title:
Using the Hills Mechanism to Generate Repeating Partial Tidal Disruption
  Events and ASASSN-14ko

abstract:
  Periodic nuclear transients have been detected with increasing frequency,
with one such system -- ASASSN-14ko -- exhibiting highly regular outbursts on a
timescale of $114 \pm 1$ days. It has been postulated that the outbursts from
this source are generated by the repeated partial disruption of a star, but how
the star was placed onto such a tightly bound orbit about the supermassive
black hole remains unclear. Here we use analytic arguments and three-body
integrations to demonstrate that the Hills mechanism, where a binary system is
destroyed by the tides of the black hole, can lead to the capture of a star on
a $\sim 114$ day orbit and with a pericenter distance that is comparable to the
tidal radius of one of the stars within the binary. Thus, Hills capture can
produce stars on tightly bound orbits that undergo repeated partial disruption,
leading to a viable mechanism for generating not only the outbursts detected
from ASASSN-14ko, but for periodic nuclear transients in general. We also show
that the rate of change of the period of the captured star due to
gravitational-wave emission is likely too small to produce the observed value
for ASASSN-14ko, indicating that in this system there must be additional
effects that contribute to the decay of the orbit. In general, however,
gravitational-wave emission can be important for limiting the lifetimes of
these systems, and could produce observable period decay rates in future
events.


title:
Dynamics of binary black holes in low and high-mass young star clusters:
  the impact of long-term star cluster evolution

abstract:
  Dynamical interactions in dense star clusters are considered one of the most
effective formation channels of binary black holes (BBHs). Here, we present
direct $N-$body simulations of two different star cluster families: low-mass
($\sim{500-800}$ M$_\odot$) and high-mass star clusters ($\ge{5000}$
M$_\odot$). We show that the formation channels of BBHs in low- and high-mass
star clusters are extremely different and lead to two completely distinct
populations of BBH mergers. Low-mass clusters host mainly low-mass BBHs born
from binary evolution, while BBHs in high-mass clusters are relatively massive
(chirp mass up to $\sim{50}$ M$_\odot$) and driven by dynamical exchanges.
Tidal disruption dramatically quenches the formation and dynamical evolution of
BBHs in low-mass clusters on a very short timescale ($\lesssim{100}$ Myr),
while BBHs in high-mass clusters undergo effective dynamical hardening until
the end of our simulations (1.5 Gyr). In high-mass clusters we find 4% of BBHs
with primary mass in the pair-instability mass gap, all of them born via
stellar collisions, while none of them forms in low-mass clusters. These
differences are crucial for the interpretation of the formation channels of
gravitational-wave sources.


title: Bounding Violations of the Weak Gravity Conjecture

abstract:
  The black hole weak gravity conjecture (WGC) is a set of linear inequalities
on the four-derivative corrections to Einstein--Maxwell theory. Remarkably, in
four dimensions, these combinations appear in the $2 \to 2$ photon amplitudes,
leading to the hope that the conjecture might be supported using dispersion
relations. However, the presence of a pole arising in the forward limit due to
graviton exchange greatly complicates the use of such arguments. In this paper,
we apply recently developed numerical techniques to handle the graviton pole,
and we find that standard dispersive arguments are not strong enough to imply
the black hole WGC. Specifically, under a fairly typical set of assumptions,
including weak coupling of the EFT and Regge boundedness, a small violation of
the black hole WGC is consistent with unitarity and causality. We quantify the
size of this violation, which vanishes in the limit where gravity decouples and
also depends logarithmically on an infrared cutoff. We discuss the meaning of
these bounds in various scenarios. We also implement a method for bounding
amplitudes without manifestly positive spectral densities, which could be
applied to any system of non-identical states, and we use it to improve bounds
on the EFT of pure photons in absence of gravity.


title: L-band Integral Field Spectroscopy of the HR 8799 Planetary System

abstract:
  Understanding the physical processes sculpting the appearance of young
gas-giant planets is complicated by degeneracies confounding effective
temperature, surface gravity, cloudiness, and chemistry. To enable more
detailed studies, spectroscopic observations covering a wide range of
wavelengths is required. Here we present the first L-band spectroscopic
observations of HR 8799 d and e and the first low-resolution wide bandwidth
L-band spectroscopic measurements of HR 8799 c. These measurements were
facilitated by an upgraded LMIRCam/ALES instrument at the LBT, together with a
new apodizing phase plate coronagraph. Our data are generally consistent with
previous photometric observations covering similar wavelengths, yet there
exists some tension with narrowband photometry for HR 8799 c. With the addition
of our spectra, each of the three innermost observed planets in the HR 8799
system have had their spectral energy distributions measured with integral
field spectroscopy covering $\sim0.9$ to $4.1~\mu\mathrm{m}$. We combine these
spectra with measurements from the literature and fit synthetic model
atmospheres. We demonstrate that the bolometric luminosity of the planets is
not sensitive to the choice of model atmosphere used to interpolate between
measurements and extrapolate beyond them. Combining luminosity with age and
mass constraints, we show that the predictions of evolutionary models are
narrowly peaked for effective temperature, surface gravity, and planetary
radius. By holding these parameters at their predicted values, we show that
more flexible cloud models can provide good fits to the data while being
consistent with the expectations of evolutionary models.


title: Constraints on Heavy Neutral Leptons interacting with a singlet scalar

abstract:
  Heavy neutral leptons (HNLs) are an attractive minimal extension of the
Standard Model, as is a singlet scalar $s$ mixing with the Higgs boson. If both
are present, it is natural for HNLs to interact with $s$. For a light singlet,
the decay $N\to s\nu$ can dominate over weak HNL decays. We reinterpret
existing constraints on HNL mixing from the DELPHI, CHARM and Belle experiments
for 0.5-100 GeV mass HNLs, taking into account the new decay channel. Although
the constraints are typically weakened, in some cases they can become stronger,
due to observable $s\to\ell^+\ell^-$ decays in the detectors. In addition, we
update and correct some errors in the analysis of the original constraints, in
the absence of the singlet.


title: 2D Percolation as a Conformal Field Theory

abstract:
  Fitting percolation into the conformal field theory framework requires
showing that connection probabilities have a conformally invariant scaling
limit. For critical site percolation on the triangular lattice, we prove that
the probability that $n$ vertices belong to the same open cluster has a
well-defined scaling limit for every $n \geq 2$. Moreover, the limiting
functions $P_n(x_1,\ldots,x_n)$ transform covariantly under M\"obius
transformations of the plane as well as under local conformal maps, i.e., they
behave like correlation functions of primary operators in conformal field
theory. In particular, they are invariant under translations, rotations and
inversions, and $P_n(sx_1,\ldots,sx_n)=s^{-5n/48}P_n(x_1,\ldots,x_n)$ for any
$s>0$. This implies that the ratio
$P_3(x_1,x_2,x_3)/\sqrt{P_2(x_1,x_2)P_2(x_1,x_3)P_2(x_2,x_3)}$ is independent
of $x_1,x_2,x_3$, which confirms a prediction of the physics literature.
  We also define a site-diluted spin model whose $n$-point correlation
functions $\mathrm{C}_{n}$ can be expressed in terms of percolation connection
probabilities and, as a consequence, have a well-defined scaling limit with the
same properties as the functions $P_n$. In particular,
$\mathrm{C}_{2}(x_1,x_2)=P_{2}(x_1,x_2)$. We prove that the magnetization field
associated with this spin model has a well-defined scaling limit in an
appropriate space of distributions. The limiting field transforms covariantly
under M\"obius transformations with exponent (scaling dimension) $5/48$. A
heuristic analysis of the four-point function of the magnetization field
reveals the presence of an additional conformal field of scaling dimension
$5/4$, which counts the number of percolation four-arm events and can be
identified with the so-called "four-leg operator" of conformal field theory.

categories: math-ph cond-mat.stat-mech math.MP math.PR


title: Feynman rules for wave turbulence

abstract:
  It has long been known that weakly nonlinear field theories can have a
late-time stationary state that is not the thermal state, but a wave turbulent
state with a far-from-equilibrium cascade of energy. We go beyond the existence
of the wave turbulent state, studying fluctuations about the wave turbulent
state. Specifically, we take a classical field theory with an arbitrary quartic
interaction and add dissipation and Gaussian-random forcing. Employing the path
integral relation between stochastic classical field theories and quantum field
theories, we give a prescription, in terms of Feynman diagrams, for computing
correlation functions in this system. We explicitly compute the two-point and
four-point functions of the field to next-to-leading order in the coupling.
Through an appropriate choice of forcing and dissipation, these correspond to
correlation functions in the wave turbulent state. As a check, we reproduce the
next-to-leading order term in the kinetic equation.

categories: cond-mat.stat-mech hep-th nlin.CD physics.flu-dyn


title:
Design and Performance of the Prototype Schwarzschild-Couder Telescope
  Camera

abstract:
  The prototype Schwarzschild-Couder Telescope (pSCT) is a candidate for a
medium-sized telescope in the Cherenkov Telescope Array. The pSCT is based on a
novel dual mirror optics design which reduces the plate scale and allows for
the use of silicon photomultipliers as photodetectors.
  The prototype pSCT camera currently has only the central sector instrumented
with 25 camera modules (1600 pixels), providing a 2.68$^{\circ}$ field of view
(FoV). The camera electronics are based on custom TARGET (TeV array readout
with GSa/s sampling and event trigger) application specific integrated
circuits. Field programmable gate arrays sample incoming signals at a
gigasample per second. A single backplane provides camera-wide triggers. An
upgrade of the pSCT camera is in progress, which will fully populate the focal
plane. This will increase the number of pixels to 11,328, the number of
backplanes to 9, and the FoV to 8.04$^{\circ}$. Here we give a detailed
description of the pSCT camera, including the basic concept, mechanical design,
detectors, electronics, current status and first light.


title: On the General Position Number of Mycielskian Graphs

abstract:
  The general position problem for graphs was inspired by the no-three-in-line
problem and the general position subset selection problem from discrete
geometry. A set $S$ of vertices of a graph $G$ is a \emph{general position set}
if no shortest path between two vertices of $S$ contains a third element of
$S$; the general position number of $G$ is the size of a largest general
position set. In this note we investigate the general position numbers of the
Mycielskian of graphs, which are of interest in chromatic graph theory. We give
tight upper and lower bounds on the general position number of the Mycielskian
of a graph $G$ and investigate the structure of the graphs meeting these
bounds. We determine this number exactly for common classes of graphs including
cubic graphs and a wide range of trees.


