title: Benders Adaptive-Cuts Method for Two-Stage Stochastic Programs

abstract:
  Benders decomposition is one of the most applied methods to solve two-stage
stochastic problems (TSSP) with a large number of scenarios. The main idea
behind the Benders decomposition is to solve a large problem by replacing the
values of the second-stage subproblems with individual variables, and
progressively forcing those variables to reach the optimal value of the
subproblems, dynamically inserting additional valid constraints, known as
Benders cuts. Most traditional implementations add a cut for each scenario
(multi-cut) or a single-cut that includes all scenarios. In this paper we
present a novel Benders adaptive-cuts method, where the Benders cuts are
aggregated according to a partition of the scenarios, which is dynamically
refined using the LP-dual information of the subproblems. This scenario
aggregation/disaggregation is based on the Generalized Adaptive Partitioning
Method (GAPM), which has been successfully applied to TSSPs. We formalize this
hybridization of Benders decomposition and the GAPM, by providing sufficient
conditions under which an optimal solution of the deterministic equivalent can
be obtained in a finite number of iterations. Our new method can be interpreted
as a compromise between the Benders single-cuts and multi-cuts methods, drawing
on the advantages of both sides, by rendering the initial iterations faster (as
for the single-cuts Benders) and ensuring the overall faster convergence (as
for the multi-cuts Benders). Computational experiments on two stochastic
network flow problems validate these statements, showing that the new method
outperforms the other implementations of Benders method, as well as other
standard methods for solving TSSPs, in particular when the number of scenarios
is very large.


title: Proton and pion distribution functions in counterpoint

abstract:
  Working with proton and pion valence distribution functions (DFs) determined
consistently at the same, unique hadron scale and exploiting the possibility
that there is an effective charge which defines an evolution scheme for DFs
that is all-orders exact, we obtain a unified body of predictions for all
proton and pion DFs - valence, glue, and four-flavour-separated sea. Whilst the
hadron light-front momentum fractions carried by identifiable parton classes
are the same for the proton and pion at any scale, the pointwise behaviour of
the DFs is strongly hadron-dependent. All calculated distributions comply with
quantum chromodynamics constraints on low- and high-$x$ scaling behaviour and,
owing to emergent hadron mass, pion DFs are the most dilated. These results aid
in elucidating the sources of similarities and differences between proton and
pion structure.


title:
Ultimate limit for optical losses in gold, revealed by quantitative
  near-field microscopy

abstract:
  We report thorough measurements of surface plasmon polaritons (SPPs) running
along nearly perfect air-gold interfaces formed by atomically flat surfaces of
chemically synthesized gold monocrystals. By means of amplitude- and
phase-resolved near-field microscopy, we obtain their propagation length and
effective mode index at visible wavelengths (532, 594, 632.8, 729, and 800 nm).
The measured values are compared with the values obtained from the dielectric
functions of gold that are reported in literature. Importantly, a reported
dielectric function of monocrystalline gold implies $\sim 1.5$ times shorter
propagation lengths than those observed in our experiments, whereas a
dielectric function reported for properly fabricated polycrystalline gold leads
to SPP propagation lengths matching our results. We argue that the SPP
propagation lengths measured in our experiments signify the ultimate limit of
optical losses in gold, encouraging further comprehensive characterization of
optical material properties of pure gold as well as other plasmonic materials.


title: Bounded Generation by semi-simple elements: quantitative results

abstract:
  We prove that for a number field $F$, the distribution of the points of a set
$\Sigma \subset \mathbb{A}_F^n$ with a purely exponential parametrization, for
example a set of matrices boundedly generated by semi-simple (diagonalizable)
elements, is of at most logarithmic size when ordered by height. As a
consequence, one obtains that a linear group $\Gamma\subset \mathrm{GL}_n(K)$
over a field $K$ of characteristic zero admits a purely exponential
parametrization if and only if it is finitely generated and the connected
component of its Zariski closure is a torus. Our results are obtained via a key
inequality about the heights of minimal $m$-tuples for purely exponential
parametrizations. One main ingredient of our proof is Evertse's strengthening
of the $S$-Unit Equation Theorem.


title: Real time spectrogram inversion on mobile phone

abstract:
  With the growth of computing power on mobile phones and privacy concerns over
user's data, on-device real time speech processing has become an important
research topic. In this paper, we focus on methods for real time spectrogram
inversion, where an algorithm receives a portion of the input signal (e.g., one
frame) and processes it incrementally, i.e., operating in streaming mode. We
present a real time Griffin Lim(GL) algorithm using a sliding window approach
in STFT domain. The proposed algorithm is 2.4x faster than real time on the ARM
CPU of a Pixel4. In addition we explore a neural vocoder operating in streaming
mode and demonstrate the impact of looking ahead on perceptual quality. As
little as one hop size (12.5ms) of lookahead is able to significantly improve
perceptual quality in comparison to a causal model. We compare GL with the
neural vocoder and show different trade-offs in terms of perceptual quality,
on-device latency, algorithmic delay, memory footprint and noise sensitivity.
For fair quality assessment of the GL approach, we use input log magnitude
spectrogram without mel transformation. We evaluate presented real time
spectrogram inversion approaches on clean, noisy and atypical speech.


title:
FlexKeys: Rapidly Customizable 3D Printed Tactile Input Devices with No
  Assembly Required

abstract:
  Physical input devices serve as a tactile interface between users and
computing systems. These devices are often complex assemblies that consist of
both electrical and mechanical components making customization difficult and
out of reach for non-engineers. While these components can now be 3D printed on
demand, they must still be independently designed and assembled. We present
FlexKeys, an approach in which devices that include both electrical and
deformable components can be created in a single print on a multi-material 3D
printer, requiring no assembly. Designers can customize devices including the
input type, travel distance and layout of keys, textures of surfaces, and route
all electrical signals directly to a microcontroller socket. In many instances,
these devices require no support material, producing a functional device the
moment a print finishes. We demonstrate this approach by creating a customized
keyboard and report on validation measurements of individual input keys as well
as highlighting additional designs. This work provides the first step towards
lowering the barrier to entry for non-engineers to design custom tactile
inputs, enabling occupational and physical therapists, clinicians, and
educators to design and create devices directly based on their assessments of
individual user needs.


