title:
Stochastic Parameterizations: Better Modelling of Temporal Correlations
  using Probabilistic Machine Learning

abstract:
  The modelling of small-scale processes is a major source of error in climate
models, hindering the accuracy of low-cost models which must approximate such
processes through parameterization. Using stochasticity and machine learning
have led to better models but there is a lack of work on combining the benefits
from both. We show that by using a physically-informed recurrent neural network
within a probabilistic framework, our resulting model for the Lorenz 96
atmospheric simulation is competitive and often superior to both a bespoke
baseline and an existing probabilistic machine-learning (GAN) one. This is due
to a superior ability to model temporal correlations compared to standard
first-order autoregressive schemes. The model also generalises to unseen
regimes. We evaluate across a number of metrics from the literature, but also
discuss how the probabilistic metric of likelihood may be a unifying choice for
future probabilistic climate models.


title: On a $j$-Santal\'{o} Conjecture

abstract:
  Let $k\geq 2$ be an integer. In the spirit of Kolesnikov-Werner [14], for
each $j\in\{1,\ldots,k\}$, we conjecture a sharp Santal\'{o} type inequality
(we call it $j$-Santal\'{o} conjecture) for many sets (or more generally for
many functions), which we are able to confirm in some cases, including the
cases $j=1$ and $j=k$ and the unconditional case. Interestingly, the extremals
of this family of inequalities are tuples of the $l_j^n$-ball. Our results also
strengthen one of the main results in [14], which corresponds to the case
$j=2$. All members of the family of our conjectured inequalities, excluding the
exceptional case $j=1$, can be interpreted as generalizations of the classical
Blaschke-Santal\'{o} inequality. Related, we discuss an analogue of a
conjecture due to K. Ball [5] in the multi-entry setting and establish a
connection to the $j$-Santal\'{o} conjecture.


title: Role of kidney stones in renal pelvis flow

abstract:
  We examine the time-dependent flow dynamics inside an idealised renal pelvis
in the context of a surgical procedure for kidney stone removal, extending
previous work by Williams et al. (2020,2021), who showed how vortical flow
structures can hinder mass transport in a canonical two-dimensional domain.
Here, we examine the time-dependent evolution of these vortical flow structures
in three-dimensions, and incorporate the presence of rigid kidney stones. We
perform direct numerical simulations, solving the transient Navier-Stokes
equations in a spherical domain. The results shed light on the crucial role of
flow circulation in the renal cavity and its effect on the trajectories of
rigid stones. We demonstrate that stones can either be washed out of the cavity
along with the fluid, or be trapped in the cavity via their interaction with
vortical flow structures. Additionally, we study the effect of multiple stones
in the flow field within the cavity in terms of the kinetic energy, entrapped
fluid volume, and the clearance rate of a passive tracer modelled via an
advection--diffusion equation. We demonstrate that the flow in the presence of
stones features a higher vorticity production within the cavity compared with
the stone-free cases.


title: Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval

abstract:
  Sketching enables many exciting applications, notably, image retrieval. The
fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal
for its widespread adoption. This paper tackles this "fear" head on, and for
the first time, proposes an auxiliary module for existing retrieval models that
predominantly lets the users sketch without having to worry. We first conducted
a pilot study that revealed the secret lies in the existence of noisy strokes,
but not so much of the "I can't sketch". We consequently design a stroke subset
selector that {detects noisy strokes, leaving only those} which make a positive
contribution towards successful retrieval. Our Reinforcement Learning based
formulation quantifies the importance of each stroke present in a given subset,
based on the extent to which that stroke contributes to retrieval. When
combined with pre-trained retrieval models as a pre-processing module, we
achieve a significant gain of 8%-10% over standard baselines and in turn report
new state-of-the-art performance. Last but not least, we demonstrate the
selector once trained, can also be used in a plug-and-play manner to empower
various sketch applications in ways that were not previously possible.


title:
Accessing the topological Mott insulator in cold atom quantum simulators
  with realistic Rydberg dressing

abstract:
  The interplay between many-body interactions and the kinetic energy gives
rise to rich phase diagrams hosting, among others, interaction-induced
topological phases. These phases are characterized by both a local order
parameter and a global topological invariant, and can exhibit exotic ground
states such as self-trapped polarons and interaction-induced edge states. In
this work, we investigate a realistic scenario for the quantum simulation of
such systems using cold Rydberg-dressed atoms in optical lattices. We consider
spinless fermions on a checkerboard lattice, interacting via the tunable-range
effective potential induced by the Rydberg dressing. We perform a detailed
analysis of the phase diagram at half- and incommensurate fillings, in the
mean-field approximation. We furthermore study the stability of the phases with
respect to temperature within the mean-field approximation and with respect to
quantum fluctuations using the density matrix renormalization group method.
Finally, we propose an implementation protocol, and in particular identify
attainable regimes of experimental parameters in which the topological
properties of the model become accessible. Our work, thereby, opens a realistic
pathway to the outstanding experimental observation of this predicted phase in
state-of-the-art cold atom quantum simulators.


title:
Nanoscale mapping of the full strain tensor, rotation and composition in
  partially relaxed In$_x$Ga$_{1-x}$N layers by scanning X-ray diffraction
  microscopy

abstract:
  Strain and composition play a fundamental role in semiconductor physics,
since they are means to tune the electronic and optical properties of a
material and hence develop new devices. Today it is still a challenge to
measure strain in epitaxial systems in a non-destructive manner which becomes
especially important in strain-engineered devices that often are subjected to
intense stress. In this work, we demonstrate a microscopic mapping of the full
tensors of strain and lattice orientation by means of scanning X-ray
diffraction microscopy. We develope a formalism to extract all components of
strain and orientation from a set of scanning diffraction measurements and
apply the technique to a patterned In$_x$Ga$_{1-x}$N double layer to study
strain relaxation and indium incorporation phenomena. The contributions due to
varying indium content and threading dislocations are separated and analyzed.


title:
Convolutional Neural Networks for Reflective Event Detection and
  Characterization in Fiber Optical Links Given Noisy OTDR Signals

abstract:
  Fast and accurate fault detection and localization in fiber optic cables is
extremely important to ensure the optical network survivability and
reliability. Hence there exists a crucial need to develop an automatic and
reliable algorithm for real time optical fiber fault detection and diagnosis
leveraging the telemetry data obtained by an optical time domain reflectometry
(OTDR) instrument. In this paper, we propose a novel data driven approach based
on convolutional neural networks (CNNs) to detect and characterize the fiber
reflective faults given noisy simulated OTDR data, whose SNR (signal-to-noise
ratio) values vary from 0 dB to 30 dB, incorporating reflective event patterns.
In our simulations, we achieved a higher detection capability with low false
alarm rate and greater localization accuracy even for low SNR values compared
to conventionally employed techniques.


title:
Vibrational and structural properties of the $R$Fe$_{4}$Sb$_{12}$
  ($R=$Na, K, Ca, Sr, Ba) filled skutterudites

abstract:
  Vibrational and elastic properties of the $R$Fe$_{4}$Sb$_{12}$ skutterudites
are investigated by, respectively, temperature $(T)$ dependent extended X-ray
absorption fine structure (EXAFS) and pressure $(P)$ dependent x-ray
diffraction (XRD) experiments. The Fe $K$-edge EXAFS experiments of the $R=$ K,
Ca and Ba materials were performed in the $T$-interval $06<T<300$ K and XRD
experiments of the $R=$ Na, K, Ca, Sr and Ba materials were performed in the
$P$-interval $1\text{atm}<P<16$ GPa. From EXAFS, we obtained the correlated
Debye-Waller parameters that were thus analyzed to extract effective spring
constants connected with the Fe-$Y$ (where $Y=$ either $R$, Fe or Sb)
scattering paths. Our findings suggest that in the case of the light cations,
$R=$ K or Ca, the $R$ atoms are relatively weakly coupled to the cage, in a
scenario reminiscent of the Einstein oscillators. From the XRD experiments, we
obtained the bulk modulus $B_{0}$ for all $R=$Na, K, Ca, Sr and Ba materials,
with values ranging from $77$ GPa ($R=$ K) to $R=99$ GPa ($R=$ Ba) as well as
the compressibility $\beta$ as a function of $P$. The trend in $\beta$ as a
function of the $R$ filler is discussed and is shown that it does not correlate
with simple geometrical considerations, and is likely related to bonding
specific properties.


title: Row monomial matrices and \v{C}erny conjecture, short proof

abstract:
  The class of row monomial matrices (one unit and rest zeros in every row)
with some non-standard operations of summation and usual multiplication is our
main object. These matrices generate a space with respect to the mentioned
operations.
  A word $w$ of letters on edges of underlying graph $\Gamma$ of deterministic
finite automaton (DFA) is called synchronizing if $w$ sends all states of the
automaton to a unique state ($|R(w)|=1$. J. \v{C}erny discovered in 1964 a
sequence of $n$-state complete DFA possessing a minimal synchronizing word of
length $(n-1)^2$.
  The hypothesis, well known today as the \v{C}erny conjecture, claims that
$(n-1)^2$ is also precise upper bound on the length of such a word for a
complete DFA. The hypothesis was formulated in 1966 by Starke. The problem has
motivated great and constantly growing number of investigations and
generalizations.
  The proof of the conjecture is based on connection between length of words
$u$ and dimension of the space generated by row monomial matrices $M_u$, the
set of synchronizing matrices placed some role.


title: Reciprocal phase transition-enabled electro-optic modulation

abstract:
  Electro-optic (EO) modulation is a well-known and essential topic in the
field of communications and sensing. Its ultrahigh efficiency is
unprecedentedly desired in the current green and data era. However,
dramatically increasing the modulation efficiency is difficult due to the
monotonic mapping relationship between the electrical signal and modulated
optical signal. Here, a new mechanism termed phase-transition EO modulation is
revealed from the reciprocal transition between two distinct phase planes
arising from the bifurcation. Remarkably, a monolithically integrated
mode-locked laser (MLL) is implemented as a prototype. A 24.8-GHz
radio-frequency signal is generated and modulated, achieving a modulation
energy efficiency of 3.06 fJ/bit improved by about four orders of magnitude and
a contrast ratio exceeding 50 dB. Thus, MLL-based phase-transition EO
modulation is characterised by ultrahigh modulation efficiency and ultrahigh
contrast ratio, as experimentally proved in radio-over-fibre and underwater
acoustic-sensing systems. This phase-transition EO modulation opens a new
avenue for green communication and ubiquitous connections.


