title:
Invariant subspaces and explicit Bethe vectors in the integrable open
  spin $1/2$ $\XYZ$ chain

abstract:
  We derive a criterion under which splitting of all eigenstates of an open
$\XYZ$ Hamiltonian with boundary fields into two invariant subspaces, spanned
by chiral shock states, occurs. The splitting is governed by an integer number,
which has the geometrical meaning of the maximal number of kinks in the basis
states. We describe the generic structure of the respective Bethe vectors. We
obtain explicit expressions for Bethe vectors, in the absence of Bethe roots,
and those generated by one Bethe root, and investigate the \multiplet. We also
describe in detail an elliptic analogue of the spin-helix state, appearing in
both the periodic and the open $\XYZ$ model, and derive the eigenstate
condition. The elliptic analogue of the spin-helix state is characterized by a
quasi-periodic modulation of the magnetization profile, governed by Jacobi
elliptic functions.

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


title: Scheduling in the high uncertainty heavy traffic regime

abstract:
  We propose a model uncertainty approach to heavy traffic asymptotics that
allows for a high level of uncertainty. That is, the uncertainty classes of
underlying distributions accommodate disturbances that are of order 1 at the
usual diffusion scale, as opposed to asymptotically vanishing disturbances
studied previously in relation to heavy traffic. A main advantage of the
approach is that the invariance principle underlying diffusion limits makes it
possible to define uncertainty classes in terms of the first two moments only.
The model we consider is a single server queue with multiple job types. The
problem is formulated as a zero sum stochastic game played between the system
controller, who determines scheduling and attempts to minimize an expected
linear holding cost, and an adversary, who dynamically controls the service
time distributions of arriving jobs, and attempts to maximize the cost. The
heavy traffic asymptotics of the game are fully solved. It is shown that an
asymptotically optimal policy for the system controller is to prioritize
according to an index rule and for the adversary it is to select distributions
based on the system's current workload. The workload-to-distribution feedback
mapping is determined by an HJB equation, which also characterizes the game's
limit value. Unlike in the vast majority of results in the heavy traffic
theory, and as a direct consequence of the diffusive size disturbances, the
limiting dynamics under asymptotically optimal play are captured by a
stochastic differential equation where both the drift and the diffusion
coefficients may be discontinuous.


title: Familywise error rate control for block response-adaptive randomization

abstract:
  Response-adaptive randomization allows the probabilities of allocating
patients to treatments in a clinical trial to change based on the previously
observed response data, in order to achieve different experimental goals. One
concern over the use of such designs in practice, particularly from a
regulatory viewpoint, is controlling the type I error rate. To address this,
Robertson and Wason (Biometrics, 2019) proposed methodology that guarantees
familywise error rate control for a large class of response-adaptive designs.
In this paper, we propose an improvement of their proposal that is conceptually
simpler, in the specific context of block-randomised trials with a fixed
allocation to the control arm. We show the modified method guarantees that
there will never be negative weights for blocks of data, and can also provide a
substantial power advantage in practice.


title:
GARF: Gaussian Activated Radiance Fields for High Fidelity
  Reconstruction and Pose Estimation

abstract:
  Despite Neural Radiance Fields (NeRF) showing compelling results in
photorealistic novel views synthesis of real-world scenes, most existing
approaches require accurate prior camera poses. Although approaches for jointly
recovering the radiance field and camera pose exist (BARF), they rely on a
cumbersome coarse-to-fine auxiliary positional embedding to ensure good
performance. We present Gaussian Activated neural Radiance Fields (GARF), a new
positional embedding-free neural radiance field architecture - employing
Gaussian activations - that outperforms the current state-of-the-art in terms
of high fidelity reconstruction and pose estimation.


title:
Quasi-Fuchsian manifolds close to the Fuchsian locus are foliated by
  constant mean curvature surfaces

abstract:
  Even though it is known that there exist quasi-Fuchsian hyperbolic
three-manifolds that do not admit any monotone foliation by constant mean
curvature (CMC) surfaces, a conjecture due to Thurston asserts the existence of
CMC foliations for all almost-Fuchsian manifolds, namely those quasi-Fuchsian
manifolds that contain a closed minimal surface with principal curvatures in
(-1,1). In this paper we prove that there exists a (unique) monotone CMC
foliation for all quasi-Fuchsian manifolds that lie in a sufficiently small
neighborhood of the Fuchsian locus.


title: LifeLonger: A Benchmark for Continual Disease Classification

abstract:
  Deep learning models have shown a great effectiveness in recognition of
findings in medical images. However, they cannot handle the ever-changing
clinical environment, bringing newly annotated medical data from different
sources. To exploit the incoming streams of data, these models would benefit
largely from sequentially learning from new samples, without forgetting the
previously obtained knowledge. In this paper we introduce LifeLonger, a
benchmark for continual disease classification on the MedMNIST collection, by
applying existing state-of-the-art continual learning methods. In particular,
we consider three continual learning scenarios, namely, task and class
incremental learning and the newly defined cross-domain incremental learning.
Task and class incremental learning of diseases address the issue of
classifying new samples without re-training the models from scratch, while
cross-domain incremental learning addresses the issue of dealing with datasets
originating from different institutions while retaining the previously obtained
knowledge. We perform a thorough analysis of the performance and examine how
the well-known challenges of continual learning, such as the catastrophic
forgetting exhibit themselves in this setting. The encouraging results
demonstrate that continual learning has a major potential to advance disease
classification and to produce a more robust and efficient learning framework
for clinical settings. The code repository, data partitions and baseline
results for the complete benchmark will be made publicly available.


title: Text-Driven Separation of Arbitrary Sounds

abstract:
  We propose a method of separating a desired sound source from a
single-channel mixture, based on either a textual description or a short audio
sample of the target source. This is achieved by combining two distinct models.
The first model, SoundWords, is trained to jointly embed both an audio clip and
its textual description to the same embedding in a shared representation. The
second model, SoundFilter, takes a mixed source audio clip as an input and
separates it based on a conditioning vector from the shared text-audio
representation defined by SoundWords, making the model agnostic to the
conditioning modality. Evaluating on multiple datasets, we show that our
approach can achieve an SI-SDR of 9.1 dB for mixtures of two arbitrary sounds
when conditioned on text and 10.1 dB when conditioned on audio. We also show
that SoundWords is effective at learning co-embeddings and that our multi-modal
training approach improves the performance of SoundFilter.


title:
A Proposed Fuzzy Logic Approach for Conserving the Energy of Data
  Transmission in the Temperature Monitoring Systems of Internet of Things

abstract:
  One of the primary challenges facing the Internet of Things is the
reservation and efficient consumption of energy resources, especially in those
types of applications that require continuous monitoring or suffer from lacking
ongoing energy resources. Despite this, the indoor temperature and humidity
monitoring systems are unconcerned about the insignificant amount of energy
consumed during critical times when sending unimportant or useless data to the
control rooms servers. This paper proposes a fuzzy logic-based approach for
reducing the amount of energy spent in indoor temperature and humidity
monitoring systems by filtering data that is sent to servers based on several
surrounding circumstances such as time of data recording and current energy
consumption amount while maintaining constant monitoring. The experimental
results on the Appliances Energy Prediction dataset show that the proposed
fuzzy-based approach successfully reduces energy consumption in temperature and
humidity monitoring systems by 11.8% .


title: Adaptive cross approximation for Tikhonov regularization in general form

abstract:
  Many problems in Science and Engineering give rise to linear integral
equations of the first kind with a smooth kernel. Discretization of the
integral operator yields a matrix, whose singular values cluster at the origin.
We describe the approximation of such matrices by adaptive cross approximation,
which avoids forming the entire matrix. The choice of the number of steps of
adaptive cross approximation is discussed. The discretized right-hand side
represents data that commonly are contaminated by measurement error. Solution
of the linear system of equations so obtained is not meaningful because the
matrix determined by adaptive cross approximation is rank-deficient. We remedy
this difficulty by using Tikhonov regularization and discuss how a fairly
general regularization matrix can be used. Computed examples illustrate that
the use of a regularization matrix different from the identity can improve the
quality of the computed approximate solutions significantly.


title:
Solutions to the Dirac Equation in Kerr-Newman Geometries including the
  black-hole region

abstract:
  We investigate the Dirac equation in Kerr-Newman space-time, using horizon
penetrating coordinates (Eddington-Finkelstein-Coordinates) and the
Newman-Penrose formalism to separate the equation into radial and angular
systems of ordinary differential equations, and deriving the asymptotics of the
radial solutions at infinity and at the Cauchy horizon.


title: On Top-$k$ Selection from $m$-wise Partial Rankings via Borda Counting

abstract:
  We analyze the performance of the Borda counting algorithm in a
non-parametric model. The algorithm needs to utilize probabilistic rankings of
the items within $m$-sized subsets to accurately determine which items are the
overall top-$k$ items in a total of $n$ items. The Borda counting algorithm
simply counts the cumulative scores for each item from these partial ranking
observations. This generalizes a previous work of a similar nature by Shah et
al. using probabilistic pairwise comparison data. The performance of the Borda
counting algorithm critically depends on the associated score separation
$\Delta_k$ between the $k$-th item and the $(k+1)$-th item. Specifically, we
show that if $\Delta_k$ is greater than certain value, then the top-$k$ items
selected by the algorithm is asymptotically accurate almost surely; if
$\Delta_k$ is below certain value, then the result will be inaccurate with a
constant probability. In the special case of $m=2$, i.e., pairwise comparison,
the resultant bound is tighter than that given by Shah et al., leading to a
reduced gap between the error probability upper and lower bounds. These results
are further extended to the approximate top-$k$ selection setting. Numerical
experiments demonstrate the effectiveness and accuracy of the Borda counting
algorithm, compared with the spectral MLE-based algorithm, particularly when
the data does not necessarily follow an assumed parametric model.


title:
On the missing magnetic flux and topological effects of a screw
  dislocation on a charged particle in an inhomogeneous magnetic field

abstract:
  We study the interaction of an electron/hole with inhomogeneous magnetic
field in the presence of a screw dislocation. We consider the internal
structure of the defect, i.e., we consider the core that gives rise to a finite
size to the defect. In addition, we assume that this core determines a
forbidden region for the electron/hole. Then, we solve the Schr\"odinger
equation and show that an Aharonov-Bohm-type effect arises from the influence
of the topological defect and the missing magnetic flux on the eigenvalues of
energy.


