title: Steady Euler flows on $\mathbb{R}^3$ with wild and universal dynamics

abstract:
  Understanding complexity in fluid mechanics is a major problem that has
attracted the attention of physicists and mathematicians during the last
decades. Using the concept of renormalization in dynamics, we show the
existence of a locally dense set $\mathscr G$ of stationary solutions to the
Euler equations in $\mathbb R^3$ such that each vector field $X\in \mathscr G$
is universal in the sense that any area preserving diffeomorphism of the disk
can be approximated (with arbitrary precision) by the Poincar\'e map of $X$ at
some transverse section. We remark that this universality is approximate but
occurs at all scales. In particular, our results establish that a steady Euler
flow may exhibit any conservative finite codimensional dynamical phenomenon;
this includes the existence of horseshoes accumulated by elliptic islands,
increasing union of horseshoes of Hausdorff dimension $3$ or homoclinic
tangencies of arbitrarily high multiplicity. The steady solutions we construct
are Beltrami fields with sharp decay at infinity. To prove these results we
introduce new perturbation methods in the context of Beltrami fields that allow
us to import deep techniques from bifurcation theory: the
Gonchenko-Shilnikov-Turaev universality theory and the Newhouse and Duarte
theorems on the geometry of wild hyperbolic sets. These perturbation methods
rely on two tools from linear PDEs: global approximation and
Cauchy-Kovalevskaya theorems. These results imply a strong version of V.I.
Arnold's vision on the complexity of Beltrami fields in Euclidean space.


title: Mechanobiology of Collective Cell Migration in 3D Microenvironments

abstract:
  Tumor cells invade individually or in groups, mediated by mechanical
interactions between cells and their surrounding matrix. These multicellular
dynamics are reminiscent of leader-follower coordination and
epithelial-mesenchymal transitions (EMT) in tissue development, which may occur
via dysregulation of associated molecular or physical mechanisms. However, it
remains challenging to elucidate such phenotypic heterogeneity and plasticity
without precision measurements of single cell behavior. The convergence of
technological developments in live cell imaging, biophysical measurements, and
3D biomaterials are highly promising to reveal how tumor cells cooperate in
aberrant microenvironments. Here, we highlight new results in collective
migration from the perspective of cancer biology and bioengineering. First, we
review the biology of collective cell migration. Next, we consider
physics-inspired analyses based on order parameters and phase transitions.
Further, we examine the interplay of metabolism and heterogeneity in collective
migration. We then review the extracellular matrix, and new modalities for
mechanical characterization of 3D biomaterials. We also explore
epithelial-mesenchymal plasticity and implications for tumor progression.
Finally, we speculate on future directions for integrating mechanobiology and
cancer cell biology to elucidate collective migration.


title:
Stochastic Gradient Descent with Dependent Data for Offline
  Reinforcement Learning

abstract:
  In reinforcement learning (RL), offline learning decoupled learning from data
collection and is useful in dealing with exploration-exploitation tradeoff and
enables data reuse in many applications. In this work, we study two offline
learning tasks: policy evaluation and policy learning. For policy evaluation,
we formulate it as a stochastic optimization problem and show that it can be
solved using approximate stochastic gradient descent (aSGD) with time-dependent
data. We show aSGD achieves $\tilde O(1/t)$ convergence when the loss function
is strongly convex and the rate is independent of the discount factor $\gamma$.
This result can be extended to include algorithms making approximately
contractive iterations such as TD(0). The policy evaluation algorithm is then
combined with the policy iteration algorithm to learn the optimal policy. To
achieve an $\epsilon$ accuracy, the complexity of the algorithm is $\tilde
O(\epsilon^{-2}(1-\gamma)^{-5})$, which matches the complexity bound for
classic online RL algorithms such as Q-learning.


title:
Machine Learning Aided Holistic Handover Optimization for Emerging
  Networks

abstract:
  In the wake of network densification and multi-band operation in emerging
cellular networks, mobility and handover management is becoming a major
bottleneck. The problem is further aggravated by the fact that holistic
mobility management solutions for different types of handovers, namely
inter-frequency and intra-frequency handovers, remain scarce. This paper
presents a first mobility management solution that concurrently optimizes
inter-frequency related A5 parameters and intra-frequency related A3
parameters. We analyze and optimize five parameters namely A5-time to trigger
(TTT), A5-threshold1, A5-threshold2, A3-TTT, and A3-offset to jointly maximize
three critical key performance indicators (KPIs): edge user reference signal
received power (RSRP), handover success rate (HOSR) and load between frequency
bands. In the absence of tractable analytical models due to system level
complexity, we leverage machine learning to quantify the KPIs as a function of
the mobility parameters. An XGBoost based model has the best performance for
edge RSRP and HOSR while random forest outperforms others for load prediction.
An analysis of the mobility parameters provides several insights: 1) there
exists a strong coupling between A3 and A5 parameters; 2) an optimal set of
parameters exists for each KPI; and 3) the optimal parameters vary for
different KPIs. We also perform a SHAP based sensitivity to help resolve the
parametric conflict between the KPIs. Finally, we formulate a maximization
problem, show it is non-convex, and solve it utilizing simulated annealing
(SA). Results indicate that ML-based SA-aided solution is more than 14x faster
than the brute force approach with a slight loss in optimality.


title:
Kolmogorov complexity as intrinsic entropy of a pure state: perspective
  from entanglement in free fermion systems

abstract:
  We consider free fermion systems in arbitrary dimensions and represent the
occupation pattern of each eigenstate as a classical binary string. We find
that the Kolmogorov complexity of the string correctly captures the scaling
behavior of its entanglement entropy (EE). In particular, the
logarithmically-enhanced area law for EE in the ground state and the volume law
for EE in typical highly excited states are reproduced. Since our approach does
not require bipartitioning the system, it allows us to distinguish typical and
atypical eigenstates directly by their intrinsic complexity. We reveal that the
fraction of atypical eigenstates which do not thermalize in the free fermion
system vanishes exponentially in the thermodynamic limit. Our results
illustrate explicitly the connection between complexity and EE of individual
pure states in quantum systems.


title: Covertly Controlling a Linear System

abstract:
  Consider the problem of covertly controlling a linear system. In this
problem, Alice desires to control (stabilize or change the parameters of) a
linear system, while keeping an observer, Willie, unable to decide if the
system is indeed being controlled or not.
  We formally define the problem, under two different models: (i) When Willie
can only observe the system's output (ii) When Willie can directly observe the
control signal. Focusing on AR(1) systems, we show that when Willie observes
the system's output through a clean channel, an inherently unstable linear
system can not be covertly stabilized. However, an inherently stable linear
system can be covertly controlled, in the sense of covertly changing its
parameter. Moreover, we give direct and converse results for two important
controllers: a minimal-information controller, where Alice is allowed to used
only $1$ bit per sample, and a maximal-information controller, where Alice is
allowed to view the real-valued output. Unlike covert communication, where the
trade-off is between rate and covertness, the results reveal an interesting
\emph{three--fold} trade--off in covert control: the amount of information used
by the controller, control performance and covertness. To the best of our
knowledge, this is the first study formally defining covert control.

categories: math.OC cs.IT cs.SY eess.SP eess.SY math.IT


title:
The Fekete--Szeg\"{o} problem for spirallike mappings and non-linear
  resolvents in Banach spaces

abstract:
  We study the Fekete--Szeg\"{o} problem on the open unit ball of a complex
Banach space. Namely, the Fekete--Szeg\"{o} inequalities are proved for the
class of spirallike mappings relative to an arbitrary strongly accretive
operator, and some of its subclasses. Next, we consider families of non-linear
resolvents for holomorphically accretive mappings vanishing at the origin. We
solve the Fekete--Szeg\"{o} problem over these families.


title:
On the Connes-Kasparov isomorphism, I: The reduced C*-algebra of a real
  reductive group and the K-theory of the tempered dual

abstract:
  This is the first of two papers dedicated to the computation of the reduced
C*-algebra of a connected, linear, real reductive group up to Morita
equivalence, and the verification of the Connes-Kasparov conjecture for these
groups. These results were originally announced by Antony Wassermann in 1987.
In Part I we shall give details of the C*-algebraic Morita equivalence, and
then compute the Connes-Kasparov morphism subject to some results in tempered
representation theory that we shall prove in Part II using tools from David
Vogan's classification of the tempered dual.


title: Deep Learning-Aided Spatial Multiplexing with Index Modulation

abstract:
  In this paper, deep learning (DL)-aided data detection of spatial
multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with
index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been
constructed by combining a zero-forcing (ZF) detector and DL technique. The
proposed method uses the significant advantages of DL techniques to learn
transmission characteristics of the frequency and spatial domains. Furthermore,
thanks to using subblockbased detection provided by IM, Deep-SMX-IM is a
straightforward method, which eventually reveals reduced complexity. It has
been shown that Deep-SMX-IM has significant error performance gains compared to
ZF detector without increasing computational complexity for different system
configurations.


title:
On the Connes-Kasparov isomorphism, II: The Vogan classification of
  essential components in the tempered dual

abstract:
  This is the second of two papers dedicated to the computation of the reduced
C*-algebra of a connected, linear, real reductive group up to C*-algebraic
Morita equivalence, and the verification of the Connes-Kasparov conjecture for
these groups. These results were originally announced by Antony Wassermann in
1987. In Part I we presented the Morita equivalence and the Connes-Kasparov
morphism. In this part we shall compute the morphism using David Vogan's
description of the tempered dual.


title: Non-degeneracy of Stochastic Line Integrals

abstract:
  We derive quantitative criteria for the existence of density for stochastic
line integrals and iterated line integrals along solutions of hypoelliptic
differential equations driven by fractional Brownian motion. As an application,
we also study the signature uniqueness problem for these rough differential
equations.


