title:
Characterizing & Finding Good Data Orderings for Fast Convergence of
  Sequential Gradient Methods

abstract:
  While SGD, which samples from the data with replacement is widely studied in
theory, a variant called Random Reshuffling (RR) is more common in practice. RR
iterates through random permutations of the dataset and has been shown to
converge faster than SGD. When the order is chosen deterministically, a variant
called incremental gradient descent (IG), the existing convergence bounds show
improvement over SGD but are worse than RR. However, these bounds do not
differentiate between a good and a bad ordering and hold for the worst choice
of order. Meanwhile, in some cases, choosing the right order when using IG can
lead to convergence faster than RR. In this work, we quantify the effect of
order on convergence speed, obtaining convergence bounds based on the chosen
sequence of permutations while also recovering previous results for RR. In
addition, we show benefits of using structured shuffling when various levels of
abstractions (e.g. tasks, classes, augmentations, etc.) exists in the dataset
in theory and in practice. Finally, relying on our measure, we develop a greedy
algorithm for choosing good orders during training, achieving superior
performance (by more than 14 percent in accuracy) over RR.


title: From quantum speed limits to energy-efficient quantum gates

abstract:
  While recent breakthroughs in quantum computing promise the nascence of the
quantum information age, quantum states remain delicate to control. Moreover,
the required energy budget for large scale quantum applications has only
sparely been considered. Addressing either of these issues necessitates a
careful study of the most energetically efficient implementation of elementary
quantum operations. In the present analysis, we show that this optimal control
problem can be solved within the powerful framework of quantum speed limits. To
this end, we derive state-independent lower bounds on the energetic cost, from
which we find the universally optimal implementation of unitary quantum gates,
for both single and $N$-qubit operations.


title: Hidden Heterogeneity: When to Choose Similarity-Based Calibration

abstract:
  Trustworthy classifiers are essential to the adoption of machine learning
predictions in many real-world settings. The predicted probability of possible
outcomes can inform high-stakes decision making, particularly when assessing
the expected value of alternative decisions or the risk of bad outcomes. These
decisions require well calibrated probabilities, not just the correct
prediction of the most likely class. Black-box classifier calibration methods
can improve the reliability of a classifier's output without requiring
retraining. However, these methods are unable to detect subpopulations where
calibration could improve prediction accuracy. Such subpopulations are said to
exhibit "hidden heterogeneity" (HH), because the original classifier did not
detect them. The paper proposes a quantitative measure for HH. It also
introduces two similarity-weighted calibration methods that can address HH by
adapting locally to each test item: SWC weights the calibration set by
similarity to the test item, and SWC-HH explicitly incorporates hidden
heterogeneity to filter the calibration set. Experiments show that the
improvements in calibration achieved by similarity-based calibration methods
correlate with the amount of HH present and, given sufficient calibration data,
generally exceed calibration achieved by global methods. HH can therefore serve
as a useful diagnostic tool for identifying when local calibration methods are
needed.


title:
Transport Score Climbing: Variational Inference Using Forward KL and
  Adaptive Neural Transport

abstract:
  Variational inference often minimizes the "reverse" Kullbeck-Leibler (KL)
KL(q||p) from the approximate distribution q to the posterior p. Recent work
studies the "forward" KL KL(p||q), which unlike reverse KL does not lead to
variational approximations that underestimate uncertainty. This paper
introduces Transport Score Climbing (TSC), a method that optimizes KL(p||q) by
using Hamiltonian Monte Carlo (HMC) and a novel adaptive transport map. The
transport map improves the trajectory of HMC by acting as a change of variable
between the latent variable space and a warped space. TSC uses HMC samples to
dynamically train the transport map while optimizing KL(p||q). TSC leverages
synergies, where better transport maps lead to better HMC sampling, which then
leads to better transport maps. We demonstrate TSC on synthetic and real data.
We find that TSC achieves competitive performance when training variational
autoencoders on large-scale data.


title:
Distributed State Estimation with Deep Neural Networks for Uncertain
  Nonlinear Systems under Event-Triggered Communication

abstract:
  Distributed state estimation is examined for a sensor network tasked with
reconstructing a system's state through the use of a distributed and
event-triggered observer. Each agent in the sensor network employs a deep
neural network (DNN) to approximate the uncertain nonlinear dynamics of the
system, which is trained using a multiple timescale approach. Specifically, the
outer weights of each DNN are updated online using a Lyapunov-based gradient
descent update law, while the inner weights and biases are trained offline
using a supervised learning method and collected input-output data. The
observer utilizes event-triggered communication to promote the efficient use of
network resources. A nonsmooth Lyapunov analysis shows the distributed
event-triggered observer has a uniformly ultimately bounded state
reconstruction error. A simulation study is provided to validate the result and
demonstrate the performance improvements afforded by the DNNs.


title: The Swapped Dragonfly

abstract:
  This paper describes the Swapped Dragonfly. It is a two-parameter family of
diameter three interconnection networks, D3(K,M), which are linearly scalable
in M. Although D3(K,M) is a Dragonfly, it differs from standard Dragonflies in
many respects. It has a K by M by M coordinate system (c;d; p). The routers
(c,d,p) and (c',p,d) are globally connected using a swap of p and d. If L < K
and/or N < M, D3(K;M) contains D3(L,N). The coordinate system enables source
vector routing on D3(K,M). A source-vector induces KM squared parallel paths on
D3(K,M). Because of this, the Swapped Dragonfly can support conflict-free
parallelism over local ports, global ports, routers and source-vectors. In
particular, there is an all-to-all algorithm which is not a pairwise exchange
algorithm.
  Keywords: interconnection network, Dragonfly network, swapped network,
source-vector routing, all-to-all exchange


title:
The welfare effects of unemployment insurance in Argentina. New
  estimates using changes in the schedule of transfers

abstract:
  Unemployment insurance transfers should balance the provision of consumption
to the unemployed with the disincentive effects on the search behavior.
Developing countries face the additional challenge of informality. Workers can
choose to hide their employment state and labor income in informal jobs, an
additional form of moral hazard. To provide evidence about the effects of this
policy in a country affected by informality we exploit kinks in the schedule of
transfers in Argentina. Our results suggest that higher benefits induce
moderate behavioral responses in job-finding rates and increase re-employment
wages. We use a sufficient statistics formula from a model with random wage
offers and we calibrate it with our estimates. We show that welfare could rise
substantially if benefits were increased in Argentina. Importantly, our
conclusion is relevant for the median eligible worker that is strongly affected
by informality.


title: Maximal quantum chaos of the critical Fermi surface

abstract:
  We investigate the many-body quantum chaos of non-Fermi liquid states with
Fermi surfaces in two spatial dimensions by computing their out-of-time-order
correlation functions. Using a recently proposed large $N$ theory for the
critical Fermi surface, and the ladder identity of Gu and Kitaev, we show that
the chaos Lyapunov exponent takes the maximal value of $2 \pi k_B T/\hbar$,
where $T$ is the absolute temperature.


title: A Population's Feasible Posterior Beliefs

abstract:
  We consider a population of Bayesian agents who share a common prior over
some finite state space and each agent is exposed to some information about the
state. We ask which distributions over empirical distributions of posteriors
beliefs in the population are feasible. We provide a necessary and sufficient
condition for feasibility. We apply this result in several domains. First, we
study the problem of maximizing the polarization of beliefs in a population.
Second, we provide a characterization of the feasible agent-symmetric product
distributions of posteriors. Finally, we study an instance of a private
Bayesian persuasion problem and provide a clean formula for the sender's
optimal value.


title:
Validation of Monte Carlo Iodine-131 radiopharmaceutical dosimetry
  workflow using a 3D printed anthropomorphic head and neck phantom

abstract:
  Purpose: In this study, we present the creation of an anthropomorphic, head
and neck, nuclear medicine phantom and its characterization for the validation
of a Monte Carlo, SPECT image based, Iodine-131 RPT dosimetry workflow.
  Methods: 3D printing techniques were used to create the anthropomorphic
phantom from a patient CT dataset. Three Iodine-131 SPECT/CT imaging studies
were performed using a homogeneous, Jaszczak, and an anthropomorphic phantom to
quantify the SPECT images. The impact of collimator detector response (CDR)
modeling and volume-based partial volume corrections (PVC) upon the absorbed
dose was calculated using an image based, Geant4 Monte Carlo RPT dosimetry
workflow and compared against a ground truth scenario. Finally, uncertainties
were quantified in accordance with recent EANM guidelines.
  Results: The 3D printed anthropomorphic phantom was an accurate re-creation
of patient anatomy including bone. The extrapolated Jaszczak recovery
coefficients were greater than that of the 3D printed insert (~22.8 ml) for
both the CDR and non-CDR cases. Utilizing Jaszczak phantom PVCs, the absorbed
dose was underpredicted by 0.7% and 4.9% without and with CDR, respectively.
Utilizing anthropomorphic phantom RCs overpredicted the absorbed dose by 3%
both with and without CDR. All dosimetry scenarios that incorporated PVC were
within the calculated uncertainty of the activity. The uncertainties in the
cumulative activity ranged from 25.6% to 113% for Jaszczak spheres ranging in
volume from 0.5 ml to 16 ml.
  Conclusion: The accuracy of Monte Carlo-based dosimetry for Iodine-131 RPT in
head and neck cancer was validated with an anthropomorphic phantom. Future
applications of the phantom could involve 3D printing and characterizing
patient specific volumes for more personalized RPT dosimetry estimates.


title: Valid predictions of group-level random effects

abstract:
  Gaussian linear models with random group-level effects are the standard for
modeling randomized experiments carried out over groups, such as locations,
farms, hospitals, or schools. Group-level effects can be summarized by
prediction intervals for group-level means or responses, but the quality of
such summaries depends on whether the intervals are valid in the sense they
attain their nominal coverage probability. Many methods for constructing
prediction intervals are available -- such as Student's t, bootstrap, and
Bayesian methods -- but none of these are guaranteed to be valid, and indeed
are not valid over a range of simulation examples. We propose a new method for
constructing valid predictions of group-level effects based on an inferential
model (IM). The proposed prediction intervals have guaranteed finite-sample
validity and outperform existing methods in simulation examples. In an on-farm
agricultural study the new IM-based prediction intervals suggest a higher level
of uncertainty in farm-specific effects compared to the standard Student's
t-based intervals, which are known to undercover.