title: There is a Time and Place for Reasoning Beyond the Image

abstract:
  Images are often more significant than only the pixels to human eyes, as we
can infer, associate, and reason with contextual information from other sources
to establish a more complete picture. For example, in Figure 1, we can find a
way to identify the news articles related to the picture through segment-wise
understandings of the signs, the buildings, the crowds, and more. This
reasoning could provide the time and place the image was taken, which will help
us in subsequent tasks, such as automatic storyline construction, correction of
image source in intended effect photographs, and upper-stream processing such
as image clustering for certain location or time.
  In this work, we formulate this problem and introduce TARA: a dataset with
16k images with their associated news, time, and location, automatically
extracted from New York Times, and an additional 61k examples as distant
supervision from WIT. On top of the extractions, we present a crowdsourced
subset in which we believe it is possible to find the images' spatio-temporal
information for evaluation purpose. We show that there exists a $70\%$ gap
between a state-of-the-art joint model and human performance, which is slightly
filled by our proposed model that uses segment-wise reasoning, motivating
higher-level vision-language joint models that can conduct open-ended reasoning
with world knowledge. The data and code are publicly available at
https://github.com/zeyofu/TARA.


title: HyperPrompt: Prompt-based Task-Conditioning of Transformers

abstract:
  Prompt-Tuning is a new paradigm for finetuning pre-trained language models in
a parameter-efficient way. Here, we explore the use of HyperNetworks to
generate hyper-prompts: we propose HyperPrompt, a novel architecture for
prompt-based task-conditioning of self-attention in Transformers. The
hyper-prompts are end-to-end learnable via generation by a HyperNetwork.
HyperPrompt allows the network to learn task-specific feature maps where the
hyper-prompts serve as task global memories for the queries to attend to, at
the same time enabling flexible information sharing among tasks. We show that
HyperPrompt is competitive against strong multi-task learning baselines with as
few as $0.14\%$ of additional task-conditioning parameters, achieving great
parameter and computational efficiency. Through extensive empirical
experiments, we demonstrate that HyperPrompt can achieve superior performances
over strong T5 multi-task learning baselines and parameter-efficient adapter
variants including Prompt-Tuning and HyperFormer++ on Natural Language
Understanding benchmarks of GLUE and SuperGLUE across many model sizes.


title:
Towards quantitative inference of nanoscale defects in irradiated metals
  and alloys

abstract:
  Quantifying the population of nanoscale defects that are formed in metals and
alloys exposed to extreme radiation environments remains a pressing challenge
in materials science. These defects both fundamentally alter material
properties and seed long-timescale performance degradation, which often limits
the lifespan of engineering systems. Unlike ceramic and semiconducting
materials, these defects in metals and alloys are not spectroscopically active,
forcing characterization to rely on indirect measurements from which the
distribution of nanoscale defects may be inferred. In this mini-review,
different experimental methodologies which have been employed for defect
inference are highlighted to capture the current state of the art. Future
directions in this area are proposed, which, by combining data streams from
multiple and complementary characterization methods in concert with multi-scale
modeling and simulation, will enable the ultimate goal of quantifying the full
spectrum of defects in irradiated metals and alloys.


title: Tricks and Plugins to GBM on Images and Sequences

abstract:
  Convolutional neural networks (CNNs) and transformers, which are composed of
multiple processing layers and blocks to learn the representations of data with
multiple abstract levels, are the most successful machine learning models in
recent years. However, millions of parameters and many blocks make them
difficult to be trained, and sometimes several days or weeks are required to
find an ideal architecture or tune the parameters. Within this paper, we
propose a new algorithm for boosting Deep Convolutional Neural Networks
(BoostCNN) to combine the merits of dynamic feature selection and BoostCNN, and
another new family of algorithms combining boosting and transformers. To learn
these new models, we introduce subgrid selection and importance sampling
strategies and propose a set of algorithms to incorporate boosting weights into
a deep learning architecture based on a least squares objective function. These
algorithms not only reduce the required manual effort for finding an
appropriate network architecture but also result in superior performance and
lower running time. Experiments show that the proposed methods outperform
benchmarks on several fine-grained classification tasks.


title: Topic Analysis for Text with Side Data

abstract:
  Although latent factor models (e.g., matrix factorization) obtain good
performance in predictions, they suffer from several problems including
cold-start, non-transparency, and suboptimal recommendations. In this paper, we
employ text with side data to tackle these limitations. We introduce a hybrid
generative probabilistic model that combines a neural network with a latent
topic model, which is a four-level hierarchical Bayesian model. In the model,
each document is modeled as a finite mixture over an underlying set of topics
and each topic is modeled as an infinite mixture over an underlying set of
topic probabilities. Furthermore, each topic probability is modeled as a finite
mixture over side data. In the context of text, the neural network provides an
overview distribution about side data for the corresponding text, which is the
prior distribution in LDA to help perform topic grouping. The approach is
evaluated on several different datasets, where the model is shown to outperform
standard LDA and Dirichlet-multinomial regression (DMR) in terms of topic
grouping, model perplexity, classification and comment generation.


title: Multi-Sentence Knowledge Selection in Open-Domain Dialogue

abstract:
  Incorporating external knowledge sources effectively in conversations is a
longstanding problem in open-domain dialogue research. The existing literature
on open-domain knowledge selection is limited and makes certain brittle
assumptions on knowledge sources to simplify the overall task (Dinan et al.,
2019), such as the existence of a single relevant knowledge sentence per
context. In this work, we evaluate the existing state of open-domain
conversation knowledge selection, showing where the existing methodologies
regarding data and evaluation are flawed. We then improve on them by proposing
a new framework for collecting relevant knowledge, and create an augmented
dataset based on the Wizard of Wikipedia (WOW) corpus, which we call WOW++.
WOW++ averages 8 relevant knowledge sentences per dialogue context, embracing
the inherent ambiguity of open-domain dialogue knowledge selection. We then
benchmark various knowledge ranking algorithms on this augmented dataset with
both intrinsic evaluation and extrinsic measures of response quality, showing
that neural rerankers that use WOW++ can outperform rankers trained on standard
datasets.


title:
Thermodynamics and optimal protocols of multidimensional quadratic
  Brownian systems

abstract:
  We characterize finite-time thermodynamic processes of multidimensional
quadratic overdamped systems. Analytic expressions are provided for heat, work,
and dissipation for any evolution of the system covariance matrix. The
Bures-Wasserstein metric between covariance matrices naturally emerges as the
local quantifier of dissipation. General principles of how to apply these
geometric tools to identify optimal protocols are discussed. Focusing on the
relevant slow-driving limit, we show how these results can be used to analyze
cases in which the experimental control over the system is partial.


