Abstract:
  The surge in the internet of things (IoT) devices seriously threatens the
current IoT security landscape, which requires a robust network intrusion
detection system (NIDS). Despite superior detection accuracy, existing machine
learning or deep learning based NIDS are vulnerable to adversarial examples.
Recently, generative adversarial networks (GANs) have become a prevailing
method in adversarial examples crafting. However, the nature of discrete
network traffic at the packet level makes it hard for GAN to craft adversarial
traffic as GAN is efficient in generating continuous data like image synthesis.
Unlike previous methods that convert discrete network traffic into a grayscale
image, this paper gains inspiration from SeqGAN in sequence generation with
policy gradient. Based on the structure of SeqGAN, we propose Attack-GAN to
generate adversarial network traffic at packet level that complies with domain
constraints. Specifically, the adversarial packet generation is formulated into
a sequential decision making process. In this case, each byte in a packet is
regarded as a token in a sequence. The objective of the generator is to select
a token to maximize its expected end reward. To bypass the detection of NIDS,
the generated network traffic and benign traffic are classified by a black-box
NIDS. The prediction results returned by the NIDS are fed into the
discriminator to guide the update of the generator. We generate malicious
adversarial traffic based on a real public available dataset with attack
functionality unchanged. The experimental results validate that the generated
adversarial samples are able to deceive many existing black-box NIDS.


Abstract:
  In sponsored search, retrieving synonymous keywords for exact match type is
important for accurately targeted advertising. Data-driven deep learning-based
method has been proposed to tackle this problem. An apparent disadvantage of
this method is its poor generalization performance on entity-level long-tail
instances, even though they might share similar concept-level patterns with
frequent instances. With the help of a large knowledge base, we find that most
commercial synonymous query-keyword pairs can be abstracted into meaningful
conceptual patterns through concept tagging. Based on this fact, we propose a
novel knowledge-driven conceptual retrieval framework to mitigate this problem,
which consists of three parts: data conceptualization, matching via conceptual
patterns and concept-augmented discrimination. Both offline and online
experiments show that our method is very effective. This framework has been
successfully applied to Baidu's sponsored search system, which yields a
significant improvement in revenue.


Abstract:
  In this paper, a unified gas-kinetic wave-particle scheme (UGKWP) for the
disperse dilute gas-particle multiphase flow is proposed. The gas phase is
always in the hydrodynamic regime. However, the particle phase covers different
flow regimes from particle trajectory crossing to the hydrodynamic wave
interaction with the variation of local particle phase Knudsen number. The
UGKWP is an appropriate method for the capturing of the multiscale transport
mechanism in the particle phase through its coupled wave-particle formulation.
In the regime with intensive particle collision, the evolution of solid
particle will be followed by the analytic wave with quasi-equilibrium
distribution; while in the rarefied regime the non-equilibrium particle phase
will be captured through particle tracking and collision, which plays a
decisive role in recovering particle trajectory crossing behavior. The
gas-kinetic scheme (GKS) is employed for the simulation of gas flow. In the
highly collision regime for the particles, no particles will be sampled in
UGKWP and the wave formulation for solid particle with the hydrodynamic gas
phase will reduce the system to the two-fluid Eulerian model. On the other
hand, in the collisionless regime for the solid particle, the free transport of
solid particle will be followed in UGKWP, and coupled system will return to the
Eulerian-Lagrangian formulation for the gas and particle. The scheme will be
tested for in all flow regimes, which include the non-equilibrium particle
trajectory crossing, the particle concentration under different Knudsen number,
and the dispersion of particle flow with the variation of Stokes number. A
experiment of shock-induced particle bed fluidization is simulated and the
results are compared with experimental measurements. These numerical solutions
validate suitability of the proposed scheme for the simulation of gas-particle
multiphase flow.


Abstract:
  Manic episodes of bipolar disorder can lead to uncritical behaviour and
delusional psychosis, often with destructive consequences for those affected
and their surroundings. Early detection and intervention of a manic episode are
crucial to prevent escalation, hospital admission and premature death. However,
people with bipolar disorder may not recognize that they are experiencing a
manic episode and symptoms such as euphoria and increased productivity can also
deter affected individuals from seeking help. This work proposes to perform
user-independent, automatic mood-state detection based on actigraphy and
electrodermal activity acquired from a wrist-worn device during mania and after
recovery (euthymia). This paper proposes a new deep learning-based ensemble
method leveraging long (20h) and short (5 minutes) time-intervals to
discriminate between the mood-states. When tested on 47 bipolar patients, the
proposed classification scheme achieves an average accuracy of 91.59% in
euthymic/manic mood-state recognition.


Abstract:
  Biological infants are naturally curious and try to comprehend their physical
surroundings by interacting, in myriad multisensory ways, with different
objects - primarily macroscopic solid objects - around them. Through their
various interactions, they build hypotheses and predictions, and eventually
learn, infer and understand the nature of the physical characteristics and
behavior of these objects. Inspired thus, we propose a model for
curiosity-driven learning and inference for real-world AI agents. This model is
based on the arousal of curiosity, deriving from observations along
discontinuities in the fundamental macroscopic solid-body physics parameters,
i.e., shape constancy, spatial-temporal continuity, and object permanence. We
use the term body-budget to represent the perceived fundamental properties of
solid objects. The model aims to support the emulation of learning from scratch
followed by substantiation through experience, irrespective of domain, in
real-world AI agents.


Abstract:
  In previous works, questioning the mathematical nature of the connection in
the translations gauge theory formulation of Teleparallel Equivalent to General
Relativity (TEGR) Theory led us to propose a new formulation using a Cartan
connection. In this review, we summarize the presentation of that proposal and
discuss it from a gauge theoretic perspective.


Abstract:
  Emotion dynamics modeling is a significant task in emotion recognition in
conversation. It aims to predict conversational emotions when building
empathetic dialogue systems. Existing studies mainly develop models based on
Recurrent Neural Networks (RNNs). They cannot benefit from the power of the
recently-developed pre-training strategies for better token representation
learning in conversations. More seriously, it is hard to distinguish the
dependency of interlocutors and the emotional influence among interlocutors by
simply assembling the features on top of RNNs. In this paper, we develop a
series of BERT-based models to specifically capture the inter-interlocutor and
intra-interlocutor dependencies of the conversational emotion dynamics.
Concretely, we first substitute BERT for RNNs to enrich the token
representations. Then, a Flat-structured BERT (F-BERT) is applied to link up
utterances in a conversation directly, and a Hierarchically-structured BERT
(H-BERT) is employed to distinguish the interlocutors when linking up
utterances. More importantly, a Spatial-Temporal-structured BERT, namely
ST-BERT, is proposed to further determine the emotional influence among
interlocutors. Finally, we conduct extensive experiments on two popular emotion
recognition in conversation benchmark datasets and demonstrate that our
proposed models can attain around 5\% and 10\% improvement over the
state-of-the-art baselines, respectively.


Abstract:
  We introduce a symmetric fractional-order reduction (SFOR) method to
construct numerical algorithms on general nonuniform temporal meshes for
semilinear fractional diffusion-wave equations. By using the novel order
reduction method, the governing problem is transformed to an equivalent coupled
system, where the explicit orders of time-fractional derivatives involved are
all $\alpha/2$ $(1<\alpha<2)$. The linearized L1 scheme and Alikhanov scheme
are then proposed on general time meshes. Under some reasonable regularity
assumptions and weak restrictions on meshes, the optimal convergence is derived
for the two kinds of difference schemes by $H^2$ energy method. An adaptive
time stepping strategy which based on the (fast linearized) L1 and Alikhanov
algorithms is designed for the semilinear diffusion-wave equations. Numerical
examples are provided to confirm the accuracy and efficiency of proposed
algorithms.


Abstract:
  The use of neural networks and reinforcement learning has become increasingly
popular in autonomous vehicle control. However, the opaqueness of the resulting
control policies presents a significant barrier to deploying neural
network-based control in autonomous vehicles. In this paper, we present a
reinforcement learning based approach to autonomous vehicle longitudinal
control, where the rule-based safety cages provide enhanced safety for the
vehicle as well as weak supervision to the reinforcement learning agent. By
guiding the agent to meaningful states and actions, this weak supervision
improves the convergence during training and enhances the safety of the final
trained policy. This rule-based supervisory controller has the further
advantage of being fully interpretable, thereby enabling traditional validation
and verification approaches to ensure the safety of the vehicle. We compare
models with and without safety cages, as well as models with optimal and
constrained model parameters, and show that the weak supervision consistently
improves the safety of exploration, speed of convergence, and model
performance. Additionally, we show that when the model parameters are
constrained or sub-optimal, the safety cages can enable a model to learn a safe
driving policy even when the model could not be trained to drive through
reinforcement learning alone.


Abstract:
  In this article, we consider the class of 2-Calabi-Yau tilted algebras that
are defined by a quiver with potential whose dual graph is a tree. We call
these algebras \emph{dimer tree algebras} because they can also be realized as
quotients of dimer algebras on a disc. These algebras are wild in general. For
every such algebra $B$, we construct a polygon $\mathcal{S}$ with a
checkerboard pattern in its interior that gives rise to a category
$\text{Diag}(\mathcal{S})$. The indecomposable objects of
$\text{Diag}(\mathcal{S})$ are the 2-diagonals in $\mathcal{S}$, and its
morphisms are given by certain pivoting moves between the 2-diagonals. We
conjecture that the category $\text{Diag}(\mathcal{S})$ is equivalent to the
stable syzygy category over the algebra $B$, such that the rotation of the
polygon corresponds to the shift functor on the syzygies. In particular, the
number of indecomposable syzygies is finite and the projective resolutions are
periodic. We prove the conjecture in the special case where every chordless
cycle in the quiver is of length three.
  As a consequence, we obtain an explicit description of the projective
resolutions. Moreover, we show that the syzygy category is equivalent to the
2-cluster category of type $\mathbb{A}$, and we introduce a new derived
invariant for the algebra $B$ that can be read off easily from the quiver.