title: Asymptotic geometric Tevelev degrees of hypersurfaces

abstract:
  Let $(C,p_1,\ldots,p_n)$ be a fixed general pointed curve and let
$(X,x_1,\ldots,x_n)$ be a smooth hypersurface of degree $e$ and dimension $r$
with $n$ general points. We consider the problem of enumerating maps $f:C\to X$
of degree $d$ (as measured in the ambient projective space) such that
$f(p_i)=x_i$. When $e$ is small compared to $r$ and $d$ is large compared to
$g$, $e$, and $r$, these numbers have been computed first by passing to a
virtual count in Gromov-Witten theory obtained by Buch-Pandharipande, and then
by showing (in work of the author with Pandharipande) that the virtual counts
are enumerative via an analysis of boundary contributions in the moduli space
of stable maps. In this note, we give a simpler computation via projective
geometry.


title:
Properties of ubiquitous magnetic reconnection events in the lower solar
  atmosphere

abstract:
  Magnetic reconnection in the deep solar atmosphere can give rise to enhanced
emission in the Balmer hydrogen lines, a phenomenon referred to as Ellerman
bombs. Recent high quality H$\beta$ observations indicate that Ellerman bombs
are more common than previously thought and it was estimated that at any time
about half a million Ellerman bombs are present in the quiet Sun. We performed
an extensive statistical characterization of the quiet Sun Ellerman bombs
(QSEBs) in these new H$\beta$ observations. We analyzed a 1 h dataset of quiet
Sun observed with the Swedish 1-m Solar Telescope that consists of spectral
imaging in the H$\beta$ and H$\alpha$ lines, as well as spectropolarimetric
imaging in Fe I 617.3 nm. We detected a total of 2809 QSEBs. The lifetime
varies between 9 s and 20.5 min with a median of 1.14 min. The maximum area
ranges between 0.0016 and 0.2603 Mm$^2$ with a median of 0.018 Mm$^2$. A subset
(14%) of the QSEBs display enhancement of the H$\beta$ line core. On average,
the line core brightening appears 0.88 min after the onset of brightening in
the wings, and the distance between these brightenings is 243 km. This gives
rise to an apparent propagation speed ranging between $-$14.3 and +23.5 km
s$^{-1}$, with an average that is upward propagating at +4.4 km $^{-1}$. The
average orientation is nearly parallel to the limbward direction. QSEBs are
nearly uniformly distributed over the field of view but we find empty areas
with the size of mesogranulation. QSEBs are located more frequent near the
magnetic network where they are often bigger, longer lived and brighter. We
conclude that QSEBs are ubiquitous in quiet Sun and appear everywhere except in
areas of mesogranular size with weakest magnetic field
($B_{\rm{LOS}}\lesssim50$~G). Our observations support the interpretation of
reconnection along vertically extended current sheets.


title: Complex Supersymmetry in Graphene

abstract:
  This work analyzes monolayer graphene in external electromagnetic fields,
which is described by the Dirac equation with minimal coupling. Supersymmetric
quantum mechanics allows building new Dirac equations with modified magnetic
fields. Here, we will use complex factorization energies and iterate the method
in order to arrive at Hermitian graphene Hamiltonians. Finally, we compare
these results with the matrix supersymmetric quantum mechanics approach.

categories: cond-mat.mes-hall math-ph math.MP quant-ph


title: Towards understanding deep learning with the natural clustering prior

abstract:
  The prior knowledge (a.k.a. priors) integrated into the design of a machine
learning system strongly influences its generalization abilities. In the
specific context of deep learning, some of these priors are poorly understood
as they implicitly emerge from the successful heuristics and tentative
approximations of biological brains involved in deep learning design. Through
the lens of supervised image classification problems, this thesis investigates
the implicit integration of a natural clustering prior composed of three
statements: (i) natural images exhibit a rich clustered structure, (ii) image
classes are composed of multiple clusters and (iii) each cluster contains
examples from a single class. The decomposition of classes into multiple
clusters implies that supervised deep learning systems could benefit from
unsupervised clustering to define appropriate decision boundaries. Hence, this
thesis attempts to identify implicit clustering abilities, mechanisms and
hyperparameters in deep learning systems and evaluate their relevance for
explaining the generalization abilities of these systems. We do so through an
extensive empirical study of the training dynamics as well as the neuron- and
layer-level representations of deep neural networks. The resulting collection
of experiments provides preliminary evidence for the relevance of the natural
clustering prior for understanding deep learning.


title:
Mini Band Gap Generation in Magnetic Beta-Borophene: Effects of Optical
  Phonon Interaction

abstract:
  In this work we report for the first time, to the best of our concern, the
tunable electronic properties of Beta-Borophene (BB) on the polar substrate
(ZrO_2). We provide an analytical prescription for the calculation of ground
state energy in presence electron-phonon (e-ph) interaction, within the
framework of the Lee-Low-Pines theory. In the theoretical investigation of the
polaron formation in BB, we describe its effective masses, polaronic band-gap,
mobility, and Fermi velocities, which are different in each coordinate due to
the out-of-plane buckling structure. We also analyze how the average effective
mass and Fermi velocities of the charge carriers in the buckling structure are
affected by the external magnetic field. It is shown that the polaronic energy
becomes more effective in presence of a magnetic field, which we confirm
through an analytical prescription. The characteristics of the evolution of
average effective mass and average effective Fermi velocity in the presence of
a magnetic field are critically discussed. We find that the average effective
Fermi velocity is less sensitive, while the other one is greatly influenced by
the magnetic field.


title:
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for
  Edge Intelligence

abstract:
  Recent advances in wearable devices and Internet-of-Things (IoT) have led to
massive growth in sensor data generated in edge devices. Labeling such massive
data for classification tasks has proven to be challenging. In addition, data
generated by different users bear various personal attributes and edge
heterogeneity, rendering it impractical to develop a global model that adapts
well to all users. Concerns over data privacy and communication costs also
prohibit centralized data accumulation and training. This paper proposes a
novel personalized semi-supervised federated learning (SemiPFL) framework to
support edge users having no label or limited labeled datasets and a sizable
amount of unlabeled data that is insufficient to train a well-performing model.
In this work, edge users collaborate to train a hyper-network in the server,
generating personalized autoencoders for each user. After receiving updates
from edge users, the server produces a set of base models for each user, which
the users locally aggregate them using their own labeled dataset. We
comprehensively evaluate our proposed framework on various public datasets and
demonstrate that SemiPFL outperforms state-of-art federated learning frameworks
under the same assumptions. We also show that the solution performs well for
users without labeled datasets or having limited labeled datasets and
increasing performance for increased labeled data and number of users,
signifying the effectiveness of SemiPFL for handling edge heterogeneity and
limited annotation. By leveraging personalized semi-supervised learning,
SemiPFL dramatically reduces the need for annotating data and preserving
privacy in a wide range of application scenarios, from wearable health to IoT.


title:
Spin-optical dynamics and quantum efficiency of single V1 center in
  silicon carbide

abstract:
  Color centers in silicon carbide are emerging candidates for distributed
spin-based quantum applications due to the scalability of host materials and
the demonstration of integration into nanophotonic resonators. Recently,
silicon vacancy centers in silicon carbide have been identified as a promising
system with excellent spin and optical properties. Here, we in-depth study the
spin-optical dynamics of single silicon vacancy center at hexagonal lattice
sites, namely V1, in 4H-polytype silicon carbide. By utilizing resonant and
above-resonant sub-lifetime pulsed excitation, we determine spin-dependent
excited-state lifetimes and intersystem-crossing rates. Our approach to
inferring the intersystem-crossing rates is based on all-optical pulsed
initialization and readout scheme, and is applicable to spin-active color
centers with similar dynamics models. In addition, the optical transition
dipole strength and the quantum efficiency of V1 defect are evaluated based on
coherent optical Rabi measurement and local-field calibration employing
electric-field simulation. The measured rates well explain the results of
spin-state polarization dynamics, and we further discuss the altered
photoemission dynamics in resonant enhancement structures such as radiative
lifetime shortening and Purcell enhancement. By providing a thorough
description of V1 center's spin-optical dynamics, our work provides deep
understanding of the system which guides implementations of scalable quantum
applications based on silicon vacancy centers in silicon carbide.


title: A unified approach to embeddings of a line in 3-space

abstract:
  While the general question of whether every closed embedding of an affine
line in affine $3$-space can be rectified remains open, there have been several
partial results proved by several different means. We provide a new approach,
namely constructing (strongly) residual coordinates, that allows us to give new
proofs of all known partial results, and in particular generalize the results
of Bhatwadekar-Roy and Kuroda on embeddings of the form $(t^n,t^m,t^l+t)$.


title: Free outer functions in complete Pick spaces

abstract:
  Jury and Martin establish an analogue of the classical inner-outer
factorization of Hardy space functions. They show that every function $f$ in a
Hilbert function space with a normalized complete Pick reproducing kernel has a
factorization of the type $f=\varphi g$, where $g$ is cyclic, $\varphi$ is a
contractive multiplier, and $\|f\|=\|g\|$. In this paper we show that if the
cyclic factor is assumed to be what we call free outer, then the factors are
essentially unique, and we give a characterization of the factors that is
intrinsic to the space. That lets us compute examples. We also provide several
applications of this factorization.


title:
Tethered Power Supply for Quadcopters: Architecture, Analysis and
  Experiments

abstract:
  Tethered quadcopters are used for extended flight operations where the
necessary power to the system is provided through the tether from an external
power source on the ground. In this work, we study the design factors such as
the tether mass, electrical resistance, voltage conversion efficiency, etc.
that influence the power requirements. We present analytical formulations to
predict the power requirement for a given setup. Additionally, we show the
existence of a critical hover height for a single-quadcopter tether system,
beyond which it is physically (electrically) impossible for the quadcopter to
hover. We then present experimental results for single and two-quadcopter
tethered systems. Power supply readings from the experiments in various
configurations are consistent with the predictions from the analysis (within
5%), which experimentally validates the presented analysis. A two-quadcopter
system powered via a single tether is flown through a corridor to demonstrate
one of the applications of having multiple quadcopters on the same tether.