title:
Numerical and geometrical aspects of flow-based variational quantum
  Monte Carlo

abstract:
  This article aims to summarize recent and ongoing efforts to simulate
continuous-variable quantum systems using flow-based variational quantum Monte
Carlo techniques, focusing for pedagogical purposes on the example of bosons in
the field amplitude (quadrature) basis. Particular emphasis is placed on the
variational real- and imaginary-time evolution problems, carefully reviewing
the stochastic estimation of the time-dependent variational principles and
their relationship with information geometry. Some practical instructions are
provided to guide the implementation of a PyTorch code. The review is intended
to be accessible to researchers interested in machine learning and quantum
information science.

categories: quant-ph cond-mat.dis-nn cs.LG hep-lat hep-th


title: HDR Reconstruction from Bracketed Exposures and Events

abstract:
  Reconstruction of high-quality HDR images is at the core of modern
computational photography. Significant progress has been made with multi-frame
HDR reconstruction methods, producing high-resolution, rich and accurate color
reconstructions with high-frequency details. However, they are still prone to
fail in dynamic or largely over-exposed scenes, where frame misalignment often
results in visible ghosting artifacts. Recent approaches attempt to alleviate
this by utilizing an event-based camera (EBC), which measures only binary
changes of illuminations. Despite their desirable high temporal resolution and
dynamic range characteristics, such approaches have not outperformed
traditional multi-frame reconstruction methods, mainly due to the lack of color
information and low-resolution sensors. In this paper, we propose to leverage
both bracketed LDR images and simultaneously captured events to obtain the best
of both worlds: high-quality RGB information from bracketed LDRs and
complementary high frequency and dynamic range information from events. We
present a multi-modal end-to-end learning-based HDR imaging system that fuses
bracketed images and event modalities in the feature domain using attention and
multi-scale spatial alignment modules. We propose a novel event-to-image
feature distillation module that learns to translate event features into the
image-feature space with self-supervision. Our framework exploits the higher
temporal resolution of events by sub-sampling the input event streams using a
sliding window, enriching our combined feature representation. Our proposed
approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic
and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the
HdM HDR dataset, respectively.


title:
Estimating SARS-CoV-2 transmission in educational settings: a
  retrospective cohort study

abstract:
  Background School closures and distance learning have been extensively
applied to control SARS-CoV-2 transmission. Despite evidence of viral
circulation in schools, the contribution of students and of in-person schooling
to the transmission remains poorly quantified. Methods We analyze 976 exposure
events, involving 460 positive individuals, as identified in early 2021 by
routine surveillance and through an extensive screening conducted on students,
school personnel, and their household members during an outbreak in a small
municipality of Italy. Results From the analysis of potential transmission
chains, we estimated that, on average, 55.1%, 17.3% and 27.6% infection
episodes were linked to household, school, and community contacts,
respectively. Clusters originated from students or school personnel showed a
larger average cluster size (3.32 vs 1.15), a larger average number of
generations in the transmission chain (1.56 vs 1.17) and a larger set of
associated close contacts (11.3 vs 3.15, on average). We found substantial
transmission heterogeneities, with 20% positive individuals seeding 75-80 of
all transmission. A higher proportion of infected individuals causing onward
transmission was found among students (48.8% vs 29.9%, on average), who also
caused a markedly higher number of secondary cases (mean: 1.3 vs 0.5).
Conclusions Uncontrolled transmission at school could disrupt the regular
conduct of teaching activities, likely seeding the transmission into other
settings, and increasing the burden on contact-tracing operations.


title:
Differentiable, learnable, regionalized process-based models with
  physical outputs can approach state-of-the-art hydrologic prediction accuracy

abstract:
  Predictions of hydrologic variables across the entire water cycle have
significant value for water resource management as well as downstream
applications such as ecosystem and water quality modeling. Recently, purely
data-driven deep learning models like long short-term memory (LSTM) showed
seemingly-insurmountable performance in modeling rainfall-runoff and other
geoscientific variables, yet they cannot predict unobserved physical variables
and remain challenging to interpret. Here we show that differentiable,
learnable, process-based models (called {\delta} models here) can approach the
performance level of LSTM for the intensively-observed variable (streamflow)
with regionalized parameterization. We use a simple hydrologic model HBV as the
backbone and use embedded neural networks, which can only be trained in a
differentiable programming framework, to parameterize, replace, or enhance the
process-based model modules. Without using an ensemble or post-processor,
{\delta} models can obtain a median Nash Sutcliffe efficiency of 0.715 for 671
basins across the USA for a particular forcing data, compared to 0.72 from a
state-of-the-art LSTM model with the same setup. Meanwhile, the resulting
learnable process-based models can be evaluated (and later, to be trained) by
multiple sources of observations, e.g., groundwater storage,
evapotranspiration, surface runoff, and baseflow. Both simulated
evapotranspiration and fraction of discharge from baseflow agreed decently with
alternative estimates. The general framework can work with models with various
process complexity and opens up the path for learning physics from big data.


title: Positive Representations with Zero Casimirs

abstract:
  In this paper, we construct a new family of generalization of the positive
representations of split-real quantum groups based on the degeneration of the
Casimir operators acting as zero on some Hilbert spaces. It is motivated by a
new observation arising from modifying the representation in the simplest case
of $\mathcal{U}_q(\mathfrak{sl}(2,\mathbb{R}))$ compatible with Faddeev's
modular double, while having a surprising tensor product decomposition. For
higher rank, the representations are obtained by the polarization of Chevalley
generators of $\mathcal{U}_q(\mathfrak{g})$ in a new realization as universally
Laurent polynomials of a certain skew-symmetrizable quantum cluster algebra. We
also calculate explicitly the Casimir actions of the maximal $A_{n-1}$
degenerate representations of $\mathcal{U}_q(\mathfrak{g}_\mathbb{R})$ for
general Lie types based on the complexification of the central parameters.


title: Reconstruction of modular data from $SL_2(\mathbb{Z})$ representations

abstract:
  Modular data is the most significant invariant of a modular tensor category.
We pursue an approach to the classification of modular data of modular tensor
categories by building the modular $S$ and $T$ matrices directly from
irreducible representations of $SL_2(\mathbb{Z}/n \mathbb{Z})$. We discover and
collect many conditions on the $SL_2(\mathbb{Z}/n \mathbb{Z})$ representations
to identify those that correspond to some modular data. To arrive at concrete
matrices from representations, we also develop methods that allow us to select
the proper basis of the $SL_2(\mathbb{Z}/n \mathbb{Z})$ representations so that
they have the form of modular data. We apply this technique to the
classification of rank-$6$ modular tensor categories, obtaining a
classification up to modular data. Most of the calculations can be automated
using a computer algebraic system, which can be employed to classify modular
data of higher rank modular tensor categories.

categories: math.QA cond-mat.str-el math-ph math.CT math.MP


title:
The higher order nonlinear Schr\"{o}dinger equation with quadratic
  nonlinearity on the real axis

abstract:
  The initial value problem is considered for a higher order nonlinear
Schr\"odinger equation with quadratic nonlinearity. Results on existence and
uniqueness of weak solutions are obtained. In the case of an effective at
infinity additional damping large-time decay of solutions without any smallness
assumptions is also established. The main difficulty of the study is the
non-smooth character of the nonlinearity.


title:
The policy is always greener: impact heterogeneity of Covid-19
  vaccination lotteries in the US

abstract:
  Covid-19 vaccination has posed crucial challenges to policymakers and health
administrations worldwide. In addition to the pressure posed by the pandemic,
government administration has to strive against vaccine hesitancy, which seems
to be considerably higher concerning previous vaccination rollouts. To increase
the vaccination protection of the population, Ohio announced a monetary
incentive as a lottery for those who decided to vaccinate. This first example
was followed by 18 other states, with varying results. In this paper, we want
to evaluate the effect of such policies within the potential outcome framework,
using the penalized synthetic control method. We treat with a panel dataset and
estimate causal effects at a disaggregated level in the context of staggered
treatment adoption. We focused on policy outcomes at the county, state, and
supra-state levels, highlighting differences between counties with different
social characteristics and time frames for policy introduction. We also studied
the nature of the treatment effect to see whether the impact of these monetary
incentives was permanent or only temporary, accelerating the vaccination of
citizens who would have been vaccinated in any case.


title: A new Nested Cross Approximation

abstract:
  In this article, we present a new Nested Cross Approximation (NCA), for
$\mathcal{H}^{2}$ matrices with well-separated admissibility condition, i.e.,
the interaction between neighboring cluster of particles is considered
full-rank, while the interaction between well-separated cluster of particles
can be efficiently approximated by a low-rank matrix. It differs from the
existing NCAs in the technique of choosing pivots, a key part of the
approximation. Our technique of choosing pivots is purely algebraic and
involves only a single tree traversal. We demonstrate its applicability by
developing an Algebraic Fast Multipole Method (AFMM), that uses NCA for the
appropriate low-rank approximations. We perform various numerical experiments
to illustrate the timing profiles and the accuracy of our method. We also
provide a comparison of the proposed NCA with the existing NCAs.


title: On characterization of balls via solutions to the Helmholtz equation

abstract:
  A new analytical characterization of balls in the Euclidean space $\RR^m$ is
obtained. Unlike previous results of this kind, using either harmonic functions
or solutions to the modified Helmholtz equation, the present one is based on
solutions to the Helmholtz equation. This is achieved at the expense of a
restriction imposed on the size of a domain -- a feature absent in the inverse
mean value properties known before.


title:
Analyzing Language-Independent Speaker Anonymization Framework under
  Unseen Conditions

abstract:
  In our previous work, we proposed a language-independent speaker
anonymization system based on self-supervised learning models. Although the
system can anonymize speech data of any language, the anonymization was
imperfect, and the speech content of the anonymized speech was distorted. This
limitation is more severe when the input speech is from a domain unseen in the
training data. This study analyzed the bottleneck of the anonymization system
under unseen conditions. It was found that the domain (e.g., language and
channel) mismatch between the training and test data affected the neural
waveform vocoder and anonymized speaker vectors, which limited the performance
of the whole system. Increasing the training data diversity for the vocoder was
found to be helpful to reduce its implicit language and channel dependency.
Furthermore, a simple correlation-alignment-based domain adaption strategy was
found to be significantly effective to alleviate the mismatch on the anonymized
speaker vectors. Audio samples and source code are available online.