Abstract:
  Transformer language models have shown remarkable ability in detecting when a
word is anomalous in context, but likelihood scores offer no information about
the cause of the anomaly. In this work, we use Gaussian models for density
estimation at intermediate layers of three language models (BERT, RoBERTa, and
XLNet), and evaluate our method on BLiMP, a grammaticality judgement benchmark.
In lower layers, surprisal is highly correlated to low token frequency, but
this correlation diminishes in upper layers. Next, we gather datasets of
morphosyntactic, semantic, and commonsense anomalies from psycholinguistic
studies; we find that the best performing model RoBERTa exhibits surprisal in
earlier layers when the anomaly is morphosyntactic than when it is semantic,
while commonsense anomalies do not exhibit surprisal at any intermediate layer.
These results suggest that language models employ separate mechanisms to detect
different types of linguistic anomalies.


Abstract:
  We search for a first-order phase transition gravitational wave signal in 45
pulsars from the NANOGrav 12.5 year dataset. We find that the data can be
explained in terms of a strong first order phase transition taking place at
temperatures below the electroweak scale. In our search, we find that the
signal from a first order phase transition is degenerate with that generated by
Supermassive Black Hole Binary mergers. An interesting open question is how
well gravitational wave observatories could separate such signals.


Abstract:
  The paper is a continuation of arXiv:2012.10364, where the approach was
developed to constructing the exact matrix model for any generalized Ising
system, and such model was constructed for certain 2d system. In this paper,
the properties of the model are specified for light block diagonalization. A
corresponding example is considered. For the example, general exact partition
function is obtained and analysed. The analysis shows that the free energy does
not depend on the amount of rows with a large amount of cells. For the example
with light boundary conditions, the partition function is obtained and the
specific free energy per spin is plotted.


Abstract:
  We consider the problem of interpretable network representation learning for
samples of network-valued data. We propose the Principal Component Analysis for
Networks (PCAN) algorithm to identify statistically meaningful low-dimensional
representations of a network sample via subgraph count statistics. The PCAN
procedure provides an interpretable framework for which one can readily
visualize, explore, and formulate predictive models for network samples. We
furthermore introduce a fast sampling-based algorithm, sPCAN, which is
significantly more computationally efficient than its counterpart, but still
enjoys advantages of interpretability. We investigate the relationship between
these two methods and analyze their large-sample properties under the common
regime where the sample of networks is a collection of kernel-based random
graphs. We show that under this regime, the embeddings of the sPCAN method
enjoy a central limit theorem and moreover that the population level embeddings
of PCAN and sPCAN are equivalent. We assess PCAN's ability to visualize,
cluster, and classify observations in network samples arising in nature,
including functional connectivity network samples and dynamic networks
describing the political co-voting habits of the U.S. Senate. Our analyses
reveal that our proposed algorithm provides informative and discriminatory
features describing the networks in each sample. The PCAN and sPCAN methods
build on the current literature of network representation learning and set the
stage for a new line of research in interpretable learning on network-valued
data. Publicly available software for the PCAN and sPCAN methods are available
at https://www.github.com/jihuilee/.


Abstract:
  Real-time visual localization of needles is necessary for various surgical
applications, including surgical automation and visual feedback. In this study
we investigate localization and autonomous robotic control of needles in the
context of our magneto-suturing system. Our system holds the potential for
surgical manipulation with the benefit of minimal invasiveness and reduced
patient side effects. However, the non-linear magnetic fields produce
unintuitive forces and demand delicate position-based control that exceeds the
capabilities of direct human manipulation. This makes automatic needle
localization a necessity. Our localization method combines neural network-based
segmentation and classical techniques, and we are able to consistently locate
our needle with 0.73 mm RMS error in clean environments and 2.72 mm RMS error
in challenging environments with blood and occlusion. The average localization
RMS error is 2.16 mm for all environments we used in the experiments. We
combine this localization method with our closed-loop feedback control system
to demonstrate the further applicability of localization to autonomous control.
Our needle is able to follow a running suture path in (1) no blood, no tissue;
(2) heavy blood, no tissue; (3) no blood, with tissue; and (4) heavy blood,
with tissue environments. The tip position tracking error ranges from 2.6 mm to
3.7 mm RMS, opening the door towards autonomous suturing tasks.


Abstract:
  We consider the propagation of acoustic waves in a 2D waveguide unbounded in
one direction and containing a compact obstacle. The wavenumber is fixed so
that only one mode can propagate. The goal of this work is to propose a method
to cloak the obstacle. More precisely, we add to the geometry thin outer
resonators of width $\varepsilon$ and we explain how to choose their positions
as well as their lengths to get a transmission coefficient approximately equal
to one as if there were no obstacle. In the process we also investigate several
related problems. In particular, we explain how to get zero transmission and
how to design phase shifters. The approach is based on asymptotic analysis in
presence of thin resonators. An essential point is that we work around
resonance lengths of the resonators. This allows us to obtain effects of order
one with geometrical perturbations of width $\varepsilon$. Various numerical
experiments illustrate the theory.


Abstract:
  Starting from an anti-symplectic involution on a K3 surface, one can consider
a natural Lagrangian subvariety inside the moduli space of sheaves over the K3.
One can also construct a Prymian integrable system following a construction of
Markushevich--Tikhomirov, extended by Arbarello--Sacc\`a--Ferretti, Matteini
and Sawon--Chen. In this article we address a question of Sawon, showing that
these integrable systems and their associated natural Lagrangians degenerate,
respectively, into fix loci of involutions considered by Heller--Schaposnik,
Garcia-Prada--Wilkins and Basu--Garcia-Prada.
  Along the way we find interesting results such as the proof that the
Donagi--Ein--Lazarsfeled degeneration is a degeneration of symplectic
varieties, a generalization of this degeneration, originally described for K3
surfaces, to the case of an arbitrary smooth projective surface, and a
description of the behaviour of certain involutions under this degeneration.


Abstract:
  Earthquakes are lethal and costly. This study aims at avoiding these
catastrophic events by the application of injection policies retrieved through
reinforcement learning. With the rapid growth of artificial intelligence,
prediction-control problems are all the more tackled by function approximation
models that learn how to control a specific task, even for systems with
unmodeled/unknown dynamics and important uncertainties. Here, we show for the
first time the possibility of controlling earthquake-like instabilities using
state-of-the-art deep reinforcement learning techniques. The controller is
trained using a reduced model of the physical system, i.e, the spring-slider
model, which embodies the main dynamics of the physical problem for a given
earthquake magnitude. Its robustness to unmodeled dynamics is explored through
a parametric study. Our study is a first step towards minimizing seismicity in
industrial projects (geothermal energy, hydrocarbons production, CO2
sequestration) while, in a second step for inspiring techniques for natural
earthquakes control and prevention.


Abstract:
  In this work we study the decidability of the global modal logic arising from
Kripke frames evaluated on certain residuated lattices (including all BL
algebras), known in the literature as crisp modal many-valued logics. We
exhibit a large family of these modal logics that are undecidable, in
opposition to classical modal logic and to the propositional logics defined
over the same classes of algebras. These include the global modal logics
arising from the standard Lukasiewicz and Product algebras. Furthermore, it is
shown that global modal Lukasiewicz and Product logics are not recursively
axiomatizable. We conclude the paper by solving negatively the open question of
whether a global modal logic coincides with the local modal logic closed under
the unrestricted necessitation rule.


Abstract:
  We study a variant of online convex optimization where the player is
permitted to switch decisions at most $S$ times in expectation throughout $T$
rounds. Similar problems have been addressed in prior work for the discrete
decision set setting, and more recently in the continuous setting but only with
an adaptive adversary. In this work, we aim to fill the gap and present
computationally efficient algorithms in the more prevalent oblivious setting,
establishing a regret bound of $O(T/S)$ for general convex losses and
$\widetilde O(T/S^2)$ for strongly convex losses. In addition, for stochastic
i.i.d.~losses, we present a simple algorithm that performs $\log T$ switches
with only a multiplicative $\log T$ factor overhead in its regret in both the
general and strongly convex settings. Finally, we complement our algorithms
with lower bounds that match our upper bounds in some of the cases we consider.


Abstract:
  Forecasting financial time series is considered to be a difficult task due to
the chaotic feature of the series. Statistical approaches have shown solid
results in some specific problems such as predicting market direction and
single-price of stocks; however, with the recent advances in deep learning and
big data techniques, new promising options have arises to tackle financial time
series forecasting. Moreover, recent literature has shown that employing a
combination of statistics and machine learning may improve accuracy in the
forecasts in comparison to single solutions. Taking into consideration the
mentioned aspects, in this work, we proposed the MegazordNet, a framework that
explores statistical features within a financial series combined with a
structured deep learning model for time series forecasting. We evaluated our
approach predicting the closing price of stocks in the S&P 500 using different
metrics, and we were able to beat single statistical and machine learning
methods.


Abstract:
  We describe an algorithm for computing a $\Q$-rational model for the quotient
of a modular curve by an automorphism group, under mild assumptions on the
curve and the automorphisms, by determining $q$-expansions for a basis of the
corresponding space of cusp forms. We also give a moduli interpretation for
general morphisms between modular curves.


Abstract:
  We show that, given an almost-source algebra $A$ of a $p$-block of a finite
group $G$, then the unit group of $A$ contains a basis stabilized by the left
and right multiplicative action of the defect group if and only if, in a sense
to be made precise, certain relative multiplicities of local pointed groups are
invariant with respect to the fusion system. We also show that, when $G$ is
$p$-solvable, those two equivalent conditions hold for some almost-source
algebra of the given $p$-block. One motive lies in the fact that, by a theorem
of Linckelmann, if the two equivalent conditions hold for $A$, then any stable
basis for $A$ is semicharacteristic for the fusion system.


Abstract:
  Sound Source Localization (SSL) are used to estimate the position of sound
sources. Various methods have been used for detecting sound and its
localization. This paper presents a system for stationary sound source
localization by cubical microphone array consisting of eight microphones placed
on four vertical adjacent faces which is mounted on three wheel
omni-directional drive for the inspection and monitoring of the disaster
victims in disaster areas. The proposed method localizes sound source on a 3D
space by grid search method using Generalized Cross Correlation Phase Transform
(GCC-PHAT) which is robust when operating in real life scenario where there is
lack of visibility. The computed azimuth and elevation angle of victimized
human voice are fed to embedded omni-directional drive system which navigates
the vehicle automatically towards the stationary sound source.