title:
Bianchi type-I Barrow holographic dark energy model in symmetric
  teleparallel gravity

abstract:
  In this work, we have discussed a spatially homogeneous and anisotropic
Bianchi type-I space-time in the presence of Barrow holographic dark energy
(infrared cut-off is the Hubble's horizon) proposed by Barrow recently (Physics
Letters B 808 (2020): 135643) and matter in the framework of $f(Q)$ gravity
where the non-metricity $Q$ is responsible for the gravitational interaction
for the specific choice of $f(Q)=\lambda Q^{2}$ (where $\lambda <0$ is a
constant). In order to find the exact solutions to the field equations we
consider the deceleration parameter $q$ is a function of the Hubble's parameter
$H$ i.e. $q=b-\frac{n}{H}$ (where $b$ and $n$ are constants). We have studied
the physical behavior of important cosmological parameters, such as the EoS
parameter, BHDE and matter density, skewness parameter, squared sound speed,
and $\omega_B-\omega_B^{^{\prime }}$ plane. We also constrain the values of the
model parameters $b$ and $n$ using $57$ Hubble's parameter measurements.


title: DSOL: A Fast Direct Sparse Odometry Scheme

abstract:
  In this paper, we describe Direct Sparse Odometry Lite (DSOL), an improved
version of Direct Sparse Odometry (DSO). We propose several algorithmic and
implementation enhancements which speed up computation by a significant factor
(on average 5x) even on resource constrained platforms. The increase in speed
allows us to process images at higher frame rates, which in turn provides
better results on rapid motions. Our open-source implementation is available at
https://github.com/versatran01/dsol.


title:
Van der Waals heteroepitaxy of air stable quasi-free standing silicene
  layers on CVD epitaxial graphene/6H-SiC

abstract:
  Graphene, consisting of an inert, thermally stable material with an
atomically flat, dangling bond-free surface is by essence an ideal template
layer for van der Waals heteroepitaxy of two-dimensional materials such as
silicene. However, depending on the synthesis method and growth parameters,
graphene (Gr) substrates could exhibit, on a single sample, various surface
structures, thicknesses, defects, and step heights. These structures noticeably
affect the growth mode of epitaxial layers, e.g. turning the layer-by-layer
growth into the Volmer-Weber growth promoted by defect-assisted nucleation. In
this work, the growth of silicon on chemical vapor deposited epitaxial Gr (1 ML
Gr/1ML Gr buffer) on 6H-SiC(0001) substrate is investigated by a combination of
atomic force microscopy (AFM), scanning tunneling microscopy (STM), x-ray
photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) and Raman
spectroscopy measurements. It is shown that the perfect control of full-scale
almost defect-free 1 ML Gr with a single surface structure and the ultra-clean
conditions for molecular beam epitaxy (MBE) deposition of silicon represent key
prerequisites for ensuring the growth of extended silicene sheets on epitaxial
graphene.


title:
Reconfigurable Intelligent Surfaces Relying on Non-Diagonal Phase Shift
  Matrices

abstract:
  Reconfigurable intelligent surfaces (RIS) have been actively researched as a
potential technique for future wireless communications, which intelligently
ameliorate the signal propagation environment. In the conventional design, each
RIS element configures and reflects its received signal independently of all
other RIS elements, which results in a diagonal phase shift matrix. By
contrast, we propose a novel RIS architecture, where the incident signal
impinging on one element can be reflected from another element after an
appropriate phase shift adjustment, which increases the flexibility in the
design of RIS phase shifts, hence, potentially improving the system
performance. The resultant RIS phase shift matrix also has off-diagonal
elements, as opposed to the pure diagonal structure of the conventional design.
Compared to the state-of-art fully-connected/group-connected RIS structures,
our proposed RIS architecture has lower complexity, while attaining a higher
channel gain than the group-connected RIS structure, and approaching that of
the fully-connected RIS structure. We formulate and solve the problem of
maximizing the achievable rate of our proposed RIS architecture by jointly
optimizing the transmit beamforming and the non-diagonal phase shift matrix
based on alternating optimization and semi-define relaxation (SDR) methods.
Moreover, the closed-form expressions of the channel gain, the outage
probability and bit error ratio (BER) are derived. Simulation results
demonstrate that our proposed RIS architecture results in an improved
performance in terms of the achievable rate compared to the conventional
architecture, both in single-user as well as in multi-user scenarios.


title: Recollections of Richard Feynman

abstract:
  Brief recollections by the author of how he interacted with Feynman and was
influenced by him.


title: Topological Superfluid Defects with Discrete Point Group Symmetries

abstract:
  Discrete symmetries are spatially ubiquitous but are often hidden in internal
states of systems where they can have especially profound consequences. In this
work we create and verify exotic magnetic phases of atomic spinor Bose-Einstein
condensates that, despite their continuous character and intrinsic spatial
isotropy, exhibit complex discrete polytope symmetries in their topological
defects. Using carefully tailored spinor rotations and microwave transitions,
we engineer singular line defects whose quantization conditions, exchange
statistics, and dynamics are fundamentally determined by these underlying
symmetries. We show how filling the vortex line singularities with atoms in a
variety of different phases leads to core structures that possess magnetic
interfaces with rich combinations of discrete and continuous symmetries. Such
defects, with their non-commutative properties, could provide unconventional
realizations of quantum information and interferometry.

categories: cond-mat.quant-gas cond-mat.supr-con hep-th quant-ph


title: Infinitesimal categorical Torelli theorems for Fano threefolds

abstract:
  Let $X$ be a smooth Fano variety and $\Ku(X)$ the Kuznetsov component.
Torelli theorems for $\Ku(X)$ says that it is uniquely determined by a
polarized abelian variety attached to it. An infinitesimal Torelli theorem for
$X$ says that the differential of the period map is injective. A categorical
variant of infinitesimal Torelli theorem for $X$ says that the morphism
$H^1(X,T_X)\xrightarrow{\gamma} HH^2(\Ku(X))$ is injective. In the present
article, we use the machinery of Hochschild (co)homology to relate the three
Torelli-type theorems for smooth Fano varieties via a commutative diagram. As
an application, we first prove infinitesimal categorical Torelli theorem for a
class of prime Fano threefolds. Then we prove a restatement of the
Debarre-Iliev-Manivel conjecture infinitesimally.


title: Category $\mathcal O$ for vertex algebras of $\mathfrak{osp}_{1|2n}$

abstract:
  We show that the affine vertex superalgebra $V^k(\mathfrak{osp}_{1|2n})$ at
generic level $k$ embeds in the equivariant $\mathcal W$-algebra of
$\mathfrak{sp}_{2n}$ times $4n$ free fermions. This has two corollaries: (1) it
provides a new proof that for generic $k$, the coset
$\text{Com}(V^k(\mathfrak{sp}_{2n}), V^k(\mathfrak{osp}_{1|2n}))$ is isomorphic
to $\mathcal W^\ell(\mathfrak{sp}_{2n})$ for $\ell = -(n+1) +
\frac{k+n+1}{2k+2n+1}$, and (2) we obtain the decomposition of ordinary
$V^k(\mathfrak{osp}_{1|2n})$-modules into $V^k(\mathfrak{sp}_{2n}) \otimes
\mathcal W^\ell(\mathfrak{sp}_{2n})$-modules. Next, if $k$ is an admissible
level and $\ell$ is a non-degenerate admissible level for $\mathfrak{sp}_{2n}$,
we show that the simple algebra $L_k(\mathfrak{osp}_{1|2n})$ is an extension of
the simple subalgebra $L_k(\mathfrak{sp}_{2n}) \otimes {\mathcal
W}_{\ell}(\mathfrak{sp}_{2n})$. Using the theory of vertex superalgebra
extensions, we prove that the category of ordinary
$L_k(\mathfrak{osp}_{1|2n})$-modules is a semisimple, rigid vertex tensor
supercategory with only finitely many inequivalent simple objects. It is
equivalent to a certain subcategory of $\mathcal
W_\ell(\mathfrak{sp}_{2n})$-modules. A similar result also holds for the
category of Ramond twisted modules. Due to a recent theorem of Robert McRae, we
get as a corollary that categories of ordinary
$L_k(\mathfrak{sp}_{2n})$-modules are rigid.


title: Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps

abstract:
  While it is not generally reflected in the `nice' datasets used for
benchmarking machine learning algorithms, the real-world is full of processes
that would be best described as many-to-many. That is, a single input can
potentially yield many different outputs (whether due to noise, imperfect
measurement, or intrinsic stochasticity in the process) and many different
inputs can yield the same output (that is, the map is not injective). For
example, imagine a sentiment analysis task where, due to linguistic ambiguity,
a single statement can have a range of different sentiment interpretations
while at the same time many distinct statements can represent the same
sentiment. When modeling such a multivalued function $f: X \rightarrow Y$, it
is frequently useful to be able to model the distribution on $f(x)$ for
specific input $x$ as well as the distribution on fiber $f^{-1}(y)$ for
specific output $y$. Such an analysis helps the user (i) better understand the
variance intrinsic to the process they are studying and (ii) understand the
range of specific input $x$ that can be used to achieve output $y$. Following
existing work which used a fiber bundle framework to better model many-to-one
processes, we describe how morphisms of fiber bundles provide a template for
building models which naturally capture the structure of many-to-many
processes.


title:
One year of AU Mic with HARPS: II -- stellar activity and star-planet
  interaction

abstract:
  We present a spectroscopic analysis of a 1-year intensive monitoring campaign
of the 22-Myr old planet-hosting M dwarf AU Mic using the HARPS spectrograph.
In a companion paper, we reported detections of the planet radial velocity (RV)
signatures of the two close-in transiting planets of the system, with
respective semi-amplitudes of 5.8 $\pm$ 2.5 m/s and 8.5 $\pm$ 2.5 m/s for AU
Mic b and AU Mic c. Here, we perform an independent measurement of the RV
semi-amplitude of AU Mic c using Doppler imaging to simultaneously model the
activity-induced distortions and the planet-induced shifts in the line
profiles. The resulting semi-amplitude of 13.3 $\pm$ 4.1 m/s for AU Mic c
reinforces the idea that the planet features a surprisingly large inner
density, in tension with current standard models of core accretion. Our
brightness maps feature significantly higher spot coverage and lower level of
differential rotation than the brightness maps obtained in late 2019 with the
SPIRou spectropolarimeter, suggesting that the stellar magnetic activity has
evolved dramatically over a $\sim$1-yr time span. Additionally, we report a
3-$\sigma$ detection of a modulation at 8.33 $\pm$ 0.04 d of the He I D3
(587.562 nm) emission flux, close to the 8.46-d orbital period of AU Mic b. The
power of this emission (a few 10$^{17}$ W) is consistent with 3D
magnetohydrodynamical simulations of the interaction between stellar wind and
the close-in planet if the latter hosts a magnetic field of $\sim$10 G.
Spectropolarimetric observations of the star are needed to firmly elucidate the
origin of the observed chromospheric variability.


