title: A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks

abstract:
  Virtual network embedding is one of the key problems of network
virtualization. Since virtual network mapping is an NP-hard problem, a lot of
research has focused on the evolutionary algorithm's masterpiece genetic
algorithm. However, the parameter setting in the traditional method is too
dependent on experience, and its low flexibility makes it unable to adapt to
increasingly complex network environments. In addition, link-mapping strategies
that do not consider load balancing can easily cause link blocking in
high-traffic environments. In the IoT environment involving medical, disaster
relief, life support and other equipment, network performance and stability are
particularly important. Therefore, how to provide a more flexible virtual
network mapping service in a heterogeneous network environment with large
traffic is an urgent problem. Aiming at this problem, a virtual network mapping
strategy based on hybrid genetic algorithm is proposed. This strategy uses a
dynamically calculated cross-probability and pheromone-based mutation gene
selection strategy to improve the flexibility of the algorithm. In addition, a
weight update mechanism based on load balancing is introduced to reduce the
probability of mapping failure while balancing the load. Simulation results
show that the proposed method performs well in a number of performance metrics
including mapping average quotation, link load balancing, mapping cost-benefit
ratio, acceptance rate and running time.


title: Heidegger's quantum phenomenology

abstract:
  The article suggests that quantum mechanics is a science of a new type, which
refutes the classical metaphysical concept of reality. The notion of a quantum
concept is introduced. The possibility of a Wittgensteinian 'dissolution' of
the measurement problem with the help of the notion of a language game and the
possibility of a metaphysical solution of this problem with the help of the
Heideggerian notion of Dasein are considered.


title: Alternative Approaches to Solve Simple Harmonic Motion

abstract:
  This paper presents two alternative approaches to solve simple harmonic
motion (SHM) without resorting to differential equations. In one approach, the
distance between the equilibrium position and the maximal displacement is
divided into N equal segments. In each segment, the motion is approximated as
one with constant acceleration under the average of two forces at each end of
the segment. Summing up the time to cover each segment and taking a large-N
limit reproduce one quarter of the period for SHM. In the other approach, the
time moving from the maximal displacement to the equilibrium position is
divided into N equal intervals. The motion during each interval is approximated
as one with constant acceleration. A second order recurrence relation for
displacement is obtained. The large-N limit of its solution results in the same
solution obtained from solving differential equation.


title:
Fundamental problems in statistical physics XIV: Lecture on Machine
  Learning

abstract:
  The recent progresses in Machine Learning opened the door to actual
applications of learning algorithms but also, to new research directions both
in the field of Machine Learning directly and at the edges with other
disciplines. The case that interests us is the interface with physics, and more
specifically statistical physics. In this short lecture, I will try to present
first a brief introduction to Machine Learning from the angle of neural
networks. After explaining quickly the based model and the global aspects of
the training procedure, I will discuss into more detail two examples to show
some interesting developments that can be done from the statistical physics
perspective.


title: Black-Scholes Option Pricing Revisited?

abstract:
  The hedging argument of Black and Scholes (1973) hinges on the assumption
that a continuously rebalanced asset portfolio satisfies the continuous-time
self-financing condition. This condition, which is a special case of the
continuous-time budget equation of Merton (1971), is believed to mathematically
formalize the economic concept of an asset portfolio that is rebalanced
continuously without requiring an inflow or outflow of external funds. Although
we sometimes find it hard to believe our results, we believe that we show with
three alternative mathematical proofs that the continuous-time self-financing
condition does not hold for rebalanced portfolios. In addition, we pinpoint the
mistake in the derivation that Merton (1971) uses to motivate the
continuous-time budget equation. Specifically, by inadvertently equating a
deterministic variable to a stochastic one, Merton (1971) implicitly assumes
that the portfolio rebalancing does not depend on changes in asset prices. If
correct, our results invalidate the continuous-time budget equation of Merton
(1971) and the hedging argument and option pricing formula of Black and Scholes
(1973).


title:
Expected yields of $^{181}$Ta (e,e') $^{181}$Ta$^*$ in the multi-keV
  range with a plasma-cathode electron beam

abstract:
  Calculations of the cross sections of inelastic electron scattering $(e,e')$
on a nucleus in the multi-keV energy range strongly depend on the description
of the screening of the nuclear Coulomb potential as well as on the deformation
of the wave functions of the incoming and outgoing electron in the vicinity of
the nucleus. These cross sections are evaluated at values lower than $10^{-30}$
cm$^2$ which vary by several orders of magnitude according to the models.
Experimental measurements would be required to constrain the models but it is a
real challenge to measure such low cross sections. In this study, we
demonstrate that inelastic electron scattering is the main nuclear excitation
mechanism in a $^{181}$Ta target irradiated with a new intense 10 - 30 keV
electron beam produced with a biased laser-plasma. Calculations show that
through the detection of conversion electrons, it should be possible to measure
the nuclear excitation yields. The effect of electron beam heating and of
plasma deposition on the tantalum target are quantified, thus allowing the
dimensioning of a possible experimental configuration to study $(e,e')$
processes in this range of energy for the first time.

categories: nucl-th nucl-ex physics.atom-ph physics.plasm-ph


title:
Channel Estimation with Simultaneous Reflecting and Sensing
  Reconfigurable Intelligent Metasurfaces

abstract:
  Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key role
in future wireless communications, enabling programmable radio propagation
environments. They are usually considered as nearly passive planar structures
that operate as adjustable reflectors, giving rise to a multitude of
implementation challenges, including an inherent difficulty in estimating the
underlying wireless channels. In this paper, we propose the concept of Hybrid
RISs (HRISs), which do not solely reflect the impinging waveform in a
controllable fashion, but are also capable of sensing and processing a portion
of it via some active reception elements. We first present implementation
details for this novel metasurface architecture and propose a simple model for
its operation, when considered for wireless communications. As an indicative
application of HRISs, we formulate and solve the individual channels
identification problem for the uplink of multi-user HRIS-empowered systems. Our
numerical results showcase that, in the high signal-to-noise regime, HRISs
enable individual channel estimation with notably reduced amounts of pilots,
compared to those needed when using a purely reflective RIS that can only
estimate the cascaded channel.


title:
Cashing Out: Assessing the risk of localised financial exclusion as the
  UK moves towards a cashless society

abstract:
  Whilst academic, commercial and policy literature on financial exclusion is
extensive and wide-ranging, there have been very few attempts to quantify and
measure localised financial exclusion anywhere in the world. This is a subject
of growing importance in modern UK society with the withdrawal of cash
infrastructure and a shift towards online banking. This research develops a
composite indicator using a wide-range of input variables, including the
locations of existing cash infrastructure, various demographic factors (such as
income and housing tenure) and other freely available lifestyle data to
identify areas at greatest risk of financial exclusion, thereby aiding
organisations to develop intervention strategies to tackle the problem. The
indicator illustrates that whilst there is no apparent correlation between
financial exclusion and deprivation, pockets of extreme financial exclusion are
generally found in deprived communities, and affluent, suburban areas tend to
score consistently more favourably and consequently carry less risk. The
attributing causes vary, from a lack of infrastructure, to low car
availability, but income levels have a pronounced influence. Three policy
proposals are put forward, including offering banking services at PayPoint
outlets, and converting cash machines to cash recyclers, but improving digital
adoption was found to be the most effective intervention, provided that it is
implemented by community organisations. Policies purely targeting
infrastructure provision or addressing social exclusion are unlikely to be
effective, as community-based initiatives coupled with wider reforms to the
financial system are needed.


title: Mechanization of scalar field theory in 1+1 dimensions

abstract:
  The `mechanization' is a procedure of replacing a scalar field in 1+1
dimensions with a piece-wise linear function, i.e. a finite graph consisting of
$N$ joints (vertices) and straight segments (edges). As a result, the field
theory is approximated by a sequence of algebraically tractable,
general-purpose collective coordinate mechanical models. We observe the
step-by-step emergence of dynamical objects and associated phenomena as the $N$
increases. Mech-kinks and mech-oscillons -- mechanical analogs of kinks and
oscillons (bions) -- appear in the simplest models, while more intricate
dynamical patterns, such as bouncing phenomenon and bion pair-production,
emerge gradually as decay states of high $N$ mech-oscillons.


title:
Deep artificial neural network for prediction of atrial fibrillation
  through the analysis of 12-leads standard ECG

abstract:
  Atrial Fibrillation (AF) is a heart's arrhythmia which, despite being often
asymptomatic, represents an important risk factor for stroke, therefore being
able to predict AF at the electrocardiogram exam, would be of great impact on
actively targeting patients at high risk. In the present work we use
Convolution Neural Networks to analyze ECG and predict Atrial Fibrillation
starting from realistic datasets, i.e. considering fewer ECG than other studies
and extending the maximal distance between ECG and AF diagnosis. We achieved
75.5% (0.75) AUC firstly increasing our dataset size by a shifting technique
and secondarily using the dilation parameter of the convolution neural network.
In addition we find that, contrarily to what is commonly used by clinicians
reporting AF at the exam, the most informative leads for the task of predicting
AF are D1 and avR. Similarly, we find that the most important frequencies to
check are in the range of 5-20 Hz. Finally, we develop a net able to manage at
the same time the electrocardiographic signal together with the electronic
health record, showing that integration between different sources of data is a
profitable path. In fact, the 2.8% gain of such net brings us to a 78.6% (std
0.77) AUC. In future works we will deepen both the integration of sources and
the reason why we claim avR is the most informative lead.


title: Cross-Block Difference Guided Fast CU Partition for VVC Intra Coding

abstract:
  In this paper, we propose a new fast CU partition algorithm for VVC intra
coding based on cross-block difference. This difference is measured by the
gradient and the content of sub-blocks obtained from partition and is employed
to guide the skipping of unnecessary horizontal and vertical partition modes.
With this guidance, a fast determination of block partitions is accordingly
achieved. Compared with VVC, our proposed method can save 41.64% (on average)
encoding time with only 0.97% (on average) increase of BD-rate.