title:
Ethics education in the quantum information science classroom: Exploring
  attitudes, barriers, and opportunities

abstract:
  Quantum information science (QIS) is an emerging interdisciplinary field at
the intersection of physics, computer science, electrical engineering, and
mathematics leveraging the laws of quantum mechanics to circumvent classical
limitations on information processing. With QIS coursework proliferating across
US institutions, including at the undergraduate level, we argue that it is
imperative that ethics and social responsibility be incorporated into QIS
education from the beginning. We discuss ethical issues of particular relevance
to QIS education that educators may wish to incorporate into their curricula.
We then report on findings from focus interviews with six faculty who have
taught introductory QIS courses, focusing on barriers to and opportunities for
incorporation of ethics and social responsibility (ESR) into the QIS classroom.
Few faculty had explicitly considered discussion of ethical issues in the
classroom prior to the interview, yet instructor attitudes shifted markedly in
support of incorporating ESR in the classroom as a result of the interview
process itself. Taking into account faculty's perception of obstacles to
discussing issues of ESR in coursework, we propose next steps toward making ESR
education in the QIS classroom a reality.


title:
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian
  Process Bandits

abstract:
  We consider the sequential optimization of an unknown, continuous, and
expensive to evaluate reward function, from noisy and adversarially corrupted
observed rewards. When the corruption attacks are subject to a suitable budget
$C$ and the function lives in a Reproducing Kernel Hilbert Space (RKHS), the
problem can be posed as corrupted Gaussian process (GP) bandit optimization. We
propose a novel robust elimination-type algorithm that runs in epochs, combines
exploration with infrequent switching to select a small subset of actions, and
plays each action for multiple time instants. Our algorithm, Robust GP Phased
Elimination (RGP-PE), successfully balances robustness to corruptions with
exploration and exploitation such that its performance degrades minimally in
the presence (or absence) of adversarial corruptions. When $T$ is the number of
samples and $\gamma_T$ is the maximal information gain, the
corruption-dependent term in our regret bound is $O(C \gamma_T^{3/2})$, which
is significantly tighter than the existing $O(C \sqrt{T \gamma_T})$ for several
commonly-considered kernels. We perform the first empirical study of robustness
in the corrupted GP bandit setting, and show that our algorithm is robust
against a variety of adversarial attacks.


title: A Note on "Assessing Generalization of SGD via Disagreement"

abstract:
  Jiang et al. (2021) give empirical evidence that the average test error of
deep neural networks can be estimated via the prediction disagreement of two
separately trained networks. They also provide a theoretical explanation that
this 'Generalization Disagreement Equality' follows from the well-calibrated
nature of deep ensembles under the notion of a proposed 'class-aggregated
calibration'. In this paper we show that the approach suggested might be
impractical because a deep ensemble's calibration deteriorates under
distribution shift, which is exactly when the coupling of test error and
disagreement would be of practical value. We present both theoretical and
experimental evidence, re-deriving the theoretical statements using a simple
Bayesian perspective and show them to be straightforward and more generic: they
apply to any discriminative model -- not only ensembles whose members output
one-hot class predictions. The proposed calibration metrics are also equivalent
to two metrics introduced by Nixon et al. (2019): 'ACE' and 'SCE'.


title:
Bounds on the Picard rank of toric Fano varieties with minimal curve
  constraints

abstract:
  We study the Picard rank of smooth toric Fano varieties possessing families
of minimal rational curves of given degree. We discuss variants of a conjecture
of Chen-Fu-Hwang and prove a version of their statement that recovers the
original conjecture in sufficiently high dimension. We also prove new cases of
the original conjecture for high degrees in all dimensions. Our main tools come
from toric Mori theory and the combinatorics of Fano polytopes.


title: Posinormal Composition Operators on $H^2$

abstract:
  A bounded linear operator $A$ on a Hilbert space is posinormal if there
exists a positive operator $P$ such that $AA^{*} = A^{*}PA$. Posinormality of
$A$ is equivalent to the inclusion of the range of $A$ in the range of its
adjoint $A^*$. Every hyponormal operator is posinormal, as is every invertible
operator. We characterize both the posinormal and coposinormal composition
operators $C_\varphi$ on the Hardy space $H^2$ of the open unit disk
$\mathbb{D}$ when $\varphi$ is a linear-fractional selfmap of $\mathbb{D}$. Our
work reveals that there are composition operators that are both posinormal and
coposinormal yet have powers that fail to be posinormal.


title: Causal emergence is widespread across measures of causation

abstract:
  Causal emergence is the theory that macroscales can reduce the noise in
causal relationships, leading to stronger causes at the macroscale. First
identified using the effective information and later the integrated information
in model systems, causal emergence has been analyzed in real data across the
sciences since. But is it simply a quirk of these original measures? To answer
this question we examined over a dozen popular measures of causation, all
independently developed and widely used, and spanning different fields from
philosophy to statistics to psychology to genetics. All showed cases of causal
emergence. This is because, we prove, measures of causation are based on a
small set of related "causal primitives." This consilience of
independently-developed measures of causation shows that macroscale causation
is a general fact about causal relationships, is scientifically detectable, and
is not a quirk of any particular measure of causation. This finding sets the
science of emergence on firmer ground, opening the door for the detection of
intrinsic scales of function in complex systems, as well as assisting with
scientific modeling and experimental interventions.


title:
Self-supervised Learning with Random-projection Quantizer for Speech
  Recognition

abstract:
  We present a simple and effective self-supervised learning approach for
speech recognition. The approach learns a model to predict the masked speech
signals, in the form of discrete labels generated with a random-projection
quantizer. In particular the quantizer projects speech inputs with a randomly
initialized matrix, and does a nearest-neighbor lookup in a
randomly-initialized codebook. Neither the matrix nor the codebook is updated
during self-supervised learning. Since the random-projection quantizer is not
trained and is separated from the speech recognition model, the design makes
the approach flexible and is compatible with universal speech recognition
architecture. On LibriSpeech our approach achieves similar word-error-rates as
previous work using self-supervised learning with non-streaming models, and
provides lower word-error-rates and latency than wav2vec 2.0 and w2v-BERT with
streaming models. On multilingual tasks the approach also provides significant
improvement over wav2vec 2.0 and w2v-BERT.


title:
Data-Driven Optimal Control via Linear Transfer Operators: A Convex
  Approach

abstract:
  This paper is concerned with data-driven optimal control of nonlinear
systems. We present a convex formulation to the optimal control problem (OCP)
with a discounted cost function. We consider OCP with both positive and
negative discount factor. The convex approach relies on lifting nonlinear
system dynamics in the space of densities using the linear Perron-Frobenius
(P-F) operator. This lifting leads to an infinite-dimensional convex
optimization formulation of the optimal control problem. The data-driven
approximation of the optimization problem relies on the approximation of the
Koopman operator using the polynomial basis function. We write the approximate
finite-dimensional optimization problem as a polynomial optimization which is
then solved efficiently using a sum-of-squares-based optimization framework.
Simulation results are presented to demonstrate the efficacy of the developed
data-driven optimal control framework.


title:
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep
  Anchor Attention Learning with Vision Transformer

abstract:
  Image-based brain cancer prediction models, based on radiomics, quantify the
radiologic phenotype from magnetic resonance imaging (MRI). However, these
features are difficult to reproduce because of variability in acquisition and
preprocessing pipelines. Despite evidence of intra-tumor phenotypic
heterogeneity, the spatial diversity between different slices within an MRI
scan has been relatively unexplored using such methods. In this work, we
propose a deep anchor attention aggregation strategy with a Vision Transformer
to predict survival risk for brain cancer patients. A Deep Anchor Attention
Learning (DAAL) algorithm is proposed to assign different weights to
slice-level representations with trainable distance measurements. We evaluated
our method on N = 326 MRIs. Our results outperformed attention multiple
instance learning-based techniques. DAAL highlights the importance of critical
slices and corroborates the clinical intuition that inter-slice spatial
diversity can reflect disease severity and is implicated in outcome.


title: Modeling unknown dynamical systems with hidden parameters

abstract:
  We present a data-driven numerical approach for modeling unknown dynamical
systems with missing/hidden parameters. The method is based on training a deep
neural network (DNN) model for the unknown system using its trajectory data. A
key feature is that the unknown dynamical system contains system parameters
that are completely hidden, in the sense that no information about the
parameters is available through either the measurement trajectory data or our
prior knowledge of the system. We demonstrate that by training a DNN using the
trajectory data with sufficient time history, the resulting DNN model can
accurately model the unknown dynamical system. For new initial conditions
associated with new, and unknown, system parameters, the DNN model can produce
accurate system predictions over longer time.


title:
A framework for quantifying the value of vibration-based structural
  health monitoring

abstract:
  The difficulty in quantifying the benefit of Structural Health Monitoring
(SHM) for decision support is one of the bottlenecks to an extensive adoption
of SHM on real-world structures. In this paper, we present a framework for such
a quantification of the value of vibration-based SHM, which can be flexibly
applied to different use cases. These cover SHM-based decisions at different
time scales, from near-real time diagnostics to the prognosis of slowly
evolving deterioration processes over the lifetime of a structure. The
framework includes an advanced model of the SHM system. It employs a Bayesian
filter for the tasks of sequential joint deterioration state-parameter
estimation and structural reliability updating, using continuously identified
modal and intermittent visual inspection data. It also includes a realistic
model of the inspection and maintenance decisions throughout the structural
life-cycle. On this basis, the Value of SHM is quantified by the difference in
expected total life-cycle costs with and without the SHM. We investigate the
framework through application on a numerical model of a two-span bridge system,
subjected to gradual and shock deterioration, as well as to changing
environmental conditions, over its lifetime. The results show that this
framework can be used as an a-priori decision support tool to inform the
decision on whether or not to install a vibration-based SHM system on a
structure, for a wide range of SHM use cases.