title: Noncommutativity, Saez-Ballester theory and kinetic inflation

abstract:
  This paper presents a noncommutative (NC) version of an extended
S\'{a}ez-Ballester (SB) theory. Concretely, considering the spatially flat
Friedmann-Lema\^{\i}tre-Robertson-Walker~(FLRW) metric, we propose an
appropriate dynamical deformation between the conjugate momenta and applying
the Hamiltonian formalism, obtain deformed equations of motion. In our model,
the NC parameter appears linearly in the deformed Poisson bracket and the
equations of the NC SB cosmology. When it goes to zero, we get the
corresponding commutative counterparts. Even by restricting our attention to a
particular case, where there is neither an ordinary matter nor a scalar
potential, we show that the effects of the noncommutativity provide interesting
results: applying numerical endeavors for very small values of the NC
parameter, we show that (i) at the early times of the universe, there is an
inflationary phase with a graceful exit, for which the relevant nominal
condition is satisfied; (ii) for the late times, there is a zero acceleration
epoch. By establishing an appropriate dynamical framework, we show that the
results (i) and (ii) can be obtained for many sets of the initial conditions
and the parameters of the model. Finally, we indicate that, at the level of the
field equations, one may find a close resemblance between our NC model and the
Starobinsky inflationary model.


title: Geodesic deviation in Saez-Ballester theory

abstract:
  We study the geodesic deviation (GD) equation in a generalized version of the
S\'{a}ez-Ballester (SB) theory in arbitrary dimensions. We first establish a
general formalism and then restrict to particular cases, where (i) the
matter-energy distribution is that of a perfect fluid, and (ii) the spacetime
geometry is described by a vanishing Weyl tensor. Furthermore, we consider the
spatially flat FLRW universe as the background geometry. Based on this setup,
we compute the GD equation as well as the convergence condition associated with
fundamental observers and past directed null vector fields. Moreover, we extend
that framework and extract the corresponding geodesic deviation in the
\emph{modified} S\'{a}ez-Ballester theory (MSBT), where the energy-momentum
tensor and potential emerge strictly from the geometry of the extra dimensions.
In order to examine our herein GD equations, we consider two novel cosmological
models within the SB framework. Moreover, we discuss a few quintessential
models and a suitable phantom dark energy scenario within the mentioned SB and
MSBT frameworks. Noticing that our herein cosmological models can suitably
include the present time of our Universe, we solve the GD equations
analytically and/or numerically. By employing the correct energy conditions
plus recent observational data, we consistently depict the behavior of the
deviation vector $\eta(z)$ and the observer area distance $r_0(z)$ for our
models. Concerning the Hubble constant problem, we specifically focus on the
observational data reported by the Planck collaboration and the SH0ES
collaboration to depict $\eta(z)$ and $r_0(z)$ for our herein phantom model.
Subsequently, we contrast our results with those associated with the
$\Lambda$CDM model.


title: On a notion of entropy for reachability properties

abstract:
  In this work, we introduce a notion of reachability entropy to characterize
the smallest data rate which is sufficient enough to enforce reach-while-stay
specification. We also define data rates of coder-controllers that can enforce
this specification in finite time. Then, we establish the data-rate theorem
which states that the reachability entropy is a tight lower bound of the data
rates that allow satisfaction of the reach-while-stay specification. For a
system which is related to an another system under feedback refinement
relation, we show that the entropy of the former will not be larger than that
of the latter. We also provide a procedure to numerically compute an upper
bound of the reachability entropy for discrete-time control systems by
leveraging their finite abstractions. Finally, we present some examples to
demonstrate the effectiveness of the proposed results.


title:
Privacy-Preserving and Communication-Efficient Causal Inference for
  Hospital Quality Measurement

abstract:
  Data sharing can improve hospital quality measurement, but sharing
patient-level data between hospitals is often infeasible due to privacy
concerns. Motivated by the problem of evaluating the quality of care provided
by candidate Cardiac Centers of Excellence (CCE), we propose a federated causal
inference framework to safely leverage information from peer hospitals to
improve the precision of quality estimates for a target hospital. We develop a
federated doubly robust estimator that is privacy-preserving (requiring only
summary statistics be shared between hospitals) and communication-efficient
(requiring only one round of communication between hospitals). We contribute to
the quality measurement and causal inference literatures by developing a
framework for assessing treatment-specific performance in hospitals without
sharing patient-level data. We also propose a penalized regression approach on
summary statistics of the influence functions for efficient estimation and
valid inference. In so doing, the proposed estimator is data-adaptive,
downweighting hospitals with different case-mixes from the target hospital for
bias reduction and upweighting hospitals with similar case-mixes for efficiency
gain. We show the improved performance of the federated global estimator in
extensive simulation studies. Studying candidate CCE, we find that the
federated global estimator improves precision of treatment effect estimates by
$34\%$ to $86\%$ for target hospitals, qualitatively altering the evaluation of
the percutaneous coronary intervention (PCI) treatment effect in $22$ of $51$
hospitals. Focusing on treatment-specific rankings, we find that hospitals
rarely excel in both PCI and medical management (MM), stressing the importance
of treatment-specific performance assessments.


title:
VOLCANO: Detecting Vulnerabilities of Ethereum Smart Contracts Using
  Code Clone Analysis

abstract:
  Ethereum Smart Contracts based on Blockchain Technology (BT) enables monetary
transactions among peers on a blockchain network independent of a central
authorizing agency. Ethereum Smart Contracts are programs that are deployed as
decentralized applications, having the building blocks of the blockchain
consensus protocol. This enables consumers to make agreements in a transparent
and conflict-free environment. However, there exist some security
vulnerabilities within these smart contracts that are a potential threat to the
applications and their consumers and have shown in the past to cause huge
financial losses. This paper presents a framework and empirical analysis that
use code clone detection techniques for identifying vulnerabilities and their
variations in smart contracts. Our empirical analysis is conducted using the
Nicad code clone detection tool on a dataset of approximately 50k Ethereum
smart contracts. We evaluated VOLCANO on two datasets, one with confirmed
vulnerabilities and another with approximately 50k random smart contracts
collected from the Etherscan. Our approach shows an improvement in the
detection of vulnerabilities in terms of coverage and efficiency when compared
to two of the publicly available static analyzers to detect vulnerabilities in
smart contracts. To the best of our knowledge, this is the first study that
uses a clone detection technique to identify vulnerabilities and their
evolution in Ethereum smart contracts.