Abstract:
  To any $k$-dimensional subspace of $\mathbb Q^n$ one can naturally associate
a point in the Grassmannian ${\rm Gr}_{n,k}(\mathbb R)$ and two shapes of
lattices of rank $k$ and $n-k$ respectively. These lattices originate by
intersecting the $k$-dimensional subspace with the lattice $\mathbb Z^n$. Using
unipotent dynamics we prove simultaneous equidistribution of all of these
objects under a congruence conditions when $(k,n) \neq (2,4)$.


Abstract:
  We find an explicit presentation of relative linear Steinberg groups
$\mathrm{St}(n, R, I)$ for any ring $R$ and $n \geq 4$ by generators and
relations as abstract groups. We also prove a similar result for relative
simply laced Steinberg groups $\mathrm{St}(\Phi; R, I)$ for commutative $R$ and
$\Phi \in \{\mathsf A_\ell, \mathsf D_\ell, \mathsf E_\ell \mid \ell \geq 3\}$.


Abstract:
  Soft electronics are a promising and revolutionary alternative for
traditional electronics when safe physical interaction between machines and the
human body is required. Among various materials architectures developed for
producing soft and stretchable electronics, Liquid-Metal Embedded Elastomers
(LMEEs), which contain Ga-based inclusions as a conductive phase, has drawn
considerable attention in various emerging fields such as wearable computing
and bio-inspired robotics. This is because LMEEs exhibit a unique combination
of desirable mechanical, electrical, and thermal properties. For instance,
these so-called multifunctional materials can undergo large deformations as
high as 600% strain without losing their electrical conductivity. Moreover, the
desperation of conductive liquid-metal inclusions within the entire medium of
an elastomer makes it possible to fabricate autonomously self-healing circuits
that maintain their electrical functionality after extreme mechanical damage
induction. The electrically self-healing property is of great importance for
further progress in autonomous soft robotics, where materials are subjected to
various modes of mechanical damage such as tearing. In this short review, we
review the fundamental characteristics of LMEEs, their advantages over other
conductive composites, materials used in LMMEs, their preparation and
activation process, and the fabrication process of self-healing circuits.
Additionally, we will review the soft-lithography-enabled techniques for
liquid-metal pattering.


Abstract:
  The research in anomaly detection lacks a unified definition of what
represents an anomalous instance. Discrepancies in the nature itself of an
anomaly lead to multiple paradigms of algorithms design and experimentation.
Predictive maintenance is a special case, where the anomaly represents a
failure that must be prevented. Related time-series research as outlier and
novelty detection or time-series classification does not apply to the concept
of an anomaly in this field, because they are not single points which have not
been seen previously and may not be precisely annotated. Moreover, due to the
lack of annotated anomalous data, many benchmarks are adapted from supervised
scenarios.
  To address these issues, we generalise the concept of positive and negative
instances to intervals to be able to evaluate unsupervised anomaly detection
algorithms. We also preserve the imbalance scheme for evaluation through the
proposal of the Preceding Window ROC, a generalisation for the calculation of
ROC curves for time-series scenarios. We also adapt the mechanism from a
established time-series anomaly detection benchmark to the proposed
generalisations to reward early detection. Therefore, the proposal represents a
flexible evaluation framework for the different scenarios. To show the
usefulness of this definition, we include a case study of Big Data algorithms
with a real-world time-series problem provided by the company ArcelorMittal,
and compare the proposal with an evaluation method.


Abstract:
  For any graph $G$ of order $p$, a bijection $f: V(G)\to [1,p]$ is called a
numbering of the graph $G$ of order $p$. The strength $str_f(G)$ of a numbering
$f: V(G)\to [1,p]$ of $G$ is defined by $str_f(G) = \max\{f(u)+f(v)\; |\; uv\in
E(G)\},$ and the strength $str(G)$ of a graph $G$ itself is $str(G) =
\min\{str_f(G)\;|\; f \mbox{ is a numbering of } G\}.$ A numbering $f$ is
called a strength labeling of $G$ if $str_f(G)=str(G)$. In this paper, we
obtained a sufficient condition for a graph to have $str(G)=|V(G)|+\d(G)$.
Consequently, many questions raised in [Bounds for the strength of graphs, {\it
Aust. J. Combin.} {\bf72(3)}, (2018) 492--508] and [On the strength of some
trees, {\it AKCE Int. J. Graphs Comb.} (Online 2019)
doi.org/10.1016/j.akcej.2019.06.002] are solved. Moreover, we showed that every
graph $G$ either has $str(G)=|V(G)|+\d(G)$ or is a proper subgraph of a graph
$H$ that has $str(H) = |V(H)| + \d(H)$ with $\d(H)=\d(G)$. Further, new good
lower bounds of $str(G)$ are also obtained. Using these, we determined the
strength of 2-regular graphs and obtained new lower bounds of $str(Q_n)$ for
various $n$, where $Q_n$ is the $n$-regular hypercube.


Abstract:
  Today's intelligent applications can achieve high performance accuracy using
machine learning (ML) techniques, such as deep neural networks (DNNs).
Traditionally, in a remote DNN inference problem, an edge device transmits raw
data to a remote node that performs the inference task. However, this may incur
high transmission energy costs and puts data privacy at risk. In this paper, we
propose a technique to reduce the total energy bill at the edge device by
utilizing model compression and time-varying model split between the edge and
remote nodes. The time-varying representation accounts for time-varying
channels and can significantly reduce the total energy at the edge device while
maintaining high accuracy (low loss). We implement our approach in an image
classification task using the MNIST dataset, and the system environment is
simulated as a trajectory navigation scenario to emulate different channel
conditions. Numerical simulations show that our proposed solution results in
minimal energy consumption and $CO_2$ emission compared to the considered
baselines while exhibiting robust performance across different channel
conditions and bandwidth regime choices.


Abstract:
  Neural networks-based learning of the distribution of non-dispatchable
renewable electricity generation from sources such as photovoltaics (PV) and
wind as well as load demands has recently gained attention. Normalizing flow
density models are particularly well suited for this task due to the training
through direct log-likelihood maximization. However, research from the field of
image generation has shown that standard normalizing flows can only learn
smeared-out versions of manifold distributions. Previous works on normalizing
flow-based scenario generation do not address this issue, and the smeared-out
distributions result in the sampling of noisy time series. In this paper, we
propose reducing the dimensionality through principal component analysis (PCA),
which sets up the normalizing flow in a lower-dimensional space while
maintaining the direct and computationally efficient likelihood maximization.
We train the resulting principal component flow (PCF) on data of PV and wind
power generation as well as load demand in Germany in the years 2013 to 2015.
The results of this investigation show that the PCF preserves critical features
of the original distributions, such as the probability density and frequency
behavior of the time series. The application of the PCF is, however, not
limited to renewable power generation but rather extends to any data set, time
series, or otherwise, which can be efficiently reduced using PCA.


Abstract:
  Cosmic rays interacting with the atmosphere result in a flux of secondary
particles including muons and electrons. Atmospheric ray tomography (ART) uses
the muons and electrons for detecting objects and their composition. This paper
presents new methods and a proof-of-concept tomography system developed for the
ART of low-Z materials. We introduce the Particle Track Filtering (PTF) and
Multi-Modality Tomographic Reconstruction (MMTR) methods. Based on Geant4
models we optimized the tomography system, the parameters of PTF and MMTR.
Based on plastic scintillating fiber arrays we achieved the spatial resolution
120 $\mu$m and 1 mrad angular resolution in the track reconstruction. We
developed a novel edge detection method to separate the logical volumes of
scanned object. We show its effectiveness on single (e.g. water, aluminum) and
double material (e.g. explosive RDX in flesh) objects. The tabletop tomograph
we built showed excellent agreement between simulations and measurements. We
are able to increase the discriminating power of ART on low-Z materials
significantly. This work opens up new routes for the commercialization of ART
tomography.


Abstract:
  Stochastic gradient descent (SGD) has become the most attractive optimization
method in training large-scale deep neural networks due to its simplicity, low
computational cost in each updating step, and good performance. Standard excess
risk bounds show that SGD only needs to take one pass over the training data
and more passes could not help to improve the performance. Empirically, it has
been observed that SGD taking more than one pass over the training data
(multi-pass SGD) has much better excess risk bound performance than the SGD
only taking one pass over the training data (one-pass SGD). However, it is not
very clear that how to explain this phenomenon in theory. In this paper, we
provide some theoretical evidences for explaining why multiple passes over the
training data can help improve performance under certain circumstance.
Specifically, we consider smooth risk minimization problems whose objective
function is non-convex least squared loss. Under Polyak-Lojasiewicz (PL)
condition, we establish faster convergence rate of excess risk bound for
multi-pass SGD than that for one-pass SGD.


Abstract:
  Two-dimensional magnetic skyrmions are particle-like magnetic domains in
magnetic thin films. The kinetic property of the magnetic skyrmions at finite
temperature is well described by the Thiele equation, including a stochastic
field and a finite mass. In this paper, the validity of the constant-mass
approximation is examined by comparing the Fourier spectrum of Brownian motions
described by the Thiele equation and the Landau-Lifshitz-Gilbert equation.
Then, the 4-dimensional Fokker-Planck equation is derived from the Thiele
equation with a mass-term. Consequently, an expression of the diffusion flow
and diffusion constant in a tensor form is derived, extending Chandrasekhar's
method for Thiele dynamics.


Abstract:
  Nano Electro Mechanical (NEM) contact switches have been widely studied as
one of the alternative for classical field effect transistor (FET). An ideal
NEM contact switch with hysteresis free switching slope (SS) of 0 mV/dec is
desired to achieve the ultimate scaling of the complementary metal oxide
semiconductor (CMOS) integrated circuits (IC) but never realized. Here we show,
low pull-in voltage, hysteresis free graphene based NEM contact switch with hBN
as a contact larger. The hysteresis voltage is greatly reduced by exploiting
the weak adhesion energy between the graphene and hexagonal boron nitride
(hBN). The graphene NEM contact switch with hBN as contact exhibits low pull-in
voltage of < 2 V, high contact life time of more than 6x10^4 switching cycles,
ON/OFF ratio of 10^4 orders of magnitude and hysteresis voltage of as small as
< 0.1 V. Our G-hBN NEM contact switch can be potentially used in ultra-low
power energy efficient CMOS IC's.