title: Epicenter localization using forward-transmission laser interferometry

abstract:
  Widely distributed optical fibers, together with phase-sensitive laser
interferometry, can expand seismic detection methods and have great potential
for epicenter localization. In this letter, we propose an integral response
method based on a forward transmission scheme. It uses spectrum analysis and
parameter fitting to localize the epicenter. With the given shape of the fiber
link, the integral phase changes of light propagating in the forward and
reverse directions can be used to determine the direction, depth, distance of
the epicenter, and seismic wave speed. For the noisy case with SNR=20 dB, the
maximum localization error for the orientation angle is ~0.004{\deg}, the
maximum error of the P-wave speed is ~0.45 m/s, the maximum localization error
for the epicenter depth is ~1000 m, and the maximum localization error for the
epicenter distance is ~2200 m.


title: Rethinking Graph Convolutional Networks in Knowledge Graph Completion

abstract:
  Graph convolutional networks (GCNs) -- which are effective in modeling graph
structures -- have been increasingly popular in knowledge graph completion
(KGC). GCN-based KGC models first use GCNs to generate expressive entity
representations and then use knowledge graph embedding (KGE) models to capture
the interactions among entities and relations. However, many GCN-based KGC
models fail to outperform state-of-the-art KGE models though introducing
additional computational complexity. This phenomenon motivates us to explore
the real effect of GCNs in KGC. Therefore, in this paper, we build upon
representative GCN-based KGC models and introduce variants to find which factor
of GCNs is critical in KGC. Surprisingly, we observe from experiments that the
graph structure modeling in GCNs does not have a significant impact on the
performance of KGC models, which is in contrast to the common belief. Instead,
the transformations for entity representations are responsible for the
performance improvements. Based on the observation, we propose a simple yet
effective framework named LTE-KGE, which equips existing KGE models with
linearly transformed entity embeddings. Experiments demonstrate that LTE-KGE
models lead to similar performance improvements with GCN-based KGC methods,
while being more computationally efficient. These results suggest that existing
GCNs are unnecessary for KGC, and novel GCN-based KGC models should count on
more ablation studies to validate their effectiveness. The code of all the
experiments is available on GitHub at https://github.com/MIRALab-USTC/GCN4KGC.


title: D-Branes in Para-Hermitian Geometries

abstract:
  We introduce T-duality invariant versions of D-branes in doubled geometry
using a global covariant framework based on para-Hermitian geometry and metric
algebroids. We define D-branes as conformal boundary conditions for the open
string version of the Born sigma-model, where they are given by maximally
isotropic vector bundles which do not generally admit the standard geometric
picture in terms of submanifolds. When reduced to the conventional sigma-model
description of a physical string background as the leaf space of a foliated
para-Hermitian manifold, integrable branes yield D-branes as leaves of
foliations which are interpreted as Dirac structures on the physical spacetime.
We define a notion of generalised para-complex D-brane, which realises our
D-branes as para-complex versions of topological A/B-branes. We illustrate how
our formalism recovers standard D-branes in the explicit example of reductions
from doubled nilmanifolds.


title: MDPCT1 quench data and performance analysis

abstract:
  MDPCT1 is a four-layer cos-theta Nb3Sn dipole demonstrator developed and
tested at FNAL in the framework of the U.S. Magnet Development Program. The
magnet reached record fields for accelerator magnets of 14.1 T at 4.5 K in the
first test and 14.5 T at 1.9 K in the second test and then showed large
degradation. While its inner coils performed exceptionally well with only two
quenches up to 14.5 T and no evidence of degradation, the outer coils degraded
over the course of testing. By adopting new measurement and analysis techniques
at FNAL we are discussing in detail what happened. Both success and failure in
our diagnostics are discussed. The evolution of techniques over the course of
two tests (and three thermal cycles) shows the path to address challenges
brought by the first four-layer magnet tested at FNAL. This paper presents the
analysis of quench data along with diagnostic features and complementary
measurements taken in support of the magnet performance analysis.


title:
GenderedNews: Une approche computationnelle des \'ecarts de
  repr\'esentation des genres dans la presse fran\c{c}aise

abstract:
  In this article, we present {\it GenderedNews}
(\url{https://gendered-news.imag.fr}), an online dashboard which gives weekly
measures of gender imbalance in French online press. We use Natural Language
Processing (NLP) methods to quantify gender inequalities in the media, in the
wake of global projects like the Global Media Monitoring Project. Such projects
are instrumental in highlighting gender imbalance in the media and its very
slow evolution. However, their generalisation is limited by their sampling and
cost in terms of time, data and staff. Automation allows us to offer
complementary measures to quantify inequalities in gender representation. We
understand representation as the presence and distribution of men and women
mentioned and quoted in the news -- as opposed to representation as
stereotypification. In this paper, we first review different means adopted by
previous studies on gender inequality in the media : qualitative content
analysis, quantitative content analysis and computational methods. We then
detail the methods adopted by {\it GenderedNews} and the two metrics
implemented: the masculinity rate of mentions and the proportion of men quoted
in online news. We describe the data collected daily (seven main titles of
French online news media) and the methodology behind our metrics, as well as a
few visualisations. We finally propose to illustrate possible analysis of our
data by conducting an in-depth observation of a sample of two months of our
database.


title:
Rare event estimation with sequential directional importance sampling
  (SDIS)

abstract:
  In this paper, we propose a sequential directional importance sampling (SDIS)
method for rare event estimation. SDIS expresses a small failure probability in
terms of a sequence of auxiliary failure probabilities, defined by magnifying
the input variability. The first probability in the sequence is estimated with
Monte Carlo simulation in Cartesian coordinates, and all the subsequent ones
are computed with directional importance sampling in polar coordinates. Samples
from the directional importance sampling densities used to estimate the
intermediate probabilities are drawn in a sequential manner through a
resample-move scheme. The latter is conveniently performed in Cartesian
coordinates and directional samples are obtained through a suitable
transformation. For the move step, we discuss two Markov Chain Monte Carlo
(MCMC) algorithms for application in low and high-dimensional problems.
Finally, an adaptive choice of the parameters defining the intermediate failure
probabilities is proposed and the resulting coefficient of variation of the
failure probability estimate is analyzed. The proposed SDIS method is tested on
five examples in various problem settings, which demonstrate that the method
outperforms existing sequential sampling reliability methods.


title:
Statistical Analysis Based Feature Selection Enhanced RF-PUF with >99.8%
  Accuracy on Unmodified Commodity Transmitters for IoT Physical Security

abstract:
  Due to the diverse and mobile nature of the deployment environment, smart
commodity devices are vulnerable to various attacks which can grant
unauthorized access to a rogue device in a large, connected network.
Traditional digital signature-based authentication methods are vulnerable to
key recovery attacks, CSRF, etc. To circumvent this, RF-PUF had been proposed
as a promising alternative that utilizes the inherent nonidealities of the
devices as physical signatures. RF-PUF offers a robust authentication method
that is resilient to key-hacking methods due to the absence of secret key
requirements and does not require any additional circuitry on the transmitter
end, eliminating additional power, area, and computational burden. In this
work, for the first time, we analyze the effectiveness of RF-PUF on commodity
devices, purchased off-the-shelf, without any modifications whatsoever. Data
were collected from 30 Xbee S2C modules and released as a public dataset. A new
feature has been engineered through statistical property analysis. With a new
and robust feature set, it has been shown that 95% accuracy can be achieved
using only ~1.8 ms of test data, reaching >99.8% accuracy with more data and a
network of higher model capacity, without any assisting digital preamble. The
design space has been explored in detail and the effect of the wireless channel
has been determined. The performance of some popular ML algorithms has been
compared with the NN approach. A thorough investigation on various PUF
properties has been done and both intra and inter-PUF distances have been
calculated. With extensive testing of 41238000 cases, the detection probability
for RF-PUF for our data is found to be 0.9987, which, for the first time,
experimentally establishes RF-PUF as a strong authentication method. Finally,
the potential attack models and the robustness of RF-PUF against them have been
discussed.


title:
SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion
  Classification

abstract:
  Convolutional neural networks (CNNs) have achieved great success in skin
lesion classification. A balanced dataset is required to train a good model.
However, due to the appearance of different skin lesions in practice, severe or
even deadliest skin lesion types (e.g., melanoma) naturally have quite small
amount represented in a dataset. In that, classification performance
degradation occurs widely, it is significantly important to have CNNs that work
well on class imbalanced skin lesion image dataset. In this paper, we propose
SuperCon, a two-stage training strategy to overcome the class imbalance problem
on skin lesion classification. It contains two stages: (i) representation
training that tries to learn a feature representation that closely aligned
among intra-classes and distantly apart from inter-classes, and (ii) classifier
fine-tuning that aims to learn a classifier that correctly predict the label
based on the learnt representations. In the experimental evaluation, extensive
comparisons have been made among our approach and other existing approaches on
skin lesion benchmark datasets. The results show that our two-stage training
strategy effectively addresses the class imbalance classification problem, and
significantly improves existing works in terms of F1-score and AUC score,
resulting in state-of-the-art performance.


title: Graphon-aided Joint Estimation of Multiple Graphs

abstract:
  We consider the problem of estimating the topology of multiple networks from
nodal observations, where these networks are assumed to be drawn from the same
(unknown) random graph model. We adopt a graphon as our random graph model,
which is a nonparametric model from which graphs of potentially different sizes
can be drawn. The versatility of graphons allows us to tackle the joint
inference problem even for the cases where the graphs to be recovered contain
different number of nodes and lack precise alignment across the graphs. Our
solution is based on combining a maximum likelihood penalty with graphon
estimation schemes and can be used to augment existing network inference
methods. We validate our proposed approach by comparing its performance against
competing methods in synthetic and real-world datasets.


title: Towards Adversarially Robust Deepfake Detection: An Ensemble Approach

abstract:
  Detecting deepfakes is an important problem, but recent work has shown that
DNN-based deepfake detectors are brittle against adversarial deepfakes, in
which an adversary adds imperceptible perturbations to a deepfake to evade
detection. In this work, we show that a modification to the detection strategy
in which we replace a single classifier with a carefully chosen ensemble, in
which input transformations for each model in the ensemble induces pairwise
orthogonal gradients, can significantly improve robustness beyond the de facto
solution of adversarial training. We present theoretical results to show that
such orthogonal gradients can help thwart a first-order adversary by reducing
the dimensionality of the input subspace in which adversarial deepfakes lie. We
validate the results empirically by instantiating and evaluating a randomized
version of such "orthogonal" ensembles for adversarial deepfake detection and
find that these randomized ensembles exhibit significantly higher robustness as
deepfake detectors compared to state-of-the-art deepfake detectors against
adversarial deepfakes, even those created using strong PGD-500 attacks.