title: Axion-induced oscillations in Josephson qubits

abstract:
  An effective quantum description of the axion is proposed as a two-level
dynamic system together with a spin-spin Hamiltonian for a coupled
axion-Josephson qubit system. The interaction between axion and Josephson
Junction can be responsible for a resonance effect, when the axion and the
Josephson frequencies match. Thus, experimentally detectable oscillations
induced in the Josephson qubit by its interaction with the axion are clearly
highlighted. This phenomenon, causing a periodic magnetization reversal in the
junction, can be exploited for the axion detection in the quantum limit of low
noise.


title:
Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep
  Learning

abstract:
  Ultrasound shear wave elasticity imaging is a valuable tool for quantifying
the elastic properties of tissue. Typically, the shear wave velocity is derived
and mapped to an elasticity value, which neglects information such as the shape
of the propagating shear wave or push sequence characteristics. We present 3D
spatio-temporal CNNs for fast local elasticity estimation from ultrasound data.
This approach is based on retrieving elastic properties from shear wave
propagation within small local regions. A large training data set is acquired
with a robot from homogeneous gelatin phantoms ranging from 17.42 kPa to 126.05
kPa with various push locations. The results show that our approach can
estimate elastic properties on a pixelwise basis with a mean absolute error of
5.01+-4.37 kPa. Furthermore, we estimate local elasticity independent of the
push location and can even perform accurate estimates inside the push region.
For phantoms with embedded inclusions, we report a 53.93% lower MAE (7.50 kPa)
and on the background of 85.24% (1.64 kPa) compared to a conventional shear
wave method. Overall, our method offers fast local estimations of elastic
properties with small spatio-temporal window sizes.


title: BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis

abstract:
  Cryptocurrencies are no longer just the preferred option for cybercriminal
activities on darknets, due to the increasing adoption in mainstream
applications. This is partly due to the transparency associated with the
underpinning ledgers, where any individual can access the record of a
transaction record on the public ledger. In this paper, we build a dataset
comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This
dataset (hereafter referred to as BABD-13) contains 13 types of Bitcoin
addresses, 5 categories of indicators with 148 features, and 544,462 labeled
data, which is the largest labeled Bitcoin address behavior dataset publicly
available to our knowledge. We then use our proposed dataset on common machine
learning models, namely: k-nearest neighbors algorithm, decision tree, random
forest, multilayer perceptron, and XGBoost. The results show that the accuracy
rates of these machine learning models for the multi-classification task on our
proposed dataset are between 93.24% and 97.13%. We also analyze the proposed
features and their relationships from the experiments, and propose a k-hop
subgraph generation algorithm to extract a k-hop subgraph from the entire
Bitcoin transaction graph constructed by the directed heterogeneous multigraph
starting from a specific Bitcoin address node (e.g., a known transaction
associated with a criminal investigation). Besides, we initially analyze the
behavior patterns of different types of Bitcoin addresses according to the
extracted features.


title: What entropy really is

abstract:
  Even today, the concept of entropy is perceived by many as quite obscure. The
main difficulty is analyzed as being fundamentally due to the subjectivity and
anthropocentrism of the concept that prevent us to have a sufficient distance
to embrace it. However, it is pointed out that the lack of coherence of certain
presentations or certain preconceived ideas do not help. They are of three
kinds : 1) axiomatic thermodynamics; 2) inconsistent solutions of certain
paradoxes; 3) reluctance of physicists to the simplification provided by
information theory. The purpose of this paper is to examine these points by
paying attention to the structure of the theory, what are the foundations, how
ideas articulate, with a peculiar focus on their consistency and economy. It is
shown how entropy can be introduced in a more consistent and economical manner,
finally allowing a more intuitive understanding.


title: Eight-year Full-depth unWISE Coadds

abstract:
  We present deep, full-sky maps built from Wide-field Infrared Survey Explorer
(WISE) and NEOWISE exposures spanning the 2010 January - 2020 December time
period. These coadds, which incorporate roughly 8 years of W1 (3.4 microns) and
W2 (4.6 microns) imaging, are the deepest ever full-sky maps at wavelengths of
3-5 microns. Photometry based on these coadds will be a component of DESI
Legacy Imaging Surveys DR10.


title:
A critical assessment of the National Entrepreneurship Context Index of
  the Global Entrepreneurship Monitor

abstract:
  Data collected through the National Expert Survey (NES) of the Global
Entrepreneurship Monitor (GEM) are widely used to assess the quality and impact
of national entrepreneurial ecosystems. By focusing on the measurement of the
National Entrepreneurship Context Index (NECI), we argue and show that the
subjective nature of the responses of the national experts precludes meaningful
cross-country analyses and cross-country rankings. Moreover, we show that the
limited precision of the NECI severely constraints the longitudinal assessment
of within-country trends. We provide recommendations for the current use of
NECI data and suggestions for future NES data collections.


title: Random matrices: Application to quantum paradoxes

abstract:
  Recently, a geometric embedding of the classical space and classical phase
space of an n-particle system into the space of states of the system was
constructed and shown to be physically meaningful. Namely, the Newtonian
dynamics of the particles was recovered from the Schroedinger dynamics by
constraining the state of the system to the classical phase space submanifold
of the space of states. A series of theorems related to the embedding and the
Schroedinger evolution with a random Hamiltonian was proven and shown to be
applicable to the process of measurement in classical and quantum mechanics.
Here, these results are applied to have a fresh look at the main
quantum-mechanical thought experiments and paradoxes and to provide a new
insight into the process of collapse and the motion of macroscopic bodies in
quantum mechanics.


title: Decomposed Meta-Learning for Few-Shot Named Entity Recognition

abstract:
  Few-shot named entity recognition (NER) systems aim at recognizing
novel-class named entities based on only a few labeled examples. In this paper,
we present a decomposed meta-learning approach which addresses the problem of
few-shot NER by sequentially tackling few-shot span detection and few-shot
entity typing using meta-learning. In particular, we take the few-shot span
detection as a sequence labeling problem and train the span detector by
introducing the model-agnostic meta-learning (MAML) algorithm to find a good
model parameter initialization that could fast adapt to new entity classes. For
few-shot entity typing, we propose MAML-ProtoNet, i.e., MAML-enhanced
prototypical networks to find a good embedding space that can better
distinguish text span representations from different entity classes. Extensive
experiments on various benchmarks show that our approach achieves superior
performance over prior methods.


title:
A Collection of Deep Learning-based Feature-Free Approaches for
  Characterizing Single-Objective Continuous Fitness Landscapes

abstract:
  Exploratory Landscape Analysis is a powerful technique for numerically
characterizing landscapes of single-objective continuous optimization problems.
Landscape insights are crucial both for problem understanding as well as for
assessing benchmark set diversity and composition. Despite the irrefutable
usefulness of these features, they suffer from their own ailments and
downsides. Hence, in this work we provide a collection of different approaches
to characterize optimization landscapes. Similar to conventional landscape
features, we require a small initial sample. However, instead of computing
features based on that sample, we develop alternative representations of the
original sample. These range from point clouds to 2D images and, therefore, are
entirely feature-free. We demonstrate and validate our devised methods on the
BBOB testbed and predict, with the help of Deep Learning, the high-level,
expert-based landscape properties such as the degree of multimodality and the
existence of funnel structures. The quality of our approaches is on par with
methods relying on the traditional landscape features. Thereby, we provide an
exciting new perspective on every research area which utilizes problem
information such as problem understanding and algorithm design as well as
automated algorithm configuration and selection.


title:
Enhancement of Pitch Controllability using Timbre-Preserving Pitch
  Augmentation in FastPitch

abstract:
  The recently developed pitch-controllable text-to-speech (TTS) model, i.e.
FastPitch, was conditioned for the pitch contours. However, the quality of the
synthesized speech degraded considerably for pitch values that deviated
significantly from the average pitch; i.e. the ability to control pitch was
limited. To address this issue, we propose two algorithms to improve the
robustness of FastPitch. First, we propose a novel timbre-preserving
pitch-shifting algorithm for natural pitch augmentation. Pitch-shifted speech
samples sound more natural when using the proposed algorithm because the
speaker's vocal timbre is maintained. Moreover, we propose a training algorithm
that defines FastPitch using pitch-augmented speech datasets with different
pitch ranges for the same sentence. The experimental results demonstrate that
the proposed algorithms improve the pitch controllability of FastPitch.


title: CyNER: A Python Library for Cybersecurity Named Entity Recognition

abstract:
  Open Cyber threat intelligence (OpenCTI) information is available in an
unstructured format from heterogeneous sources on the Internet. We present
CyNER, an open-source python library for cybersecurity named entity recognition
(NER). CyNER combines transformer-based models for extracting
cybersecurity-related entities, heuristics for extracting different indicators
of compromise, and publicly available NER models for generic entity types. We
provide models trained on a diverse corpus that users can readily use. Events
are described as classes in previous research - MALOnt2.0 (Christian et al.,
2021) and MALOnt (Rastogi et al., 2020) and together extract a wide range of
malware attack details from a threat intelligence corpus. The user can combine
predictions from multiple different approaches to suit their needs. The library
is made publicly available.


title: $\Lambda$CDM $\cup$ MOND

abstract:
  It has been demonstrated that the difference between the Renormalised Brane
World (RBW) model and the Lambda Cold Dark Matter ($\Lambda$CDM) model occurs
only at sufficiently distant times. In this paper, it is shown that for
spherically symmetric situations an analog deviation between the RBW model and
Newton's theory occurs at large distances. More precisely, this deviation of
the RBW model is nothing other than the explanation of Milgrom's hypothesis and
follows from itself. Therefore, the results of this paper explains flat
rotation curves of galaxies without dark matter.


