered optical lattice
  clock

abstract:
  Simulating physics with large SU(N) symmetry is one of the unique advantages
of Alkaline-earth atoms.Introducing periodical driving modes to the system may
provide more rich SU(N) physics that static one could not reach. However,
whether the driving modes will break the SU(N) symmetry is still lack of
discussions. Here we experimentally study a Floquet engineered degenerate Sr-87
optical lattice clock (OLC) by periodically shaking the lattice. With the help
of Rabi spectroscopy, we find that the atoms at different Zeeman sublevels are
tuned by the same driven function. Meanwhile, our experimental results suggest
that uniform distribution among the sublevels will not change despite the
driving. Our experimental demonstrations may pave the way to implementation of
FE on tailoring the SU(N) physics in OLC system.


title:
Sufficient Reasons for A Zero-Day Intrusion Detection Artificial Immune
  System

abstract:
  The Internet is the most complex machine humankind has ever built, and how to
defense it from intrusions is even more complex. With the ever increasing of
new intrusions, intrusion detection task rely on Artificial Intelligence more
and more. Interpretability and transparency of the machine learning model is
the foundation of trust in AI-driven intrusion detection results. Current
interpretation Artificial Intelligence technologies in intrusion detection are
heuristic, which is neither accurate nor sufficient. This paper proposed a
rigorous interpretable Artificial Intelligence driven intrusion detection
approach, based on artificial immune system. Details of rigorous interpretation
calculation process for a decision tree model is presented. Prime implicant
explanation for benign traffic flow are given in detail as rule for negative
selection of the cyber immune system. Experiments are carried out in real-life
traffic.


title:
The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation
  Optimization under Uncertain Feature Positions

abstract:
  The estimation of the relative pose of two camera views is a fundamental
problem in computer vision. Kneip et al. proposed to solve this problem by
introducing the normal epipolar constraint (NEC). However, their approach does
not take into account uncertainties, so that the accuracy of the estimated
relative pose is highly dependent on accurate feature positions in the target
frame. In this work, we introduce the probabilistic normal epipolar constraint
(PNEC) that overcomes this limitation by accounting for anisotropic and
inhomogeneous uncertainties in the feature positions. To this end, we propose a
novel objective function, along with an efficient optimization scheme that
effectively minimizes our objective while maintaining real-time performance. In
experiments on synthetic data, we demonstrate that the novel PNEC yields more
accurate rotation estimates than the original NEC and several popular relative
rotation estimation algorithms. Furthermore, we integrate the proposed method
into a state-of-the-art monocular rotation-only odometry system and achieve
consistently improved results for the real-world KITTI dataset.


title: Toughness, hamiltonicity and spectral radius in graphs

abstract:
  The study of the existence of hamiltonian cycles in a graph is a classic
problem in graph theory. By incorporating toughness and spectral conditions, we
can consider Chv\'{a}tal's conjecture from another perspective: what is the
spectral condition to guarantee the existence of a hamiltonian cycle among
$t$-tough graphs? We first give the answer to $1$-tough graphs, i.e. if
$\rho(G)\geq\rho(M_{n})$, then $G$ contains a hamiltonian cycle, unless $G\cong
M_{n}$, where $M_{n}=K_{1}\nabla K_{n-4}^{+3}$ and $K_{n-4}^{+3}$ is the graph
obtained from $3K_{1}\cup K_{n-4}$ by adding three independent edges between
$3K_{1}$ and $K_{n-4}$. The Brouwer's toughness theorem states that every
$d$-regular connected graph always has $t(G)>\frac{d}{\lambda}-1$ where
$\lambda$ is the second largest absolute eigenvalue of the adjacency matrix. In
this paper, we extend the result in terms of its spectral radius, i.e. we
provide a spectral condition for a graph to be 1-tough with minimum degree
$\delta$ and to be $t$-tough, respectively.


title:
Probabilistic surrogate modeling of offshore wind-turbine loads with
  chained Gaussian processes

abstract:
  Heteroscedastic Gaussian process regression, based on the concept of chained
Gaussian processes, is used to build surrogates to predict site-specific loads
on an offshore wind turbine. Stochasticity in the inflow turbulence and
irregular waves results in load responses that are best represented as random
variables rather than deterministic values. Moreover, the effect of these
stochastic sources on the loads depends strongly on the mean environmental
conditions -- for instance, at low mean wind speeds, inflow turbulence produces
much less variability in loads than at high wind speeds. Statistically, this is
known as heteroscedasticity. Deterministic and most stochastic surrogates do
not account for the heteroscedastic noise, giving an incomplete and potentially
misleading picture of the structural response. In this paper, we draw on the
recent advancements in statistical inference to train a heteroscedastic
surrogate model on a noisy database to predict the conditional pdf of the
response. The model is informed via 10-minute load statistics of the
IEA-10MW-RWT subject to both aero- and hydrodynamic loads, simulated with
OpenFAST. Its performance is assessed against the standard Gaussian process
regression. The predicted mean is similar in both models, but the
heteroscedastic surrogate approximates the large-scale variance of the
responses significantly better.


title: Nonlinear chirped Doppler interferometry for \c{hi}(3) spectroscopy

abstract:
  Four-wave mixing processes are ubiquitous in ultrafast optics and the
determination of the coefficients of the $\chi^{(3)}$ tensor is thus essential.
We introduce a novel time-resolved ultrafast spectroscopic method to
characterize the third-order nonlinearity on the femtosecond time-scale. This
approach, coined as "nonlinear chirped Doppler interferometry", makes use of
the variation of the optical group delay of a transmitted probe under the
effect of an intense pump pulse in the nonlinear medium of interest. The
observable is the spectral interference between the probe and a reference pulse
sampled upstream. We show that the detected signal is enhanced when the pulses
are chirped, and that, although interferometric, the method is immune to
environmental phase fluctuations and drifts. By chirping adequately the
reference pulse, the transient frequency shift of the probe pulses is also
detected in the time domain and the detected nonlinear signal is enhanced.
Nonlinear phase shifts as low as 10\,mrad, corresponding to a frequency shift
of 30\,GHz, i.e. 0.01\% of the carrier frequency, are detected without
heterodyne detection or active phase-stabilization. The diagonal and/or
non-diagonal terms of reference glasses (SiO$_2$) and crystals (Al$_2$O$_3$,
BaF$_2$, CaF$_2$) are characterized. The method is finally applied to measure
the soft vibration mode of KTiOAsO$_4$ (KTA).


title: Heavy baryons in hot stellar matter with light nuclei and hypernuclei

abstract:
  The production of light nuclei and hypernuclei together with heavy baryons,
both hyperons and $\Delta$-baryons, in low density matter as found in stellar
environments such as supernova or binary mergers is studied within relativistic
mean-field models. Five light nuclei were considered together with three light
hypernuclei. The presence of both hyperons and $\Delta$-baryons shift the
dissolution of clusters to larger densities and increase the abundance of
clusters. This effect is larger the smaller the charge fraction and the higher
the temperature. The couplings of the $\Delta$-baryons were chosen imposing
that the nucleon effective mass remains finite inside neutron stars.


title:
Improving Generalizability in Implicitly Abusive Language Detection with
  Concept Activation Vectors

abstract:
  Robustness of machine learning models on ever-changing real-world data is
critical, especially for applications affecting human well-being such as
content moderation. New kinds of abusive language continually emerge in online
discussions in response to current events (e.g., COVID-19), and the deployed
abuse detection systems should be updated regularly to remain accurate. In this
paper, we show that general abusive language classifiers tend to be fairly
reliable in detecting out-of-domain explicitly abusive utterances but fail to
detect new types of more subtle, implicit abuse. Next, we propose an
interpretability technique, based on the Testing Concept Activation Vector
(TCAV) method from computer vision, to quantify the sensitivity of a trained
model to the human-defined concepts of explicit and implicit abusive language,
and use that to explain the generalizability of the model on new data, in this
case, COVID-related anti-Asian hate speech. Extending this technique, we
introduce a novel metric, Degree of Explicitness, for a single instance and
show that the new metric is beneficial in suggesting out-of-domain unlabeled
examples to effectively enrich the training data with informative, implicitly
abusive texts.


title: Random Thoughts on Quantum Mechanics

abstract:
  Here are a few random thoughts on the interpretations of the quantum double
slit experiment, the Mach Zehnder experiment, the delayed-choice experiment and
the measurement problem.