title: Radical-induced Low-field Relaxation and Thermal Mixing in Pyruvic Acid

abstract:
  In dynamic nuclear polarization, polarization is transferred from electron to
nuclear spins at low temperatures. So far, it is not possible to predict the
relaxation of hyperpolarized solids, and applications are developed largely by
trial and error. Here, we study the low-field nuclear spin relaxation in
pyruvic acid, doped with trityl. We find that, at low fields, the relaxation
time constants for both $^{13}$C and $^{1}$H scale linearly with the applied
field. We model the data using a thermodynamic approach, in which heat is
transferred via triple-spin-flips involving two electron spins and one nuclear
spin. The triple-spin-flip rate is calculated from first principles using a
formalism developed by Wenckebach. The heat capacity of the Non-Zeeman
reservoir is determined from the $^1$H relaxation data at intermediate fields,
which leads to a parameter-free, yet nearly quantitative description of the
observed $^1$H relaxation rates from 5~mT to 2~T. The observed $^{13}$C
relaxation is consistent with the formalism, provided that the direct energy
exchange between the $^1$H and $^{13}$C reservoirs, and the diffusion barrier
are accounted for at low and high fields, respectively. The formalism also
describes the observed electron-mediated $^1$H-$^{13}$C thermal mixing.


title: Conservative Extensions for Existential Rules

abstract:
  We study the problem to decide, given sets T1,T2 of tuple-generating
dependencies (TGDs), also called existential rules, whether T2 is a
conservative extension of T1. We consider two natural notions of conservative
extension, one pertaining to answers to conjunctive queries over databases and
one to homomorphisms between chased databases. Our main results are that these
problems are undecidable for linear TGDs, undecidable for guarded TGDs even
when T1 is empty, and decidable for frontier-one TGDs.


title:
HaT5: Hate Language Identification using Text-to-Text Transfer
  Transformer

abstract:
  We investigate the performance of a state-of-the art (SoTA) architecture T5
(available on the SuperGLUE) and compare with it 3 other previous SoTA
architectures across 5 different tasks from 2 relatively diverse datasets. The
datasets are diverse in terms of the number and types of tasks they have. To
improve performance, we augment the training data by using an autoregressive
model. We achieve near-SoTA results on a couple of the tasks - macro F1 scores
of 81.66% for task A of the OLID 2019 dataset and 82.54% for task A of the hate
speech and offensive content (HASOC) 2021 dataset, where SoTA are 82.9% and
83.05%, respectively. We perform error analysis and explain why one of the
models (Bi-LSTM) makes the predictions it does by using a publicly available
algorithm: Integrated Gradient (IG). This is because explainable artificial
intelligence (XAI) is essential for earning the trust of users. The main
contributions of this work are the implementation method of T5, which is
discussed; the data augmentation using a new conversational AI model
checkpoint, which brought performance improvements; and the revelation on the
shortcomings of HASOC 2021 dataset. It reveals the difficulties of poor data
annotation by using a small set of examples where the T5 model made the correct
predictions, even when the ground truth of the test set were incorrect (in our
opinion). We also provide our model checkpoints on the HuggingFace hub1 to
foster transparency.


title:
A Tight $(\frac{3}{2}+\epsilon)$-Approximation for Unsplittable
  Capacitated Vehicle Routing on Trees

abstract:
  We give a polynomial time $(3/2+\epsilon)$-approximation algorithm for the
unsplittable capacitated vehicle routing problem (CVRP) on trees. Our
approximation ratio is tight, given that it is NP-hard to approximate this
problem to better than a $3/2$ factor.


title:
Effect of infill pattern and build orientation on mechanical properties
  of FDM printed parts: An experimental modal analysis approach

abstract:
  Fused Deposition Modeling (FDM) is one of the most widely explored additive
manufacturing method that uses thermoplastic materials to manufacture products.
Mechanical properties of parts manufactured using FDM are influenced by
different process parameters involved during manufacturing as they impact the
bonding among different layers of cross-section. In this study, the effect of
infill patterns and build orientations on the mechanical properties of PLA
based parts manufactured using FDM method is studied. Six different infill
patterns (triangles, cubic, concentric, tetrahedral, lines, and zigzag) and
three different orientations (flatwise, edgewise, and upright) are considered
for rectangular beam type parts. For determining mechanical properties of parts
with different infill patterns and orientations, flexural bending test is
performed. Experimental modal analysis of manufactured parts is performed to
observe the relation of natural frequencies with elastic modulus of the parts
obtained from flexural bending test. The possibility of experimental modal
analysis as an alternative non-destructive method for testing mechanical
properties of FDM 3D printed parts is explored.


title:
Black-box Identity Testing of Noncommutative Rational Formulas of
  Inversion Height Two in Deterministic Quasipolynomial-time

abstract:
  Hrube\v{s} and Wigderson (2015) initiated the complexity-theoretic study of
noncommutative formulas with inverse gates. They introduced the Rational
Identity Testing (RIT) problem which is to decide whether a noncommutative
rational formula computes zero in the free skew field. In the white-box
setting, deterministic polynomial-time algorithms are known for this problem
following the works of Garg, Gurvits, Oliveira, and Wigderson (2016) and
Ivanyos, Qiao, and Subrahmanyam (2018).
  A central open problem in this area is to design efficient deterministic
black-box identity testing algorithm for rational formulas. In this paper, we
solve this problem for the first nested inverse case. More precisely, we obtain
a deterministic quasipolynomial-time black-box RIT algorithm for noncommutative
rational formulas of inversion height two via a hitting set construction.
Several new technical ideas are involved in the hitting set construction,
including key concepts from matrix coefficient realization theory
(Vol\v{c}i\v{c}, 2018) and properties of cyclic division algebra (Lam, 2001).
En route to the proof, an important step is to embed the hitting set of Forbes
and Shpilka for noncommutative formulas (2013) inside a cyclic division algebra
of small index.


title: Continual Learning with Invertible Generative Models

abstract:
  Catastrophic forgetting (CF) happens whenever a neural network overwrites
past knowledge while being trained on new tasks. Common techniques to handle CF
include regularization of the weights (using, e.g., their importance on past
tasks), and rehearsal strategies, where the network is constantly re-trained on
past data. Generative models have also been applied for the latter, in order to
have endless sources of data. In this paper, we propose a novel method that
combines the strengths of regularization and generative-based rehearsal
approaches. Our generative model consists of a normalizing flow (NF), a
probabilistic and invertible neural network, trained on the internal embeddings
of the network. By keeping a single NF throughout the training process, we show
that our memory overhead remains constant. In addition, exploiting the
invertibility of the NF, we propose a simple approach to regularize the
network's embeddings with respect to past tasks. We show that our method
performs favorably with espect to state-of-the-art approaches in the
literature, with bounded computational power and memory overheads.


title: Positive-Unlabeled Domain Adaptation

abstract:
  Domain Adaptation methodologies have shown to effectively generalize from a
labeled source domain to a label scarce target domain. Previous research has
either focused on unlabeled domain adaptation without any target supervision or
semi-supervised domain adaptation with few labeled target examples per class.
On the other hand Positive-Unlabeled (PU-) Learning has attracted increasing
interest in the weakly supervised learning literature since in quite some real
world applications positive labels are much easier to obtain than negative
ones. In this work we are the first to introduce the challenge of
Positive-Unlabeled Domain Adaptation where we aim to generalise from a fully
labeled source domain to a target domain where only positive and unlabeled data
is available. We present a novel two-step learning approach to this problem by
firstly identifying reliable positive and negative pseudo-labels in the target
domain guided by source domain labels and a positive-unlabeled risk estimator.
This enables us to use a standard classifier on the target domain in a second
step. We validate our approach by running experiments on benchmark datasets for
visual object recognition. Furthermore we propose real world examples for our
setting and validate our superior performance on parking occupancy data.


title: On Higgs inflation in non-minimally coupled models of gravity

abstract:
  Models of inflation in which the Higgs field is non-minimally coupled to
gravity lead, after a recaling of the metric, to a complicated expression for
the potential of the inflaton field. Nevertheless, this potential produces the
desired features, both theoretical and experimental, after some approximations
are made. In this note, we also allow for a modification of the Higgs kinetic
term in such a way that the resulting potential for the inflaton field is,
without any approximation, of the simplest form after the rescaling of the
metric.


title:
XIGA: An eXtended IsoGeometric Analysis approach for multi-material
  problems

abstract:
  Multi-material problems often exhibit complex geometries along with physical
responses presenting large spatial gradients or discontinuities. In these
cases, providing high-quality body-fitted finite element analysis meshes and
obtaining accurate solutions remain challenging. Immersed boundary techniques
provide elegant solutions for such problems. Enrichment methods alleviate the
need for generating conforming analysis grids by capturing discontinuities
within mesh elements. Additionally, increased accuracy of physical responses
and geometry description can be achieved with higher-order approximation bases.
In particular, using B-splines has become popular with the development of
IsoGeometric Analysis. In this work, an eXtended IsoGeometric Analysis (XIGA)
approach is proposed for multi-material problems. The computational domain
geometry is described implicitly by level set functions. A novel generalized
Heaviside enrichment strategy is employed to accommodate an arbitrary number of
materials without artificially stiffening the physical response. Higher-order
B-spline functions are used for both geometry representation and analysis.
Boundary and interface conditions are enforced weakly via Nitsche's method, and
a new face-oriented ghost stabilization methodology is used to mitigate
numerical instabilities arising from small material integration subdomains.
Two- and three-dimensional heat transfer and elasticity problems are solved to
validate the approach. Numerical studies provide insight into the ability to
handle multiple materials considering sharp-edged and curved interfaces, as
well as the impact of higher-order bases and stabilization on the solution
accuracy and conditioning.