title:
Short-Packet Interleaver against Impulse Interference in Practical
  Industrial Environments

abstract:
  The most common cause of transmission failure in Wireless High Performance
(WirelessHP) target industry environments is impulse interference. As
interleavers are commonly used to improve the reliability on the Orthogonal
Frequency Division Multiplexing (OFDM) symbol level for long packet
transmission, this paper considers the feasibility of applying short-packet bit
interleaving to enhance the impulse/burst interference resisting capability on
both OFDM symbol and frame level. Using the Universal Software Radio
Peripherals (USRP) and PC hardware platform, the Packet Error Rate (PER)
performance of interleaved coded short-packet transmission with Convolutional
Codes (CC), Reed-Solomon codes (RS) and RS+CC concatenated codes are tested and
analyzed. Applying the IEEE 1613 standard for impulse interference generation,
extensive PER tests of CC(1=2) and RS(31; 21)+CC(1=2) concatenated codes are
performed. With practical experiments, we prove the effectiveness of bit in
terleaved coded short-packet transmission in real factory environments. We also
investigate how PER performance depends on the interleavers, codes and impulse
interference power and frequency.


title: Mining Domain Models in Ethereum DApps using Code Cloning

abstract:
  This research study explores the use of near-miss clone detection to support
the characterization of domain models of smart contracts for each of the
popular domains in which smart contracts are being rapidly adopted. In this
paper, we leverage the code clone detection techniques to detect similarities
in functions of the smart contracts deployed onto the Ethereum blockchain
network. We analyze the clusters of code clones and the semantics of the code
fragments in the clusters in an attempt to categorize them and discover the
structural models of the patterns in code clones.


title:
Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling
  efficient CNN retraining on edge devices

abstract:
  With the increased deployment of Convolutional Neural Networks (CNNs) on edge
devices, the uncertainty of the observed data distribution upon deployment has
led researchers to to utilise large and extensive datasets such as ILSVRC'12 to
train CNNs. Consequently, it is likely that the observed data distribution upon
deployment is a subset of the training data distribution. In such cases, not
adapting a network to the observed data distribution can cause performance
degradation due to negative transfer and alleviating this is the focus of
Partial Domain Adaptation (PDA). Current works targeting PDA do not focus on
performing the domain adaptation on an edge device, adapting to a changing
target distribution or reducing the cost of deploying the adapted network. This
work proposes a novel PDA methodology that targets all of these directions and
opens avenues for on-device PDA. LoCO-PDA adapts a deployed network to the
observed data distribution by enabling it to be retrained on an edge device.
Across subsets of the ILSVRC12 dataset, LoCO-PDA improves classification
accuracy by 3.04pp on average while achieving up to 15.1x reduction in
retraining memory consumption and 2.07x improvement in inference latency on the
NVIDIA Jetson TX2. The work is open-sourced at \emph{link removed for
anonymity}.


title:
Parametric exploration of zero-energy modes in three-terminal InSb-Al
  nanowire devices

abstract:
  We systematically study three-terminal InSb-Al nanowire devices by using
radio-frequency reflectometry. Tunneling spectroscopy measurements on both ends
of the hybrid nanowires are performed while systematically varying the chemical
potential, magnetic field and junction transparencies. Identifying the
lowest-energy state allows for the construction of lowest- and zero-energy
state diagrams, which show how the states evolve as a function of the
aforementioned parameters. Importantly, comparing the diagrams taken for each
end of the hybrids enables the identification of states which do not coexist
simultaneously, ruling out a significant amount of the parameter space as
candidates for a topological phase. Furthermore, altering junction
transparencies filters out zero-energy states sensitive to a local gate
potential. Such a measurement strategy significantly reduces the time necessary
to identify a potential topological phase and minimizes the risk of falsely
recognizing trivial bound states as Majorana zero modes.


title:
Multi-Layer Perceptron Neural Network for Improving Detection
  Performance of Malicious Phishing URLs Without Affecting Other Attack Types
  Classification

abstract:
  The hypothesis here states that neural network algorithms such as Multi-layer
Perceptron (MLP) have higher accuracy in differentiating malicious and
semi-structured phishing URLs. Compared to classical machine learning
algorithms such as Logistic Regression and Multinomial Naive Bayes, the
classical algorithms rely heavily on substantial corpus data training and
machine learning experts' domain knowledge to perform complex feature
engineering. MLP could perform non-linear separable multi-classes
classification and focus less on corpus feature training. In addition,
backpropagation weight adjustment could learn which features are more important
in differentiating phishing from other attack types.


title: Tight convergence rates of the gradient method on hypoconvex functions

abstract:
  We perform the first tight convergence analysis of the gradient method with
fixed step sizes applied to the class of smooth hypoconvex (weakly-convex)
functions, i.e., smooth nonconvex functions whose curvature belongs to the
interval $[\mu, L]$, with $\mu<0$. The results fill the gap between convergence
rates for smooth nonconvex and smooth convex functions. The convergence rates
were identified using the performance estimation framework adapted to
hypoconvex functions. We provide mathematical proofs for a large range of step
sizes and prove that the convergence rates are tight when all step sizes are
smaller or equal to $1/L$. Finally, we identify the optimal constant step size
that minimizes the worst-case of the gradient method applied to hypoconvex
functions.


title: Grasp Transfer for Deformable Objects by Functional Map Correspondence

abstract:
  Handling object deformations for robotic grasping is still a major problem to
solve. In this paper, we propose an efficient learning-free solution for this
problem where generated grasp hypotheses of a region of an object are adapted
to its deformed configurations. To this end, we investigate the applicability
of functional map (FM) correspondence, where the shape matching problem is
treated as searching for correspondences between geometric functions in a
reduced basis. For a user selected region of an object, a ranked list of grasp
candidates is generated with local contact moment (LoCoMo) based grasp planner.
The proposed FM-based methodology maps these candidates to an instance of the
object that has suffered arbitrary level of deformation. The best grasp, by
analysing its kinematic feasibility while respecting the original finger
configuration as much as possible, is then executed on the object. We have
compared the performance of our method with two different state-of-the-art
correspondence mapping techniques in terms of grasp stability and region
grasping accuracy for 4 different objects with 5 different deformations.