title: Multilingual and Multimodal Abuse Detection

abstract:
  The presence of abusive content on social media platforms is undesirable as
it severely impedes healthy and safe social media interactions. While automatic
abuse detection has been widely explored in textual domain, audio abuse
detection still remains unexplored. In this paper, we attempt abuse detection
in conversational audio from a multimodal perspective in a multilingual social
media setting. Our key hypothesis is that along with the modelling of audio,
incorporating discriminative information from other modalities can be highly
beneficial for this task. Our proposed method, MADA, explicitly focuses on two
modalities other than the audio itself, namely, the underlying emotions
expressed in the abusive audio and the semantic information encapsulated in the
corresponding textual form. Observations prove that MADA demonstrates gains
over audio-only approaches on the ADIMA dataset. We test the proposed approach
on 10 different languages and observe consistent gains in the range 0.6%-5.2%
by leveraging multiple modalities. We also perform extensive ablation
experiments for studying the contributions of every modality and observe the
best results while leveraging all the modalities together. Additionally, we
perform experiments to empirically confirm that there is a strong correlation
between underlying emotions and abusive behaviour.


title:
Constraints from $\Lambda$ hypernuclei on the $\Lambda NN$ content of
  the $\Lambda$-nucleus potential and the `hyperon puzzle'

abstract:
  A depth of $D_{\Lambda}\approx -28$ MeV for the $\Lambda$-nucleus potential
was confirmed in 1988 by studying $\Lambda$ binding energies deduced from
$(\pi^+,K^+)$ spectra measured across the periodic table. Modern two-body
hyperon-nucleon interaction models require additional interaction terms, most
likely $\Lambda NN$ three-body terms, to reproduce $D_{\Lambda}$. In this work
we apply a suitably constructed $\Lambda$-nucleus density dependent optical
potential to binding energy calculations of observed $1s_{\Lambda}$ and
$1p_{\Lambda}$ states in the mass range $12\leq A\leq 208$. The resulting
$\Lambda NN$ contribution to $D_{\Lambda}$, about 14 MeV repulsion at symmetric
nuclear matter density $\rho_0=0.17$ fm$^{-3}$, has a potential to resolve the
`hyperon puzzle'.


title:
Fiat-Shamir for Proofs Lacks a Proof Even in the Presence of Shared
  Entanglement

abstract:
  We explore the cryptographic power of arbitrary shared physical resources.
The most general such resource is access to a fresh entangled quantum state at
the outset of each protocol execution. We call this the Common Reference
Quantum State (CRQS) model, in analogy to the well-known Common Reference
String (CRS). The CRQS model is a natural generalization of the CRS model but
appears to be more powerful: in the two-party setting, a CRQS can sometimes
exhibit properties associated with a Random Oracle queried once by measuring a
maximally entangled state in one of many mutually unbiased bases. We formalize
this notion as a Weak One-Time Random Oracle (WOTRO), where we only ask of the
m-bit output to have some randomness when conditioned on the n-bit input.
  We show that WOTRO with $n - m \in {\omega}(\lg n)$ is black-box impossible
in the CRQS model, meaning that no protocol can have its security black-box
reduced to a cryptographic game. We define a (inefficient) quantum adversary
against any WOTRO protocol that can be efficiently simulated in polynomial
time, ruling out any reduction to a secure game that only makes black-box
queries to the adversary. On the other hand, we introduce a non-game quantum
assumption for hash functions that implies WOTRO in the CRQ\$ model (where the
CRQS consists only of EPR pairs). We first build a statistically secure WOTRO
protocol where $m = n$, then hash the output.
  The impossibility of WOTRO has the following consequences. First, we show the
black-box impossibility of a quantum Fiat-Shamir transform, extending the
impossibility result of Bitansky et al. (TCC '13) to the CRQS model. Second, we
show a black-box impossibility result for a strenghtened version of quantum
lightning (Zhandry, Eurocrypt '19) where quantum bolts have an additional
parameter that cannot be changed without generating new bolts.


title: Combinatoric topological string theories and group theory algorithms

abstract:
  A number of finite algorithms for constructing representation theoretic data
from group multiplications in a finite group G have recently been shown to be
related to amplitudes for combinatoric topological strings (G-CTST) based on
Dijkgraaf-Witten theory of flat G-bundles on surfaces. We extend this result to
projective representations of G using twisted Dijkgraaf-Witten theory. New
algorithms for characters are described, based on handle creation operators and
minimal multiplicative generating subspaces for the centers of group algebras
and twisted group algebras. Such minimal generating subspaces are of interest
in connection with information theoretic aspects of the AdS/CFT correspondence.
For the untwisted case, we describe the integrality properties of certain
character sums and character power sums which follow from these constructive
G-CTST algorithms. These integer sums appear as residues of singularities in
G-CTST generating functions. S-duality of the combinatoric topological strings
motivates the definition of an inverse handle creation operator in the centers
of group algebras and twisted group algebras.


title:
Multi-Agent Distributed Reinforcement Learning for Making Decentralized
  Offloading Decisions

abstract:
  We formulate computation offloading as a decentralized decision-making
problem with autonomous agents. We design an interaction mechanism that
incentivizes agents to align private and system goals by balancing between
competition and cooperation. The mechanism provably has Nash equilibria with
optimal resource allocation in the static case. For a dynamic environment, we
propose a novel multi-agent online learning algorithm that learns with partial,
delayed and noisy state information, and a reward signal that reduces
information need to a great extent. Empirical results confirm that through
learning, agents significantly improve both system and individual performance,
e.g., 40% offloading failure rate reduction, 32% communication overhead
reduction, up to 38% computation resource savings in low contention, 18%
utilization increase with reduced load variation in high contention, and
improvement in fairness. Results also confirm the algorithm's good convergence
and generalization property in significantly different environments.


title:
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with
  Long-Term and Sparse Reward in Repeated Auction Games

abstract:
  We propose a multi-agent distributed reinforcement learning algorithm that
balances between potentially conflicting short-term reward and sparse, delayed
long-term reward, and learns with partial information in a dynamic environment.
We compare different long-term rewards to incentivize the algorithm to maximize
individual payoff and overall social welfare. We test the algorithm in two
simulated auction games, and demonstrate that 1) our algorithm outperforms two
benchmark algorithms in a direct competition, with cost to social welfare, and
2) our algorithm's aggressive competitive behavior can be guided with the
long-term reward signal to maximize both individual payoff and overall social
welfare.


title:
Repeat after me: Self-supervised learning of acoustic-to-articulatory
  mapping by vocal imitation

abstract:
  We propose a computational model of speech production combining a pre-trained
neural articulatory synthesizer able to reproduce complex speech stimuli from a
limited set of interpretable articulatory parameters, a DNN-based internal
forward model predicting the sensory consequences of articulatory commands, and
an internal inverse model based on a recurrent neural network recovering
articulatory commands from the acoustic speech input. Both forward and inverse
models are jointly trained in a self-supervised way from raw acoustic-only
speech data from different speakers. The imitation simulations are evaluated
objectively and subjectively and display quite encouraging performances.


title: The Problem of Irreversible Change in Quantum Mechanics

abstract:
  I prove that, if a change happens in a closed quantum system so that its
state is perfectly distinguishable from all past or future states, the
Hamiltonian is $\widehat{H}=-i\hbar\frac{\partial\ }{\partial\tau}$. A time
operator $\widehat{\tau}$ emerges as its canonical conjugate. This Hamiltonian
is usually rejected because it has no ground state, but I show that even a
weaker form of irreversibility is inconsistent with a ground state.
  What is the right choice, that the world's Hamiltonian is
$-i\hbar\frac{\partial\ }{\partial\tau}$, or that no change is irreversible?

categories: quant-ph math-ph math.MP physics.hist-ph


title:
Methods for Combining Probability and Nonprobability Samples Under
  Unknown Overlaps

abstract:
  Nonprobability (convenience) samples are increasingly sought to stabilize
estimations for one or more population variables of interest that are performed
using a randomized survey (reference) sample by increasing the effective sample
size. Estimation of a population quantity derived from a convenience sample
will typically result in bias since the distribution of variables of interest
in the convenience sample is different from the population. A recent set of
approaches estimates conditional (on sampling design predictors) inclusion
probabilities for convenience sample units by specifying reference
sample-weighted pseudo likelihoods. This paper introduces a novel approach that
derives the propensity score for the observed sample as a function of
conditional inclusion probabilities for the reference and convenience samples
as our main result. Our approach allows specification of an exact likelihood
for the observed sample. We construct a Bayesian hierarchical formulation that
simultaneously estimates sample propensity scores and both conditional and
reference sample inclusion probabilities for the convenience sample units. We
compare our exact likelihood with the pseudo likelihoods in a Monte Carlo
simulation study.