Abstract:
  In recent work [arXiv:2003.06939v2] a novel fermion to qubit mapping --
called the compact encoding -- was introduced which outperforms all previous
local mappings in both the qubit to mode ratio, and the locality of mapped
operators. There the encoding was demonstrated for square and hexagonal
lattices. Here we present an extension of that work by illustrating how to
apply the compact encoding to other regular lattices. We give constructions for
variants of the compact encoding on all regular tilings with maximum degree 4.
These constructions yield edge operators with Pauli weight at most 3 and use
fewer than 1.67 qubits per fermionic mode. Additionally we demonstrate how the
compact encoding may be applied to a cubic lattice, yielding edge operators
with Pauli weight no greater than 4 and using approximately 2.5 qubits per
mode. In order to properly analyse the compact encoding on these lattices a
more general group theoretic framework is required, which we elaborate upon in
this work. We expect this framework to find use in the design of fermion to
qubit mappings more generally.


Abstract:
  We propose and demonstrate a method to characterize a gated InGaAs
single-photon detector (SPD). Ultrashort weak coherent pulses, from a
mode-locked sub-picosecond pulsed laser, were used to measure photon counts, at
varying arrival times relative to the start of the SPD gate voltage. The uneven
detection probabilities within the gate window were used to estimate the
afterpulse probability with respect to various detector parameters: excess
bias, width of gate window and hold-off time. We estimated a lifetime of 2.1
microseconds for the half-life of trapped carriers, using a power-law fit to
the decay in afterpulse probability. Finally, we quantify the timing jitter of
the SPD using a time to digital converter with a resolution of 55 ps.


Abstract:
  A review is made of the field of contextuality in quantum mechanics. We study
the historical emergence of the concept from philosophical and logical issues.
We present and compare the main theoretical frameworks that have been derived.
Finally, we focus on the complex task of establishing experimental tests of
contextuality. Throughout this work, we try to show that the conceptualisation
of contextuality has progressed through different complementary perspectives,
before summoning them together to analyse the signification of contextuality
experiments. Doing so, we argue that contextuality emerged as a discrete
logical problem and developed into a quantifiable quantum resource.


Abstract:
  High-performance hybrid automatic speech recognition (ASR) systems are often
trained with clustered triphone outputs, and thus require a complex training
pipeline to generate the clustering. The same complex pipeline is often
utilized in order to generate an alignment for use in frame-wise cross-entropy
training. In this work, we propose a flat-start factored hybrid model trained
by modeling the full set of triphone states explicitly without relying on
clustering methods. This greatly simplifies the training of new models.
Furthermore, we study the effect of different alignments used for Viterbi
training. Our proposed models achieve competitive performance on the
Switchboard task compared to systems using clustered triphones and other
flat-start models in the literature.


Abstract:
  Motivated by M-theory, we study rank n K-theoretic Donaldson-Thomas theory on
a toric threefold X. In the presence of compact four-cycles, we discuss how to
include the contribution of D4-branes wrapping them. Combining this with a
simple assumption on the (in)dependence on Coulomb moduli in the 7d theory, we
show that the partition function factorizes and, when X is Calabi-Yau and it
admits an ADE ruling, it reproduces the 5d master formula for the geometrically
engineered theory on A(n-1) ALE space, thus extending the usual geometric
engineering dictionary to n>1. We finally speculate about implications for
instanton counting on Taub-NUT.


Abstract:
  We consider a finite abelian group $M$ of odd exponent $n$ with a symplectic
form $\omega: M\times M\to \mu_n$ and the Heisenberg extension $1\to \mu_n\to
H\to M\to 1$ with the commutator $\omega$. According to the Stone - von Neumann
theorem, $H$ admits an irreducible representation with the tautological central
character (defined up to a non-unique isomorphism). We construct such
irreducible representation of $H$ defined up to a unique isomorphism, so
canonical in this sense.


Abstract:
  In this paper, we study communication-efficient distributed stochastic
gradient descent (SGD) with data sets of users distributed over a certain area
and communicating through wireless channels. Since the time for one iteration
in the proposed approach is independent of the number of users, it is
well-suited to scalable distributed SGD. Furthermore, since the proposed
approach is based on preamble-based random access, which is widely adopted for
machine-type communication (MTC), it can be easily employed for training models
with a large number of devices in various Internet-of-Things (IoT) applications
where MTC is used for their connectivity. For fading channel, we show that
noncoherent combining can be used. As a result, no channel state information
(CSI) estimation is required. From analysis and simulation results, we can
confirm that the proposed approach is not only scalable, but also provides
improved performance as the number of devices increases.


Abstract:
  Using a systematic, symmetry-preserving continuum approach to the Standard
Model strong-interaction bound-state problem, we deliver parameter-free
predictions for all semileptonic $B_c \to \eta_c, J/\psi$ transition form
factors on the complete domains of empirically accessible momentum transfers.
Working with branching fractions calculated therefrom, the following values of
the ratios for $\tau$ over $\mu$ final states are obtained:
$R_{\eta_c}=0.313(22)$ and $R_{J/\psi}=0.242(47)$. Combined with other recent
results, our analysis confirms a $2\sigma$ discrepancy between the Standard
Model prediction for $R_{J/\psi}$ and the single available experimental result.


Abstract:
  Session-based recommendation aims to predict user the next action based on
historical behaviors in an anonymous session. For better recommendations, it is
vital to capture user preferences as well as their dynamics. Besides, user
preferences evolve over time dynamically and each preference has its own
evolving track. However, most previous works neglect the evolving trend of
preferences and can be easily disturbed by the effect of preference drifting.
In this paper, we propose a novel Preference Evolution Networks for
session-based Recommendation (PEN4Rec) to model preference evolving process by
a two-stage retrieval from historical contexts. Specifically, the first-stage
process integrates relevant behaviors according to recent items. Then, the
second-stage process models the preference evolving trajectory over time
dynamically and infer rich preferences. The process can strengthen the effect
of relevant sequential behaviors during the preference evolution and weaken the
disturbance from preference drifting. Extensive experiments on three public
datasets demonstrate the effectiveness and superiority of the proposed model.


Abstract:
  The objective of this work is to localize sound sources that are visible in a
video without using manual annotations. Our key technical contribution is to
show that, by training the network to explicitly discriminate challenging image
fragments, even for images that do contain the object emitting the sound, we
can significantly boost the localization performance. We do so elegantly by
introducing a mechanism to mine hard samples and add them to a contrastive
learning formulation automatically. We show that our algorithm achieves
state-of-the-art performance on the popular Flickr SoundNet dataset.
Furthermore, we introduce the VGG-Sound Source (VGG-SS) benchmark, a new set of
annotations for the recently-introduced VGG-Sound dataset, where the sound
sources visible in each video clip are explicitly marked with bounding box
annotations. This dataset is 20 times larger than analogous existing ones,
contains 5K videos spanning over 200 categories, and, differently from Flickr
SoundNet, is video-based. On VGG-SS, we also show that our algorithm achieves
state-of-the-art performance against several baselines.


Abstract:
  In this paper, several weighted summation formulas of $q$-hyperharmonic
numbers are derived. As special cases, several formulas of hyperharmonic
numbers of type $\sum_{\ell=1}^{n} {\ell}^{p} H_{\ell}^{(r)}$ and
$\sum_{\ell=0}^{n} {\ell}^{p} H_{n-\ell}^{(r)}$ are obtained.


Abstract:
  Estimating 3D bounding boxes from monocular images is an essential component
in autonomous driving, while accurate 3D object detection from this kind of
data is very challenging. In this work, by intensive diagnosis experiments, we
quantify the impact introduced by each sub-task and found the `localization
error' is the vital factor in restricting monocular 3D detection. Besides, we
also investigate the underlying reasons behind localization errors, analyze the
issues they might bring, and propose three strategies. First, we revisit the
misalignment between the center of the 2D bounding box and the projected center
of the 3D object, which is a vital factor leading to low localization accuracy.
Second, we observe that accurately localizing distant objects with existing
technologies is almost impossible, while those samples will mislead the learned
network. To this end, we propose to remove such samples from the training set
for improving the overall performance of the detector. Lastly, we also propose
a novel 3D IoU oriented loss for the size estimation of the object, which is
not affected by `localization error'. We conduct extensive experiments on the
KITTI dataset, where the proposed method achieves real-time detection and
outperforms previous methods by a large margin. The code will be made available
at: https://github.com/xinzhuma/monodle.


Abstract:
  We introduce and analyze a space-time hybridized discontinuous Galerkin
method for the evolutionary Navier--Stokes equations. Key features of the
numerical scheme include point-wise mass conservation, energy stability, and
pressure robustness. We prove that there exists a solution to the resulting
nonlinear algebraic system in two and three spatial dimensions, and that this
solution is unique in two spatial dimensions under a small data assumption. A
priori error estimates are derived for the velocity in a mesh-dependent energy
norm.


Abstract:
  Modeling a crystal as a periodic point set, we present a fingerprint
consisting of density functions that facilitates the efficient search for new
materials and material properties. We prove invariance under isometries,
continuity, and completeness in the generic case, which are necessary features
for the reliable comparison of crystals. The proof of continuity integrates
methods from discrete geometry and lattice theory, while the proof of generic
completeness combines techniques from geometry with analysis. The fingerprint
has a fast algorithm based on Brillouin zones and related inclusion-exclusion
formulae. We have implemented the algorithm and describe its application to
crystal structure prediction.


Abstract:
  The decomposition of the overall effect of a treatment into direct and
indirect effects is here investigated with reference to a recursive system of
binary random variables. We show how, for the single mediator context, the
marginal effect measured on the log odds scale can be written as the sum of the
indirect and direct effects plus a residual term that vanishes under some
specific conditions. We then extend our definitions to situations involving
multiple mediators and address research questions concerning the decomposition
of the total effect when some mediators on the pathway from the treatment to
the outcome are marginalized over. Connections to the counterfactual
definitions of the effects are also made. Data coming from an encouragement
design on students' attitude to visit museums in Florence, Italy, are
reanalyzed. The estimates of the defined quantities are reported together with
their standard errors to compute p-values and form confidence intervals.