title:
Measurement of the $\textit{CP}$ structure of the Higgs-tau Yukawa
  coupling

abstract:
  The CMS experiment at the LHC has performed the first measurement of the CP
structure of the Yukawa coupling between the Higgs boson and tau leptons. The
measurement is based on data collected in proton-proton collisions at $\sqrt{s}
= 13$ TeV during 2016-18, corresponding to an integrated luminosity of
$137\text{ fb}^{-1}$. The analysis utilizes the angular correlation between the
decay planes of tau leptons produced in Higgs boson decays, where dedicated
analysis techniques are used to optimise the reconstruction of tau decay
planes. The measured value of the CP mixing angle is $4\pm17^{\circ}$, at
$68\%$ confidence level. The pure $\textit{CP-}$odd hypothesis is excluded by
$3.2$ standard deviations. The analysis strategies and the results of the
measurement are presented.


title: Current Status and Future Prospects for the Light Dark Matter eXperiment

abstract:
  The constituents of dark matter are still unknown, and the viable
possibilities span a vast range of masses. The physics community has
established searching for sub-GeV dark matter as a high priority and identified
accelerator-based experiments as an essential facet of this search strategy. A
key goal of the accelerator-based dark matter program is testing the broad idea
of thermally produced sub-GeV dark matter through experiments designed to
directly produce dark matter particles. The most sensitive way to search for
the production of light dark matter is to use a primary electron beam to
produce it in fixed-target collisions. The Light Dark Matter eXperiment (LDMX)
is an electron-beam fixed-target missing-momentum experiment that realizes this
approach and provides unique sensitivity to light dark matter in the sub-GeV
range. This contribution provides an overview of the theoretical motivation,
the main experimental challenges, how LDMX addresses these challenges, and
projected sensitivities. We further describe the capabilities of LDMX to
explore other interesting new and standard physics, such as visibly-decaying
axion and vector mediators or rare meson decays, and to provide timely
electronuclear scattering measurements that will inform the modeling of
neutrino-nucleus scattering for DUNE.


title: Point Separation and Obstacle Removal by Finding and Hitting Odd Cycles

abstract:
  Suppose we are given a pair of points $s, t$ and a set $S$ of $n$ geometric
objects in the plane, called obstacles. We show that in polynomial time one can
construct an auxiliary (multi-)graph $G$ with vertex set $S$ and every edge
labeled from $\{0, 1\}$, such that a set $S_d \subseteq S$ of obstacles
separates $s$ from $t$ if and only if $G[S_d]$ contains a cycle whose sum of
labels is odd. Using this structural characterization of separating sets of
obstacles we obtain the following algorithmic results.
  In the Obstacle-Removal problem the task is to find a curve in the plane
connecting s to t intersecting at most q obstacles. We give a
$2.3146^qn^{O(1)}$ algorithm for Obstacle-Removal, significantly improving upon
the previously best known $q^{O(q^3)} n^{O(1)}$ algorithm of Eiben and
Lokshtanov (SoCG'20). We also obtain an alternative proof of a constant factor
approximation algorithm for Obstacle-Removal, substantially simplifying the
arguments of Kumar et al. (SODA'21).
  In the Generalized Points-Separation problem, the input consists of the set S
of obstacles, a point set A of k points and p pairs $(s_1, t_1),... (s_p, t_p)$
of points from A. The task is to find a minimum subset $S_r \subseteq S$ such
that for every $i$, every curve from $s_i$ to $t_i$ intersects at least one
obstacle in $S_r$. We obtain $2^{O(p)} n^{O(k)}$-time algorithm for Generalized
Points-Separation problem. This resolves an open problem of Cabello and
Giannopoulos (SoCG'13), who asked about the existence of such an algorithm for
the special case where $(s_1, t_1), ... (s_p, t_p)$ contains all the pairs of
points in A. Finally, we improve the running time of our algorithm to $f(p,k)
n^{O(\sqrt{k})}$ when the obstacles are unit disks, where $f(p,k) = 2^O(p)
k^{O(k)}$, and show that, assuming the Exponential Time Hypothesis (ETH), the
running time dependence on $k$ of our algorithms is essentially optimal.


title:
UNet Architectures in Multiplanar Volumetric Segmentation -- Validated
  on Three Knee MRI Cohorts

abstract:
  UNet has become the gold standard method for segmenting 2D medical images
that any new method must be validated against. However, in recent years,
several variations of the seminal UNet have been proposed with promising
results. However, there is no clear consensus on the generalisability of these
architectures, and UNet currently remains the methodological gold standard. The
purpose of this study was to evaluate some of the most promising UNet-inspired
architectures for 3D segmentation. For the segmentation of 3D scans,
UNet-inspired methods are also dominant, but there is a larger variety across
applications. By evaluating the architectures in a different dimensionality,
embedded in a different method, and for a different task, we aimed to evaluate
if any of these UNet-alternatives are promising as a new gold standard that
generalizes even better than UNet. Specifically, we investigated the
architectures as the central 2D segmentation core in the Multi-Planar Unet 3D
segmentation method that previously demonstrated excellent generalization in
the MICCAI Segmentation Decathlon. Generalisability can be demonstrated if a
promising UNet-variant consistently outperforms UNet in this setting. For this
purpose, we evaluated four architectures for cartilage segmentation from three
different cohorts with knee MRIs.


title: DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection

abstract:
  Lidars and cameras are critical sensors that provide complementary
information for 3D detection in autonomous driving. While prevalent multi-modal
methods simply decorate raw lidar point clouds with camera features and feed
them directly to existing 3D detection models, our study shows that fusing
camera features with deep lidar features instead of raw points, can lead to
better performance. However, as those features are often augmented and
aggregated, a key challenge in fusion is how to effectively align the
transformed features from two modalities. In this paper, we propose two novel
techniques: InverseAug that inverses geometric-related augmentations, e.g.,
rotation, to enable accurate geometric alignment between lidar points and image
pixels, and LearnableAlign that leverages cross-attention to dynamically
capture the correlations between image and lidar features during fusion. Based
on InverseAug and LearnableAlign, we develop a family of generic multi-modal 3D
detection models named DeepFusion, which is more accurate than previous
methods. For example, DeepFusion improves PointPillars, CenterPoint, and 3D-MAN
baselines on Pedestrian detection for 6.7, 8.9, and 6.2 LEVEL_2 APH,
respectively. Notably, our models achieve state-of-the-art performance on Waymo
Open Dataset, and show strong model robustness against input corruptions and
out-of-distribution data. Code will be publicly available at
https://github.com/tensorflow/lingvo/tree/master/lingvo/.


title:
Optimal Damping with Hierarchical Adaptive Quadrature for Efficient
  Fourier Pricing of Multi-Asset Options in L\'evy Models

abstract:
  Efficient pricing of multi-asset options is a challenging problem in
quantitative finance. When the Fourier transform of the density function is
available, Fourier-based pricing methods become very competitive compared to
alternative techniques because the integrand in the frequency space has often
higher regularity than in the physical space. However, when designing a
numerical quadrature method for most of these Fourier pricing approaches, two
key aspects affecting the numerical complexity should be carefully considered:
(i) the choice of the damping parameters that ensure integrability and control
the regularity class of the integrand and (ii) the effective treatment of the
high dimensionality of the integration problem. To address these challenges,
based on the extension of the one-dimensional Fourier valuation formula to the
multivariate case, we propose an efficient numerical method for pricing
European multi-asset options based on two complementary ideas. First, we smooth
the Fourier integrand via an optimized choice of damping parameters based on a
proposed heuristic optimization rule. Second, we use the adaptive sparse grid
quadrature based on sparsification and dimension-adaptivity techniques to
accelerate the convergence of the numerical quadrature in high dimensions.
Through an extensive numerical study on the basket and rainbow options under
the multivariate geometric Brownian motion and some multivariate L\'evy models,
we demonstrate the advantages of adaptivity and our damping parameter rule on
the numerical complexity of the quadrature methods. Moreover, we reveal that
our approach achieves substantial computational gains compared to the Monte
Carlo method for different dimensions and parameter constellations.


title:
A Universal Formulation of Uncertainty Relation for Errors under Local
  Representability

abstract:
  A universal formulation of uncertainty relations for quantum measurements is
presented with additional focus on the representability of quantum observables
by classical observables over a given state. Owing to the simplicity and
operational tangibility of the framework, the resultant general relations admit
natural operational interpretations and characterisations, and are thus also
experimentally verifiable. In view of the universal formulation, Heisenberg's
philosophy of the uncertainty principle is also revisited; it is reformulated
and restated as a refined no-go theorem, albeit perhaps in a weaker form than
was originally intended. In fact, the relations entail, in essence as
corollaries to their special cases, several previously known relations,
including most notably the Arthurs-Kelly-Goodman, Ozawa, and
Watanabe-Sagawa-Ueda relations for quantum measurements. The Schr{\"o}dinger
relation (hence the standard Kennard-Robertson relation as its trivial
corollary as well) is also shown to be a special case when the measurement is
non-informative.


title: ergm 4: Computational Improvements

abstract:
  The ergm package supports the statistical analysis and simulation of network
data. It anchors the statnet suite of packages for network analysis in R
introduced in a special issue in Journal of Statistical Software in 2008. This
article provides an overview of the performance improvements in the 2021
release of ergm version 4. These include performance enhancements to the Markov
chain Monte Carlo and maximum likelihood estimation algorithms as well as
broader and faster searching for networks with certain target statistics using
simulated annealing.


title: (Re)Politicizing Digital Well-Being: Beyond User Engagements

abstract:
  The psychological costs of the attention economy are often considered through
the binary of harmful design and healthy use, with digital well-being chiefly
characterised as a matter of personal responsibility. This article adopts an
interdisciplinary approach to highlight the empirical, ideological, and
political limits of embedding this individualised perspective in computational
discourses and designs of digital well-being measurement. We will reveal
well-being to be a culturally specific and environmentally conditioned concept
and will problematize user engagement as a universal proxy for well-being.
Instead, the contributing factors of user well-being will be located in
environing social, cultural, and political conditions far beyond the control of
individual users alone. In doing so, we hope to reinvigorate the issue of
digital well-being measurement as a nexus point of political concern, through
which multiple disciplines can study experiences of digital ill as symptomatic
of wider social inequalities and (capitalist) relations of power.