title:
Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference
  of spatio-temporal heat fluxes in rotating disc systems

abstract:
  We study the Bayesian inverse problem of inferring the Biot number, a
spatio-temporal heat-flux parameter in a PDE model. This is an ill-posed
problem where standard optimisation yields unphysical inferences. We introduce
a training scheme that uses temperature data to adaptively train a
neural-network surrogate to simulate the parametric forward model. This
approach approximates forward and inverse solution together, by simultaneously
identifying an approximate posterior distribution over the Biot number, and
weighting the forward training loss according to this approximation. Utilising
random Chebyshev series, we outline how to approximate an arbitrary Gaussian
process prior, and using the surrogate we apply Hamiltonian Monte Carlo (HMC)
to efficiently sample from the corresponding posterior distribution. We derive
convergence of the surrogate posterior to the true posterior distribution in
the Hellinger metric as our adaptive loss function approaches zero.
Furthermore, we describe how this surrogate-accelerated HMC approach can be
combined with a traditional PDE solver in a delayed-acceptance scheme to
a-priori control the posterior accuracy, thus overcoming a major limitation of
deep learning-based surrogate approaches, which do not achieve guaranteed
accuracy a-priori due to their non-convex training. Biot number calculations
are involved turbo-machinery design, which is safety critical and highly
regulated, therefore it is important that our results have such mathematical
guarantees. Our approach achieves fast mixing in high-dimensional parameter
spaces, whilst retaining the convergence guarantees of a traditional PDE
solver, and without the burden of evaluating this solver for proposals that are
likely to be rejected. Numerical results compare the accuracy and efficiency of
the adaptive and general training regimes, as well as various Markov chain
Monte Carlo proposals strategies.


title: Arbitrary-Scale Image Synthesis

abstract:
  Positional encodings have enabled recent works to train a single adversarial
network that can generate images of different scales. However, these approaches
are either limited to a set of discrete scales or struggle to maintain good
perceptual quality at the scales for which the model is not trained explicitly.
We propose the design of scale-consistent positional encodings invariant to our
generator's layers transformations. This enables the generation of
arbitrary-scale images even at scales unseen during training. Moreover, we
incorporate novel inter-scale augmentations into our pipeline and partial
generation training to facilitate the synthesis of consistent images at
arbitrary scales. Lastly, we show competitive results for a continuum of scales
on various commonly used datasets for image synthesis.


title: Grounding of the Functional Object-Oriented Network in Industrial Tasks

abstract:
  In this preliminary work, we propose to design an activity recognition system
that is suitable for Industrie 4.0 (I4.0) applications, especially focusing on
Learning from Demonstration (LfD) in collaborative robot tasks. More precisely,
we focus on the issue of data exchange between an activity recognition system
and a collaborative robotic system. We propose an activity recognition system
with linked data using functional object-oriented network (FOON) to facilitate
industrial use cases. Initially, we drafted a FOON for our use case.
Afterwards, an action is estimated by using object and hand recognition systems
coupled with a recurrent neural network, which refers to FOON objects and
states. Finally, the detected action is shared via a context broker using an
existing linked data model, thus enabling the robotic system to interpret the
action and execute it afterwards. Our initial results show that FOON can be
used for an industrial use case and that we can use existing linked data models
in LfD applications.


title:
Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin
  Lesion Detection in Highly Imbalanced Datasets

abstract:
  Melanoma is a fatal skin cancer that is curable and has dramatically
increasing survival rate when diagnosed at early stages. Learning-based methods
hold significant promise for the detection of melanoma from dermoscopic images.
However, since melanoma is a rare disease, existing databases of skin lesions
predominantly contain highly imbalanced numbers of benign versus malignant
samples. In turn, this imbalance introduces substantial bias in classification
models due to the statistical dominance of the majority class. To address this
issue, we introduce a deep clustering approach based on the latent-space
embedding of dermoscopic images. Clustering is achieved using a novel
center-oriented margin-free triplet loss (COM-Triplet) enforced on image
embeddings from a convolutional neural network backbone. The proposed method
aims to form maximally-separated cluster centers as opposed to minimizing
classification error, so it is less sensitive to class imbalance. To avoid the
need for labeled data, we further propose to implement COM-Triplet based on
pseudo-labels generated by a Gaussian mixture model. Comprehensive experiments
show that deep clustering with COM-Triplet loss outperforms clustering with
triplet loss, and competing classifiers in both supervised and unsupervised
settings.


title:
A Discrete Analog of General Covariance: Could the world be
  fundamentally set on a lattice?

abstract:
  A crucial step in the history of General Relativity was Einstein's adoption
of the principle of general covariance which demands a coordinate independent
formulation for our spacetime theories. General covariance helps us to
disentangle a theory's substantive content from its merely representational
artifacts. It is an indispensable tool for a modern understanding of spacetime
theories. Motivated by quantum gravity, one may wish to extend these notions to
quantum spacetime theories (whatever those are). Relatedly, one might want to
extend these notions to discrete spacetime theories (i.e., lattice theories).
This paper delivers such an extension with surprising consequences.
  One's first intuition regarding discrete spacetime theories may be that they
introduce a great deal of fixed background structure (i.e., a lattice) and
thereby limit our theory's possible symmetries down to those which preserve
this fixed structure (i.e., discrete symmetries). However, as I will discuss,
these intuitions are doubly wrong and overhasty. Discrete spacetime theories
can and do have continuous translation and rotation symmetries. Moreover, the
exact same theory can be given a wide variety of lattice structures and can
even be described with no lattice at all. As my discrete analog of general
covariance will reveal: lattice structure is rather less like a fixed
background structure or part of an underlying manifold and rather more like a
coordinate system, i.e., merely a representational artifact. Thus, the world
cannot be "fundamentally set on a square lattice" (or any other lattice) any
more than it could be "fundamentally set in a certain coordinate system". Like
coordinate systems, lattice structures are just not the sort of thing that can
be fundamental; they are both thoroughly representational. Spacetime cannot be
discrete (even when it might be representable as such).


title: An Introduction to Complex Game Theory

abstract:
  We make an extension of Game Theory to the complex space. We use the extreme
points of convex polytope $S^{m}_\alpha$ to generalize the definitions of
strategy sets and payoff functions, and we prove that the properties of Nash
equilibria and the relations between the security levels of players, in the
case of two-player zero-sum games, that appear in real space are extented to
the complex one. We give a constructive proof of the Minimax Theorem in complex
space, as well as a solution method for two-player zero-sum complex games which
is based on the solutions of complex linear systems of the form $Bz=b$ and on
the solutions of dual complex linear programming problems. A conjecture
equivalent to the Riemann Hypothesis is also obtained.


title:
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics
  Data

abstract:
  Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and
classification of cancer subtypes are indispensable for effective clinical
treatment. Promising results on automatic cancer subtyping systems have been
published recently with the emergence of various deep learning methods.
However, such automatic systems often overfit the data due to the high
dimensionality and scarcity. In this paper, we propose to investigate automatic
subtyping from an unsupervised learning perspective by directly constructing
the underlying data distribution itself, hence sufficient data can be generated
to alleviate the issue of overfitting. Specifically, we bypass the strong
Gaussianity assumption that typically exists but fails in the unsupervised
learning subtyping literature due to small-sized samples by vector
quantization. Our proposed method better captures the latent space features and
models the cancer subtype manifestation on a molecular basis, as demonstrated
by the extensive experimental results.


title:
How Information on Acoustic Scenes and Sound Events Mutually Benefits
  Event Detection and Scene Classification Tasks

abstract:
  Acoustic scene classification (ASC) and sound event detection (SED) are
fundamental tasks in environmental sound analysis, and many methods based on
deep learning have been proposed. Considering that information on acoustic
scenes and sound events helps SED and ASC mutually, some researchers have
proposed a joint analysis of acoustic scenes and sound events by multitask
learning (MTL). However, conventional works have not investigated in detail how
acoustic scenes and sound events mutually benefit SED and ASC. We, therefore,
investigate the impact of information on acoustic scenes and sound events on
the performance of SED and ASC by using domain adversarial training based on a
gradient reversal layer (GRL) or model training with fake labels. Experimental
results obtained using the TUT Acoustic Scenes 2016/2017 and TUT Sound Events
2016/2017 show that pieces of information on acoustic scenes and sound events
are effectively used to detect sound events and classify acoustic scenes,
respectively. Moreover, upon comparing GRL- and fake-label-based methods with
single-task-based ASC and SED methods, single-task-based methods are found to
achieve better performance. This result implies that even when using
single-task-based ASC and SED methods, information on acoustic scenes may be
implicitly utilized for SED and vice versa.


title:
Opportunities for precision QCD physics in hadronization at Belle II --
  a snowmass whitepaper

abstract:
  This document presents a selection of QCD studies accessible to
high-precision studies with hadronic final states in $e^+e^-$ collisions at
Belle II. The exceptionally clean environment and the state-of-the-art
capabilities of the Belle~II detector (including excellent particle
identification and improved vertex reconstruction), coupled with an
unprecedented data-set size, will make possible to carry out multiple valuable
measurements of the strong interaction including hadronic contributions to the
muon $(g-2)$ and the QCD coupling, as well as advanced studies of parton
hadronization and dynamical quark mass generation.