title:
New insights on divertor parallel flows, ExB drifts, and fluctuations
  from in situ, two-dimensional probe measurement in the Tokamak \`a
  Configuration Variable

abstract:
  In-situ, two-dimensional (2D) Langmuir probe measurements across a large part
of the TCV divertor are reported in L-mode discharges with and without divertor
baffles. This provides detailed insights into time averaged profiles, particle
fluxes, and fluctuations behavior in different divertor regimes. The presence
of the baffles is shown to substantially increase the divertor neutral pressure
for a given upstream density and to facilitate the access to detachment, an
effect that increases with plasma current. The detailed, 2D probe measurements
allow for a divertor particle balance, including ion flux contributions from
parallel flows and ExB drifts. The poloidal flux contribution from the latter
is often comparable or even larger than the former, such that the divertor
parallel flow direction reverses in some conditions, pointing away from the
target. In most conditions, the integrated particle flux at the outer target
can be predominantly ascribed to ionization along the outer divertor leg,
consistent with a closed-box approximation of the divertor. The exception is a
strongly detached divertor, achieved here only with baffles, where the total
poloidal ion flux even decreases towards the outer target, indicative of
significant plasma recombination. The most striking observation from relative
density fluctuation measurements along the outer divertor leg is the transition
from poloidally uniform fluctuation levels in attached conditions to
fluctuations strongly peaking near the X-point when approaching detachment.


title:
Dealing with quantum computer readout noise through high energy physics
  unfolding methods

abstract:
  Quantum computers have the potential to solve problems that are intractable
to classical computers, nevertheless they have high error rates. One
significant kind of errors is known as Readout Errors. Current methods, as the
matrix inversion and least-squares, are used to unfold (correct) readout
errors. But these methods present many problems like oscillatory behavior and
unphysical outcomes. In 2020 Benjamin Nachman et al. suggested a technique
currently used in HEP, to correct detector effects. This method is known as the
Iterative Bayesian Unfolding (IBU), and they have proven its effectiveness in
mitigating readout errors, avoiding problems of the mentioned methods.
Therefore, the main objective of our thesis is to mitigate readout noise of
quantum computers, using this powerful unfolding method. For this purpose we
generated a uniform distribution in the Yorktown IBM Q Machine, for 5 Qubits,
in order to unfold it by IBU after being distorted by noise. Then we repeated
the same experiment with a Gaussian distribution. Very satisfactory results and
consistent with those of B. Nachman et al., were obtained. After that, we took
a second purpose to explore unfolding in a larger qubit system, where we
succeed to unfold a uniform distribution for 7 Qubits, distorted by noise from
the Melbourne IBM Q Machine. In this case, the IBU method showed much better
results than other techniques.


title:
Backdoor Attack against NLP models with Robustness-Aware Perturbation
  defense

abstract:
  Backdoor attack intends to embed hidden backdoor into deep neural networks
(DNNs), such that the attacked model performs well on benign samples, whereas
its prediction will be maliciously changed if the hidden backdoor is activated
by the attacker defined trigger. This threat could happen when the training
process is not fully controlled, such as training on third-party data-sets or
adopting third-party models. There has been a lot of research and different
methods to defend such type of backdoor attacks, one being robustness-aware
perturbation-based defense method. This method mainly exploits big gap of
robustness between poisoned and clean samples. In our work, we break this
defense by controlling the robustness gap between poisoned and clean samples
using adversarial training step.


title:
Some Liouville-type theorems for the stationary 3D magneto-micropolar
  fluids

abstract:
  In this paper we prove some Liouville-type theorems for the stationary
magneto-micropolar fluids under suitable conditions in three space dimensions.
We first prove that the solutions are trivial under the assumption of certain
growth conditions for the mean oscillations of the potentials. And then we show
similar results assuming that the the solutions are contained in
$L^p(\mathbb{R}^3)$ with $p\in[2,9/2)$. Finally we show the same result for
lower values of $p\in[1,9/4)$ with the further assumption that the solutions
vanish at infinity.


title:
Explaining The New CDF II W-Boson Mass Data In The Georgi-Machacek
  Extension Models

abstract:
  Georgi-Machacek model can hardly account for the new CDF II data on W-boson
mass in its original form. As anticipated, unless additional tree level
$SU(2)_c$ custodial symmetry breaking effects are non-negligible, the new
physics contributions to $\Delta m_W$ is always very small. Our numerical
results show that ordinary GM model can contribute to $\Delta m_W$ a maximal
amount $0.0012$ GeV, which can not explain the new CDF II data on W boson mass.
We propose to introduce small misalignment among the triplet VEVs to increase
$\Delta m_W$, which can reach 0.06 GeV for $v_\xi-v_\chi\sim 6$ GeV, marginally
explain the new data in $2\sigma$ range. We also propose to extend the GM model
with low scale RH neutrino sector, which can adopt the leptogenesis mechanism
and act as a large $SU(2)_c$ breaking source because of its correlation with
$h_{ij}$. With low scale RH neutrino mass scale of order $10^2\sim 10^4$ TeV,
the new physics contributions to $\Delta m_W$ can reach $0.03$ GeV. Combining
both small $SU(2)_c$ breaking effects, the small misalignment among the triplet
VEVs and large $h_{ij}$ couplings, the $1\sigma$ range of CDF II data on W
boson mass can be obtained even for small splitting among the triplet VEVs with
$v_\xi-v_\chi\approx 1 $ GeV.


title:
Generalization and stabilization of exact scattering solutions for
  spherical symmetric scatterers

abstract:
  In a previous work the authors described a fast high-fidelity computer model
for acoustic scattering from multi-layered elastic spheres. This work is now
extended with a scaling strategy significantly mitigating the problem of
overflow and thus expanding the useful frequency range of the model. % Several
new capabilities are also implemented.
  Moreover, new boundary conditions and loads are implemented. Most important
are the fluid-fluid and solid-solid couplings, which allow a completely general
layering of the scattering object. Sound hard and sound soft boundary
conditions are implemented for solids and fluids respectively. In addition to
the existing acoustic excitation, mechanical excitation in the form of
point-excitation and surface excitation are implemented. Attenuation in the
form of hysteresis damping as well as viscous fluid layers are also included.
  Several numerical examples are included, with the purpose of validating the
code against existing reference solutions. The examples include air bubbles, a
coated steel shell and a point-exited steel shell.


title: Nanomatrix: Scalable Construction of Crowded Biological Environments

abstract:
  We present a novel method for interactive construction and rendering of
extremely large molecular scenes, capable of representing multiple biological
cells at atomistic detail. Our method is tailored for scenes, which are
procedurally constructed, based on a given set of building rules. Rendering of
large scenes normally requires the entire scene available in-core, or
alternatively, it requires out-of-core management to load data into the memory
hierarchy as a part of the rendering loop. Instead of out-of-core memory
management, we propose to procedurally generate the scene on-demand on the fly.
The key idea is a positional- and view-dependent procedural scene-construction
strategy, where only a fraction of the atomistic scene around the camera is
available in the GPU memory at any given time. The atomistic detail is
populated into a uniform-space partitioning using a grid that covers the entire
scene. Most of the grid cells are not filled with geometry, only those are
populated that are potentially seen by the camera. The atomistic detail is
populated in a compute shader and its representation is connected with
acceleration data structures for hardware ray-tracing of modern GPUs. Objects
which are far away, where atomistic detail is not perceivable from a given
viewpoint, are represented by a triangle mesh mapped with a seamless texture,
generated from the rendering of geometry from atomistic detail. The algorithm
consists of two pipelines, the construction computes pipeline and the rendering
pipeline, which work together to render molecular scenes at an atomistic
resolution far beyond the limit of the GPU memory containing trillions of
atoms. We demonstrate our technique on multiple models of SARS-CoV-2 and the
red blood cell.


title: Discretised Hilbert Space and Superdeterminism

abstract:
  In computational physics it is standard to approximate continuum systems with
discretised representations. Here we consider a specific discretisation of the
continuum complex Hilbert space of quantum mechanics - a discretisation where
squared amplitudes and complex phases are rational numbers. The fineness of
this discretisation is determined by a finite (prime-number) parameter $p$. As
$p \rightarrow \infty$, unlike standard discretised representations in
computational physics, this model does not tend smoothly to the continuum
limit. Instead, the state space of quantum mechanics is a singular limit of the
discretised model at $p=\infty$. Using number theoretic properties of
trigonometric functions, it is shown that for large enough values of $p$,
discretised Hilbert space accurately describes ensemble representations of
quantum systems within an inherently superdeterministic framework, one where
the Statistical Independence assumption in Bell's theorem is formally violated.
In this sense, the discretised model can explain the violation of Bell
inequalities without appealing to nonlocality or indefinite reality. It is
shown that this discretised framework is not fine tuned (and hence not
conspiratorial) with respect to its natural state-space $p$-adic metric. As
described by Michael Berry, old theories of physics are typically the singular
limits of new theories as a parameter of the new theory is set equal to zero or
infinity. Using this, we can answer the challenge posed by Scott Aaronson,
critic of superderminism: to explain when a great theory in physics (here
quantum mechanics) has ever been `grudgingly accommodated' rather than
`gloriously explained' by its candidate successor theory (here a
superdeterministic theory of quantum physics based on discretised Hilbert
space).


title:
Examining the Proximity of Adversarial Examples to Class Manifolds in
  Deep Networks

abstract:
  Deep neural networks achieve remarkable performance in multiple fields.
However, after proper training they suffer from an inherent vulnerability
against adversarial examples (AEs). In this work we shed light on inner
representations of the AEs by analysing their activations on the hidden layers.
We test various types of AEs, each crafted using a specific norm constraint,
which affects their visual appearance and eventually their behavior in the
trained networks. Our results in image classification tasks (MNIST and
CIFAR-10) reveal qualitative differences between the individual types of AEs,
when comparing their proximity to the class-specific manifolds on the inner
representations. We propose two methods that can be used to compare the
distances to class-specific manifolds, regardless of the changing dimensions
throughout the network. Using these methods, we consistently confirm that some
of the adversarials do not necessarily leave the proximity of the manifold of
the correct class, not even in the last hidden layer of the neural network.
Next, using UMAP visualisation technique, we project the class activations to
2D space. The results indicate that the activations of the individual AEs are
entangled with the activations of the test set. This, however, does not hold
for a group of crafted inputs called the rubbish class. We also confirm the
entanglement of adversarials with the test set numerically using the soft
nearest neighbour loss.