title: Shape Dynamics of $N$ Point Vortices on the Sphere

abstract:
  We give a geometric account of the relative motion or the shape dynamics of
$N$ point vortices on the sphere exploiting the $\mathsf{SO}(3)$-symmetry of
the system. The main idea is to bypass the technical difficulty of the
$\mathsf{SO}(3)$-reduction by first lifting the dynamics from $\mathbb{S}^{2}$
to $\mathbb{C}^{2}$. We then perform the $\mathsf{U}(2)$-reduction using a dual
pair to obtain a Lie--Poisson dynamics for the shape dynamics. This
Lie--Poisson structure helps us find a family of Casimirs for the shape
dynamics. We further reduce the system by $\mathbb{T}^{N-1}$-symmetry to obtain
a Poisson structure for the shape dynamics involving less shape variables than
those of the previous work by Borisov and Pavlov. As an application of the
shape dynamics, we prove that the tetrahedron relative equilibria are stable
when all of their circulations have the same sign, generalizing some existing
results on tetrahedron relative equilibria of identical vortices.


title: Quantum-inspired classical algorithm for molecular vibronic spectra

abstract:
  Molecular vibronic spectra is an essential property of molecules, which is an
important tool to analyze chemical components and study molecular structures.
Computing molecular vibronic spectra has been a challenging task, and its
best-known classical algorithm scales combinatorially in the system size.
Recently, a quantum simulator, specifically Gaussian boson sampling, has been
proposed to generate the spectra efficiently. Thus, molecular vibronic spectra
is a candidate of tasks for which a quantum device provides a computational
advantage. In this work, we propose a quantum-inspired classical algorithm for
molecular vibronic spectra. We find an exact solution of the Fourier components
of molecular vibronic spectra at zero temperature using the positive
$P$-representation method. Thus, we can obtain the molecular vibronic spectra
exactly by the inverse Fourier transformation for the solution for a given
resolution. We also show that the same method applies to a finite temperature
case. Therefore, computing molecular vibronic spectra, which can be mapped to a
Gaussian boson sampling, does not require a quantum device. We then generalize
the method beyond the Gaussian framework and find that the Fourier components
are written as a loop hafnian, the exact computation of which is known to be
\#P-hard. It presents an interesting theoretical separation between
non-Gaussian boson sampling and Gaussian boson sampling. Meanwhile, since
running an actual boson sampling circuit would incur an $1/\text{poly}(N)$
additive error for $N$ samples, we also study the possibility of achieving the
same accuracy using a classical algorithm. Interestingly, when there is no
squeezing in the process, one may use Gurvits's algorithm to compute the
Fourier components within an additive error. We finally provide possible
approaches to approximate the Fourier components and discuss their limitations.


title: Practical Imitation Learning in the Real World via Task Consistency Loss

abstract:
  Recent work in visual end-to-end learning for robotics has shown the promise
of imitation learning across a variety of tasks. Such approaches are expensive
both because they require large amounts of real world training demonstrations
and because identifying the best model to deploy in the real world requires
time-consuming real-world evaluations. These challenges can be mitigated by
simulation: by supplementing real world data with simulated demonstrations and
using simulated evaluations to identify high performing policies. However, this
introduces the well-known "reality gap" problem, where simulator inaccuracies
decorrelate performance in simulation from that of reality. In this paper, we
build on top of prior work in GAN-based domain adaptation and introduce the
notion of a Task Consistency Loss (TCL), a self-supervised loss that encourages
sim and real alignment both at the feature and action-prediction levels. We
demonstrate the effectiveness of our approach by teaching a mobile manipulator
to autonomously approach a door, turn the handle to open the door, and enter
the room. The policy performs control from RGB and depth images and generalizes
to doors not encountered in training data. We achieve 72% success across
sixteen seen and unseen scenes using only ~16.2 hours of teleoperated
demonstrations in sim and real. To the best of our knowledge, this is the first
work to tackle latched door opening from a purely end-to-end learning approach,
where the task of navigation and manipulation are jointly modeled by a single
neural network.


title:
Best Practices and Scoring System on Reviewing A.I. based Medical
  Imaging Papers: Part 1 Classification

abstract:
  With the recent advances in A.I. methodologies and their application to
medical imaging, there has been an explosion of related research programs
utilizing these techniques to produce state-of-the-art classification
performance. Ultimately, these research programs culminate in submission of
their work for consideration in peer reviewed journals. To date, the criteria
for acceptance vs. rejection is often subjective; however, reproducible science
requires reproducible review. The Machine Learning Education Sub-Committee of
SIIM has identified a knowledge gap and a serious need to establish guidelines
for reviewing these studies. Although there have been several recent papers
with this goal, this present work is written from the machine learning
practitioners standpoint. In this series, the committee will address the best
practices to be followed in an A.I.-based study and present the required
sections in terms of examples and discussion of what should be included to make
the studies cohesive, reproducible, accurate, and self-contained. This first
entry in the series focuses on the task of image classification. Elements such
as dataset curation, data pre-processing steps, defining an appropriate
reference standard, data partitioning, model architecture and training are
discussed. The sections are presented as they would be detailed in a typical
manuscript, with content describing the necessary information that should be
included to make sure the study is of sufficient quality to be considered for
publication. The goal of this series is to provide resources to not only help
improve the review process for A.I.-based medical imaging papers, but to
facilitate a standard for the information that is presented within all
components of the research study. We hope to provide quantitative metrics in
what otherwise may be a qualitative review process.


title:
Exploiting Independent Instruments: Identification and Distribution
  Generalization

abstract:
  Instrumental variable models allow us to identify a causal function between
covariates X and a response Y, even in the presence of unobserved confounding.
Most of the existing estimators assume that the error term in the response Y
and the hidden confounders are uncorrelated with the instruments Z. This is
often motivated by a graphical separation, an argument that also justifies
independence. Posing an independence condition, however, leads to strictly
stronger identifiability results. We connect to existing literature in
econometrics and provide a practical method for exploiting independence that
can be combined with any gradient-based learning procedure. We see that even in
identifiable settings, taking into account higher moments may yield better
finite sample results. Furthermore, we exploit the independence for
distribution generalization. We prove that the proposed estimator is invariant
to distributional shifts on the instruments and worst-case optimal whenever
these shifts are sufficiently strong. These results hold even in the
under-identified case where the instruments are not sufficiently rich to
identify the causal function.


title:
Collective neutrino oscillations with tensor networks using a
  time-dependent variational principle

abstract:
  A system of oscillating neutrinos at high densities, wherein
neutrino-neutrino coherent forward scatterings are non-negligible, represents a
many-body problem engendered by the weak interaction. Whether an interplay
between the one-body and two-body interaction terms in the neutrino Hamiltonian
could result in significant nontrivial quantum entanglement developing between
the constituent neutrinos remains an open question. Numerical computations of
the time-evolution of many-body quantum systems are challenging because the
size of the Hilbert space scales exponentially with the number of particles in
the system. Such calculations therefore tend to become extremely memory
intensive even at a relatively small number of particles. As a result,
approximate numerical treatments become necessary in order to facilitate
comparisons with mean-field calculations at larger values of N. Here we
investigate the efficacy of tensor network methods for extending the
calculations of time-evolving systems of interacting neutrinos to larger values
of N than are possible with conventional methods. In particular, we introduce
the use of time-dependent variational principle methods to address the
long-range (in momentum space) interactions of the neutrino Hamiltonian when
including many distinct vacuum oscillation frequencies. Furthermore, we define
new error measures based upon the instantaneously conserved charge operators
known for this Hamiltonian to determine validity of large-N tensor network
calculations.


title: Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks

abstract:
  Organ at risk (OAR) segmentation is a crucial step for treatment planning and
outcome determination in radiotherapy treatments of cancer patients. Several
deep learning based segmentation algorithms have been developed in recent
years, however, U-Net remains the de facto algorithm designed specifically for
biomedical image segmentation and has spawned many variants with known
weaknesses. In this study, our goal is to present simple architectural changes
in U-Net to improve its accuracy and generalization properties. Unlike many
other available studies evaluating their algorithms on single center data, we
thoroughly evaluate several variations of U-Net as well as our proposed
enhanced architecture on multiple data sets for an extensive and reliable study
of the OAR segmentation problem. Our enhanced segmentation model includes
(a)architectural changes in the loss function, (b)optimization framework, and
(c)convolution type. Testing on three publicly available multi-object
segmentation data sets, we achieved an average of 80% dice score compared to
the baseline U-Net performance of 63%.


title: Lieb's permanental dominance conjecture

abstract:
  We survey the impact of Lieb's influential paper "Proofs of some conjectures
on permanents" [J. Math. Mech. 16 1966, 127-134], which introduced the famous
permanental dominance conjecture. This conjecture has defied all attacks for
over half a century, although a number of related conjectures have recently
been resolved.


title:
A nearby galaxy perspective on interstellar dust properties and their
  evolution

abstract:
  Interstellar dust is a key physical ingredient of galaxies, obscuring star
formation, regulating the heating and cooling of the gas, and building-up
chemical complexity. In this manuscript, I give a wide review of interstellar
dust properties and some of the modern techniques used to study it. I start
with a general introduction presenting the main concepts, in molecular and
solid-state physics, required to understand the contemporary literature on the
subject. I then review the empirical evidence we currently use to constrain
state-of-the-art dust models. Follows a long discussion about our current
understanding of the grain properties of nearby galaxies, with an emphasis on
the results from spectral energy distribution modeling. The following chapter
presents dust evolution at all scales. I review the different microphysical
evolution processes, and the way they are accounted for in cosmic dust
evolution models. I give my take on the origin of interstellar dust in galaxies
of different metallicities. The last chapter focusses on methodology. I give an
introduction to the Bayesian method and compare it to frequentist techniques. I
discuss the epistemological consequences of the two approaches, and show why
the field of interstellar dust requires a probabilistic viewpoint. I end the
manuscript with a summary of the major breakthroughs achieved in the past
decade, and delineate a few prospectives for the next decade.