title:
A Cautionary Tale of LyC Escape Fraction Estimates from High Redshift
  Galaxies

abstract:
  Measuring the escape fraction, $f_{\rm esc}$, of ionizing, Lyman Continuum
(LyC) radiation is key to our understanding of the process of cosmic
reionization. In this paper we provide a methodology for recovering the
posterior probability distribution of the LyC escape fraction, $f_{\rm
esc}^{\rm PDF}$, considering both the observational uncertainties and ensembles
of simulated transmission functions through the intergalactic medium (IGM). We
present an example of this method applied to a VUDS galaxy at $z=3.64$ and find
$f_{\rm esc}^{\rm PDF}$ = 0.51$^{+0.33}_{-0.34}$ and compare this to the values
computed assuming averaged IGM transmission with and without consideration of
detection bias along average sightlines yielding $f_{\rm esc}^{\langle T
\rangle}$ = 1.40$^{+0.80}_{-0.42}$, and $f_{\rm esc}^{\rm bias}$ =
0.82$^{+0.33}_{-0.16}$. Our results highlight the limitations of methods
assuming average, smooth transmission functions. We also present MOSFIRE data
for a sample of seven LyC candidates selected based on photometric redshifts at
$z > 3.4$, but find that all seven have overestimated photometric redshifts by
$\Delta z \sim 0.2$ making them unsuitable for LyC measurements. This results
likely due to a bias induced by our selection criteria.


title:
MIMO Systems with One-bit ADCs: Capacity Gains using Nonlinear Analog
  Operations

abstract:
  Analog to Digital Converters (ADCs) are a major contributor to the energy
consumption on the receiver side of millimeter-wave multiple-input
multiple-output (MIMO) systems with large antenna arrays. Consequently, there
has been significant interest in using low-resolution ADCs along with hybrid
beam-forming at MIMO receivers for energy efficiency. However, decreasing the
ADC resolution results in performance loss -- in terms of achievable rates --
due to increased quantization error. In this work, we study the application of
practically implementable nonlinear analog operations, prior to sampling and
quantization at the ADCs, as a way to mitigate the aforementioned rate-loss. A
receiver architecture consisting of linear analog combiners, implementable
nonlinear analog operators, and one-bit threshold ADCs is designed. The
fundamental information theoretic performance limits of the resulting
communication system, in terms of achievable rates, are investigated under
various assumptions on the set of implementable nonlinear analog functions. In
order to justify the feasibility of the nonlinear operations in the proposed
receiver architecture, an analog circuit is introduced, and circuit simulations
exhibiting the generation of the desired nonlinear analog operations are
provided.


title: New Time-Resolved, Multi-Band Flares In The GJ 65 System With gPhoton

abstract:
  Characterizing the distribution of flare properties and occurrence rates is
important for understanding habitability of M dwarf exoplanets. The GALEX space
telescope observed the GJ 65 system, composed of the active, flaring M stars BL
Cet and UV Cet, for 15900 seconds (~4.4 hours) in two ultraviolet bands. The
contrast in flux between flares and the photospheres of cool stars is maximized
at ultraviolet wavelengths, and GJ 65 is the brightest and nearest flaring M
dwarf system with significant GALEX coverage. It therefore represents the best
opportunity to measure low energy flares with GALEX. We construct high cadence
light curves from calibrated photon events and find 13 new flare events with
NUV energies ranging from 10^28.5 - 10^29.5 ergs and recover one previously
reported flare with an energy of 10^31 ergs. The newly reported flares are
among the smallest M dwarf flares observed in the ultraviolet with sufficient
time resolution to discern light curve morphology. The estimated flare
frequency at these low energies is consistent with extrapolation from the
distributions of higher-energy flares on active M dwarfs measured by other
surveys. The largest flare in our sample is bright enough to exceed the local
non-linearity threshold of the GALEX detectors, which precludes color analysis.
However, we detect quasi-periodic pulsations (QPP) during this flare in both
the FUV and NUV bands at a period of ~50 seconds, which we interpret as a
modulation of the flare's chromospheric thermal emission through periodic
triggering of reconnection by external MHD oscillations in the corona.


title:
Asymptotic behavior of the forecast-assimilation process with unstable
  dynamics

abstract:
  Extensive numerical evidence shows that the assimilation of observations has
a stabilizing effect on unstable dynamics, in numerical weather prediction and
elsewhere. In this paper, we apply mathematically rigorous methods to showing
why this is so. Our stabilization results do not assume a full set of
observations and we provide examples where it suffices to observe the model's
unstable degrees of freedom.


title:
Towards Modeling Human Motor Learning Dynamics in High-Dimensional
  Spaces

abstract:
  Designing effective rehabilitation strategies for upper extremities,
particularly hands and fingers, warrants the need for a computational model of
human motor learning. The presence of large degrees of freedom (DoFs) available
in these systems makes it difficult to balance the trade-off between learning
the full dexterity and accomplishing manipulation goals. The motor learning
literature argues that humans use motor synergies to reduce the dimension of
control space. Using the low-dimensional space spanned by these synergies, we
develop a computational model based on the internal model theory of motor
control. We analyze the proposed model in terms of its convergence properties
and fit it to the data collected from human experiments. We compare the
performance of the fitted model to the experimental data and show that it
captures human motor learning behavior well.


title: Alpha Blending with No Division Operations

abstract:
  Highly accurate alpha blending can be performed entirely with integer
operations, and no divisions. To reduce the number of integer multiplications,
multiple color components can be blended in parallel in the same 32-bit or
64-bit register. This tutorial explains how to avoid division operations when
alpha blending with 32-bit RGBA pixels. An RGBA pixel contains four 8-bit
components (red, green, blue, and alpha) whose values range from 0 to 255.
Alpha blending requires multiplication of the color components by an alpha
value, after which (for greatest accuracy) each of these products is divided by
255 and then rounded to the nearest integer. This tutorial presents an
approximate alpha-blending formula that replaces the division operation with an
integer shift and add -- and also enables the number of multiplications to be
reduced. When the same blending calculation is carried out to high precision
using double-precision floating-point division operations, the results are
found to exactly match those produced by this approximation. C++ code examples
are included.


title:
Packed swarms on dirt: two dimensional incompressible flocks with
  quenched and annealed disorder

abstract:
  We show that incompressible polar active fluids can exhibit an ordered,
coherently moving phase even in the presence of quenched disorder in two
dimensions. Unlike such active fluids with annealed (i.e., time-dependent)
disorder, which behave like equilibrium ferromagnets with long-ranged
interactions, this robustness against quenched disorder is a fundamentally
non-equilibrium phenomenon. The ordered state belongs to a new universality
class, whose scaling laws we calculate using three different renormalization
group schemes, which all give scaling exponents within 0.02 of each other,
indicating that our results are quite accurate. Our predictions can be
quantitatively tested in readily available artificial active systems, and imply
that biological systems such as cell layers can move coherently on substrates
despite the presence of surface inhomogeneities.


title:
Ab initio calculations of spin-nonconserving exciton-phonon scattering
  in monolayer transition metal dichalcogenides

abstract:
  We investigate the spin-nonconserving relaxation channel of excitons by their
couplings with phonons in two-dimensional transition metal dichalcogenides
using $\textit{ab initio}$ approaches. Combining $\text{GW}$-Bethe-Salpeter
equation method and density functional perturbation theory, we calculate the
electron-phonon and exciton-phonon coupling matrix elements for the spin-flip
scattering in monolayer WSe$_{\text{2}}$, and further analyze the microscopic
mechanisms influencing these scattering strengths. We find that phonons could
produce effective in-plane magnetic fields which flip spin of excitons, giving
rise to relaxation channels complimentary to the spin-conserving relaxation.
Finally, we calculate temperature-dependent spin-flip exciton-phonon relaxation
times. Our method and analysis can be generalized to study other
two-dimensional materials and would stimulate experimental measurements of
spin-flip exciton relaxation dynamics.


title:
On simultaneous fitting of nonlinear and linear rheology data:
  Preventing a false sense of certainty

abstract:
  Uncertainty propagates through calculations, down to molecular scales to
infer microstructural features, and up to macroscopic scales with predictive
flow simulations. Here we study uncertainty quantification for sequential
(two-step) versus simultaneous (all at once) fitting methods with linear and
weakly-nonlinear rheological data. Using an example of a combined dataset on
small-amplitude oscillatory shear (SAOS) and medium-amplitude oscillatory shear
(MAOS) for a linear entangled polymer melt (cis-1,4-polyisoprene), we
demonstrate with a multi-mode Giesekus model how the fit parameter
uncertainties are significantly under-estimated with the sequential fit because
of the neglect of model parameter correlations. These results are surprising
because weakly-nonlinear data is only an asymptotic step away from the linear
data, yet it has significant impact on calibrating the linear model parameters.
Similarly, the nonlinear parameter estimates and uncertainties are impacted by
considering the linear data in a simultaneous fit. To compare multi-mode
spectra of nonlinear parameters (mobility parameters {\alpha_i} from the
Giesekus model), we derive new average measures based on moments of the spectra
related to the high-frequency MAOS limit. The spectral averages are also
sensitive to sequential versus simultaneous fitting. Our results reveal the
importance of using simultaneous fitting for honest uncertainty quantification,
even with weakly-nonlinear data.


title:
Applications of Machine Learning in Healthcare and Internet of Things
  (IOT): A Comprehensive Review

abstract:
  In recent years, smart healthcare IoT devices have become ubiquitous, but
they work in isolated networks due to their policy. Having these devices
connected in a network enables us to perform medical distributed data analysis.
However, the presence of diverse IoT devices in terms of technology, structure,
and network policy, makes it a challenging issue while applying traditional
centralized learning algorithms on decentralized data collected from the IoT
devices. In this study, we present an extensive review of the state-of-the-art
machine learning applications particularly in healthcare, challenging issues in
IoT, and corresponding promising solutions. Finally, we highlight some
open-ended issues of IoT in healthcare that leaves further research studies and
investigation for scientists.


title: Longitudinally Modulated Dynamo Action in Simulated M-Dwarf Stars

abstract:
  M-dwarf stars are well known for the intense magnetic activity that many of
them exhibit. In cool stars with near-surface convection zones, this magnetic
activity is thought to be driven largely by the interplay of convection and the
large scale differential rotation and circulations it establishes. The highly
nonlinear nature of these flows yields a fascinatingly sensitive and diverse
parameter space, with a wide range of possible dynamics. We report here on a
set of three global MHD simulations of rapidly rotating M2 (0.4 $M_\odot$)
stars. Each of these three models established nests of vigorous convection that
were highly modulated in longitude at low latitudes. Slight differences in
their magnetic parameters led each model to disparate dynamo states, but the
effect of the convective nest was a unifying feature. In each case, the action
of longitudinally modulated convection led to localized (and in one case,
global) reversals of the toroidal magnetic field, as well as the formation of
an active longitude, with enhanced poloidal field amplitudes and flux
emergence.