title: Topological phases in the dynamics of the simple exclusion process

abstract:
  We study the dynamical large deviations of the classical stochastic symmetric
simple exclusion process (SSEP) by means of numerical matrix product states. We
show that for half-filling, long-time trajectories with a large enough
imbalance between the number hops in even and odd bonds of the lattice belong
to distinct symmetry protected topological (SPT) phases. Using tensor network
techniques, we obtain the large deviation (LD) phase diagram in terms of
counting fields conjugate to the dynamical activity and the total hop
imbalance. We show the existence of high activity trivial and non-trivial SPT
phases (classified according to string-order parameters) separated by either a
critical phase or a critical point. Using the leading eigenstate of the tilted
generator, obtained from infinite-system density matrix renormalisation group
(DMRG) simulations, we construct a near-optimal dynamics for sampling the LDs,
and show that the SPT phases manifest at the level of rare stochastic
trajectories. We also show how to extend these results to other filling
fractions, and discuss generalizations to asymmetric SEPs.


title: Evolution of waves in liquid films on moving substrates

abstract:
  Accurate and computationally accessible mo\-dels of liquid film flows allow
for optimizing coating processes such as hot-dip galvanization and vertical
slot-die coating. This paper extends the classic three-dimen\-sional integral
boundary layer (IBL) model for falling liquid films to account for a substrate
motion. We analyze the stability of the liquid films on vertically moving
substrates in a linear and a nonlinear setting. In the linear analysis, we
derive the dispersion relation and the temporal growth rates of an
infinitesimal disturbance using normal modes and linearized governing
equations. In the nonlinear analysis, we consider disturbances of finite size
and numerically compute their evolution using the complete set of nonlinear
equations. We present the region of (linear) stability of both conditions, and
we place the operating conditions of an industrial galvanizing line in these
maps. A wide range of flow conditions were analyzed and shown to be stable
according to both linear and nonlinear stability analysis. Moreover, the
nonlinear analysis, carried out in the absence of surface tension reveals a
nonlinear stabilizing mechanism for the interface dynamics of a liquid films
dragged by an upward-moving substrate.


title:
Factorization and Sudakov Resummation in Leptonic Radiative $B$ Decay --
  A Reappraisal

abstract:
  The $B$-meson light-cone distribution amplitude is an important
non-perturbative quantity arising in the factorization of the amplitudes for
many exclusive decays of $B$ mesons, such as $B^-\to\gamma\,\ell^-\bar\nu$. We
reconsider the renormalization-group (RG) equation satisfied by this function
and present its solution at next-to-leading order (NLO) in RG-improved
perturbation theory in Laplace space and, for the first time, in momentum space
and the so-called diagonal (or dual) space. Since the information needed to
describe the $B$ decay processes at leading order in $\Lambda_{\rm QCD}/m_b$ is
most directly contained in the distribution amplitude in Laplace space
evaluated near the origin, we propose an unbiased parameterization of this
object in terms of a small set of uncorrelated hadronic parameters. Using
recent results on the three-loop anomalous dimension for heavy-light current
operators, we derive an expression for the convolution integral appearing in
the $B^-\to\gamma\,\ell^-\bar\nu$ factorization formula that is explicitly
scale independent, and we evaluate this formula at (approximate) NNLO.


title:
Solitonic Boson Stars: Numerical solutions beyond the thin-wall
  approximation

abstract:
  In this paper we present several set of solutions of static and spherically
symmetric solitonic boson stars. Each set is characterized by the value of
{\sigma} that defines the solitonic potential in the complex scalar field
theory. The main features peculiar to this potential occur for small values of
{\sigma}, but for which the equations become so stiff as to pose numerical
challenges. Without making approximations we build the sets for decreasing
{\sigma} values and show how they change their behavior in the parameter space,
giving special attention to the region where thin-wall configurations dwell.
The validity of the thin-wall approximation is explored as well as the
possibility of the solution sets being discontinuous. We investigate five
different possible definitions of a radius for boson stars and employ them to
calculate the compactness of each solution in order to assess how different the
outcomes might be.


title:
Snowmass White Paper: Effective Field Theories for Dark Matter
  Phenomenology

abstract:
  The quest to discover the nature of dark matter continues to drive many of
the experimental and observational frontiers in particle physics, astronomy,
and cosmology. While there are no definitive signatures to date, there exists a
rich ecosystem of experiments searching for signals for a broad class of dark
matter models, at different epochs of cosmic history, and through a variety of
processes with different characteristic energy scales. Given the multitude of
candidates and search strategies, effective field theory has been an important
tool for parametrizing the possible interactions between dark matter and
Standard Model probes, for quantifying and improving model-independent
uncertainties, and for robust estimation of detection rates in the presence of
large perturbative corrections. This white paper summarizes a wide range of
effective field theory applications for connecting dark matter theories to
experiments.


title:
Learning Deep Implicit Fourier Neural Operators (IFNOs) with
  Applications to Heterogeneous Material Modeling

abstract:
  Constitutive modeling based on continuum mechanics theory has been a
classical approach for modeling the mechanical responses of materials. However,
when constitutive laws are unknown or when defects and/or high degrees of
heterogeneity are present, these classical models may become inaccurate. In
this work, we propose to use data-driven modeling, which directly utilizes
high-fidelity simulation and/or experimental measurements to predict a
material's response without using conventional constitutive models.
Specifically, the material response is modeled by learning the implicit
mappings between loading conditions and the resultant displacement and/or
damage fields, with the neural network serving as a surrogate for a solution
operator. To model the complex responses due to material heterogeneity and
defects, we develop a novel deep neural operator architecture, which we coin as
the Implicit Fourier Neural Operator (IFNO). In the IFNO, the increment between
layers is modeled as an integral operator to capture the long-range
dependencies in the feature space. As the network gets deeper, the limit of
IFNO becomes a fixed point equation that yields an implicit neural operator and
naturally mimics the displacement/damage fields solving procedure in material
modeling problems. We demonstrate the performance of our proposed method for a
number of examples, including hyperelastic, anisotropic and brittle materials.
As an application, we further employ the proposed approach to learn the
material models directly from digital image correlation (DIC) tracking
measurements, and show that the learned solution operators substantially
outperform the conventional constitutive models in predicting displacement
fields.


title: Probing the Electroweak Phase Transition with Exotic Higgs Decays

abstract:
  An essential goal of the Higgs physics program at the LHC and beyond is to
explore the nature of the Higgs potential and shed light on the mechanism of
electroweak symmetry breaking. An important class of models defining the
strength and order of the electroweak phase transition is driven by the Higgs
boson coupling to a light new state. This Snowmass white paper points out the
existence of a region of parameter space where a strongly first order
electroweak phase transition is compatible with exotic decays of the SM-like
Higgs boson. A dedicated search for exotic Higgs decays can actively explore
this framework at the Large Hadron Collider (LHC), while future exotic Higgs
decay searches at the high-luminosity LHC and future Higgs factories will be
vital to conclusively probe the scenario.


title: SocialVAE: Human Trajectory Prediction using Timewise Latents

abstract:
  Predicting pedestrian movement is critical for human behavior analysis and
also for safe and efficient human-agent interactions. However, despite
significant advancements, it is still challenging for existing approaches to
capture the uncertainty and multimodality of human navigation decision making.
In this paper, we propose SocialVAE, a novel approach for human trajectory
prediction. The core of SocialVAE is a timewise variational autoencoder
architecture that exploits stochastic recurrent neural networks to perform
prediction, combined with a social attention mechanism and backward posterior
approximation to allow for better extraction of pedestrian navigation
strategies. We show that SocialVAE improves current state-of-the-art
performance on several pedestrian trajectory prediction benchmarks, including
the ETH/UCY benchmark, the Stanford Drone Dataset and SportVU NBA movement
dataset. Code is available at: https://github.com/xupei0610/SocialVAE.


title:
Snowmass white paper: Need for amplitude analysis in the discovery of
  new hadrons

abstract:
  We highlight the need for the development of comprehensive amplitude analysis
methods to further our understanding of hadron spectroscopy. Reaction
amplitudes constrained by first principles of $S$-matrix theory and by QCD
phenomenology are needed to extract robust interpretations of the data from
experiments and from lattice calculations.


title:
A Differentiable Approach to Combinatorial Optimization using Dataless
  Neural Networks

abstract:
  The success of machine learning solutions for reasoning about discrete
structures has brought attention to its adoption within combinatorial
optimization algorithms. Such approaches generally rely on supervised learning
by leveraging datasets of the combinatorial structures of interest drawn from
some distribution of problem instances. Reinforcement learning has also been
employed to find such structures. In this paper, we propose a radically
different approach in that no data is required for training the neural networks
that produce the solution. In particular, we reduce the combinatorial
optimization problem to a neural network and employ a dataless training scheme
to refine the parameters of the network such that those parameters yield the
structure of interest. We consider the combinatorial optimization problems of
finding maximum independent sets and maximum cliques in a graph. In principle,
since these problems belong to the NP-hard complexity class, our proposed
approach can be used to solve any other NP-hard problem. Additionally, we
propose a universal graph reduction procedure to handle large scale graphs. The
reduction exploits community detection for graph partitioning and is applicable
to any graph type and/or density. Experimental evaluation on both synthetic
graphs and real-world benchmarks demonstrates that our method performs on par
with or outperforms state-of-the-art heuristic, reinforcement learning, and
machine learning based methods without requiring any data.


title:
A short overview on low mass scalars at future lepton colliders --
  Snowmass White Paper

abstract:
  In this whitepaper, I give a short summary on possible channels of low-mass
scenarios and their discovery potential at future $e^+e^-$ colliders. This is a
summary of talks I recently gave at the CEPC workshop, FCC week and ECFA future
collider workshop.


title:
Higgs-Energy LEptoN (HELEN) Collider based on advanced superconducting
  radio frequency technology

abstract:
  This Snowmass 2021 contributed paper discusses a Higgs-Energy LEptoN (HELEN)
$e^+e^-$ linear collider based on advances superconducting radio frequency
technology. The proposed collider offers cost and AC power savings, smaller
footprint (relative to the ILC), and could be built at Fermilab with an
Interaction Region within the site boundaries. After the initial physics run at
250 GeV, the collider could be upgraded either to higher luminosity or to
higher (up to 500 GeV) energies. If the ILC could not be realized in Japan in a
timely fashion, the HELEN collider would be a viable option to build a Higgs
factory in the U.S.