title: Flexible Triggering Kernels for Hawkes Process Modeling

abstract:
  Recently proposed encoder-decoder structures for modeling Hawkes processes
use transformer-inspired architectures, which encode the history of events via
embeddings and self-attention mechanisms. These models deliver better
prediction and goodness-of-fit than their RNN-based counterparts. However, they
often require high computational and memory complexity requirements and
sometimes fail to adequately capture the triggering function of the underlying
process. So motivated, we introduce an efficient and general encoding of the
historical event sequence by replacing the complex (multilayered) attention
structures with triggering kernels of the observed data. Noting the similarity
between the triggering kernels of a point process and the attention scores, we
use a triggering kernel to replace the weights used to build history
representations. Our estimate for the triggering function is equipped with a
sigmoid gating mechanism that captures local-in-time triggering effects that
are otherwise challenging with standard decaying-over-time kernels. Further,
taking both event type representations and temporal embeddings as inputs, the
model learns the underlying triggering type-time kernel parameters given pairs
of event types. We present experiments on synthetic and real data sets widely
used by competing models, while further including a COVID-19 dataset to
illustrate a scenario where longitudinal covariates are available. Results show
the proposed model outperforms existing approaches while being more efficient
in terms of computational complexity and yielding interpretable results via
direct application of the newly introduced kernel.


title: Localized orbital scaling correction for periodic systems

abstract:
  Density functional theory offers accurate structure prediction at acceptable
computational cost, but commonly used approximations suffer from delocalization
error; this results in inaccurate predictions of quantities such as energy band
gaps of finite and bulk systems, the energy level alignments, and electron
distributions at interfaces. The localized orbital scaling correction (LOSC)
was developed to correct delocalization error by using orbitals localized in
space and energy. These localized orbitals span both the occupied and
unoccupied spaces and can have fractional occupations in order to address
corrections to both the total energies and the one-electron energy eigenvalues.
We extend the LOSC method to periodic systems, where the localized orbitals
employed are dually localized Wannier functions. In light of the effect of the
bulk environment on the electrostatic interaction between localized orbitals,
we modify the LOSC energy correction to include a screened Coulomb kernel. For
a test set of semiconductors and large-gap insulators, we show that the
screened LOSC (sLOSC) method consistently improves the band gap compared to the
parent density functional approximation.


title:
A Bibliometric Perspective of Social Science Scientific Communities of
  Pakistan and India

abstract:
  In this study, we use research publication data from the field of social
science to identify collaboration networks among social science research
communities of India and Pakistan. We have used Scopus database to extract
information of social science journals for both countries India and Pakistan.
Study of this data is significant as both countries have common social issues
and many of common social values. Keywords analysis has been done to see common
research areas in both communities like poverty, education, the issue of gender
etc. Despite having many of the common social issues, collaboration among
social science research communities of both countries is not strong.


title:
Existence results for a class of quasilinear Schr\"odinger equations
  with singular or vanishing potentials

abstract:
  Given two continuous functions $V\left(r \right)\geq 0$ and $K\left(r\right)>
0$ ($r>0$), which may be singular or vanishing at zero as well as at infinity,
we study the quasilinear elliptic equation \[ -\Delta w+ V\left( \left|
x\right| \right) w - w \left( \Delta w^2 \right)= K(|x|) g(w) \quad \text{in
}\mathbb{R}^{N}, \] where $N\geq3$. To study this problem we apply a change of
variables $w=f(u)$, already used by several authors, and find existence results
for nonnegative solutions by the application of variational methods. The main
features of our results are that they do not require any compatibility between
how the potentials $V$ and $K$ behave at the origin and at infinity, and that
they essentially rely on power type estimates of the relative growth of $V$ and
$K$, not of the potentials separately. Our solutions satisfy a weak
formulations of the above equation, but we are able to prove that they are in
fact classical solutions in $\mathbb{R}^{N} \backslash \{ 0\}$. To apply
variational methods, we have to study the compactness of the embedding of a
suitable function space into the sum of Lebesgue spaces
$L_{K}^{q_{1}}+L_{K}^{q_{2}}$, and thus into $L_{K}^{q}$
($=L_{K}^{q}+L_{K}^{q}$) as a particular case.
  The nonlinearity $g$ has a double-power behavior, whose standard example is
$g(t) = \min \{ t^{q_1 -1}, t^{q_2 -1} \}$, recovering the usual case of a
single-power behavior when $q_1 = q_2$.


title: Biphoton entanglement of topologically-distinct modes

abstract:
  The robust generation and manipulation of entangled multiphoton states
on-chip has an essential role in quantum computation and communication. Lattice
topology has emerged as a means of protecting photonic states from disorder but
entanglement across different topologies remained unexplored. We report
biphoton entanglement between topologically distinct spatial modes in a
bipartite array of silicon waveguides. The results highlight topology as an
additional degree of freedom for entanglement and open avenues for
investigating information teleportation between trivial and topological modes.


title:
Intermittency in the Expanding Solar Wind: Observations from Parker
  Solar Probe (0.16au), Helios 1 (0.3-1au), and Voyager 1 (1-10au)

abstract:
  We examine statistics of magnetic field vector components to explore how
intermittency evolves from near sun plasma to radial distances as large as 10
au. Statistics entering the analysis include auto-correlation, magnetic
structure functions of order n (SFn), and scale dependent kurtosis (SDK), each
grouped in ranges of heliocentric distance. The Goddard Space Flight Center
Space Physics Data Facility (SPDF) provides magnetic field measurements for
resolutions of 6.8ms for Parker Solar Probe, 6s for Helios, and 1.92s for
Voyager 1. We compute SF2 to determine the scales encompassing the inertial
range and examine SDK to investigate degree of non-Gaussianity.
Auto-correlations are used to resolve correlation scales. Correlation lengths
and ion inertial lengths provide an estimate of effective Reynolds number (Re).
Variation in Re allows us to examine for the first time the relationship
between SDK and Re in an interplanetary plasma. A conclusion from this observed
relationship is that regions with lower Re at a fixed physical scale have on
average lower kurtosis, implying less intermittent behavior. Kolmogorov refined
similarity hypothesis is applied to magnetic SFn and kurtosis to calculate
intermittency parameters and fractal scaling in the inertial range. A refined
Voyager 1 magnetic field dataset is generated.


title: Rethinking Explainability as a Dialogue: A Practitioner's Perspective

abstract:
  As practitioners increasingly deploy machine learning models in critical
domains such as health care, finance, and policy, it becomes vital to ensure
that domain experts function effectively alongside these models. Explainability
is one way to bridge the gap between human decision-makers and machine learning
models. However, most of the existing work on explainability focuses on
one-off, static explanations like feature importances or rule lists. These
sorts of explanations may not be sufficient for many use cases that require
dynamic, continuous discovery from stakeholders. In the literature, few works
ask decision-makers about the utility of existing explanations and other
desiderata they would like to see in an explanation going forward. In this
work, we address this gap and carry out a study where we interview doctors,
healthcare professionals, and policymakers about their needs and desires for
explanations. Our study indicates that decision-makers would strongly prefer
interactive explanations in the form of natural language dialogues. Domain
experts wish to treat machine learning models as "another colleague", i.e., one
who can be held accountable by asking why they made a particular decision
through expressive and accessible natural language interactions. Considering
these needs, we outline a set of five principles researchers should follow when
designing interactive explanations as a starting place for future work.
Further, we show why natural language dialogues satisfy these principles and
are a desirable way to build interactive explanations. Next, we provide a
design of a dialogue system for explainability and discuss the risks,
trade-offs, and research opportunities of building these systems. Overall, we
hope our work serves as a starting place for researchers and engineers to
design interactive explainability systems.


title: Sobolev Sampling of Free Energy Landscapes

abstract:
  A family of fast sampling methods is introduced here for molecular
simulations of systems having rugged free energy landscapes. The methods
represent a generalization of a strategy consisting of adjusting a model for
the free energy as a function of one- or more collective variables as a
simulation proceeds. Such a model is gradually built as a system evolves
through phase space from both the frequency of visits to distinct states and
generalized force estimates corresponding to such states. A common feature of
the methods is that the underlying functional models and their gradients are
easily expressed in terms of the same parameters, thereby providing faster and
more effective fitting of the model from simulation data than other available
sampling techniques. They also eliminate the need to train simultaneously
separate neural networks, while retaining the advantage of generating smooth
and continuous functional estimates that enable biasing outside the support
grid. Implementation of the methods is relatively simple and, more importantly,
they are found to provide gains of up to several orders of magnitude in
computational efficiency over existing approaches.


title: Stackelberg Strategic Guidance for Heterogeneous Robots Collaboration

abstract:
  In this study, we explore the application of game theory, in particular
Stackelberg games, to address the issue of effective coordination strategy
generation for heterogeneous robots with one-way communication. To that end,
focusing on the task of multi-object rearrangement, we develop a theoretical
and algorithmic framework that provides strategic guidance for a pair of robot
arms, a leader and a follower where the leader has a model of the follower's
decision-making process, through the computation of a feedback Stackelberg
equilibrium. With built-in tolerance of model uncertainty, the strategic
guidance generated by our planning algorithm not only improves the overall
efficiency in solving the rearrangement tasks, but is also robust to common
pitfalls in collaboration, e.g., chattering.