title:
Self-adjusting optimization algorithm for solving the setunion knapsack
  problem

abstract:
  The set-union knapsack problem (SUKP) is a constrained composed optimization
problem. It is more difficulty for solving because values and weights depend on
items and elements respectively. In this paper, we present two self-adjusting
optimization algorithms for approximating SUKP from items and elements
perspective respectively. By analyzing the dynamic characters in the SUKP, we
design two types of self-adjusting repair and optimization operators that are
based on the different loading process. We use the novel
teaching-learning-based optimization algorithm (TLBO) to design a general
discrete framework (DTLBO) suitable for these two types of operators. In
addition, we introduce elite opposite search and natural selection mechanism
into DTLBO to furtherly improve the performance of the algorithm from the
perspective of population. Finally, we performed experimental comparisons on
benchmark sets to verify the effectiveness of the proposed algorithm. The
experimental results show that the item-based self-adjusting optimization
algorithm I-DTLBO is outstanding, and the algorithm is superior to the other
swarm intelligence methods for solving SUKP. IDTLBO algorithm reaches the upper
boundary of the current swarm intelligence algorithms for solving SUKP in 10
instances, and gotten new upper boundary in 15 instances. The algorithm E-DTLBO
based on element loading only perform slightly better on small and middle data
sets, but worse on large-scale instances. It shows that element-based design is
not suitable for solving SUKP.


title: The activity of the post-nova V1363 Cyg on long timescales

abstract:
  V1363 Cyg is a cataclysmic variable (CV) and a post-nova. Our analysis of its
long-term optical activity used the archival data from the AAVSO database and
literature. We showed that the accretion disk of V1363 Cyg is exposed to the
thermal-viscous instability (TVI) for at least part of the time. The time
fraction spent in the high state or the outbursts dramatically changed on the
timescale of decades. Highly variable brightness of V1363 Cyg displayed several
episodes of a strong brightening (bumps in the light curve) from a cool disk in
the TVI zone. In the interpretation, their vastly discrepant decay rates show
that only some of these bumps can be attributed to the dwarf nova outbursts
without strong irradiation of the disk by the hot white dwarf. The Bailey
relation of the decay rate, if ascribed to a DN outburst of V1363 Cyg, speaks
in favor of its orbital period $P_{\rm orb}$ very long for a CV, about 20-40
hr. A dominant cycle length of about 435 d was present in the brightness
changes all the time, even when the disk was well inside the TVI zone. We
interpret it as modulation of the companion's mass outflow by differential
rotation of the active region(s).


title: Axiomatizing consciousness, with applications

abstract:
  Consciousness will be introduced axiomatically, inspired by Buddhist insight
meditation and psychology, logic in computer science, and cognitive
neuroscience, as consisting of a stream of $configurations$ that is $compound$,
$discrete$, and (non-deterministically) $computable$. Within this context the
notions of self, concentration, mindfulness, and various forms of suffering can
be defined. As an application of this set up, it will be shown how a combined
development of concentration and mindfulness can attenuate and eventually
eradicate some of the forms of suffering.


title: Minimality Notions via Factorization Systems and Examples

abstract:
  For the minimization of state-based systems (i.e. the reduction of the number
of states while retaining the system's semantics), there are two obvious
aspects: removing unnecessary states of the system and merging redundant states
in the system. In the present article, we relate the two aspects on coalgebras
by defining an abstract notion of minimality.
  The abstract notions minimality and minimization live in a general category
with a factorization system. We will find criteria on the category that ensure
uniqueness, existence, and functoriality of the minimization aspects. The
proofs of these results instantiate to those for reachability and observability
minimization in the standard coalgebra literature. Finally, we will see how the
two aspects of minimization interact and under which criteria they can be
sequenced in any order, like in automata minimization.


title: Machine Learning for Stock Prediction Based on Fundamental Analysis

abstract:
  Application of machine learning for stock prediction is attracting a lot of
attention in recent years. A large amount of research has been conducted in
this area and multiple existing results have shown that machine learning
methods could be successfully used toward stock predicting using stocks
historical data. Most of these existing approaches have focused on short term
prediction using stocks historical price and technical indicators. In this
paper, we prepared 22 years worth of stock quarterly financial data and
investigated three machine learning algorithms: Feed-forward Neural Network
(FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS)
for stock prediction based on fundamental analysis. In addition, we applied RF
based feature selection and bootstrap aggregation in order to improve model
performance and aggregate predictions from different models. Our results show
that RF model achieves the best prediction results, and feature selection is
able to improve test performance of FNN and ANFIS. Moreover, the aggregated
model outperforms all baseline models as well as the benchmark DJIA index by an
acceptable margin for the test period. Our findings demonstrate that machine
learning models could be used to aid fundamental analysts with decision-making
regarding stock investment.


title:
Molecule Generation from Input-Attributions over Graph Convolutional
  Networks

abstract:
  It is well known that Drug Design is often a costly process both in terms of
time and economic effort. While good Quantitative Structure-Activity
Relationship models (QSAR) can help predicting molecular properties without the
need to synthesize them, it is still required to come up with new molecules to
be tested. This is mostly done in lack of tools to determine which
modifications are more promising or which aspects of a molecule are more
influential for the final activity/property. Here we present an automatic
process which involves Graph Convolutional Network models and input-attribution
methods to generate new molecules. We also explore the problems of
over-optimization and applicability, recognizing them as two important aspects
in the practical use of such automatic tools.


title:
Semi-Supervised GCN for learning Molecular Structure-Activity
  Relationships

abstract:
  Since the introduction of artificial intelligence in medicinal chemistry, the
necessity has emerged to analyse how molecular property variation is modulated
by either single atoms or chemical groups. In this paper, we propose to train
graph-to-graph neural network using semi-supervised learning for attributing
structure-property relationships. As initial case studies we apply the method
to solubility and molecular acidity while checking its consistency in
comparison with known experimental chemical data. As final goal, our approach
could represent a valuable tool to deal with problems such as activity cliffs,
lead optimization and de-novo drug design.


title:
The FRB 20121102A November rain in 2018 observed with the Arecibo
  Telescope

abstract:
  We present 849 new bursts from FRB 20121102A detected with the 305-m Arecibo
Telescope. Observations were conducted as part of our regular campaign to
monitor the activity and evolution of burst properties. The 10 reported
observations were carried out between 1150 and 1730 MHz and fall in the active
period around November 2018. All bursts were dedispersed at the same dispersion
measure and are consistent with a single value of 562.4(1) pc/cm$^3$. The burst
rate varies between 0 bursts and 218(16) bursts per hour, the highest rate
observed to date. The times between consecutive bursts show a bimodal
distribution. We find that arrival times with separations > 0.1 s are best
described as a Poisson process with varying rate, compared to models with
additional parameters. Clustering on time-scales of 22 ms is reflecting a
characteristic time-scale of the source and possibly the emission mechanism. We
analyse the spectro-temporal structure of the bursts by fitting 2D Gaussians
with a temporal drift to each sub-burst in the dynamic spectra. We find a
linear relationship between the sub-burst's drift and its duration. At the same
time the drifts are consistent with coming from the sad-trombone effect. This
has not been predicted by current models. The energy distribution shows an
excess of high energy bursts and is insufficiently modeled by a single
power-law even within single observations. We find long-term changes in the
energy distribution and the average spectrum compared to earlier and later
published observations. Finally, despite the large burst rate we find no strict
short-term periodicity.


title: Tiered trees and Theta operators

abstract:
  In [Dugan-Glennon-Gunnells-Steingrimsson-2019], the authors introduce tiered
trees to define combinatorial objects counting absolutely indecomposable
representations of certain quivers, and torus orbits on certain homogeneous
varieties. In this paper, we use Theta operators, introduced in
[D'Adderio-Iraci-VandenWyngaerd-Theta-2021], to give a symmetric function
formula that enumerates these trees. We then formulate a general conjecture
that extends this result, a special case of which might give some insight about
how to formulate a unified Delta conjecture [Haglund-Remmel-Wilson-2018].


title:
Uncertainties associated with the backward integration of dwarf
  satellites using simple parametric potentials

abstract:
  In order to backward integrate the orbits of Milky Way (MW) dwarf galaxies,
much effort has been invested in recent years to constrain their initial
phase-space coordinates. Yet equally important are the assumptions on the
potential that the dwarf galaxies experience over time, especially given the
fact that the MW is currently accreting the Large Magellanic Cloud (LMC). In
this work, using a dark matter-only zoom-in simulation, we test whether the use
of common parametric forms of the potential is adequate to successfully
backward integrate the orbits of the subhaloes from their present-day
positions. We parametrise the recovered orbits and compare them with those from
the simulations. We find that simple symmetric parametric forms of the
potential fail to capture the complexities and the inhomogeneities of the true
potential experienced by the subhaloes. More specifically, modelling a recent
massive accretion like that of the LMC as a sum of two spherical parametric
potentials leads to substantial errors in the recovered parameters of the
orbits. These errors rival those caused due to a) a 30\% uncertainty in the
virial mass of the MW and b) not modelling the potential of the recently
accreted massive satellite. Our work suggests that i) the uncertainties in the
parameters of the recovered orbits of some MW dwarfs may be under-estimated and
that ii) researchers should characterise the uncertainties inherent to their
choice of integration techniques and assumptions of the potential against
cosmological zoom-in simulations of the MW, which include a recently-accreted
LMC.


title: Dynamics near Couette flow for the $\beta$-plane equation

abstract:
  In this paper, we study stationary structures near the planar Couette flow in
Sobolev spaces on a channel $\mathbb{T}\times[-1,1]$, and asymptotic behavior
of Couette flow in Gevrey spaces on $\mathbb{T}\times\mathbb{R}$ for the
$\beta$-plane equation. Let $T>0$ be the horizontal period of the channel and
$\alpha={2\pi\over T}$ be the wave number. We obtain a sharp region $O$ in the
whole $(\alpha,\beta)$ half-plane such that non-parallel steadily traveling
waves do not exist for $(\alpha,\beta)\in O$ and such traveling waves exist for
$(\alpha,\beta)$ in the remaining regions, near Couette flow for $H^{\geq5}$
velocity perturbation. The borderlines between the region $O$ and its remaining
are determined by two curves of the principal eigenvalues of singular
Rayleigh-Kuo operators. Our results reveal that there exists $\beta_*>0$ such
that if $|\beta|\leq \beta_*$, then non-parallel traveling waves do not exist
for any $T>0$, while if $|\beta|>\beta_*$, then there exists a critical period
$T_\beta>0$ so that such traveling waves exist for $T\in
\left[T_\beta,\infty\right)$ and do not exist for $T\in
\left(0,T_\beta\right)$, near Couette flow for $H^{\geq5}$ velocity
perturbation. This contrasting dynamics plays an important role in studying the
long time dynamics near Couette flow with Coriolis effects. Moreover, for any
$\beta\neq0$ and $T>0$, there exist no non-parallel traveling waves with speeds
converging in $(-1,1)$ near Couette flow for $H^{\geq5}$ velocity perturbation,
in contrast to this, we construct non-shear stationary solutions near Couette
flow for $H^{<{5\over2}}$ velocity perturbation, which is a generalization of
Theorem 1 in [22] but the construction is more difficult due to the $\beta$'s
term. Finally, we prove nonlinear inviscid damping for Couette flow in some
Gevrey spaces by extending the method of [4] to the $\beta$-plane equation on
$\mathbb{T}\times\mathbb{R}$.