title:
Ap\'{e}ry-Type Series with Summation Indices of Mixed Parities and
  Colored Multiple Zeta Values, II

abstract:
  In this paper, we study Ap\'{e}ry-type series involving the central binomial
coefficients \begin{align*} \sum_{n_1>\cdots>n_d>0}
\frac1{4^{n_1}}\binom{2n_1}{n_1} \frac{1}{n_1^{s_1}\cdots n_d^{s_d}}
\end{align*} and its variations where the summation indices may have mixed
parities and some or all "$>$" are replaced by "$\ge$", as long as the series
are defined. We show that all these sums can be expressed as $\mathbb Q$-linear
combinations of the real and/or imaginary parts of the colored multiple zeta
values at level four, i.e., special values of multiple polylogarithms at fourth
roots of unity. We also show that the corresponding series where
${\binom{2n_1}{n_1}}/4^{n_1}$ is replaced by ${\binom{2n_1}{n_1}}^2/16^{n_1}$
can be expressed in a similar way except for a possible extra factor of
$1/\pi$.


title: Permutation rotation-symmetric Sboxes, liftings and affine equivalence

abstract:
  In this paper, we investigate permutation rotation-symmetric
(shift-invariant) vectorial Boolean functions on $n$ bits that are liftings
from Boolean functions on $k$ bits, for $k\leq n$. These functions generalize
the well-known map used in the current Keccak hash function, which is generated
via the Boolean function $x_1+x_1x_2+x_3$. We provide some general
constructions, and also study the affine equivalence between rotation-symmetric
Sboxes and describe the corresponding relationship between the Boolean function
they are associated with. In the process, we point out some inaccuracies in the
existing literature.


title:
A mathematical modelling portrait of Wnt signalling in early vertebrate
  embryogenesis

abstract:
  There are two phases of Wnt signalling in early vertebrate embryogenesis:
very early, maternal Wnt signalling promotes dorsal development, and slightly
later, zygotic Wnt signalling promotes ventral and lateral mesoderm induction.
However, recent molecular biology analysis has revealed more complexity among
the direct Wnt target genes, with at least five classes. Here we tested the
logic and the dynamics of a new Gene Regulatory Network model suggested by
these discoveries using mathematical modelling based on ordinary differential
equations (ODEs). Our mathematical modelling of this Gene Regulatory Network
reveals that a simplified model, with one "super-gene" for each class is
sufficient to a large extent to describe the regulatory behaviour observed
experimentally.


title:
Distributed and Localized Model Predictive Control. Part II: Theoretical
  Guarantees

abstract:
  Engineered cyberphysical systems are growing increasingly large and complex.
These systems require scalable controllers that robustly satisfy state and
input constraints in the presence of additive noise -- such controllers should
also be accompanied by theoretical guarantees on feasibility and stability. In
our companion paper, we introduced Distributed and Localized Model Predictive
Control (DLMPC) for large-scale linear systems; DLMPC is a scalable closed-loop
MPC scheme in which subsystems need only exchange local information in order to
synthesize and implement local controllers. In this paper, we provide recursive
feasibility and asymptotic stability guarantees for DLMPC. We leverage the
System Level Synthesis framework to express the maximal positive robust
invariant set for the closed-loop system and its corresponding Lyapunov
function, both in terms of the closed-loop system responses. We use the
invariant set as the terminal set for DLMPC, and show that this guarantees
feasibility with minimal conservatism. We use the Lyapunov function as the
terminal cost, and show that this guarantees stability. We provide fully
distributed and localized algorithms to compute the terminal set offline, and
also provide necessary additions to the online DLMPC algorithm to accommodate
coupled terminal constraint and cost. In all algorithms, only local information
exchanges are necessary, and computational complexity is independent of the
global system size -- we demonstrate this analytically and experimentally. This
is the first distributed MPC approach that provides minimally conservative yet
fully distributed guarantees for recursive feasibility and asymptotic
stability, for both nominal and robust settings.


title: Enhanced Nearest Neighbor Classification for Crowdsourcing

abstract:
  In machine learning, crowdsourcing is an economical way to label a large
amount of data. However, the noise in the produced labels may deteriorate the
accuracy of any classification method applied to the labelled data. We propose
an enhanced nearest neighbor classifier (ENN) to overcome this issue. Two
algorithms are developed to estimate the worker quality (which is often unknown
in practice): one is to construct the estimate based on the denoised worker
labels by applying the $k$NN classifier to the expert data; the other is an
iterative algorithm that works even without access to the expert data. Other
than strong numerical evidence, our proposed methods are proven to achieve the
same regret as its oracle version based on high-quality expert data. As a
technical by-product, a lower bound on the sample size assigned to each worker
to reach the optimal convergence rate of regret is derived.


title: Connecting Gaits in Energetically Conservative Legged Systems

abstract:
  In this work, we present a nonlinear dynamics perspective on generating and
connecting gaits for energetically conservative models of legged systems. In
particular, we show that the set of conservative gaits constitutes a connected
space of locally defined 1D submanifolds in the gait space. These manifolds are
coordinate-free parameterized by energy level. We present algorithms for
identifying such families of gaits through the use of numerical continuation
methods, generating sets and bifurcation points. To this end, we also introduce
several details for the numerical implementation. Most importantly, we
establish the necessary condition for the Delassus' matrix to preserve energy
across impacts. An important application of our work is with simple models of
legged locomotion that are often able to capture the complexity of legged
locomotion with just a few degrees of freedom and a small number of physical
parameters. We demonstrate the efficacy of our framework on a one-legged hopper
with four degrees of freedom.


title:
Synthesizing Fine-Grained Synchronization Protocols for Implicit
  Monitors (Extended Version)

abstract:
  A monitor is a widely-used concurrent programming abstraction that
encapsulates all shared state between threads. Monitors can be classified as
being either implicit or explicit depending on the primitives they provide.
Implicit monitors are much easier to program but typically not as efficient. To
address this gap, there has been recent research on automatically synthesizing
explicit-signal monitors from an implicit specification, but prior work does
not exploit all paralellization opportunities due to the use of a single lock
for the entire monitor. This paper presents a new technique for synthesizing
fine-grained explicit-synchronization protocols from implicit monitors. Our
method is based on two key innovations: First, we present a new static analysis
for inferring safe interleavings that allow violating mutual exclusion of
monitor operations without changing its semantics. Second, we use the results
of this static analysis to generate a MaxSAT instance whose models correspond
to correct-by-construction synchronization protocols. We have implemented our
approach in a tool called Cortado and evaluate it on monitors that contain
parallelization opportunities. Our evaluation shows that Cortado can synthesize
synchronization policies that are competitive with, or even better than,
expert-written ones on these benchmarks.