title: On the Benefit of Cooperation in Relay Networks

abstract:
  This work addresses the cooperation facilitator (CF) model, in which network
nodes coordinate through a rate limited communication device. For independent
multiple-access channel (MAC) encoders, the CF model is known to show
significant rate benefits, even when the rate of cooperation is negligible.
Specifically, the benefit in MAC sum-rate, as a function of the cooperation
rate $C_{CF}$, sometimes has an infinite slope at $C_{CF}=0$. This work studies
the question of whether cooperation through a CF can yield similar
infinite-slope benefits when applied to internal network encoders in which
dependence among MAC transmitters can be established without the help of the
CF. Towards this end, this work studies the CF model when applied to relay
nodes of a single-source, single-terminal, diamond network consisting of a
broadcast channel followed by a MAC. In the relay channel with orthogonal
receiver components, careful generalization of the
partial-decode-forward/compress-forward lower bound to the CF model yields
sufficient conditions for an infinite-slope benefit. Additional results include
derivation of a family of diamond networks for which the infinite-slope
rate-benefit derives directly from the properties of the corresponding MAC
component when studied in isolation.


title: Learning from a Learning User for Optimal Recommendations

abstract:
  In real-world recommendation problems, especially those with a formidably
large item space, users have to gradually learn to estimate the utility of any
fresh recommendations from their experience about previously consumed items.
This in turn affects their interaction dynamics with the system and can
invalidate previous algorithms built on the omniscient user assumption. In this
paper, we formalize a model to capture such "learning users" and design an
efficient system-side learning solution, coined Noise-Robust Active Ellipsoid
Search (RAES), to confront the challenges brought by the non-stationary
feedback from such a learning user. Interestingly, we prove that the regret of
RAES deteriorates gracefully as the convergence rate of user learning becomes
worse, until reaching linear regret when the user's learning fails to converge.
Experiments on synthetic datasets demonstrate the strength of RAES for such a
contemporaneous system-user learning problem. Our study provides a novel
perspective on modeling the feedback loop in recommendation problems.


title:
Directional-dependence of the event-by-event neutron-$\gamma$
  multiplicity correlations in $^{252}$Cf(sf)

abstract:
  We differentiate the event-by-event n-$\gamma$ multiplicity data from
\ce{^{252}Cf}(sf) with respect to the energies of the emitted particles as well
as their relative angles of emission. We determine that neutron emission
enhances $\gamma$-ray emission around $0.7$ and $1.2$ MeV, but the only
directional alignment was observed for $E_\gamma \leq 0.7$ MeV and tended to be
parallel and antiparallel to neutrons emitted in the same event. The emission
of $\gamma$ rays at other energies was determined to be nearly isotropic. The
presence of the emission and alignment enhancements is explained by positive
correlations between neutron emission and quadrupole $\gamma$-ray emission
along rotational bands in the de-exciting fragments. This observation
corroborates the hypothesis of positive correlations between the angular
momentum of a fragment and its intrinsic excitation energy. The results of this
work are especially relevant in view of the recent theoretical and experimental
interest in the generation of angular momentum in fission. Specifically, we
have determined an alignment of the fragments angular momenta in a direction
perpendicular to the direction of motion. We interpret the lack of $n$-$\gamma$
angular correlations for fission fragments near closed shells as a weakening of
the alignment process for spherical nuclei. Lastly, we have observed that
statistical $\gamma$ rays are emitted isotropically, indicating that the
average angular momentum removed by this radiation is small. These results, and
the analysis tools presented in this work, represent a stepping stone for
future analysis of $n$-$\gamma$ emission correlations and their connection to
angular momentum properties.


title: Functionalizing Aromatic Compounds with Optical Cycling Centers

abstract:
  Molecular design principles provide guidelines for augmenting a molecule with
a smaller group of atoms to realize a desired property or function. We
demonstrate that these concepts can be used to create an optical cycling center
that can be attached to a number of aromatic ligands, allowing the scattering
of many photons from the resulting molecules without changing the molecular
vibrational states. We provide further design principles that indicate the
ability to expand this work. This represents a significant step towards a
quantum functional group, which may serve as a generic qubit moiety that can be
attached to a wide range of molecular structures and surfaces.


title:
Theoretical scheme on shape-programming of thin hyperelastic plates
  through differential growth

abstract:
  In this paper, a theoretical scheme is proposed for shape-programming of thin
hyperelastic plates through differential growth. First, starting from the 3D
governing system of a hyperelastic (neo-Hookean) plate, a consistent
finite-strain plate equation system is formulated through a series-expansion
and truncation approach. Based on the plate equation system, the problem of
shape-programming is studied under the stress-free assumption. By equating the
stress components in the plate equations to be zero, the explicit relations
between growth functions and geometrical quantities of the target shape of the
plate are derived. Then, a theoretical scheme of shape-programming is proposed,
which can be used to identify the growth fields corresponding to arbitrary 3D
shapes of the plate. To demonstrate the efficiency of the scheme, some typical
examples are studied. The predicted growth functions in these examples are
adopted in the numerical simulations, from which the target shapes of the plate
can be recovered completely. The scheme of shape-programming proposed in the
current work is applicable for manufacture of intelligent soft devices.


title:
Reduced polynomial invariant integrity basis for in-plane
  magneto-mechanical loading

abstract:
  The description of the behavior of a material subjected to multi-physics
loadings requires the formulation of constitutive laws that usually derive from
Gibbs free energies, using invariant quantities depending on the considered
physics and material symmetries. On the other hand, most of crystalline
materials can be described by their crystalline texture and the associated
preferred directions of strong crystalline symmetry (the so-called fibers).
Moreover, among the materials produced industrially, many are manufactured in
the form of sheets or of thin layers. This article has for object the study of
the magneto-mechanical coupling which is a function of the stress $\sigma$ and
the magnetization M. We consider a material with cubic symmetry whose texture
can be described by one of three fibers denoted as $\theta$, $\gamma$ or
$\alpha$ ' , and which is thin enough so that both the stress and the
magnetization can be considered as in-plane quantities. We propose an algorithm
able to derive linear relations between the 30 cubic invariants I k of a
minimal integrity basis describing a magneto-elastic problem, when they are
restricted to in-plane loading conditions and for different fiber orientations.
The algorithm/program output is a reduced list of invariants of cardinal 7 for
the {100}-oriented $\theta$ fiber, of cardinal 15 for the {110}-oriented
$\alpha$ ' fiber and of cardinal 8 for the {111}-oriented $\gamma$ fiber. This
reduction (compared to initial cardinal 30) can be of great help for the
formulation of low-parameter macroscopic magneto-mechanical models.


title: Research on Patch Attentive Neural Process

abstract:
  Attentive Neural Process (ANP) improves the fitting ability of Neural Process
(NP) and improves its prediction accuracy, but the higher time complexity of
the model imposes a limitation on the length of the input sequence. Inspired by
models such as Vision Transformer (ViT) and Masked Auto-Encoder (MAE), we
propose Patch Attentive Neural Process (PANP) using image patches as input and
improve the structure of deterministic paths based on ANP, which allows the
model to extract image features more accurately and efficiently reconstruction.


title:
Machine-learning convex and texture-dependent macroscopic yield from
  crystal plasticity simulations

abstract:
  The influence of the microstructure of a polycrystalline material on its
macroscopic deformation response is still one of the major problems in
materials engineering. For materials characterized by elastic-plastic
deformation responses, predictive computational models to characterize
crystal-plasticity (CP) have been developed. However, due to their large demand
of computational resources, CP simulations cannot be straightforwardly
implemented in hierarchical computational models such as FE$^{2}$. This
bottleneck intensifies the need for the development of macroscopic simulation
tools that can be directly informed by microstructural quantities. Using a 3D
Finite-Element solver for CP, we generate a macroscopic yield function database
based on general loading conditions and crystallographic texture. We
furthermore assume an independence of the yield function to hydrostatic
pressure of the yield function. Leveraging the advancement in statistical
modeling we describe and apply a machine learning framework for predicting
macroscopic yield as a function of crystallographic texture. The convexity of
the data-driven yield function is guaranteed by using partially input convex
neural networks as the predictive tool. Furthermore, in order to allow for the
predicted yield function to be directly incorporated in time-integration
schemes, as needed for the Finite Element method, the yield surfaces are
interpreted as the boundaries of signed distance function level sets.


title:
On the abnormal temperature dependent elastic properties of fused silica
  irradiated by ultrafast lasers

abstract:
  Materials with thermal-invariant elastic properties of materials are of
interest for resonant device frequency and dimensional stability of precision
devices. Here, we demonstrate that the temperature coefficient of elasticity
(TCE) of amorphous silica can be locally reduced using femtosecond laser
irradiation. Notably, a self-organized laser-induced modification shows a
decrease of the TCE beyond 50%. The origin of this dramatic decrease is
attributed to the presence of sub-micron permanent densified zones within the
fine multi-layers structure of the laser-modified zone.


title: Tests on High-Directivity Unconventional Biconical Type Antennas

abstract:
  Biconical-type antennas featuring high directivity have been designed,
created, and tested in anechoic chamber. Results in the range between 1 and 5
GHz are presented in this article. In particular, two different configurations
have been tested, with and without dielectric lenses, both involving rapid
prototyping tools (3D printing) for the dielectric and the antenna support. A
very high directivity is nowadays demanded by efficient and sustainable
point-to-point communications or energy transfer protocols, to avoid releasing
energy in neighboring areas and preserve data transfer security. As
demonstrated here, special biconical type antennas featuring a 3D printed
polylactic acid (PLA) dielectric lens can achieve a good directivity, with a
corresponding emission lobe centered around 8.4 degrees, featuring a FWHM of
6.4 degrees. Dielectric lens-free antennas, featuring an unconventional shape,
can also achieve a good directivity, with a corresponding emission lobe
centered around 10.0 degrees, featuring a FWHM of 14.2 degrees. The preliminary
results shown here explore some of the aspects of the vast configuration space
(which include fabrication techniques, dielectric materials, conductive
supports, etc.) and open the route for further optimization studies. The aim
would be to adjust the various degrees of freedom in order to achieve what can
be defined as "infinite" directivity.