title: Design Guidelines for Inclusive Speaker Verification Evaluation Datasets

abstract:
  Speaker verification (SV) provides billions of voice-enabled devices with
access control, and ensures the security of voice-driven technologies. As a
type of biometrics, it is necessary that SV is unbiased, with consistent and
reliable performance across speakers irrespective of their demographic, social
and economic attributes. Current SV evaluation practices are insufficient for
evaluating bias: they are over-simplified and aggregate users, not
representative of real-life usage scenarios, and consequences of errors are not
accounted for. This paper proposes design guidelines for constructing SV
evaluation datasets that address these short-comings. We propose a schema for
grading the difficulty of utterance pairs, and present an algorithm for
generating inclusive SV datasets. We empirically validate our proposed method
in a set of experiments on the VoxCeleb1 dataset. Our results confirm that the
count of utterance pairs/speaker, and the difficulty grading of utterance pairs
have a significant effect on evaluation performance and variability. Our work
contributes to the development of SV evaluation practices that are inclusive
and fair.


title:
Implicit and Explicit Dual Model Predictive Control with an Application
  to Steel Recycling

abstract:
  We present a formulation for both implicit and explicit dual model predictive
control with chance constraints. The formulation is applicable to systems that
are affine in the state and disturbances, but possibly nonlinear in the
controls. Awareness of uncertainty and dual control effect is achieved by
including the covariance of a Kalman Filter state estimate in the predictions.
For numerical stability, these predictions are obtained from a square-root
Kalman filter update based on a QR decomposition. In the implicit formulation,
the incentive for uncertainty reduction is given indirectly via the impact of
active constraints on the objective, as large uncertainty leads to large safety
backoffs from the constraint set boundary. The explicit formulation
additionally uses a heuristic cost term on uncertainty to encourage its active
exploration. We evaluate the methods based on numerical simulation of a
simplified but representative industrial steel recycling problem. Here, new
steel needs to be produced by choosing a combination of several different steel
scraps with unknown pollutant content. The pollutant content can only be
measured after a scrap combination is molten, allowing for inference on the
pollutants in the different scrap heaps. The cost should be minimized while
ensuring high quality of the product through constraining the maximum amount of
pollutant. The numerical simulations demonstrate the superiority of the two
dual formulations with respect to a robustified but non-dual formulation.
Specifically we achieve lower cost for the closed-loop trajectories while
ensuring constraint satisfaction with a given probability.


title:
Lost in Latent Space: Disentangled Models and the Challenge of
  Combinatorial Generalisation

abstract:
  Recent research has shown that generative models with highly disentangled
representations fail to generalise to unseen combination of generative factor
values. These findings contradict earlier research which showed improved
performance in out-of-training distribution settings when compared to entangled
representations. Additionally, it is not clear if the reported failures are due
to (a) encoders failing to map novel combinations to the proper regions of the
latent space or (b) novel combinations being mapped correctly but the
decoder/downstream process is unable to render the correct output for the
unseen combinations. We investigate these alternatives by testing several
models on a range of datasets and training settings. We find that (i) when
models fail, their encoders also fail to map unseen combinations to correct
regions of the latent space and (ii) when models succeed, it is either because
the test conditions do not exclude enough examples, or because excluded
generative factors determine independent parts of the output image. Based on
these results, we argue that to generalise properly, models not only need to
capture factors of variation, but also understand how to invert the generative
process that was used to generate the data.


title: Free gs-monoidal categories and free Markov categories

abstract:
  Categorical probability has recently seen significant advances through the
formalism of Markov categories, within which several classical theorems have
been proven in entirely abstract categorical terms. Closely related to Markov
categories are gs-monoidal categories, also known as CD categories. These omit
a condition that implements the normalization of probability. Extending work of
Corradini and Gadducci, we construct free gs-monoidal and free Markov
categories generated by a collection of morphisms of arbitrary arity and
coarity. For free gs-monoidal categories, this comes in the form of an explicit
combinatorial description of their morphisms as structured cospans of labeled
hypergraphs. These can be thought of as a formalization of gs-monoidal string
diagrams ($=$term graphs) as a combinatorial data structure. We formulate the
appropriate $2$-categorical universal property based on ideas of Walters and
prove that our categories satisfy it.
  We expect our free categories to be relevant for computer implementations and
we also argue that they can be used as statistical causal models generalizing
Bayesian networks.


title:
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context
  in Visual Question Answering

abstract:
  While Visual Question Answering (VQA) has progressed rapidly, previous works
raise concerns about robustness of current VQA models. In this work, we study
the robustness of VQA models from a novel perspective: visual context. We
suggest that the models over-rely on the visual context, i.e., irrelevant
objects in the image, to make predictions. To diagnose the model's reliance on
visual context and measure their robustness, we propose a simple yet effective
perturbation technique, SwapMix. SwapMix perturbs the visual context by
swapping features of irrelevant context objects with features from other
objects in the dataset. Using SwapMix we are able to change answers to more
than 45 % of the questions for a representative VQA model. Additionally, we
train the models with perfect sight and find that the context over-reliance
highly depends on the quality of visual representations. In addition to
diagnosing, SwapMix can also be applied as a data augmentation strategy during
training in order to regularize the context over-reliance. By swapping the
context object features, the model reliance on context can be suppressed
effectively. Two representative VQA models are studied using SwapMix: a
co-attention model MCAN and a large-scale pretrained model LXMERT. Our
experiments on the popular GQA dataset show the effectiveness of SwapMix for
both diagnosing model robustness and regularizing the over-reliance on visual
context. The code for our method is available at
https://github.com/vipulgupta1011/swapmix


title: Influence of misfit strain on the physical properties of Fe thin films

abstract:
  We investigate the growth of thin Fe layers on MgAl$_2$O$_4$ (001) and MgO
(001) substrates using dc magnetron sputtering. The crystal quality of Fe
layers deposited on MgAl$_2$O$_4$ is found to be substantially higher as
compared to Fe grown on MgO substrates. The effects of the crystal quality on
the magnetic and electric transport properties are discussed.


title: Rethinking Visual Geo-localization for Large-Scale Applications

abstract:
  Visual Geo-localization (VG) is the task of estimating the position where a
given photo was taken by comparing it with a large database of images of known
locations. To investigate how existing techniques would perform on a real-world
city-wide VG application, we build San Francisco eXtra Large, a new dataset
covering a whole city and providing a wide range of challenging cases, with a
size 30x bigger than the previous largest dataset for visual geo-localization.
We find that current methods fail to scale to such large datasets, therefore we
design a new highly scalable training technique, called CosPlace, which casts
the training as a classification problem avoiding the expensive mining needed
by the commonly used contrastive learning. We achieve state-of-the-art
performance on a wide range of datasets and find that CosPlace is robust to
heavy domain changes. Moreover, we show that, compared to the previous
state-of-the-art, CosPlace requires roughly 80% less GPU memory at train time,
and it achieves better results with 8x smaller descriptors, paving the way for
city-wide real-world visual geo-localization. Dataset, code and trained models
are available for research purposes at https://github.com/gmberton/CosPlace.


title:
Approximation of Generating Function Barcode for Hamiltonian
  Diffeomorphisms

abstract:
  Persistence modules and barcodes are used in symplectic topology to define
various invariants of Hamiltonian diffeomorphisms, however numerical methods
for computing these barcodes are not yet well developed. In this paper we
define one such invariant called the generating function barcode of compactly
supported Hamiltonian diffeomorphisms of $ \mathbb{R}^{2n} $ by applying Morse
theory to generating functions quadratic at infinity associated to such
Hamiltonian diffeomorphisms and provide an algorithm (i.e a finite sequence of
explicit calculation steps) that approximates it.


title: Neural Convolutional Surfaces

abstract:
  This work is concerned with a representation of shapes that disentangles
fine, local and possibly repeating geometry, from global, coarse structures.
Achieving such disentanglement leads to two unrelated advantages: i) a
significant compression in the number of parameters required to represent a
given geometry; ii) the ability to manipulate either global geometry, or local
details, without harming the other. At the core of our approach lies a novel
pipeline and neural architecture, which are optimized to represent one specific
atlas, representing one 3D surface. Our pipeline and architecture are designed
so that disentanglement of global geometry from local details is accomplished
through optimization, in a completely unsupervised manner. We show that this
approach achieves better neural shape compression than the state of the art, as
well as enabling manipulation and transfer of shape details. Project page at
http://geometry.cs.ucl.ac.uk/projects/2022/cnnmaps/ .


title:
This Sample seems to be good enough! Assessing Coverage and Temporal
  Reliability of Twitter's Academic API

abstract:
  Because of its willingness to share data with academia and industry, Twitter
has been the primary social media platform for scientific research as well as
for the consulting of businesses and governments in the last decade. In recent
years, a series of publications have studied and criticized Twitter's APIs and
Twitter has partially adapted its existing data streams. The newest Twitter API
for Academic Research allows to "access Twitter's real-time and historical
public data with additional features and functionality that support collecting
more precise, complete, and unbiased datasets." The main new feature of this
API is the possibility of accessing the full archive of all historic Tweets. In
this article, we will take a closer look at the Academic API and will try to
answer two questions. First, are the datasets collected with the Academic API
complete? Secondly, since Twitter's Academic API delivers historic Tweets as
represented on Twitter at the time of data collection, we need to understand
how much data is lost over time due to Tweet and account removal from the
platform. Our work shows evidence that Twitter's Academic API can indeed create
(almost) complete samples of Twitter data based on a wide variety of search
terms. We also provide evidence that Twitter's data endpoint v2 delivers better
samples than the previously used endpoint v1.1. Furthermore, collecting Tweets
with the Academic API at the time of studying a phenomenon rather than creating
local archives of stored Tweets, allows for a straightforward way of following
Twitter's developer agreement. Finally, we will also discuss technical
artifacts and implications of the Academic API. We hope that our work can add
another layer of understanding of Twitter data collections leading to more
reliable studies of human behavior via social media data.