title: Multilingual Simultaneous Speech Translation

abstract:
  Applications designed for simultaneous speech translation during events such
as conferences or meetings need to balance quality and lag while displaying
translated text to deliver a good user experience. One common approach to
building online spoken language translation systems is by leveraging models
built for offline speech translation. Based on a technique to adapt end-to-end
monolingual models, we investigate multilingual models and different
architectures (end-to-end and cascade) on the ability to perform online speech
translation. On the multilingual TEDx corpus, we show that the approach
generalizes to different architectures. We see similar gains in latency
reduction (40% relative) across languages and architectures. However, the
end-to-end architecture leads to smaller translation quality losses after
adapting to the online model. Furthermore, the approach even scales to
zero-shot directions.


title:
RF Low Noise Amplifiers and Power Amplifiers using Tunnelling Barrier
  modulated Superconductor-Semiconductor-Superconductor Junctions

abstract:
  In high-performance RF transceivers the thrust of has been on lower noise
LNAs and high-power high-speed PAs. The performance trade-off has forced many
solutions to have the LNA and PA on a separate die compared to the rest of
baseband. Often fabricated in GaAs or InP, they need to be properly interfaced
with the rest of signal chain leading to signal integrity issues. Applications
for such technologies range from defence, aerospace to scientific
instrumentation. In this paper we propose a device using
superconductor-semiconductor-superconductor (SC-Sm-SC) junction with a
controllable gate terminal to modulate the tunnelling resistance. This
arrangement in a typical resonant-tank LNA or an impedance matching PA would
reduce the parasitic capacitances leading to higher frequency operation. Also
by virtue of Cooper-pair bosons being the bulk carriers, noise due to
carrier-carrier and carrier-lattice scattering will be much lower than its
conventional CMOS counterparts. Our calculation shows that it can be used in a
LNA with 20dB gain till 36GHz and in a PA to deliver -10dBm power till 350GHz.


title: Christoffel functions for multiple orthogonal polynomials

abstract:
  We study weak asymptotic behaviour of the Christoffel--Darboux kernel on the
main diagonal corresponding to multiple orthogonal polynomials. We show that
under some hypotheses the weak limit of $\tfrac{1}{n} K_n(x,x)\, d\mu$ is the
same as the limit of the normalized zero counting measure of type II multiple
orthogonal polynomials. We also study an extension of Nevai's operators to our
context.


title: Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition

abstract:
  Noise-robust automatic speech recognition degrades significantly in face of
over-suppression problem, which usually exists in the front-end speech
enhancement module. To alleviate such issue, we propose novel dual-path style
learning for end-to-end noise-robust automatic speech recognition (DPSL-ASR).
Specifically, the proposed DPSL-ASR approach introduces clean feature along
with fused feature by the IFF-Net as dual-path inputs to recover the
over-suppressed information. Furthermore, we propose style learning to learn
abundant details and latent information by mapping fused feature to clean
feature. Besides, we also utilize the consistency loss to minimize the distance
of decoded embeddings between two paths. Experimental results show that the
proposed DPSL-ASR approach achieves relative word error rate (WER) reductions
of 10.6% and 8.6%, on RATS Channel-A dataset and CHiME-4 1-Channel Track
dataset, respectively. The visualizations of intermediate embeddings also
indicate that the proposed DPSL-ASR can learn more details than the best
baseline. Our code implementation is available at Github:
https://github.com/YUCHEN005/DPSL-ASR.


title:
Defect free strain relaxation of microcrystals on mesoporous patterned
  silicon

abstract:
  A perfectly compliant substrate would allow the monolithic integration of
high-quality semiconductor materials such as Ge and III-V on Silicon (Si)
substrate, enabling novel functionalities on the well-established low-cost Si
technology platform. Here, we demonstrate a compliant Si substrate allowing
defect-free epitaxial growth of lattice mismatched materials. The method is
based on the deep patterning of the Si substrate to form micrometer-scale
pillars and subsequent electrochemical porosification. The investigation of the
epitaxial Ge crystalline quality by X-ray diffraction, transmission electron
microscopy and etch-pits counting demonstrates the full elastic relaxation of
defect-free microcrystals. The achievement of dislocation free heteroepitaxy
relies on the interplay between elastic deformation of the porous micropillars,
set under stress by the lattice mismatch between Ge and Si, and on the
diffusion of Ge into the mesoporous patterned substrate attenuating the
mismatch strain at the Ge/Si interface.


title:
A Framework of Meta Functional Learning for Regularising Knowledge
  Transfer

abstract:
  Machine learning classifiers' capability is largely dependent on the scale of
available training data and limited by the model overfitting in data-scarce
learning tasks. To address this problem, this work proposes a novel framework
of Meta Functional Learning (MFL) by meta-learning a generalisable functional
model from data-rich tasks whilst simultaneously regularising knowledge
transfer to data-scarce tasks. The MFL computes meta-knowledge on functional
regularisation generalisable to different learning tasks by which functional
training on limited labelled data promotes more discriminative functions to be
learned. Based on this framework, we formulate three variants of MFL: MFL with
Prototypes (MFL-P) which learns a functional by auxiliary prototypes, Composite
MFL (ComMFL) that transfers knowledge from both functional space and
representational space, and MFL with Iterative Updates (MFL-IU) which improves
knowledge transfer regularisation from MFL by progressively learning the
functional regularisation in knowledge transfer. Moreover, we generalise these
variants for knowledge transfer regularisation from binary classifiers to
multi-class classifiers. Extensive experiments on two few-shot learning
scenarios, Few-Shot Learning (FSL) and Cross-Domain Few-Shot Learning (CD-FSL),
show that meta functional learning for knowledge transfer regularisation can
improve FSL classifiers.


title: The Manin-Peyre conjecture for smooth spherical Fano threefolds

abstract:
  The Manin-Peyre conjecture is established for smooth spherical Fano
threefolds of semisimple rank one and type N. Together with the previously
solved case T and the toric cases, this covers all types of smooth spherical
Fano threefolds. The case N features a number of structural novelties; most
notably, one may lose regularity of the ambient toric variety, the height
conditions may contain fractional exponents, and it may be necessary to exclude
a thin subset with exceptionally many rational points from the count, as
otherwise Manin's conjecture in its original form would turn out to be
incorrect.


title: Antimagic Labeling for Unions of Graphs with Many Three-Paths

abstract:
  Let $G$ be a graph with $m$ edges and let $f$ be a bijection from $E(G)$ to
$\{1,2, \dots, m\}$. For any vertex $v$, denote by $\phi_f(v)$ the sum of
$f(e)$ over all edges $e$ incident to $v$. If $\phi_f(v) \neq \phi_f(u)$ holds
for any two distinct vertices $u$ and $v$, then $f$ is called an antimagic
labeling of $G$. We call $G$ antimagic if such a labeling exists. Hartsfield
and Ringel in 1990 conjectured that all connected graphs except $P_2$ are
antimagic. Denote the disjoint union of graphs $G$ and $H$ by $G \cup H$, and
the disjoint union of $t$ copies of $G$ by $tG$. For an antimagic graph $G$
(connected or disconnected), let $\tau(G)$ be the maximum integer such that $G
\cup tP_3$ is antimagic for all $t \leq \tau(G)$. The existence of finite
$\tau(G)$ was presented by Chang, Chen, Li, and Pan [Graphs and Combinatorics
37 (2021), 1065--1182]. Further, Shang, Lin, Liaw [Util. Math. 97 (2015),
373--385] and Li [Master Thesis, National Chung Hsing University, Taiwan, 2019]
showed tight bounds of $\tau(G)$ for star forests and balanced double stars,
respectively. We generalize these results by proving an upper bound of
$\tau(G)$ for all graphs without isolated vertices and $P_2$ as components.
Besides star forests and balanced double stars, this general bound is also
tight on many other families of graphs, including cycles $C_n$, $3 \leq n \leq
9$, double triangle $2C_3$ and others. In addition, we discuss properties of
$\tau(G)$ and pose open questions, including whether our bound is always tight.


title: Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches

abstract:
  The human visual system is remarkable in learning new visual concepts from
just a few examples. This is precisely the goal behind few-shot class
incremental learning (FSCIL), where the emphasis is additionally placed on
ensuring the model does not suffer from "forgetting". In this paper, we push
the boundary further for FSCIL by addressing two key questions that bottleneck
its ubiquitous application (i) can the model learn from diverse modalities
other than just photo (as humans do), and (ii) what if photos are not readily
accessible (due to ethical and privacy constraints). Our key innovation lies in
advocating the use of sketches as a new modality for class support. The product
is a "Doodle It Yourself" (DIY) FSCIL framework where the users can freely
sketch a few examples of a novel class for the model to learn to recognize
photos of that class. For that, we present a framework that infuses (i)
gradient consensus for domain invariant learning, (ii) knowledge distillation
for preserving old class information, and (iii) graph attention networks for
message passing between old and novel classes. We experimentally show that
sketches are better class support than text in the context of FSCIL, echoing
findings elsewhere in the sketching literature.


title:
Toward a Quantum Memory in a Fiber Cavity Controlled by Intracavity
  Frequency Translation

abstract:
  We propose a quantum memory protocol based on trapping photons in a
fiber-integrated cavity, comprised of a birefringent fiber with dichroic
reflective end facets. Photons are switched into resonance with the fiber
cavity by intracavity Bragg-scattering frequency translation, driven by
ancillary control pulses. After the storage delay, photons are switched out of
resonance with the cavity, again by intracavity frequency translation. We
demonstrate storage of quantum-level THz-bandwidth coherent states for a
lifetime up to 16 cavity round trips, or 200ns, and a maximum overall
efficiency of 73%.