title: A Python Tool for Object-Centric Process Mining Comparison

abstract:
  Object-centric process mining provides a more holistic view of processes
where we analyze processes with multiple case notions. However, most
object-centric process mining techniques consider the whole event log rather
than the comparison of existing behaviors in the log. In this paper, we
introduce a stand-alone object-centric process cube tool built on the PM4PY-MDL
process mining framework. Our infrastructure uses both object and event
attributes to build the process cube which leads to different types of
materialization. Furthermore, our tool is equipped with the state of the art
object-centric process mining techniques. Through our tool the user can
visualize the extracted object-centric event log from process cube operations,
export the object-centric event log, discover the state-of-the-art
object-centric process model for the extracted log, and compare the process
models side-by-side.


title: Collaborative Dispersion by Silent Robots

abstract:
  In the dispersion problem, a set of $k$ co-located mobile robots must
relocate themselves in distinct nodes of an unknown network. The network is
modeled as an anonymous graph $G=(V,E)$, where the nodes of the graph are not
labeled. The edges incident to a node $v$ with degree $d$ are labeled with port
numbers in the range $0,1, \cdots, d-1$ at $v$. The robots have unique ids in
the range $[0,L]$, where $L \ge k$, and are initially placed at a source node
$s$. Each robot knows only its own id but does not know the ids of the other
robots or the values of $L,k$. The task of dispersion was traditionally
achieved with the assumption of two types of communication abilities: (a) when
some robots are at the same node, they can communicate by exchanging messages
between them (b) any two robots in the network can exchange messages between
them.
  In this paper, we ask whether this ability of communication among co-located
robots is necessary to achieve dispersion. We show that even if the ability of
communication is not available, the task of dispersion by a set of mobile
robots can be achieved in a much weaker model where a robot at a node $v$ has
the access of following very restricted information at the beginning of any
round: (1) am I alone at $v$? (2) the number of robots at $v$ increased or
decreased compare to the previous round?
  We propose a deterministic algorithm that achieves dispersion on any given
graph $G=(V,E)$ in time $O\left( k\log L+k^2 \log \Delta\right)$, where
$\Delta$ is the maximum degree of a node in $G$. Each robot uses $O(\log L+
\log \Delta)$ additional memory. We also prove that the task of dispersion
cannot be achieved by a set of mobile robots with $o(\log L + \log \Delta)$
additional memory.


title:
Global Optimization of Data Pipelines in Heterogeneous Cloud
  Environments

abstract:
  Modern production data processing and machine learning pipelines on the cloud
are critical components for many cloud-based companies. These pipelines are
typically composed of complex workflows represented by directed acyclic graphs
(DAGs). Cloud environments are attractive to these workflows due to the wide
range of choice with heterogeneous instances and prices that can provide the
flexibility for different cost-performance needs. However, this flexibility
also leads to the complexity of selecting the right resource configuration
(e.g., instance type, resource demands) for each task in the DAG, while
simultaneously scheduling the tasks with the selected resources to reach the
optimal end-to-end performance and cost. These two decisions are often
codependent resulting in an NP-hard scheduling optimization bottleneck.
Existing solutions only focus solely on either problem and ignore the co-effect
on the end-to-end optimum. We propose AGORA, a scheduler that considers both
task-level resource allocation and execution for DAG workflows as a whole in
heterogeneous cloud environments. AGORA first (1) studies the characteristics
of the tasks from prior runs and gives predictions on resource configurations,
and (2) automatically finds the best configuration with its corresponding
schedules for the entire workflow with a cost-performance objective. We
evaluate AGORA in a heterogeneous Amazon Web Services (AWS) cloud environment
with multi-tenant workflows served by Airflow and demonstrate a performance
improvement up to 45% and cost reduction up to 77% compared to state-of-the-art
schedulers. In addition, we apply AGORA to a real-world production trace from
Alibaba and show cost reduction of 65% and DAG completion time reduction of
57%.


title:
SABRE Enhancement with Oscillating Pulse Sequences: Symmetry Reduces
  Robustness

abstract:
  SABRE (Signal Amplification by Reversible Exchange) methods provide a simple,
fast, and cost-effective method to hyperpolarize a wide variety of molecules in
solution, and have been demonstrated with protons and, more recently, with
heteronuclei (X-SABRE). The conventional analysis of the SABRE effect is based
on level anti-crossings (LACs), which requires very low magnetic fields (~
0.6uT) to achieve resonance and transfer spin order from the para-hydrogen to
target heteronuclei. We have demonstrated in our recent study that the validity
of LACs used in SABRE is very limited, so the maximum SABRE polarization
predicted with LACs is not correct. Here, we present several oscillating pulse
sequences that use magnetic fields far away from the resonance condition and
can commonly triple the polarization. An analysis with average Hamiltonian
theory indicates that the oscillating pulse, in effect, adjusts the J-couplings
between hydrides and target nuclei and that a much weaker coupling produces
maximum polarization. This theoretical treatment, combined with simulations and
experiment, show substantial magnetization improvements relative to traditional
X-SABRE methods. It also shows that, in contrast to most pulse sequence
applications, waveforms with reduced time symmetry in the toggling frame make
magnetization generation more robust to experimental imperfections.


title: Cross Domain Few-Shot Learning via Meta Adversarial Training

abstract:
  Few-shot relation classification (RC) is one of the critical problems in
machine learning. Current research merely focuses on the set-ups that both
training and testing are from the same domain. However, in practice, this
assumption is not always guaranteed. In this study, we present a novel model
that takes into consideration the afore-mentioned cross-domain situation. Not
like previous models, we only use the source domain data to train the
prototypical networks and test the model on target domain data. A meta-based
adversarial training framework (MBATF) is proposed to fine-tune the trained
networks for adapting to data from the target domain. Empirical studies confirm
the effectiveness of the proposed model.


title:
Modeling Reservoir Release Using Pseudo-Prospective Learning and
  Physical Simulations to Predict Water Temperature

abstract:
  This paper proposes a new data-driven method for predicting water temperature
in stream networks with reservoirs. The water flows released from reservoirs
greatly affect the water temperature of downstream river segments. However, the
information of released water flow is often not available for many reservoirs,
which makes it difficult for data-driven models to capture the impact to
downstream river segments. In this paper, we first build a state-aware graph
model to represent the interactions amongst streams and reservoirs, and then
propose a parallel learning structure to extract the reservoir release
information and use it to improve the prediction. In particular, for reservoirs
with no available release information, we mimic the water managers' release
decision process through a pseudo-prospective learning method, which infers the
release information from anticipated water temperature dynamics. For reservoirs
with the release information, we leverage a physics-based model to simulate the
water release temperature and transfer such information to guide the learning
process for other reservoirs. The evaluation for the Delaware River Basin shows
that the proposed method brings over 10\% accuracy improvement over existing
data-driven models for stream temperature prediction when the release data is
not available for any reservoirs. The performance is further improved after we
incorporate the release data and physical simulations for a subset of
reservoirs.


title:
Jets from main sequence and white dwarf companions during common
  envelope evolution

abstract:
  It has long been speculated that jet feedback from accretion onto the
companion during a common envelope (CE) event could affect the orbital
evolution and envelope unbinding process, but this conjecture has heretofore
remained largely untested. We present global 3D hydrodynamical simulations of
CE evolution (CEE) that include a jet subgrid model and compare them with an
otherwise identical model without a jet. Our binary consists of a $2M_\odot$
red giant branch primary and a $1M_\odot$ or $0.5M_\odot$ main sequence or
white dwarf secondary companion modeled as a point particle. We run the
simulations for 10 orbits (40 days). Our jet model adds mass at a constant rate
$\dot{M}_\mathrm{j}$ of order the Eddington rate, with maximum velocity
$v_\mathrm{j}$ of order the escape speed, to two spherical sectors with the jet
axis perpendicular to the orbital plane, and supplies kinetic energy at the
rate $\sim\dot{M}_\mathrm{j} v_\mathrm{j}^2/40$. We explore the influence of
the jet on orbital evolution, envelope morphology and envelope unbinding, and
assess the dependence of the results on jet mass-loss rate, launch speed,
companion mass, opening angle, and whether or not subgrid accretion is turned
on. In line with our theoretical estimates, we find that in all cases the jet
becomes choked around the time of first periastron passage. We also find that
jets lead to increases in unbound mass of up to $\sim10\%$, as compared to
simulations which do not include a jet.


title:
Choices, Risks, and Reward Reports: Charting Public Policy for
  Reinforcement Learning Systems

abstract:
  In the long term, reinforcement learning (RL) is considered by many AI
theorists to be the most promising path to artificial general intelligence.
This places RL practitioners in a position to design systems that have never
existed before and lack prior documentation in law and policy. Public agencies
could intervene on complex dynamics that were previously too opaque to
deliberate about, and long-held policy ambitions would finally be made
tractable. In this whitepaper we illustrate this potential and how it might be
technically enacted in the domains of energy infrastructure, social media
recommender systems, and transportation. Alongside these unprecedented
interventions come new forms of risk that exacerbate the harms already
generated by standard machine learning tools. We correspondingly present a new
typology of risks arising from RL design choices, falling under four
categories: scoping the horizon, defining rewards, pruning information, and
training multiple agents. Rather than allowing RL systems to unilaterally
reshape human domains, policymakers need new mechanisms for the rule of reason,
foreseeability, and interoperability that match the risks these systems pose.
We argue that criteria for these choices may be drawn from emerging subfields
within antitrust, tort, and administrative law. It will then be possible for
courts, federal and state agencies, and non-governmental organizations to play
more active roles in RL specification and evaluation. Building on the "model
cards" and "datasheets" frameworks proposed by Mitchell et al. and Gebru et
al., we argue the need for Reward Reports for AI systems. Reward Reports are
living documents for proposed RL deployments that demarcate design choices.