title: Aggregating distribution forecasts from deep ensembles

abstract:
  The importance of accurately quantifying forecast uncertainty has motivated
much recent research on probabilistic forecasting. In particular, a variety of
deep learning approaches has been proposed, with forecast distributions
obtained as output of neural networks. These neural network-based methods are
often used in the form of an ensemble based on multiple model runs from
different random initializations, resulting in a collection of forecast
distributions that need to be aggregated into a final probabilistic prediction.
With the aim of consolidating findings from the machine learning literature on
ensemble methods and the statistical literature on forecast combination, we
address the question of how to aggregate distribution forecasts based on such
deep ensembles. Using theoretical arguments, simulation experiments and a case
study on wind gust forecasting, we systematically compare probability- and
quantile-based aggregation methods for three neural network-based approaches
with different forecast distribution types as output. Our results show that
combining forecast distributions can substantially improve the predictive
performance. We propose a general quantile aggregation framework for deep
ensembles that shows superior performance compared to a linear combination of
the forecast densities. Finally, we investigate the effects of the ensemble
size and derive recommendations of aggregating distribution forecasts from deep
ensembles in practice.


title:
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual
  Retrieval

abstract:
  State-of-the-art neural (re)rankers are notoriously data hungry which - given
the lack of large-scale training data in languages other than English - makes
them rarely used in multilingual and cross-lingual retrieval settings. Current
approaches therefore typically transfer rankers trained on English data to
other languages and cross-lingual setups by means of multilingual encoders:
they fine-tune all the parameters of a pretrained massively multilingual
Transformer (MMT, e.g., multilingual BERT) on English relevance judgments and
then deploy it in the target language. In this work, we show that two
parameter-efficient approaches to cross-lingual transfer, namely Sparse
Fine-Tuning Masks (SFTMs) and Adapters, allow for a more lightweight and more
effective zero-shot transfer to multilingual and cross-lingual retrieval tasks.
We first train language adapters (or SFTMs) via Masked Language Modelling and
then train retrieval (i.e., reranking) adapters (SFTMs) on top while keeping
all other parameters fixed. At inference, this modular design allows us to
compose the ranker by applying the task adapter (or SFTM) trained with source
language data together with the language adapter (or SFTM) of a target
language. Besides improved transfer performance, these two approaches offer
faster ranker training, with only a fraction of parameters being updated
compared to full MMT fine-tuning. We benchmark our models on the CLEF-2003
benchmark, showing that our parameter-efficient methods outperform standard
zero-shot transfer with full MMT fine-tuning, while enabling modularity and
reducing training times. Further, we show on the example of Swahili and Somali
that, for low(er)-resource languages, our parameter-efficient neural re-rankers
can improve the ranking of the competitive machine translation-based ranker.


title:
Two years of optical and NIR observations of the superluminous supernova
  UID 30901 discovered by the UltraVISTA SN survey

abstract:
  We present deep optical and near-infrared photometry of UID 30901, a
superluminous supernova (SLSN) discovered during the UltraVISTA survey. The
observations were obtained with VIRCAM ($YJHK_{s}$) mounted on the VISTA
telescope, DECam ($griz$) on the Blanco telescope, and SUBARU Hyper Suprime-Cam
(HSC; $grizy$). These multi-band observations comprise +700 days making UID
30901 one of the best photometrically followed SLSNe to date. The host galaxy
of UID 30901 is detected in a deep HST F814W image with an AB magnitude of
$27.3 \pm 0.2$. While no spectra exist for the SN or its host galaxy, we
perform our analysis assuming $z = 0.37$, based on the photometric redshift of
a possible host galaxy found at a projected distance of 7 kpc. Fitting a
blackbody to the observations, the radius, temperature, and bolometric light
curve are computed. We find a maximum bolometric luminosity of $5.4 \pm 0.34
\times 10^{43}$ erg s$^{-1}$. A flattening in the light curve beyond 600 days
is observed and several possible causes are discussed. We find the observations
to clearly favour a SLSN type I, and plausible power sources such as the
radioactive decay of $^{56}$Ni and the spin-down of a magnetar are compared to
the data. We find that the magnetar model yields a good fit to the observations
with the following parameters: a magnetic field $B = 1.4 \pm 0.3 \times 10^{14}
\ G$, spin period of $P = 6.0 \pm 0.1 \ ms$ and ejecta mass $M_{ej} =
11.9^{+4.8}_{-6.4} M_{\odot}$.


title:
ZETAR: Modeling and Computational Design of Strategic and Adaptive
  Compliance Policies

abstract:
  Security compliance management plays an important role in mitigating insider
threats. Incentive design is a proactive and non-invasive approach to achieving
compliance by aligning an employee's incentive with the defender's security
objective. Controlling insiders' incentives to elicit proper actions is
challenging because they are neither precisely known nor directly controllable.
To this end, we develop ZETAR, a zero-trust audit and recommendation framework,
to provide a quantitative approach to model incentives of the insiders and
design customized and strategic recommendation policies to improve their
compliance. We formulate primal and dual convex programs to compute the optimal
bespoke recommendation policies. We create a theoretical underpinning for
understanding trust and compliance, and it leads to security insights,
including fundamental limits of Completely Trustworthy (CT) recommendation, the
principle of compliance equivalency, and strategic information disclosure. This
work proposes finite-step algorithms to efficiently learn the CT policy set
when employees' incentives are unknown. Finally, we present a case study to
corroborate the design and illustrate a formal way to achieve compliance for
insiders with different risk attitudes. Our results show that the optimal
recommendation policy leads to a significant improvement in compliance for
risk-averse insiders. Moreover, CT recommendation policies promote insiders'
satisfaction.


title:
Three-body molecules $\bar{D}\bar{D}^{\ast}\Sigma_{c}$- understanding
  the nature of $T_{cc}$, $P_{c}(4312)$, $P_{c}(4440)$ and $P_{c}(4457)$

abstract:
  The nature of the three pentaquark states, $P_{c}(4312)$, $P_{c}(4440)$ and
$P_{c}(4457)$, discovered by the LHCb Collaboration in 2019, is still under
debate, although the $\bar{D}^{(\ast)}\Sigma_{c}$ molecular interpretation
seems to be the most popular. In this work, by adding a $\bar{D}$ meson into
the $\bar{D}^{\ast}\Sigma_{c}$ pair, we investigate the mass and decay width of
the three-body molecules $\bar{D}\bar{D}^{\ast}\Sigma_{c}$ and explore the
correlation between the existence of the $\bar{D}\bar{D}^{\ast}\Sigma_{c}$
molecules with the existence of $\bar{D}^{(\ast)}\Sigma_{c}$ and
$\bar{D}^{\ast}\bar{D}$ two-body molecules. The latter can be identified with
the doubly charmed tetraquark state $T_{cc}$ recently discovered by the LHCb
Collaboration. Based on the molecular nature of $P_{c}(4312)$, $P_{c}(4440)$,
$P_{c}(4457)$, and $T_{cc}$, our results indicate that there exist two
three-body bound states of $\bar{D}\bar{D}^{\ast}\Sigma_{c}$ with
$I(J^{P})=1(1/2^{+})$ and $I(J^{P})=1(3/2^{+})$, and binding energies $37.24$
MeV and $29.63$ MeV below the $\bar{D}\bar{D}^{\ast}\Sigma_{c}$ mass threshold.
In addition, we find that the mass splitting of these two three-body molecules
are correlated to the mass splitting of $P_{c}(4440)$ and $P_{c}(4457)$, which
offers a non-trivial way to reveal the molecular nature of these states. The
partial widths of two $\bar{D}\bar{D}^{\ast}\Sigma_{c}$ molecules decaying into
$J/\psi p \bar{D}$ and $J/\psi p \bar{D}^{\ast}$ are found to be several MeV.
We recommend the experimental searches for the
$\bar{D}\bar{D}^{\ast}\Sigma_{c}$ molecules in the $J/\psi p \bar{D}$ and
$J/\psi p \bar{D}^{\ast}$ invariant mass distributions.