title: Warping Cache Simulation of Polyhedral Programs

abstract:
  Techniques to evaluate a program's cache performance fall into two camps: 1.
Traditional trace-based cache simulators precisely account for sophisticated
real-world cache models and support arbitrary workloads, but their runtime is
proportional to the number of memory accesses performed by the program under
analysis. 2. Relying on implicit workload characterizations such as the
polyhedral model, analytical approaches often achieve problem-size-independent
runtimes, but so far have been limited to idealized cache models.
  We introduce a hybrid approach, warping cache simulation, that aims to
achieve applicability to real-world cache models and problem-size-independent
runtimes. As prior analytical approaches, we focus on programs in the
polyhedral model, which allows to reason about the sequence of memory accesses
analytically. Combining this analytical reasoning with information about the
cache behavior obtained from explicit cache simulation allows us to soundly
fast-forward the simulation. By this process of warping, we accelerate the
simulation so that its cost is often independent of the number of memory
accesses.


title:
Discovering dynamic laws from observations: the case of self-propelled,
  interacting colloids

abstract:
  Active matter spans a wide range of time and length scales, from groups of
cells and synthetic self-propelled particles to schools of fish, flocks of
birds, or even human crowds. The theoretical framework describing these systems
has shown tremendous success at finding universal phenomenology. However,
further progress is often burdened by the difficulty of determining the forces
that control the dynamics of the individual elements within each system.
Accessing this local information is key to understanding the physics dominating
the system and to create the models that can explain the observed collective
phenomena. In this work, we present a machine-learning model, a graph neural
network, that uses the collective movement of the system to learn the active
and two-body forces controlling the individual dynamics of the particles. We
verify our approach using numerical simulations of active brownian particles,
considering different interaction potentials and levels of activity. Finally,
we apply our model to experiments of electrophoretic Janus particles,
extracting the active and two-body forces that control the dynamics of the
colloids. Due to this, we can uncover the physics dominating the behavior of
the system. We extract an active force that depends on the electric field and
also area fraction. We also discover a dependence of the two-body interaction
with the electric field that leads us to propose that the dominant force
between these colloids is a screened electrostatic interaction with a constant
length scale. We expect that this methodology can open a new avenue for the
study and modeling of experimental systems of active particles.


title:
Correlation of local densities of states on mesoscopic energy scales in
  random band matrices

abstract:
  We are interested in the phase transition of the correlation function of
local densities of states at mesoscopic scales of random band matrices of width
$W$ in dimension $2$. As a result, we show that the local densities of states
are alternately positively and negatively correlated in the diffusive regime
$O(\log(L/W))$ times, $L$ being the size of he system.


title:
Transverse dynamics of two-dimensional traveling periodic
  gravity-capillary water waves

abstract:
  We study the transverse dynamics of two-dimensional traveling periodic waves
for the gravity--capillary water-wave problem. The governing equations are the
Euler equations for the irrotational flow of an inviscid fluid layer with free
surface under the forces of gravity and surface tension. We focus on two open
sets of dimensionless parameters $(\alpha,\beta)$, where $\alpha$ and $\beta$
are the inverse square of the Froude number and the Weber number, respectively.
For each arbitrary but fixed pair $(\alpha,\beta)$ in one of these sets,
two-dimensional traveling periodic waves bifurcate from the trivial constant
flow. In one open set we find a one-parameter family of periodic waves, whereas
in the other open set we find two geometrically distinct one-parameter families
of periodic waves. Starting from a transverse spatial dynamics formulation of
the governing equations, we investigate the transverse linear instability of
these periodic waves and the induced dimension-breaking bifurcation. The two
results share a common analysis of the purely imaginary spectrum of the
linearization at a periodic wave. We apply a simple general criterion for the
transverse linear instability problem and a Lyapunov center theorem for the
dimension-breaking bifurcation. For parameters $(\alpha,\beta)$ in the open set
where there is only one family of periodic waves, we prove that these waves are
linearly transversely unstable. For the other open set, we show that the waves
with larger wavenumber are transversely linearly unstable. We also identify an
open subset of parameters for which both families of periodic waves are
tranversely linearly unstable. For each of these transversely linearly unstable
periodic waves, a dimension-breaking bifurcation occurs in which
three-dimensional doubly periodic waves bifurcate from the two-dimensional
periodic wave.


title: Safe Active Learning for Multi-Output Gaussian Processes

abstract:
  Multi-output regression problems are commonly encountered in science and
engineering. In particular, multi-output Gaussian processes have been emerged
as a promising tool for modeling these complex systems since they can exploit
the inherent correlations and provide reliable uncertainty estimates. In many
applications, however, acquiring the data is expensive and safety concerns
might arise (e.g. robotics, engineering). We propose a safe active learning
approach for multi-output Gaussian process regression. This approach queries
the most informative data or output taking the relatedness between the
regressors and safety constraints into account. We prove the effectiveness of
our approach by providing theoretical analysis and by demonstrating empirical
results on simulated datasets and on a real-world engineering dataset. On all
datasets, our approach shows improved convergence compared to its competitors.


title:
A Fly in the Ointment: An Empirical Study on the Characteristics of
  Ethereum Smart Contracts Code Weaknesses and Vulnerabilities

abstract:
  Context: Smart contracts are computer programs that are automatically
executed on the blockchain. Vulnerabilities in their implementation have led to
severe loss of cryptocurrency. Smart contracts become immutable when deployed
to the Ethereum blockchain. Therefore, it is essential to understand the nature
of vulnerabilities in Ethereum smart contracts to prevent them in the future.
Existing classifications exist, but are limited in several ways. Objective: We
aim to characterize vulnerabilities in Ethereum smart contracts written in
Solidity, and unify existing classifications schemes. Method: We extracted 2143
vulnerabilities from public coding platforms and popular vulnerability
databases and categorized them using a card sorting approach. We targeted the
Ethereum blockchain in this paper, as it is the first and most popular
blockchain to support the deployment of smart contracts, and Solidity as the
most widely used language to implement smart contracts. We devised a
classification scheme of smart contract vulnerabilities according to their
error source and impact. Afterwards, we mapped existing classification schemes
to our classification. Results: The resulting classification consists of 11
categories describing the error source of a vulnerability and 13 categories
describing potential impacts. Our findings show that the language specific
coding and the structural data flow categories are the dominant categories, but
that the frequency of occurrence differs substantially between the data
sources. Conclusions: Our findings enable researchers to better understand
smart contract vulnerabilities by defining various dimensions of the problem
and supporting our classification with mappings with literature-based
classifications and frequency distributions of the defined categories.


title: Axion Electrodynamics in Strong Magnetic Backgrounds

abstract:
  The overcritical regime of axion-electrodynamics (AED) is investigated. For
magnetic fields larger than the characteristic scale linked to AED, quantum
vacuum fluctuations due to axion-like fields can dominate over those associated
with the electron-positron fields. This hypothetical regime of the dominance of
AED over QED is predicted to induce strong birefringence and screening effects.
We show that, if the magnetic field lines are curved, extraordinary photons
could be canalized along the field direction. It is also shown that the running
QED coupling depends on the magnetic field strength, and for certain energy
regimes, it could be screened almost to zero, making the QED building blocks
very weakly interacting between each other. The impact of this phenomenon on
the radiation mechanism of pulsars is discussed.


title:
Learning Sketches for Decomposing Planning Problems into Subproblems of
  Bounded Width: Extended Version

abstract:
  Recently, sketches have been introduced as a general language for
representing the subgoal structure of instances drawn from the same domain.
Sketches are collections of rules of the form C -> E over a given set of
features where C expresses Boolean conditions and E expresses qualitative
changes. Each sketch rule defines a subproblem: going from a state that
satisfies C to a state that achieves the change expressed by E or a goal state.
Sketches can encode simple goal serializations, general policies, or
decompositions of bounded width that can be solved greedily, in polynomial
time, by the SIW_R variant of the SIW algorithm. Previous work has shown the
computational value of sketches over benchmark domains that, while tractable,
are challenging for domain-independent planners. In this work, we address the
problem of learning sketches automatically given a planning domain, some
instances of the target class of problems, and the desired bound on the sketch
width. We present a logical formulation of the problem, an implementation using
the ASP solver Clingo, and experimental results. The sketch learner and the
SIW_R planner yield a domain-independent planner that learns and exploits
domain structure in a crisp and explicit form.


title:
Provably Positive Central DG Schemes via Geometric Quasilinearization
  for Ideal MHD Equations

abstract:
  In the numerical simulation of ideal MHD, keeping the pressure and density
positive is essential for both physical considerations and numerical stability.
This is a challenge, due to the underlying relation between such
positivity-preserving (PP) property and the magnetic divergence-free (DF)
constraint as well as the strong nonlinearity of the MHD equations. This paper
presents the first rigorous PP analysis of the central discontinuous Galerkin
(CDG) methods and constructs arbitrarily high-order PP CDG schemes for ideal
MHD. By the recently developed geometric quasilinearization (GQL) approach, our
analysis reveals that the PP property of standard CDG methods is closely
related to a discrete DF condition, whose form was unknown and differs from the
non-central DG and finite volume cases in [K. Wu, SIAM J. Numer. Anal. 2018].
This result lays the foundation for the design of our PP CDG schemes. In 1D
case, the discrete DF condition is naturally satisfied, and we prove the
standard CDG method is PP under a condition that can be enforced with a PP
limiter. However, in the multidimensional cases, the discrete DF condition is
highly nontrivial yet critical, and we prove the the standard CDG method, even
with the PP limiter, is not PP in general, as it fails to meet the discrete DF
condition. We address this issue by carefully analyzing the structure of the
discrete divergence and then constructing new locally DF CDG schemes for
Godunov's modified MHD equations with an additional source. The key point is to
find out the suitable discretization of the source term such that it exactly
offsets all the terms in the discrete DF condition. Based on the GQL approach,
we prove the PP property of the new multidimensional CDG schemes. The
robustness and accuracy of PP CDG schemes are validated by several demanding
examples, including the high-speed jets and blast problems with very low plasma
beta.