title:
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient
  Hyper-parameter Tuning

abstract:
  Deep neural networks have seen great success in recent years; however,
training a deep model is often challenging as its performance heavily depends
on the hyper-parameters used. In addition, finding the optimal hyper-parameter
configuration, even with state-of-the-art (SOTA) hyper-parameter optimization
(HPO) algorithms, can be time-consuming, requiring multiple training runs over
the entire dataset for different possible sets of hyper-parameters. Our central
insight is that using an informative subset of the dataset for model training
runs involved in hyper-parameter optimization, allows us to find the optimal
hyper-parameter configuration significantly faster. In this work, we propose
AUTOMATA, a gradient-based subset selection framework for hyper-parameter
tuning. We empirically evaluate the effectiveness of AUTOMATA in
hyper-parameter tuning through several experiments on real-world datasets in
the text, vision, and tabular domains. Our experiments show that using
gradient-based data subsets for hyper-parameter tuning achieves significantly
faster turnaround times and speedups of 3$\times$-30$\times$ while achieving
comparable performance to the hyper-parameters found using the entire dataset.


title:
HUMUS-Net: Hybrid unrolled multi-scale network architecture for
  accelerated MRI reconstruction

abstract:
  In accelerated MRI reconstruction, the anatomy of a patient is recovered from
a set of under-sampled and noisy measurements. Deep learning approaches have
been proven to be successful in solving this ill-posed inverse problem and are
capable of producing very high quality reconstructions. However, current
architectures heavily rely on convolutions, that are content-independent and
have difficulties modeling long-range dependencies in images. Recently,
Transformers, the workhorse of contemporary natural language processing, have
emerged as powerful building blocks for a multitude of vision tasks. These
models split input images into non-overlapping patches, embed the patches into
lower-dimensional tokens and utilize a self-attention mechanism that does not
suffer from the aforementioned weaknesses of convolutional architectures.
However, Transformers incur extremely high compute and memory cost when 1) the
input image resolution is high and 2) when the image needs to be split into a
large number of patches to preserve fine detail information, both of which are
typical in low-level vision problems such as MRI reconstruction, having a
compounding effect. To tackle these challenges, we propose HUMUS-Net, a hybrid
architecture that combines the beneficial implicit bias and efficiency of
convolutions with the power of Transformer blocks in an unrolled and
multi-scale network. HUMUS-Net extracts high-resolution features via
convolutional blocks and refines low-resolution features via a novel
Transformer-based multi-scale feature extractor. Features from both levels are
then synthesized into a high-resolution output reconstruction. Our network
establishes new state of the art on the largest publicly available MRI dataset,
the fastMRI dataset. We further demonstrate the performance of HUMUS-Net on two
other popular MRI datasets and perform fine-grained ablation studies to
validate our design.


title:
Free-spin dominated magnetocaloric effect in dense Gd$^{3+}$ double
  perovskites

abstract:
  Frustrated lanthanide oxides with dense magnetic lattices are of fundamental
interest for their potential in cryogenic refrigeration due to a large ground
state entropy and suppressed ordering temperatures, but can often be limited by
short-range correlations. Here, we present examples of frustrated fcc oxides,
Ba$_2$GdSbO$_6$ and Sr$_2$GdSbO$_6$ and the new site-disordered analog
Ca$_2$GdSbO$_6$ ([CaGd]$_A$[CaSb]$_B$O$_6$), in which the magnetocaloric effect
is influenced by minimal superexchange ($J_1 \sim 10$ mK). We report on the
crystal structures using powder x-ray diffraction and the bulk magnetic
properties through low-field susceptibility and isothermal magnetization
measurements. The Gd compounds exhibit a magnetic entropy change of up to -15.8
J/K/mol$_\textrm{Gd}$ in a field of 7 T at 2 K, a 20 % excess compared to the
value of -13.0 J/K/mol$_\textrm{Gd}$ for a standard in magnetic refrigeration,
Gd$_3$Ga$_5$O$_{12}$. Heat capacity measurements indicate a lack of magnetic
ordering down to 0.4 K for Ba$_2$GdSbO$_6$ and Sr$_2$GdSbO$_6$, suggesting
cooling down through the liquid 4-He regime. A mean-field model is used to
elucidate the role of primarily free spin behavior in the magnetocaloric
performance of these compounds in comparison to other top-performing Gd-based
oxides. The chemical flexibility of the double perovskites raises the
possibility of further enhancement of the magnetocaloric effect in the
Gd$^{3+}$ fcc lattices.


title:
Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait
  Task Videos

abstract:
  In this paper, we investigated whether we can 1) detect participants with
ataxia-specific gait characteristics (risk-prediction), and 2) assess severity
of ataxia from gait (severity-assessment) using computer vision. We created a
dataset of 155 videos from 89 participants, 24 controls and 65 diagnosed with
(or are pre-manifest) spinocerebellar ataxias (SCAs), performing the gait task
of the Scale for the Assessment and Rating of Ataxia (SARA) from 11 medical
sites located in 8 different states across the United States. We develop a
computer vision pipeline to detect, track, and separate out the participants
from their surroundings and construct several features from their body pose
coordinates to capture gait characteristics like step width, step length,
swing, stability, speed, etc. Our risk-prediction model achieves 83.06%
accuracy and an 80.23% F1 score. Similarly, our severity-assessment model
achieves a mean absolute error (MAE) score of 0.6225 and a Pearson's
correlation coefficient score of 0.7268. Our models still performed
competitively when evaluated on data from sites not used during training.
Furthermore, through feature importance analysis, we found that our models
associate wider steps, decreased walking speed, and increased instability with
greater ataxia severity, which is consistent with previously established
clinical knowledge. Our models create possibilities for remote ataxia
assessment in non-clinical settings in the future, which could significantly
improve accessibility of ataxia care. Furthermore, our underlying dataset was
assembled from a geographically diverse cohort, highlighting its potential to
further increase equity. The code used in this study is open to the public, and
the anonymized body pose landmark dataset is also available upon request.


title: Interactive Portrait Harmonization

abstract:
  Current image harmonization methods consider the entire background as the
guidance for harmonization. However, this may limit the capability for user to
choose any specific object/person in the background to guide the harmonization.
To enable flexible interaction between user and harmonization, we introduce
interactive harmonization, a new setting where the harmonization is performed
with respect to a selected \emph{region} in the reference image instead of the
entire background. A new flexible framework that allows users to pick certain
regions of the background image and use it to guide the harmonization is
proposed. Inspired by professional portrait harmonization users, we also
introduce a new luminance matching loss to optimally match the color/luminance
conditions between the composite foreground and select reference region. This
framework provides more control to the image harmonization pipeline achieving
visually pleasing portrait edits. Furthermore, we also introduce a new dataset
carefully curated for validating portrait harmonization. Extensive experiments
on both synthetic and real-world datasets show that the proposed approach is
efficient and robust compared to previous harmonization baselines, especially
for portraits. Project Webpage at
\href{https://jeya-maria-jose.github.io/IPH-web/}{https://jeya-maria-jose.github.io/IPH-web/}


title: A Wearables-Driven Attack on Examination Proctoring

abstract:
  Multiple choice questions are at the heart of many standardized tests and
examinations at academic institutions allover the world. In this paper, we
argue that recent advancements in sensing and human-computer interaction expose
these types of questions to highly effective attacks that today's proctor's are
simply not equipped to detect. We design one such attack based on a protocol of
carefully orchestrated wrist movements combined with haptic and visual feedback
mechanisms designed for stealthiness. The attack is done through collaboration
between a knowledgeable student (i.e., a mercenary) and a weak student (i.e.,
the beneficiary) who depends on the mercenary for solutions. Through a
combination of experiments and theoretical modeling, we show the attack to be
highly effective. The paper makes the case for an outright ban on all tech
gadgets inside examination rooms, irrespective of whether their usage appears
benign to the plain eye.


title:
New predictions for radiation-driven, steady-state mass-loss and
  wind-momentum from hot, massive stars III. Updated mass-loss rates for
  stellar evolution

abstract:
  Massive stars lose a large fraction of their mass to radiation-driven winds
throughout their entire life. These outflows impact both the life and death of
these stars and their surroundings. Theoretical mass-loss rates of hot, massive
stars are derived to be used in applications such as stellar evolution. The
behaviour of these rates in the OB-star regime is analysed, and their effects
on massive-star evolution predictions is studied. Dynamically-consistent models
are computed by solving the spherically symmetric, steady-state
equation-of-motion for a large grid of hot, massive stars. The radiative
acceleration is derived from non-local thermodynamic equilibrium radiative
transfer in the co-moving frame. The resulting mass-loss rates are used to
derive a simple scaling recipe with stellar parameters and to evaluate some
first impacts upon evolution tracks. We provide a new prescription for
steady-state, radiation-driven mass-loss from hot, massive stars depending on
their fundamental parameters. The rates for O-stars are lower by about a factor
~3 than the rates typically used in previous stellar-evolution calculations,
where differences generally decrease with increasing luminosity and
temperature. For cooler B-giants/supergiants we find larger discrepancies. This
arises because we do not find any systematic increase in mass-loss rates below
the so-called bi-stability region; indeed, our results do not show any sign of
a significant bi-stability jump within the parameter range covered by the grid.
Due to the lower mass-loss rates we find that envelopes are not easily stripped
by means of standard steady-state winds, making it difficult to create
classical Wolf-Rayet stars via this channel. However, a remaining key
uncertainty regarding these predictions concerns unsteady mass loss for very
high-luminosity stars close to the Eddington limit as well as the impact of
non-line-driven winds.


title: CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP

abstract:
  Existing state-of-the-art crowd counting algorithms rely excessively on
location-level annotations, which are burdensome to acquire. When only
count-level (weak) supervisory signals are available, it is arduous and
error-prone to regress total counts due to the lack of explicit spatial
constraints. To address this issue, a novel and efficient counter (referred to
as CrowdMLP) is presented, which probes into modelling global dependencies of
embeddings and regressing total counts by devising a multi-granularity MLP
regressor. In specific, a locally-focused pre-trained frontend is cascaded to
extract crude feature maps with intrinsic spatial cues, which prevent the model
from collapsing into trivial outcomes. The crude embeddings, along with raw
crowd scenes, are tokenized at different granularity levels. The
multi-granularity MLP then proceeds to mix tokens at the dimensions of
cardinality, channel, and spatial for mining global information. An effective
proxy task, namely Split-Counting, is also proposed to evade the barrier of
limited samples and the shortage of spatial hints in a self-supervised manner.
Extensive experiments demonstrate that CrowdMLP significantly outperforms
existing weakly-supervised counting algorithms and performs on par with
state-of-the-art location-level supervised approaches.