title: iSDF: Real-Time Neural Signed Distance Fields for Robot Perception

abstract:
  We present iSDF, a continual learning system for real-time signed distance
field (SDF) reconstruction. Given a stream of posed depth images from a moving
camera, it trains a randomly initialised neural network to map input 3D
coordinate to approximate signed distance. The model is self-supervised by
minimising a loss that bounds the predicted signed distance using the distance
to the closest sampled point in a batch of query points that are actively
sampled. In contrast to prior work based on voxel grids, our neural method is
able to provide adaptive levels of detail with plausible filling in of
partially observed regions and denoising of observations, all while having a
more compact representation. In evaluations against alternative methods on real
and synthetic datasets of indoor environments, we find that iSDF produces more
accurate reconstructions, and better approximations of collision costs and
gradients useful for downstream planners in domains from navigation to
manipulation. Code and video results can be found at our project page:
https://joeaortiz.github.io/iSDF/ .


title:
Non-self similar blowup solutions to the higher dimensional Yang-Mills
  heat flows

abstract:
  In this paper, we consider the Yang-Mills heat flow on $\mathbb R^d \times
SO(d)$ with $d \ge 11$. Under a certain symmetry preserved by the flow, the
Yang-Mills equation can be reduced to: $$ \partial_t u =\partial_r^2 u
+\frac{d+1}{r} \partial_r u -3(d-2) u^2 - (d-2) r^2 u^3, \text{ and } (r,t) \in
\mathbb R_+ \times \mathbb R_+. $$ We are interested in describing the
singularity formation of this parabolic equation. We construct non-self-similar
blowup solutions for $d \ge 11$ and prove that the asymptotic of the solution
is of the form $$ u(r,t) \sim \frac{1}{\lambda_\ell(t)} \mathcal{Q} \left(
\frac{r}{\sqrt{\lambda_\ell (t)}} \right), \text{ as } t \to T ,$$ where
$\mathcal{Q}$ is the ground state with boundary conditions $\mathcal{Q}(0)=-1,
\mathcal{Q}'(0)=0$ and the blowup speed $\lambda_\ell$ verifies $$\lambda_\ell
(t) = \left( C(u_0) +o_{t\to T}(1) \right) (T-t)^{\frac{2\ell }{\alpha}} \text{
as } t \to T,~~ \ell \in \mathbb{N}^*_+, ~~\alpha>1.$$ In particular, when
$\ell = 1$, this asymptotic is stable whereas for $ \ell \ge 2$ it becomes
stable on a space of codimension $\ell-1$. Our approach here is not based on
energy estimates but on a careful construction of time dependent eigenvectors
and eigenvalues combined with maximum principle and semigroup pointwise
estimates.


title:
A rigidity result of spectral gap on Finsler manifolds and its
  application

abstract:
  We investigate the rigidity problem for the sharp spectral gap on Finsler
manifolds of weighted Ricci curvature bound $\text{Ric}_{\infty} \geq K > 0$.
Our main results show that if the equality holds, the manifold necessarily
admits a diffeomorphic splitting (or isometric splitting in the particular
class of Berwald spaces). This splitting phenomenon is comparable to the
Cheeger-Gromoll type splitting theorem by Ohta. We also obtain the rigidity
results of logarithmic Sobolev and Bakry-Ledoux isoperimetric inequalities via
needle decomposition as corollaries.


title: Theoretical properties of Bayesian Student-$t$ linear regression

abstract:
  Student-$t$ linear regression is a commonly used alternative to the normal
model in Bayesian analysis when one wants to gain robustness against outliers.
The assumption of heavy-tailed error distribution makes the model more adapted
to a potential presence of outliers by assigning higher probabilities to
extreme values. Even though the Student-$t$ model is often used in practice,
not a lot is known about its theoretical properties. In this paper, we aim to
fill some gaps by providing analyses in two different asymptotic scenarios. In
the first one, outliers are considered to be further and further away from the
bulk of the data. The analysis allows to characterize the limiting posterior
distribution, a distribution in which a trace of the outliers is present,
making the approach partially robust. The impact of the trace is seen to
increase with the degrees of freedom of the Student-$t$ distribution assumed.
The second asymptotic scenario is one where the sample size increases and the
normal model is the true generating process to be able to compare the
efficiency of the robust estimator to the ordinary-least-squares one when the
latter is the benchmark. The asymptotic efficiency is comparable, in the sense
that the variance of the robust estimator is inflated but only by a factor, and
this factor converges to 1 as the degrees of freedom increase. The trade-off
between robustness and efficiency controlled through the degrees of freedom is
thus precisely characterized (at least asymptotically).


title: Interfacial flow around a pusher bacterium

abstract:
  Motile bacteria play essential roles in biology that rely on their dynamic
behaviors, including their ability to navigate, interact, and self-organize.
However, bacteria dynamics on fluid interfaces are not understood, despite the
importance of interfaces in nature and the complexity of these highly
anisotropic milieu. Fluid interfaces are highly non-ideal, complex domains that
impose constraints that alter swimming behavior. We study flow fields generated
by Pseudomonas aeruginosa PA01 in the pusher mode at aqueous-hexadecane
interfaces. Analysis of correlated displacements of tracers and bacteria
reveals flow fields with unexpected asymmetries that differ significantly from
their counterparts in bulk fluids. These flow fields can be decomposed into
fundamental hydrodynamic modes for swimmers in incompressible fluid interfaces.
Experiments reveal an expected force-doublet mode corresponding to propulsion
and drag at the interface plane, and a second dipolar mode, associated with
forces exerted by the flagellum on the cell body in the aqueous phase that are
countered by stresses in the interface. The balance of these modes depends on
the bacteria's trapped interfacial configurations. The implications of these
flows on enhanced transport and swimmer pair interactions are explored by
investigating diffusion dynamics of tracer particles. This study identifies
important factors of general interest regarding swimmers on or near fluid
boundaries, and in the design of biomimetic swimmers to enhance transport at
interfaces.


title:
A multiphysics modeling approach for in-stent restenosis: Theoretical
  aspects and finite element implementation

abstract:
  Development of in silico models are intrinsic in understanding disease
progression in soft biological tissues. Within this work, we propose a
fully-coupled Lagrangian finite element framework which replicates the process
of in-stent restenosis observed post stent implantation in a coronary artery.
Coupled advection-reaction-diffusion reactions are set up that track the
evolution of the concentrations of the platelet-derived growth factor, the
transforming growth factor-$\beta$, the extracellular matrix, and the density
of the smooth muscle cells. A continuum mechanical description of growth
incorporating the evolution of arterial wall constituents is developed, and a
suitable finite element implementation discussed. Qualitative validation of the
computational model are presented by emulating a stented artery.
Patient-specific data can be integrated into the model to predict the risk of
restenosis and thereby assist in tuning of stent implantation parameters to
mitigate the risk.


title:
Observation of Global Spin Alignment of $\phi$ and $K^{*0}$ Vector
  Mesons in Nuclear Collisions

abstract:
  The strong force, as one of the four fundamental forces at work in the
universe, governs interactions of quarks and gluons, and binds together the
atomic nucleus. Notwithstanding decades of progress since Yukawa first
developed a description of the force between nucleons in terms of meson
exchange, a full understanding of the strong interaction remains a major
challenge in modern science. One remaining difficulty arises from the
non-perturbative nature of the strong force, which leads to the phenomenon of
quark confinement at distance scales on the order of the size of the proton.
Here we show that in relativistic heavy-ion collisions, where quarks and gluons
are set free over an extended volume, two species of produced vector (spin-1)
mesons, namely $\phi$ and $K^{*0}$, emerge with a surprising pattern of global
spin alignment. In particular, the global spin alignment for $\phi$ is
unexpectedly large, while that for $K^{*0}$ is consistent with zero. The
observed spin-alignment pattern and magnitude for the $\phi$ cannot be
explained by conventional mechanisms, while a model with strong force fields
accommodates the current data. This is the first time that the strong force
field is experimentally supported as a key mechanism that leads to global spin
alignment. We extract a quantity proportional to the intensity of the field of
the strong force. Within the framework of the Standard Model, where the strong
force is typically described in the quark and gluon language of Quantum
Chromodynamics, the field being considered here is an effective proxy
description. This is a qualitatively new class of measurement, which opens a
new avenue for studying the behaviour of strong force fields via their imprint
on spin alignment.


title:
Dispersal of protoplanetary discs: How stellar properties and the local
  environment determine the pathway of evolution

abstract:
  We study the evolution and final dispersal of protoplanetary discs that
evolve under the action of internal and external photoevaporation, and
different degrees of viscous transport. We identify five distinct dispersal
pathways, which are i) very long lived discs ($>20\,$Myr), ii) inside-out
dispersal where internal photoevaporation dominates and opens inner holes, iii)
outside-in dispersal where external photoevaporation dominates through disc
truncation and two intermediate regimes characterised by lingering material in
the inner disc with the outer disc dispersed predominantly by either internal
or external photoevaporation. We determine how the lifetime, relative impact of
internal and external winds and clearing pathway varies over a wide, plausible,
parameter space of stellar/disc/radiation properties. There are a number of
implications, for example in high UV environments because the outer disc
lifetime is shorter than the time-scale for clearing the inner disc we do not
expect transition discs to be common, which appears to be reflected in the
location of transition disc populations towards the Orion Nebular Cluster.
Irrespective of environment, we find that ongoing star formation is required to
reproduce observed disc fractions as a function of stellar cluster age. This
work demonstrates the importance of including both internal and external winds
for understanding protoplanetary disc evolution.