categories: math.NA astro-ph.IM cs.NA physics.comp-ph physics.flu-dyn physics.plasm-ph


title: Turnover in close friendships: age and gender differences

abstract:
  Humans are social animals and the interpersonal bonds formed between them are
crucial for their development and well being in a society. These relationships
are usually structured into several layers (Dunbar's layers of friendship)
depending on their significance in an individual's life with closest friends
and family being the most important ones taking major part of their time and
communication effort. However, we have little idea how the initiation and
termination of these relationships occurs across the lifespan. To explore this,
we analyse a national cellphone database to determine how and when changes in
close relationships occur in the two genders. In general, membership of this
inner circle of intimate relationships is extremely stable, at least over a
three-year period. However, around 1-4% of alters change every year, with the
rate of change being higher among 17-21 year olds than older adults. Young
adult females terminate more of their opposite-gender relationships, while
older males are more persistent in trying to maintain relationships in decline.
These results emphasise the variability in relationship dynamics across age and
gender, and remind us that individual differences play an important role in the
structure of social networks. Overall, our study provides a holistic
understanding of the dynamic nature of relationships during the life-course of
humans.


title:
Modular Adaptive Policy Selection for Multi-Task Imitation Learning
  through Task Division

abstract:
  Deep imitation learning requires many expert demonstrations, which can be
hard to obtain, especially when many tasks are involved. However, different
tasks often share similarities, so learning them jointly can greatly benefit
them and alleviate the need for many demonstrations. But, joint multi-task
learning often suffers from negative transfer, sharing information that should
be task-specific. In this work, we introduce a method to perform multi-task
imitation while allowing for task-specific features. This is done by using
proto-policies as modules to divide the tasks into simple sub-behaviours that
can be shared. The proto-policies operate in parallel and are adaptively chosen
by a selector mechanism that is jointly trained with the modules. Experiments
on different sets of tasks show that our method improves upon the accuracy of
single agents, task-conditioned and multi-headed multi-task agents, as well as
state-of-the-art meta learning agents. We also demonstrate its ability to
autonomously divide the tasks into both shared and task-specific
sub-behaviours.


title:
WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit
  Algorithm for Multi-Layer Convolutional Sparse Coding

abstract:
  The pursuit algorithms integrated in multi-layer convolutional sparse coding
(ML-CSC) can interpret the convolutional neural networks (CNNs). However, many
current state-of-art (SOTA) pursuit algorithms require multiple iterations to
optimize the solution of ML-CSC, which limits their applications to deeper CNNs
due to high computational cost and large number of resources for getting very
tiny gain of performance. In this study, we focus on the 0th iteration in
pursuit algorithm by introducing an effective initialization strategy for each
layer, by which the solution for ML-CSC can be improved. Specifically, we first
propose a novel width-depth synchronous extension-based basis pursuit (WSEBP)
algorithm which solves the ML-CSC problem without the limitation of the number
of iterations compared to the SOTA algorithms and maximizes the performance by
an effective initialization in each layer. Then, we propose a simple and
unified ML-CSC-based classification network (ML-CSC-Net) which consists of an
ML-CSC-based feature encoder and a fully-connected layer to validate the
performance of WSEBP on image classification task. The experimental results
show that our proposed WSEBP outperforms SOTA algorithms in terms of accuracy
and consumption resources. In addition, the WSEBP integrated in CNNs can
improve the performance of deeper CNNs and make them interpretable. Finally,
taking VGG as an example, we propose WSEBP-VGG13 to enhance the performance of
VGG13, which achieves competitive results on four public datasets, i.e., 87.79%
vs. 86.83% on Cifar-10 dataset, 58.01% vs. 54.60% on Cifar-100 dataset, 91.52%
vs. 89.58% on COVID-19 dataset, and 99.88% vs. 99.78% on Crack dataset,
respectively. The results show the effectiveness of the proposed WSEBP, the
improved performance of ML-CSC with WSEBP, and interpretation of the CNNs or
deeper CNNs.


title:
Randomized trials and their observational emulations: a framework for
  benchmarking and joint analysis

abstract:
  A randomized trial and an analysis of observational data designed to emulate
the trial sample observations separately, but have the same eligibility
criteria, collect information on some shared baseline covariates, and compare
the effects of the same treatments on the same outcomes. Treatment effect
estimates from the trial and its emulation can be compared to benchmark
observational analysis methods. In a simplified setting with complete adherence
to the assigned treatment strategy and no loss-to-follow-up, we show that
benchmarking relies on an exchangeability condition between the populations
underlying the trial and its emulation, to account for differences in the
distribution of covariates between them. When this exchangeability condition
holds, and the usual conditions needed for the estimates from the trial and its
emulation to have a causal interpretation also hold, we derive restrictions on
the law of the observed data. When the data are compatible with the
restrictions, joint analysis of the trial and its emulation is possible. When
the data are incompatible with the restrictions, a discrepancy between (1)
estimates based on extending inferences from the trial to the population
underlying the emulation and (2) the emulation itself may reflect either
inability to benchmark (e.g., due to selective participation into the trial) or
a failure of the emulation (e.g., due to unmeasured confounding), but we cannot
use the data to determine which is the case. Our analysis reveals how
benchmarking attempts combine causal assumptions, data analysis methods, and
substantive knowledge to examine the validity of observational analysis
methods.


title:
Phase Properties of Interacting Bosons in Presence of Quasiperiodic and
  Random Disorder

abstract:
  Motivated by two different types of disorder that occur in quantum systems
with ubiquity, namely, the random and the quasiperiodic (QP) disorder, we have
performed a systematic comparison of the emerging phase properties
corresponding to these two cases for a system of interacting bosons in a two
dimensional square lattice. Such a comparison is imperative as a random
disorder at each lattice is completely uncorrelated, while a quasiperiodic
disorder is deterministic in nature. Using a site decoupled mean-field
approximation followed by a percolation analysis on a BoseHubbard model,
several different phases are realized, such as the familiar Bose-glass (BG),
Mott insulator (MI), superfluid (SF) phases, and, additionally, we observe a
mixed phase, specific to the QP disorder, which we call as a QM phase.
Incidentally, the QP disorder stabilizes the BG phase more efficiently than the
case of random disorder. Further, we have employed a finite-size scaling
analysis to characterize various phase transitions via computing the critical
transition points and the corresponding critical exponents. The results show
that for both types of disorder, the transition from the BG phase to the SF
phase belongs to the same universality class. However, the QM to the SF
transition for the QP disorder comprises of different critical exponents,
thereby hinting at the involvement of a different universality class therein.
The critical exponents that depict all the various phase transitions occurring
as a function of the disorder strength are found to be in good agreement with
the quantum Monte-Carlo results available in the literature.

categories: cond-mat.dis-nn cond-mat.other cond-mat.quant-gas cond-mat.stat-mech


title: FaasKeeper: a Blueprint for Serverless Services

abstract:
  FaaS (Function-as-a-Service) brought a fundamental shift into cloud
computing: (persistent) virtual machines have been replaced with dynamically
allocated resources, trading locality and statefulness for a pay-as-you-go
model more suitable for varying and infrequent workloads. However, adapting
services to function within the serverless paradigm while still fulfilling
requirements is challenging. In this work, we introduce a design blueprint for
creating complex serverless services and contribute a set of requirements for
efficient and scalable FaaS computing. To showcase our approach, we focus on
ZooKeeper, a centralized coordination service that offers a safe and wait-free
consensus mechanism but requires a persistent allocation of computing resources
that does not offer the flexibility needed to handle variable workloads. We
design FaaSKeeper, the first coordination service built on serverless functions
and cloud-native services. FaaSKeeper provides the same consistency guarantees
and interface as ZooKeeper with a price model proportional to the activity in
the system. In addition, we define synchronization primitives to extend the
capabilities of scalable cloud storage ser- vices with consensus semantics
needed for strong data consistency.


title: Time-inhomogeneous diffusion geometry and topology

abstract:
  Diffusion condensation is a dynamic process that yields a sequence of
multiscale data representations that aim to encode meaningful abstractions. It
has proven effective for manifold learning, denoising, clustering, and
visualization of high-dimensional data. Diffusion condensation is constructed
as a time-inhomogeneous process where each step first computes and then applies
a diffusion operator to the data. We theoretically analyze the convergence and
evolution of this process from geometric, spectral, and topological
perspectives. From a geometric perspective, we obtain convergence bounds based
on the smallest transition probability and the radius of the data, whereas from
a spectral perspective, our bounds are based on the eigenspectrum of the
diffusion kernel. Our spectral results are of particular interest since most of
the literature on data diffusion is focused on homogeneous processes. From a
topological perspective, we show diffusion condensation generalizes
centroid-based hierarchical clustering. We use this perspective to obtain a
bound based on the number of data points, independent of their location. To
understand the evolution of the data geometry beyond convergence, we use
topological data analysis. We show that the condensation process itself defines
an intrinsic diffusion homology. We use this intrinsic topology as well as an
ambient topology to study how the data changes over diffusion time. We
demonstrate both homologies in well-understood toy examples. Our work gives
theoretical insights into the convergence of diffusion condensation, and shows
that it provides a link between topological and geometric data analysis.


title: Bloch Theorem Dictated Wave Chaos in Microcavity Crystals

abstract:
  Chaotic microcavity has been the primary testing ground for the study of
quantum chaos. In this work, we generalize the wave chaos theory to the cavity
lattice systems. We discover the intrinsic coupling of the crystal momentum to
the internal cavity dynamics, which substitutes the role of the deformed
boundary shape in the ordinary microcavity problem. This provides a new
platform enabling in-situ study of microcavity light dynamics. In the
perturbative regime, a small momentum coupling leads to the dynamical
localization transition, where the degenerate scar-mode pairs hybridizes and
non-trivially localizes around regular islands in phase space. Different from
the weak coupling case, as the momentum coupling becomes maximal at the
Brillouin zone boundary, we find that the intercavity chaotic modes coupling
and wave confinement are significantly altered. Our work pioneers the study of
intertwining quantum chaos in periodic systems and provide useful applications
in light dynamics control.