title: Membership-Mappings for Practical Secure Distributed Deep Learning

abstract:
  This study leverages the data representation capability of fuzzy based
membership-mappings for practical secure distributed deep learning using fully
homomorphic encryption. The impracticality issue of secure machine (deep)
learning with fully homomorphic encrypted data, arising from large
computational overhead, is addressed via applying fuzzy attributes. Fuzzy
attributes are induced by globally convergent and robust variational
membership-mappings based local deep models. Fuzzy attributes combine the local
deep models in a robust and flexible manner such that the global model can be
evaluated homomorphically in an efficient manner using a boolean circuit
composed of bootstrapped binary gates. The proposed method, while preserving
privacy in a distributed learning scenario, remains accurate, practical, and
scalable. The method is evaluated through numerous experiments including
demonstrations through MNIST dataset and Freiburg Groceries Dataset. Further, a
biomedical application related to mental stress detection on individuals is
considered.


title:
Prospective pathways of green graphene-based lab-on-chip devices: the
  pursuit toward sustainability

abstract:
  At present, analytical lab-on-chip devices find their usage in different
facets of chemical analysis, biological analysis, point of care analysis,
biosensors, etc. In addition, graphene has already established itself as an
essential component of advanced lab-on-chip devices. Graphene-based lab-on-chip
devices have achieved appreciable admiration because of their peerless
performance in comparison to others. However, to accomplish a sustainable
future a device must undergo Green-Screening to check its environmental
compatibility. Thus, extensive research is carried out globally to make the
graphene-based lab-on-chip green, though it is yet to be achieved.
Nevertheless, as a ray of hope, there are few existing strategies that can be
stitched together for feasible fabrication of environment-friendly green
graphene-based analytical lab-on-chip, and those prospective pathways are
reviewed in this paper.


title: A brute-force search for R-symmetric Wess-Zumino models

abstract:
  This work makes an exhaustive search for generic R-symmetric Wess-Zumino
models with up to 5 chiral fields, and checks the consistency of their vacuum
solutions with predictions from the Nelson-Seiberg theorem and its
generalization. Each model is recorded as the R-charge assignment of fields,
which uniquely determines the cubic polynomial superpotentials with generic
coefficients. Redundancy from permutation symmetries and reducible models are
properly eliminated in the searching algorithm. We found that among 925 models
in total, 20 of them violate the Nelson-Seiberg theorem, with SUSY vacua which
the theorem fails to predict. However, all these counterexamples have their
specific R-charge assignments covered by the previously found sufficient
condition. The search result can be used to estimate the accuracy of the field
counting method for finding SUSY models in the string landscape. More
applications of the dataset are expected in future work.


title:
High Fidelity 12-Mode Quantum Photonic Processor Operating at InGaAs
  Quantum Dot Wavelength

abstract:
  Reconfigurable quantum photonic processors are an essential technology for
photonic quantum computing. Although most large-scale reconfigurable quantum
photonic processors were demonstrated at the telecommunications C band around
1550 nm, high-performance single photon light sources utilizing quantum dots
that are well-suited for photonic quantum computing operate at a variety of
wavelengths. Thus, a demand exists for the compatibility of quantum photonic
processors with a larger wavelength range. Silicon nitride (SiN) has a high
confinement and wide transparency window, enabling compact, low-loss quantum
photonic processors at wavelengths outside the C band. Here, we report a SiN
universal 12-mode quantum photonic processor with optimal operation at a
wavelength of 940 nm, which is compatible with InGaAs quantum dot light sources
that emit light in the 900 nm to 970 nm wavelength range. The processor can
implement arbitrary unitary transformations on its 12 input modes with a
fidelity of 98.6 %, with a mean optical loss of 3.4 dB/mode.


title: On irrationality measure functions for several real numbers

abstract:
  We prove a new result about the mutual behavior of irrationality measure
functions $\psi_{\alpha_j}(t)$ for $n$ different real numbers $\alpha_j, j = 1,
\dots, n$.


title: Non-factorisable contribution to $t$-channel single-top production

abstract:
  We compute the non-factorisable ${\cal O}(\alpha_s^2)$ corrections to
$t$-channel single-top quark production at the LHC. These peculiar corrections
arise because of interactions between the heavy- and the light-quark lines and
appear for the very first time at next-to-next-to-leading order in perturbative
QCD. We find that the non-factorisable corrections change the single-top
production cross section and the relevant kinematic distributions in this
process by about half a percent.


title:
Molten globule-like transition state of protein barnase measured with
  calorimetric force spectroscopy

abstract:
  Understanding how proteins fold into their native structure is a fundamental
problem in biophysics, crucial for protein design. It has been hypothesized
that the formation of a molten globule intermediate precedes folding to the
native conformation of globular proteins; however, its thermodynamic properties
are poorly known. We perform single-molecule pulling experiments of protein
barnase in the range of 7$^\circ$C to 37$^\circ$C using a temperature-jump
optical trap. We derive the folding free energy, entropy and enthalpy, and heat
capacity change ($\Delta C_p = 1050\pm50$ cal/mol$\cdot$K) at low ionic
strength conditions. From the measured unfolding and folding kinetic rates, we
also determine the thermodynamic properties of the transition state, finding a
significant change in $\Delta C_p$ ($\sim$ 90$\%$) between the unfolded and the
transition states. In contrast, the major change in enthalpy ($\sim$ 80$\%$)
occurs between the transition and native states. These results highlight a
transition state of high energy and low configurational entropy structurally
similar to the native state, in agreement with the molten globule hypothesis.


title: Temperature dependent latency of jumper cables

abstract:
  The temperature dependence of the latency of an optical signal transmitted
over an optical fiber was investigated by means of a correlation OTDR. While
the bare fiber showed a linear latency increase over temperature, the jumper
cable inside a jacket with tight buffer as inner jacket showed an increased
elongation effect, especially in the range of lower tempera-tures. This could
be attributed to the tight buffer. For future latency sensitive applications in
the area of 5G networks these effects have to be taken into account.


title: Binary Control Pulse Optimization for Quantum Systems

abstract:
  Quantum control aims to manipulate quantum systems toward specific quantum
states or desired operations. Designing highly accurate and effective control
steps is vitally important to various quantum applications, including energy
minimization and circuit compilation. In this paper we focus on discrete binary
quantum control problems and apply different optimization algorithms and
techniques to improve computational efficiency and solution quality.
Specifically, we develop a generic model and extend it in several ways. We
introduce a squared $L_2$-penalty function to handle additional side
constraints, to model requirements such as allowing at most one control to be
active. We introduce a total variation (TV) regularizer to reduce the number of
switches in the control. We modify the popular gradient ascent pulse
engineering (GRAPE) algorithm, develop a new alternating direction method of
multipliers (ADMM) algorithm to solve the continuous relaxation of the
penalized model, and then apply rounding techniques to obtain binary control
solutions. We propose a modified trust-region method to further improve the
solutions. Our algorithms can obtain high-quality control results, as
demonstrated by numerical studies on diverse quantum control examples.


title: Weak Indestructibility and Reflection

abstract:
  This work is a part of my upcoming thesis [7]. We establish an
equiconsistency between (1) weak indestructibility for all $\kappa +2$-degrees
of strength for cardinals $\kappa $ in the presence of a proper class of strong
cardinals, and (2) a proper class of cardinals that are strong reflecting
strongs. We in fact get weak indestructibility for degrees of strength far
beyond $\kappa +2$, well beyond the next inaccessible limit of measurables (of
the ground model). One direction is proven using forcing and the other using
core model techniques from inner model theory. Additionally, connections
between weak indestructibility and the reflection properties associated with
Woodin cardinals are discussed.


title:
Numerical Regge pole analysis of resonance structures in state-to-state
  reactive differential cross sections

abstract:
  This is the third (and the last) code in a collection of three programs
[Sokolovski et al (2011), Akhmatskaya et al (2014)] dedicated to the analysis
of numerical data, obtained in an accurate simulation of an atom-diatom
chemical reaction. Our purpose is to provide a detailed description of a
FORTRAN code for complex angular momentum (CAM) analysis of the resonance
effects in reactive angular scattering [for CAM analysis of integral reactive
cross sections see [Akhmatskaya et al (2014)]. The code evaluates the
contributions of a Regge trajectory (or trajectories) to a differential cross
section in a specified range of energies. The contribution is computed with the
help of the methods described in [Dobbyn et al (2007), Sokolovski and Msezane
(2004), Sokolovski et al (2007)]. Regge pole positions and residues are
obtained by analytically continuing S-matrix element, calculated numerically
for the physical integer values of the total angular momentum, into the complex
angular momentum plane using the PADE II program [Sokolovski et al (2011)]. The
code represents a reactive scattering amplitude as a sum of the components
corresponding to a rapid "direct" exchange of the atom, and the various
scenarios in which the reactants form long-lived intermediate complexes, able
to complete several rotations before breaking up into products. The package has
been successfully tested on the representative models, as well as the F +
H2->HF+H benchmark reaction. Several detailed examples are given in the text.
PACS:34.50.Lf,34.50.Pi


title:
GORDA: Graph-based ORientation Distribution Analysis of SLI
  scatterometry Patterns of Nerve Fibres

abstract:
  Scattered Light Imaging (SLI) is a novel approach for microscopically
revealing the fibre architecture of unstained brain sections. The measurements
are obtained by illuminating brain sections from different angles and measuring
the transmitted (scattered) light under normal incidence. The evaluation of
scattering profiles commonly relies on a peak picking technique and feature
extraction from the peaks, which allows quantitative determination of parallel
and crossing in-plane nerve fibre directions for each image pixel. However, the
estimation of the 3D orientation of the fibres cannot be assessed with the
traditional methodology. We propose an unsupervised learning approach using
spherical convolutions for estimating the 3D orientation of neural fibres,
resulting in a more detailed interpretation of the fibre orientation
distributions in the brain.


title: The first Cotangent Cohomology Module for Matroids

abstract:
  We find a combinatorial formula which computes the first cotangent cohomology
module of Stanley-Reisner rings associated to matroids. For arbitrary
simplicial complexes we provide upper bounds for the dimensions of the
multigraded components of T^1. For specific degrees we prove that these bounds
are reached if and only if the simplicial complex is a matroid, obtaining thus
a new characterization for matroids. Furthermore, the graded first cotangent
cohomology turns out to be a complete invariant for nondiscrete matroids.