title:
Generalized L-product for hight order tensors with applications using
  GPU computation

abstract:
  In this paper, we will present a generalization of the L-tensor product
(L-product) including generalization of the well known tensor cosine and
T-products that were defined for third-order tensors and based on fast Fourier
transform and discrete cosine transform (DCT). We will give some applications
on tensor completion. To solve some optimization problems linked with the
problem of tensor completion, we will use the Proximal Gradient Algorithm (PGA)
to solve some derived optimization problems. Numerical tests are given to show
the effectiveness of the proposed methods and also present some tests using GPU
computation.


title: Thermal Properties of Relativistic Dunkl Oscillators

abstract:
  For a better understanding of the physical systems, even at the quantum
level, the thermal quantities can be investigated. Recently, we realized that
the parity of the system can also be examined simultaneously, by substituting
the Dunkl operator to the ordinary differential operator in quantum mechanics.
In this manuscript, we consider two relativistic Dunkl-oscillators and
investigate their thermal quantities with well-known statistical methods.


title: Differentiable Economics for Randomized Affine Maximizer Auctions

abstract:
  A recent approach to automated mechanism design, differentiable economics,
represents auctions by rich function approximators and optimizes their
performance by gradient descent. The ideal auction architecture for
differentiable economics would be perfectly strategyproof, support multiple
bidders and items, and be rich enough to represent the optimal (i.e.
revenue-maximizing) mechanism. So far, such an architecture does not exist.
There are single-bidder approaches (MenuNet, RochetNet) which are always
strategyproof and can represent optimal mechanisms. RegretNet is multi-bidder
and can approximate any mechanism, but is only approximately strategyproof. We
present an architecture that supports multiple bidders and is perfectly
strategyproof, but cannot necessarily represent the optimal mechanism. This
architecture is the classic affine maximizer auction (AMA), modified to offer
lotteries. By using the gradient-based optimization tools of differentiable
economics, we can now train lottery AMAs, competing with or outperforming prior
approaches in revenue.


title:
Diffusive transport in the lowest Landau level of disordered 2d
  semimetals: the mean-square-displacement approach

abstract:
  We study the electronic transport in the lowest Landau level of disordered
two-dimensional semimetals placed in a homogeneous perpendicular magnetic
field. The material system is modeled by the Bernevig-Hughes-Zhang Hamiltonian,
which has zero energy Landau modes due to the material's intrinsic Berry
curvature. These turn out to be crucially important for the density of states
and the static conductivity of the disordered system. We develop an analytical
approach to the diffusion and conductivity based on a self-consistent equation
of motion for the mean squared displacement. The obtained value of the zero
mode conductivity is close to the conductivity of disordered Dirac electrons
without magnetic fields, which have zero energy points in the spectrum as well.
Our analysis is applicable in a broader context of disordered two-dimensional
electron gases in strong magnetic fields.

categories: cond-mat.mes-hall cond-mat.dis-nn cond-mat.str-el hep-th


title: Bubble Lattices I: Structure

abstract:
  C. Greene introduced the shuffle lattice as an idealized model for DNA
mutation and discovered remarkable combinatorial and enumerative properties of
this structure. We attempt an explanation of these properties from a
lattice-theoretic point of view. To that end, we introduce and study an order
extension of the shuffle lattice, the bubble lattice. We characterize the
bubble lattice both locally (via certain transformations of shuffle words) and
globally (using a notion of inversion set). We then prove that the bubble
lattice is extremal and constructable by interval doublings. Lastly, we prove
that our bubble lattice is a generalization of the Hochschild lattice studied
earlier by Chapoton, Combe and the second author.


title:
To See a World in a Grain of Sand -- The Scientific Life of Shoucheng
  Zhang

abstract:
  Our friend and colleague, Prof. Shoucheng Zhang, passed away in 2018, which
was a great loss for the entire physics community. For all of us who knew
Shoucheng, it is difficult to overcome the sadness and shock of his early
departure. However, we are very fortunate that Shoucheng has left us such a
rich legacy and so many memories in his 55 years of life as a valuable friend,
a world-leading physicist, a remarkable advisor, and a great thinker. On May
2-4, 2019, a memorial workshop for Shoucheng was organized at Stanford
University, where we displayed a small exhibition of 12 posters, as a brief
overview of Shoucheng's wonderful scientific life. This article is prepared
based on those posters.

categories: physics.hist-ph cond-mat.mes-hall cond-mat.str-el hep-th


title:
Deep Convolutional Learning-Aided Detector for Generalized Frequency
  Division Multiplexing with Index Modulation

abstract:
  In this paper, a deep convolutional neural network-based symbol detection and
demodulation is proposed for generalized frequency division multiplexing with
index modulation (GFDM-IM) scheme in order to improve the error performance of
the system. The proposed method first pre-processes the received signal by
using a zero-forcing (ZF) detector and then uses a neural network consisting of
a convolutional neural network (CNN) followed by a fully-connected neural
network (FCNN). The FCNN part uses only two fully-connected layers, which can
be adapted to yield a trade-off between complexity and bit error rate (BER)
performance. This two-stage approach prevents the getting stuck of neural
network in a saddle point and enables IM blocks processing independently. It
has been demonstrated that the proposed deep convolutional neural network-based
detection and demodulation scheme provides better BER performance compared to
ZF detector with a reasonable complexity increase. We conclude that
non-orthogonal waveforms combined with IM schemes with the help of deep
learning is a promising physical layer (PHY) scheme for future wireless
networks


title: HARFE: Hard-Ridge Random Feature Expansion

abstract:
  We propose a random feature model for approximating high-dimensional sparse
additive functions called the hard-ridge random feature expansion method
(HARFE). This method utilizes a hard-thresholding pursuit-based algorithm
applied to the sparse ridge regression (SRR) problem to approximate the
coefficients with respect to the random feature matrix. The SRR formulation
balances between obtaining sparse models that use fewer terms in their
representation and ridge-based smoothing that tend to be robust to noise and
outliers. In addition, we use a random sparse connectivity pattern in the
random feature matrix to match the additive function assumption. We prove that
the HARFE method is guaranteed to converge with a given error bound depending
on the noise and the parameters of the sparse ridge regression model. Based on
numerical results on synthetic data as well as on real datasets, the HARFE
approach obtains lower (or comparable) error than other state-of-the-art
algorithms.


title:
When to pull data from sensors for minimum Distance-based Age of
  incorrect Information metric

abstract:
  The age of Information (AoI) has been introduced to capture the notion of
freshness in real-time monitoring applications. However, this metric falls
short in many scenarios, especially when quantifying the mismatch between the
current and the estimated states. To circumvent this issue, in this paper, we
adopt the age of incorrect of information metric (AoII) that considers the
quantified mismatch between the source and the knowledge at the destination. We
consider for that a problem where a central entity pulls the information from
remote sources that evolve according to a Markovian Process. It selects at each
time slot which sources should send their updates. As the scheduler does not
know the real state of the remote sources, it estimates at each time the value
of AoII based on the Markovian sources' parameters. Its goal is to keep the
time average of AoII function as small as possible. We develop a scheduling
scheme based on Whittle's index policy for that purpose. To that extent, we
proceed by using the Lagrangian Relaxation Approach and establish that the dual
problem has an optimal threshold policy. Building on that, we compute the
expressions of Whittle's indices. Finally, we provide some numerical results to
highlight the performance of our derived policy compared to the classical AoI
metric.


title: An Empirical Analysis of AI Contributions to Sustainable Cities (SDG11)

abstract:
  Artificial Intelligence (AI) presents opportunities to develop tools and
techniques for addressing some of the major global challenges and deliver
solutions with significant social and economic impacts. The application of AI
has far-reaching implications for the 17 Sustainable Development Goals (SDGs)
in general, and sustainable urban development in particular. However, existing
attempts to understand and use the opportunities offered by AI for SDG 11 have
been explored sparsely, and the shortage of empirical evidence about the
practical application of AI remains. In this chapter, we analyze the
contribution of AI to support the progress of SDG 11 (Sustainable Cities and
Communities). We address the knowledge gap by empirically analyzing the AI
systems (N = 29) from the AIxSDG database and the Community Research and
Development Information Service (CORDIS) database. Our analysis revealed that
AI systems have indeed contributed to advancing sustainable cities in several
ways (e.g., waste management, air quality monitoring, disaster response
management, transportation management), but many projects are still working for
citizens and not with them. This snapshot of AI's impact on SDG11 is inherently
partial, yet useful to advance our understanding as we move towards more mature
systems and research on the impact of AI systems for social good.


title:
Continuous-Time Channel Gain Control for Minimum-Information Kalman-Bucy
  Filtering

abstract:
  We consider the problem of estimating a continuous-time Gauss-Markov source
process observed through a vector Gaussian channel with an adjustable channel
gain matrix. For a given (generally time-varying) channel gain matrix, we
provide formulas to compute (i) the mean-square estimation error attainable by
the classical Kalman-Bucy filter, and (ii) the mutual information between the
source process and its Kalman-Bucy estimate. We then formulate a novel "optimal
channel gain control problem" where the objective is to control the channel
gain matrix strategically to minimize the weighted sum of these two performance
metrics. To develop insights into the optimal solution, we first consider the
problem of controlling a time-varying channel gain over a finite time interval.
A necessary optimality condition is derived based on Pontryagin's minimum
principle. For a scalar system, we show that the optimal channel gain is a
piece-wise constant signal with at most two switches. We also consider the
problem of designing the optimal time-invariant gain to minimize the average
cost over an infinite time horizon. A novel semidefinite programming (SDP)
heuristic is proposed and the exactness of the solution is discussed.