title:
Instantaneous smoothing and exponential decay of solutions for a
  degenerate evolution equation with application to Boltzmann's equation

abstract:
  We establish an instantaneous smoothing property for decaying solutions on
the half-line $(0,+\infty)$ of certain degenerate Hilbert space-valued
evolution equations arising in kinetic theory, including in particular the
steady Boltzmann equation. Our results answer the two main open problems posed
by Pogan and Zumbrun in their treatment of $H^1$ stable manifolds of such
equations, showing that $L^2_{loc}$ solutions that remain sufficiently small in
$L^\infty$ (i) decay exponentially, and (ii) are $C^\infty$ for $t>0$, hence
lie eventually in the $H^1$ stable manifold constructed by Pogan and Zumbrun


title: HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars

abstract:
  A promising direction for recovering the lost information in low-resolution
headshot images is utilizing a set of high-resolution exemplars from the same
identity. Complementary images in the reference set can improve the generated
headshot quality across many different views and poses. However, it is
challenging to make the best use of multiple exemplars: the quality and
alignment of each exemplar cannot be guaranteed. Using low-quality and
mismatched images as references will impair the output results. To overcome
these issues, we propose an efficient Headshot Image Super-Resolution with
Multiple Exemplars network (HIME) method. Compared with previous methods, our
network can effectively handle the misalignment between the input and the
reference without requiring facial priors and learn the aggregated reference
set representation in an end-to-end manner. Furthermore, to reconstruct more
detailed facial features, we propose a correlation loss that provides a rich
representation of the local texture in a controllable spatial range.
Experimental results demonstrate that the proposed framework not only has
significantly fewer computation cost than recent exemplar-guided methods but
also achieves better qualitative and quantitative performance.


title: Chess is hard even for a single player

abstract:
  We introduce a generalization of "Solo Chess", a single-player variant of the
game that can be played on chess.com. The standard version of the game is
played on a regular 8 x 8 chessboard by a single player, with only white
pieces, using the following rules: every move must capture a piece, no piece
may capture more than 2 times, and if there is a King on the board, it must be
the final piece. The goal is to clear the board, i.e, make a sequence of
captures after which only one piece is left.
  We generalize this game to unbounded boards with $n$ pieces, each of which
have a given number of captures that they are permitted to make. We show that
Generalized Solo Chess is NP-complete, even when it is played by only rooks
that have at most two captures remaining. It also turns out to be NP-complete
even when every piece is a queen with exactly two captures remaining in the
initial configuration. In contrast, we show that solvable instances of
Generalized Solo Chess can be completely characterized when the game is: a)
played by rooks on a one-dimensional board, and b) played by pawns with two
captures left on a 2D board.
  Inspired by Generalized Solo Chess, we also introduce the Graph Capture Game,
which involves clearing a graph of tokens via captures along edges. This game
subsumes Generalized Solo Chess played by knights. We show that the Graph
Capture Game is NP-complete for undirected graphs and DAGs.


title:
Towards Transferable Speech Emotion Representation: On loss functions
  for cross-lingual latent representations

abstract:
  In recent years, speech emotion recognition (SER) has been used in wide
ranging applications, from healthcare to the commercial sector. In addition to
signal processing approaches, methods for SER now also use deep learning
techniques which provide transfer learning possibilities. However, generalizing
over languages, corpora and recording conditions is still an open challenge. In
this work we address this gap by exploring loss functions that aid in
transferability, specifically to non-tonal languages. We propose a variational
autoencoder (VAE) with KL annealing and a semi-supervised VAE to obtain more
consistent latent embedding distributions across data sets. To ensure
transferability, the distribution of the latent embedding should be similar
across non-tonal languages (data sets). We start by presenting a low-complexity
SER based on a denoising-autoencoder, which achieves an unweighted
classification accuracy of over 52.09% for four-class emotion classification.
This performance is comparable to that of similar baseline methods. Following
this, we employ a VAE, the semi-supervised VAE and the VAE with KL annealing to
obtain a more regularized latent space. We show that while the DAE has the
highest classification accuracy among the methods, the semi-supervised VAE has
a comparable classification accuracy and a more consistent latent embedding
distribution over data sets.


title: Unified dark sectors in scalar-torsion theories of gravity

abstract:
  We present a unified description of the matter and dark energy epochs, using
a class of scalar-torsion theories. We provide a Hamiltonian description, and
by applying Noether's theorem and by requiring the field equations to admit
linear-in-momentum conservation laws we obtain two specific classes of
scalar-field potentials. We extract analytic solutions and we perform a
detailed dynamical analysis. We show that the system possesses critical points
that correspond to scaling solutions in which the effective, total
equation-of-state parameter is close to zero and points in which it is equal to
the cosmological constant value $-1$. Therefore, during evolution, the Universe
remains for sufficiently long times at the epoch corresponding to dust-matter
domination, while at later times it enters the accelerated epoch and it
eventually results in the de Sitter phase. Finally, in contrast to other
unified scenarios, such as Chaplygin gas-based models as well as
Horndeski-based constructions, the present scenario is free from instabilities
and pathologies at the perturbative level.


title:
Continuous Metric Learning For Transferable Speech Emotion Recognition
  and Embedding Across Low-resource Languages

abstract:
  Speech emotion recognition~(SER) refers to the technique of inferring the
emotional state of an individual from speech signals. SERs continue to garner
interest due to their wide applicability. Although the domain is mainly founded
on signal processing, machine learning, and deep learning, generalizing over
languages continues to remain a challenge. However, developing generalizable
and transferable models are critical due to a lack of sufficient resources in
terms of data and labels for languages beyond the most commonly spoken ones. To
improve performance over languages, we propose a denoising autoencoder with
semi-supervision using a continuous metric loss based on either activation or
valence. The novelty of this work lies in our proposal of continuous metric
learning, which is among the first proposals on the topic to the best of our
knowledge. Furthermore, to address the lack of activation and valence labels in
the transfer datasets, we annotate the signal samples with activation and
valence levels corresponding to a dimensional model of emotions, which were
then used to evaluate the quality of the embedding over the transfer datasets.
We show that the proposed semi-supervised model consistently outperforms the
baseline unsupervised method, which is a conventional denoising autoencoder, in
terms of emotion classification accuracy as well as correlation with respect to
the dimensional variables. Further evaluation of classification accuracy with
respect to the reference, a BERT based speech representation model, shows that
the proposed method is comparable to the reference method in classifying
specific emotion classes at a much lower complexity.


title: Matrix Whittaker processes

abstract:
  We study a discrete-time Markov dynamics on triangular arrays of matrices of
order $d\geq 1$, driven by inverse Wishart random matrices. The components of
the right edge evolve as multiplicative random walks on positive definite
matrices with one-sided interactions and can be viewed as a $d$-dimensional
generalisation of log-gamma polymer partition functions. We establish
intertwining relations to prove that, for suitable initial configurations of
the triangular process, the bottom edge has an autonomous Markovian evolution
with an explicit transition kernel. We then show that, for a singular initial
configuration, the fixed-time law of the bottom edge is a Whittaker measure,
which we define. To achieve this, we perform a Laplace approximation that
requires solving a constrained minimisation problem for certain energy
functions of matrix arguments on directed graphs.


title: Simultaneous beam based alignment measurement for multiple magnets

abstract:
  We propose a method to simultaneously determine the magnetic centers of
multiple quadrupoles in a transport line or a storage ring. The method finds
the magnet centers by correcting the orbit shift due to a change of the
quadrupole gradient strengths with orbit correctors. The quadrupoles are
selected with orbit corrector magnets and beam position monitors in between to
ensure that orbit correction at the quadrupole locations can be achieved. The
correction of the induced orbit shift is done by steering the orbit toward the
quadrupole centers with correctors, using its response matrix with respect to
the correctors. The response matrix can be measured or calculated. Simulations
with a section of the Linac Coherent Light Source (LCLS) II and the SPEAR3
storage ring are done to demonstrate the feasibility and performance of the
method. It is also experimentally tested on SPEAR3. The method can be extended
for beam based alignment measurement of nonlinear magnets.


title: Biophysical models of intrinsic homeostasis: Firing rates and beyond

abstract:
  In view of ever-changing conditions both in the external world and in
intrinsic brain states, maintaining the robustness of computations poses a
challenge, adequate solutions to which we are only beginning to understand. At
the level of cell-intrinsic properties, biophysical models of neurons permit
one to identify relevant physiological substrates that can serve as regulators
of neuronal excitability and to test how feedback loops can stabilize crucial
variables such as long-term calcium levels and firing rates. Mathematical
theory has also revealed a rich set of complementary computational properties
arising from distinct cellular dynamics and even shaping processing at the
network level. Here, we provide an overview over recently explored homeostatic
mechanisms derived from biophysical models and hypothesize how multiple
dynamical characteristics of cells, including their intrinsic neuronal
excitability classes, can be stably controlled.


title:
The question of selective absorption of light in space viewed from the
  viewpoint of the dynamics of the universe

abstract:
  The selective light absorption in space has been raised in astronomical
literature. The substance producing the absorption must have some mass; thus
the question is how large it is. We develop a dynamical model of the Milky Way
system, assuming that it can be represented by a flattened ellipsoid of
rotation. We use the spatial distribution of $\delta$-Cephei and Algol type
variable stars, and mean velocities of stars according to Campbell to calculate
the dynamical density of the Milky Way near the Sun, $0.100\,M_\odot/pc^3$. We
find that the dynamical density is equal to the mean density of stars in the
vicinity of the Sun. Our conclusion is that the intrinsic gravity of stars
fully explains their motion, and the existence of any other matter in any
significant quantity seems unlikely. Therefore, the existence of noticeable
selective absorption seems to be absolutely improbable, unless one admits the
existence in the space of particles much smaller than atoms of elements known
to us. Normal absorption may exist if the particle diameter is of the order of
a millimetre or less, and their mass is comparatively small. This absorption
has not yet been reliably detected; the fact that the number of stars increases
with stellar magnitude more slowly than theory requires in case of uniform
distribution of stars in space, can be equally explained by both light
absorption and decrease in number of stars with distance.


title: Closed geodesics on compact symmetric spaces of higher rank

abstract:
  In this article, we consider a compact symmetric space $M$ of higher rank.
Let $P(t)$ be the set of free-homotopy classes containing a closed geodesic on
$M$ with length at most $t$, and $\# P(t)$ its cardinality. We obtain the
following asymptotic estimates: \[\#P(t)=\frac{e^{ht}}{ht}(1+O(e^{-ut}))\] for
some $u>0$, where $h$ is the topological entropy of the geodesic flow.


