Abstract:
  While most moving groups are young and nearby, a small number have been
identified in the Galactic halo. Understanding the origin and evolution of
these groups is an important piece of reconstructing the formation history of
the halo. Here we report on our analysis of three putative halo moving groups:
G03-37, G18-39, and G21-22. Based on Gaia EDR3 data, the stars associated with
each group show some scatter in velocity (e.g., Toomre diagram) and integrals
of motion (energy, angular momentum) spaces, counter to expectations of
moving-group stars. We choose the best candidate of the three groups, G21-22,
for follow-up chemical analysis based on high-resolution spectroscopy of six
presumptive members. Using a new Python code that uses a Bayesian method to
self-consistently propagate uncertainties from stellar atmosphere solutions in
calculating individual abundances and spectral synthesis, we derive the
abundances of $\alpha$- (Mg, Si, Ca, Ti), Fe-peak (Cr, Sc, Mn, Fe, Ni), odd-$Z$
(Na, Al, V), and neutron-capture (Ba, Eu) elements for each star. We find that
the G21-22 stars are not chemically homogeneous. Based on the kinematic
analysis for all three groups and the chemical analysis for G21-22, we conclude
the three are not genuine moving groups. The case for G21-22 demonstrates the
benefit of combining kinematic and chemical information in identifying conatal
populations when either alone may be insufficient. Comparing the integrals of
motion and velocities of the six G21-22 stars with those of known structures in
the halo, we tentatively associate them with the Gaia-Enceladus accretion
event.


Abstract:
  The goal of this short paper is to find Lagrangian for bosonic string with
deformed dispersion relation proposed by J.Magueijo and L. Smolin in 2004.


Abstract:
  We perform a detailed calculation of the various contributions to the
fluctuation conductivity of a granular metal close to its superconducting
transition. We find three distinct regions of power law behavior in reduced
temperature, $\eta=(T-T_c)/T_c$, with crossovers at $\Gamma/T_c$ and
$E_{Th}/T_c$, where $\Gamma$ is the electron tunneling rate, and $E_{Th}$ is
the Thouless energy of a grain. The calculation includes both intergrain and
intragrain degrees of freedom. This complete theory of the fluctuation region
in granular superconductors is then compared to experimental results from
boron-doped nanocrystalline diamond, using the assumption of a constant phase
breaking rate, $\tau_{\phi}^{-1}$. We find a semi-quantitative agreement
between the theoretical and experimental results only in the case of large
phase breaking. We argue that there may be a novel phase breaking mechanism in
granular metals worthy of further experimental and theoretical investigation.


Abstract:
  In 2017, Google announced Kotlin as an official Android programming language,
and more recently, as the preferred programming language to build applications.
These facts motivated developers to migrate their applications, which is
challenging because each migrated piece of code must be tested after the
migration to ensure it preserves the expected behavior. Due to the
interoperability between Java and Kotlin, most developers decided to migrate
their applications gradually. Thus, developers have to decide which file(s) to
migrate first on each migration step. However, there are no tools available to
help developers make these choices. This paper presents an approach to support
a gradual migration of Android applications that given a version of an
application written in Java and eventually, in Kotlin, it suggests the most
convenient files to migrate. To this end, we built a large-scale corpus of
open-source projects that migrated Java files to Kotlin. Then, we trained a
learning to rank model using the information extracted from these projects. To
validate our model, we verify whether these recommendations made by them
correspond to real migrations. The results showed our approach modestly
outperforms random approaches. Since most Android applications are written in
Java, we conclude that our approach may significantly impact Android
applications' development. Therefore, we consider this result is the first step
into long-term research towards a model capable of predicting precisely
file-level migration, establishing the initial baseline on file migrations.


Abstract:
  By successfully solving the problem of forecasting, the processes in the work
of various companies are optimized and savings are achieved. In this process,
the analysis of time series data is of particular importance. Since the
creation of Facebook's Prophet, and Amazon's DeepAR+ and CNN-QR forecasting
models, algorithms have attracted a great deal of attention. The paper presents
the application and comparison of the above algorithms for sales forecasting in
distribution companies. A detailed comparison of the performance of algorithms
over real data with different lengths of sales history was made. The results
show that Prophet gives better results for items with a longer history and
frequent sales, while Amazon's algorithms show superiority for items without a
long history and items that are rarely sold.


Abstract:
  Corrupted sensing concerns the problem of recovering a high-dimensional
structured signal from a collection of measurements that are contaminated by
unknown structured corruption and unstructured noise. In the case of linear
measurements, the recovery performance of different convex programming
procedures (e.g., generalized Lasso and its variants) is well established in
the literature. However, in practical applications of digital signal
processing, the quantization process is inevitable, which often leads to
non-linear measurements. This paper is devoted to studying corrupted sensing
under quantized measurements. Specifically, we demonstrate that, with the aid
of uniform dithering, both constrained and unconstrained Lassos are able to
recover signal and corruption from the quantized samples when the measurement
matrix is sub-Gaussian. Our theoretical results reveal the role of quantization
resolution in the recovery performance of Lassos. Numerical experiments are
provided to confirm our theoretical results.


Abstract:
  One of the well-known measurements of vulnerability in graph theory is
domination. There are many kinds of dominating and relative types of sets in
graphs. However, we are going to focus on Roman domination, which is a type of
domination that came up with an article by Ian Stewart. The Roman domination
numbers of the comet, double comet, and comb graphs are given in this paper.


Abstract:
  We revisit the computation of string worldsheet correlators on Euclidean
$\text{AdS}_3$ with pure NS-NS background. We compute correlation functions
with insertions of spectrally flowed operators. We explicitly solve all the
known constraints of the model and for the first time conjecture a closed
formula for three-point functions with arbitrary amount of spectral flow. We
explain the relation of our results with previous computations in the
literature and derive the fusion rules of the model. This paper is the first in
a series with several installments.


Abstract:
  Fano Bott manifolds bijectively correspond to signed rooted forests with some
equivalence relation. Using this bijective correspondence, we enumerate the
isomorphism classes of Fano Bott manifolds and the diffeomorphism classes of
indecomposable Fano Bott manifolds. We also observe that the signed rooted
forests with the equivalence relation bijectively correspond to rooted
triangular cacti.


Abstract:
  Recent advancements in deep learning-based modeling of molecules promise to
accelerate in silico drug discovery. A plethora of generative models is
available, building molecules either atom-by-atom and bond-by-bond or
fragment-by-fragment. However, many drug discovery projects require a fixed
scaffold to be present in the generated molecule, and incorporating that
constraint has only recently been explored. Here, we propose MoLeR, a
graph-based model that naturally supports scaffolds as initial seed of the
generative procedure, which is possible because it is not conditioned on the
generation history. Our experiments show that MoLeR performs comparably to
state-of-the-art methods on unconstrained molecular optimization tasks, and
outperforms them on scaffold-based tasks, while being an order of magnitude
faster to train and sample from than existing approaches. Furthermore, we show
the influence of a number of seemingly minor design choices on the overall
performance.


Abstract:
  Avalanche photodiodes fabricated from AlInAsSb grown as a digital alloy
exhibit low excess noise. In this paper, we investigate the band
structure-related mechanisms that influence impact ionization. Band-structures
calculated using an empirical tight-binding method and Monte Carlo simulations
reveal that the mini-gaps in the conduction band do not inhibit electron impact
ionization. Good agreement between the full band Monte Carlo simulations and
measured noise characteristics is demonstrated.


Abstract:
  In this work, we consider symmetric positive definite pencils depending on
two parameters. That is, we are concerned with the generalized eigenvalue
problem $A(x)-\lambda B(x)$, where $A$ and $B$ are symmetric matrix valued
functions in ${\mathbb R}^{n\times n}$, smoothly depending on parameters $x\in
\Omega\subset {\mathbb R}^2$; further, $B$ is also positive definite. In
general, the eigenvalues of this multiparameter problem will not be smooth, the
lack of smoothness resulting from eigenvalues being equal at some parameter
values (conical intersections). We first give general theoretical results on
the smoothness of eigenvalues and eigenvectors for the present generalized
eigenvalue problem, and hence for the corresponding projections, and then
perform a numerical study of the statistical properties of coalescing
eigenvalues for pencils where $A$ and $B$ are either full or banded, for
several bandwidths. Our numerical study will be performed with respect to a
random matrix ensemble which respects the underlying engineering problems
motivating our study.


Abstract:
  A quantity of interest to characterise continuous-valued stochastic processes
is the differential entropy rate. The rate of convergence of many properties of
LRD processes is slower than might be expected, based on the intuition for
conventional processes, e.g. Markov processes. Is this also true of the entropy
rate?
  In this paper we consider the properties of the differential entropy rate of
stochastic processes that have an autocorrelation function that decays as a
power law. We show that power law decaying processes with similar
autocorrelation and spectral density functions, Fractional Gaussian Noise and
ARFIMA(0,d,0), have different entropic properties, particularly for negatively
correlated parameterisations. Then we provide an equivalence between the mutual
information between past and future and the differential excess entropy for
stationary Gaussian processes, showing the finiteness of this quantity is the
boundary between long and short range dependence. Finally, we analyse the
convergence of the conditional entropy to the differential entropy rate and
show that for short range dependence that the rate of convergence is of the
order $O(n^{-1})$, but it is slower for long range dependent processes and
depends on the Hurst parameter.


Abstract:
  The computer science research community and the broader public have become
increasingly aware of negative consequences of algorithmic systems. In
response, the top-tier Neural Information Processing Systems (NeurIPS)
conference for machine learning and artificial intelligence research required
that authors include a statement of broader impact to reflect on potential
positive and negative consequences of their work. We present the results of a
qualitative thematic analysis of a sample of statements written for the 2020
conference. The themes we identify broadly fall into categories related to how
consequences are expressed (e.g., valence, specificity, uncertainty), areas of
impacts expressed (e.g., bias, the environment, labor, privacy), and
researchers' recommendations for mitigating negative consequences in the
future. In light of our results, we offer perspectives on how the broader
impact statement can be implemented in future iterations to better align with
potential goals.