title:
Power-Based Side-Channel Attack for AES Key Extraction on the ATMega328
  Microcontroller

abstract:
  We demonstrate the extraction of an AES secret key from flash memory on the
ATMega328 microcontroller (the microcontroller used on the popular Arduino
Genuino/Uno board). We loaded a standard AVR-architecture AES-128
implementation onto the chip and encrypted randomly chosen plaintexts with
several different keys. We measured the chip's power consumption during
encryption, correlated observed power consumption with the expected power
consumption of the plaintexts with every possible key, and ultimately extracted
the 128-bit key used during AES. We describe here our test infrastructure for
automated power trace collection, an overview of our correlation attack,
sanitization of the traces and stumbling blocks encountered during data
collection and analysis, and results of our attack.


title: Development of Decision Support System for Effective COVID-19 Management

abstract:
  This paper discusses a Decision Support System (DSS) for cases prediction,
allocation of resources, and lockdown management for managing COVID-19 at
different levels of a government authority. Algorithms incorporated in the DSS
are based on a data-driven modeling approach and independent of physical
parameters of the region, and hence the proposed DSS is applicable to any area.
Based on predicted active cases, the demand of lower-level units and total
availability, allocation, and lockdown decision is made. A MATLAB-based GUI is
developed based on the proposed DSS and could be implemented by the local
authority.


title: Zipfian environments for Reinforcement Learning

abstract:
  As humans and animals learn in the natural world, they encounter
distributions of entities, situations and events that are far from uniform.
Typically, a relatively small set of experiences are encountered frequently,
while many important experiences occur only rarely. The highly-skewed,
heavy-tailed nature of reality poses particular learning challenges that humans
and animals have met by evolving specialised memory systems. By contrast, most
popular RL environments and benchmarks involve approximately uniform variation
of properties, objects, situations or tasks. How will RL algorithms perform in
worlds (like ours) where the distribution of environment features is far less
uniform? To explore this question, we develop three complementary RL
environments where the agent's experience varies according to a Zipfian
(discrete power law) distribution. On these benchmarks, we find that standard
Deep RL architectures and algorithms acquire useful knowledge of common
situations and tasks, but fail to adequately learn about rarer ones. To
understand this failure better, we explore how different aspects of current
approaches may be adjusted to help improve performance on rare events, and show
that the RL objective function, the agent's memory system and self-supervised
learning objectives can all influence an agent's ability to learn from uncommon
experiences. Together, these results show that learning robustly from skewed
experience is a critical challenge for applying Deep RL methods beyond
simulations or laboratories, and our Zipfian environments provide a basis for
measuring future progress towards this goal.


title:
On the dependence structure of the trade/no trade sequence of illiquid
  assets

abstract:
  In this paper, we propose to consider the dependence structure of the
trade/no trade categorical sequence of individual illiquid stocks returns. The
framework considered here is wide as constant and time-varying zero returns
probability are allowed. The ability of our approach in highlighting illiquid
stock's features is underlined for a variety of situations. More specifically,
we show that long-run effects for the trade/no trade categorical sequence may
be spuriously detected in presence of a non-constant zero returns probability.
Monte Carlo experiments, and the analysis of stocks taken from the Chilean
financial market, illustrate the usefulness of the tools developed in the
paper.


title:
Predicting Value at Risk for Cryptocurrencies Using Generalized Random
  Forests

abstract:
  We study the estimation and prediction of the risk measure Value at Risk for
cryptocurrencies. Using Generalized Random Forests (GRF) (Athey et al., 2019)
that can be adapted to specifically fit the framework of quantile prediction,
we show their superior performance over other established methods such as
quantile regression and CAViaR, particularly in unstable times. We investigate
the small-sample prediction properties in comparison to standard techniques in
a Monte Carlo simulation study. In a comprehensive empirical assessment, we
study the performance not only for the major cryptocurrencies but also in the
stock market. Generally, we find that GRF outperforms established methods
especially in crisis situations. We further identify important predictors
during such times and show their influence on forecasting over time.


title:
Confinement Effects of External Fields and Topological Defect on
  Hydrogen Atom in a Quantum-plasma Environment

abstract:
  This study looks at the confinement effects of Aharonov-Bohm (AB) flux and
magnetic fields, as well as topological defects in a quantum plasma, on the
hydrogen atom. The joint effects show that the system is extremely attractive.
Furthermore, as we've shown, the joint effect of the fields is greater than the
sum of the individual effects, resulting in a significant change in the
system's bound state energy. The magnetic field can be used as a control
parameter or booster, whereas the topological defect and AB field are needed to
hold the hydrogen atom in quantum plasmas at a low energy. The findings of our
research may be extended to atomic structure and plasma collisions.


title:
Numerical analysis of effective refractive index bio-sensor based on
  graphene-embedded slot-based dual-micro-ring resonator

abstract:
  We propose a novel bio-sensor structure composed of slot dual-micro-ring
resonators and mono-layer graphene.Based on the electromagnetically induced
transparency (EIT)-like phenomenon and the light-absorption characteristics of
graphene,we present a theoretical analysis of transmission by using the coupled
mode theory and Kubo formula.The results demonstrate the EIT-like spectrum with
asymmetric line profile.The mode-field distributions of transmission spectrum
obtained from 3D simulations based on finite-difference time-domain (FDTD)
method.Our bio-sensor exhibit theoretical sensitivity of 330nm/RIU, a minimum
detection limit of 3600RIU,the maximum extinction ratio of 4.4dB,the quality
factor of 1288 and a compact structure of 15um*10um.Finally, the bio-sensor's
performance is simulated for glucose solution.Our proposed design provide a
promising candidate for on-chip integration with other silicon photonic
element.


title:
Sex Trouble: Common pitfalls in incorporating sex/gender in medical
  machine learning and how to avoid them

abstract:
  False assumptions about sex and gender are deeply embedded in the medical
system, including that they are binary, static, and concordant. Machine
learning researchers must understand the nature of these assumptions in order
to avoid perpetuating them. In this perspectives piece, we identify three
common mistakes that researchers make when dealing with sex/gender data: "sex
confusion", the failure to identity what sex in a dataset does or doesn't mean;
"sex obsession", the belief that sex, specifically sex assigned at birth, is
the relevant variable for most applications; and "sex/gender slippage", the
conflation of sex and gender even in contexts where only one or the other is
known. We then discuss how these pitfalls show up in machine learning studies
based on electronic health record data, which is commonly used for everything
from retrospective analysis of patient outcomes to the development of
algorithms to predict risk and administer care. Finally, we offer a series of
recommendations about how machine learning researchers can produce both
research and algorithms that more carefully engage with questions of
sex/gender, better serving all patients, including transgender people.


title:
Snowmass2021 Cosmic Frontier White Paper: Future Gravitational-Wave
  Detector Facilities

abstract:
  The next generation of gravitational-wave observatories can explore a wide
range of fundamental physics phenomena throughout the history of the universe.
These phenomena include access to the universe's binary black hole population
throughout cosmic time, to the universe's expansion history independent of the
cosmic distance ladders, to stochastic gravitational-waves from early-universe
phase transitions, to warped space-time in the strong-field and high-velocity
limit, to the equation of state of nuclear matter at neutron star and
post-merger densities, and to dark matter candidates through their interaction
in extreme astrophysical environments or their interaction with the detector
itself. We present the gravitational-wave detector concepts than can drive the
future of gravitational-wave astrophysics. We summarize the status of the
necessary technology, and the research needed to be able to build these
observatories in the 2030s.


title: Metric embeddings of Laakso graphs into Banach spaces

abstract:
  Let $X$ be Banach space which is not super-reflexive. Then, for each $n\ge1$
and $\varepsilon>0$, we exhibit metric embeddings of the Laakso graph
$\mathcal{L}_n$ into $X$ with distortion less than $2+\varepsilon$ and into
$L_1[0,1]$ with distortion $4/3$. The distortion of an embedding of
$\mathcal{L}_2$ (respectively, the diamond graph $D_2$) into $L_1[0,1]$ is at
least $9/8$ (respectively, $5/4$).


title:
A standard convention for particle-level Monte Carlo event-variation
  weights

abstract:
  Streams of event weights in particle-level Monte Carlo event generators are a
convenient and immensely CPU-efficient approach to express systematic
uncertainties in phenomenology calculations, providing systematic variations on
the nominal prediction within a single event sample. But the lack of a common
standard for labelling these variation streams across different tools has
proven to be a major limitation for event-processing tools and analysers alike.
Here we propose a well-defined, extensible community standard for the naming,
ordering, and interpretation of weight streams that will serve as the basis for
semantically correct parsing and combination of such variations in both
theoretical and experimental studies.


title:
Actions of diagonalizable groups and attractors: I. Definitions, affine
  case and root subgroups

abstract:
  For a diagonalizable group scheme $D(M)$ acting on an algebraic space $X$, we
introduce for any subsemigroup $N$ of $M$ an attractor space $X^N$. We explain
that this formalism allows to see root groups of reductive groups as
attractors.


title: Snowmass White Paper: Effective Field Theories in Cosmology

abstract:
  Small fluctuations around homogeneous and isotropic expanding backgrounds are
the main object of study in cosmology. Their origin and evolution is sensitive
to the physical processes that happen during inflation and in the late
Universe. As such, they hold the key to answering many of the major open
questions in cosmology. Given a large separation of relevant scales in many
examples of interest, the most natural description of these fluctuations is
formulated in terms of effective field theories. This was the main avenue for
many of the important modern developments in theoretical cosmology, which
provided a unifying framework for a plethora of cosmological models and made a
clear connection between the fundamental cosmological parameters and
observables. In this review we summarize these results in the context of
effective field theories of inflation, large-scale structure, and dark energy.


title:
On the probability of irreducibility of random polynomials with integer
  coefficients

abstract:
  In this article we study asymptotic behavior of the probability that a random
monic polynomial with integer coefficients is irreducible over the integers. We
consider the cases where the coefficients grow together with the degree of the
random polynomials. Our main result is a generalization of a theorem proved by
Konyagin in 1999. We also generalize Hilbert's Irreducibility Theorem and
present an analog of this result with centered Binomial distributed
coefficients.