title:
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with
  impured training data

abstract:
  The detection of lesions in magnetic resonance imaging (MRI)-scans of human
brains remains challenging, time-consuming and error-prone. Recently,
unsupervised anomaly detection (UAD) methods have shown promising results for
this task. These methods rely on training data sets that solely contain healthy
samples. Compared to supervised approaches, this significantly reduces the need
for an extensive amount of labeled training data. However, data labelling
remains error-prone. We study how unhealthy samples within the training data
affect anomaly detection performance for brain MRI-scans. For our evaluations,
we consider three publicly available data sets and use autoencoders (AE) as a
well-established baseline method for UAD. We systematically evaluate the effect
of impured training data by injecting different quantities of unhealthy samples
to our training set of healthy samples from T1-weighted MRI-scans. We evaluate
a method to identify falsely labeled samples directly during training based on
the reconstruction error of the AE. Our results show that training with impured
data decreases the UAD performance notably even with few falsely labeled
samples. By performing outlier removal directly during training based on the
reconstruction-loss, we demonstrate that falsely labeled data can be detected
and removed to mitigate the effect of falsely labeled data. Overall, we
highlight the importance of clean data sets for UAD in brain MRI and
demonstrate an approach for detecting falsely labeled data directly during
training.


title: Towards Polyglot Data Stores -- Overview and Open Research Questions

abstract:
  Nowadays, data-intensive applications face the problem of handling
heterogeneous data with sometimes mutually exclusive use cases and soft
non-functional goals such as consistency and availability. Since no single
platform copes everything, various stores (RDBMS, NewSQL, NoSQL) for different
workloads and use-cases have been developed. However, since each store is only
a specialization, this motivates progress in polyglot data management emerged
new systems called Mult- and Polystores. They are trying to access different
stores transparently and combine their capabilities to achieve one or multiple
given use-cases. This paper describes representative real-world use cases for
data-intensive applications (OLTP and OLAP). It derives a set of requirements
for polyglot data stores. Subsequently, we discuss the properties of selected
Multi- and Polystores and evaluate them based on given needs illustrated by
three common application use cases. We classify them into functional features,
query processing technique, architecture and adaptivity and reveal a lack of
capabilities, especially in changing conditions tightly integration. Finally,
we outline the benefits and drawbacks of the surveyed systems and propose
future research directions and current challenges in this area.


title:
A Machine Learning and Computer Vision Approach to Geomagnetic Storm
  Forecasting

abstract:
  Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of
charged particles being emitted from the Sun, are an uncontrollable threat to
modern technology. Notably, they have the potential to damage satellites and
cause instability in power grids on Earth, among other disasters. They result
from high sun activity, which are induced from cool areas on the Sun known as
sunspots. Forecasting the storms to prevent disasters requires an understanding
of how and when they will occur. However, current prediction methods at the
National Oceanic and Atmospheric Administration (NOAA) are limited in that they
depend on expensive solar wind spacecraft and a global-scale magnetometer
sensor network. In this paper, we introduce a novel machine learning and
computer vision approach to accurately forecast geomagnetic storms without the
need of such costly physical measurements. Our approach extracts features from
images of the Sun to establish correlations between sunspots and geomagnetic
storm classification and is competitive with NOAA's predictions. Indeed, our
prediction achieves a 76% storm classification accuracy. This paper serves as
an existence proof that machine learning and computer vision techniques provide
an effective means for augmenting and improving existing geomagnetic storm
forecasting methods.


title:
Cryptocurrency Return Prediction Using Investor Sentiment Extracted by
  BERT-Based Classifiers from News Articles, Reddit Posts and Tweets

abstract:
  This paper studies the extent at which investor sentiment contributes to
cryptocurrency return prediction. Investor sentiment is extracted from news
articles, Reddit posts and Tweets using BERT-based classifiers fine-tuned on
this specific text data. As this data is unlabeled, a weak supervision approach
by pseudo-labeling using a zero-shot classifier is used. Contribution of
sentiment is then examined using a variety of machine learning models. Each
model is trained on data with and without sentiment separately. The conclusion
is that sentiment leads to higher prediction accuracy and additional investment
profit when the models are analyzed collectively, although this does not hold
true for every single model.


title:
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by
  a Linear Dynamical System

abstract:
  The stochastic multi-armed bandit has provided a framework for studying
decision-making in unknown environments. We propose a variant of the stochastic
multi-armed bandit where the rewards are sampled from a stochastic linear
dynamical system. The proposed strategy for this stochastic multi-armed bandit
variant is to learn a model of the dynamical system while choosing the optimal
action based on the learned model. Motivated by mathematical finance areas such
as Intertemporal Capital Asset Pricing Model proposed by Merton and Stochastic
Portfolio Theory proposed by Fernholz that both model asset returns with
stochastic differential equations, this strategy is applied to quantitative
finance as a high-frequency trading strategy, where the goal is to maximize
returns within a time period.


title:
Stock Price Prediction using Sentiment Analysis and Deep Learning for
  Indian Markets

abstract:
  Stock market prediction has been an active area of research for a
considerable period. Arrival of computing, followed by Machine Learning has
upgraded the speed of research as well as opened new avenues. As part of this
research study, we aimed to predict the future stock movement of shares using
the historical prices aided with availability of sentiment data. Two models
were used as part of the exercise, LSTM was the first model with historical
prices as the independent variable. Sentiment Analysis captured using Intensity
Analyzer was used as the major parameter for Random Forest Model used for the
second part, some macro parameters like Gold, Oil prices, USD exchange rate and
Indian Govt. Securities yields were also added to the model for improved
accuracy of the model. As the end product, prices of 4 stocks viz. Reliance,
HDFC Bank, TCS and SBI were predicted using the aforementioned two models. The
results were evaluated using RMSE metric.


title:
A Note on the Blockchain Trilemma for Decentralized Identity: Learning
  from Experiments with Hyperledger Indy

abstract:
  The challenge to establish and verify human identity over the Internet in a
secure and privacy-respecting way is long-standing. In this paper, we explore
the blockchain trilemma of scalability, security, and decentralization in the
context of the Trust Registry: a root of trust for a decentralized identity
scheme that enables read and write access to shared records and is
tamper-resistant. We make a case study of Hyperledger Indy -- an open-source
technology bespoke for decentralized identity -- and conduct two empirical
experiments to measure the latency of more than 45,000 transactions in the
naturalistic environment of Amazon Web Services. We conclude that issues of
Trust Registry scalability have multiple facets. While Hyperledger Indy
captures data useful to underpin a decentralized identity scheme, the knock-on
effect of its scalability limitations may indeed place constraints on
properties of security and decentralization. The current credential
verification process relies on transaction processing by a ledger with
transaction processing bottlenecks, which may constrain the ideal of
non-repudiation.


title:
The structure of cluster merger shocks: turbulent width and the electron
  heating timescale

abstract:
  We present a new 2 Ms Chandra observation of the cluster merger Abell 2146,
which hosts two huge M~2 shock fronts each ~500 kpc across. For the first time,
we resolve and measure the width of cluster merger shocks. The best-fit width
for the bow shock is 17+/-1 kpc and for the upstream shock is 10.7+/-0.3 kpc. A
narrow collisionless shock will appear broader in projection if its smooth
shape is warped by local gas motions. We show that both shock widths are
consistent with collisionless shocks blurred by local gas motions of 290+/-30
km/s. The upstream shock forms later on in the merger than the bow shock and is
therefore expected to be significantly narrower. From the electron temperature
profile behind the bow shock, we measure the timescale for the electrons and
ions to come back into thermal equilibrium. We rule out rapid thermal
equilibration of the electrons with the shock-heated ions at the 6 sigma level.
The observed temperature profile instead favours collisional equilibration. We
find no evidence for electron heating over that produced by adiabatic
compression. This supports the existing picture from collisionless shocks in
the solar wind and supernova remnants. The upstream shock is consistent with
this result but has a more complex structure, including a ~2 keV increase in
temperature ~50 kpc ahead of the shock.


title:
A Simulation Driven Deep Learning Approach for Separating Mergers and
  Star Forming Galaxies: The Formation Histories of Clumpy Galaxies in all the
  CANDELS Fields

abstract:
  Being able to distinguish between galaxies that have recently undergone major
merger events, or are experiencing intense star formation, is crucial for
making progress in our understanding of the formation and evolution of
galaxies. As such, we have developed a machine learning framework based on a
convolutional neural network (CNN) to separate star forming galaxies from
post-mergers using a dataset of 160,000 simulated images from IllustrisTNG100
that resemble observed deep imaging of galaxies with Hubble. We improve upon
previous methods of machine learning with imaging by developing a new approach
to deal with the complexities of contamination from neighbouring sources in
crowded fields and define a quality control limit based on overlapping sources
and background flux. Our pipeline successfully separates post-mergers from star
forming galaxies in IllustrisTNG $80\%$ of the time, which is an improvement by
at least 25\% in comparison to a classification using the asymmetry ($A$) of
the galaxy. Compared with measured S\'ersic profiles, we show that star forming
galaxies in the CANDELS fields are predominantly disc-dominated systems while
post-mergers show distributions of transitioning discs to bulge-dominated
galaxies. With these new measurements, we trace the rate of post-mergers among
asymmetric galaxies in the universe finding an increase from $20\%$ at $z=0.5$
to $50\%$ at $z=2$. Additionally, we do not find strong evidence that the
scattering above the Star Forming Main Sequence (SFMS) can be attributed to
major post-mergers. Finally, we use our new approach to update our previous
measurements of galaxy merger rates $\mathcal{R} = 0.022 \pm 0.006 \times
(1+z)^{2.71\pm0.31}$


title:
Biermann Battery Powered by Resistive Heating Induced by Cosmic Ray
  Streaming

abstract:
  It is recently proposed that cosmic rays generate a seed magnetic field in
the early universe. In this paper, we propose another generation mechanism of
magnetic fields by cosmic rays, which is the Biermann battery driven by
resistive heating induced by the streaming of cosmic rays. This new mechanism
is dominant in small-scale, low-temperature, and strongly-ionized regions,
compared with other previously proposed mechanisms. Because cosmic rays are
expected to be accelerated after the death of the first stars, this mechanism
can work during structure formation in the early universe. We show that it
makes the seed magnetic field with sufficient strength for the subsequent
dynamo to amplify it to the micro Gauss level in the current galaxies.