title: Trusted Approximate Policy Iteration with Bisimulation Metrics

abstract:
  Bisimulation metrics define a distance measure between states of a Markov
decision process (MDP) based on a comparison of reward sequences. Due to this
property they provide theoretical guarantees in value function approximation.
In this work we first prove that bisimulation metrics can be defined via any
$p$-Wasserstein metric for $p\geq 1$. Then we describe an approximate policy
iteration (API) procedure that uses $\epsilon$-aggregation with
$\pi$-bisimulation and prove performance bounds for continuous state spaces. We
bound the difference between $\pi$-bisimulation metrics in terms of the change
in the policies themselves. Based on these theoretical results, we design an
API($\alpha$) procedure that employs conservative policy updates and enjoys
better performance bounds than the naive API approach. In addition, we propose
a novel trust region approach which circumvents the requirement to explicitly
solve a constrained optimization problem. Finally, we provide experimental
evidence of improved stability compared to non-conservative alternatives in
simulated continuous control.


title:
Diversity Multiplexing Trade-off and Selection Gain in Media-Based
  Modulation

abstract:
  The idea of Media-based Modulation (MBM) is to embed information in the
variations of the transmission media (channel states). Using an RF closure with
$w$ RF walls, MBM creates a set of $2^w$ select-able states for the end-to-end
channel. Each state represents an index of an MBM constellation point. In each
state, the wave (tone) emanating from the transmit antenna experiences many
pseudo-random back-and-forth reflections within the RF closure. The RF signal,
upon finally leaving the RF closure, further propagates in the rich scattering
environment to reach the receiver. This results in an independent complex
channel gain to each receive antenna. As a result, coordinates of different MBM
constellation points (vectors of channel gains formed over received antennas)
will be independent of each other. This is unlike legacy transmission schemes
where a fixed constellation structure used at the transmitter will be
multiplied by a fixed, but random, channel gain. Due to this independence
property, MBM offers several advantages vs. legacy systems, including
"additivity of information over multiple receive antennas (regardless of the
number of transmit antennas)", and "inherent diversity over a static fading
channel". This work studies the Diversity-Multiplexing Trade-off of an MBM
constellation. Analytical expressions are provided that demonstrate the
advantages of MBM vs. legacy systems. In particular, it is shown that a
$1\times N_r$ SIMO-MBM constellation equipped with an MDS code (even with a
relatively small code length) significantly outperforms an $N_r\times N_r$
legacy MIMO.


title:
MAXI and NuSTAR observations of the faint X-ray transient MAXI J1848-015
  in the GLIMPSE-C01 Cluster

abstract:
  We present the results of MAXI monitoring and two NuSTAR observations of the
recently discovered faint X-ray transient MAXI J1848-015. Analysis of the MAXI
light-curve shows that the source underwent a rapid flux increase beginning on
2020 December 20, followed by a rapid decrease in flux after only $\sim5$ days.
NuSTAR observations reveal that the source transitioned from a bright soft
state with unabsorbed, bolometric ($0.1$-$100$ keV) flux $F=6.9 \pm 0.1 \times
10^{-10}\,\mathrm{erg\,cm^{-2}\,s^{-1}}$, to a low hard state with flux $F=2.85
\pm 0.04 \times 10^{-10}\,\mathrm{erg\,cm^{-2}\,s^{-1}}$. Given a distance of
$3.3$ kpc, inferred via association of the source with the GLIMPSE-C01 cluster,
these fluxes correspond to an Eddington fraction of order $10^{-3}$ for an
accreting neutron star of mass $M=1.4M_\odot$, or even lower for a more massive
accretor. However, the source spectra exhibit strong relativistic reflection
features, indicating the presence of an accretion disk which extends close to
the accretor, for which we measure a high spin, $a=0.967\pm0.013$. In addition
to a change in flux and spectral shape, we find evidence for other changes
between the soft and hard states, including moderate disk truncation with the
inner disk radius increasing from $R_\mathrm{in}\approx3\,R_\mathrm{g}$ to
$R_\mathrm{in}\approx8\,R_\mathrm{g}$, narrow Fe emission whose centroid
decreases from $6.8\pm0.1$ keV to $6.3 \pm 0.1$ keV, and an increase in
low-frequency ($10^{-3}$-$10^{-1}$ Hz) variability. Due to the high spin we
conclude that the source is likely to be a black hole rather than a neutron
star, and we discuss physical interpretations of the low apparent luminosity as
well as the narrow Fe emission.


title: On Using Transformers for Speech-Separation

abstract:
  Transformers have enabled major improvements in deep learning. They often
outperform recurrent and convolutional models in many tasks while taking
advantage of parallel processing. Recently, we have proposed SepFormer, which
uses self-attention and obtains state-of-the art results on WSJ0-2/3 Mix
datasets for speech separation. In this paper, we extend our previous work by
providing results on more datasets including LibriMix, and WHAM!, WHAMR! which
include noisy and noisy-reverberant conditions. Moreover we provide denoising,
and denoising+dereverberation results in the context of speech enhancement,
respectively on WHAM! and WHAMR! datasets. We also investigate incorporating
recently proposed efficient self-attention mechanisms inside the SepFormer
model, and show that by using efficient self-attention mechanisms it is
possible to reduce the memory requirements significantly while performing
better than the popular convtasnet model on WSJ0-2Mix dataset.


title: Limit theorems for multifractal products of random fields

abstract:
  This paper investigates asymptotic properties of multifractal products of
random fields. The obtained limit theorems provide sufficient conditions for
the convergence of cumulative fields in the spaces $L_q.$ New results on the
rate of convergence of cumulative fields are presented. Simple unified
conditions for the limit theorems and the calculation of the R\'enyi function
are given. They are less restrictive than those in the known one-dimensional
results. The developed methodology is also applied to multidimensional
multifractal measures. Finally, a new class of examples of geometric
$\varphi$-sub-Gaussian random fields is presented. In this case, the general
assumptions have a simple form and can be expressed in terms of covariance
functions only.


title:
Leveraging Approximate Symbolic Models for Reinforcement Learning via
  Skill Diversity

abstract:
  Creating reinforcement learning (RL) agents that are capable of accepting and
leveraging task-specific knowledge from humans has been long identified as a
possible strategy for developing scalable approaches for solving long-horizon
problems. While previous works have looked at the possibility of using symbolic
models along with RL approaches, they tend to assume that the high-level action
models are executable at low level and the fluents can exclusively characterize
all desirable MDP states. This need not be true and this assumption overlooks
one of the central technical challenges of incorporating symbolic task
knowledge, namely, that these symbolic models are going to be an incomplete
representation of the underlying task. To this end, we introduce Symbolic-Model
Guided Reinforcement Learning, wherein we will formalize the relationship
between the symbolic model and the underlying MDP that will allow us to capture
the incompleteness of the symbolic model. We will use these models to extract
high-level landmarks that will be used to decompose the task, and at the low
level, we learn a set of diverse policies for each possible task sub-goal
identified by the landmark. We evaluate our system by testing on three
different benchmark domains and we show how even with incomplete symbolic model
information, our approach is able to discover the task structure and
efficiently guide the RL agent towards the goal.


title:
Monte Carlo Methods for Estimating the Diagonal of a Real Symmetric
  Matrix

abstract:
  For real symmetric matrices that are accessible only through matrix vector
products, we present Monte Carlo estimators for computing the diagonal
elements. Our probabilistic bounds for normwise absolute and relative errors
apply to Monte Carlo estimators based on random Rademacher, sparse Rademacher,
normalized and unnormalized Gaussian vectors, and to vectors with bounded
fourth moments. The novel use of matrix concentration inequalities in our
proofs represents a systematic model for future analyses. Our bounds mostly do
not depend on the matrix dimension, target different error measures than
existing work, and imply that the accuracy of the estimators increases with the
diagonal dominance of the matrix. An application to derivative-based global
sensitivity metrics corroborates this, as do numerical experiments on synthetic
test matrices. We recommend against the use in practice of sparse Rademacher
vectors, which are the basis for many randomized sketching and sampling
algorithms, because they tend to deliver barely a digit of accuracy even under
large sampling amounts.


title: Weighted enumeration of nonbacktracking walks on weighted graphs

abstract:
  We extend the notion of nonbacktracking walks from unweighted graphs to
graphs whose edges have a nonnegative weight. Here the weight associated with a
walk is taken to be the product over the weights along the individual edges. We
give two ways to compute the associated generating function, and corresponding
node centrality measures. One method works directly on the original graph and
one uses a line graph construction followed by a projection. The first method
is more efficient, but the second has the advantage of extending naturally to
time-evolving graphs. Computational results are also provided.


title:
The join-the-shortest-queue system in the Halfin-Whitt regime: rates of
  convergence to the diffusion limit

abstract:
  We show that the steady-state distribution of the join-the-shortest-queue
(JSQ) system converges, in the Halfin-Whitt regime, to its diffusion limit at a
rate of at least $1/\sqrt{n}$, where $n$ is the number of servers. Our proof
uses Stein's method, and, specifically, the recently proposed \emph{prelimit}
generator comparison approach. Being the first application of the prelimit
approach to a non-trivial and high-dimensional model, this paper may be helpful
to readers wishing to apply the approach to their own setting. For example, the
approach has the potential to be applied to other heavy-traffic regimes or
load-balancing policies.


title:
Rates of convergence for nonparametric estimation of singular
  distributions using generative adversarial networks

abstract:
  We consider generative adversarial networks (GAN) for estimating parameters
in a deep generative model. The data-generating distribution is assumed to
concentrate around some low-dimensional structure, making the target
distribution singular to the Lebesgue measure. Under this assumption, we obtain
convergence rates of a GAN type estimator with respect to the Wasserstein
metric. The convergence rate depends only on the noise level, intrinsic
dimension and smoothness of the underlying structure. Furthermore, the rate is
faster than that obtained by likelihood approaches, which provides insights
into why GAN approaches perform better in many real problems. A lower bound of
the minimax optimal rate is also investigated.