title:
A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State
  Estimation

abstract:
  Full-field traffic state information (i.e., flow, speed, and density) is
critical for the successful operation of Intelligent Transportation Systems
(ITS) on freeways. However, incomplete traffic information tends to be directly
collected from traffic detectors that are insufficiently installed in most
areas, which is a major obstacle to the popularization of ITS. To tackle this
issue, this paper introduces an innovative traffic state estimation (TSE)
framework that hybrid regression machine learning techniques (e.g., artificial
neural network (ANN), random forest (RF), and support vector machine (SVM))
with a traffic physics model (e.g., second-order macroscopic traffic flow
model) using limited information from traffic sensors as inputs to construct
accurate and full-field estimated traffic state for freeway systems. To examine
the effectiveness of the proposed TSE framework, this paper conducted empirical
studies on a real-world data set collected from a stretch of I-15 freeway in
Salt Lake City, Utah. Experimental results show that the proposed method has
been proved to estimate full-field traffic information accurately. Hence, the
proposed method could provide accurate and full-field traffic information, thus
providing the basis for the popularization of ITS.


title: Generalizing to New Physical Systems via Context-Informed Dynamics Model

abstract:
  Data-driven approaches to modeling physical systems fail to generalize to
unseen systems that share the same general dynamics with the learning domain,
but correspond to different physical contexts. We propose a new framework for
this key problem, context-informed dynamics adaptation (CoDA), which takes into
account the distributional shift across systems for fast and efficient
adaptation to new dynamics. CoDA leverages multiple environments, each
associated to a different dynamic, and learns to condition the dynamics model
on contextual parameters, specific to each environment. The conditioning is
performed via a hypernetwork, learned jointly with a context vector from
observed data. The proposed formulation constrains the search hypothesis space
to foster fast adaptation and better generalization across environments. It
extends the expressivity of existing methods. We theoretically motivate our
approach and show state-ofthe-art generalization results on a set of nonlinear
dynamics, representative of a variety of application domains. We also show, on
these systems, that new system parameters can be inferred from context vectors
with minimal supervision.


title: Advances in MetaDL: AAAI 2021 challenge and workshop

abstract:
  To stimulate advances in metalearning using deep learning techniques
(MetaDL), we organized in 2021 a challenge and an associated workshop. This
paper presents the design of the challenge and its results, and summarizes
presentations made at the workshop. The challenge focused on few-shot learning
classification tasks of small images. Participants' code submissions were run
in a uniform manner, under tight computational constraints. This put pressure
on solution designs to use existing architecture backbones and/or pre-trained
networks. Winning methods featured various classifiers trained on top of the
second last layer of popular CNN backbones, fined-tuned on the meta-training
data (not necessarily in an episodic manner), then trained on the labeled
support and tested on the unlabeled query sets of the meta-test data.


title: Weighted Random Cut Forest Algorithm for Anomaly Detection

abstract:
  Random cut forest (RCF) algorithms have been developed for anomaly detection,
particularly for anomaly detection in time-series data. The RCF algorithm is
the improved version of the isolation forest algorithm. Unlike the isolation
forest algorithm, the RCF algorithm has the power of determining whether the
real-time input has anomaly by inserting the input in the constructed tree
network. There have been developed various RCF algorithms including Robust RCF
(RRCF) with which the cutting procedure is adaptively chosen probabilistically.
The RRCF algorithm shows better performance than the isolation forest as the
cutting dimension is decided based on the geometric range of the data. The
overall data structure is, however, not considered in the adaptive cutting
algorithm with the RRCF algorithm. In this paper, we propose a new RCF
algorithm, so-called the weighted RCF (WRCF) algorithm. In order to introduce
the WRCF, we first introduce a new geometric measure, a density measure which
is crucial for the construction of the WRCF. We provide various mathematical
properties of the density measure. The proposed WRCF also cuts the tree network
adaptively, but with the consideration of the denseness of the data. The
proposed algorithm is more efficient when the data is non-uniformly structured
and achieves the desired anomaly scores more rapidly than the RRCF. We provide
theorems that prove our claims with numerical examples.


title: Univalent foundations and the equivalence principle

abstract:
  In this paper, we explore the 'equivalence principle' (EP): roughly,
statements about mathematical objects should be invariant under an appropriate
notion of equivalence for the kinds of objects under consideration. In set
theoretic foundations, EP may not always hold: for instance, the statement '1
\in N' is not invariant under isomorphism of sets. In univalent foundations, on
the other hand, EP has been proven for many mathematical structures. We first
give an overview of earlier attempts at designing foundations that satisfy EP.
We then describe how univalent foundations validates EP.


title:
A Machine Learning Smartphone-based Sensing for Driver Behavior
  Classification

abstract:
  Driver behavior profiling is one of the main issues in the insurance
industries and fleet management, thus being able to classify the driver
behavior with low-cost mobile applications remains in the spotlight of
autonomous driving. However, using mobile sensors may face the challenge of
security, privacy, and trust issues. To overcome those challenges, we propose
to collect data sensors using Carla Simulator available in smartphones
(Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using
speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll)
taking into account the speed limit of the current road and weather conditions
to better identify the risky behavior. Secondly, after fusing inter-axial data
from multiple sensors into a single file, we explore different machine learning
algorithms for time series classification to evaluate which algorithm results
in the highest performance.


title:
A new Model of City Growth and its Application to a middle sized French
  City

abstract:
  In the first part of this paper we propose a new theoretical model of city
growth based on percolation. The second half oh the paper is devoted to a
concrete application of the model, namely to the city of Montargis. It appears
that the embedded algorithm is quite efficient in terms of computational time
and allows to exploit big data type ressources such as individual land lots.


title:
Appraising scattering theories for polycrystals of any symmetry using
  finite elements

abstract:
  This paper uses 3D grain-scale finite element (FE) simulations to appraise
the classical scattering theory of plane longitudinal wave propagation in
untextured polycrystals with statistically equiaxed grains belonging to the
seven crystal symmetries. As revealed from the results of 10,390 materials, the
classical theory has a linear relationship with the elastic scattering factor
at the quasi-static velocity limit, whereas the reference FE and
self-consistent (SC) results generally exhibit a quadratic relationship. As
supported by the results of 90 materials, such order difference also extends to
the attenuation and phase velocity, leading to larger differences between the
classical theory and the FE results for more strongly scattering materials.
Alternatively, two approximate models are proposed to achieve more accurate
calculations by including an additional quadratic term. One model uses
quadratic coefficients from quasi-static SC velocity fits and is thus
symmetry-specific, while the other uses theoretically-determined coefficients
and is valid for any individual material. These simple models generally deliver
more accurate attenuation and phase velocity (particularly the second model)
than the classical theory, especially for strongly scattering materials.
However, the models are invalid for the attenuation of materials with negative
quadratic coefficients.


title: Yordle: An Efficient Imitation Learning for Branch and Bound

abstract:
  Combinatorial optimization problems have aroused extensive research interests
due to its huge application potential. In practice, there are highly redundant
patterns and characteristics during solving the combinatorial optimization
problem, which can be captured by machine learning models. Thus, the 2021
NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition is
proposed with the goal of improving state-of-the-art combinatorial optimization
solvers by replacing key heuristic components with machine learning techniques.
This work presents our solution and insights gained by team qqy in the dual
task of the competition. Our solution is a highly efficient imitation learning
framework for performance improvement of Branch and Bound (B&B), named Yordle.
It employs a hybrid sampling method and an efficient data selection method,
which not only accelerates the model training but also improves the decision
quality during branching variable selection. In our experiments, Yordle greatly
outperforms the baseline algorithm adopted by the competition while requiring
significantly less time and amounts of data to train the decision model.
Specifically, we use only 1/4 of the amount of data compared to that required
for the baseline algorithm, to achieve around 50% higher score than baseline
algorithm. The proposed framework Yordle won the championship of the student
leaderboard.


title:
AtmoDist: Self-supervised Representation Learning for Atmospheric
  Dynamics

abstract:
  Representation learning has proven to be a powerful methodology in a wide
variety of machine learning applications. For atmospheric dynamics, however, it
has so far not been considered, arguably due to the lack of large-scale,
labeled datasets that could be used for training. In this work, we show that
the difficulty is benign and introduce a self-supervised learning task that
defines a categorical loss for a wide variety of unlabeled atmospheric
datasets. Specifically, we train a neural network on the simple yet intricate
task of predicting the temporal distance between atmospheric fields, e.g. the
components of the wind field, from distinct but nearby times. Despite this
simplicity, a neural network will provide good predictions only when it
develops an internal representation that captures intrinsic aspects of
atmospheric dynamics. We demonstrate this by introducing a data-driven distance
metric for atmospheric states based on representations learned from ERA5
reanalysis. When employ as a loss function for downscaling, this Atmodist
distance leads to downscaled fields that match the true statistics more closely
than the previous state-of-the-art based on an l2-loss and whose local behavior
is more realistic. Since it is derived from observational data, AtmoDist also
provides a novel perspective on atmospheric predictability.


title:
Some Fuzzy Korovkin type Approximation Theorems via Power Series
  Summability Method

abstract:
  This article provides a power series summability based Korovkin type
approximation theorem for any fuzzy sequence of positive linear operators.
Using the notion of fuzzy modulus of smoothness, we also derive an associated
approximation theorem concerning the fuzzy rate of convergence of these
operators. Furthermore, through an example, we illustrate that our summability
based Korovin type theorem is an advantage over the fuzzy Korovkin type theorem
proved in the seminal paper by Anastassiou (Stud. Univ. Babe\c{s}-Bolyai Math.
4 (2005), 3--10).