title:
Subgroups of hyperbolic groups, finiteness properties and complex
  hyperbolic lattices

abstract:
  We prove that in a cocompact complex hyperbolic arithmetic lattice $\Gamma <
{\rm PU}(m,1)$ of the simplest type, deep enough finite index subgroups admit
plenty of homomorphisms to $\mathbb{Z}$ with kernel of type $\mathscr{F}_{m-1}$
but not of type $\mathscr{F}_{m}$. This provides many finitely presented
non-hyperbolic subgroups of hyperbolic groups and answers an old question of
Brady. Our method also yields a proof of a special case of Singer's conjecture
for aspherical K\"ahler manifolds.


title: On the applicability of holography in thermodynamic equilibrium

abstract:
  For a strongly coupled system that has a gravity dual description, we show
that the standard holographic dictionary yields a nonnegative susceptibility
when the system is in thermodynamic equilibrium. When the system has no
spontaneous condensation or has a spontaneous $\mathbb{Z}_2$-symmetry breaking,
we find that the "trace energy condition" must be violated in many cases. There
is a normalized grand potential density is monotonic as accessing to lower
scales, providing a candidate $c$-function characterizing the number of
effective degrees of freedom. Moreover, we find that some extensively discussed
holography models in the literature are not consistent with holography.


title:
Generalized warped products and the $\kappa$-nullity of Riemannian
  curvature

abstract:
  In this short survey, we show how two (classes of) known examples of
inhomogeneous, curvature homogeneous Riemannian manifolds with nontrivial
$\kappa$-nullity can be seen as deformations of homogeneous metrics along the
vertical distribution of an integrable Riemannian submersion. We also pose two
open questions.


title: Square coloring planar graphs with automatic discharging

abstract:
  The discharging method is a powerful proof technique, especially for graph
coloring problems. Its major downside is that it often requires lengthy case
analyses, which are sometimes given to a computer for verification. However, it
is much less common to use a computer to actively look for a discharging proof.
In this paper, we use a Linear Programming approach to automatically look for a
discharging proof. While our system is not entirely autonomous, we manage to
make some progress towards Wegner's conjecture for distance-$2$ coloring of
planar graphs, by showing that $12$ colors are sufficient to color at distance
$2$ every planar graph with maximum degree $4$.


title: High-dimensional nonconvex lasso-type $M$-estimators

abstract:
  This paper proposes a theory for $\ell_1$-norm penalized high-dimensional
$M$-estimators, with nonconvex risk and unrestricted domain. Under high-level
conditions, the estimators are shown to attain the rate of convergence
$s_0\sqrt{\log(nd)/n}$, where $s_0$ is the number of nonzero coefficients of
the parameter of interest. Sufficient conditions for our main assumptions are
then developed and finally used in several examples including robust linear
regression, binary classification and nonlinear least squares.


title: Coarse Personalization

abstract:
  Advances in heterogeneous treatment effects estimation enable firms to
personalize marketing mix elements and target individuals at an unmatched level
of granularity, but feasibility constraints limit such personalization. In
practice, firms choose which unique treatments to offer and which individuals
to assign to each treatment to maximize profits, and we denote the firm's
problem as the coarse personalization problem. We solve the coarse
personalization problem using a two-step procedure that forms segmentation and
targeting decisions in concert. First, the firm personalizes by estimating
conditional average treatment effects. Second, the firm discretizes by
utilizing treatment effects to choose which unique treatments to offer and who
to assign to these treatments. The second step is the main contribution of our
paper and is a novel application of optimal transport methods. We adapt Lloyd's
Algorithm to computationally solve the second step. With data from a
large-scale field experiment for promotions management, we find that our
methodology outperforms classical marketing techniques that segment on consumer
characteristics or preferences. Using our procedure, the firm recoups over
99.5% of its expected incremental profits under fully granular personalization
while offering only five unique treatments. We conclude by discussing how
coarse personalization arises in other marketing areas.


title:
Generation of highly retrievable atom photon entanglement with a
  millisecond lifetime via a spatially multiplexed cavity

abstract:
  Qubit memory that is entangled with photonic qubit is the building block for
long distance quantum repeaters. Cavity enhanced and long lived spin wave
photon entanglement has been demonstrated by applying dual laser beams onto
optical-lattice atoms. However, owing to cross readouts by two beams, retrieval
efficiency of spin wave qubit is decreased by one quarter compared to that of
single mode spin wave at all storage times. Here, by coupling cold atoms to two
modes of a polarization interferometer based cavity, we achieve perfect qubit
retrieval in cavity enhanced and long lived atom photon entanglement. A write
laser beam is applied onto cold atoms, we then create a magnetic field
insensitive spin wave qubit that is entangled with the photonic qubit encoded
onto two arms of the interferometer. The spin wave qubit is retrieved by a read
beam, which avoids the cross readouts. Our experiment demonstrates 540{\mu}s
storage time at 50% intrinsic qubit retrieval efficiency, which is 13.5 times
longer than the best reported result.


title: Surrogate Ensemble Forecasting for Dynamic Climate Impact Models

abstract:
  As acute climate change impacts weather and climate variability, there is
increased demand for robust climate impact model predictions from which
forecasts of the impacts can be derived. The quality of those predictions are
limited by the climate drivers for the impact models which are nonlinear and
highly variable in nature. One way to estimate the uncertainty of the model
drivers is to assess the distribution of ensembles of climate forecasts. To
capture the uncertainty in the impact model outputs associated with the
distribution of the input climate forecasts, each individual forecast ensemble
member has to be propagated through the physical model which can imply high
computational costs. It is therefore desirable to train a surrogate model which
allows predictions of the uncertainties of the output distribution in ensembles
of climate drivers, thus reducing resource demands. This study considers a
climate driven disease model, the Liverpool Malaria Model (LMM), which predicts
the malaria transmission coefficient R0. Seasonal ensembles forecasts of
temperature and precipitation with a 6-month horizon are propagated through the
model, predicting the distribution of transmission time series. The input and
output data is used to train surrogate models in the form of a Random Forest
Quantile Regression (RFQR) model and a Bayesian Long Short-Term Memory (BLSTM)
neural network. Comparing the predictive performance, the RFQR better predicts
the time series of the individual ensemble member, while the BLSTM offers a
direct way to construct a combined distribution for all ensemble members. An
important element of the proposed methodology is that accounting for non-normal
distributions of climate forecast ensembles can be captured naturally by a
Bayesian formulation.


title:
A deep learning method for solving stochastic optimal control problems
  driven by fully-coupled FBSDEs

abstract:
  In this paper, we mainly focus on the numerical solution of high-dimensional
stochastic optimal control problem driven by fully-coupled forward-backward
stochastic differential equations (FBSDEs in short) through deep learning. We
first transform the problem into a stochastic Stackelberg differential
game(leader-follower problem), then a cross-optimization method (CO method) is
developed where the leader's cost functional and the follower's cost functional
are optimized alternatively via deep neural networks. As for the numerical
results, we compute two examples of the investment-consumption problem solved
through stochastic recursive utility models, and the results of both examples
demonstrate the effectiveness of our proposed algorithm.


title:
Sub-percent accuracy for the intensity of a near infrared water line at
  10670 cm$^{-1}$: experiment and analysis

abstract:
  Laser measurements of the intensity of (201) 3$_{22}$ -- (000) 2$_{21}$
near-infrared water absorption line at 10670.1 \cm\ are made using three
different Herriott cells. These measurements determine the line intensity with
an standard deviation below of 0.3~\% by consideration of the new geometrically
derived formula for the opt ical path length without approximations. This
determination together with the current accepted value leads to an overall
uncertainty of 0.7~\% of the experimentally assessed line intensity which is
compared with previous {\it ab initio} predictions. It is found that steady
improvements in the both the dipole moment surface (DMS) and potential energy
surface (PES) used in the theoretical studies leads to systematic better
agreement with the observation, with the most recent prediction agreeing
closely with the experiment.


title: Multi-View Breast Cancer Classification via Hypercomplex Neural Networks

abstract:
  Traditionally, deep learning-based methods for breast cancer classification
perform a single-view analysis. However, radiologists simultaneously analyze
all four views that compose a mammography exam, owing to the correlations
contained in mammography views, which present crucial information for
identifying tumors. In light of this, some studies have started to propose
multi-view methods. Nevertheless, in such existing architectures, mammogram
views are processed as independent images by separate convolutional branches,
thus losing correlations among them. To overcome such limitations, in this
paper we propose a novel approach for multi-view breast cancer classification
based on parameterized hypercomplex neural networks. Thanks to hypercomplex
algebra properties, our networks are able to model, and thus leverage, existing
correlations between the different views that comprise a mammogram exam, thus
mimicking the reading process performed by clinicians. As a consequence, the
proposed method is able to handle the information of a patient altogether
without breaking the multi-view nature of the exam. Starting from the proposed
hypercomplex approach, we define architectures designed to process two-view
exams, namely PHResNets, and four-view exams, i.e., PHYSEnet and PHYSBOnet,
with the ability to grasp inter-view correlations in a wide range of clinical
use cases. Through an extensive experimental evaluation conducted with two
publicly available datasets, CBIS-DDSM and INbreast, we demonstrate that our
parameterized hypercomplex models clearly outperform real-valued counterparts
and also state-of-the-art methods, proving that breast cancer classification
benefits from the proposed multi-view architecture. Full code and pretrained
models for complete reproducibility of our experiments are freely available at:
https://github.com/ispamm/PHBreast.


title:
EVOPS Benchmark: Evaluation of Plane Segmentation from RGBD and LiDAR
  Data

abstract:
  This paper provides the EVOPS dataset for plane segmentation from 3D data,
both from RGBD images and LiDAR point clouds (PC). We have designed two
annotation methodologies (RGBD and LiDAR) running on well-known and widely-used
datasets and we have provided a complete set of benchmarking tools including
point, planes and segmentation metrics. The data includes a total number of 10k
RGBD and 7K LiDAR frames over different selected scenes which consist of high
quality segmented planes. The experiments report quality of SOTA methods for
RGBD plane segmentation on our annotated data. All labeled data and benchmark
tools used have been made publicly available.