Abstract:
  Room temperature two-dimensional (2D) ferromagnetism is highly desired in
practical spintronics applications. Recently, 1T phase CrTe2 (1T-CrTe2)
nanosheets with five and thicker layers have been successfully synthesized,
which all exhibit the properties of ferromagnetic (FM) metals with Curie
temperatures around 305 K. However, whether the ferromagnetism therein can be
maintained when continuously reducing the nanosheet's thickness to monolayer
limit remains unknown. Here, through first-principles calculations, we explore
the evolution of magnetic properties of 1 to 6 layers CrTe2 nanosheets and
several interesting points are found: First, unexpectedly, monolayer CrTe2
prefers a zigzag antiferromagnetic (AFM) state with its energy much lower than
that of FM state. Second, in 2 to 4 layers CrTe2, both the intralayer and
interlayer magnetic coupling are AFM. Last, when the number of layers is equal
to or greater than five, the intralayer and interlayer magnetic coupling become
FM. Theoretical analysis reveals that the in-plane lattice contraction of few
layer CrTe2 compared to bulk is the main factor producing intralayer AFM-FM
magnetic transition. At the same time, as long as the intralayer coupling gets
FM, the interlayer coupling will concomitantly switch from AFM to FM. Such
highly thickness dependent magnetism provides a new perspective to control the
magnetic properties of 2D materials.


Abstract:
  Whereas the Si photonic platform is highly attractive for scalable optical
quantum information processing, it lacks practical solutions for efficient
photon generation. Self-assembled semiconductor quantum dots (QDs) efficiently
emitting photons in the telecom bands ($1460-1625$ nm) allow for heterogeneous
integration with Si. In this work, we report on a novel, robust, and
industry-compatible approach for achieving single-photon emission from InAs/InP
QDs heterogeneously integrated with a Si substrate. As a proof of concept, we
demonstrate a simple vertical emitting device, employing a metallic mirror
beneath the QD emitter, and experimentally obtained photon extraction
efficiencies of $\sim10\%$. Nevertheless, the figures of merit of our
structures are comparable with values previously only achieved for QDs emitting
at shorter wavelength or by applying technically demanding fabrication
processes. Our architecture and the simple fabrication procedure allows for the
demonstration of a single-photon generation with purity $\mathcal{P}>98\%$ at
the liquid helium temperature and $\mathcal{P}=75\%$ at $80$ K.


Abstract:
  Crowd-sourced traffic data offer great promise in environmental modeling.
However, archives of such traffic data are typically not made available for
research; instead, the data must be acquired in real time. The objective of
this paper is to present methods we developed for acquiring and analyzing time
series of real-time crowd-sourced traffic data. We present scripts, which can
be run in Unix/Linux like computational environments, to automatically download
tiles of crowd-sourced Google traffic congestion maps for a user-specifiable
region of interest. Broad and international applicability of our method is
demonstrated for Manhattan in New York City and Mexico City. We also
demonstrate that Google traffic data can be used to quantify decreases in
traffic congestion due to social distancing policies implemented to curb the
COVID-19 pandemic in the South Bronx in New York City.


Abstract:
  Research has shown that Educational Robotics (ER) enhances student
performance, interest, engagement and collaboration. However, until now, the
adoption of robotics in formal education has remained relatively scarce. Among
other causes, this is due to the difficulty of determining the alignment of
educational robotic learning activities with the learning outcomes envisioned
by the curriculum, as well as their integration with traditional, non-robotics
learning activities that are well established in teachers' practices. This work
investigates the integration of ER into formal mathematics education, through a
quasi-experimental study employing the Thymio robot and Scratch programming to
teach geometry to two classes of 15-year-old students, for a total of 26
participants. Three research questions were addressed: (1) Should an ER-based
theoretical lecture precede, succeed or replace a traditional theoretical
lecture? (2) What is the students' perception of and engagement in the ER-based
lecture and exercises? (3) Do the findings differ according to students' prior
appreciation of mathematics? The results suggest that ER activities are as
valid as traditional ones in helping students grasp the relevant theoretical
concepts. Robotics activities seem particularly beneficial during exercise
sessions: students freely chose to do exercises that included the robot, rated
them as significantly more interesting and useful than their traditional
counterparts, and expressed their interest in introducing ER in other
mathematics lectures. Finally, results were generally consistent between the
students that like and did not like mathematics, suggesting the use of robotics
as a means to broaden the number of students engaged in the discipline.


Abstract:
  Recent studies on the analysis of the multilingual representations focus on
identifying whether there is an emergence of language-independent
representations, or whether a multilingual model partitions its weights among
different languages. While most of such work has been conducted in a
"black-box" manner, this paper aims to analyze individual components of a
multilingual neural translation (NMT) model. In particular, we look at the
encoder self-attention and encoder-decoder attention heads (in a many-to-one
NMT model) that are more specific to the translation of a certain language pair
than others by (1) employing metrics that quantify some aspects of the
attention weights such as "variance" or "confidence", and (2) systematically
ranking the importance of attention heads with respect to translation quality.
Experimental results show that surprisingly, the set of most important
attention heads are very similar across the language pairs and that it is
possible to remove nearly one-third of the less important heads without hurting
the translation quality greatly.


Abstract:
  The CMS experiment at the LHC has measured the differential cross sections of
Z bosons decaying to pairs of leptons, as functions of transverse momentum and
rapidity, in lead-lead collisions at a nucleon-nucleon center-of-mass energy of
5.02 TeV. The measured Z boson elliptic azimuthal anisotropy coefficient is
compatible with zero, showing that Z bosons do not experience significant
final-state interactions in the medium produced in the collision. Yields of Z
bosons are compared to Glauber model predictions and are found to deviate from
these expectations in peripheral collisions, indicating the presence of initial
collision geometry and centrality selection effects. The precision of the
measurement allows, for the first time, for a data-driven determination of the
nucleon-nucleon integrated luminosity as a function of lead-lead centrality,
thereby eliminating the need for its estimation based on a Glauber model.


Abstract:
  We study the statistical theory of offline reinforcement learning (RL) with
deep ReLU network function approximation. We analyze a variant of fitted-Q
iteration (FQI) algorithm under a new dynamic condition that we call Besov
dynamic closure, which encompasses the conditions from prior analyses for deep
neural network function approximation. Under Besov dynamic closure, we prove
that the FQI-type algorithm enjoys the sample complexity of
$\tilde{\mathcal{O}}\left( \kappa^{1 + d/\alpha} \cdot \epsilon^{-2 -
2d/\alpha} \right)$ where $\kappa$ is a distribution shift measure, $d$ is the
dimensionality of the state-action space, $\alpha$ is the (possibly fractional)
smoothness parameter of the underlying MDP, and $\epsilon$ is a user-specified
precision. This is an improvement over the sample complexity of
$\tilde{\mathcal{O}}\left( K \cdot \kappa^{2 + d/\alpha} \cdot \epsilon^{-2 -
d/\alpha} \right)$ in the prior result [Yang et al., 2019] where $K$ is an
algorithmic iteration number which is arbitrarily large in practice.
Importantly, our sample complexity is obtained under the new general dynamic
condition and a data-dependent structure where the latter is either ignored in
prior algorithms or improperly handled by prior analyses. This is the first
comprehensive analysis for offline RL with deep ReLU network function
approximation under a general setting.


Abstract:
  As a means for testing whether a group of agents jointly maximize random
utility, we introduce the correlated random utility model. The correlated
random utility model asks that agents face correlated random draws of
preferences which govern their decisions. We study joint random utility
maximization through the lens of joint stochastic choice data (correlated
choice rule), a novel type of data to the stochastic choice framework. Key is
the property of marginality, which demands the independence of any given
agent's marginal choices from the budgets faced by the remaining agents.
Marginality permits the construction of well-defined marginal stochastic choice
functions. Marginality and non-negativity of an analogue of the Block-Marschak
polynomials characterize joint random utility maximization for small
environments. For larger environments, we offer an example of a correlated
choice rule establishing that each of the marginal stochastic choice rule may
be stochastically rational while the correlated choice rule is not.


Abstract:
  (abridged) Context. The origin of hot exozodiacal dust and its connection
with outer dust reservoirs remains unclear. Aims. We aim to explore the
possible connection between hot exozodiacal dust and warm dust reservoirs (>
100 K) in asteroid belts. Methods. We use precision near-infrared
interferometry with VLTI/PIONIER to search for resolved emission at H band
around a selected sample of nearby stars. Results. Our observations reveal the
presence of resolved near-infrared emission around 17 out of 52 stars, four of
which are shown to be due to a previously unknown stellar companion. The 13
other H-band excesses are thought to originate from the thermal emission of hot
dust grains. Taking into account earlier PIONIER observations, and after
reevaluating the warm dust content of all our PIONIER targets through spectral
energy distribution modeling, we find a detection rate of 17.1(+8.1)(-4.6)% for
H-band excess around main sequence stars hosting warm dust belts, which is
statistically compatible with the occurrence rate of 14.6(+4.3)(-2.8)% found
around stars showing no signs of warm dust. After correcting for the
sensitivity loss due to partly unresolved hot disks, under the assumption that
they are arranged in a thin ring around their sublimation radius, we however
find tentative evidence at the 3{\sigma} level that H-band excesses around
stars with outer dust reservoirs (warm or cold) could be statistically larger
than H-band excesses around stars with no detectable outer dust. Conclusions.
Our observations do not suggest a direct connection between warm and hot dust
populations, at the sensitivity level of the considered instruments, although
they bring to light a possible correlation between the level of H-band excesses
and the presence of outer dust reservoirs in general.


Abstract:
  We perform Brownian dynamics simulations of active stiff polymers undergoing
run-reverse dynamics, and so mimic bacterial swimming, in porous media. In
accord with recent experiments of \emph{Escherichia coli}, the polymer dynamics
are characterized by trapping phases interrupted by directed hopping motion
through the pores. We find that the effective translational diffusivities of
run-reverse agents can be enhanced up to two orders in magnitude, compared to
their non-reversing counterparts, and exhibit a non-monotonic behavior as a
function of the reversal rate, which we rationalize using a coarse-grained
model. Furthermore, we discover a geometric criterion for the optimal
spreading, which emerges when their run lengths are comparable to the longest
straight path available in the porous medium. More significantly, our criterion
unifies results for porous media with disparate pore sizes and shapes and thus
provides a fundamental principle for optimal transport of microorganisms and
cargo-carriers in densely-packed biological and environmental settings.


Abstract:
  In this paper, we study normal magnetic curves in $C$-manifolds. We prove
that magnetic trajectories with respect to the contact magnetic fields are
indeed $\theta_{\alpha }$-slant curves with certain curvature functions. Then,
we give the parametrizations of normal magnetic curves in $\mathbb{R}^{2n+s}$
with its structures as a $C$-manifold.