title: Causal Inference Using Tractable Circuits

abstract:
  The aim of this paper is to discuss a recent result which shows that
probabilistic inference in the presence of (unknown) causal mechanisms can be
tractable for models that have traditionally been viewed as intractable. This
result was reported recently to facilitate model-based supervised learning but
it can be interpreted in a causality context as follows. One can compile a
non-parametric causal graph into an arithmetic circuit that supports inference
in time linear in the circuit size. The circuit is also non-parametric so it
can be used to estimate parameters from data and to further reason (in linear
time) about the causal graph parametrized by these estimates. Moreover, the
circuit size can sometimes be bounded even when the treewidth of the causal
graph is not, leading to tractable inference on models that have been deemed
intractable previously. This has been enabled by a new technique that can
exploit causal mechanisms computationally but without needing to know their
identities (the classical setup in causal inference). Our goal is to provide a
causality-oriented exposure to these new results and to speculate on how they
may potentially contribute to more scalable and versatile causal inference.


title: Learning under Storage and Privacy Constraints

abstract:
  Storage-efficient privacy-guaranteed learning is crucial due to enormous
amounts of sensitive user data required for increasingly many learning tasks.
We propose a framework for reducing the storage cost while at the same time
providing privacy guarantees, without essential loss in the utility of the data
for learning. Our method comprises noise injection followed by lossy
compression. We show that, when appropriately matching the lossy compression to
the distribution of the added noise, the compressed examples converge, in
distribution, to that of the noise-free training data. In this sense, the
utility of the data for learning is essentially maintained, while reducing
storage and privacy leakage by quantifiable amounts. We present experimental
results on the CelebA dataset for gender classification and find that our
suggested pipeline delivers in practice on the promise of the theory: the
individuals in the images are unrecognizable (or less recognizable, depending
on the noise level), overall storage of the data is substantially reduced, with
no essential loss of the classification accuracy. As an added bonus, our
experiments suggest that our method yields a substantial boost to robustness in
the face of adversarial test data.


title: Inferring rheology from free-surface observations

abstract:
  We develop direct inversion methods for inferring the rheology of a fluid
from observations of its shallow flow. First, the evolution equation for the
free-surface flow of an inertia-less current with general constitutive law is
derived. The relationship between the volume flux of fluid and the basal
stress, $\tau_b$ is encapsulated by a single function $F(\tau_b)$, which
depends only on the constitutive law. The inversion method consists of (i)
determining the flux and basal stress from the free-surface evolution, (ii)
comparing the flux to the basal stress to constrain $F$ and (iii) inferring the
constitutive law from $F$. Examples are presented for both steady and transient
free-surface flows demonstrating that a wide range of constitutive laws can be
directly obtained. For flows in which the free-surface velocity is known, we
derive a different method, which circumvents the need to calculate the flux.


title:
Effects of Parametric and Non-Parametric Methods on High Dimensional
  Sparse Matrix Representations

abstract:
  The semantics are derived from textual data that provide representations for
Machine Learning algorithms. These representations are interpretable form of
high dimensional sparse matrix that are given as an input to the machine
learning algorithms. Since learning methods are broadly classified as
parametric and non-parametric learning methods, in this paper we provide the
effects of these type of algorithms on the high dimensional sparse matrix
representations. In order to derive the representations from the text data, we
have considered TF-IDF representation with valid reason in the paper. We have
formed representations of 50, 100, 500, 1000 and 5000 dimensions respectively
over which we have performed classification using Linear Discriminant Analysis
and Naive Bayes as parametric learning method, Decision Tree and Support Vector
Machines as non-parametric learning method. We have later provided the metrics
on every single dimension of the representation and effect of every single
algorithm detailed in this paper.


title: An Automated Approach for Privacy Leakage Identification in IoT Apps

abstract:
  This paper presents a fully automated static analysis approach and a tool,
Taint-Things, for the identification of tainted flows in SmartThings IoT apps.
Taint-Things accurately identifies all tainted flows reported by one of the
state-of-the-art tools with at least 4 times improved performance. Our approach
reports potential vulnerable tainted flows in a form of a concise security
slice, where the relevant parts of the code are given with the lines affecting
the sensitive information, which could provide security auditors with an
effective and precise tool to pinpoint security issues in SmartThings apps
under test. We also present and test ways to add precision to Taint-Things by
adding extra sensitivities; we provide different approaches for flow, path and
context sensitive analyses through modules that can be added to Taint-Things.
We present experiments to evaluate Taint-Things by running it on a SmartThings
app dataset as well as testing for precision and recall on a set generated by a
mutation framework to see how much coverage is achieved without adding false
positives. This shows an improvement in performance both in terms of speed up
to 4 folds, as well as improving the precision avoiding false positives by
providing a higher level of flow and path sensitivity analysis in comparison
with one of state of the art tools.


title: Evaluation Methods and Measures for Causal Learning Algorithms

abstract:
  The convenient access to copious multi-faceted data has encouraged machine
learning researchers to reconsider correlation-based learning and embrace the
opportunity of causality-based learning, i.e., causal machine learning (causal
learning). Recent years have therefore witnessed great effort in developing
causal learning algorithms aiming to help AI achieve human-level intelligence.
Due to the lack-of ground-truth data, one of the biggest challenges in current
causal learning research is algorithm evaluations. This largely impedes the
cross-pollination of AI and causal inference, and hinders the two fields to
benefit from the advances of the other. To bridge from conventional causal
inference (i.e., based on statistical methods) to causal learning with big data
(i.e., the intersection of causal inference and machine learning), in this
survey, we review commonly-used datasets, evaluation methods, and measures for
causal learning using an evaluation pipeline similar to conventional machine
learning. We focus on the two fundamental causal-inference tasks and
causality-aware machine learning tasks. Limitations of current evaluation
procedures are also discussed. We then examine popular causal inference
tools/packages and conclude with primary challenges and opportunities for
benchmarking causal learning algorithms in the era of big data. The survey
seeks to bring to the forefront the urgency of developing publicly available
benchmarks and consensus-building standards for causal learning evaluation with
observational data. In doing so, we hope to broaden the discussions and
facilitate collaboration to advance the innovation and application of causal
learning.


title:
Proof of a conjectured M\"obius inversion formula for Grothendieck
  polynomials

abstract:
  Schubert polynomials $\mathfrak{S}_w$ are polynomial representatives for
cohomology classes of Schubert varieties in a complete flag variety, while
Grothendieck polynomials $\mathfrak{G}_w$ are analogous representatives for the
$K$-theory classes of the structure sheaves of Schubert varieties. In the
special case that $\mathfrak{S}_w$ is a multiplicity-free sum of monomials, K.
M\'esz\'aros, L. Setiabrata, and A. St. Dizier conjectured that
$\mathfrak{G}_w$ can be easily computed from $\mathfrak{S}_w$ via M\"obius
inversion on a certain poset. We prove this conjecture.


title:
Gradient boosting machines and careful pre-processing work best: ASHRAE
  Great Energy Predictor III lessons learned

abstract:
  The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late
2019 as one of the largest machine learning competitions ever held focused on
building performance. It was hosted on the Kaggle platform and resulted in
39,402 prediction submissions, with the top five teams splitting $25,000 in
prize money. This paper outlines lessons learned from participants, mainly from
teams who scored in the top 5% of the competition. Various insights were gained
from their experience through an online survey, analysis of publicly shared
submissions and notebooks, and the documentation of the winning teams. The
top-performing solutions mostly used ensembles of Gradient Boosting Machine
(GBM) tree-based models, with the LightGBM package being the most popular. The
survey participants indicated that the preprocessing and feature extraction
phases were the most important aspects of creating the best modeling approach.
All the survey respondents used Python as their primary modeling tool, and it
was common to use Jupyter-style Notebooks as development environments. These
conclusions are essential to help steer the research and practical
implementation of building energy meter prediction in the future.


title:
Deep learning of inverse water waves problems using multi-fidelity data:
  Application to Serre-Green-Naghdi equations

abstract:
  We consider strongly-nonlinear and weakly-dispersive surface water waves
governed by equations of Boussinesq type, known as the Serre-Green-Naghdi
system; it describes future states of the free water surface and depth averaged
horizontal velocity, given their initial state. The lack of knowledge of the
velocity field as well as the initial states provided by measurements lead to
an ill-posed problem that cannot be solved by traditional techniques. To this
end, we employ physics-informed neural networks (PINNs) to generate solutions
to such ill-posed problems using only data of the free surface elevation and
depth of the water. PINNs can readily incorporate the physical laws and the
observational data, thereby enabling inference of the physical quantities of
interest. In the present study, both experimental and synthetic (generated by
numerical methods) training data are used to train PINNs. Furthermore,
multi-fidelity data are used to solve the inverse water wave problem by
leveraging both high- and low-fidelity data sets. The applicability of the PINN
methodology for the estimation of the impact of water waves onto solid
obstacles is demonstrated after deriving the corresponding equations. The
present methodology can be employed to efficiently design offshore structures
such as oil platforms, wind turbines, etc. by solving the corresponding
ill-posed inverse water waves problem.


title: A Passivity Based Framework for Safe Physical Human Robot Interaction

abstract:
  In this paper, the problem of making a safe compliant contact between a human
and an assistive robot is considered. Users with disabilities have a need to
utilize their assistive robots for physical human-robot interaction (PHRI)
during certain activities of daily living (ADLs). Specifically, we propose a
hybrid force/velocity/attitude control for a PHRI system based on measurements
from a 6-axis force/torque sensor mounted on the robot wrist. While
automatically aligning the end-effector surface with the unknown environmental
(human) surface, a desired commanded force is applied in the normal direction
while following desired velocity commands in the tangential directions. A
Lyapunov based stability analysis is provided to prove both convergence as well
as passivity of the interaction to ensure both performance and safety.
Simulation as well as experimental results verify the performance and
robustness of the proposed hybrid controller in the presence of dynamic
uncertainties as well as safe physical human-robot interactions for a
kinematically redundant robotic manipulator.


title:
Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual
  Recognition

abstract:
  This paper tackles the problem of subject adaptive EEG-based visual
recognition. Its goal is to accurately predict the categories of visual stimuli
based on EEG signals with only a handful of samples for the target subject
during training. The key challenge is how to appropriately transfer the
knowledge obtained from abundant data of source subjects to the subject of
interest. To this end, we introduce a novel method that allows for learning
subject-independent representation by increasing the similarity of features
sharing the same class but coming from different subjects. With the dedicated
sampling principle, our model effectively captures the common knowledge shared
across different subjects, thereby achieving promising performance for the
target subject even under harsh problem settings with limited data.
Specifically, on the EEG-ImageNet40 benchmark, our model records the top-1 /
top-3 test accuracy of 72.6% / 91.6% when using only five EEG samples per class
for the target subject. Our code is available at
https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Inter_Subject_Contrastive_Learning_for_EEG.