title:
Images of locally finite $\mathcal{E}$-derivations of bivariate
  polynomial algebras

abstract:
  This paper presents an $\mathcal{E}$-derivation analogue of a result on
derivations due to van den Essen, Wright and Zhao. We prove that the image of a
locally finite $K$-$\mathcal{E}$-derivation of polynomial algebras in two
variables over a field $K$ of characteristic zero is a Mathieu-Zhao subspace.
This result together with that of van den Essen, Wright and Zhao confirms the
LFED conjecture in the case of polynomial algebras in two variables.


title: Generalized quadratic commutator algebras of PBW-type

abstract:
  In recent years, various nonlinear algebraic structures have been obtained in
the context of quantum systems as symmetry algebras, Painlev\'{e} transcendent
models and missing label problems. In this paper we treat all of these algebras
as instances of the class of quadratic (and higher degree) commutator bracket
algebras of PBW type. We provide a general approach for simplifying the
constraints arising from the diamond lemma, and apply this in particular to
give a comprehensive analysis of the quadratic case. We present new examples of
quadratic algebras, which admit a cubic Casimir invariant. The connection with
other approaches such as Gr\"{o}bner bases is developed, and we suggest how our
explicit and computational techniques can be relevant in other contexts.


title: Mechanical signaling cascades

abstract:
  Mechanical computing has seen resurgent interest recently owing to the
potential to embed sensing and computation into new classes of programmable
metamaterials. To realize this, however, one must push signals from one part of
a device to another, and do so in a way that can be reset robustly. We
investigate the propagation of signals in a bistable mechanical cascade uphill
in energy. By identifying a penetration length for perturbations, we show that
signals can propagate uphill for finite distances and map out parameters for
this to occur. Experiments on soft elastomers corroborate our results.


title: Analytic treatment of 3-flavor neutrino oscillation and decay in matter

abstract:
  We present compact analytic expressions for 3-flavor neutrino oscillation
probabilities with invisible neutrino decay, where matter effects have been
explicitly included. We take into account the possibility that the oscillation
and decay components of the effective Hamiltonian do not commute. This is
achieved by employing the techniques of inverse Baker-Campbell-Hausdorff (BCH)
expansion and the Cayley-Hamilton theorem applied in the 3-flavor framework. If
only the vacuum mass eigenstate $\nu_3$ decays, we show that the treatment of
neutrino propagation may be reduced to an effective 2-flavor analysis in the
One Mass Scale Dominance (OMSD) approximation. The oscillation probabilities
for $P_{\mu\mu}$, $P_{ee}$, $P_{e\mu}$ and $P_{\mu e}$ -- relevant for reactor,
long baseline and atmospheric neutrino experiments -- are obtained as
perturbative expansions for the case of only $\nu_3$ decay, as well as for the
more general scenario where all components of the decay matrix are non-zero.
The analytic results thus obtained match the exact numerical results for
constant density matter to a high precision and provide physical insights into
possible effects of the decay of neutrinos as they propagate through Earth
matter. We find that the effects of neutrino decay are most likely to be
observable in $P_{\mu\mu}$. We also point out that at any long baseline, the
oscillation dips in $P_{\mu\mu}$ can show higher survival probabilities in the
case with decay than without decay, and explain this feature using our analytic
approximations.


title: Analytic capacity and dimension of sets with plenty of big projections

abstract:
  Our main result marks progress on an old conjecture of Vitushkin. We show
that a compact set in the plane with plenty of big projections (PBP) has
positive analytic capacity, along with a quantitative lower bound. A higher
dimensional counterpart is also proved for capacities related to the Riesz
kernel, including the Lipschitz harmonic capacity. The proof uses a
construction of a doubling Frostman measure on a lower content regular set,
which may be of independent interest. Our second main result is the Analyst's
Traveling Salesman Theorem for sets with plenty of big projections. As a
corollary, we obtain a lower bound for the Hausdorff dimension of uniformly
wiggly sets with PBP. The second corollary is an estimate for the capacities of
subsets of sets with PBP, in the spirit of the quantitative solution to
Denjoy's conjecture.


title: Learning to Express in Knowledge-Grounded Conversation

abstract:
  Grounding dialogue generation by extra knowledge has shown great potentials
towards building a system capable of replying with knowledgeable and engaging
responses. Existing studies focus on how to synthesize a response with proper
knowledge, yet neglect that the same knowledge could be expressed differently
by speakers even under the same context. In this work, we mainly consider two
aspects of knowledge expression, namely the structure of the response and style
of the content in each part. We therefore introduce two sequential latent
variables to represent the structure and the content style respectively. We
propose a segmentation-based generation model and optimize the model by a
variational approach to discover the underlying pattern of knowledge expression
in a response. Evaluation results on two benchmarks indicate that our model can
learn the structure style defined by a few examples and generate responses in
desired content style.


abstract:
  We propose Variational Heteroscedastic Volatility Model (VHVM) -- an
end-to-end neural network architecture capable of modelling heteroscedastic
behaviour in multivariate financial time series. VHVM leverages recent advances
in several areas of deep learning, namely sequential modelling and
representation learning, to model complex temporal dynamics between different
asset returns. At its core, VHVM consists of a variational autoencoder to
capture relationships between assets, and a recurrent neural network to model
the time-evolution of these dependencies. The outputs of VHVM are time-varying
conditional volatilities in the form of covariance matrices. We demonstrate the
effectiveness of VHVM against existing methods such as Generalised
AutoRegressive Conditional Heteroscedasticity (GARCH) and Stochastic Volatility
(SV) models on a wide range of multivariate foreign currency (FX) datasets.


title:
Research on accurate stereo portrait generation algorithm of scientific
  research team

abstract:
  In order to smoothly promote the establishment of scientific research
projects, accurately identify the excellent scientific research team, and
intuitively and comprehensively describe the scientific research team, it is of
great significance for the scientific research management department to
comprehensively understand and objectively evaluate the scientific research
team. At present, the research work on the construction of accurate
three-dimensional portrait of scientific research team is relatively less. In
view of the practical demand of scientific research management department, this
paper proposes an accurate stereo portrait generation algorithm of scientific
research team. The algorithm includes three modules: research team
identification, research topic extraction and research team portrait
generation. Firstly, the leader of the scientific research team is identified
based on the iterative middle centrality ranking method, and the members of the
scientific research team are identified through the 2-faction and snowball
methods, so as to realize the identification of the scientific research team.
Then, considering the statistical information of words and the co-occurrence
features of words in the research team, the research topics of the research
team are extracted to improve the accuracy of research topic extraction.
Finally, the research team portrait generation module generates the accurate
three-dimensional portrait of the research team through the generation of the
research team profile, the construction of the research cooperation
relationship, and the construction of the research team topic cloud. The
research team is identified on the data set of scientific research
achievements, and the accurate three-dimensional portraits of the research team
are generated and visualized. Experiments verify the effectiveness of the
proposed algorithm.


title: Top degree $\ell^p$-homology and conformal dimension of buildings

abstract:
  For a non-compact finite thickness building whose Davis apartment is an
orientable pseudomanifold, we compute the supremum of the set of $p>1$ such
that its top dimensional reduced $\ell^p$-cohomology is nonzero. We adapt the
non-vanishing assertion of this result to any finite thickness building using
the Bestvina realization. Using similar techniques, we generalize bounds
obtained by Clais on the conformal dimension of some Gromov-hyperbolic
buildings to any such building.


title: 1-Extendability of independent sets

abstract:
  In the 70s, Berge introduced 1-extendable graphs (also called B-graphs),
which are graphs where every vertex belongs to a maximum independent set.
Motivated by an application in the design of wireless networks, we study the
computational complexity of 1-extendability, the problem of deciding whether a
graph is 1-extendable. We show that, in general, 1-extendability cannot be
solved in $2^{o(n)}$ time assuming the Exponential Time Hypothesis, where $n$
is the number of vertices of the input graph, and that it remains NP-hard in
subcubic planar graphs and in unit disk graphs (which is a natural model for
wireless networks). Although 1-extendability seems to be very close to the
problem of finding an independent set of maximum size (a.k.a. Maximum
Independent Set), we show that, interestingly, there exist 1-extendable graphs
for which Maximum Independent Set is NP-hard. Finally, we investigate a
parameterized version of 1-extendability.


title:
Into the Depths: a new activity metric for high-precision radial
  velocity measurements based on line depth variations

abstract:
  The discovery and characterization of extrasolar planets using radial
velocity (RV) measurements is limited by noise sources from the surfaces of
host stars. Current techniques to suppress stellar magnetic activity rely on
decorrelation using an activity indicator (e.g., strength of the Ca II lines,
width of the cross-correlation function, broadband photometry) or measurement
of the RVs using only a subset of spectral lines that have been shown to be
insensitive to activity. Here, we combine the above techniques by constructing
a high signal-to-noise activity indicator, the depth metric $\mathcal{D}(t)$,
from the most activity-sensitive spectral lines using the "line-by-line" method
of Dumusque (2018). Analogous to photometric decorrelation of RVs or Gaussian
progress regression modeling of activity indices, time series modeling of
$\mathcal{D}(t)$ reduces the amplitude of magnetic activity in RV measurements;
in an $\alpha$CenB RV time series from HARPS, the RV RMS was reduced from 2.67
to 1.02 m s$^{-1}$. $\mathcal{D}(t)$ modeling enabled us to characterize
injected planetary signals as small as 1 m s$^{-1}$. In terms of noise
reduction and injected signal recovery, $\mathcal{D}(t)$ modeling outperforms
activity mitigation via the selection of activity-insensitive spectral lines.
For Sun-like stars with activity signals on the m s$^{-1}$ level, the depth
metric independently tracks rotationally modulated and multiyear stellar
activity with a level of quality similar to that of the FWHM of the CCF and
log$R^{\prime}_{HK}$. The depth metric and its elaborations will be a powerful
tool in the mitigation of stellar magnetic activity, particularly as a means of
connecting stellar activity to physical processes within host stars.