title: Single domain stripe order in a high-temperature superconductor

abstract:
  The coupling of spin, charge and lattice degrees of freedom results in the
emergence of novel states of matter across many classes of strongly correlated
electron materials. A model example is unconventional superconductivity, which
is widely believed to arise from the coupling of electrons via spin
excitations. In cuprate high-temperature superconductors, the interplay of
charge and spin degrees of freedom is also reflected in a zoo of charge and
spin-density wave orders that are intertwined with superconductivity. A key
question is whether the different types of density waves merely coexist or are
indeed directly coupled. Here we use a novel neutron diffraction technique with
superior beam-focusing that allows us to probe the subtle spin-density wave
order in the prototypical high-temperature superconductor La1.88Sr0.12CuO4
under applied uniaxial pressure to demonstrate that it is immediately coupled
with charge-density wave order. Our result shows that suitable models for
high-temperature superconductivity must equally account for charge and spin
degrees of freedom via uniaxial charge-spin stripe fluctuations.


title: A monotone convergence theorem for strong Feller semigroups

abstract:
  For an increasing sequence $(T_n)$ of one-parameter semigroups of sub
Markovian kernel operators over a Polish space, we study the limit semigroup
and prove sufficient conditions for it to be strongly Feller. In particular, we
show that the strong Feller property carries over from the approximating
semigroups to the limit semigroup if the resolvent of the latter maps the
constant 1 function to a continuous function.
  This is instrumental in the study of elliptic operators on $\mathbb{R}^d$
with unbounded coefficients: our abstract result enables us to assign a
semigroup to such an operator and to show that the semigroup is strongly Feller
under very mild regularity assumptions on the coefficients.
  We also provide counterexamples to demonstrate that the assumptions in our
main result are close to optimal.


title:
Leveraging Speech Separation for Conversational Telephone Speaker
  Diarization

abstract:
  Speech separation and speaker diarization have strong similarities. In
particular with respect to end-to-end neural diarization (EEND) methods.
Separation aims at extracting each speaker from overlapped speech, while
diarization identifies time boundaries of speech segments produced by the same
speaker. In this paper, we carry out an analysis of the use of speech
separation guided diarization (SSGD) where diarization is performed simply by
separating the speakers signals and applying voice activity detection. In
particular we compare two speech separation (SSep) models, both in offline and
online settings. In the online setting we consider both the use of continuous
source separation (CSS) and causal SSep models architectures. As an additional
contribution, we show a simple post-processing algorithm which reduces
significantly the false alarm errors of a SSGD pipeline. We perform our
experiments on Fisher Corpus Part 1 and CALLHOME datasets evaluating both
separation and diarization metrics. Notably, without fine-tuning, our SSGD
DPRNN-based online model achieves 12.7% DER on CALLHOME, comparable with
state-of-the-art EEND models despite having considerably lower latency, i.e.,
50 ms vs 1 s.


title:
Effect of Cu(II), Pb(II), Mg(II) ions on gamma-irradiated Spirulina
  platensis

abstract:
  Influence of metal ions Cu(II), Pb(II), Mg(II) on Spirulina platensis cells
were studied after three times Cs137 gamma irradiation discrete dose (in each
case 20 kGy irradiation dose) and recultivation using UV-VIS spectrometry. It
was shown, that metal ions Mg(II), Pb(II), Cu(II) differ from each other in
terms of interaction efficiency. In particular, Mg(II) ions act as a stimulant
to increase content for Spirulina platensis components after several times with
discrete doses of radiation and cultivation . For Pb (II) ions, the change in
absorption intensity at low concentrations slowly decreases, and then with an
increase in the Pb (II) concentration, the absorption intensity increases for
C-phycocyanin, Ch1a, which implies an increase in the quantity content of
Spirulina platensis constituents. Such effect for Cu(II) not observed.


title:
CalmResponses: Displaying Collective Audience Reactions in Remote
  Communication

abstract:
  We propose a system displaying audience eye gaze and nod reactions for
enhancing synchronous remote communication. Recently, we have had increasing
opportunities to speak to others remotely. In contrast to offline situations,
however, speakers often have difficulty observing audience reactions at once in
remote communication, which makes them feel more anxious and less confident in
their speeches. Recent studies have proposed methods of presenting various
audience reactions to speakers. Since these methods require additional devices
to measure audience reactions, they are not appropriate for practical
situations. Moreover, these methods do not present overall audience reactions.
In contrast, we design and develop CalmResponses, a browser-based system which
measures audience eye gaze and nod reactions only with a built-in webcam and
collectively presents them to speakers. The results of our two user studies
indicated that the number of fillers in speaker's speech decreases when
audiences' eye gaze is presented, and their self-rating score increases when
audiences' nodding is presented. Moreover, comments from audiences suggested
benefits of CalmResponses for them in terms of co-presence and privacy
concerns.


title:
Cr (VI) biosorption at different Na (I) concentrations after Zn(II)
  uptake during Arthrobacter species growth

abstract:
  The biosorption of Cr(VI) Arthrobacter species (Arthrobacter globiformis 151B
and Arthrobacter oxides 61B) after growth in the presence of Zn (II) ions at
different Na (I) concentrations was studied application using dialysis and
atomic absorption analysis. It was shown that after absorption of Zn(II) during
growth of types of Arthrobacter the biosorption of Cr (VI) with types
Arthrobacter (Arthrobacter globiformis 151B, Arthrobacter oxidas 61B) increases
at various concentration of Na (I) comparative without Zn(II), at identical
concentration of Na (I). It is seen, that zinc(II) ions promote for Cr(VI) part
of the active centers of Arthrobacter species and Na(I) ions already has
relatively little effect on them. However, this change is more pronounced for
Cr (VI) _Arthrobacter globiformis 151B + Zn (II) than for Cr (VI) _Arthrobacter
oxides of 61B + Zn (II).


title:
Improving Human-AI Partnerships in Child Welfare: Understanding Worker
  Practices, Challenges, and Desires for Algorithmic Decision Support

abstract:
  AI-based decision support tools (ADS) are increasingly used to augment human
decision-making in high-stakes, social contexts. As public sector agencies
begin to adopt ADS, it is critical that we understand workers' experiences with
these systems in practice. In this paper, we present findings from a series of
interviews and contextual inquiries at a child welfare agency, to understand
how they currently make AI-assisted child maltreatment screening decisions.
Overall, we observe how workers' reliance upon the ADS is guided by (1) their
knowledge of rich, contextual information beyond what the AI model captures,
(2) their beliefs about the ADS's capabilities and limitations relative to
their own, (3) organizational pressures and incentives around the use of the
ADS, and (4) awareness of misalignments between algorithmic predictions and
their own decision-making objectives. Drawing upon these findings, we discuss
design implications towards supporting more effective human-AI decision-making.


title: PaLM: Scaling Language Modeling with Pathways

abstract:
  Large language models have been shown to achieve remarkable performance
across a variety of natural language tasks using few-shot learning, which
drastically reduces the number of task-specific training examples needed to
adapt the model to a particular application. To further our understanding of
the impact of scale on few-shot learning, we trained a 540-billion parameter,
densely activated, Transformer language model, which we call Pathways Language
Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML
system which enables highly efficient training across multiple TPU Pods. We
demonstrate continued benefits of scaling by achieving state-of-the-art
few-shot learning results on hundreds of language understanding and generation
benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough
performance, outperforming the finetuned state-of-the-art on a suite of
multi-step reasoning tasks, and outperforming average human performance on the
recently released BIG-bench benchmark. A significant number of BIG-bench tasks
showed discontinuous improvements from model scale, meaning that performance
steeply increased as we scaled to our largest model. PaLM also has strong
capabilities in multilingual tasks and source code generation, which we
demonstrate on a wide array of benchmarks. We additionally provide a
comprehensive analysis on bias and toxicity, and study the extent of training
data memorization with respect to model scale. Finally, we discuss the ethical
considerations related to large language models and discuss potential
mitigation strategies.


title:
Anisotropic tomography of heavy quark dissociation by using general
  propagator structure at finite magnetic field

abstract:
  In this work we have explored the imaginary part of the Heavy Quark (HQ)
potential and subsequently the dissociation of heavy quarkonia, within the most
general scenario of magnetized hot medium. We have used the general structure
of the gauge boson propagator in a hot magnetized medium and derived the most
general result for the imaginary HQ potential and the decay width for the heavy
quarkonia. In the process we have investigated the rich anisotropic structure
of the complex HQ potential which explicitly depends on the longitudinal and
transverse distance. We have also compared our full structure rich result with
various approximated results available in the literature and explained the
differences between them.