Abstract:
  The population protocol model describes a network of $n$ anonymous agents who
cannot control with whom they interact. The agents collectively solve some
computational problem through random pairwise interactions, each agent updating
its own state in response to seeing the state of the other agent. They are
equivalent to the model of chemical reaction networks, describing abstract
chemical reactions such as $A+B \rightarrow C+D$, when the latter is subject to
the restriction that all reactions have two reactants and two products, and all
rate constants are 1. The counting problem is that of designing a protocol so
that $n$ agents, all starting in the same state, eventually converge to states
where each agent encodes in its state an exact or approximate description of
population size $n$. In this survey paper, we describe recent algorithmic
advances on the counting problem.


Abstract:
  For mission-critical sensing and control applications such as those to be
enabled by 5G Ultra-Reliable, Low-Latency Communications (URLLC), it is
critical to ensure the communication quality of individual packets.
  Prior studies have considered Probabilistic Per-packet Real-time
Communications (PPRC) guarantees for single-cell, single-channel networks with
implicit deadline constraints, but they have not considered real-world
complexities such as inter-cell interference and multiple communication
channels.
  Towards ensuring PPRC in multi-cell, multi-channel wireless networks, we
propose a real-time scheduling algorithm based on
\emph{local-deadline-partition (LDP)}. The LDP algorithm is suitable for
distributed implementation, and it ensures probabilistic per-packet real-time
guarantee for multi-cell, multi-channel networks with general deadline
constraints. We also address the associated challenge of the schedulability
test of PPRC traffic. In particular, we propose the concept of \emph{feasible
set} and identify a closed-form sufficient condition for the schedulability of
PPRC traffic.
  We propose a distributed algorithm for the schedulability test, and the
algorithm includes a procedure for finding the minimum sum work density of
feasible sets which is of interest by itself. We also identify a necessary
condition for the schedulability of PPRC traffic, and use numerical studies to
understand a lower bound on the approximation ratio of the LDP algorithm.
  We experimentally study the properties of the LDP algorithm and observe that
the PPRC traffic supportable by the LDP algorithm is significantly higher than
that of a state-of-the-art algorithm.


Abstract:
  Reinforcement learning (RL)-based neural architecture search (NAS) generally
guarantees better convergence yet suffers from the requirement of huge
computational resources compared with gradient-based approaches, due to the
rollout bottleneck -- exhaustive training for each sampled generation on proxy
tasks. In this paper, we propose a general pipeline to accelerate the
convergence of the rollout process as well as the RL process in NAS. It is
motivated by the interesting observation that both the architecture and the
parameter knowledge can be transferred between different experiments and even
different tasks. We first introduce an uncertainty-aware critic (value
function) in Proximal Policy Optimization (PPO) to utilize the architecture
knowledge in previous experiments, which stabilizes the training process and
reduces the searching time by 4 times. Further, an architecture knowledge pool
together with a block similarity function is proposed to utilize parameter
knowledge and reduces the searching time by 2 times. It is the first to
introduce block-level weight sharing in RLbased NAS. The block similarity
function guarantees a 100% hitting ratio with strict fairness. Besides, we show
that a simply designed off-policy correction factor used in "replay buffer" in
RL optimization can further reduce half of the searching time. Experiments on
the Mobile Neural Architecture Search (MNAS) search space show the proposed
Fast Neural Architecture Search (FNAS) accelerates standard RL-based NAS
process by ~10x (e.g. ~256 2x2 TPUv2 x days / 20,000 GPU x hour -> 2,000 GPU x
hour for MNAS), and guarantees better performance on various vision tasks.


Abstract:
  This study creates a physiologically realistic virtual patient database
(VPD), representing the human arterial system, for the primary purpose of
studying the affects of arterial disease on haemodynamics. A low dimensional
representation of an anatomically detailed arterial network is outlined, and a
physiologically realistic posterior distribution for its parameters is
constructed through a Bayesian approach. This approach combines both
physiological/geometrical constraints and the available measurements reported
in the literature. A key contribution of this work is to present a framework
for including all such available information for the creation of virtual
patients (VPs). The Markov Chain Monte Carlo (MCMC) method is used to sample
random VPs from this posterior distribution, and the pressure and flow-rate
profiles associated with the VPs are computed through a model of pulse wave
propagation. This combination of the arterial network parameters (representing
the VPs) and the haemodynamics waveforms of pressure and flow-rates at various
locations (representing functional response of the VPs) makes up the VPD. While
75,000 VPs are sampled from the posterior distribution, 10,000 are discarded as
the initial burn-in period. A further 12,857 VPs are subsequently removed due
to the presence of negative average flow-rate. Due to an undesirable behaviour
observed in some VPs -- asymmetric under- and over-damped pressure and
flow-rate profiles in the left and right sides of the arterial system -- a
filter is proposed for their removal. The final VPD has 28,868 subjects. It is
shown that the methodology is appropriate by comparing the VPD statistics to
those reported in literature across real populations. A good agreement between
the two is found while respecting physiological/geometrical constraints. The
pre-filter database is made available at
https://doi.org/10.5281/zenodo.4549764.


Abstract:
  Stuttering is a complex speech disorder identified by repeti-tions,
prolongations of sounds, syllables or words and blockswhile speaking. Specific
stuttering behaviour differs strongly,thus needing personalized therapy.
Therapy sessions requirea high level of concentration by the therapist. We
introduceSTAN, a system to aid speech therapists in stuttering therapysessions.
Such an automated feedback system can lower thecognitive load on the therapist
and thereby enable a more con-sistent therapy as well as allowing analysis of
stuttering overthe span of multiple therapy sessions.


Abstract:
  We present a study of far and near-ultraviolet emission from the accretion
disk in a powerful Seyfert 1 galaxy IC4329A using observations performed with
the Ultraviolet Imaging Telescope (UVIT) onboard AstroSat. These data provide
the highest spatial resolution and deepest images of IC4329A in the far and
near UV bands acquired to date. The excellent spatial resolution of the UVIT
data has allowed us to accurately separate the extended emission from the host
galaxy and the AGN emission in the far and near UV bands. We derive the
intrinsic AGN flux after correcting for the Galactic and internal reddening, as
well as for the contribution of emission lines from the broad and narrow-line
regions. The intrinsic UV continuum emission shows a marked deficit compared to
that expected from the "standard" models of the accretion disk around an
estimated black hole mass of 1-2x10^8Msun when the disk extends to the
innermost stable circular orbit. We find that the intrinsic UV continuum is
fully consistent with the standard disk models, but only if the disk emits from
distances larger than 80-150 gravitational radii.


Abstract:
  Modern programming follows the continuous integration (CI) and continuous
deployment (CD) approach rather than the traditional waterfall model. Even the
development of modern programming languages uses the CI/CD approach to swiftly
provide new language features and to adapt to new development environments.
Unlike in the conventional approach, in the modern CI/CD approach, a language
specification is no more the oracle of the language semantics because both the
specification and its implementations can co-evolve. In this setting, both the
specification and implementations may have bugs, and guaranteeing their
correctness is non-trivial.
  In this paper, we propose a novel N+1-version differential testing to resolve
the problem. Unlike the traditional differential testing, our approach consists
of three steps: 1) to automatically synthesize programs guided by the syntax
and semantics from a given language specification, 2) to generate conformance
tests by injecting assertions to the synthesized programs to check their final
program states, 3) to detect bugs in the specification and implementations via
executing the conformance tests on multiple implementations, and 4) to localize
bugs on the specification using statistical information. We actualize our
approach for the JavaScript programming language via JEST, which performs
N+1-version differential testing for modern JavaScript engines and ECMAScript,
the language specification describing the syntax and semantics of JavaScript in
a natural language. We evaluated JEST with four JavaScript engines that support
all modern JavaScript language features and the latest version of ECMAScript
(ES11, 2020). JEST automatically synthesized 1,700 programs that covered 97.78%
of syntax and 87.70% of semantics from ES11. Using the assertion-injection, it
detected 44 engine bugs in four engines and 27 specification bugs in ES11.


Abstract:
  Wilson's theorem for the factorial got generalized to the moduli $p^2$ in
1900 and $p^3$ in 2000 by J.W.L. Glaisher and Z-H. Sun respectively. This paper
which studies more generally the multiple harmonic sums $\mathcal{H}_{\lbrace
s\rbrace^{2l}=1;p-1},2\leq 2l\leq p-1$ modulo $p^4$ in association with the
Stirling numbers $\left[\begin{array}{l}\;\;\;p\\2s-1\end{array}\right], 2\leq
2s\leq p-1$ modulo $p^4$ is concerned with establishing a generalization of
Wilson, Glaisher and Sun's results to the modulus $p^4$. We also break
p-residues of convolutions of three divided Bernoulli numbers of respective
orders $p-1$, $p-3$ and $p-5$ into smaller pieces and generalize some results
of Sun for some of the generalized harmonic numbers of order $p-1$ modulo
$p^4$.


Abstract:
  We address the two issues raised by Bayle, Vallisneri, Babak, and Petiteau
(in their gr-qc document arXiv:2106.03976) about our matrix formulation of
Time-Delay Interferometry (TDI) (arXiv:2105.02054) \cite{TDJ21}. In so doing we
explain and quantify our concerns about the results derived by Vallisneri,
Bayle, Babak and Petiteau \cite{Vallisneri2020} by applying their data
processing technique (named TDI-$\infty$) to the two heterodyne measurements
made by a two-arm space-based GW interferometer. First we show that the
solutions identified by the TDI-$\infty$ algorithm derived by Vallisneri,
Bayle, Babak and Petiteau \cite{Vallisneri2020} {\underbar {do}} depend on the
boundary-conditions selected for the two-way Doppler data. We prove this by
adopting the (non-physical) boundary conditions used by Vallisneri {\it et al.}
and deriving the corresponding analytic expression for a laser-noise-canceling
combination. We show it to be characterized by a number of Doppler measurement
terms that grows with the observation time and works for any time-dependent
time delays. We then prove that, for a constant-arm-length interferometer whose
two-way light times are equal to twice and three-times the sampling time, the
solutions identified by TDI-$\infty$ are linear combinations of the TDI
variable $X$. In the second part of this document we address the concern
expressed by Bayle {\it et al.} regarding our matrix formulation of TDI when
the two-way light-times are constant but not equal to integer multiples of the
sampling time. We mathematically prove the homomorphism between the delay
operators and their matrix representation \cite{TDJ21} holds in general. By
sequentially applying two order-$m$ Fractional-Delay (FD) Lagrange filters of
delays $l_1$, $l_2$ we find its result to be equal to applying an order-$m$ FD
Lagrange filter of delay $l_1 + l_2$.