title:
Jet-Cocoon Geometry in the Optically Dark, Very High Energy Gamma-ray
  Burst 201216C

abstract:
  We present the results of a radio observing campaign on GRB 201216C, combined
with publicly available optical and X-ray data. The detection of very high
energy (VHE, >100GeV) emission by MAGIC makes this the fifth VHE GRB at time of
publication. Comparison between the optical and X-ray light curves show that
GRB 201216C is a dark GRB, i.e. the optical emission is significantly absorbed
and is fainter than expected from the X-ray detections. Our e-MERLIN data also
shows evidence of diffractive interstellar scintillation. We can study the
column density along the line-of-sight to the GRB in both the host galaxy, from
the damped optical light curve, and the Milky Way, via scintillation studies.
We find that the afterglow is best modelled using a jet-cocoon geometry within
a stellar wind environment. Fitting the data with a multi-component model we
estimate that the optical, X-ray and higher-frequency radio data before ~25days
originates from an ultra-relativistic jet with an isotropic equivalent kinetic
energy of (0.6-10)x10^52erg and an opening angle of ~1-9deg. The
lower-frequency radio emission detected by MeerKAT, from day 28 onwards, is
produced by the cocoon with a kinetic energy that is between two and seven
orders of magnitude lower (0.02-50)x10^48erg. The energies of the two
components are comparable to those derived in simulations of such scenarios.


title:
On the banded Toeplitz structured distance to symmetric positive
  semidefiniteness

abstract:
  This paper is concerned with the determination of a close real banded
positive definite Toeplitz matrix in the Frobenius norm to a given square real
banded matrix. While it is straightforward to determine the closest banded
Toeplitz matrix to a given square matrix, the additional requirement of
positive definiteness makes the problem difficult. We review available
theoretical results and provide a simple approach to determine a banded
positive definite Toeplitz matrix.


title: Explicit models of motions to understand protein side-chain dynamics

abstract:
  Nuclear magnetic relaxation is widely used to probe protein dynamics. For
decades, most analyses of relaxation in proteins have relied successfully on
the model-free approach, forgoing mechanistic descriptions of motions.
Model-free types of correlation functions cannot describe a large carbon-13
relaxation dataset in protein sidechains. Here, we use molecular dynamics
simulations to design explicit models of motion and solve Fokker-Planck
diffusion equations. These models of motion provide better agreement with
relaxation data, mechanistic insight and a direct link to configuration
entropy.


title:
MuCoT: Multilingual Contrastive Training for Question-Answering in
  Low-resource Languages

abstract:
  Accuracy of English-language Question Answering (QA) systems has improved
significantly in recent years with the advent of Transformer-based models
(e.g., BERT). These models are pre-trained in a self-supervised fashion with a
large English text corpus and further fine-tuned with a massive English QA
dataset (e.g., SQuAD). However, QA datasets on such a scale are not available
for most of the other languages. Multi-lingual BERT-based models (mBERT) are
often used to transfer knowledge from high-resource languages to low-resource
languages. Since these models are pre-trained with huge text corpora containing
multiple languages, they typically learn language-agnostic embeddings for
tokens from different languages. However, directly training an mBERT-based QA
system for low-resource languages is challenging due to the paucity of training
data. In this work, we augment the QA samples of the target language using
translation and transliteration into other languages and use the augmented data
to fine-tune an mBERT-based QA model, which is already pre-trained in English.
Experiments on the Google ChAII dataset show that fine-tuning the mBERT model
with translations from the same language family boosts the question-answering
performance, whereas the performance degrades in the case of cross-language
families. We further show that introducing a contrastive loss between the
translated question-context feature pairs during the fine-tuning process,
prevents such degradation with cross-lingual family translations and leads to
marginal improvement. The code for this work is available at
https://github.com/gokulkarthik/mucot.


title:
Giant density-of-states van Hove singularities in the face-centered
  cubic lattice

abstract:
  All van Hove singularities in the density of states (DOS) of face-centered
cubic lattice in the nearest and next-nearest neighbour approximation, focusing
on higher-order ones, are found and classified. At special values of the ratio
$\tau$ of nearest and next-nearest neighbour hopping integrals, $t$ and $t'$,
giant DOS singularities, caused by van Hove lines or surfaces, are formed. An
exact formula for DOS which provides efficient numerical implementation is
proposed. The standard tetrahedron method is demonstrated to be inapplicable
due to its poor convergence in the vicinity of kinks caused by van Hove
singularities. A comparison with the case of large space dimensionality
(infinite coordination number) including next-nearest neighbours is performed.


title: Free-space-coupled wavelength-scale disk resonators

abstract:
  Optical microresonators with low quality factor ($Q$) can be efficiently
excited by and scatter freely propagating optical waves, but those with high
$Q$ typically cannot. Here, we present a universal model for resonators
interacting with freely propagating waves and show that the stored energy of a
resonator excited by a plane wave is proportional to the product of its $Q$ and
directivity. Guided by this result, we devise a microdisk with periodic
protrusions in its circumference that couples efficiently to normally incident
plane waves. We experimentally demonstrate several microdisk designs, including
one with a radius of $0.75{\lambda_0}$ and $Q$ of 15,000. Our observation of
thermally-induced bistability in this resonator at input powers as low as 0.7
mW confirms strong excitation. Their small footprints and mode volumes and the
simplicity of their excitation and fabrication make wavelength-scale,
free-space-coupled microdisks attractive for sensing, enhancing emission and
nonlinearity, and as micro-laser cavities.


title: Hard Problems are Easier for Success-based Parameter Control

abstract:
  Recent works showed that simple success-based rules for self-adjusting
parameters in evolutionary algorithms (EAs) can match or outperform the best
fixed parameters on discrete problems. Non-elitism in a (1,$\lambda$) EA
combined with a self-adjusting offspring population size $\lambda$ outperforms
common EAs on the multimodal Cliff problem. However, it was shown that this
only holds if the success rate $s$ that governs self-adjustment is small
enough. Otherwise, even on OneMax, the self-adjusting (1,$\lambda$) EA
stagnates on an easy slope, where frequent successes drive down the offspring
population size. We show that self-adjustment works as intended in the absence
of easy slopes. We define everywhere hard functions, for which successes are
never easy to find and show that the self-adjusting (1,$\lambda$) EA is robust
with respect to the choice of success rates $s$. We give a general
fitness-level upper bound on the number of evaluations and show that the
expected number of generations is at most $O(d + \log(1/p_{\min}))$ where $d$
is the number of non-optimal fitness values and $p_{\min}$ is the smallest
probability of finding an improvement from a non-optimal search point. We
discuss implications for the everywhere hard function LeadingOnes and a new
class OneMaxBlocks of everywhere hard functions with tunable difficulty.


title:
GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine
  Glacier Mapping

abstract:
  In recent decades, climate change has significantly affected glacier
dynamics, resulting in mass loss and an increased risk of glacier-related
hazards including supraglacial and proglacial lake development, as well as
catastrophic outburst flooding. Rapidly changing conditions dictate the need
for continuous and detailed observations and analysis of climate-glacier
dynamics. Thematic and quantitative information regarding glacier geometry is
fundamental for understanding climate forcing and the sensitivity of glaciers
to climate change, however, accurately mapping debris-cover glaciers (DCGs) is
notoriously difficult based upon the use of spectral information and
conventional machine-learning techniques. The objective of this research is to
improve upon an earlier proposed deep-learning-based approach, GlacierNet,
which was developed to exploit a convolutional neural-network segmentation
model to accurately outline regional DCG ablation zones. Specifically, we
developed an enhanced GlacierNet2 architecture thatincorporates multiple
models, automatic post-processing, and basin-level hydrological flow techniques
to improve the mapping of DCGs such that it includes both the ablation and
accumulation zones. Experimental evaluations demonstrate that GlacierNet2
improves the estimation of the ablation zone and allows a high level of
intersection over union (IOU: 0.8839) score. The proposed architecture provides
complete glacier (both accumulation and ablation zone) outlines at regional
scales, with an overall IOU score of 0.8619. This is a crucial first step in
automating complete glacier mapping that can be used for accurate glacier
modeling or mass-balance analysis.


title:
Towards Few-shot Entity Recognition in Document Images: A Label-aware
  Sequence-to-Sequence Framework

abstract:
  Entity recognition is a fundamental task in understanding document images.
Traditional sequence labeling frameworks treat the entity types as class IDs
and rely on extensive data and high-quality annotations to learn semantics
which are typically expensive in practice. In this paper, we aim to build an
entity recognition model requiring only a few shots of annotated document
images. To overcome the data limitation, we propose to leverage the label
surface names to better inform the model of the target entity type semantics
and also embed the labels into the spatial embedding space to capture the
spatial correspondence between regions and labels. Specifically, we go beyond
sequence labeling and develop a novel label-aware seq2seq framework, LASER. The
proposed model follows a new labeling scheme that generates the label surface
names word-by-word explicitly after generating the entities. During training,
LASER refines the label semantics by updating the label surface name
representations and also strengthens the label-region correlation. In this way,
LASER recognizes the entities from document images through both semantic and
layout correspondence. Extensive experiments on two benchmark datasets
demonstrate the superiority of LASER under the few-shot setting.


title:
The Gaia-ESO Survey: Constraining evolutionary models and ages for young
  low mass stars with measurements of lithium depletion and rotation

abstract:
  A growing disquiet has emerged in recent years that standard stellar models
are at odds with observations of the colour-magnitude diagrams (CMDs) and
lithium depletion patterns of pre main sequence (PMS) stars in clusters. In
this work we select 1,246 high probability K/M-type constituent members of 5
young open clusters (5--125\,Myr) in the Gaia-ESO Survey to test a series of
models that use standard input physics and others that incorporate surface
magnetic fields or cool starspots. We find that: standard models provide
systematically under-luminous isochrones for low-mass stars in the CMD and fail
to predict Li-depletion of the right strength at the right colour; magnetic
models provide better CMD fits with isochrones that are $\sim 1.5-2$ times
older, and provide better matches to Li depletion patterns. We investigate how
rotation periods, most of which are determined here for the first time from
Transiting Exoplanet Survey Satellite data, correlate with CMD position and Li.
Among the K-stars in the older clusters we find the brightest and least
Li-depleted are the fastest rotators, demonstrating the classic "Li-rotation
connection" for the first time at $\sim 35$ Myr in NGC 2547, and finding some
evidence that it exists in the early M-stars of NGC 2264 at $<10\,$Myr.
However, the wide dispersion in Li depletion observed in fully-convective
M-dwarfs in the $\gamma$ Vel cluster at $\sim 20$ Myr appears not to be
correlated with rotation and is challenging to explain without a very large
($>10$ Myr) age spread.