Abstract:
  Many phenomena in the climate system lie in the gray zone between weather and
climate: they are not amenable to deterministic forecast, but they still depend
on the initial condition. A natural example is medium-range forecasting, which
is inherently probabilistic because it lies beyond the deterministic
predictability time of the atmosphere, but for which statistically significant
prediction can be made which depend on the current state of the system.
Similarly, one may ask the probability of occurrence of an El Ni\~no event
several months ahead of time. In this paper, we introduce a quantity which
corresponds precisely to this type of prediction problem: the committor
function is the probability that an event takes place within a given time
window, as a function of the initial condition. We explain the main
mathematical properties of this probabilistic concept, and compute it in the
case of a low-dimensional stochastic model for El-Ni\~no, the Jin and Timmerman
model. In this context, we show that the ability to predict the probability of
occurrence of the event of interest may differ strongly depending on the
initial state. The main result is the new distinction between intrinsic
probabilistic predictability (when the committor function is smooth and
probability can be computed which does not depend sensitively on the initial
condition) and intrinsic probabilistic unpredictability (when the committor
function depends sensitively on the initial condition). We also demonstrate
that the Jin and Timmerman model might be the first example of a stochastic
differential equation with weak noise for which transition between attractors
do not follow the Arrhenius law, which is expected based on large deviation
theory and generic hypothesis.


Abstract:
  We generalize our previous new definition of Euler Gamma function to higher
Gamma functions. With this unified approach, we characterize Barnes higher
Gamma functions, Mellin Gamma functions, Barnes multiple Gamma functions,
Jackson $q$-Gamma function, and Nishizawa higher $q$-Gamma functions. This
approach extends to more general functional equations. This generalization
reveals the multiplicative group structure of solutions of the functional
equation that appears as a cocycle equation. We also generalize Barnes
hierarchy of higher Gamma function and multiple Gamma functions. In this new
approach, Barnes-Hurwitz zeta functions are no longer required for the
definition of Barnes multiple Gamma functions. This simplifies the classical
definition, without the necessary analytic preliminaries about the meromorphic
extension of Barnes-Hurwitz zeta functions, and defines a larger class of Gamma
functions. For some algebraic independence conditions on the parameters, we
have uniqueness of the solutions, which implies the coincidence of our multiple
Gamma functions with Barnes multiple Gamma functions.


Abstract:
  Extending the Standard Model with three right-handed neutrinos and a simple
QCD axion sector can account for neutrino oscillations, dark matter and baryon
asymmetry; at the same time, it solves the strong CP problem, stabilizes the
electroweak vacuum and can implement critical Higgs inflation (satisfying all
current observational bounds). We perform here a general analysis of dark
matter (DM) in such a model, which we call the $a\nu$MSM. Although critical
Higgs inflation features a (quasi) inflection point of the inflaton potential
we show that DM cannot receive a contribution from primordial black holes in
the $a\nu$MSM. This leads to a multicomponent axion-sterile-neutrino DM and
allows us to relate the axion parameters, such as the axion decay constant, to
the neutrino parameters. We include several DM production mechanisms: the axion
production via misalignment and decay of topological defects as well as the
sterile-neutrino production through the resonant and non-resonant mechanisms
and in the recently proposed CPT-symmetric universe.


Abstract:
  Continuous-variable quantum information processing through quantum optics
offers a promising platform for building the next generation of scalable
fault-tolerant information processors. To achieve quantum computational
advantages and fault tolerance, non-Gaussian resources are essential. In this
work, we propose and analyze a method to generate a variety of non-Gaussian
states using coherent photon subtraction from a two-mode squeezed state
followed by photon-number-resolving measurements. The proposed method offers a
promising way to generate rotation-symmetric states conventionally used for
quantum error correction with binomial codes and truncated Schr\"{o}dinger cat
codes. We consider the deleterious effects of experimental imperfections such
as detection inefficiencies and losses in the state engineering protocol. Our
method can be readily implemented with current quantum photonic technologies.


Abstract:
  It is often desired to train 6D pose estimation systems on synthetic data
because manual annotation is expensive. However, due to the large domain gap
between the synthetic and real images, synthesizing color images is expensive.
In contrast, this domain gap is considerably smaller and easier to fill for
depth information. In this work, we present a system that regresses 6D object
pose from depth information represented by point clouds, and a lightweight data
synthesis pipeline that creates synthetic point cloud segments for training. We
use an augmented autoencoder (AAE) for learning a latent code that encodes 6D
object pose information for pose regression. The data synthesis pipeline only
requires texture-less 3D object models and desired viewpoints, and it is cheap
in terms of both time and hardware storage. Our data synthesis process is up to
three orders of magnitude faster than commonly applied approaches that render
RGB image data. We show the effectiveness of our system on the LineMOD, LineMOD
Occlusion, and YCB Video datasets. The implementation of our system is
available at: https://github.com/GeeeG/CloudAAE.


Abstract:
  We apply the Dirac procedure for constrained systems to the
Arnowitt-Deser-Misner formalism linearized around the Bianchi I universe. We
discuss and employ basic concepts such as Dirac observables, Dirac brackets,
gauge-fixing conditions, reduced phase space, physical Hamiltonian, canonical
isomorphism between different gauge-fixing surfaces and spacetime
reconstruction. We relate this approach to the gauge-fixing procedure for
non-perturbative canonical relativity. We discuss the issue of propagating a
basis for the scalar-vector-tensor decomposition as, in an anisotropic
universe, the wavefronts of plane waves undergo a nontrivial evolution. We show
that the definition of a gravitational wave as a traceless-transverse mode of
the metric perturbation needs to be revised. Moreover there exist coordinate
systems in which a polarization mode of the gravitational wave is given
entirely in terms of a scalar metric perturbation. We first develop the
formalism for the universe with a single scalar field and then extend it to the
multi-field case. The obtained fully canonical formalism will serve as a
starting point for a complete quantization of the cosmological perturbations
and the cosmological background.


Abstract:
  Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play
key roles in the biology of microbes. As the most abundant biological entities
on the planet, the number of discovered phages is only the tip of the iceberg.
Recently, many new phages have been revealed using high throughput sequencing,
particularly metagenomic sequencing. Compared to the fast accumulation of
phage-like sequences, there is a serious lag in taxonomic classification of
phages. High diversity, abundance, and limited known phages pose great
challenges for taxonomic analysis. In particular, alignment-based tools have
difficulty in classifying fast accumulating contigs assembled from metagenomic
data. Results: In this work, we present a novel semi-supervised learning model,
named PhaGCN, to conduct taxonomic classification for phage contigs. In this
learning model, we construct a knowledge graph by combining the DNA sequence
features learned by convolutional neural network (CNN) and protein sequence
similarity gained from gene-sharing network. Then we apply graph convolutional
network (GCN) to utilize both the labeled and unlabeled samples in training to
enhance the learning ability. We tested PhaGCN on both simulated and real
sequencing data. The results clearly show that our method competes favorably
against available phage classification tools.


Abstract:
  In this article, we introduce the first degrees of a cochain complex
associated to a strict Lie 2-group whose cohomology is shown to extend the
classical cohomology theory of Lie groups. In particular, we show that the
second cohomology group classifies an appropriate type of extension. We
conclude by putting forward evidence that this complex can be extended to
arbitrary degrees.


Abstract:
  We present tight bounds and heuristics for personalized, multi-product
pricing problems. Under mild conditions we show that the best price in the
direction of a positive vector results in profits that are guaranteed to be at
least as large as a fraction of the profits from optimal personalized pricing.
For unconstrained problems, the fraction depends on the factor and on optimal
price vectors for the different customer types. For constrained problems the
factor depends on the factor and a ratio of the constraints. Using a factor
vector with equal components results in uniform pricing and has exceedingly
mild sufficient conditions for the bound to hold. A robust factor is presented
that achieves the best possible performance guarantee. As an application, our
model yields a tight lower-bound on the performance of linear pricing relative
to optimal personalized non-linear pricing, and suggests effective non-linear
price heuristics relative to personalized solutions. Additionally, our model
provides guarantees for simple strategies such as bundle-size pricing and
component-pricing with respect to optimal personalized mixed bundle pricing.
Heuristics to cluster customer types are also developed with the goal of
improving performance by allowing each cluster to price along its own factor.
Numerical results are presented for a variety of demand models that illustrate
the tradeoffs between using the economic factor and the robust factor for each
cluster, as well as the tradeoffs between using a clustering heuristic with a
worst case performance of two and a machine learning clustering algorithm. In
our experiments economically motivated factors coupled with machine learning
clustering heuristics performed best.


Abstract:
  Resilience to class imbalance and confounding biases, together with the
assurance of fairness guarantees are highly desirable properties of autonomous
decision-making systems with real-life impact. Many different targeted
solutions have been proposed to address separately these three problems,
however a unifying perspective seems to be missing. With this work, we provide
a general formalization, showing that they are different expressions of
unbalance. Following this intuition, we formulate a unified loss correction to
address issues related to Fairness, Biases and Imbalances (FBI-loss). The
correction capabilities of the proposed approach are assessed on three
real-world benchmarks, each associated to one of the issues under
consideration, and on a family of synthetic data in order to better investigate
the effectiveness of our loss on tasks with different complexities. The
empirical results highlight that the flexible formulation of the FBI-loss leads
also to competitive performances with respect to literature solutions
specialised for the single problems.


Abstract:
  Quantization is a technique used in deep neural networks (DNNs) to increase
execution performance and hardware efficiency. Uniform post-training
quantization (PTQ) methods are common, since they can be implemented
efficiently in hardware and do not require extensive hardware resources or a
training set. Mapping FP32 models to INT8 using uniform PTQ yields models with
negligible accuracy degradation; however, reducing precision below 8 bits with
PTQ is challenging, as accuracy degradation becomes noticeable, due to the
increase in quantization noise. In this paper, we propose a sparsity-aware
quantization (SPARQ) method, in which the unstructured and dynamic activation
sparsity is leveraged in different representation granularities. 4-bit
quantization, for example, is employed by dynamically examining the bits of
8-bit values and choosing a window of 4 bits, while first skipping zero-value
bits. Moreover, instead of quantizing activation-by-activation to 4 bits, we
focus on pairs of 8-bit activations and examine whether one of the two is equal
to zero. If one is equal to zero, the second can opportunistically use the
other's 4-bit budget; if both do not equal zero, then each is dynamically
quantized to 4 bits, as described. SPARQ achieves minor accuracy degradation
and a practical hardware implementation. The code is available at
https://github.com/gilshm/sparq.