title: The separating variety for 2x2 matrix invariants

abstract:
  Let $G$ be a linear algebraic group acting linearly on a $G$-variety
$\mathcal{V}$, and let $k[\mathcal{V}]^G$ be the corresponding algebra of
invariant polynomial functions. A separating set $S \subseteq k[\mathcal{V}]^G$
is a set of polynomials with the property that for all $v,w \in \mathcal{V}$,
if there exists $f \in k[\mathcal{V}]^G$ separating $v$ and $w$, then there
exists $f \in S$ separating $v$ and $w$.
  In this article we consider the action of $G = \mathrm{GL}_2(\mathbb{C})$ on
the variety $\mathcal{M}_2^n$ of $n$-tuples of $2 \times 2$ matrices by
simultaneous conjugation. Minimal generating sets $S_n$ of
$\mathbb{C}[\mathcal{M}_2^n]^G$ are well-known, and $|S_n| = \frac16(n^3+11n)$.
In recent work, Kaygorodov, Lopatin and Popov showed that for all $n \geq 1$,
$S_n$ is a minimal separating set by inclusion, i.e. that no proper subset of
$S_n$ is a separating set. This does not necessarily mean that $S_n$ has
minimum cardinality among all separating sets for
$\mathbb{C}[\mathcal{M}_2^n]^G$. Our main result shows that any separating set
for $\mathbb{C}[\mathcal{M}_2^n]^G$ has cardinality $\geq 5n-5$. In particular,
there is no separating set of size $\dim(\mathbb{C}[\mathcal{M}_2^n]) = 4n-3$
for $n \geq 3$. Further, $S_3$ has indeed minimum cardinality as a separating
set, but for $n \geq 4$ there may exist a smaller separating set than $S_n$. We
show that for $n \geq 5$ there does, in fact, exist a smaller separating set
than $S_n$. We also prove similar results for the left-right action of
$\mathrm{SL}_2(\mathbb{C}) \times \mathrm{SL}_2(\mathbb{C})$ on
$\mathcal{M}_2^n$.


title: Audio Defect Detection in Music with Deep Networks

abstract:
  With increasing amounts of music being digitally transferred from production
to distribution, automatic means of determining media quality are needed.
Protection mechanisms in digital audio processing tools have not eliminated the
need of production entities located downstream the distribution chain to assess
audio quality and detect defects inserted further upstream. Such analysis often
relies on the received audio and scarce meta-data alone. Deliberate use of
artefacts such as clicks in popular music as well as more recent defects
stemming from corruption in modern audio encodings call for data-centric and
context sensitive solutions for detection. We present a convolutional network
architecture following end-to-end encoder decoder configuration to develop
detectors for two exemplary audio defects. A click detector is trained and
compared to a traditional signal processing method, with a discussion on
context sensitivity. Additional post-processing is used for data augmentation
and workflow simulation. The ability of our models to capture variance is
explored in a detector for artefacts from decompression of corrupted MP3
compressed audio. For both tasks we describe the synthetic generation of
artefacts for controlled detector training and evaluation. We evaluate our
detectors on the large open-source Free Music Archive (FMA) and genre-specific
datasets.


title: Impact of satellite glints on the transient science on ZTF scale

abstract:
  Thousands of active artificial objects are orbiting around Earth along with
much more non-operational ones -- derelict satellites or rocket bodies,
collision debris, or spacecraft payloads, significant part of them being
uncatalogued. They all impact observations of the sky by ground-based
telescopes by producing a large number of streaks polluting the images, as well
as generating false alerts hindering the search for new astrophysical
transients. While the former threat for astronomy is widely discussed nowadays
in regard of rapidly growing satellite mega-constellations, the latter one --
false transients -- still lacks attention on the similar level.
  In this work we assess the impact of satellite glints -- rapid flashes
produced by reflections of a sunlight from flat surfaces of rotating satellites
-- on current and future deep sky surveys such as the ones conducted by the
Zwicky Transient Facility (ZTF) and the Vera Rubin Observatory Legacy Survey of
Space and Time (LSST). For that, we propose a simple routine that detects, in a
single exposure, a series of repeated flashes along the trajectories of
otherwise invisible satellites, and describe its implementation in FINK alert
broker. Application of the routine to ZTF alert stream revealed about 73,000
individual events polluting 3.6\% of all ZTF science images between November
2019 and December 2021 and linked to more than 300 different glinting
satellites on all kinds of orbits, from low-Earth up to geostationary ones. The
timescales of individual flashes are as short as $0.1$--$10^{-3}$ seconds, with
instant brightness of 4--14 magnitudes, peak amplitudes of at least 2--4
magnitudes, and generally complex temporal patterns of flashing activity. We
expect LSST to see much more such satellite glints of even larger amplitudes
due to its better sensitivity.


title: Hereditary uniform property $\Gamma$

abstract:
  We study the uniform property $\Gamma$ for separable simple $C^*$-algebras
which have quasitraces and may not be exact.
  We show that a stably finite separable simple $C^*$-algebra $A$ with strict
comparison and uniform property $\Gamma$ has tracial approximate oscillation
zero and stable rank one. Moreover in this case, its hereditary
$C^*$-subalgebras also have a version of uniform property $\Gamma.$ If a
separable non-elementary simple amenable $C^*$-algebra $A$ with strict
comparison has this hereditary uniform property $\Gamma,$ then $A$ is ${\cal
Z}$-stable.


title:
Mechanical excitation and marginal triggering during avalanches in
  sheared amorphous solids

abstract:
  We study plastic strain during individual avalanches in overdamped
particle-scale molecular dynamics (MD) and meso-scale elasto-plastic models
(EPM) for amorphous solids sheared in the athermal quasi-static limit. We show
that the spatial correlations in plastic activity exhibit a short lengthscale
that grows as $t^{3/4}$ in MD and ballistically in EPM, and is generated by
mechanical excitation of nearby sites not necessarily close to their stability
thresholds, and a longer lengthscale that grows diffusively for both models and
is associated with remote marginally stable sites. These similarities in
spatial correlations explain why simple EPMs accurately capture the size
distribution of avalanches observed in MD, though the temporal profiles and
dynamical critical exponents are quite different.


title: Recovering Stochastic Dynamics via Gaussian Schr\"odinger Bridges

abstract:
  We propose a new framework to reconstruct a stochastic process
$\left\{\mathbb{P}_{t}: t \in[0, T]\right\}$ using only samples from its
marginal distributions, observed at start and end times $0$ and $T$. This
reconstruction is useful to infer population dynamics, a crucial challenge,
e.g., when modeling the time-evolution of cell populations from single-cell
sequencing data. Our general framework encompasses the more specific
Schr\"odinger bridge (SB) problem, where $\mathbb{P}_{t}$ represents the
evolution of a thermodynamic system at almost equilibrium. Estimating such
bridges is notoriously difficult, motivating our proposal for a novel adaptive
scheme called the GSBflow. Our goal is to rely on Gaussian approximations of
the data to provide the reference stochastic process needed to estimate SB. To
that end, we solve the \acs{SB} problem with Gaussian marginals, for which we
provide, as a central contribution, a closed-form solution and
SDE-representation. We use these formulas to define the reference process used
to estimate more complex SBs, and show that this does indeed help with its
numerical solution. We obtain notable improvements when reconstructing both
synthetic processes and single-cell genomics experiments.


title: Thermodynamic limit of the Pieces' Model V2

abstract:
  We study the ground states of the pieces' model in the Fermi-Dirac statistics
in the thermodynamic limit. In other words, we consider the minimizing
configurations of $ n $ interacting fermions in an interval $ \Lambda $ divided
into pieces by a Poisson point process, when $ \frac{n}{\vert \Lambda\vert}\to
\rho>0 $ as $ \vert \Lambda \vert \to \infty $. We notice that a decomposition
into groups of pieces arises from the hypothesis of finite-range pairwise
interaction. Under assumptions of convexity and non-degeneracy of the
subsystems, we get an almost complete factorization of any ground state. This
method applies at least for groups comprising one or two particles. It improves
the expansion of the thermodynamic limit of the ground state energy per
particle up to the error $ O(\rho^{2-\delta}) $, with $ 0<\delta<1 $. It also
provides an approximate ground state for the pieces' model.


title:
On RT CW THz Cyclotron Resonance lasing in graphene in crossed E, H
  fields

abstract:
  In framework of classical consideration of electron trajectories in crossed
electric and magnetic fields and conductivity of electron system on cyclotron
resonance in single layer graphene possibility to achieve cyclotron lasing in
hexagonal boron nitride single layer graphene sandwiches is discussed. By
simplified consideration with known data on scattering rate in the sandwiches
it is demonstrated that the continuing wave laser action can be achieved in
high quality sandwiches at room temperature at frequencies above one tera hertz
in magnetic field above one Tesla.


title:
On the Detection of Adaptive Adversarial Attacks in Speaker Verification
  Systems

abstract:
  Speaker verification systems have been widely used in smart phones and
Internet of things devices to identify a legitimate user. In recent work, it
has been shown that adversarial attacks, such as FAKEBOB, can work effectively
against speaker verification systems. The goal of this paper is to design a
detector that can distinguish an original audio from an audio contaminated by
adversarial attacks. Specifically, our designed detector, called MEH-FEST,
calculates the minimum energy in high frequencies from the short-time Fourier
transform of an audio and uses it as a detection metric. Through both analysis
and experiments, we show that our proposed detector is easy to implement, fast
to process an input audio, and effective in determining whether an audio is
corrupted by FAKEBOB attacks. The experimental results indicate that the
detector is extremely effective: with near zero false positive and false
negative rates for detecting FAKEBOB attacks in Gaussian mixture model (GMM)
and i-vector speaker verification systems. Moreover, adaptive adversarial
attacks against our proposed detector and their countermeasures are discussed
and studied, showing the game between attackers and defenders.


title: Fundamental scalar field with zero dimension from anomaly cancelations

abstract:
  In this article a novel mechanism for dynamical electroweak symmetry breaking
and the ensuing appearance of fermion mass terms in the action is proposed. The
action contains massless fermions of the SM coupled to gravity through a new
type of non-minimal coupling to the vielbein field. The corresponding coupling
constants in our approach become zero-dimension scalar fields. Such scalar
fields provide the cancellation of the Weyl anomaly \cite{Boyle:2021jaz}.