title:
DeepQMLP: A Scalable Quantum-Classical Hybrid DeepNeural Network
  Architecture for Classification

abstract:
  Quantum machine learning (QML) is promising for potential speedups and
improvements in conventional machine learning (ML) tasks (e.g.,
classification/regression). The search for ideal QML models is an active
research field. This includes identification of efficient classical-to-quantum
data encoding scheme, construction of parametric quantum circuits (PQC) with
optimal expressivity and entanglement capability, and efficient output decoding
scheme to minimize the required number of measurements, to name a few. However,
most of the empirical/numerical studies lack a clear path towards scalability.
Any potential benefit observed in a simulated environment may diminish in
practical applications due to the limitations of noisy quantum hardware (e.g.,
under decoherence, gate-errors, and crosstalk). We present a scalable
quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired
by classical deep neural network architectures. In DeepQMLP, stacked shallow
Quantum Neural Network (QNN) models mimic the hidden layers of a classical
feed-forward multi-layer perceptron network. Each QNN layer produces a new and
potentially rich representation of the input data for the next layer. This new
representation can be tuned by the parameters of the circuit. Shallow QNN
models experience less decoherence, gate errors, etc. which make them (and the
network) more resilient to quantum noise. We present numerical studies on a
variety of classification problems to show the trainability of DeepQMLP. We
also show that DeepQMLP performs reasonably well on unseen data and exhibits
greater resilience to noise over QNN models that use a deep quantum circuit.
DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during
inference under noise than QMLP.


title: Atom interferometers and a small-scale test of general relativity

abstract:
  Since the first appearance of general relativity in 1916, various experiments
have been conducted to test the theory. Due to the weakness of the interactions
involved, all of the documented tests were carried out in a gravitational field
generated by objects of an astronomical scale. We propose an idea for an
experiment that could detect purely general-relativistic effects in a
lab-generated gravitational field. It is shown that a set of dense
rapidly-revolving cylinders produce a frame-dragging effect substantial enough
to be two orders of magnitude away from the observable range of the next
generation of atomic interferometers.. The metric tensor due to a uniform
rotating axisymmetric body in the weak-field limit is calculated and the
phaseshift formula for the interferometer is derived. This article is meant to
demonstrate feasibility of the concept and stimulate further research into the
field of low-scale experiments in general relativity. It is by no means a fully
developed experiment proposal.


title: Bunched Fuzz: Sensitivity for Vector Metrics

abstract:
  "Program sensitivity" measures the distance between the outputs of a program
when it is run on two related inputs. This notion, which plays an important
role in areas such as data privacy and optimization, has been the focus of
several program analysis techniques introduced in recent years. One approach
that has proved particularly fruitful for this domain is the use of type
systems inspired by linear logic, as pioneered by Reed and Pierce in the Fuzz
programming language. In Fuzz, each type is equipped with its own notion of
distance, and the typing rules explain how those distances can be treated
soundly when analyzing the sensitivity of a program. In particular, Fuzz
features two products types, corresponding to two different sensitivity
analyses: the "tensor product" combines the distances of each component by
adding them, while the "with product" takes their maximum.
  In this work, we show that products in Fuzz can be generalized to arbitrary
$L^p$ distances, metrics that are often used in privacy and optimization. The
original Fuzz products, tensor and with, correspond to the special cases $L^1$
and $L^\infty$. To simplify the handling of such products, we extend the Fuzz
type system with bunches -- as in the logic of bunched implications -- where
the distances of different groups of variables can be combined using different
$L^p$ distances. We show that our extension can be used to reason about
important examples of metrics between probability distributions in a natural
way.


title:
Microscopic structure of three-dimensional charge order in kagome
  superconductor AV3Sb5 and its tunability

abstract:
  Correlated electronic systems are naturally susceptible to develop
collective, symmetry-breaking electronic phases as observed in Cu- and Fe-based
high-temperature superconductors, and twisted Moir\'e superlattices. The family
of kagome metals AV3Sb5 (A = K, Rb, Cs) is a recently discovered, rich platform
to study many of these phenomena and their interplay. In these systems,
three-dimensional charge order (3D-CO) is the primary instability that sets the
stage in which other ordered phases emerge, including unidirectional stripe
order, orbital flux order, and superconductivity. Therefore, determining the
exact nature of the 3D-CO is key to capture the broader phenomenology in
AV3Sb5. Here, we use high-resolution angle-resolved photoemission spectroscopy
to resolve the microscopic structure and symmetry of 3D-CO in AV3Sb5. Our
approach is based on identifying an unusual splitting of kagome bands induced
by 3D-CO, which provides a sensitive way to refine the spatial charge patterns
in neighboring kagome planes. Notably, we found a marked dependence of the
3D-CO structure on alkali metal and doping: the 3D-CO in CsV3Sb5 is composed of
kagome layers with alternating Star-of-David and Tri-Hexagonal distortions,
while KV3Sb5, RbV3Sb5, and Sn-doped CsV3Sb5 realize a staggered charge pattern
breaking C6 rotational symmetry. These results establish the microscopic
structure of 3D-CO and its evolution with chemical composition for the first
time, providing fresh insights on the origin of the cascade of exotic
electronic phases in AV3Sb5.


title:
An isolated mass gap black hole or neutron star detected with
  astrometric microlensing

abstract:
  We present the analysis of five black hole (BH) candidates identified from
gravitational microlensing surveys. HST astrometric data and densely sampled
lightcurves from ground-based microlensing surveys are fit with a
single-source, single-lens microlensing model in order to measure the mass and
luminosity of each lens and determine if it is a black hole. One of the five
targets (OGLE-2011-BLG-0462/MOA-2011-BLG-191 or OB110462 for short) shows a
significant $>1$ mas coherent astrometric shift, little to no lens flux, and
has an inferred lens mass of 1.6 - 4.2 $M_\odot$. This makes OB110462 the first
definitive discovery of a compact object through astrometric microlensing and
it is most likely either a neutron star or a low-mass black hole. This compact
object lens is relatively nearby (690 - 1370 pc) and has a slow transverse
motion of $<$ 25 km/s. OB110462 shows significant tension between models
well-fit to photometry vs. astrometry, making it currently difficult to
distinguish between a neutron star and a black hole. Additional observations
and modeling with more complex system geometries, such as binary sources are
needed to resolve the puzzling nature of this object. For the remaining four
candidates, the lens masses are $<2 M_\odot$ and they are unlikely to be black
holes; but two of the four are likely white dwarfs or neutron stars. We compare
the full sample of 5 candidates to theoretical expectations on the number
($\sim 10^8$) of BHs in the Milky Way and find reasonable agreement given the
small sample size.


title: A Note on the Conditional Probabilities of the Telegraph Process

abstract:
  We consider the telegraph process with two velocities,
$a_1>a_2\in\mathbb{R}$, and two rates of reversal, $\lambda_1,\lambda_2>0$. We
study some of its features with respect to the conditional probability measure
where both the initial speed and the number of changes of direction are known.
We exhibit a new proof by induction of the (conditional) probability law and a
detailed study of the distribution of the motion at time $t>0$ conditioned on
its position at a previous time $0<s<t$. In the case of a symmetric process, we
present some results on the joint distribution of the position of the motion at
time $t>0$, its maximum and its minimum up to that moment.


title:
Modified ResNet Model for MSI and MSS Classification of Gastrointestinal
  Cancer

abstract:
  In this work, a modified ResNet model is proposed for the classification of
Microsatellite instability(MSI) and Microsatellite stability(MSS) of
gastrointestinal cancer. The performance of this model is analyzed and compared
with existing models. The proposed model surpassed the existing models with an
accuracy of 0.8981 and F1 score of 0.9178.


title:
Net benefit, calibration, threshold selection, and training objectives
  for algorithmic fairness in healthcare

abstract:
  A growing body of work uses the paradigm of algorithmic fairness to frame the
development of techniques to anticipate and proactively mitigate the
introduction or exacerbation of health inequities that may follow from the use
of model-guided decision-making. We evaluate the interplay between measures of
model performance, fairness, and the expected utility of decision-making to
offer practical recommendations for the operationalization of algorithmic
fairness principles for the development and evaluation of predictive models in
healthcare. We conduct an empirical case-study via development of models to
estimate the ten-year risk of atherosclerotic cardiovascular disease to inform
statin initiation in accordance with clinical practice guidelines. We
demonstrate that approaches that incorporate fairness considerations into the
model training objective typically do not improve model performance or confer
greater net benefit for any of the studied patient populations compared to the
use of standard learning paradigms followed by threshold selection concordant
with patient preferences, evidence of intervention effectiveness, and model
calibration. These results hold when the measured outcomes are not subject to
differential measurement error across patient populations and threshold
selection is unconstrained, regardless of whether differences in model
performance metrics, such as in true and false positive error rates, are
present. In closing, we argue for focusing model development efforts on
developing calibrated models that predict outcomes well for all patient
populations while emphasizing that such efforts are complementary to
transparent reporting, participatory design, and reasoning about the impact of
model-informed interventions in context.


title:
Unified Fake News Detection using Transfer Learning of Bidirectional
  Encoder Representation from Transformers model

abstract:
  Automatic detection of fake news is needed for the public as the
accessibility of social media platforms has been increasing rapidly. Most of
the prior models were designed and validated on individual datasets separately.
But the lack of generalization in models might lead to poor performance when
deployed in real-world applications since the individual datasets only cover
limited subjects and sequence length over the samples. This paper attempts to
develop a unified model by combining publicly available datasets to detect fake
news samples effectively.


title: Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space

abstract:
  We demonstrate for the first time that ill-conditioned, non-smooth,
constrained distributions in very high dimension, upwards of 100,000, can be
sampled efficiently $\textit{in practice}$. Our algorithm incorporates
constraints into the Riemannian version of Hamiltonian Monte Carlo and
maintains sparsity. This allows us to achieve a mixing rate independent of
smoothness and condition numbers.
  On benchmark data sets in systems biology and linear programming, our
algorithm outperforms existing packages by orders of magnitude. In particular,
we achieve a 1,000-fold speed-up for sampling from the largest published human
metabolic network (RECON3D). Our package has been incorporated into the COBRA
toolbox.