Abstract:
  Several applications in medical imaging and non-destructive material testing
lead to inverse elliptic coefficient problems, where an unknown coefficient
function in an elliptic PDE is to be determined from partial knowledge of its
solutions. This is usually a highly non-linear ill-posed inverse problem, for
which unique reconstructability results, stability estimates and global
convergence of numerical methods are very hard to achieve.
  The aim of this note is to point out a new connection between inverse
coefficient problems and semidefinite programming that may help addressing
these challenges. We show that an inverse elliptic Robin transmission problem
with finitely many measurements can be equivalently rewritten as a uniquely
solvable convex non-linear semidefinite optimization problem. This allows to
explicitly estimate the number of measurements that is required to achieve a
desired resolution, to derive an error estimate for noisy data, and to overcome
the problem of local minima that usually appears in optimization-based
approaches for inverse coefficient problems.


Abstract:
  We conducted a field study at a K-12 private school in the suburbs of
Melbourne, Australia. The data capture contained two elements: First, a 5-month
longitudinal field study In-Gauge using two outdoor weather stations, as well
as indoor weather stations in 17 classrooms and temperature sensors on the
vents of occupant-controlled room air-conditioners; these were collated into
individual datasets for each classroom at a 5-minute logging frequency,
including additional data on occupant presence. The dataset was used to derive
predictive models of how occupants operate room air-conditioning units. Second,
we tracked 23 students and 6 teachers in a 4-week cross-sectional study
En-Gage, using wearable sensors to log physiological data, as well as daily
surveys to query the occupants' thermal comfort, learning engagement, emotions
and seating behaviours. This is the first publicly available dataset studying
the daily behaviours and engagement of high school students using heterogeneous
methods. The combined data could be used to analyse the relationships between
indoor climates and mental states of school students.


Abstract:
  Recent work has highlighted several advantages of enforcing orthogonality in
the weight layers of deep networks, such as maintaining the stability of
activations, preserving gradient norms, and enhancing adversarial robustness by
enforcing low Lipschitz constants. Although numerous methods exist for
enforcing the orthogonality of fully-connected layers, those for convolutional
layers are more heuristic in nature, often focusing on penalty methods or
limited classes of convolutions. In this work, we propose and evaluate an
alternative approach to directly parameterize convolutional layers that are
constrained to be orthogonal. Specifically, we propose to apply the Cayley
transform to a skew-symmetric convolution in the Fourier domain, so that the
inverse convolution needed by the Cayley transform can be computed efficiently.
We compare our method to previous Lipschitz-constrained and orthogonal
convolutional layers and show that it indeed preserves orthogonality to a high
degree even for large convolutions. Applied to the problem of certified
adversarial robustness, we show that networks incorporating the layer
outperform existing deterministic methods for certified defense against
$\ell_2$-norm-bounded adversaries, while scaling to larger architectures than
previously investigated. Code is available at
https://github.com/locuslab/orthogonal-convolutions.


Abstract:
  The critical nature of vehicular communications requires their extensive
testing and evaluation. Analytical models can represent an attractive and
cost-effective approach for such evaluation if they can adequately model all
underlying effects that impact the performance of vehicular communications.
Several analytical models have been proposed to date to model vehicular
communications based on the IEEE 802.11p (or DSRC) standard. However, existing
models normally model in detail the MAC (Medium Access Control), and generally
simplify the propagation and interference effects. This reduces their value as
an alternative to evaluate the performance of vehicular communications. This
paper addresses this gap, and presents new analytical models that accurately
model the performance of vehicle-to-vehicle communications based on the IEEE
802.11p standard. The models jointly account for a detailed modeling of the
propagation and interference effects, as well as the impact of the hidden
terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a
function of the distance between transmitter and receiver. The paper also
presents new analytical models to quantify the probability of the four
different types of packet errors in IEEE 802.11p. In addition, the paper
presents the first analytical model capable to accurately estimate the Channel
Busy Ratio (CBR) metric even under high channel load levels. All the analytical
models are validated by means of simulation for a wide range of parameters,
including traffic densities, packet transmission frequencies, transmission
power levels, data rates and packet sizes. An implementation of the models is
provided openly to facilitate their use by the community.


Abstract:
  We synthesized lead sulfide (PbS) nanowires with high crystallinity by
solvothermal method and successfully fabricated devices based on single PbS
nanowires. The devices exhibited memristive property which depends on the
applied voltage and the power density of light. In summary, these results
indicated that PbS nanowires prepared by solvothermal method have potential
applications in the fields of electronics and optoelectronics.


Abstract:
  Transformer-based language models have been changing the modern Natural
Language Processing (NLP) landscape for high-resource languages such as
English, Chinese, Russian, etc. However, this technology does not yet exist for
any Ghanaian language. In this paper, we introduce the first of such models for
Twi or Akan, the most widely spoken Ghanaian language. The specific
contribution of this research work is the development of several pretrained
transformer language models for the Akuapem and Asante dialects of Twi, paving
the way for advances in application areas such as Named Entity Recognition
(NER), Neural Machine Translation (NMT), Sentiment Analysis (SA) and
Part-of-Speech (POS) tagging. Specifically, we introduce four different
flavours of ABENA -- A BERT model Now in Akan that is fine-tuned on a set of
Akan corpora, and BAKO - BERT with Akan Knowledge only, which is trained from
scratch. We open-source the model through the Hugging Face model hub and
demonstrate its use via a simple sentiment classification example.


Abstract:
  The time- or temperature-resolved detector signal from a thermoluminescence
dosimeter can reveal additional information about circumstances of an exposure
to ionizing irradiation. We present studies using deep neural networks to
estimate the date of a single irradiation with 12 mSv within a monitoring
interval of 42 days from glow curves of novel TL-DOS personal dosimeters
developed by the Materialpr\"ufungsamt NRW in cooperation with TU Dortmund
University. Using a deep convolutional network, the irradiation date can be
predicted from raw time-resolved glow curve data with an uncertainty of roughly
1-2 days on a 68% confidence level without the need for a prior transformation
into temperature space and a subsequent glow curve deconvolution. This
corresponds to a significant improvement in prediction accuracy compared to a
prior publication, which yielded a prediction uncertainty of 2-4 days using
features obtained from a glow curve deconvolution as input to a neural network.


Abstract:
  The vast majority of image recovery tasks are ill-posed problems. As such,
methods that are based on optimization use cost functions that consist of both
fidelity and prior (regularization) terms. A recent line of works imposes the
prior by the Regularization by Denoising (RED) approach, which exploits the
good performance of existing image denoising engines. Yet, the relation of RED
to explicit prior terms is still not well understood, as previous work requires
too strong assumptions on the denoisers. In this paper, we make two
contributions. First, we show that the RED gradient can be seen as a
(sub)gradient of a prior function--but taken at a denoised version of the
point. As RED is typically applied with a relatively small noise level, this
interpretation indicates a similarity between RED and traditional gradients.
This leads to our second contribution: We propose to combine RED with the
Back-Projection (BP) fidelity term rather than the common Least Squares (LS)
term that is used in previous works. We show that the advantages of BP over LS
for image deblurring and super-resolution, which have been demonstrated for
traditional gradients, carry on to the RED approach.


Abstract:
  Single-electron avalanche distributions in gaseous multipliers affect their
efficient detection and that of single UV-photons. In this work, we
investigated the shape of single-photo-electron spectra in single- and
double-stage Resistive Plate WELL (RPWELL) detector configurations, operated in
$\mathrm{Ne/CH_{4}}$ and $\mathrm{Ar/CH_{4}}$. Discharge-free operation was
reached over a broad dynamic range, with charge gains of \numrange[range-phrase
= -]{e4}{e6}. Compared to the usual exponential ones, the observed Polya-like
charge spectra pave the way towards higher single-electrons detection
efficiencies. The latter were evaluated here, using experimental data combined
with numerical simulations. The effects of the gas mixtures, electric field
configuration and detector geometry on the Polya spectra and their related
"$\theta$" parameter are presented.


Abstract:
  Optically trapped mixed-species single atom arrays with arbitrary geometries
are an attractive and promising platform for various applications, because
tunable quantum systems with multiple components provide extra degrees of
freedom for experimental control. Here, we report the first demonstration of
two-dimensional $6\times4$ dual-species atom assembly with a filling fraction
of 0.88 (0.89) for $^{85}$Rb ($^{87}$Rb) atoms. This mixed-species atomic
synthetic is achieved via rearranging initially randomly distributed atoms
using a sorting algorithm (heuristic heteronuclear algorithm) which is proposed
for bottom-up atom assembly with both user-defined geometries and two-species
atom number ratios. Our fully tunable hybrid-atom system of scalable advantages
is a good starting point for high-fidelity quantum logic, many-body quantum
simulation and forming defect-free single molecule arrays.


Abstract:
  We study the van der Waals (vdW) interaction between a polarizable particle
and a grounded conducting corrugated surface. For sinusoidal corrugations, one
knows that, under the action of the lateral vdW force, an isotropic particle is
always attracted to the nearest corrugation peak, with such behavior called in
the present paper as peak regime. Here, considering an anisotropic polarizable
particle, and making analytical calculations valid beyond the proximity force
approximation (PFA), we show that the attraction is not only toward the peaks,
but, for certain particle orientations and distances from the surface, the
lateral force attracts the particle to the nearest corrugation valley (valley
regime), or even to an intermediate point between a peak and a valley
(intermediate regime). We also show that in the configurations of transition
between the peak and valley regimes the lateral vdW force vanishes, even in the
presence of a corrugated surface. In addition, we find that these new regimes
occur in general, for periodic and nonperiodic corrugated surfaces. Moreover,
we demonstrate that similar regimes arise in the classical interaction between
a neutral polarized particle and a rough surface. The description of these
valley and intermediate regimes, which are out of reach of the predictions
based on the PFA, may be relevant for a better understanding of the interaction
between anisotropic particles and corrugated surfaces in classical and quantum
physics, with experimental verifications feasible in both domains.