title:
Bayesian adaptive and interpretable functional regression for exposure
  profiles

abstract:
  Pollutant exposures during gestation are a known and adverse factor for birth
and health outcomes. However, the links between prenatal air pollution
exposures and educational outcomes are less clear, in particular the critical
windows of susceptibility during pregnancy. Using a large cohort of students in
North Carolina, we study prenatal $\mbox{PM}_{2.5}$ exposures recorded at
near-continuous resolutions and linked to 4th end-of-grade reading scores. We
develop a locally-adaptive Bayesian regression model for scalar responses with
functional and scalar predictors. The proposed model pairs a B-spline basis
expansion with dynamic shrinkage priors to capture both smooth and
rapidly-changing features in the regression surface. The local adaptivity is
manifested in more accurate point estimates and more precise uncertainty
quantification than existing methods on simulated data. The model is
accompanied by a highly scalable Gibbs sampler for fully Bayesian inference on
large datasets. In addition, we describe broad limitations with the
interpretability of scalar-on-function regression models, and introduce new
decision analysis tools to guide the model interpretation. Using these methods,
we identify a period within the third trimester as the critical window of
susceptibility to $\mbox{PM}_{2.5}$ exposure.


title: Back to Boundaries in Billiards

abstract:
  We prove Poisson limit laws for open billiards where the holes are on the
boundaries of billiard tables (rather than some abstract holes in the phase
space of a billiard). Such holes are of the main interest for billiard systems,
especially for applications. Sinai billiards with or without a finite horizon,
diamond billiards, and semi-dispersing billiards, as well as focusing billiards
with slow decay of correlations, are considered.


title:
VVVX near-IR photometry for 99 low-mass stars in the Gaia EDR3 Catalog
  of Nearby Stars

abstract:
  Red dwarf stars, which represent 75% of stars in the Milky Way, can be
studied in great detail in the solar neighborhood where the sample is more
complete. We intend to better characterize red dwarf candidates selected from
the Gaia Catalog of Nearby Stars using optical and near-infrared photometry
from the VVVX Survey, DECaPS, Pan-STARRS, and WISE. We performed a
cross-matching procedure among the positions of a color-selected sample of M
dwarfs in the VVVX Survey and the Gaia EDR3 sub-catalog of nearby stars. We
explored their stellar parameters and spectral types using VOSA. Radii were
also obtained using the Stefan-Boltzmann equation. Masses and ages were
computed for some of the objects using evolutionary tracks and isochrones.
Additional mass estimations were obtained with the MKs - M* relation. We then
validated our results for the stellar parameters of two of our objects with
spectra obtained with the TripleSpec instrument at the SOAR telescope, as well
as those of our total amount of stars through a direct comparison with an
independent sample from the literature. We revised the objects in our sample
and compared their proper motion vectors with other sources within 30" to
identify possible companions and probed their RUWE values to identify
unresolved companions. We present a catalog of physical parameters for 99
low-mass objects with distances from 43.2 to 111.3 pc. Teffs range from 2500 to
3400 K, with the majority of stars in the sample compatible with being M4
dwarfs. We obtained a good agreement between the stellar parameters computed
with VOSA and the estimations from observed spectra, also when comparing with
an independent sample from the literature. The distribution of masses obtained
with VOSA is concentrated toward the very low-mass regime. Eight objects
present values of RUWE >= 1.4 and seven are consistent with being part of a
binary system.


title: Image analysis for automatic measurement of crustose lichens

abstract:
  Lichens, organisms resulting from a symbiosis between a fungus and an algae,
are frequently used as age estimators, especially in recent geological deposits
and archaeological structures, using the correlation between lichen size and
age. Current non-automated manual lichen and measurement (with ruler, calipers
or using digital image processing tools) is a time-consuming and laborious
process, especially when the number of samples is high.
  This work presents a workflow and set of image acquisition and processing
tools developed to efficiently identify lichen thalli in flat rocky surfaces,
and to produce relevant lichen size statistics (percentage cover, number of
thalli, their area and perimeter).
  The developed workflow uses a regular digital camera for image capture along
with specially designed targets to allow for automatic image correction and
scale assignment. After this step, lichen identification is done in a flow
comprising assisted image segmentation and classification based on interactive
foreground extraction tool (GrabCut) and automatic classification of images
using Simple Linear Iterative Clustering (SLIC) for image segmentation and
Support Vector Machines (SV) and Random Forest classifiers.
  Initial evaluation shows promising results. The manual classification of
images (for training) using GrabCut show an average speedup of 4 if compared
with currently used techniques and presents an average precision of 95\%. The
automatic classification using SLIC and SVM with default parameters produces
results with average precision higher than 70\%. The developed system is
flexible and allows a considerable reduction of processing time, the workflow
allows it applicability to data sets of new lichen populations.


title:
Error estimates for a vorticity-based velocity-stress formulation of the
  Stokes eigenvalue problem

abstract:
  The aim of this paper is to analyze a mixed formulation for the two
dimensional Stokes eigenvalue problem where the unknowns are the stress and the
velocity, whereas the pressure can be recovered with a simple postprocess of
the stress. The stress tensor is written in terms of the vorticity of the
fluid, leading to an alternative mixed formulation that incorporates this
physical feature. We propose a mixed numerical method where the stress is
approximated with suitable N\'edelec finite elements, whereas the velocity is
approximated with piecewise polynomials of degree $k\geq 0$. With the aid of
the compact operators theory we derive convergence of the method and spectral
correctness. Moreover, we propose a reliable and efficient a posteriori error
estimator for our spectral problem. We report numerical tests in different
domains, computing the spectrum and convergence orders, together with a
computational analysis for the proposed estimator. In addition, we use the
corresponding error estimator to drive an adaptive scheme, and we report the
results of a numerical test, that allow us to assess the performance of this
approach.