title:
Institutionalization of Software Product Line: An Empirical
  Investigation of Key Organizational Factors

abstract:
  A good fit between the person and the organization is essential in a better
organizational performance. This is even more crucial in case of
institutionalization of a software product line practice within an
organization. Employees participation, organizational behavior and management
contemplation play a vital role in successfully institutionalizing software
product lines in a firm. Organizational dimension has been weighted as one of
the critical dimensions in software product line theory and practice. A
comprehensive empirical investigation to study the impact of some
organizational factors on the performance of software product line practice is
presented in this work. This is the first study to empirically investigate and
demonstrate the relationships between some of the key organizational factors
and software product line performance of an organization. The results of this
investigation provide empirical evidence and further support the theoretical
foundations that in order to institutionalize software product lines within an
organization, organizational factors play an important role.


title: Note: Gas-liquid crossover in the Lennard-Jones system

abstract:
  It is demonstrated that the crossover between gas- and liquid-like regions on
the phase diagram of the Lennard-Jones system occurs at a fixed value of the
density divided by its value at the freezing point, $\rho/\rho_{\rm fr}\simeq
0.35$. This definition is consistent with other definitions proposed recently.
As a result a very simple practical expression for the gas-to-liquid crossover
line emerges.


title: FLDP: Flexible strategy for local differential privacy

abstract:
  Local differential privacy (LDP), a technique applying unbiased statistical
estimations instead of real data, is often adopted in data collection. In
particular, this technique is used with frequency oracles (FO) because it can
protect each user's privacy and prevent leakage of sensitive information.
However, the definition of LDP is so conservative that it requires all inputs
to be indistinguishable after perturbation. Indeed, LDP protects each value;
however, it is rarely used in practical scenarios owing to its cost in terms of
accuracy. In this paper, we address the challenge of providing weakened but
flexible protection where each value only needs to be indistinguishable from
part of the domain after perturbation. First, we present this weakened but
flexible LDP (FLDP) notion. We then prove the association with LDP and DP.
Second, we design an FHR approach for the common FO issue while satisfying
FLDP. The proposed approach balances communication cost, computational
complexity, and estimation accuracy. Finally, experimental results using
practical and synthetic datasets verify the effectiveness and efficiency of our
approach.


title: Finnish Parliament ASR corpus - Analysis, benchmarks and statistics

abstract:
  Public sources like parliament meeting recordings and transcripts provide
ever-growing material for the training and evaluation of automatic speech
recognition (ASR) systems. In this paper, we publish and analyse the Finnish
parliament ASR corpus, the largest publicly available collection of manually
transcribed speech data for Finnish with over 3000 hours of speech and 449
speakers for which it provides rich demographic metadata. This corpus builds on
earlier initial work, and as a result the corpus has a natural split into two
training subsets from two periods of time. Similarly, there are two official,
corrected test sets covering different times, setting an ASR task with
longitudinal distribution-shift characteristics. An official development set is
also provided. We develop a complete Kaldi-based data preparation pipeline, and
hidden Markov model (HMM), hybrid deep neural network (HMM-DNN) and
attention-based encoder-decoder (AED) ASR recipes. We set benchmarks on the
official test sets, as well as multiple other recently used test sets. Both
temporal corpus subsets are already large, and we observe that beyond their
scale, ASR performance on the official test sets plateaus, whereas other
domains benefit from added data. The HMM-DNN and AED approaches are compared in
a carefully matched equal data setting, with the HMM-DNN system consistently
performing better. Finally, the variation of the ASR accuracy is compared
between the speaker categories available in the parliament metadata to detect
potential biases based on factors such as gender, age, and education.


title:
Role of a magnetic field in the context of inhomogeneous gravitational
  collapse

abstract:
  Magnetic fields are found to have an inherent capability of acting against
gravity. An important question posed in the literature is whether presence of a
magnetic field can alter the dynamics of a gravitational collapse and prevent
the final formation of a singularity. Promising conclusions regarding this have
been found in a few recent works considering nearly homogeneous models.
However, whether this can happen in realistic collapse scenarios is still not
very clear and remains an open problem. In the present work we investigate the
role of magnetic fields in the evolution of inhomogeneous cylindrically
symmetric models. We use an approach based on the Raychaudhuri equation for
such an analysis. We show that it is quite possible for the magnetic field to
avert the gravitational collapse in these models.


title: Diffeomorphisms of 4-manifolds with boundary and exotic embeddings

abstract:
  We define family versions of the invariant of 4-manifolds with contact
boundary due to Kronheimer and Mrowka and using these to detect exotic
diffeomorphisms of 4-manifolds with boundary. Further we develop some
techniques that have been used to show the existence of exotic codimension-2
submanifolds and exotic codimension-1 submanifolds in 4-manifolds.


title:
Integrality in the Matching-Jack conjecture and the Farahat-Higman
  algebra

abstract:
  Using Jack polynomials, Goulden and Jackson have introduced a one parameter
deformation $\tau_b$ of the generating series of bipartite maps, which
generalizes the partition function of $\beta$-ensembles of random matrices. The
Matching-Jack conjecture suggests that the coefficients $c^\lambda_{\mu,\nu}$
of the function $\tau_b$ in the power-sum basis are non-negative integer
polynomials in the deformation parameter $b$. Do{\l}\k{e}ga and F\'eray have
proved in 2016 the "polynomiality" part in the Matching-Jack conjecture, namely
that coefficients $c^\lambda_{\mu,\nu}$ are in $\mathbb{Q}[b]$. In this paper,
we prove the "integrality" part, i.e that the coefficients
$c^\lambda_{\mu,\nu}$ are in $\mathbb{Z}[b]$.
  The proof is based on a recent work of the author that deduces the
Matching-Jack conjecture for marginal sums from an analog result for the
$b$-conjecture, established in 2020 by Chapuy and Do{\l}\k{e}ga. A key step in
the proof involves a new connection with the graded Farahat-Higman algebra.


title:
A reduced order model for the finite element approximation of eigenvalue
  problems

abstract:
  In this paper we consider a reduced order method for the approximation of the
eigensolutions of the Laplace problem with Dirichlet boundary condition. We use
a time continuation technique that consists in the introduction of a fictitious
time parameter. We use a POD approach and we present some theoretical results
showing how to choose the optimal dimension of the POD basis. The results of
our computations, related to the first eigenvalue, confirm the optimal behavior
of our approximate solution.


title:
An algebraic preconditioner for the exactly divergence-free
  discontinuous Galerkin method for Stokes

abstract:
  We present an optimal preconditioner for the exactly divergence-free
discontinuous Galerkin (DG) discretization of Cockburn, Kanschat, and
Sch\"otzau [J. Sci. Comput., 31 (2007), pp. 61--73] and Wang and Ye [SIAM J.
Numer. Anal., 45 (2007), pp. 1269--1286] for the Stokes problem. This DG method
uses finite elements that use an $H({\rm div})$-conforming basis, thereby
significantly complicating its solution by iterative methods. Several
preconditioners for this Stokes discretization have been developed, but each is
based on specialized solvers or decompositions, and do not offer a clear
framework to generalize to Navier--Stokes. To avoid requiring custom solvers,
we hybridize the $H({\rm div})$-conforming finite element so that the velocity
lives in a standard $L^2$-DG space, and present a simple algebraic
preconditioner for the extended hybridized system. The proposed preconditioner
is optimal in $h$, super robust in element order (demonstrated up to 5th
order), effective in 2d and 3d, and only relies on standard relaxation and
algebraic multigrid methods available in many packages. The hybridization also
naturally extends to Navier--Stokes, providing a potential pathway to effective
black-box preconditioners for exactly divergence-free DG discretizations of
Navier--Stokes.


title: Vector In Memory Architecture for simple and high efficiency computing

abstract:
  Data movement is one of the main challenges of contemporary system
architectures. Near-Data Processing (NDP) mitigates this issue by moving
computation closer to the memory, avoiding excessive data movement. Our
proposal, Vector-In-Memory Architecture(VIMA), executes large vector
instructions near 3D-stacked memories using vector functional units and uses a
small data cache to enable short-term data reuse. It provides an easy
programming interface and guarantees precise exceptions. When executing
stream-behaved applications using a single core, VIMA offers a speedup of up to
26x over a CPU system baseline with vector operations in a single-core
processor while spending 93% less energy.


title:
TGL: A General Framework for Temporal GNN Training on Billion-Scale
  Graphs

abstract:
  Many real world graphs contain time domain information. Temporal Graph Neural
Networks capture temporal information as well as structural and contextual
information in the generated dynamic node embeddings. Researchers have shown
that these embeddings achieve state-of-the-art performance in many different
tasks. In this work, we propose TGL, a unified framework for large-scale
offline Temporal Graph Neural Network training where users can compose various
Temporal Graph Neural Networks with simple configuration files. TGL comprises
five main components, a temporal sampler, a mailbox, a node memory module, a
memory updater, and a message passing engine. We design a Temporal-CSR data
structure and a parallel sampler to efficiently sample temporal neighbors to
formtraining mini-batches. We propose a novel random chunk scheduling technique
that mitigates the problem of obsolete node memory when training with a large
batch size. To address the limitations of current TGNNs only being evaluated on
small-scale datasets, we introduce two large-scale real-world datasets with 0.2
and 1.3 billion temporal edges. We evaluate the performance of TGL on four
small-scale datasets with a single GPU and the two large datasets with multiple
GPUs for both link prediction and node classification tasks. We compare TGL
with the open-sourced code of five methods and show that TGL achieves similar
or better accuracy with an average of 13x speedup. Our temporal parallel
sampler achieves an average of 173x speedup on a multi-core CPU compared with
the baselines. On a 4-GPU machine, TGL can train one epoch of more than one
billion temporal edges within 1-10 hours. To the best of our knowledge, this is
the first work that proposes a general framework for large-scale Temporal Graph
Neural Networks training on multiple GPUs.


title: AWAPart: Adaptive Workload-Aware Partitioning of Knowledge Graphs

abstract:
  Large-scale knowledge graphs are increasingly common in many domains. Their
large sizes often exceed the limits of systems storing the graphs in a
centralized data store, especially if placed in main memory. To overcome this,
large knowledge graphs need to be partitioned into multiple sub-graphs and
placed in nodes in a distributed system. But querying these fragmented
sub-graphs poses new challenges, such as increased communication costs, due to
distributed joins involving cut edges. To combat these problems, a good
partitioning should reduce the edge cuts while considering a given query
workload. However, a partitioned graph needs to be continually re-partitioned
to accommodate changes in the query workload and maintain a good average
processing time. In this paper, an adaptive partitioning method for large-scale
knowledge graphs is introduced, which adapts the partitioning in response to
changes in the query workload. Our evaluation demonstrates that the performance
of processing time for queries is improved after dynamically adapting the
partitioning of knowledge graph triples.