title:
Narrow-line imaging of single strontium atoms in shallow optical
  tweezers

abstract:
  Single strontium atoms held in optical tweezers have so far only been imaged
using the broad $^{1\hspace{-0.3ex}}S_0$-$^{1\hspace{-0.3ex}}P_1$ transition.
For Yb, use of the narrow (183 kHz-wide)
$^{1\hspace{-0.3ex}}S_0$-$^{3\hspace{-0.3ex}}P_1$ transition for simultaneous
imaging and cooling has been demonstrated in tweezers with a magic wavelength
for the imaging transition. We demonstrate high-fidelity imaging of single Sr
atoms using its even narrower (7.4 kHz-wide) $^{1\hspace{-0.3ex}}S_0$ -
$^{3\hspace{-0.3ex}}P_1$ transition. The atoms are trapped in
\textit{non}-magic-wavelength tweezers. We detect the photons scattered during
Sisyphus cooling, thus keeping the atoms near the motional ground state of the
tweezer throughout imaging. The fidelity of detection is 0.9991(4) with a
survival probability of 0.97(2). An atom in a tweezer can be held under imaging
conditions for 79(3) seconds allowing for hundreds of images to be taken,
limited mainly by background gas collisions. We detect atoms in an arrary of 36
tweezers with 813.4-nm light and trap depths of 135(20) $\mu$K. This trap depth
is three times shallower than typically used for imaging on the broad
$^{1\hspace{-0.3ex}}S_0$ - $^{1\hspace{-0.3ex}}P_1$ transition. Narrow-line
imaging opens the possibility to even further reduce this trap depth, as long
as all trap frequencies are kept larger than the imaging transition linewidth.
Imaging using a narrow-linewidth transition in a non-magic-wavelength tweezer
also allows for selective imaging of a given tweezer. As a demonstration, we
selectively image (hide) a single tweezer from the array. This provides a
useful tool for quantum error correction protocols.


title:
Deep soccer captioning with transformer: dataset, semantics-related
  losses, and multi-level evaluation

abstract:
  This work aims at generating captions for soccer videos using deep learning.
In this context, this paper introduces a dataset, model, and triple-level
evaluation. The dataset consists of 22k caption-clip pairs and three visual
features (images, optical flow, inpainting) for ~500 hours of \emph{SoccerNet}
videos. The model is divided into three parts: a transformer learns language,
ConvNets learn vision, and a fusion of linguistic and visual features generates
captions. The paper suggests evaluating generated captions at three levels:
syntax (the commonly used evaluation metrics such as BLEU-score and CIDEr),
meaning (the quality of descriptions for a domain expert), and corpus (the
diversity of generated captions). The paper shows that the diversity of
generated captions has improved (from 0.07 reaching 0.18) with
semantics-related losses that prioritize selected words. Semantics-related
losses and the utilization of more visual features (optical flow, inpainting)
improved the normalized captioning score by 28\%. The web page of this work:
https://sites.google.com/view/soccercaptioning}{https://sites.google.com/view/soccercaptioning


title:
Effect of size distribution, skewness and roughness on the optical
  properties of colloidal plasmonic nanoparticles

abstract:
  It is a generally accepted idea that the particle size distribution strongly
affects the optical spectra of colloidal plasmonic nanoparticles. It is often
quoted as one of the main reasons while explaining the mismatch between the
theoretical and experimental optical spectra of such nanoparticles. In this
work, these aspects are critically analyzed by means of a bottom up statistical
approach that considers variables such as mean, standard deviation and skewness
of the nanoparticle size distribution independently from one another. By
assuming normal and log-normal distributions of the particle size, the effect
of the statistical parameters on the Mie analytical optical spectra of
colloidal nanoparticles was studied. The effect of morphology was also studied
numerically in order to understand to what extent it can play a role. It is our
finding that the particle polydispersity, skewness and surface morphology in
fact only weakly impact the optical spectra. While, the selection of suitable
optical constants with regard to the crystallinity of the nanoparticles is a
far more influential factor for correctly predicting both the plasmon band
position and the plasmon bandwidth in theoretical simulations of the optical
spectra. It is shown that the mean particle size can be correctly estimated
directly from the plasmon band position, as it is the mean that determines the
resonance wavelength. The standard deviation can on the other hand be estimated
from the intensity distribution data obtained from dynamic light scattering
experiments. The results reported herein clear the ambiguity around particle
size distribution and optical response of colloidal plasmonic nanoparticles.

categories: physics.optics cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.atm-clus


title: A 4.9-GHz Low Power, Low Jitter, LC Phase Locked Loop

abstract:
  This paper presents a low power, low jitter LC phase locked loop (PLL) which
has been designed and fabricated in a commercial 0.25-um Silicon-on-Sapphire
CMOS technology. Random jitter and deterministic jitter of the PLL are 1.3 ps
and 7.5 ps, respectively. The measured tuning range, from 4.6 to 5.0 GHz, is
narrower than the expected one, from 3.8 to 5.0 GHz. The narrow tuning range
issue has been investigated and traced to the first stage of the divider chain.
The power consumption at the central frequency is 111 mW.


title:
Flags, Landscapes and Signaling: Contact-mediated inter-cellular
  interactions enable plasticity in fate determination driven by positional
  information

abstract:
  Multicellular organisms exhibit a high degree of structural organization with
specific cell types always occurring in characteristic locations. The
conventional framework for describing the emergence of such consistent spatial
patterns is provided by Wolpert's "French flag" paradigm. According to this
view, intra-cellular genetic regulatory mechanisms use positional information
provided by morphogen concentration gradients to differentially express
distinct fates, resulting in a characteristic pattern of differentiated cells.
However, recent experiments have shown that suppression of inter-cellular
interactions can alter these spatial patterns, suggesting that cell fates are
not exclusively determined by the regulation of gene expression by local
morphogen concentration. Using an explicit model where adjacent cells
communicate by Notch signaling, we provide a mechanistic description of how
contact-mediated interactions allow information from the cellular environment
to be incorporated into cell fate decisions. Viewing cellular differentiation
in terms of trajectories along an epigenetic landscape (as first enunciated by
Waddington), our results suggest that the contours of the landscape are moulded
differently in a cell position-dependent manner, not only by the global signal
provided by the morphogen but also by the local environment via cell-cell
interactions. We show that our results are robust with respect to different
choices of coupling between the inter-cellular signaling apparatus and the
intra-cellular gene regulatory dynamics. Indeed, we show that the broad
features can be observed even in abstract spin models. Our work reconciles
interaction-mediated self-organized pattern formation with boundary-organized
mechanisms involving signals that break symmetry.


title: CAP-VMs: Capability-Based Isolation and Sharing for Microservices

abstract:
  Cloud stacks must isolate microservices, while permitting efficient data
sharing between isolated services deployed on the same physical host.
Traditionally, the MMU enforces isolation and permits sharing at a page
granularity. MMU approaches, however, lead to cloud stacks with large TCBs in
kernel space, and the page granularity requires inefficient OS interfaces for
data sharing. Forthcoming CPUs with hardware support for memory capabilities
offer new opportunities to implement isolation and sharing at a finer
granularity.
  We describe cVMs, a new VM-like abstraction that uses memory capabilities to
isolate application components while supporting efficient data sharing, all
without mandating application code to be capability-aware. cVMs share a single
virtual address space safely, each having only capabilities to access its own
memory. A cVM may include a library OS, minimizing its dependency on the cloud
environment. cVMs efficiently exchange data through two capability-based
primitives assisted by a small trusted monitor: (i) an asynchronous read/write
interface to buffers shared between cVMs; and (ii) a call interface to transfer
control between cVMs. Using these two primitives, we build more expressive
mechanisms for efficient cross-cVM communication. Our prototype implementation
using CHERI RISC-V capabilities shows that cVMs isolate microservices (Redis
and Python) with low overhead while improving data sharing.


title:
Optical Links for ATLAS Liquid Argon Calorimeter Front-end Electronics
  Readout

abstract:
  We present the optical data links for the ATLAS liquid argon calorimeter. The
current status of the vertical cavity surface emitting laser failures, the
up-to-date results in searching for the failure cause, experiences gained in
the searching process, possible backup plans for the optical transmitters and
the lessons learned are also discussed.


title:
An assessment of implicit-explicit time integrators for the
  pseudo-spectral approximation of Boussinesq thermal convection in an annulus

abstract:
  We analyze the behaviour of an ensemble of time integrators applied to the
semi-discrete problem resulting from the spectral discretization of the
equations describing Boussinesq convection in a cylindrical annulus. The
equations are cast in a vorticity-streamfunction formulation that yields a
differential algebraic equation (DAE). The ensemble comprises 28 members: 4
implicit-explicit multistep schemes, 22 implicit-explicit Runge-Kutta (IMEX-RK)
schemes, and 2 fully explicit schemes used for reference. The schemes are
assessed for 11 different physical setups that cover laminar and turbulent
regimes. Multistep and order 2 IMEX-RK methods exhibit their expected order of
convergence under all circumstances. IMEX-RK methods of higher-order show
occasional order reduction that impacts both algebraic and differential field
variables. We ascribe the order reduction to the stiffness of the problem and,
to a larger extent, the presence of the DAE. Using the popular Crank-Nicolson
Adams-Bashforth of order 2 (CNAB2) integrator as reference, performance is
defined by the ratio of maximum admissible time step to the cost of performing
one iteration; the maximum admissible time step is determined by inspection of
the time series of viscous dissipation within the system, which guarantees a
physically acceptable solution. Relative performance is bounded between 0.5 and
1.5 across all studied configurations. Considering accuracy jointly with
performance, we find that 6 schemes consistently outperform CNAB2, meaning that
in addition to allowing for a more efficient calculation, the accuracy that
they achieve at their operational limit of stability yields a lower error. In
our most turbulent setup, where the behaviour of the methods is almost entirely
dictated by their explicit component, 13 IMEX-RK integrators outperform CNAB2
in terms of accuracy and efficiency.