Abstract:
  Interpretability is a critical factor in applying complex deep learning
models to advance the understanding of brain disorders in neuroimaging studies.
To interpret the decision process of a trained classifier, existing techniques
typically rely on saliency maps to quantify the voxel-wise or feature-level
importance for classification through partial derivatives. Despite providing
some level of localization, these maps are not human-understandable from the
neuroscience perspective as they do not inform the specific meaning of the
alteration linked to the brain disorder. Inspired by the image-to-image
translation scheme, we propose to train simulator networks that can warp a
given image to inject or remove patterns of the disease. These networks are
trained such that the classifier produces consistently increased or decreased
prediction logits for the simulated images. Moreover, we propose to couple all
the simulators into a unified model based on conditional convolution. We
applied our approach to interpreting classifiers trained on a synthetic dataset
and two neuroimaging datasets to visualize the effect of the Alzheimer's
disease and alcohol use disorder. Compared to the saliency maps generated by
baseline approaches, our simulations and visualizations based on the Jacobian
determinants of the warping field reveal meaningful and understandable patterns
related to the diseases.


Abstract:
  We prove that a complete properly embedded surface in $\mathbb
H^2\times\mathbb R$ with constant mean curvature $0<H\leq \frac{1}{2}$ and an
annular end can not be contained in any horizontal slab. Moreover, we show that
a properly embedded surface with constant mean curvature $0<H\leq \frac{1}{2}$
contained in $\mathbb H^2\times[0,+\infty)$ and with finite topology is
necessarily a graph. For the case $H=\frac{1}{2}$, the graph is entire.


Abstract:
  We apply the Hybrid-Multi-Determinant method using the recent chiral two-body
interactions of Entem-Machleidt-Nosyk (EMN) without renormalization to few
nuclei up to A=48. Mostly we use the bare fifth order NN interaction N4LO-450.
For ${}^{24}Mg$ and ${}^{48}Cr$ the excitation energies of the $2^+_1$ states
are far larger than the corresponding experimental values.


Abstract:
  We prove an equation of Euler characteristics of tautological sheaves on the
Hilbert scheme of points on the fibers of a simple degeneration.


Abstract:
  Generative Adversarial Networks can learn the mapping of random noise to
realistic images in a semi-supervised framework. This mapping ability can be
used for semi-supervised image classification to detect images of an unknown
class where there is no training data to be used for supervised classification.
However, if the unknown class shares similar characteristics to the known
class(es), GANs can learn to generalize and generate images that look like both
classes. This generalization ability can hinder the classification performance.
In this work, we propose the Vanishing Twin GAN. By training a weak GAN and
using its generated output image parallel to the regular GAN, the Vanishing
Twin training improves semi-supervised image classification where image
similarity can hurt classification tasks.


Abstract:
  We report measurements of the branching fractions of singly
Cabibbo-suppressed decays $\Lambda_c^+ \to p \eta$ and $\Lambda_c^+ \to p
\pi^0$ using the full Belle data sample corresponding to an integrated
luminosity of 980.6 $\rm fb^{-1}$. The data were collected by the Belle
detector at the KEKB $e^{+}$$e^{-}$ asymmetric-energy collider. A clear
$\Lambda_c^+$ signal is seen in the invariant mass distribution of $p \eta$.
The signal yield of the $\Lambda_c^+ \to p \eta$ process is $7734 \pm 263$;
from this, we measure the ratio of branching fractions ${\cal B}(\Lambda_c^+
\to p \eta)/{\cal B}(\Lambda_c^+ \to p K^- \pi^+) = (2.258 \pm 0. 077(\rm stat.
) \pm 0.136(\rm syst. ))\times 10^{-2}$, from which we infer the branching
fraction ${\cal B}(\Lambda_c^+ \to p \eta) = (1.42 \pm 0.05(\rm stat.) \pm
0.11(\rm syst.)) \times 10^{-3}$. In addition, no significant signal for
$\Lambda_c^+ \to p \pi^0$ is found so an upper limit on the branching fraction
of ${\cal B}(\Lambda_c^+ \to p \pi^0)<8.0 \times 10^{-5}$ at 90\% credibility
level is set, more than three times better than the best current upper limit.


Abstract:
  Higher Spin Gravities are scarce, but covariant actions for them are even
scarcer. We construct covariant actions for contractions of Chiral Higher Spin
Gravity that represent higher spin extensions of self-dual Yang-Mills and
self-dual Gravity theories. The actions give examples of complete higher spin
theories both in flat and (anti)-de Sitter spaces that feature gauge and
gravitational interactions. The actions are based on a new description of
higher spin fields, whose origin can be traced to early works on twistor
theory. The new description simplifies the structure of interactions. In
particular, we find a covariant form of the minimal gravitational interaction
for higher spin fields both in flat and anti-de Sitter space, which resolves
some of the puzzles in the literature.


Abstract:
  Sounds recorded with smartphones or IoT devices often have partially
unreliable observations caused by clipping, wind noise, and completely missing
parts due to microphone failure and packet loss in data transmission over the
network. In this paper, we investigate the impact of the partially missing
channels on the performance of acoustic scene classification using multichannel
audio recordings, especially for a distributed microphone array. Missing
observations cause not only losses of time-frequency and spatial information on
sound sources but also a mismatch between a trained model and evaluation data.
We thus investigate how a missing channel affects the performance of acoustic
scene classification in detail. We also propose simple data augmentation
methods for scene classification using multichannel observations with partially
missing channels and evaluate the scene classification performance using the
data augmentation methods.


Abstract:
  We argue that primordial black-hole formation must be described by means of
extreme-value theory. This is a consequence of the large values of the energy
density required to initiate the collapse of black holes in the early Universe
and the finite duration of their collapse. Compared to the Gaussian description
of the most extreme primordial density fluctuations, the holes' mass function
is narrower and peaks towards larger masses. Secondly, thanks to the shallower
fall-off of extreme-value distributions, the predicted abundance of primordial
black holes is boosted by $10^{7}$ orders of magnitude when extrapolating the
observed nearly scale-free power spectrum of the cosmic large-scale structure
to primordial black-hole mass scales.


Abstract:
  We investigate the entanglement entropy (EE) and charge fluctuations in
models where the low energy physics is governed by massless Dirac fermions. We
focus on the response to flux insertion which, for the EE, is widely assumed to
be universal, \emph{i.e.}, independent of the microscopic details. We provide
an analytical derivation of the EE and charge fluctuations for the seminal
example of graphene, using the dimensional reduction of its tight-binding model
to the one-dimensional Su-Schrieffer-Heeger model. Our asymptotic expression
for the EE matches the conformal field theory prediction. We show that the
charge variance has the same asymptotic behavior, up to a constant prefactor.
To check the validity of universality arguments, we numerically consider
several models, with different geometries and number of Dirac cones, and either
for strictly two-dimensional models or for gapless surface mode of
three-dimensional topological insulators. We also show that the flux response
does not depend on the entangling surface geometry as long as it encloses the
flux. Finally we consider the universal corner contributions to the EE. We show
that in the presence of corners, the Kitaev-Preskill subtraction scheme
provides non-universal, geometry dependent results.


Abstract:
  Let $\Sigma_{g,p}$ be a oriented connected surface of genus $g$ with $p$
punctures. We denote by $\mathcal{M}_{g,p}$ and $\mathcal{M}_{g,p}^\pm$ the
mapping class group and the extended mapping class group of $\Sigma_{g,p}$,
respectively. In this paper, we show that $\mathcal{M}_{g,p}$ and
$\mathcal{M}_{g,p}^\pm$ are generated by two element for $g\geq 3$ and $p\geq
0$.


Abstract:
  Recently, we have shown that thermal effects play a crucial role in speeding
up the rate of bond-dissociation reactions. This was done by applying a simple
temperature-shifted Arrhenius Law to the experimental data, corroborated with
detailed account of the heat diffusion occurring within the relevant samples
and identification of errors in the temperature measurements. Here, we provide
three important extensions of our previous studies. First, we analyze thermal
effects in reduction-oxidation (redox) reactions, where charge transfer is an
integral part of the reaction. Second, we analyze not only the spatial
distribution of the temperature, but also its temporal dynamics. Third, we also
model the fluid convection and stirring. An analysis of two exemplary
experimental studies allows us to show that thermal effects can explain the
experimental data in one of experiments (Baumberg and coworkers), but not in
the other (Jain and coworkers), showing that redox reactions are not
necessarily driven by non-thermal charge carriers.


Abstract:
  Millimeter-wave (mmWave) radars are being increasingly integrated in
commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS)
features that require accurate location and Doppler velocity estimates of
objects, independent of environmental conditions. To explore radar-based ADAS
applications, we have updated our test-bed with Texas Instrument's 4-chip
cascaded FMCW radar (TIDEP-01012) that forms a non-uniform 2D MIMO virtual
array. In this paper, we develop the necessary received signal models for
applying different direction of arrival (DoA) estimation algorithms and
experimentally validating their performance on formed virtual array under
controlled scenarios. To test the robustness of mmWave radars under adverse
weather conditions, we collected raw radar dataset (I-Q samples post
demodulated) for various objects by a driven vehicle-mounted platform,
specifically for snowy and foggy situations where cameras are largely
ineffective. Initial results from radar imaging algorithms to this dataset are
presented.


Abstract:
  In this work, with the help of fractional calculus, it is shown a time
dependence of entropy more general than the well known Pesin relation is
derived. Here the equiprobability postulate is not assumed, the system dynamic
in the phase space is not necessarily Markovian and the system is not in a
steady state at all. Different possibilities for the time evolution of entropy
by considering different features of the phase space and processes involved are
obtained.