Abstract:
  We propose a semantic similarity metric for image registration. Existing
metrics like Euclidean Distance or Normalized Cross-Correlation focus on
aligning intensity values, giving difficulties with low intensity contrast or
noise. Our approach learns dataset-specific features that drive the
optimization of a learning-based registration model. We train both an
unsupervised approach using an auto-encoder, and a semi-supervised approach
using supplemental segmentation data to extract semantic features for image
registration. Comparing to existing methods across multiple image modalities
and applications, we achieve consistently high registration accuracy. A learned
invariance to noise gives smoother transformations on low-quality images.


Abstract:
  This paper surveys several recent abstract summarization methods: T5,
Pegasus, and ProphetNet. We implement the systems in two languages: English and
Indonesian languages. We investigate the impact of pre-training models (one T5,
three Pegasuses, three ProphetNets) on several Wikipedia datasets in English
and Indonesian language and compare the results to the Wikipedia systems'
summaries. The T5-Large, the Pegasus-XSum, and the ProphetNet-CNNDM provide the
best summarization. The most significant factors that influence ROUGE
performance are coverage, density, and compression. The higher the scores, the
better the summary. Other factors that influence the ROUGE scores are the
pre-training goal, the dataset's characteristics, the dataset used for testing
the pre-trained model, and the cross-lingual function. Several suggestions to
improve this paper's limitation are: 1) assure that the dataset used for the
pre-training model must sufficiently large, contains adequate instances for
handling cross-lingual purpose; 2) Advanced process (finetuning) shall be
reasonable. We recommend using the large dataset consists of comprehensive
coverage of topics from many languages before implementing advanced processes
such as the train-infer-train procedure to the zero-shot translation in the
training stage of the pre-training model.


Abstract:
  We discuss $C^1$ regularity and developability of isometric immersions of
flat domains into $\mathbb R^3$ enjoying a local fractional Sobolev $W^{1+s,
\frac2s}$ regularity for $2/3 \le s< 1 $, generalizing the known results on
Sobolev and H\"older regimes. Ingredients of the proof include analysis of the
weak Codazzi-Mainardi equations of the isometric immersions and study of
$W^{2,\frac2s}$ planar deformations with symmetric Jacobian derivative and
vanishing distributional Jacobian determinant. On the way, we also show that
the distributional Jacobian determinant, conceived as an operator defined on
the Jacobian matrix, behaves like determinant of gradient matrices under
products by scalar functions.


Abstract:
  SPT-3G is the third survey receiver operating on the South Pole Telescope
dedicated to high-resolution observations of the cosmic microwave background
(CMB). Sensitive measurements of the temperature and polarization anisotropies
of the CMB provide a powerful dataset for constraining cosmology. Additionally,
CMB surveys with arcminute-scale resolution are capable of detecting galaxy
clusters, millimeter-wave bright galaxies, and a variety of transient
phenomena. The SPT-3G instrument provides a significant improvement in mapping
speed over its predecessors, SPT-SZ and SPTpol. The broadband optics design of
the instrument achieves a 430 mm diameter image plane across observing bands of
95 GHz, 150 GHz, and 220 GHz, with 1.2 arcmin FWHM beam response at 150 GHz. In
the receiver, this image plane is populated with 2690 dual-polarization,
tri-chroic pixels (~16000 detectors) read out using a 68X digital
frequency-domain multiplexing readout system. In 2018, SPT-3G began a multiyear
survey of 1500 deg$^{2}$ of the southern sky. We summarize the unique optical,
cryogenic, detector, and readout technologies employed in SPT-3G, and we report
on the integrated performance of the instrument.


Abstract:
  We clarify and generalize the ant on a rubber rope paradox, which is a
mathematical puzzle with a solution that appears counterintuitive. In this
paper, we show that the ant can still reach the end of the rope even if we
consider the step length of the ant and stretching length of the rubber rope as
random variables.


Abstract:
  The young T Tauri star WW Cha was recently proposed to be a close binary
object with strong infrared and submillimeter excess associated with
circum-system emission. This makes WW Cha a very interesting source for
studying the influence of dynamical effects on circumstellar as well as
circumbinary material. We derive the relative astrometric positions and flux
ratios of the stellar companion in WW Cha from the interferometric model
fitting of observations made with the VLTI instruments AMBER, PIONIER, and
GRAVITY in the near-infrared from 2011 to 2020. For two epochs, the resulting
uv-coverage in spatial frequencies permits us to perform the first image
reconstruction of the system in the K band. The positions of nine epochs are
used to determine the orbital elements and the total mass of the system. We
find the secondary star orbiting the primary with a period of T=206.55 days, a
semimajor axis of a=1.01 au, and a relatively high eccentricity of e=0.45.
Combining the orbital solution with distance measurements from Gaia DR2 and the
analysis of evolutionary tracks, the dynamical mass of Mtot=3.20 Msol can be
explained by a mass ratio between ~0.5 and 1. The orbital angular momentum
vector is in close alignment with the angular momentum vector of the outer disk
as measured by ALMA and SPHERE. The analysis of the relative photometry
suggests the presence of infrared excess surviving in the system and likely
originating from truncated circumstellar disks. The flux ratio between the two
components appears variable, in particular in the K band, and may hint at
periods of triggered higher and lower accretion or changes in the disks'
structures. The knowledge of the orbital parameters, combined with a relatively
short period, makes WW Cha an ideal target for studying the interaction of a
close young T Tauri binary with its surrounding material, such as
time-dependent accretion phenomena.


Abstract:
  This paper describes a system by which Unmanned Aerial Vehicles (UAVs) can
gather high-quality face images that can be used in biometric identification
tasks. Success in face-based identification depends in large part on the image
quality, and a major factor is how frontal the view is. Face recognition
software pipelines can improve identification rates by synthesizing frontal
views from non-frontal views by a process call {\em frontalization}. Here we
exploit the high mobility of UAVs to actively gather frontal images using
components of a synthetic frontalization pipeline. We define a frontalization
error and show that it can be used to guide an UAVs to capture frontal views.
Further, we show that the resulting image stream improves matching quality of a
typical face recognition similarity metric. The system is implemented using an
off-the-shelf hardware and software components and can be easily transfered to
any ROS enabled UAVs.


Abstract:
  The article presents a matrix differential operator and a pseudoinverse
matrix differential operator for finding a particular solution to
nonhomogeneous linear ordinary differential equations (ODE) with constant
coefficients with special types of the right-hand side. Calculation requires
the determination of an inverse or pseudoinverse matrix. If the matrix is
singular, the Moore-Penrose pseudoinverse matrix is used for the calculation,
which is simply calculated as the inverse submatrix of the considered matrix.
It is shown that block matrices are effectively used to calculate a particular
solution.


Abstract:
  The role of bipolar jets in the formation of stars, and in particular how
they are launched, is still not well understood. We probe the protostellar jet
launching mechanism, via high resolution observations of the near-IR [FeII]
1.53,1.64 micron lines. We consider the bipolar jet from the Classical T Tauri
star, DO Tau, & investigate jet morphology & kinematics close to the star,
using AO-assisted IFU observations from GEMINI/NIFS. The brighter, blue-shifted
jet is collimated quickly after launch. This early collimation requires the
presence of magnetic fields. We confirm velocity asymmetries between the two
jet lobes, & confirm no time variability in the asymmetry over a 20 year
interval. This sustained asymmetry is in accordance with recent simulations of
magnetised disk-winds. We examine the data for jet rotation. We report an upper
limit on differences in radial velocity of 6.3 & 8.7 km/s for the blue &
red-shifted jets, respectively. Interpreting this as an upper limit on jet
rotation implies that any steady, axisymmetric magneto-centrifugal model of jet
launching is constrained to a launch radius in the disk-plane of 0.5 & 0.3 au
for the blue & red-shifted jets, respectively. This supports an X-wind or
narrow disk-wind model. This pertains only to the observed high velocity [FeII]
emission, & does not rule out a wider flow launched from a wider radius. We
report detection of small amplitude jet axis wiggling in both lobes. We rule
out orbital motion of the jet source as the cause. Precession can better
account for the observations but requires double the precession angle, & a
different phase for the counter-jet. Such non-solid body precession could arise
from an inclined massive Jupiter companion, or a warping instability induced by
launching a magnetic disk-wind. Overall, our observations are consistent with
an origin of the DO Tau jets from the inner regions of the disk.


Abstract:
  Infrared dark clouds (IRDCs) are potential hosts of the elusive early phases
of high-mass star formation (HMSF). Here we conduct an in-depth analysis of the
fragmentation properties of a sample of 10 IRDCs, which have been highlighted
as some of the best candidates to study HMSF within the Milky Way. To do so, we
have obtained a set of large mosaics covering these IRDCs with ALMA at band 3
(or 3mm). These observations have a high angular resolution (~3arcsec or
~0.05pc), and high continuum and spectral line sensitivity (~0.15mJy/beam and
~0.2K per 0.1km/s channel at the N2H+(1-0) transition). From the dust continuum
emission, we identify 96 cores ranging from low- to high-mass (M = 3.4 to
50.9Msun) that are gravitationally bound (alpha_vir = 0.3 to 1.3) and which
would require magnetic field strengths of B = 0.3 to 1.0mG to be in virial
equilibrium. We combine these results with a homogenised catalogue of
literature cores to recover the hierarchical structure within these clouds over
four orders of magnitude in spatial scale (0.01pc to 10pc). Using supplementary
observations at an even higher angular resolution, we find that the smallest
fragments (<0.02pc) within this hierarchy do not currently have the mass and/or
the density required to form high-mass stars. Nonetheless, the new ALMA
observations presented in this paper have facilitated the identification of 19
(6 quiescent and 13 star-forming) cores that retain >16Msun without further
fragmentation. These high-mass cores contain trans-sonic non-thermal motions,
are kinematically sub-virial, and require moderate magnetic field strengths for
support against collapse. The identification of these potential sites of
high-mass star formation represents a key step in allowing us to test the
predictions from high-mass star and cluster formation theories.