title:
Unified Physical Threat Monitoring System Aided by Virtual Building
  Simulation

abstract:
  With increasing physical threats in recent years targeted at critical
infrastructures, it is crucial to establish a reliable threat monitoring system
integrating video surveillance and digital sensors based on cutting-edge
technologies. A physical threat monitoring solution unifying the floorplan,
cameras, and sensors for smart buildings has been set up in our study. Computer
vision and deep learning models are used for video streams analysis. When a
threat is detected by a rule engine based on the real-time analysis results
combining with feedback from related digital sensors, an alert is sent to the
Video Management System so that human operators can take further action. A
physical threat monitoring system typically needs to address complex and even
destructive incidents, such as fire, which is unrealistic to simulate in real
life. Restrictions imposed during the Covid-19 pandemic and privacy concerns
have added to the challenges. Our study utilises the Unreal Engine to simulate
some typical suspicious and intrusion scenes with photorealistic qualities in
the context of a virtual building. Add-on programs are implemented to transfer
the video stream from virtual PTZ cameras to the Milestone Video Management
System and enable users to control those cameras from the graphic client
application. Virtual sensors such as fire alarms, temperature sensors and door
access controls are implemented similarly, fulfilling the same programmatic VMS
interface as real-life sensors. Thanks to this simulation system's
extensibility and repeatability, we have consolidated this unified physical
threat monitoring system and verified its effectiveness and user-friendliness.
Both the simulated Unreal scenes and the software add-ons developed during this
study are highly modulated and thereby are ready for reuse in future projects
in this area.


title:
Presymplectic integrators for optimal control problems via retraction
  maps

abstract:
  Retractions maps are used to define a discretization of the tangent bundle of
the configuration manifold as two copies of the configuration manifold where
the dynamics take place. Such discretization maps can be conveniently lifted to
the cotangent bundle so that symplectic integrators are constructed for
Hamilton's equations. Optimal control problems are provided with a Hamiltonian
framework by Pontryagin's Maximum Principle. That is why we use discretization
maps and the integrability algorithm to obtain presymplectic integrators for
optimal control problems.


title: Privacy of Fitness Applications and Consent Management in Blockchain

abstract:
  The rapid advances in fitness wearable devices are redefining privacy around
interactions. Fitness wearables devices record a considerable amount of
sensitive and private details about exercise, blood oxygen level, and heart
rate. Privacy concerns have emerged about the interactions between an
individual's raw fitness data and data analysis by the providers of fitness
apps and wearable devices. This paper describes the importance of adopting and
applying legal frameworks within the fitness tracker ecosystem. In this review,
we describe the studies on the current privacy policies of fitness app
providers, heuristically evaluate the methods for consent management by fitness
providers, summarize the gaps identified in our review of these studies, and
discuss potential solutions for filling the gaps identified. We have identified
four main problems related to preserving the privacy of users of fitness apps:
lack of system transparency, lack of privacy policy legibility, concerns
regarding one-time consent, and issues of noncompliance regarding consent
management. After discussing feasible solutions, we conclude by describing how
blockchain is suitable for solving these privacy issues.


title: Equivalent definitions of the preprojective algebra

abstract:
  Following the article of C. M. Ringel we introduce preprojective algebras of
a Dynkin quiver $Q$ starting from three definitions which, despite concerning
completely different algebraic structures, turn out to be equivalent. Our main
result is a new version of Ringel's proofs that applies a theorem by Happel and
exploits the techniques of homological algebra. Moreover we show that the
definition of the preprojective algebra given with the usual notion of
commutator is equivalent to the definition with the "generalised" commutator.


title:
Two-Level Supervised Contrastive Learning for Response Selection in
  Multi-Turn Dialogue

abstract:
  Selecting an appropriate response from many candidates given the utterances
in a multi-turn dialogue is the key problem for a retrieval-based dialogue
system. Existing work formalizes the task as matching between the utterances
and a candidate and uses the cross-entropy loss in learning of the model. This
paper applies contrastive learning to the problem by using the supervised
contrastive loss. In this way, the learned representations of positive examples
and representations of negative examples can be more distantly separated in the
embedding space, and the performance of matching can be enhanced. We further
develop a new method for supervised contrastive learning, referred to as
two-level supervised contrastive learning, and employ the method in response
selection in multi-turn dialogue. Our method exploits two techniques: sentence
token shuffling (STS) and sentence re-ordering (SR) for supervised contrastive
learning. Experimental results on three benchmark datasets demonstrate that the
proposed method significantly outperforms the contrastive learning baseline and
the state-of-the-art methods for the task.


title:
Comparison of the canonical transformation and energy functional
  formalisms for ab initio calculations of self-localized polarons

abstract:
  In materials with strong electron-phonon (e-ph) interactions, charge carriers
can distort the surrounding lattice and become trapped, forming self-localized
(small) polarons. We recently developed an ab initio approach based on
canonical transformations to efficiently compute the formation and energetics
of small polarons[1]. A different approach based on a Landau-Pekar energy
functional has been proposed in the recent literature [2,3]. In this work, we
analyze and compare these two methods in detail. We show that the small polaron
energy is identical in the two formalisms when using the same polaron wave
function. We also show that our canonical transformation formalism can predict
polaron band structures and can properly treat zero- and finite-temperature
lattice vibration effects, although at present using a fixed polaron wave
function. Conversely, the energy functional approach can compute the polaron
wave function, but as we show here it neglects lattice vibrations and cannot
address polaron self-localization and thermal band narrowing. Taken together,
this work relates two different methods developed recently to study polarons
from first-principles, highlighting their merits and shortcomings and
discussing them both in a unified formalism.


title: Amplitude Control for Parallel Lattices of Docked Modboats

abstract:
  The Modboat is a low-cost, underactuated, modular robot capable of surface
swimming. It is able to swim individually, dock to other Modboats, and undock
from them using only a single motor and two passive flippers. Undocking without
additional actuation is achieved by causing intentional self-collision between
the tails of neighboring modules; this becomes a challenge when group swimming
as one connected component is desirable. In this work, we develop a control
strategy to allow parallel lattices of Modboats to swim as a single unit, which
conventionally requires holonomic modules. We show that the control strategy is
guaranteed to avoid unintentional undocking and minimizes internal forces
within the lattice. Experimental verification shows that the controller
performs well and is consistent for lattices of various sizes. Controllability
is maintained while swimming, but pure yaw control causes lateral movement that
cannot be counteracted by the presented framework.


title: Flow-Based Control of Marine Robots in Gyre-Like Environments

abstract:
  We present a flow-based control strategy that enables resource-constrained
marine robots to patrol gyre-like flow environments on an orbital trajectory
with a periodicity in a given range. The controller does not require a detailed
model of the flow field and relies only on the robot's location relative to the
center of the gyre. Instead of precisely tracking a pre-defined trajectory, the
robots are tasked to stay in between two bounding trajectories with known
periodicity. Furthermore, the proposed strategy leverages the surrounding flow
field to minimize control effort. We prove that the proposed strategy enables
robots to cycle in the flow satisfying the desired periodicity requirements.
Our method is tested and validated both in simulation and in experiments using
a low-cost, underactuated, surface swimming robot, i.e. the Modboat.