Abstract:
  The study of coordinated manipulation of conversations on social media has
become more prevalent as social media's role in amplifying misinformation,
hate, and polarization has come under scrutiny. We discuss the implications of
successful coordination detection algorithms based on shifts of power, and
consider how responsible coordination detection may be carried out through
synchronized action. We then propose a Synchronized Action Framework for
detection of automated coordination through construction and analysis of
multi-view networks. We validate our framework by examining the Reopen America
conversation on Twitter, discovering three coordinated campaigns. We further
investigate covert coordination surrounding the protests and find the task to
be far more complex than examples seen in prior work, demonstrating the need
for our multi-view approach. A cluster of suspicious users is identified and
the activity of three members is detailed. These users amplify protest messages
using the same hashtags at very similar times, though they all focus on
different states. Through this analysis, we emphasize both the potential
usefulness of coordination detection algorithms in investigating amplification,
and the need for careful and responsible deployment of such tools.


Abstract:
  Layered tin selenide (SnSe) has recently emerged as a high-performance
thermoelectric material with the current record for the figure of merit (ZT)
observed in the high-temperature Cmcm phase. So far, access of the Cmcm phase
has been mainly obtained via thermal equilibrium methods based on sample
heating or application of external pressure, thus restricting the current
understanding only to ground-state conditions. Here, we investigate the
ultrafast carrier and phononic dynamics in SnSe. Our results demonstrate that
optical excitations can transiently switch the point-group symmetry of the
crystal from Pnma to Cmcm at room temperature in a few hundreds of femtoseconds
with an ultralow threshold for the excitation carrier density. This
non-equilibrium Cmcm phase is found to be driven by the displacive excitation
of coherent Ag phonons and, given the absence of low-energy thermal phonons,
exists in SnSe with the status of 'cold lattice with hot carriers'. Our
findings provide important insight for understanding non-equilibrium
thermoelectric properties of SnSe.


Abstract:
  Social networks are widely used for information consumption and
dissemination, especially during time-critical events such as natural
disasters. Despite its significantly large volume, social media content is
often too noisy for direct use in any application. Therefore, it is important
to filter, categorize, and concisely summarize the available content to
facilitate effective consumption and decision-making. To address such issues
automatic classification systems have been developed using supervised modeling
approaches, thanks to the earlier efforts on creating labeled datasets.
However, existing datasets are limited in different aspects (e.g., size,
contains duplicates) and less suitable to support more advanced and data-hungry
deep learning models. In this paper, we present a new large-scale dataset with
~77K human-labeled tweets, sampled from a pool of ~24 million tweets across 19
disaster events that happened between 2016 and 2019. Moreover, we propose a
data collection and sampling pipeline, which is important for social media data
sampling for human annotation. We report multiclass classification results
using classic and deep learning (fastText and transformer) based models to set
the ground for future studies. The dataset and associated resources are
publicly available. https://crisisnlp.qcri.org/humaid_dataset.html


Abstract:
  Although ferromagnets are found in all kinds of technological applications,
only few substances are known to be intrinsically ferromagnetic at room
temperature. In the past twenty years, a plethora of new artificial
ferromagnetic materials have been found by introducing defects into
non-magnetic host materials. In contrast to the intrinsic ferromagnetic
materials, they offer an outstanding degree of material engineering freedom,
provided one finds a type of defect to functionalize every possible host
material to add magnetism to its intrinsic properties. Still, one controversial
question remains: Are these materials really technologically relevant
ferromagnets? To answer this question, in this work the emergence of a
ferromagnetic phase upon ion irradiation is systematically investigated both
theoretically and experimentally. Quantitative predictions are validated
against experimental data from the literature of SiC hosts irradiated with high
energy Ne ions and own experiments on low energy Ar ion irradiation of TiO$_2$
hosts. In the high energy regime, a bulk magnetic phase emerges, which is
limited by host lattice amorphization, whereas at low ion energies an ultrathin
magnetic layer forms at the surface and evolves into full magnetic percolation.
Lowering the ion energy, the magnetic layer thickness reduces down to a
bilayer, where a perpendicular magnetic anisotropy appears due to magnetic
surface states.


Abstract:
  Commercial application of facial recognition demands robustness to a variety
of challenges such as illumination, occlusion, spoofing, disguise, etc.
Disguised face recognition is one of the emerging issues for access control
systems, such as security checkpoints at the borders. However, the lack of
availability of face databases with a variety of disguise addons limits the
development of academic research in the area. In this paper, we present a
multimodal disguised face dataset to facilitate the disguised face recognition
research. The presented database contains 8 facial add-ons and 7 additional
combinations of these add-ons to create a variety of disguised face images.
Each facial image is captured in visible, visible plus infrared, infrared, and
thermal spectra. Specifically, the database contains 100 subjects divided into
subset-A (30 subjects, 1 image per modality) and subset-B (70 subjects, 5 plus
images per modality). We also present baseline face detection results performed
on the proposed database to provide reference results and compare the
performance in different modalities. Qualitative and quantitative analysis is
performed to evaluate the challenging nature of disguise addons. The dataset
will be publicly available with the acceptance of the research article. The
database is available at: https://github.com/usmancheema89/SejongFaceDatabase.


Abstract:
  This paper revisits the classical question of the stability of the nonlinear
Wonham filter. The novel contributions of this paper are two-fold: (i)
definition of the stabilizability for the (control-theoretic) dual to the
nonlinear filter; and (ii) the use of this definition to obtain conclusions on
the stability of the Wonham filter. Specifically, it is shown that the
stabilizability of the dual system is necessary for filter stability and
conversely stabilizability implies that the filter asymptotically detects the
correct ergodic class. The formulation and the proofs are based upon a recently
discovered duality result whereby the nonlinear filtering problem is cast as a
stochastic optimal control problem for a backward stochastic differential
equation (BSDE). The control-theoretic proof techniques and results may be
viewed as a generalization of the classical work on the stability of the Kalman
filter.


Abstract:
  In any other circumstance, it might make sense to define the extent of the
terrain (Data Science) first, and then locate and describe the landmarks
(Principles). But this data revolution we are experiencing defies a cadastral
survey. Areas are continually being annexed into Data Science. For example,
biometrics was traditionally statistics for agriculture in all its forms but
now, in Data Science, it means the study of characteristics that can be used to
identify an individual. Examples of non-intrusive measurements include height,
weight, fingerprints, retina scan, voice, photograph/video (facial landmarks
and facial expressions), and gait. A multivariate analysis of such data would
be a complex project for a statistician, but a software engineer might appear
to have no trouble with it at all. In any applied-statistics project, the
statistician worries about uncertainty and quantifies it by modelling data as
realisations generated from a probability space. Another approach to
uncertainty quantification is to find similar data sets, and then use the
variability of results between these data sets to capture the uncertainty. Both
approaches allow 'error bars' to be put on estimates obtained from the original
data set, although the interpretations are different. A third approach, that
concentrates on giving a single answer and gives up on uncertainty
quantification, could be considered as Data Engineering, although it has staked
a claim in the Data Science terrain. This article presents a few (actually
nine) statistical principles for data scientists that have helped me, and
continue to help me, when I work on complex interdisciplinary projects.


Abstract:
  In 2008, Langevin and Leander determined the dual function of three classes
of monomial bent functions with the help of Stickelberger's theorem: Dillon,
Gold and Kasami. In their paper, they proposed one very strong condition such
that their method works, and showed that both Gold exponent and Kasami exponent
satisfy this condition. In 2018, Pott {\em et al.} investigated the issue of
vectorial functions with maximal number of bent components. They found one
class of binomial functions which attains the upper bound. They also proposed
an open problem regarding monomial function with maximal number of bent
components.
  In this paper, we obtain an interesting result about the condition of
Langevin and Leander, and solve the open problem of Pott {\em et al.}.
Specifically, we show that: 1) for a monomial bent function over
$\mathbb{F}_{2^{2k}}$, if the exponent satisfies the first part of the
condition of Langevin and Leander, then it satisfies the entire condition; 2)
$x^{2^k+1}$ is the only monomial function over $\mathbb{F}_{2^{2k}}$ which has
maximal number of bent components. Fortunately, as a consequence, we also solve
an open problem of Ness and Helleseth in 2006.


Abstract:
  In this paper, we propose a path re-planning algorithm that makes robots able
to work in scenarios with moving obstacles. The algorithm switches between a
set of pre-computed paths to avoid collisions with moving obstacles. It also
improves the current path in an anytime fashion. The use of informed sampling
enhances the search speed. Numerical results show the effectiveness of the
strategy in different simulation scenarios.


Abstract:
  The perpendicular shape anisotropy-spin transfer torque-magnetic random
access memories (PSASTT-MRAMs) takes advantage of the nanopillar free-layer
geometry for securing a good thermal stability factor from the shape anisotropy
of the nanomagnet. Such a concept is particularly well-suited for small
junctions down to a few nanometers. At such a volume size, the nanopillar can
be effectively modeled as a Stoner-Wohlfarth (SW) particle, and the shape
anisotropy scales with the spontaneous magnetization by ~ Ms^2. For almost all
ferromagnets, Ms is a strong function of temperature, therefore, the
temperature-dependent shape anisotropy is an important factor to be considered
in any modeling of the temperature-dependent performance of PSA-STT-MRAMs. In
this work, we summarize and discuss various possible temperature-dependent
contributions to the thermal stability factor and coercivity of the
PSA-STT-MRAMs by modeling and comparing different temperature scaling and
parameters. We reveal nontrivial corrections to the thermal stability factor by
considering both temperature-dependent shape and interfacial anisotropies. The
coercivity, blocking temperature, and electrical switching characteristics that
resulted from incorporating such a temperature dependence are also discussed,
in conjugation with the nanomagnet dimension and coherence volume.