title:
SleepPPG-Net: a deep learning algorithm for robust sleep staging from
  continuous photoplethysmography

abstract:
  Introduction: Sleep staging is an essential component in the diagnosis of
sleep disorders and management of sleep health. It is traditionally measured in
a clinical setting and requires a labor-intensive labeling process. We
hypothesize that it is possible to perform robust 4-class sleep staging using
the raw photoplethysmography (PPG) time series and modern advances in deep
learning (DL). Methods: We used two publicly available sleep databases that
included raw PPG recordings, totalling 2,374 patients and 23,055 hours. We
developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG
time series. SleepPPG-Net was trained end-to-end and consists of a residual
convolutional network for automatic feature extraction and a temporal
convolutional network to capture long-range contextual information. We
benchmarked the performance of SleepPPG-Net against models based on the
best-reported state-of-the-art (SOTA) algorithms. Results: When benchmarked on
a held-out test set, SleepPPG-Net obtained a median Cohen's Kappa ($\kappa$)
score of 0.75 against 0.69 for the best SOTA approach. SleepPPG-Net showed good
generalization performance to an external database, obtaining a $\kappa$ score
of 0.74 after transfer learning. Perspective: Overall, SleepPPG-Net provides
new SOTA performance. In addition, performance is high enough to open the path
to the development of wearables that meet the requirements for usage in
clinical applications such as the diagnosis and monitoring of obstructive sleep
apnea.


title: Estimating flow fields with Reduced Order Models of Centrifugal Pumps

abstract:
  The estimation of fluid flows inside a centrifugal pump in realtime is a
challenging task that cannot be achieved with long-established methods like CFD
due to their computational demands. We use a projection-based reduced order
model (ROM) instead. Based on this ROM, a realtime observer can be devised that
estimates the temporally and spatially resolved velocity and pressure fields
inside the pump. The entire fluid-solid domain is treated as a fluid in order
to be able to consider moving rigid bodies in the reduction method. A greedy
algorithm is introduced for finding suitable and as few measurement locations
as possible. Robust observability is ensured with an extended Kalman filter,
which is based on a time-variant observability matrix obtained from the
nonlinear velocity ROM. We present the results of the velocity and pressure
ROMs based on a unsteady Reynolds-averaged Navier-Stokes CFD simulation of a 2D
centrifugal model pump, as well as the results for the extended Kalman filter.


title: Improving Generalization via Uncertainty Driven Perturbations

abstract:
  Recently Shah et al., 2020 pointed out the pitfalls of the simplicity bias -
the tendency of gradient-based algorithms to learn simple models - which
include the model's high sensitivity to small input perturbations, as well as
sub-optimal margins. In particular, while Stochastic Gradient Descent yields
max-margin boundary on linear models, such guarantee does not extend to
non-linear models. To mitigate the simplicity bias, we consider
uncertainty-driven perturbations (UDP) of the training data points, obtained
iteratively by following the direction that maximizes the model's estimated
uncertainty. The uncertainty estimate does not rely on the input's label and it
is highest at the decision boundary, and - unlike loss-driven perturbations -
it allows for using a larger range of values for the perturbation magnitude.
Furthermore, as real-world datasets have non-isotropic distances between data
points of different classes, the above property is particularly appealing for
increasing the margin of the decision boundary, which in turn improves the
model's generalization. We show that UDP is guaranteed to achieve the maximum
margin decision boundary on linear models and that it notably increases it on
challenging simulated datasets. For nonlinear models, we show empirically that
UDP reduces the simplicity bias and learns more exhaustive features.
Interestingly, it also achieves competitive loss-based robustness and
generalization trade-off on several datasets.


title:
Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy
  for place recognition

abstract:
  Visual Place Recognition (VPR) in areas with similar scenes such as urban or
indoor scenarios is a major challenge. Existing VPR methods using global
descriptors have difficulty capturing local specific regions (LSR) in the scene
and are therefore prone to localization confusion in such scenarios. As a
result, finding the LSR that are critical for location recognition becomes key.
To address this challenge, we introduced Patch-NetVLAD+, which was inspired by
patch-based VPR researches. Our method proposed a fine-tuning strategy with
triplet loss to make NetVLAD suitable for extracting patch-level descriptors.
Moreover, unlike existing methods that treat all patches in an image equally,
our method extracts patches of LSR, which present less frequently throughout
the dataset, and makes them play an important role in VPR by assigning proper
weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that
our approach achieved up to 6.35\% performance improvement than existing
patch-based methods.


title: The duals of annihilator conditions for modules

abstract:
  Let R be a commutative ring with identity and let M be an R-module. The
purpose of this paper is to introduce and investigate the submodules of an
R-module M which satisfy the dual of Property A, the dual of strong Property A,
and the dual of proper strong Property A. Moreover, a submodule N of M which
satisfy Property SJ (N) and Property IM J (N) will be introduced and
investigated.


title: Quantum phase diagram of high-pressure hydrogen

abstract:
  The interplay between electron correlation and nuclear quantum effects makes
our understanding of elemental hydrogen a formidable challenge. Here, we
present the phase diagram of hydrogen and deuterium at low temperatures and
high-pressure ($P > 300$ GPa by accounting for highly accurate electronic and
nuclear enthalpies. We evaluated internal electronic energies by diffusion
quantum Monte Carlo, while nuclear quantum motion and anharmonicity have been
included by the stochastic self-consistent harmonic approximation. Our results
show that the long-sought atomic metallic hydrogen, predicted to host
room-temperature superconductivity, forms at $577\pm 10$ GPa ($640\pm 14$ GPa
in deuterium). Indeed, anharmonicity pushes the stability of this phase towards
pressures much larger than previous theoretical estimates or attained
experimental values. Before atomization, molecular hydrogen transforms from a
conductive phase III to another metallic structure that is still molecular
(phase VI) at $422\pm 40$ GPa ($442\pm30$ GPa in deuterium). We predict
clear-cut signatures in optical spectroscopy and DC conductivity that can be
used experimentally to distinguish between the two structural transitions.
According to our findings, the experimental evidence of metallic hydrogen has
so far been limited to molecular phases.

categories: cond-mat.mtrl-sci cond-mat.supr-con physics.comp-ph quant-ph


title:
Neural-Network Decoders for Quantum Error Correction using Surface
  Codes:A Space Exploration of the Hardware Cost-Performance Trade-Offs

abstract:
  Quantum Error Correction (QEC) is required in quantum computers to mitigate
the effect of errors on physical qubits. When adopting a QEC scheme based on
surface codes, error decoding is the most computationally expensive task in the
classical electronic back-end. Decoders employing neural networks (NN) are
well-suited for this task but their hardware implementation has not been
presented yet. This work presents a space exploration of fully-connected
feed-forward NN decoders for small distance surface codes. The goal is to
optimize the neural network for high decoding performance, while keeping a
minimalistic hardware implementation. This is needed to meet the tight delay
constraints of real-time surface code decoding. We demonstrate that hardware
based NN-decoders can achieve high decoding performance comparable to other
state-of-the-art decoding algorithms whilst being well below the tight delay
requirements $(\approx 440\ \mathrm{ns})$ of current solid-state qubit
technologies for both ASIC designs $(<30\ \mathrm{ns})$ and FPGA
implementations $(<90\ \mathrm{ns})$. These results designates NN-decoders as
fitting candidates for an integrated hardware implementation in future
large-scale quantum computers.


title:
On the computation of Gr\"obner bases for pluriweighted-homogeneous
  systems

abstract:
  In this paper, we examine the structure of systems which are weighted
homogeneous for several systems of weights, and how it impacts Gr\"obner basis
computations. We present different ways to compute Gr\"obner bases for systems
with this structure, either directly or by reducing to existing structures. We
also present optimization techniques which are suitable for this structure.
  The most natural orderings to compute a Gr\"obner basis for systems with this
structure are weighted orderings following the systems of weights, and we
discuss the possibility to use the algorithms in order to directly compute a
basis for such an order, regardless of the structure of the system.
  We discuss applicable notions of regularity which could be used to evaluate
the complexity of the algorithm, and prove that they are generic if non-empty.
Finally, we present experimental data from a prototype implementation of the
algorithms in SageMath.


title:
Inflation and income inequality: Does the level of income inequality
  matter?

abstract:
  In the recent times of global Covid pandemic, the Federal Reserve has raised
the concerns of upsurges in prices. Given the complexity of interaction between
inflation and inequality, we examine whether the impact of inflation on
inequality differs among distinct levels of income inequality across the US
states. Results reveal that there is a negative contemporaneous effect of
inflation on the inequality which becomes stronger with higher levels of income
inequality. However, over a one year period, we find higher inflation rate to
further increase income inequality only when income inequality is initially
relatively low.


title:
The xmuspeech system for multi-channel multi-party meeting transcription
  challenge

abstract:
  This paper describes the system developed by the XMUSPEECH team for the
Multi-channel Multi-party Meeting Transcription Challenge (M2MeT). For the
speaker diarization task, we propose a multi-channel speaker diarization system
that obtains spatial information of speaker by Difference of Arrival (DOA)
technology. Speaker-spatial embedding is generated by x-vector and s-vector
derived from Filter-and-Sum Beamforming (FSB) which makes the embedding more
robust. Specifically, we propose a novel multi-channel sequence-to-sequence
neural network architecture named Discriminative Multi-stream Neural Network
(DMSNet) which consists of Attention Filter-and-Sum block (AFSB) and Conformer
encoder. We explore DMSNet to address overlapped speech problem on
multi-channel audio. Compared with LSTM based OSD module, we achieve a
decreases of 10.1% in Detection Error Rate(DetER). By performing DMSNet based
OSD module, the DER of cluster-based diarization system decrease significantly
form 13.44% to 7.63%. Our best fusion system achieves 7.09% and 9.80% of the
diarization error rate (DER) on evaluation set and test set.


title:
Response of a Commercial 0.25 um Thin-Film Silicon-on-Sapphire CMOS
  Technology to Total Ionizing Dose

abstract:
  The radiation response of a 0.25 um silicon-on-sapphire CMOS technology is
characterized at the transistor and circuit levels utilizing both standard and
enclosed layout devices. Device-level characterization showed threshold voltage
change of less than 170 mV and leakage current change of less than 1 nA for
individual nMOSFET and pMOSFET devices at a total dose of 100 krad(SiO2). The
increase in power supply current at the circuit level was less than 5%,
consistent with the small change in off-state transistor leakage current. The
technology exhibits good characteristics for use in the electronics of the
ATLAS experiment at the Large Hadron Collider.