title:
Is it worth the effort? Understanding and contextualizing physical
  metrics in soccer

abstract:
  We present a framework that gives a deep insight into the link between
physical and technical-tactical aspects of soccer and it allows associating
physical performance with value generation thanks to a top-down approach.
First, we estimate physical indicators from tracking data. Then, we
contextualize each player's run to understand better the purpose and
circumstances in which it is done, adding a new dimension to the creation of
team and player profiles. Finally, we assess the value-added by off-ball
high-intensity runs by linking with a possession-value model. This novel
approach allows answering practical questions from very different profiles of
practitioners within a soccer club, from analysts, coaches, and scouts to
physical coaches and readaptation physiotherapists.


title: Slow Star Formation in the Milky Way: Theory Meets Observations

abstract:
  The observed star formation rate of the Milky Way can be explained by
applying a metallicity-dependent factor to convert CO luminosity to molecular
gas mass and a star formation efficiency per free-fall time that depends on the
virial parameter of a molecular cloud. These procedures also predict the trend
of star formation rate surface density with Galactocentric radius. The
efficiency per free-fall time variation with virial parameter plays the major
role in bringing theory into agreement with observations for the total star
formation rate, while the metallicity dependence of the CO luminosity to mass
conversion is most notable in the variation with Galactocentric radius.
Application of these changes resolves a factor of over 100 discrepancy between
observed and theoretical star formation rates that has been known for nearly 50
years.


title:
Reverse shock forming condition for magnetized relativistic outflows:
  reconciling theories and simulations

abstract:
  Reverse shock (RS) emission can be used to probe the properties of the
relativistic ejecta, especially the degree of magnetization $\sigma$, in
gamma-ray burst (GRB) afterglows. However, there has been confusion in the
literature regarding the physical condition for the RS formation, and the role
of magnetic fields in the RS dynamics in the Poynting-flux-dominated regime is
not fully understood. Exploiting the shock jump conditions, we characterize the
properties of a magnetized RS. We compare the RS dynamics and forming
conditions from different theories and numerical simulations, and reconcile the
discrepancies among them. The strict RS forming condition is found to be
$\sigma < \sigma_\mathrm{cr}=(8/3)\gamma_4^2(n_1/n_4)$, where $n_4$ and $n_1$
are the rest-frame number densities of the ejecta and the ambient medium,
respectively, $\gamma_4$ is the bulk Lorentz factor, and $\sigma_\mathrm{cr}$
is the critical magnetization. Contrary to previous claims, we prove that this
condition agrees with other theoretical and simulated results, which can be
further applied to the setup and consistency check of future numerical
simulations. Using this condition, we propose a characteristic radius for RS
formation, and categorize the magnetized shell into three regimes: 'thick
shell' (relativistic RS), 'thin shell' (trans-relativistic RS), and 'no RS'
regimes. The critical magnetization $\sigma_\mathrm{cr}$ is generally below
unity for thin shells, but can potentially reaches $\sim 100-1000$ in the
'thick shell' regime. Our results could be applied to the dynamical evolution
of Poynting-flux-dominated ejecta, with potential applications to
self-consistent lightcurve modelling of magnetized relativistic outflows.


title:
Economies and Diseconomies of Scale in Segmented Mobility Sharing
  Markets

abstract:
  On-demand mobility sharing, provided by one or several transportation network
companies (TNCs), is realized by real-time optimization algorithms to connect
trips among tens of thousands of drivers and fellow passengers. In a market of
mobility sharing comprised of TNCs, there are two competing principles, the
economies of network scale and the healthy competition between TNCs, which can
lead to "segmentation" of market. To understand the substantiality and
relationship of the two competing principles, we need to answer how much
efficiency loss is generated due to the segmentation of market, and which
factors are related to it. Here we show how four critical factors of market
structure and characteristics of mobility sharing services -- density of trips
(thickness), maximum detour allowed for sharing (tightness), market shares
(unevenness), and spatial segregation of the TNCs (dissolvedness) -- are
associated with the efficiency loss, represented as the difference in vehicle
miles traveled (VMT) under different market structures. We found that 1) while
VMT shows a simple power function with thickness, the corresponding exponent
term can be expressed as a non-monotonic function with tightness -- essentially
showing how economies and diseconomies of scale in this market arise, and
appearing a very similar form to the Lennard--Jones model in inter-molecular
potentials; and 2) the efficiency loss is higher when unevenness is closer to
0.5 (50-50 market share) and dissolvedness is larger. Our results give a
comprehensive analysis of how the inefficiency of market segmentation is
generated, and how potentially it may be avoided through market mechanism
design.


title: Learning new physics efficiently with nonparametric methods

abstract:
  We present a machine learning approach for model-independent new physics
searches. The corresponding algorithm is powered by recent large-scale
implementations of kernel methods, nonparametric learning algorithms that can
approximate any continuous function given enough data. Based on the original
proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the
compatibility between experimental data and a reference model, by implementing
a hypothesis testing procedure based on the likelihood ratio.
Model-independence is enforced by avoiding any prior assumption about the
presence or shape of new physics components in the measurements. We show that
our approach has dramatic advantages compared to neural network implementations
in terms of training times and computational resources, while maintaining
comparable performances. In particular, we conduct our tests on higher
dimensional datasets, a step forward with respect to previous studies.


title:
ee$\in$MC: Simulation of $\bf e^{+}e^{-} \to \mu^{+} \mu^{-} (\gamma) $
  and $\bf e^{+}e^{-} \to \tau^{+}\tau^{-} (\gamma) $ Events

abstract:
  We present a new Monte-Carlo generator, {\tt ee$\in$MC}, for the simulation
of $e^{+}e^{-} \to \mu^{+} \mu^{-} (\gamma) $, $e^{+}e^{-} \to \tau^{+}\tau^{-}
(\gamma) $ and $\tau$ lepton decays which is suitable for $e^{+}e^{-}$
colliders from threshold up to energy of $\sim 10.58GeV$. The $e^{+}e^{-} \to
\mu^{+} \mu^{-}(\gamma) $, $e^{+}e^{-} \to \tau^{+}\tau^{-}(\gamma)$
interactions are computed up to the exact LO matrix element where the running
of $\alpha_{QED}$ includes both the LO leptonic and hadronic vacuum
polarization. Infra-red divergences are investigated within the
Yennie-Frautschi-Suura Exponentiation formalism, where several well-known
approximations and a formulation based on the exact LO contribution to the soft
and virtual photons are applied. The simulation of the $\tau$ lepton consists
of the leptonic decay modes at Born level and theoretical models based on
Flux-Tube Breaking Models and Chiral Resonance Lagrangian Models as well as
experimental models for the majority of semi-leptonic decay modes. This
includes hadronic models for investigating the origin of low mass scalar sector
recently observed in decays of the $\tau$ lepton and their association with the
origin of the constituent-quark mass through Chiral-symmetry Breaking.


title:
Thermodynamic insights into the intricate magnetic phase diagram of
  EuAl$_{4}$

abstract:
  The tetragonal intermetallic compound EuAl$_{4}$ hosts an exciting variety of
low temperature phases. In addition to a charge density wave below 140 K, four
ordered magnetic phases are observed below 15.4 K. Recently, a skyrmion phase
was proposed based on Hall effect measurements under a $c$-axis magnetic field.
We present a detailed investigation of the phase transitions in EuAl$_{4}$
under $c$-axis magnetic field. Our dilatometry, heat capacity, DC magnetometry,
AC magnetic susceptibility, and resonant ultrasound spectroscopy measurements
reveal three magnetic phase transitions not previously reported. We discuss
what our results reveal about the character of the magnetic phases. Our first
key result is a detailed $H \parallel [001]$ magnetic phase diagram mapping the
seven phases we observe. Second, we identify a new high-field phase, phase VII,
which directly corresponds to the region were skyrmions have been suggested.
Our results provide guidance for future studies exploring the complex magnetic
interactions and spin structures in EuAl$_{4}$.


title:
Learning Generalizable Dexterous Manipulation from Human Grasp
  Affordance

abstract:
  Dexterous manipulation with a multi-finger hand is one of the most
challenging problems in robotics. While recent progress in imitation learning
has largely improved the sample efficiency compared to Reinforcement Learning,
the learned policy can hardly generalize to manipulate novel objects, given
limited expert demonstrations. In this paper, we propose to learn dexterous
manipulation using large-scale demonstrations with diverse 3D objects in a
category, which are generated from a human grasp affordance model. This
generalizes the policy to novel object instances within the same category. To
train the policy, we propose a novel imitation learning objective jointly with
a geometric representation learning objective using our demonstrations. By
experimenting with relocating diverse objects in simulation, we show that our
approach outperforms baselines with a large margin when manipulating novel
objects. We also ablate the importance on 3D object representation learning for
manipulation. We include videos, code, and additional information on the
project website - https://kristery.github.io/ILAD/ .