Abstract:
  2D Convolutional neural network (CNN) has arguably become the de facto
standard for computer vision tasks. Recent findings, however, suggest that CNN
may not be the best option for 1D pattern recognition, especially for datasets
with over 1 M training samples, e.g., existing CNN-based methods for 1D signals
are highly reliant on human pre-processing. Common practices include utilizing
discrete Fourier transform (DFT) to reconstruct 1D signal into 2D array. To add
to extant knowledge, in this paper, a novel 1D data processing algorithm is
proposed for 1D big data analysis through learning a deep
deconvolutional-convolutional network. Rather than resorting to human-based
techniques, we employed deconvolution layers to convert 1 D signals into 2D
data. On top of the deconvolution model, the data was identified by a 2D CNN.
Compared with the existing 1D signal processing algorithms, DCNet boasts the
advantages of less human-made inference and higher generalization performance.
Our experimental results from a varying number of training patterns (50 K to 11
M) from classification and regression demonstrate the desirability of our new
approach.


Abstract:
  Dinuclear systems that occur in the post-saddle to scission stage in nuclear
fission process are special transient formations. The diabatic evolution at
this stage has been studied using the methods of non-equilibrium
thermodynamics. A novel explanation for the spontaneous emergence of collective
rotational modes in a dinucleus has been developed by identifying these modes
together as an open subsystem that exchange matter and energy with its
environment. The environment consists of all remaining degrees of freedom of
the dinucleus, and a weak coupling of the subsystem to this environment
facilitate a diabatic energy exchange. Under appropriate non-equilibrium
conditions, the available energy will be coupled to the work needed for the
emergence of self organizing rotational modes. This comes at the expense of
increasing microscopic disorder in the form of intrinsic excitations in the
fragments. A simple formalism is presented such that the magnitudes of relevant
energy flow through the subsystem are obtained in terms of entropy production
rate, entropy expulsion rate and net rate of change of entropy in unit of $MeV
zs^{-1} K^{-1}$. An experimental approach with which the entropy rates can be
measured is discussed schematically.


Abstract:
  Despite frequent references in modern reviews to a seventeenth-century
Venetian longitude prize, only a single, circumstantial reference to the
alleged prize is known from contemporary sources. Edward Harrison's scathing
assessment of the conditions governing the award of an alleged Venetian
longitude prize simultaneously disparages the rewards offered by the Dutch
States General. However, the latter had long run its course by 1696, the year
of the citation, thus rendering Harrison's reference unreliable. Whereas other
longitude awards offered by the leading European maritime nations attracted
applicants from far and wide, often accompanied by extensive, self-published
pamphlets, the alleged Venetian prize does not seem to have been subject to
similar hype. The alleged existence of seventeenth-century Venetian award is
particularly curious, because the city's fortune was clearly in decline, and
longitude determination on the open seas does not appear to have been a
priority; the city's mariners already had access to excellent "portolan"
charts. It is therefore recommended that authors refrain from referring to a
potentially phantom Venetian longitude prize in the same context as the major
sixteenth- to eighteenth-century European awards offered by the dominant
sea-faring nations.


Abstract:
  As our lives migrate to the digital realm, our online identity has evolved to
become an increasingly robust collection of data about every aspect of our
online and offline lives. This data is extremely appealing to companies who
wish to use it for a variety of analytics. In this report, we create awareness
for the consumers around the world who have only a vague understanding of how
much of their data is being tracked, where, when, and by which companies.


Abstract:
  This presentation for the AIES 21 doctoral consortium examines the Latin
American crowdsourcing market through a decolonial lens. This research is based
on the analysis of the web traffic of ninety-three platforms, interviews with
Venezuelan data workers of four platforms, and the analysis of the
documentation issued by these organizations. The findings show that (1)
centuries-old global divisions of labor persist, in this case, with requesters
located in advanced economies and workers in the Global South. (2) That the
platforms' configuration of the labor process constrains the agency of these
workers when producing annotations. And, (3) that ideologies originating from
the Global North serve to legitimize and reinforce this global labor market
configuration.


Abstract:
  Recognizing and categorizing human actions is an important task with
applications in various fields such as human-robot interaction, video analysis,
surveillance, video retrieval, health care system and entertainment industry.
This thesis presents a novel computational approach for human action
recognition through different implementations of multi-layer architectures
based on artificial neural networks. Each system level development is designed
to solve different aspects of the action recognition problem including online
real-time processing, action segmentation and the involvement of objects. The
analysis of the experimental results are illustrated and described in six
articles. The proposed action recognition architecture of this thesis is
composed of several processing layers including a preprocessing layer, an
ordered vector representation layer and three layers of neural networks. It
utilizes self-organizing neural networks such as Kohonen feature maps and
growing grids as the main neural network layers. Thus the architecture presents
a biological plausible approach with certain features such as topographic
organization of the neurons, lateral interactions, semi-supervised learning and
the ability to represent high dimensional input space in lower dimensional
maps. For each level of development the system is trained with the input data
consisting of consecutive 3D body postures and tested with generalized input
data that the system has never met before. The experimental results of
different system level developments show that the system performs well with
quite high accuracy for recognizing human actions.


Abstract:
  In this paper, an enhancement technique for the class activation mapping
methods such as gradient-weighted class activation maps or excitation
backpropagation is proposed to present the visual explanations of decisions
from convolutional neural network-based models. The proposed idea, called
Gradual Extrapolation, can supplement any method that generates a heatmap
picture by sharpening the output. Instead of producing a coarse localization
map that highlights the important predictive regions in the image, the proposed
method outputs the specific shape that most contributes to the model output.
Thus, the proposed method improves the accuracy of saliency maps. The effect
has been achieved by the gradual propagation of the crude map obtained in the
deep layer through all preceding layers with respect to their activations. In
validation tests conducted on a selected set of images, the faithfulness,
interpretability, and applicability of the method are evaluated. The proposed
technique significantly improves the localization detection of the neural
networks attention at low additional computational costs. Furthermore, the
proposed method is applicable to a variety deep neural network models. The code
for the method can be found at
https://github.com/szandala/gradual-extrapolation


Abstract:
  Conservation properties of iterative methods applied to implicit finite
volume discretizations of nonlinear conservation laws are analyzed. It is shown
that any consistent multistep or Runge-Kutta method is globally conservative.
Further, it is shown that Newton's method, Krylov subspace methods and
pseudo-time iterations are globally conservative while the Jacobi and
Gauss-Seidel methods are not in general. If pseudo-time iterations using an
explicit Runge-Kutta method are applied to a locally conservative
discretization, then the resulting scheme is also locally conservative.
However, the corresponding numerical flux can be inconsistent with the
conservation law. We prove an extension of the Lax-Wendroff theorem, which
reveals that numerical solutions based on these methods converge to weak
solutions of a modified conservation law where the flux function is multiplied
by a particular constant. This constant depends on the choice of Runge-Kutta
method but is independent of both the conservation law and the discretization.
Consistency is maintained by ensuring that this constant equals unity and a
strategy for achieving this is presented. Experiments show that this strategy
improves the convergence rate of the pseudo-time iterations.


Abstract:
  In common law, the outcome of a new case is determined mostly by precedent
cases, rather than by existing statutes. However, how exactly does the
precedent influence the outcome of a new case? Answering this question is
crucial for guaranteeing fair and consistent judicial decision-making. We are
the first to approach this question computationally by comparing two
longstanding jurisprudential views; Halsbury's, who believes that the arguments
of the precedent are the main determinant of the outcome, and Goodhart's, who
believes that what matters most is the precedent's facts. We base our study on
the corpus of legal cases from the European Court of Human Rights (ECtHR),
which allows us to access not only the case itself, but also cases cited in the
judges' arguments (i.e. the precedent cases). Taking an information-theoretic
view, and modelling the question as a case outcome classification task, we find
that the precedent's arguments share 0.38 nats of information with the case's
outcome, whereas precedent's facts only share 0.18 nats of information (i.e.,
58% less); suggesting Halsbury's view may be more accurate in this specific
court. We found however in a qualitative analysis that there are specific
statues where Goodhart's view dominates, and present some evidence these are
the ones where the legal concept at hand is less straightforward.


Abstract:
  Textures contain a wealth of image information and are widely used in various
fields such as computer graphics and computer vision. With the development of
machine learning, the texture synthesis and generation have been greatly
improved. As a very common element in everyday life, wallpapers contain a
wealth of texture information, making it difficult to annotate with a simple
single label. Moreover, wallpaper designers spend significant time to create
different styles of wallpaper. For this purpose, this paper proposes to
describe wallpaper texture images by using multi-label semantics. Based on
these labels and generative adversarial networks, we present a framework for
perception driven wallpaper texture generation and style transfer. In this
framework, a perceptual model is trained to recognize whether the wallpapers
produced by the generator network are sufficiently realistic and have the
attribute designated by given perceptual description; these multi-label
semantic attributes are treated as condition variables to generate wallpaper
images. The generated wallpaper images can be converted to those with
well-known artist styles using CycleGAN. Finally, using the aesthetic
evaluation method, the generated wallpaper images are quantitatively measured.
The experimental results demonstrate that the proposed method can generate
wallpaper textures conforming to human aesthetics and have artistic
characteristics.


Abstract:
  This paper addresses the challenge of classifying polarimetric SAR images by
leveraging the peculiar characteristics of the polarimetric covariance matrix
(PCM). To this end, a general framework to solve a multiple hypothesis test is
introduced with the aim to detect and classify contextual spatial variations in
polarimetric SAR images. Specifically, under the null hypothesis, only an
unknown structure is assumed for data belonging to a 2-dimensional spatial
sliding window, whereas under each alternative hypothesis, data are partitioned
into subsets sharing different structures. The problem of partition estimation
is solved by resorting to hidden random variables representative of covariance
structure classes and the expectation-maximization algorithm. The effectiveness
of the proposed detection strategies is demonstrated on both simulated and real
polarimetric SAR data also in comparison with existing classification
algorithms.