title:
Redactor: Targeted Disinformation Generation using Probabilistic
  Decision Boundaries

abstract:
  Information leakage is becoming a critical problem as various information
becomes publicly available by mistake, and machine learning models train on
that data to provide services. As a result, one's private information could
easily be memorized by such trained models. Unfortunately, deleting information
is out of the question as the data is already exposed to the Web or third-party
platforms. Moreover, we cannot necessarily control the labeling process and the
model trainings by other parties either. In this setting, we study the problem
of targeted disinformation where the goal is to lower the accuracy of inference
attacks on a specific target (e.g., a person's profile) only using data
insertion. While our problem is related to data privacy and defenses against
exploratory attacks, our techniques are inspired by targeted data poisoning
attacks with some key differences. We show that our problem is best solved by
finding the closest points to the target in the input space that will be
labeled as a different class. Since we do not control the labeling process, we
instead conservatively estimate the labels probabilistically by combining
decision boundaries of multiple classifiers using data programming techniques.
We also propose techniques for making the disinformation realistic. Our
experiments show that a probabilistic decision boundary can be a good proxy for
labelers, and that our approach outperforms other targeted poisoning methods
when using end-to-end training on real datasets.


title: Difference in Differences with Time-Varying Covariates

abstract:
  This paper considers identification and estimation of causal effect
parameters from participating in a binary treatment in a difference in
differences (DID) setup when the parallel trends assumption holds after
conditioning on observed covariates. Relative to existing work in the
econometrics literature, we consider the case where the value of covariates can
change over time and, potentially, where participating in the treatment can
affect the covariates themselves. We propose new empirical strategies in both
cases. We also consider two-way fixed effects (TWFE) regressions that include
time-varying regressors, which is the most common way that DID identification
strategies are implemented under conditional parallel trends. We show that,
even in the case with only two time periods, these TWFE regressions are not
generally robust to (i) time-varying covariates being affected by the
treatment, (ii) treatment effects and/or paths of untreated potential outcomes
depending on the level of time-varying covariates in addition to only the
change in the covariates over time, (iii) treatment effects and/or paths of
untreated potential outcomes depending on time-invariant covariates, (iv)
treatment effect heterogeneity with respect to observed covariates, and (v)
violations of strong functional form assumptions, both for outcomes over time
and the propensity score, that are unlikely to be plausible in most DID
applications. Thus, TWFE regressions can deliver misleading estimates of causal
effect parameters in a number of empirically relevant cases. We propose both
doubly robust estimands and regression adjustment/imputation strategies that
are robust to these issues while not being substantially more challenging to
implement.


title: A Least Square Approach to Semi-supervised Local Cluster Extraction

abstract:
  A least square semi-supervised local clustering algorithm based on the idea
of compressed sensing are proposed to extract clusters from a graph with known
adjacency matrix. The algorithm is based on a two stage approaches similar to
the one in \cite{LaiMckenzie2020}. However, under a weaker assumption and with
less computational complexity than the one in \cite{LaiMckenzie2020}, the
algorithm is shown to be able to find a desired cluster with high probability.
Several numerical experiments including the synthetic data and real data such
as MNIST, AT\&T and YaleB human faces data sets are conducted to demonstrate
the performance of our algorithm.


title:
Channel Capacity for Adversaries with Computationally Bounded
  Observations

abstract:
  We study reliable communication over point-to-point adversarial channels in
which the adversary can observe the codeword via some function that takes the
$n$-bit codeword as input and computes an $rn$-bit output. We consider the
scenario where the $rn$-bit observation is computationally bounded -- the
adversary is free to choose an arbitrary observation function as long as the
function can be computed using a polynomial amount of computational resources.
This observation-based restriction differs from conventional channel-based
computational limitations, where in the later case, the resource limitation
applies to the computation of the channel error. For some number $r^* \in
[0,1]$ and for $r \in [0,r^*]$, we characterize the capacity of the above
channel. For this range of $r$, we find that the capacity is identical to the
completely obvious setting ($r=0$). This result can be viewed as a
generalization of known results on myopic adversaries and channels with active
eavesdroppers for which the observation process depends on a fixed distribution
and fixed-linear structure, respectively, that cannot be chosen arbitrarily by
the adversary.


title:
Universality of parametric Coupling Flows over parametric
  diffeomorphisms

abstract:
  Invertible neural networks based on Coupling Flows CFlows) have various
applications such as image synthesis and data compression. The approximation
universality for CFlows is of paramount importance to ensure the model
expressiveness. In this paper, we prove that CFlows can approximate any
diffeomorphism in C^k-norm if its layers can approximate certain
single-coordinate transforms. Specifically, we derive that a composition of
affine coupling layers and invertible linear transforms achieves this
universality. Furthermore, in parametric cases where the diffeomorphism depends
on some extra parameters, we prove the corresponding approximation theorems for
our proposed parametric coupling flows named Para-CFlows. In practice, we apply
Para-CFlows as a neural surrogate model in contextual Bayesian optimization
tasks, to demonstrate its superiority over other neural surrogate models in
terms of optimization performance.


title:
Fluctuations of partition functions of directed polymers in weak
  disorder beyond the $L^2$-phase

abstract:
  We study the directed polymer model in a bounded environment in weak disorder
but without $L^2$-boundedness, specifically the speed of homogenization for the
field $(W_n^{0,x})_{x\in\mathbb Z^d}$, where $W_n^{0,x}$ denotes the associated
martingale for the polymer starting from $x$. We show that a suitably
re-centered spatial average over a set of diameter $n^{1/2}$ convergence to
zero at rate $n^{-\xi+o(1)}$, where the exponent is an explicit function of the
inverse temperature $\beta$.


title:
Interaction between deformation twinning and dislocation slip in
  polycrystalline solids

abstract:
  Deformation twinning is a form of permanent deformation that is commonly
observed in low symmetry crystals such as hexagonal close-packed (hcp) metals.
With recent increased interest in using hcp metals, such as magnesium, in
structural, automotive, and armor applications due to their high strength to
weight ratio, there is a need for a comprehensive understanding of deformation
twinning and its interaction with dislocation slip. A great deal has been
learned at the microscopic level where individual dislocations interact with
twin boundaries through atomistic simulations, and at the macroscopic level by
ignoring morphology and treating twinning as `pseudo-slip'. However, twins form
collectively across multiple grains with complex morphology that affects the
bulk behavior. These mesoscale aspects have been less studied and are the focus
of this paper. We present a model that describes the twin and slip morphology,
its evolution, and interactions in a unified manner at the scale of several
grains and use it to study the implications on macroscopic behavior. The key
ideas are to combine a phase-field model of twinning with a crystal plasticity
model of slip, and to implement it in parallel on graphic processing units for
fast computations.


title:
Identification of topological phases using classically-optimized
  variational quantum eigensolver

abstract:
  Variational quantum eigensolver (VQE) is regarded as a promising candidate of
hybrid quantum-classical algorithm for the near-term quantum computers.
Meanwhile, VQE is confronted with a challenge that statistical error associated
with the measurement as well as systematic error could significantly hamper the
optimization. To circumvent this issue, we propose classically-optimized VQE
(co-VQE), where the whole process of the optimization is efficiently conducted
on a classical computer. The efficacy of the method is guaranteed by the
observation that quantum circuits with a constant (or logarithmic) depth are
classically tractable via simulations of local subsystems. In co-VQE, we only
use quantum computers to measure nonlocal quantities after the parameters are
optimized. As proof-of-concepts, we present numerical experiments on quantum
spin models with topological phases. After the optimization, we identify the
topological phases by nonlocal order parameters as well as unsupervised machine
learning on inner products between quantum states. The proposed method
maximizes the advantage of using quantum computers while avoiding strenuous
optimization on noisy quantum devices. Furthermore, in terms of quantum machine
learning, our study shows an intriguing approach that employs quantum computers
to generate data of quantum systems while using classical computers for the
learning process.


title:
Semiclassical magnetotransport including the effects of the Berry
  curvature and Lorentz force

abstract:
  In topological semimetals and insulators, negative longitudinal
magnetoresistance and angle-dependent planar Hall effect have been reported
arising from the Berry curvature. Using the Boltzmann transport theory, we
present a closed-form expression for the nonequilibrium distribution function
which includes both the effects of the Berry curvature and Lorentz force. Using
this formulation, we obtain analytical expressions for conductivity and
resistivity tensors in Weyl semimetals demonstrating a non-monotonic field
dependence arising from the competition between the two effects.


title: Traffic-aware Gateway Placement and Queue Management in Flying Networks

abstract:
  Unmanned Aerial Vehicles (UAVs) have emerged as adequate platforms to carry
communications nodes, including Wi-Fi Access Points and cellular Base Stations.
This has led to the concept of flying networks composed of UAVs as a flexible
and agile solution to provide on-demand wireless connectivity anytime,
anywhere. However, state of the art works have been focused on optimizing the
placement of the access network providing connectivity to ground users,
overlooking the backhaul network design. In order to improve the overall
Quality of Service (QoS) offered to ground users, the placement of Flying
Gateways (FGWs) and the size of the queues configured in the UAVs need to be
carefully defined to meet strict performance requirements. The main
contribution of this article is a traffic-aware gateway placement and queue
management (GPQM) algorithm for flying networks. GPQM takes advantage of
knowing in advance the positions of the UAVs and their traffic demand to
determine the FGW position and the queue size of the UAVs, in order to maximize
the aggregate throughput and provide stochastic delay guarantees. GPQM is
evaluated by means of ns-3 simulations, considering a realistic wireless
channel model. The results demonstrate significant gains in the QoS offered
when GPQM is used.