title:
The effect of heterogeneous distributions of social norms on the spread
  of infectious diseases

abstract:
  The emergence due to the outbreak of the COVID-19 disease, caused by the
SARS-CoV-2 virus, suddenly erupted at the beginning of 2020 in China and soon
spread worldwide. This has caused an outstanding increase on research about the
virus itself and, more in general, epidemics in many scientific fields. In this
work we focus on the dynamics of the epidemic spreading and how it can be
affected by the individual variability in compliance with social norms, i.e.,
in the adoption of health and hygienic social norms by population's members.


title:
Molecular simulations matching denaturation experiments for
  N6-Methyladenosine

abstract:
  Post-transcriptional modifications are crucial for RNA function and can
affect its structure and dynamics. Force-field based classical molecular
dynamics simulations are a fundamental tool to characterize biomolecular
dynamics and their application to RNA is flourishing. Here we show that the set
of force-field parameters for N$^6$-methyladenosine (m$^6$A) developed for the
commonly used AMBER force field does not reproduce duplex denaturation
experiments and, specifically, cannot be used to describe both paired and
unpaired states. Then we use reweighting techniques to derive new parameters
matching available experimental data. The resulting force field can be used to
properly describe paired and unpaired m$^6$A in both syn and anti conformation,
and thus opens the way to the use of molecular simulations to investigate the
effects of N6 methylations on RNA structural dynamics.


title: HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation

abstract:
  A high-performance unsupervised nuclei instance segmentation (HUNIS) method
is proposed in this work. HUNIS consists of two-stage block-wise operations.
The first stage includes: 1) adaptive thresholding of pixel intensities, 2)
incorporation of nuclei size/shape priors and 3) removal of false positive
nuclei instances. Then, HUNIS conducts the second stage segmentation by
receiving guidance from the first one. The second stage exploits the
segmentation masks obtained in the first stage and leverages color and shape
distributions for a more accurate segmentation. The main purpose of the
two-stage design is to provide pixel-wise pseudo-labels from the first to the
second stage. This self-supervision mechanism is novel and effective.
Experimental results on the MoNuSeg dataset show that HUNIS outperforms all
other unsupervised methods by a substantial margin. It also has a competitive
standing among state-of-the-art supervised methods.


title: WawPart: Workload-Aware Partitioning of Knowledge Graphs

abstract:
  Large-scale datasets in the form of knowledge graphs are often used in
numerous domains, today. A knowledge graphs size often exceeds the capacity of
a single computer system, especially if the graph must be stored in main
memory. To overcome this, knowledge graphs can be partitioned into multiple
sub-graphs and distributed as shards among many computing nodes. However,
performance of many common tasks performed on graphs, such as querying,
suffers, as a result. This is due to distributed joins mandated by graph edges
crossing (cutting) the partitions. In this paper, we propose a method of
knowledge graph partitioning that takes into account a set of queries
(workload). The resulting partitioning aims to reduces the number of
distributed joins and improve the workload performance. Critical features
identified in the query workload and the knowledge graph are used to cluster
the queries and then partition the graph. Queries are rewritten to account for
the graph partitioning. Our evaluation results demonstrate the performance
improvement in workload processing time.


title:
LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL
  Applications

abstract:
  Spark SQL has been widely deployed in industry but it is challenging to tune
its performance. Recent studies try to employ machine learning (ML) to solve
this problem, but suffer from two drawbacks. First, it takes a long time (high
overhead) to collect training samples. Second, the optimal configuration for
one input data size of the same application might not be optimal for others. To
address these issues, we propose a novel Bayesian Optimization (BO) based
approach named LOCAT to automatically tune the configurations of Spark SQL
applications online. LOCAT innovates three techniques. The first technique,
named QCSA, eliminates the configuration-insensitive queries by Query
Configuration Sensitivity Analysis (QCSA) when collecting training samples. The
second technique, dubbed DAGP, is a Datasize-Aware Gaussian Process (DAGP)
which models the performance of an application as a distribution of functions
of configuration parameters as well as input data size. The third technique,
called IICP, Identifies Important Configuration Parameters (IICP) with respect
to performance and only tunes the important ones. As such, LOCAT can tune the
configurations of a Spark SQL application with low overhead and adapt to
different input data sizes. We employ Spark SQL applications from benchmark
suites TPC-DS, TPC-H, and HiBench running on two significantly different
clusters, a four-node ARM cluster and an eight-node x86 cluster, to evaluate
LOCAT. The experimental results on the ARM cluster show that LOCAT accelerates
the optimization procedures of the state-of-the-art approaches by at least 4.1x
and up to 9.7x; moreover, LOCAT improves the application performance by at
least 1.9x and up to 2.4x. On the x86 cluster, LOCAT shows similar results to
those on the ARM cluster.


title:
Cyclotron cooling to cryogenic temperature in a Penning-Malmberg trap
  with a large solid angle acceptance

abstract:
  Magnetized nonneutral plasma composed of electrons or positrons couples to
the local microwave environment via cyclotron radiation. The equilibrium plasma
temperature depends on the microwave energy density near the cyclotron
frequency. Fine copper meshes and cryogenic microwave absorbing material were
used to lower the effective temperature of the radiation environment in
ASACUSA's Cusp trap, resulting in significantly reduced plasma temperature.


title: How Functorial Are (Deep) GADTs?

abstract:
  It is well-known that GADTs do not admit standard map functions of the kind
supported by ADTs and nested types. In addition, standard map functions are
insufficient to distribute their data-changing argument functions over all of
the structure present in elements of deep GADTs, even just deep ADTs or nested
types. This paper develops an algorithm for detecting exactly which functions
are mappable over data whose types are (deep) GADTs. The algorithm takes as
input a term t whose type is an instance of a deep GADT D and a function f to
be mapped over t. It detects a minimal possible shape of t as an element of D,
and returns a minimal set of constraints f must satisfy to be mappable over t.
The crux of the algorithm is its ability to separate t's essential structure as
an element of D -- i.e., the part of t that is essential for it to have the
shape of an element of D -- from its incidental structure as an element of D --
i.e., the part of t that is simply data in the positions of this shape. The
algorithm ensures that the constraints on f come only from t's essential
structure. This work is part of an ongoing effort to define initial algebra
semantics for GADTs that properly generalizes the usual semantics for ADTs and
nested types as least fixpoints of higher-order endofunctors.


title:
Using dynamic mode decomposition to predict the dynamics of a two-time
  non-equilibrium Green's function

abstract:
  Computing the numerical solution of the Kadanoff-Baym equations, a set of
nonlinear integral differential equations satisfied by two-time Green's
functions derived from many-body perturbation theory for a quantum many-body
system away from equilibrium, is a challenging task. Recently, we have
successfully applied dynamic mode decomposition (DMD) to construct a data
driven reduced order model that can be used to extrapolate the time-diagonal of
a two-time Green's function from numerical solution of the KBE within a small
time window. In this paper, we extend the previous work and use DMD to predict
off-diagonal elements of the two-time Green's function. We partition the
two-time Green's function into a number of one-time functions along the
diagonal and subdiagonls of the two-time window as well as in horizontal and
vertical directions. We use DMD to construct separate reduced order models to
predict the dynamics of these one-time functions in a two-step procedure. We
extrapolate along diagonal and several subdiagonals within a subdiagonal band
of a two-time window in the first step. In the second step, we use DMD to
extrapolate the Green's function outside of the sub-diagonal band. We
demonstrate the efficiency and accuracy of this approach by applying it to a
two-band Hubbard model problem.


title:
Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA
  for length-normalized embeddings

abstract:
  In speaker recognition, where speech segments are mapped to embeddings on the
unit hypersphere, two scoring backends are commonly used, namely cosine scoring
or PLDA. Both have advantages and disadvantages, depending on the context.
Cosine scoring follows naturally from the spherical geometry, but for PLDA the
blessing is mixed -- length normalization Gaussianizes the between-speaker
distribution, but violates the assumption of a speaker-independent
within-speaker distribution. We propose PSDA, an analogue to PLDA that uses Von
Mises-Fisher distributions on the hypersphere for both within and between-class
distributions. We show how the self-conjugacy of this distribution gives
closed-form likelihood-ratio scores, making it a drop-in replacement for PLDA
at scoring time. All kinds of trials can be scored, including single-enroll and
multi-enroll verification, as well as more complex likelihood-ratios that could
be used in clustering and diarization. Learning is done via an EM-algorithm
with closed-form updates. We explain the model and present some first
experiments.


title: Readout Technologies for Future Detectors

abstract:
  It is essential to develop novel cost-effective readout technologies to
maximize the discovery potential of future HEP experiments.


title: Predicting frictional ageing from bulk relaxation measurements

abstract:
  The coefficient of static friction between solids generally depends on the
time they have remained in static contact before the measurement. Such
frictional aging is at the origin of the difference between static and dynamic
friction coefficients, but has remained difficult to understand. It is usually
attributed to a slow increase in the area of atomic contact as the interface
changes under pressure. This is however very difficult to quantify as surfaces
have roughness at all length scales, and friction is not always proportional to
the contact area. Here, we show that plastic flow of surface irregularities
within a polymer-on-glass frictional interface exhibits identical relaxation
dynamics as that of the bulk, allowing to predict the rate of frictional aging.


title: Multi-Task Learning for Visual Scene Understanding

abstract:
  Despite the recent progress in deep learning, most approaches still go for a
silo-like solution, focusing on learning each task in isolation: training a
separate neural network for each individual task. Many real-world problems,
however, call for a multi-modal approach and, therefore, for multi-tasking
models. Multi-task learning (MTL) aims to leverage useful information across
tasks to improve the generalization capability of a model. This thesis is
concerned with multi-task learning in the context of computer vision. First, we
review existing approaches for MTL. Next, we propose several methods that
tackle important aspects of multi-task learning. The proposed methods are
evaluated on various benchmarks. The results show several advances in the
state-of-the-art of multi-task learning. Finally, we discuss several
possibilities for future work.