Abstract:
  We consider the lepton-number-violating processes in $\Lambda_b$/$\Lambda_c$
decays mediated by on-shell GeV-scale sterile neutrino. We calculate the
branching ratio for the following processes: $\Lambda_b^{0} \rightarrow
\mathcal{B}^+ \mu^{-}\mu^- \pi^+$, where $\mathcal{B}^+$ is $\Lambda_c^+$ or
proton, and $\Lambda_c^{+} \rightarrow \Lambda \mu^{+}\mu^+ \pi^-$, as function
of the mass of the sterile neutrino $m_N$ and the heavy-light mixing
coefficient of the extended Pontecorvo-Maki-Nakagawa-Sakata (PMNS) matrix
$|U_{\mu N}|^2$. The effect of finite detector size is included in our
calculation. After comparing the theoretical effective branching ratio with the
expected experimental ability of LHCb, we give the sensitivity upper bounds on
$|U_{\mu N}|^2$ on the ($|U_{\mu N}|^2$-$m_N$) plane. These channels give
comparable results with the bounds given by different search strategies (e.g.,
at Belle, NuTeV, DELPHI and BEBC) in the mass region $m_N \simeq$ 0.25 GeV -
4.5 GeV, and the limits are stronger in the mass range 2 GeV $<m_N<$ 4.5 GeV.


Abstract:
  Dielectric and antiferroelectric materials are particularly promising for
high-power energy-storage applications. However, relatively low energy density
greatly hinders their usage in storage technologies. Here, we report
first-principles-based calculations predicting that epitaxial and initially
non-polar AlN/ScN superlattices can achieve an ultrahigh energy density of up
to 200 J/cm$^{\textrm{3}}$, accompanied by an ideal efficiency of 100%. We also
show that high energy density requires the system being neither too close nor
too far from a ferroelectric phase transition under zero electric field. A
phenomenological model is further proposed to rationalize such striking
features.


Abstract:
  We present HyperMorph, a learning-based strategy for deformable image
registration that removes the need to tune important registration
hyperparameters during training. Classical registration methods solve an
optimization problem to find a set of spatial correspondences between two
images, while learning-based methods leverage a training dataset to learn a
function that generates these correspondences. The quality of the results for
both types of techniques depends greatly on the choice of hyperparameters.
Unfortunately, hyperparameter tuning is time-consuming and typically involves
training many separate models with various hyperparameter values, potentially
leading to suboptimal results. To address this inefficiency, we introduce
amortized hyperparameter learning for image registration, a novel strategy to
learn the effects of hyperparameters on deformation fields. The proposed
framework learns a hypernetwork that takes in an input hyperparameter and
modulates a registration network to produce the optimal deformation field for
that hyperparameter value. In effect, this strategy trains a single, rich model
that enables rapid, fine-grained discovery of hyperparameter values from a
continuous interval at test-time. We demonstrate that this approach can be used
to optimize multiple hyperparameters considerably faster than existing search
strategies, leading to a reduced computational and human burden as well as
increased flexibility. We also show several important benefits, including
increased robustness to initialization and the ability to rapidly identify
optimal hyperparameter values specific to a registration task, dataset, or even
a single anatomical region, all without retraining the HyperMorph model. Our
code is publicly available at http://voxelmorph.mit.edu.


Abstract:
  In recent times, federated machine learning has been very useful in building
intelligent intrusion detection systems for IoT devices. As IoT devices are
equipped with a security architecture vulnerable to various attacks, these
security loopholes may bring a risk during federated training of decentralized
IoT devices. Adversaries can take control over these IoT devices and inject
false gradients to degrade the global model performance. In this paper, we have
proposed an approach that detects the adversaries with the help of a clustering
algorithm. After clustering, it further rewards the clients for detecting
honest and malicious clients. Our proposed gradient filtration approach does
not require any processing power from the client-side and does not use
excessive bandwidth, making it very much feasible for IoT devices. Further, our
approach has been very successful in boosting the global model accuracy, up to
99% even in the presence of 40% adversaries.


Abstract:
  Edit-based approaches have recently shown promising results on multiple
monolingual sequence transduction tasks. In contrast to conventional
sequence-to-sequence (Seq2Seq) models, which learn to generate text from
scratch as they are trained on parallel corpora, these methods have proven to
be much more effective since they are able to learn to make fast and accurate
transformations while leveraging powerful pre-trained language models. Inspired
by these ideas, we present TST, a simple and efficient Text Simplification
system based on sequence Tagging, leveraging pre-trained Transformer-based
encoders. Our system makes simplistic data augmentations and tweaks in training
and inference on a pre-existing system, which makes it less reliant on large
amounts of parallel training data, provides more control over the outputs and
enables faster inference speeds. Our best model achieves near state-of-the-art
performance on benchmark test datasets for the task. Since it is fully
non-autoregressive, it achieves faster inference speeds by over 11 times than
the current state-of-the-art text simplification system.


Abstract:
  In this paper, we study the low-rank matrix minimization problem, where the
loss function is convex but nonsmooth and the penalty term is defined by the
cardinality function. We first introduce an exact continuous relaxation, that
is, both problems have the same minimzers and the same optimal value. In
particular, we introduce a class of lifted stationary point of the relaxed
problem and show that any local minimizer of the relaxed problem must be a
lifted stationary point. In addition, we derive lower bound property for the
nonzero singular values of the lifted stationary point and hence also of the
local minimizers of the relaxed problem. Then the smoothing proximal gradient
(SPG) algorithm is proposed to find a lifted stationary point of the continuous
relaxation model. Moreover, it is shown that the whole sequence generated by
SPG algorithm converges to a lifted stationary point. At last, numerical
examples show the efficiency of the SPG algorithm.


Abstract:
  In the pioneering work of Stern, level sets of harmonic functions have been
shown to be an effective tool in the study of scalar curvature in dimension 3.
Generalizations of this idea, utilizing level sets of so called spacetime
harmonic functions as well as other elliptic equations, are similarly effective
in treating geometric inequalities involving the ADM mass. In this paper, we
survey recent results in this context, focusing on applications of spacetime
harmonic functions to the asymptotically flat and asymptotically hyperbolic
versions of the spacetime positive mass theorem, and additionally introduce a
new concept of total mass valid in both settings which is encoded in
interpolation regions between generic initial data and model geometries.
Furthermore, a novel and elementary proof of the positive mass theorem with
charge is presented, and the level set approach to the Penrose inequality given
by Huisken and Ilmanen is related to the current developments. Lastly, we
discuss several open problems.


Abstract:
  Asymptotic efficiency of targeted maximum likelihood estimators (TMLE) of
target features of the data distribution relies on a a second order remainder
being asymptotically negligible. In previous work we proposed a nonparametric
MLE termed Highly Adaptive Lasso (HAL) which parametrizes the relevant
functional of the data distribution in terms of a multivariate real valued
cadlag function that is assumed to have finite variation norm. We showed that
the HAL-MLE converges in Kullback-Leibler dissimilarity at a rate n-1/3 up till
logn factors. Therefore, by using HAL as initial density estimator in the TMLE,
the resulting HAL-TMLE is an asymptotically efficient estimator only assuming
that the relevant nuisance functions of the data density are cadlag and have
finite variation norm. However, in finite samples, the second order remainder
can dominate the sampling distribution so that inference based on asymptotic
normality would be anti-conservative.
  In this article we propose a new higher order TMLE, generalizing the regular
first order TMLE. We prove that it satisfies an exact linear expansion, in
terms of efficient influence functions of sequentially defined higher order
fluctuations of the target parameter, with a remainder that is a k+1th order
remainder. As a consequence, this k-th order TMLE allows statistical inference
only relying on the k+1th order remainder being negligible. We also provide a
rationale for the higher order TMLE that it will be superior to the first order
TMLE by (iteratively) locally minimizing the exact finite sample remainder of
the first order TMLE. The second order TMLE is demonstrated for nonparametric
estimation of the integrated squared density and for the treatment specific
mean outcome. We also provide an initial simulation study for the second order
TMLE of the treatment specific mean confirming the theoretical analysis.


Abstract:
  The transition kernel of a continuous-state-action Markov decision process
(MDP) admits a natural tensor structure. This paper proposes a tensor-inspired
unsupervised learning method to identify meaningful low-dimensional state and
action representations from empirical trajectories. The method exploits the
MDP's tensor structure by kernelization, importance sampling and
low-Tucker-rank approximation. This method can be further used to cluster
states and actions respectively and find the best discrete MDP abstraction. We
provide sharp statistical error bounds for tensor concentration and the
preservation of diffusion distance after embedding.


Abstract:
  A systematic procedure is proposed for inclusion of Stueckelberg fields. The
procedure begins with the involutive closure when the original Lagrangian
equations are complemented by all the lower order consequences. The involutive
closure can be viewed as Lagrangian analogue of complementing constrained
Hamiltonian system with secondary constraints. The involutively closed form of
the field equations allows for explicitly covariant degree of freedom number
count, which is stable with respect to deformations. If the original Lagrangian
equations are not involutive, the involutive closure will be a non-Lagrangian
system. The Stueckelberg fields are assigned to all the consequences included
into the involutive closure of the Lagrangian system. The iterative procedure
is proposed for constructing the gauge invariant action functional involving
Stueckelberg fields such that Lagrangian equations are equivalent to the
involutive closure of the original theory. The generators of the Stueckelberg
gauge symmetry begin with the operators generating the closure of original
Lagrangian system. These operators are not assumed to be a generators of gauge
symmetry of any part of the original action, nor are they supposed to form an
on shell integrable distribution. With the most general closure generators, the
consistent Stueckelberg gauge invariant theory is iteratively constructed,
without obstructions at any stage. The Batalin-Vilkovisky form of inclusion the
Stueckelberg fields is worked out and existence theorem for the Stueckelberg
action is proven.