title:
User Satisfaction Estimation with Sequential Dialogue Act Modeling in
  Goal-oriented Conversational Systems

abstract:
  User Satisfaction Estimation (USE) is an important yet challenging task in
goal-oriented conversational systems. Whether the user is satisfied with the
system largely depends on the fulfillment of the user's needs, which can be
implicitly reflected by users' dialogue acts. However, existing studies often
neglect the sequential transitions of dialogue act or rely heavily on annotated
dialogue act labels when utilizing dialogue acts to facilitate USE. In this
paper, we propose a novel framework, namely USDA, to incorporate the sequential
dynamics of dialogue acts for predicting user satisfaction, by jointly learning
User Satisfaction Estimation and Dialogue Act Recognition tasks. In specific,
we first employ a Hierarchical Transformer to encode the whole dialogue
context, with two task-adaptive pre-training strategies to be a second-phase
in-domain pre-training for enhancing the dialogue modeling ability. In terms of
the availability of dialogue act labels, we further develop two variants of
USDA to capture the dialogue act information in either supervised or
unsupervised manners. Finally, USDA leverages the sequential transitions of
both content and act features in the dialogue to predict the user satisfaction.
Experimental results on four benchmark goal-oriented dialogue datasets across
different applications show that the proposed method substantially and
consistently outperforms existing methods on USE, and validate the important
role of dialogue act sequences in USE.


title:
Two-loop infrared singularities in the production of a Higgs boson
  associated with a top-quark pair

abstract:
  The associated production of a Higgs boson and a top-quark pair is important
for probing the Yukawa coupling of the top quark, and calls for better
theoretical modeling. In this paper, we calculate the two-loop infrared
divergences in $t\bar{t}H$ production at hadron colliders. To do that we
compute the one-loop amplitudes to higher orders in the dimensional regulator
$\epsilon$. Numeric results for the infrared poles are given as a reference at
several representative phase-space points. The result in this work servers as a
part of the ongoing efforts towards the $t\bar{t}H$ cross sections at the
next-to-next-to-leading order.


title:
Global convergence and asymptotic optimality of the heavy ball method
  for a class of non-convex optimization problems

abstract:
  In this letter we revisit the famous heavy ball method and study its global
convergence for a class of non-convex problems with sector-bounded gradient. We
characterize the parameters that render the method globally convergent and
yield the best $R$-convergence factor. We show that for this family of
functions, this convergence factor is superior to the factor obtained from the
triple momentum method.


title: Mobile Based Gradebook with Student Outcomes Analytics

abstract:
  Mobile applications and other integration of information and communication
technology (ICT) have become well-known in education to monitor teaching and
learning activities. The analysis of student learning through evaluation is a
growing area of interest for teachers in higher education aiming to enhance
students learning experience. This paper describes a development of student
outcomes monitoring tool that applies analytics to provide feedback to students
as they progress in the ladder of achieving the intended learning outcomes. The
student outcomes focus on the core elements of the curriculum; it offers
detailed student outcomes where the result in courses evaluations and
recordings are tracked and analyzed. The data revealed that the student
outcomes monitoring and analytics tool is adequate in providing constant
feedback to students on the achievement of the desired learning outcomes as
well as support teachers in planning the teaching and learning activities,
enhance feedback system, academic planning and improvement.


title: Dataset Condensation with Contrastive Signals

abstract:
  Recent studies have demonstrated that gradient matching-based dataset
synthesis, or dataset condensation (DC), methods can achieve state-of-the-art
performance when applied to data-efficient learning tasks. However, in this
study, we prove that the existing DC methods can perform worse than the random
selection method when task-irrelevant information forms a significant part of
the training dataset. We attribute this to the lack of participation of the
contrastive signals between the classes resulting from the class-wise gradient
matching strategy. To address this problem, we propose Dataset Condensation
with Contrastive signals (DCC) by modifying the loss function to enable the DC
methods to effectively capture the differences between classes. In addition, we
analyze the new loss function in terms of training dynamics by tracking the
kernel velocity. Furthermore, we introduce a bi-level warm-up strategy to
stabilize the optimization. Our experimental results indicate that while the
existing methods are ineffective for fine-grained image classification tasks,
the proposed method can successfully generate informative synthetic datasets
for the same tasks. Moreover, we demonstrate that the proposed method
outperforms the baselines even on benchmark datasets such as SVHN, CIFAR-10,
and CIFAR-100. Finally, we demonstrate the high applicability of the proposed
method by applying it to continual learning tasks.


title: Nonequilibrium topological spin textures in momentum space

abstract:
  Nonequilibrium quantum dynamics of many-body systems is the frontier of
condensed matter physics; recent advances in various time-resolved
spectroscopic techniques continue to reveal rich phenomena. Angle-resolved
photoemission spectroscopy (ARPES) as one powerful technique can resolve
electronic energy, momentum, and spin along the time axis after excitation.
However, dynamics of spin textures in momentum space remains mostly unexplored.
Here we demonstrate theoretically that the photoexcited surface state of
genuine or magnetically doped topological insulators shows novel topological
spin textures, i.e., tornado-like patterns, in the spin-resolved ARPES. We
systematically reveal its origin as a unique nonequilibrium photoinduced
topological winding phenomenon. As all intrinsic and extrinsic topological
helicity factors of both material and light are embedded in a robust and
delicate manner, the tornado patterns not only allow a remarkable tomography of
such important system information, but also enable various unique dichroic
topological switchings of the momentum-space spin texture. These results open a
new direction of nonequilibrium topological spin states in quantum materials.


title: Soft Actor-Critic with Inhibitory Networks for Faster Retraining

abstract:
  Reusing previously trained models is critical in deep reinforcement learning
to speed up training of new agents. However, it is unclear how to acquire new
skills when objectives and constraints are in conflict with previously learned
skills. Moreover, when retraining, there is an intrinsic conflict between
exploiting what has already been learned and exploring new skills. In soft
actor-critic (SAC) methods, a temperature parameter can be dynamically adjusted
to weight the action entropy and balance the explore $\times$ exploit
trade-off. However, controlling a single coefficient can be challenging within
the context of retraining, even more so when goals are contradictory. In this
work, inspired by neuroscience research, we propose a novel approach using
inhibitory networks to allow separate and adaptive state value evaluations, as
well as distinct automatic entropy tuning. Ultimately, our approach allows for
controlling inhibition to handle conflict between exploiting less risky,
acquired behaviors and exploring novel ones to overcome more challenging tasks.
We validate our method through experiments in OpenAI Gym environments.


title: On some non-rigid unit distance patterns

abstract:
  A recent generalization of the Erd\H{o}s Unit Distance Problem, proposed by
Palsson, Senger and Sheffer, asks for the maximum number of unit distance paths
with a given number of vertices in the plane and in $3$-space. Studying a
variant of this question, we prove sharp bounds on the number of unit distance
paths and cycles on the sphere of radius $1/\sqrt{2}$. We also consider a
similar problem about $3$-regular unit distance graphs in $\mathbb{R}^3$.


title: Monolithic Kerr and electro-optic hybrid microcombs

abstract:
  Advances in microresonator-based soliton generation promise chip-scale
integration of optical frequency comb for applications spanning from time
keeping to frequency synthesis. Miniaturized cavities harness Kerr nonlinearity
and enable terahertz soliton repetition rates. However, such high repetition
rates are not amenable to direct electronic detection. Here, we demonstrate
hybrid Kerr and electro-optic microcombs using the lithium niobate thin film
that exhibits both Kerr and Pockels nonlinearities. By interleaving the
high-repetition-rate Kerr soliton comb with the low-repetition-rate
electro-optic comb on the same waveguide, the wide Kerr soliton mode spacing is
divided within a single chip, allowing for subsequent electronic detection and
feedback control of the soliton repetition rate. Our work establishes an
integrated electronic interface to Kerr solitons of terahertz repetition rates,
paving the path towards chipscale optical-to-microwave frequency division and
comb locking.


title:
Wall Ion Loss Reduction by Acceleration Zone Shifting in Anode-Layer
  Hall Thruster

abstract:
  In this study, wall ion loss and erosion rate are evaluated in the RAIJIN
thruster. The novelty of this work is the detailed analysis of measured
guard-ring (metallic channel wall) current, which enables a fast evaluation of
the wall ion loss and erosion rate. The current-voltage characteristics of the
guard-ring current indicate that the sheath on the guard rings is in the ion
saturation regime and that the guard-ring current can be used as a wall ion
flux indicator. The experimental results demonstrate that the wall ion loss can
be significantly reduced in the magnetic configuration of RAIJIN thrusters with
the acceleration zone shifting.


title: Combining Evidence

abstract:
  The problem of combining the evidence concerning an unknown, contained in
each of $k$ Bayesian inference bases, is discussed. This can be considered as a
generalization of the problem of pooling $k$ priors to determine a consensus
prior. The linear opinion pool of Stone (1961) is seen to have the most
appropriate properties for this role. In particular, linear pooling preserves a
consensus with respect to the evidence and other rules do not. While linear
pooling does not preserve prior independence, it is shown that it still behaves
appropriately with respect to the expression of evidence in such a context. For
the general problem of combining evidence, Jeffrey conditionalization plays a
key role.


title:
Bayesian calibration of simulation models: A tutorial and an Australian
  smoking behaviour model

abstract:
  Simulation models of epidemiological, biological, ecological, and
environmental processes are increasingly being calibrated using Bayesian
statistics. The Bayesian approach provides simple rules to synthesise multiple
data sources and to calculate uncertainty in model output due to uncertainty in
the calibration data. As the number of tutorials and studies published grow,
the solutions to common difficulties in Bayesian calibration across these
fields have become more apparent, and a step-by-step process for successful
calibration across all these fields is emerging. We provide a statement of the
key steps in a Bayesian calibration, and we outline analyses and approaches to
each step that have emerged from one or more of these applied sciences. Thus we
present a synthesis of Bayesian calibration methodologies that cut across a
number of scientific disciplines.
  To demonstrate these steps and to provide further detail on the computations
involved in Bayesian calibration, we calibrated a compartmental model of
tobacco smoking behaviour in Australia. We found that the proportion of a birth
cohort estimated to take up smoking before they reach age 20 years in 2016 was
at its lowest value since the early 20th century, and that quit rates were at
their highest. As a novel outcome, we quantified the rate that ex-smokers
switched to reporting as a 'never smoker' when surveyed later in life; a
phenomenon that, to our knowledge, has never been quantified using
cross-sectional survey data.