Abstract:
  Recent observations of the HDO/H$_2$O ratio toward protostars in isolated and
clustered environments show an apparent dichotomy, where isolated sources show
higher D/H ratios than clustered counterparts. Establishing which physical and
chemical processes create this differentiation can provide insights into the
chemical evolution of water during star formation and the chemical diversity
during the star formation process and in young planetary systems. Methods: The
evolution of water is modeled using 3D physicochemical models of a dynamic
star-forming environment. The physical evolution during the protostellar
collapse is described by tracer particles from a 3D MHD simulation of a
molecular cloud region. Each particle trajectory is post-processed using
RADMC-3D to calculate the temperature and radiation field. The chemical
evolution is simulated using a three-phase grain-surface chemistry model and
the results are compared with interferometric observations of H$_2$O, HDO, and
D$_2$O in hot corinos toward low-mass protostars. Results: The physicochemical
model reproduces the observed HDO/H$_2$O and D$_2$O/HDO ratios in hot corinos,
but shows no correlation with cloud environment for similar identical
conditions. The observed dichotomy in water D/H ratios requires variation in
the initial conditions (e.g., the duration and temperature of the prestellar
phase). Reproducing the observed D/H ratios in hot corinos requires a
prestellar phase duration $t\sim$1-3 Myr and temperatures in the range $T \sim$
10-20 K prior to collapse. This work demonstrates that the observed
differentiation between clustered and isolated protostars stems from
differences in the molecular cloud or prestellar core conditions and does not
arise during the protostellar collapse itself.


Abstract:
  In this paper we report on detailed temperature and magnetic field dependence
of m agnetization of IV-VI semiconductor PbTe doped with mixed valence
transition metal Cr$^{2+/3+}$. The material is studied solely by an integral
superconducting quantum interference device magnetometer in order to
quantitatively determine the contribution of single substitutional Cr$^{3+}$ as
well as of various Cr-Te magnetic nanocrystals, including their identification.
The applied experimental procedure reveals the presence of about
$10^{19}$~cm$^{-3}$ paramagnetic Cr$^{3+}$ ions formed via self-ionization of
Cr$^{2+}$ resonant donors. These are known to improve the thermoelectric figure
of merit parameter zT of this semiconductor. The magnetic finding excellently
agrees with previous Hall effect studies thus providing a new experimental
support for the proposed electronic structure model of PbTe:Cr system with
resonant Cr$^{2+/3+}$ state located (at low temperatures) about 100 meV above
the bottom of the conduction band. Below room temperature a ferromagnetic-like
signal points to the presence of Cr-rich nanocrystalline precipitates. Two most
likely candidates, namely: Cr$_2$Te$_3$ and Cr$_5$Te$_8$ are identified upon
dedicated temperature cycling of the sample at the remnant state. As an
ensemble, the nanocrystals exhibits (blocked) superparamagnetic properties. The
magnetic susceptibility of both n- and p-type PbTe in the temperature range
$100 < T < 400$~K has been established. These magnitudes are essential in
proper accounting for the high temperature magnetic susceptibility of PbTe:Cr.


Abstract:
  The largest experiments in machine learning now require resources far beyond
the budget of all but a few institutions. Fortunately, it has recently been
shown that the results of these huge experiments can often be extrapolated from
the results of a sequence of far smaller, cheaper experiments. In this work, we
show that not only can the extrapolation be done based on the size of the
model, but on the size of the problem as well. By conducting a sequence of
experiments using AlphaZero and Hex, we show that the performance achievable
with a fixed amount of compute degrades predictably as the game gets larger and
harder. Along with our main result, we further show that the test-time and
train-time compute available to an agent can be traded off while maintaining
performance.


Abstract:
  Deep Reinforcement Learning (DRL) has shown outstanding performance on
inducing effective action policies that maximize expected long-term return on
many complex tasks. Much of DRL work has been focused on sequences of events
with discrete time steps and ignores the irregular time intervals between
consecutive events. Given that in many real-world domains, data often consists
of temporal sequences with irregular time intervals, and it is important to
consider the time intervals between temporal events to capture latent
progressive patterns of states. In this work, we present a general Time-Aware
RL framework: Time-aware Q-Networks (TQN), which takes into account physical
time intervals within a deep RL framework. TQN deals with time irregularity
from two aspects: 1) elapsed time in the past and an expected next observation
time for time-aware state approximation, and 2) action time window for the
future for time-aware discounting of rewards. Experimental results show that by
capturing the underlying structures in the sequences with time irregularities
from both aspects, TQNs significantly outperform DQN in four types of contexts
with irregular time intervals. More specifically, our results show that in
classic RL tasks such as CartPole and MountainCar and Atari benchmark with
randomly segmented time intervals, time-aware discounting alone is more
important while in the real-world tasks such as nuclear reactor operation and
septic patient treatment with intrinsic time intervals, both time-aware state
and time-aware discounting are crucial. Moreover, to improve the agent's
learning capacity, we explored three boosting methods: Double networks, Dueling
networks, and Prioritized Experience Replay, and our results show that for the
two real-world tasks, combining all three boosting methods with TQN is
especially effective.


Abstract:
  Anti-unification in logic programming refers to the process of capturing
common syntactic structure among given goals, computing a single new goal that
is more general called a generalization of the given goals. Finding an
arbitrary common generalization for two goals is trivial, but looking for those
common generalizations that are either as large as possible (called largest
common generalizations) or as specific as possible (called most specific
generalizations) is a non-trivial optimization problem, in particular when
goals are considered to be \textit{unordered} sets of atoms. In this work we
provide an in-depth study of the problem by defining two different
generalization relations. We formulate a characterization of what constitutes a
most specific generalization in both settings. While these generalizations can
be computed in polynomial time, we show that when the number of variables in
the generalization needs to be minimized, the problem becomes NP-hard. We
subsequently revisit an abstraction of the largest common generalization when
anti-unification is based on injective variable renamings, and prove that it
can be computed in polynomially bounded time.


Abstract:
  Wavelet scattering networks, which are convolutional neural networks (CNNs)
with fixed filters and weights, are promising tools for image analysis.
Imposing symmetry on image statistics can improve human interpretability, aid
in generalization, and provide dimension reduction. In this work, we introduce
a fast-to-compute, translationally invariant and rotationally equivariant
wavelet scattering network (EqWS) and filter bank of wavelets (triglets). We
demonstrate the interpretability and quantify the invariance/equivariance of
the coefficients, briefly commenting on difficulties with implementing scale
equivariance. On MNIST, we show that training on a rotationally invariant
reduction of the coefficients maintains rotational invariance when generalized
to test data and visualize residual symmetry breaking terms. Rotation
equivariance is leveraged to estimate the rotation angle of digits and
reconstruct the full rotation dependence of each coefficient from a single
angle. We benchmark EqWS with linear classifiers on EMNIST and CIFAR-10/100,
introducing a new second-order, cross-color channel coupling for the color
images. We conclude by comparing the performance of an isotropic reduction of
the scattering coefficients and RWST, a previous coefficient reduction, on an
isotropic classification of magnetohydrodynamic simulations with astrophysical
relevance.


Abstract:
  From spiking activity in neuronal networks to force chains in granular
materials, the behavior of many real-world systems depends on a network of both
strong and weak interactions. These interactions give rise to complex and
higher-order system behaviors, and are encoded using data as the network's
edges. However, distinguishing between true weak edges and low-weight edges
caused by noise remains a challenge. We address this problem by examining the
higher-order structure of noisy, weak edges added to model networks. We find
that the structure of low-weight, noisy edges varies according to the topology
of the model network to which it is added. By investigating this variation more
closely, we see that at least three qualitative classes of noise structure
emerge. Furthermore, we observe that the structure of noisy edges contains
enough model-specific information to classify the model networks with moderate
accuracy. Finally, we offer network generation rules that can drive different
types of structure in added noisy edges. Our results demonstrate that noise
does not present as a monolithic nuisance, but rather as a nuanced,
topology-dependent, and even useful entity in characterizing higher-order
network interactions. Hence, we provide an alternate approach to noise
management by embracing its role in such interactions.


Abstract:
  We argue that long optical storage times are required to establish
entanglement at high rates over large distances using memory-based quantum
repeaters. Triggered by this conclusion, we investigate the $^3$H$_6$
$\leftrightarrow$ $^3$H$_4$ transition at 795.325 nm of Tm:Y$_3$Ga$_5$O$_{12}$
(Tm:YGG). Most importantly, we show that the optical coherence time can reach
1.1 ms, and, using laser pulses, we demonstrate optical storage based on the
atomic frequency comb protocol up to 100 $\mu$s as well as a memory decay time
T$_M$ of 13.1 $\mu$s. Possibilities of how to narrow the gap between the
measured value of T$_m$ and its maximum of 275 $\mu$s are discussed. In
addition, we demonstrate quantum state storage using members of non-classical
photon pairs. Our results show the potential of Tm:YGG for creating quantum
memories with long optical storage times, and open the path to building
extended quantum networks.


Abstract:
  The asteroid exploration project "Hayabusa2" has successfully returned
samples from the asteroid (162173) Ryugu. In this study, we measured the linear
polarization degrees of Ryugu using four ground-based telescopes from 2020
September 27 to December 25, covering a wide-phase angle (Sun-target-observer's
angle) range from 28$^\circ$ to 104$^\circ$. We found that the polarization
degree of Ryugu reached 53$\%$ around a phase angle of 100$^\circ$, the highest
value among all asteroids and comets thus far reported. The high polarization
degree of Ryugu can be attributed to the scattering properties of its surface
layers, in particular the relatively small contribution of multiply-scattered
light. Our polarimetric results indicate that Ryugu's surface is covered with
large grains. On the basis of a comparison with polarimetric measurements of
pulverized meteorites, we can infer the presence of submillimeter-sized grains
on the surface layer of Ryugu. We also conjecture that this size boundary
represents the grains that compose the aggregate. It is likely that a very
brittle structure has been lost in the recovered samples, although they may
hold a record of its evolution. Our data will be invaluable for future
experiments aimed at reproducing the surface structure of Ryugu.