Abstract:
  SPICA is a mid to far infra-red space mission to explore the processes that
form galaxies, stars and planets. SPICA/SAFARI is the far infrared spectrometer
that provides near-background limited observations between 34 and 230
micrometers. The core of SAFARI consists of 4 grating modules, dispersing light
onto 5 arrays of TES detectors per module. The grating modules provide low
resolution (250) instantaneous spectra over the entire wavelength range. The
high resolution (1500 to 12000) mode is accomplished by placing a Fourier
Transform Spectrometer (FTS) in front of the gratings. Each grating module
detector sees an interferogram from which the high resolution spectrum can be
constructed. SAFARI data will be a convolution of complex spectral, temporal
and spatial information. Along with spectral calibration accuracy of <1%, a
relative flux calibration of 1% and an absolute flux calibration accuracy of
10% are required. This paper will discuss the calibration strategy and its
impact on the instrument design of SAFARI


Abstract:
  In this paper, we define a new class of dynamic games played in large
populations of anonymous agents. The behavior of agents in these games depends
on a time-homogeneous type and a time-varying state, which are private to each
agent and characterize their available actions and motifs. We consider finite
type, state, and action spaces. On the individual agent level, the state
evolves in discrete-time as the agent participates in interactions, in which
the state transitions are affected by the agent's individual action and the
distribution of other agents' states and actions. On the societal level, we
consider that the agents form a continuum of mass and that interactions occur
either synchronously or asynchronously, and derive models for the evolution of
the agents' state distribution. We characterize the stationary equilibrium as
the solution concept in our games, which is a condition where all agents are
playing their best response and the state distribution is stationary. At least
one stationary equilibrium is guaranteed to exist in every dynamic population
game. Our approach intersects with previous works on anonymous sequential
games, mean-field games, and Markov decision evolutionary games, but it is
novel in how we relate the dynamic setting to a classical, static population
game setting. In particular, stationary equilibria can be reduced to standard
Nash equilibria in classical population games. This simplifies the analysis of
these games and inspires the formulation of an evolutionary model for the
coupled dynamics of both the agents' actions and states.


Abstract:
  We consider the lower bounds of differentially private empirical risk
minimization for general convex functions in this paper. For convex generalized
linear models (GLMs), the well-known tight bound of DP-ERM in the constrained
case is $\tilde{\Theta}(\frac{\sqrt{p}}{\epsilon n})$, while recently,
\cite{sstt21} find the tight bound of DP-ERM in the unconstrained case is
$\tilde{\Theta}(\frac{\sqrt{\text{rank}}}{\epsilon n})$ where $p$ is the
dimension, $n$ is the sample size and $\text{rank}$ is the rank of the feature
matrix of the GLM objective function. As $\text{rank}\leq \min\{n,p\}$, a
natural and important question arises that whether we can evade the curse of
dimensionality for over-parameterized models where $n\ll p$, for more general
convex functions beyond GLM. We answer this question negatively by giving the
first and tight lower bound of unconstrained private ERM for the general convex
function, matching the current upper bound
$\tilde{O}(\frac{\sqrt{p}}{n\epsilon})$ for unconstrained private ERM. We also
give an $\Omega(\frac{p}{n\epsilon})$ lower bound for unconstrained pure-DP ERM
which recovers the result in the constrained case.


Abstract:
  We survey partial geometric designs and investigate their concurrences of
points. The concurrence matrix of a design, which encodes the concurrences of
pairs of points, can be used in the classification of designs in some extent.
An ordinary 2-$(v,k,\lambda)$ design has concurrence $\lambda$ for any pair of
distinct points, and its concurrence matrix is circulant. A partial geometry
has two concurrences $1$ and $0$ and a transversal design TD$_{\lambda}(k, u)$
has two concurrences $\lambda$ and $0$. It is also known that the concurrence
matrix of a partial geometric design can have at most three distinct
eigenvalues, all of which are non-negative integers. In this paper, we show the
existence of other partial geometric designs having two or three distinct
concurrences, and investigate which symmetric circulant matrices are realized
as the concurrence matrices of partial geometric designs. We collect known
sources of partial geometric designs and study their structural characteristics
and construction methods. We then give a list of feasible parameter sets for
partial geometric designs of order up to 12 each of which has a circulant
concurrence matrix. We also consider the combinatorial properties and
constructions of partial geometric designs satisfying these parameter sets.


Abstract:
  The ability for an Open Source Software (OSS) project to attract, onboard,
and retain any newcomer is vital to its livelihood. Evidence suggests more new
users are joining GitHub, however, the extent to which they contribute to OSS
projects is unknown. In this study, we coin the term newcomer candidate to
describe a novice developer that is a new user to the GitHub platform, with the
intention to later onboard an OSS project. Our objective is to track and
characterize their initial contributions using a mixed-method approach. Our
results show that 68% of newcomer candidates are more likely to practice
non-social coding, 86% tend to work on forward-engineering activities in their
first commits, and 53% show their interest of targeting non-software
repositories. Our quantitative analysis did match only 3% of newcomer
candidates contributions to established OSS repositories, yet 70% of newcomer
candidates claim to already onboard an OSS project. This study opens up new
avenues for future work, especially in terms of targeting potential
contributors to onboard an existing OSS project. More practical applications
would be tool support to (i) recommend practical examples that OSS project
teams can use to lower their barriers for a newcomer candidate to successfully
make a contribution and (ii) recommend suitable repositories for newcomer
candidates based on their preference. Researchers can explore strategies to
sustain newcomer candidate activities until they are ready to onboard an OSS
project.


Abstract:
  This paper proposes a novel nonlinear activation mechanism typically for
convolutional neural network (CNN), named as reborn mechanism. In sharp
contrast to ReLU which cuts off the negative phase value, the reborn mechanism
enjoys the capacity to reborn and reconstruct dead neurons. Compared to other
improved ReLU functions, reborn mechanism introduces a more proper way to
utilize the negative phase information. Extensive experiments validate that
this activation mechanism is able to enhance the model representation ability
more significantly and make the better use of the input data information while
maintaining the advantages of the original ReLU function. Moreover, reborn
mechanism enables a non-symmetry that is hardly achieved by traditional CNNs
and can act as a channel compensation method, offering competitive or even
better performance but with fewer learned parameters than traditional methods.
Reborn mechanism was tested on various benchmark datasets, all obtaining better
performance than previous nonlinear activation functions.


Abstract:
  In this paper we study gapless fermionic and bosonic systems in
$d$-dimensional continuum space with $U(1)$ particle-number conservation and
$\mathbb{R}^d$ translation symmetry. We write down low energy effective field
theories for several gapless phases where $U(1)\times \mathbb{R}^d$ is viewed
as internal symmetry. The $U(1)\times \mathbb{R}^d$ symmetry, when viewed as an
internal symmetry, has a mixed anomaly, and the different effective field
theories for different phases must have the same mixed anomaly. Such a mixed
anomaly is proportional to the particle number density, and can be measured
from the distribution of the total momentum $\boldsymbol{k}_\text{tot}$ for low
energy many-body states (\ie how such a distribution is shifted by $U(1)$
symmetry twist $\boldsymbol{a}$), as well as some other low energy universal
properties of the systems. In particular, we write down low energy effective
field theory for Fermi liquid with infinite number of fields, in the presence
of both real space magnetic field and $\boldsymbol{k}$-space "magnetic" field.
The effective field theory also captures the mixed anomaly, which constraints
the low energy dynamics, such as determine the volume of Fermi surface (which
is another formulation of Luttinger-Ward-Oshikawa theorem).


Abstract:
  Unabated coal power in India must be phased out by mid-century to achieve
global climate targets under the Paris Agreement. Here we estimate the costs of
hybrid power plants - lithium-ion battery storage with wind and solar PV - to
replace coal generation. We design least cost mixes of these technologies to
supply baseload and load-following generation profiles in three Indian states -
Karnataka, Gujarat, and Tamil Nadu. Our analysis shows that availability of low
cost capital, solar PV installation costs of $<$\$300/kW, and battery storage
capacity costs of $<$\$75/kWh will be required to phase out existing coal power
plants. Phaseout by 2040 requires a 5% annual decline in the cost of hybrid
systems over the next two decades. Solar PV is more suited to pairing with
short duration storage than wind power. Our results describe the challenging
technological and policy advances needed to achieve the goals of the Paris
Agreement.


Abstract:
  We propose a new method in which a generative adversarial network (GAN) is
used to quantify the uncertainty of forward simulations in the presence of
observed data. Previously, a method has been developed which enables GANs to
make time series predictions and data assimilation by training a GAN with
unconditional simulations of a high-fidelity numerical model. After training,
the GAN can be used to predict the evolution of the spatial distribution of the
simulation states and observed data is assimilated. In this paper, we describe
the process required in order to quantify uncertainty, during which no
additional simulations of the high-fidelity numerical model are required. These
methods take advantage of the adjoint-like capabilities of generative models
and the ability to simulate forwards and backwards in time. Set within a
reduced-order model framework for efficiency, we apply these methods to a
compartmental model in epidemiology to predict the spread of COVID-19 in an
idealised town. The results show that the proposed method can efficiently
quantify uncertainty in the presence of measurements using only unconditional
simulations of the high-fidelity numerical model.


Abstract:
  Adoption processes in socio-technological systems have been widely studied
both empirically and theoretically. The way in which social norms, behaviors,
and even items such as books, music, or other commercial or technological
product spread in a population is usually modeled as a process of social
contagion, in which the agents of a social system can infect their neighbors on
the underlying network of social contacts. More recently, various models have
also been proposed to reproduce the typical dynamics of a process of discovery,
in which an agent explores a space of relations between ideas or items in
search for novelties. In both types of processes, the structure of the
underlying networks, respectively, the network of social contacts in the first
case, and the network of relations among items in the second one, plays a
fundamental role. However, the two processes have been traditionally seen and
studied independently. Here, we provide a brief overview of the existing models
of social spreading and exploration and of the latest advancements in both
directions. We propose to look at them as two complementary aspects of the same
adoption process: on the one hand, there are items spreading over a social
network of individuals influencing each other, and on the other hand,
individuals explore a network of similarities among items to adopt. The
two-fold nature of the approach proposed opens up new stimulating challenges
for the scientific community of network and data scientists. We conclude by
outlining some possible directions that we believe may be relevant to be
explored in the coming years.