title: H2CS deuteration maps towards the pre-stellar core L1544

abstract:
  Deuteration is a crucial tool to understand the complexity of interstellar
chemical processes, especially when they involve the interplay of gas-phase and
grain-surface chemistry. In the case of multiple deuteration, comparing
observation with the results of chemical modelling is particularly effective to
study how molecules are inherited in the different stages within the process of
star and planet formation. We aim to study the the D/ H ratio in H2CS across
the prototypical pre-stellar core L1544. This study allows us to test current
gas-dust chemical models involving sulfur in dense cores. We present here
single-dish observations of H2CS, HDCS and D2CS with the IRAM 30m telescope. We
analyse their column densities and distributions, and compare these
observations with gas-grain chemical models. The deuteration maps of H2CS in
L1544 are compared with the deuteration maps of methanol, H2CO, N2H+ and HCO+
towards the same source. Furthermore, the single and double deuteration of H2CS
towards the dust peak of L1544 is compared with H2CO and c-C3H2. The difference
between the deuteration of these molecules in L1544 is discussed and compared
with the prediction of chemical models. The maximum deuterium fractionation for
the first deuteration of H2CS is N(HDCS)/N(H2CS)$\sim$30$\%$ and is located
towards the north-east at a distance of about 10000 AU from the dust peak.
While for c-C3H2 the first and second deuteration have a similar efficiency,
for H2CS and H2CO the second deuteration is more efficient, leading to
D2CX/HDCX$\sim$100$\%$ (with X= O or S). Our results imply that the large
deuteration of H2CO and H2CS observed in protostellar cores as well as in
comets is likely inherited from the pre-stellar phase. However, the comparison
with state-of-the-art chemical models suggests that the reaction network for
the formation of the doubly deuterated H2CS and H2CO it is not complete yet.


title: A Deep Dive into Dataset Imbalance and Bias in Face Identification

abstract:
  As the deployment of automated face recognition (FR) systems proliferates,
bias in these systems is not just an academic question, but a matter of public
concern. Media portrayals often center imbalance as the main source of bias,
i.e., that FR models perform worse on images of non-white people or women
because these demographic groups are underrepresented in training data. Recent
academic research paints a more nuanced picture of this relationship. However,
previous studies of data imbalance in FR have focused exclusively on the face
verification setting, while the face identification setting has been largely
ignored, despite being deployed in sensitive applications such as law
enforcement. This is an unfortunate omission, as 'imbalance' is a more complex
matter in identification; imbalance may arise in not only the training data,
but also the testing data, and furthermore may affect the proportion of
identities belonging to each demographic group or the number of images
belonging to each identity. In this work, we address this gap in the research
by thoroughly exploring the effects of each kind of imbalance possible in face
identification, and discuss other factors which may impact bias in this
setting.


title:
Comment on "Using Dipole Interaction to Achieve Nonvolatile Voltage
  Control of Magnetism in Multiferroic Heterostructures" [Adv. Mater., 2021,
  2105902]

abstract:
  In a recent article, A. Chen et al. claimed that they have switched (rotated
by ~90 degrees) the magnetization of the soft layer of a magnetic tunnel
junction (MTJ) in a non-volatile way with volatile strain by exploiting dipole
interaction with the hard layer. I show that dipole interaction cannot cause
this effect and the authors' explanation cannot be correct. An alternate
explanation is offered.


title: Closed relations with non-zero entropy that generate no periodic points

abstract:
  The paper is motivated by E. Akin's book about dynamical systems and closed
relations [A], and by J. Kennedy's and G. Erceg's recent paper about the
entropy of closed relations on closed intervals [EK]. In present paper, we
introduce the entropy of a closed relation G on any compact metric space X and
show its basic properties. We also introduce when such a relation G generates a
periodic point or finitely generates a Cantor set. Then we show that periodic
points, finitely generated Cantor sets, Mahavier products and the entropy of
closed relations are preserved by topological conjugations. Among other things,
this generalizes the well-known results about the topological conjugacy of
continuous mappings. Finally, we prove a theorem, giving sufficient conditions
for a closed relation G on [0,1] to have a non-zero entropy. Then we present
various examples of closed relations G on [0,1] such that (1) the entropy of G
is non-zero, (2) no periodic point or exactly one periodic point is generated
by G, and (3) no Cantor set is finitely generated by G.


title:
A method to predict 3D structural ensembles of chromatins from pairwise
  distances: Applications to Interphase Chromosomes and Structural Variants

abstract:
  The principles that govern the organization of genomes, which are needed for
a deeper understanding of how chromosomes are packaged and function in
eukaryotic cells, could be deciphered if the three-dimensional (3D) structures
are known. Recently, single-cell imaging experiments have determined the 3D
coordinates of a number of loci in a chromosome. Here, we introduce a
computational method (Distance Matrix to Ensemble of Structures, DIMES), based
on the maximum entropy principle, with experimental pair-wise distances between
loci as constraints, to generate a unique ensemble of 3D chromatin structures.
Using the ensemble of structures, we quantitatively account for the
distribution of pair-wise distances, three-body co-localization and
higher-order interactions. We demonstrate that the DIMES method can be applied
to both small length-scale and chromosome-scale imaging data to quantify the
extent of heterogeneity and fluctuations in the shapes on various length
scales. We develop a perturbation method that is used in conjunction with DIMES
to predict the changes in 3D structures from structural variations. Our method
also reveals quantitative differences between the 3D structures inferred from
Hi-C and the ones measured in imaging experiments. Finally, the physical
interpretation of the parameters extracted from DIMES provides insights into
the origin of phase separation between euchromatin and heterochromatin domains.

categories: cond-mat.soft physics.bio-ph q-bio.BM q-bio.QM


title: Irradiation Facilities and Irradiation Methods for High Power Target

abstract:
  High Power Target systems are key elements in future neutrino and other rare
particle production in accelerators. These systems transform an intense source
of protons into secondary particles of interest to enable new scientific
discoveries. As beam intensity and energies increase, target systems face
significant challenges. Radiation damages and thermal shocks in target
materials were identified as the leading cross-cutting challenges of high-power
target facilities. Target material R&D to address these challenges are
essential to enable and ensure reliable operation of future-generation
accelerators. Irradiation facilities and alternative methods are critical to
provide a full support of material R&D and better address these critical
challenges.


title: Chaotic Dynamics of the Mass Deformed ABJM Model

abstract:
  We explore the chaotic dynamics of the mass deformed ABJM model. To do so, we
first perform a dimensional reduction of this model from $2+1$- to
$0+1$-dimensions, considering that the fields are spatially uniform. Working in
the 't Hooft limit and tracing over ansatz configurations involving fuzzy
two-spheres, which are described in terms of the GRVV matrices with collective
time dependence, we obtain a family of reduced effective Lagrangians and
demonstrate that they have chaotic dynamics by computing the associated
Lyapunov exponents. In particular, we focus on how the largest Lyapunov
exponent, $\lambda_L$, changes as a function of $E/N^2$. Depending on the
structure of the effective potentials, we find either $\lambda_L \propto
(E/N^2)^{1/3}$ or $\lambda_L \propto (E/N^2 - \gamma_N)^{1/3}$, where
$\gamma_N(k, \mu)$ is a constant determined in terms of the Chern-Simons
coupling $k$, the mass $\mu$, and the matrix level $N$. Using our results, we
investigate the temperature dependence of the largest Lyapunov exponents and
give upper bounds on the temperature above which $\lambda_L$ values comply with
the MSS bound, $ \lambda_L \leq 2 \pi T $, and below which it will eventually
be violated.


title:
Cold Quark-Gluon Plasma EOS Applied to a Magnetically Deformed Quark
  Star with an Anomalous Magnetic Moment

abstract:
  We consider a QCD cold plasma motivated Equation of State (EOS) to examine
the impact of an Anomalous Magnetic Moment (AMM) coupling and small shape
deformations for static oblate and prolate core shapes of quark stars. Using
the Foga\c{c}a QCD motivated EOS which shifts from the high temperature low
chemical potential quark gluon plasma environment to the low temperature high
chemical potential quark stellar core environment we consider the impact of an
AMM coupling with a metric induced shape deformation parameter in the TOV
equations. The EOS is developed using a hard gluon and soft gluon decomposition
of the gluon field tensor using a mean field effective mass for the gluons. The
AMM is considered using the Dirac spin tensor coupled to the EM field tensor
with quark flavor based magnetic moments. The shape parameter is introduced in
a metric ansatz that represents oblate and prolate static stellar cores for
modified TOV equations. These equations are numerically solved for the final
mass and radius states representing the core collapse of a massive star with a
phase transition leading to an unbound quark-gluon plasma. We find that the
combined shape parameter and AMM effects can alter the coupled EOS-TOV
equations resulting in an increase in the final mass and a decrease in the
final equatorial radius without collapsing the core into a black hole and
without violating causality constraints, we find maximum mass values in the
range: 2.3 Solar Masses < M < 2.7 Solar Masses. These states are consistent
with some astrophysical high mass magnetar/pulsar and gravity wave systems
which may provide evidence for a core that has undergone a quark-gluon phase
transition such as PSR 0943+10 and the secondary from the GW 190814 event.


title: Data Contamination: From Memorization to Exploitation

abstract:
  Pretrained language models are typically trained on massive web-based
datasets, which are often "contaminated" with downstream test sets. It is not
clear to what extent models exploit the contaminated data for downstream tasks.
We present a principled method to study this question. We pretrain BERT models
on joint corpora of Wikipedia and labeled downstream datasets, and fine-tune
them on the relevant task. Comparing performance between samples seen and
unseen during pretraining enables us to define and quantify levels of
memorization and exploitation. Experiments with two models and three downstream
tasks show that exploitation exists in some cases, but in others the models
memorize the contaminated data, but do not exploit it. We show that these two
measures are affected by different factors such as the number of duplications
of the contaminated data and the model size. Our results highlight the
importance of analyzing massive web-scale datasets to verify that progress in
NLP is obtained by better language understanding and not better data
exploitation.


title: Unified Visual Transformer Compression

abstract:
  Vision transformers (ViTs) have gained popularity recently. Even without
customized image operators such as convolutions, ViTs can yield competitive
performance when properly trained on massive data. However, the computational
overhead of ViTs remains prohibitive, due to stacking multi-head self-attention
modules and else. Compared to the vast literature and prevailing success in
compressing convolutional neural networks, the study of Vision Transformer
compression has also just emerged, and existing works focused on one or two
aspects of compression. This paper proposes a unified ViT compression framework
that seamlessly assembles three effective techniques: pruning, layer skipping,
and knowledge distillation. We formulate a budget-constrained, end-to-end
optimization framework, targeting jointly learning model weights, layer-wise
pruning ratios/masks, and skip configurations, under a distillation loss. The
optimization problem is then solved using the primal-dual algorithm.
Experiments are conducted with several ViT variants, e.g. DeiT and T2T-ViT
backbones on the ImageNet dataset, and our approach consistently outperforms
recent competitors. For example, DeiT-Tiny can be trimmed down to 50\% of the
original FLOPs almost without losing accuracy. Codes are available
online:~\url{https://github.com/VITA-Group/UVC}.