title: Ad-datasets: a meta-collection of data sets for autonomous driving

abstract:
  Autonomous driving is among the largest domains in which deep learning has
been fundamental for progress within the last years. The rise of datasets went
hand in hand with this development. All the more striking is the fact that
researchers do not have a tool available that provides a quick, comprehensive
and up-to-date overview of data sets and their features in the domain of
autonomous driving. In this paper, we present ad-datasets, an online tool that
provides such an overview for more than 150 data sets. The tool enables users
to sort and filter the data sets according to currently 16 different
categories. ad-datasets is an open-source project with community contributions.
It is in constant development, ensuring that the content stays up-to-date.


title: Cross-Study Replicability in Cluster Analysis

abstract:
  In cancer research, clustering techniques are widely used for exploratory
analyses and dimensionality reduction, playing a critical role in the
identification of novel cancer subtypes, often with direct implications for
patient management. As data collected by multiple research groups grows, it is
increasingly feasible to investigate the replicability of clustering
procedures, that is, their ability to consistently recover biologically
meaningful clusters across several datasets.
  In this paper, we review existing methods to assess replicability of
clustering analyses, and discuss a framework for evaluating cross-study
clustering replicability, useful when two or more studies are available. These
approaches can be applied to any clustering algorithm and can employ different
measures of similarity between partitions to quantify replicability, globally
(i.e. for the whole sample) as well as locally (i.e. for individual clusters).
Using experiments on synthetic and real gene expression data, we illustrate the
utility of replicability metrics to evaluate if the same clusters are
identified consistently across a collection of datasets.


title: Twisted moments of GL(3)xGL(2) L-functions

abstract:
  We compute an asymptotic formula for the twisted moment of GL(3)xGL(2)
L-functions and their derivatives. As an application we prove that
symmetric-square lifts of GL(2) Maass forms are uniquely determined by the
central values of the derivatives of GL(3)xGL(2) L-functions.


title:
Role of temperature and alignment activity on kinetics of coil-globule
  transition of a flexible polymer

abstract:
  We study the nonequilibrium kinetics during the coil-globule transition of a
flexible polymer chain with active beads after a quench from good to poor
solvent condition using molecular dynamics simulation. Activity for each bead
is introduced via the well-known Vicsek-like alignment rule due to which the
velocity of a bead tries to align towards the average direction of its
neighbors. We investigate the role of quenching temperature with varying
activity during collapse of this polymer. We find that although for lower
activities the kinetics remains qualitatively similar for different
temperatures, for higher activity noticeable differences can be identified.


title: Time-Constrained Learning

abstract:
  Consider a scenario in which we have a huge labeled dataset ${\cal D}$ and a
limited time to train some given learner using ${\cal D}$. Since we may not be
able to use the whole dataset, how should we proceed? Questions of this nature
motivate the definition of the Time-Constrained Learning Task (TCL): Given a
dataset ${\cal D}$ sampled from an unknown distribution $\mu$, a learner ${\cal
L}$ and a time limit $T$, the goal is to obtain in at most $T$ units of time
the classification model with highest possible accuracy w.r.t. to $\mu$, among
those that can be built by ${\cal L}$ using the dataset ${\cal D}$.
  We propose TCT, an algorithm for the TCL task designed based that on
principles from Machine Teaching. We present an experimental study involving 5
different Learners and 20 datasets where we show that TCT consistently
outperforms two other algorithms: the first is a Teacher for black-box learners
proposed in [Dasgupta et al., ICML 19] and the second is a natural adaptation
of random sampling for the TCL setting. We also compare TCT with Stochastic
Gradient Descent training -- our method is again consistently better.
  While our work is primarily practical, we also show that a stripped-down
version of TCT has provable guarantees. Under reasonable assumptions, the time
our algorithm takes to achieve a certain accuracy is never much bigger than the
time it takes the batch teacher (which sends a single batch of examples) to
achieve similar accuracy, and in some case it is almost exponentially better.


title: Tsetlin Machine for Solving Contextual Bandit Problems

abstract:
  This paper introduces an interpretable contextual bandit algorithm using
Tsetlin Machines, which solves complex pattern recognition tasks using
propositional logic. The proposed bandit learning algorithm relies on
straightforward bit manipulation, thus simplifying computation and
interpretation. We then present a mechanism for performing Thompson sampling
with Tsetlin Machine, given its non-parametric nature. Our empirical analysis
shows that Tsetlin Machine as a base contextual bandit learner outperforms
other popular base learners on eight out of nine datasets. We further analyze
the interpretability of our learner, investigating how arms are selected based
on propositional expressions that model the context.


title: Possible evidence for double precessing nozzle structure in QSO 3C345

abstract:
  The precessing jet-nozzle scenario has been applied to interpret the
VLBI-kinematics of twenty-seven superluminal components in blazar 3C345
measured during an about 38-yr period. The superluminal knots could be ejected
from a double jet-nozzle system forming a double-jet structure.Both nozzles
could precess with a period of about 7.30yr and in the same direction.For both
jets a steady precessing common trajectory could exist, along which different
knots moved according to their precession phases. Most knots were
model-simulated to be accelerated with their bulk Lorentz factor in the range
of about 4 to 30 and their Lorentz/Doppler factor profiles were derived. The
radio light-curves of knot C9 were well coincident with its Doppler boosting
profile. The double precessing nozzle scenario has been applied to interpret
the VLBI-kinematics for four blazars (3C279, OJ287, 3C454.3 and 3C345). The
charateristic parameters of the four putative supermassive black hole binaries
(including hole masses, orbital separation and gravitational radiation
lifetime, etc.) were derived and compared, showing that they are in physically
reasonable ranges, and well consistent with some theoretical arguments for
close black hole binaries.


title: Artificial Intelligence Powered Material Search Engine

abstract:
  Many data-driven applications in material science have been made possible
because of recent breakthroughs in artificial intelligence(AI). The use of AI
in material engineering is becoming more viable as the number of material data
such as X-Ray diffraction, various spectroscopy, and microscope data grows. In
this work, we have reported a material search engine that uses the interatomic
space (d value) from X-ray diffraction to provide material information. We have
investigated various techniques for predicting prospective material using X-ray
diffraction data. We used the Random Forest, Naive Bayes (Gaussian), and Neural
Network algorithms to achieve this. These algorithms have an average accuracy
of 88.50\%, 100.0\%, and 88.89\%, respectively. Finally, we combined all these
techniques into an ensemble approach to make the prediction more generic. This
ensemble method has a ~100\% accuracy rate. Furthermore, we are designing a
graph neural network (GNN)-based architecture to improve interpretability and
accuracy. Thus, we want to solve the computational and time complexity of
traditional dictionary-based and metadata-based material search engines and to
provide a more generic prediction.


title:
Interface inter-atomic electron-transition induced photon emission in
  contact-electrification

abstract:
  Contact electrification (CE) (or triboelectrification) is the phenomena where
charges are produced through physical contact between two materials. Here we
report the atomic featured photon emission spectra during CE between two solid
materials. Photon emission provides the evidence that electron transfer takes
place at the interface from an atom in one material to another atom in the
other material during CE. This process is the contact electrification induced
interface photon emission spectroscopy (CEIIPES). It naturally paves a way to a
spectroscopy corresponding to the CE at an interface, which might impact our
understanding of the interaction between solids, liquids, and gases. The
physics presented here could be expanded to Auger electron excitation, x-ray
emission, and electron emission in CE for general cases, which remain to be
explored. This could lead to a general field that may be termed as contact
electrification induced interface spectroscopy (CEIIS).


title:
An Investigation into the Kinetics and Mechanism of Phase Transitions in
  Optical Phase Change Ternary Alloy $Ge_2Sb_2Te_5$

abstract:
  Ternary alloys of \textit{Ge-Sb-Te (GST)} have been extensively studied due
to their unique ability display a reversible change in their phase upon
stimulation by optical pulses i.e amorphous \textit{(a-GST)} to crystalline
\textit{(c-GST)} and vice-versa. The two phases exhibit remarkably different
electrical and optical properties like conductivity, reflectivity, refractive
index and optical loss, this coupled with their high phase switching speeds,
low power phase switching, large switching cycles, large measurable optical and
electrical contrast, and phase stability makes GST alloys stand out from other
phase change materials \textit{(PCM)}. GST alloys have already found extensive
use in optical disks and electronic memories due to their non-volatility and
zero static power consumption, but the precise mechanism of the phase change is
not clearly understood. The phase change mechanism has usually been attributed
to the optical pulse, usually a high power short pulse laser, heating up the
\textit{c-GST} alloy to above its melting temperature (\textit{$T_m$}) after
which, if it is cooled rapidly enough to below the glass transition temperature
(\textit{$T_g$}), the atoms are fixed in place due to the drastic reduction in
their mobility, resulting in a phase which exhibits structure similar to a
frozen liquid and lacks long range order i.e amorphous. If alternatively the
\textit{a-GST} is heated above \textit{$T_g$} with a intermediate power laser
pulse for a significant amount of time to induce nucleation, then this favours
the shift back to the energetically favourable crystalline phase. With the
growing interest in next generation data storage technology for photonic and
neuromorphic computing, the research into understanding and improving the
properties of GST alloys has also been rekindled. In this term paper we hope to
investigate and gain a deeper understanding and app


title: Theoretical Exploration of Solutions of Feedforward ReLU networks

abstract:
  This paper aims to interpret the mechanism of feedforward ReLU networks by
exploring their solutions for piecewise linear functions through basic rules.
The constructed solutions should be universal enough to explain the network
architectures of engineering. In order for that, we borrow the methodology of
theoretical physics to develop the theories. Some of the consequences of our
theories include: Under geometric backgrounds, the solutions of both
three-layer networks and deep-layer networks are presented, and the solution
universality is ensured by several ways; We give clear and intuitive
interpretations of each component of network architectures, such as the
parameter-sharing mechanism for multi-output, the function of each layer, the
advantage of deep layers, the redundancy of parameters, and so on. We explain
three typical network architectures: the subnetwork of last three layers of
convolutional networks, multi-layer feedforward networks, and the decoder of
autoencoders. This paper is expected to provide a basic foundation of theories
of feedforward ReLU networks for further investigations.