title:
3TO: THz-Enabled Throughput and Trajectory Optimization of UAVs in 6G
  Networks by Proximal Policy Optimization Deep Reinforcement Learning

abstract:
  Next-generation networks need to meet ubiquitous and high data-rate demand.
Therefore, this paper considers the throughput and trajectory optimization of
terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation
(6G) communication networks. In the considered scenario, multiple UAVs must
provide on-demand terabits per second (TB/s) services to an urban area along
with existing terrestrial networks. However, THz-empowered UAVs pose some new
constraints, e.g., dynamic THz-channel conditions for ground users (GUs)
association and UAV trajectory optimization to fulfill GU's throughput demands.
Thus, a framework is proposed to address these challenges, where a joint
UAVs-GUs association, transmit power, and the trajectory optimization problem
is studied. The formulated problem is mixed-integer non-linear programming
(MINLP), which is NP-hard to solve. Consequently, an iterative algorithm is
proposed to solve three sub-problems iteratively, i.e., UAVs-GUs association,
transmit power, and trajectory optimization. Simulation results demonstrate
that the proposed algorithm increased the throughput by up to 10%, 68.9%, and
69.1% respectively compared to baseline algorithms.


title: Benchmarking Deep Models for Salient Object Detection

abstract:
  In recent years, deep network-based methods have continuously refreshed
state-of-the-art performance on Salient Object Detection (SOD) task. However,
the performance discrepancy caused by different implementation details may
conceal the real progress in this task. Making an impartial comparison is
required for future researches. To meet this need, we construct a general
SALient Object Detection (SALOD) benchmark to conduct a comprehensive
comparison among several representative SOD methods. Specifically, we
re-implement 14 representative SOD methods by using consistent settings for
training. Moreover, two additional protocols are set up in our benchmark to
investigate the robustness of existing methods in some limited conditions. In
the first protocol, we enlarge the difference between objectness distributions
of train and test sets to evaluate the robustness of these SOD methods. In the
second protocol, we build multiple train subsets with different scales to
validate whether these methods can extract discriminative features from only a
few samples. In the above experiments, we find that existing loss functions
usually specialized in some metrics but reported inferior results on the
others. Therefore, we propose a novel Edge-Aware (EA) loss that promotes deep
networks to learn more discriminative features by integrating both pixel- and
image-level supervision signals. Experiments prove that our EA loss reports
more robust performances compared to existing losses.


title:
Feedback Linearization Based Tracking Control of A Tilt-rotor with
  Cat-trot Gait Plan

abstract:
  With the introduction of the laterally bounded forces, the tilt-rotor gains
more flexibility in the controller design. Typical feedback linearization
methods utilize all the inputs in controlling this vehicle; the magnitudes as
well as the directions of the thrusts are maneuvered simultaneously based on a
unified control rule. Although several promising results indicate that these
controllers may track the desired complicated trajectories, the tilting angles
are required to change relatively fast or in large scale during the flight,
which turns to be a challenge in application. The recent gait plan for a
tilt-rotor may solve this problem; the tilting angles are fixed or vary in a
predetermined pattern without being maneuvered by the control algorithm.
Carefully avoiding the singular decoupling matrix, several attitudes can be
tracked without changing the tilting angles frequently. While the position was
not directly regulated in that research, which left the position-tracking still
an open question. In this research, we elucidate the coupling relationship
between the position and the attitude. Based on this, we design the
position-tracking controller, adopting feedback linearization. A cat-trot gait
is further designed for a tilt-rotor to track the reference; three types of
references are designed for our tracking experiments: setpoint, uniform
rectilinear motion, and uniform circular motion. The significant improvement
with less steady state error is witnessed after equipping with our modified
attitude-position decoupler. It is also found that the frequency of the
cat-trot gait highly influenced the steady state error.


title: Reinforcement Learning for Shared Autonomy Drone Landings

abstract:
  Novice pilots find it difficult to operate and land unmanned aerial vehicles
(UAVs), due to the complex UAV dynamics, challenges in depth perception, lack
of expertise with the control interface and additional disturbances from the
ground effect. Therefore we propose a shared autonomy approach to assist pilots
in safely landing a UAV under conditions where depth perception is difficult
and safe landing zones are limited. Our approach comprises of two modules: a
perception module that encodes information onto a compressed latent
representation using two RGB-D cameras and a policy module that is trained with
the reinforcement learning algorithm TD3 to discern the pilot's intent and to
provide control inputs that augment the user's input to safely land the UAV.
The policy module is trained in simulation using a population of simulated
users. Simulated users are sampled from a parametric model with four
parameters, which model a pilot's tendency to conform to the assistant,
proficiency, aggressiveness and speed. We conduct a user study (n = 28) where
human participants were tasked with landing a physical UAV on one of several
platforms under challenging viewing conditions. The assistant, trained with
only simulated user data, improved task success rate from 51.4% to 98.2%
despite being unaware of the human participants' goal or the structure of the
environment a priori. With the proposed assistant, regardless of prior piloting
experience, participants performed with a proficiency greater than the most
experienced unassisted participants.


title:
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving
  Cooperative Learning

abstract:
  Cooperative learning, that enables two or more data owners to jointly train a
model, has been widely adopted to solve the problem of insufficient training
data in machine learning. Nowadays, there is an urgent need for institutions
and organizations to train a model cooperatively while keeping each other's
data privately. To address the issue of privacy-preserving in collaborative
learning, secure outsourced computation and federated learning are two typical
methods. Nevertheless, there are many drawbacks for these two methods when they
are leveraged in cooperative learning. For secure outsourced computation,
semi-honest servers need to be introduced. Once the outsourced servers collude
or perform other active attacks, the privacy of data will be disclosed. For
federated learning, it is difficult to apply to the scenarios where vertically
partitioned data are distributed over multiple parties. In this work, we
propose a multi-party mixed protocol framework, ABG$^n$, which effectively
implements arbitrary conversion between Arithmetic sharing (A), Boolean sharing
(B) and Garbled-Circuits sharing (G) for $n$-party scenarios. Based on ABG$^n$,
we design a privacy-preserving multi-party cooperative learning system, which
allows different data owners to cooperate in machine learning in terms of data
security and privacy-preserving. Additionally, we design specific
privacy-preserving computation protocols for some typical machine learning
methods such as logistic regression and neural networks. Compared with previous
work, the proposed method has a wider scope of application and does not need to
rely on additional servers. Finally, we evaluate the performance of ABG$^n$ on
the local setting and on the public cloud setting. The experiments indicate
that ABG$^n$ has excellent performance, especially in the network environment
with low latency.


title: Model-Based Offline Meta-Reinforcement Learning with Regularization

abstract:
  Existing offline reinforcement learning (RL) methods face a few major
challenges, particularly the distributional shift between the learned policy
and the behavior policy. Offline Meta-RL is emerging as a promising approach to
address these challenges, aiming to learn an informative meta-policy from a
collection of tasks. Nevertheless, as shown in our empirical studies, offline
Meta-RL could be outperformed by offline single-task RL methods on tasks with
good quality of datasets, indicating that a right balance has to be delicately
calibrated between "exploring" the out-of-distribution state-actions by
following the meta-policy and "exploiting" the offline dataset by staying close
to the behavior policy. Motivated by such empirical analysis, we explore
model-based offline Meta-RL with regularized Policy Optimization (MerPO), which
learns a meta-model for efficient task structure inference and an informative
meta-policy for safe exploration of out-of-distribution state-actions. In
particular, we devise a new meta-Regularized model-based Actor-Critic (RAC)
method for within-task policy optimization, as a key building block of MerPO,
using conservative policy evaluation and regularized policy improvement; and
the intrinsic tradeoff therein is achieved via striking the right balance
between two regularizers, one based on the behavior policy and the other on the
meta-policy. We theoretically show that the learnt policy offers guaranteed
improvement over both the behavior policy and the meta-policy, thus ensuring
the performance improvement on new tasks via offline Meta-RL. Experiments
corroborate the superior performance of MerPO over existing offline Meta-RL
methods.


title:
Towards Micro-video Thumbnail Selection via a Multi-label
  Visual-semantic Embedding Model

abstract:
  The thumbnail, as the first sight of a micro-video, plays a pivotal role in
attracting users to click and watch. While in the real scenario, the more the
thumbnails satisfy the users, the more likely the micro-videos will be clicked.
In this paper, we aim to select the thumbnail of a given micro-video that meets
most users` interests. Towards this end, we present a multi-label
visual-semantic embedding model to estimate the similarity between the pair of
each frame and the popular topics that users are interested in. In this model,
the visual and textual information is embedded into a shared semantic space,
whereby the similarity can be measured directly, even the unseen words.
Moreover, to compare the frame to all words from the popular topics, we devise
an attention embedding space associated with the semantic-attention projection.
With the help of these two embedding spaces, the popularity score of a frame,
which is defined by the sum of similarity scores over the corresponding visual
information and popular topic pairs, is achieved. Ultimately, we fuse the
visual representation score and the popularity score of each frame to select
the attractive thumbnail for the given micro-video. Extensive experiments
conducted on a real-world dataset have well-verified that our model
significantly outperforms several state-of-the-art baselines.


title: TRGP: Trust Region Gradient Projection for Continual Learning

abstract:
  Catastrophic forgetting is one of the major challenges in continual learning.
To address this issue, some existing methods put restrictive constraints on the
optimization space of the new task for minimizing the interference to old
tasks. However, this may lead to unsatisfactory performance for the new task,
especially when the new task is strongly correlated with old tasks. To tackle
this challenge, we propose Trust Region Gradient Projection (TRGP) for
continual learning to facilitate the forward knowledge transfer based on an
efficient characterization of task correlation. Particularly, we introduce a
notion of `trust region' to select the most related old tasks for the new task
in a layer-wise and single-shot manner, using the norm of gradient projection
onto the subspace spanned by task inputs. Then, a scaled weight projection is
proposed to cleverly reuse the frozen weights of the selected old tasks in the
trust region through a layer-wise scaling matrix. By jointly optimizing the
scaling matrices and the model, where the model is updated along the directions
orthogonal to the subspaces of old tasks, TRGP can effectively prompt knowledge
transfer without forgetting. Extensive experiments show that our approach
achieves significant improvement over related state-of-the-art methods.