title:
Proximity Induced Chiral Quantum Light Generation in Strain-Engineered
  WSe2/NiPS3 Heterostructures

abstract:
  Quantum light emitters (QEs) capable of generating single photons of
well-defined circular polarization could enable non-reciprocal single photon
devices and deterministic spin-photon interfaces critical for realizing complex
quantum networks. To date, emission of such chiral quantum light has been
achieved via the application of intense external magnetic field
electrical/optical injection of spin polarized carriers/excitons, or coupling
with complex photonic/meta-structures. Here we report free-space generation of
highly chiral single photons from QEs created in monolayer WSe2 - NiPS3
heterostructures at zero external magnetic field. These QEs emit in the 760-800
nm range with a degree of circular polarization and single photon purity as
high as 0.71 and 80% respectively, independent of pump laser polarization. QEs
are deterministically created by pressing a scanning probe microscope tip into
a two-dimensional heterostructure comprising a WSe2 monolayer and a ~50 nm
thick layer of the antiferromagnetic (AFM) insulator NiPS3. Temperature
dependent magneto-photoluminescence studies indicate that the chiral quantum
light emission arises from magnetic proximity interactions between localized
excitons in the WSe2 monolayer and the out-of-plane magnetization of AFM
defects in NiPS3, both of which are co-localized by the strain field arising
from the nanoscale indentations.


title:
TANDEM: Learning Joint Exploration and Decision Making with Tactile
  Sensors

abstract:
  Inspired by the human ability to perform complex manipulation in the complete
absence of vision (like retrieving an object from a pocket), the robotic
manipulation field is motivated to develop new methods for tactile-based object
interaction. However, tactile sensing presents the challenge of being an active
sensing modality: a touch sensor provides sparse, local data, and must be used
in conjunction with effective exploration strategies in order to collect
information. In this work, we focus on the process of guiding tactile
exploration, and its interplay with task-related decision making. We propose
TANDEM (TActile exploration aNd DEcision Making), an architecture to learn
efficient exploration strategies in conjunction with decision making. Our
approach is based on separate but co-trained modules for exploration and
discrimination. We demonstrate this method on a tactile object recognition
task, where a robot equipped with a touch sensor must explore and identify an
object from a known set based on tactile feedback alone. TANDEM achieves higher
accuracy with fewer actions than alternative methods and is also shown to be
more robust to sensor noise.


title: Weighted domination models and randomized heuristics

abstract:
  We consider the minimum weight and smallest weight minimum-size dominating
set problems in vertex-weighted graphs and networks. The latter problem is a
two-objective optimization problem, which is different from the classic minimum
weight dominating set problem that requires finding a dominating set of the
smallest weight in a graph without trying to optimize its cardinality. First,
we show how to reduce the two-objective optimization problem to the minimum
weight dominating set problem by using Integer Linear Programming formulations.
Then, under different assumptions, the probabilistic method is developed to
obtain upper bounds on the minimum weight dominating sets in graphs. The
corresponding randomized algorithms for finding small-weight dominating sets in
graphs are described as well. Computational experiments are used to illustrate
the results for two different types of random graphs.


title:
Finite-sample concentration of the empirical relative entropy around its
  mean

abstract:
  In this note, we show that the relative entropy of an empirical distribution
of $n$ samples drawn from a set of size $k$ with respect to the true underlying
distribution is exponentially concentrated around its expectation, with central
moment generating function bounded by that of a gamma distribution with shape
$2k$ and rate $n/2$. This improves on recent work of Bhatt and Pensia (arXiv
2021) on the same problem, who showed such a similar bound with an additional
polylogarithmic factor of $k$ in the shape, and also confirms a recent
conjecture of Mardia et al. (Information and Inference 2020). The proof
proceeds by reducing the case $k>3$ of the multinomial distribution to the
simpler case $k=2$ of the binomial, for which the desired bound follows from
standard results on the concentration of the binomial.


title: Architectures of Compact Super-Earth Systems Shaped by Instabilities

abstract:
  Compact non-resonant systems of sub-Jovian planets are the most common
outcome of the planet formation process. Despite exhibiting broad overall
diversity, these planets also display dramatic signatures of intra-system
uniformity in their masses, radii, and orbital spacings. Although the details
of their formation and early evolution are poorly known, sub-Jovian planets are
expected to emerge from their natal nebulae as multi-resonant chains, owing to
planet-disk interactions. Within the context of this scenario, the
architectures of observed exoplanet systems can be broadly replicated if
resonances are disrupted through post-nebular dynamical instabilities. Here, we
generate an ad-hoc sample of resonant chains and use a suite of N-body
simulations to show that instabilities can not only reproduce the observed
period ratio distribution, but that the resulting collisions also modify the
mass uniformity in a way that is consistent with the data. Furthermore, we
demonstrate that primordial mass uniformity, motivated by the sample of
resonant chains coupled with dynamical sculpting, naturally generates
uniformity in orbital period spacing similar to what is observed. Finally, we
find that almost all collisions lead to perfect mergers, but some form of
post-instability damping is likely needed to fully account for the present-day
dynamically cold architectures of sub-Jovian exoplanets.


title:
Accelerated Bregman Primal-Dual methods applied to Optimal Transport and
  Wasserstein Barycenter problems

abstract:
  This paper discusses the efficiency of Hybrid Primal-Dual (HDP) type
algorithms to approximate solve discrete Optimal Transport (OT) and Wasserstein
Barycenter (WB) problems without smoothing. Our first contribution is an
analysis showing that these methods yield state-of-the-art convergence rates,
both theoretically and practically. Next, we extend the HPD algorithm with
line-search proposed by Malitsky and Pock in 2018 to the setting where the dual
space has a Bregman divergence, and the dual function is relatively strongly
convex to the Bregman's kernel. This extension yields a new method for OT and
WB problems based on smoothing of the objective and the solution that also
achieves state-of-the-art convergence rates. Finally, we introduce a new
Bregman divergence based on a scaled entropy function that makes the algorithm
numerically stable and reduces the smoothing, leading to sparse solutions of OT
and WB problems. We complement our findings with numerical experiments and
comparisons.


title: Code Smells in Machine Learning Systems

abstract:
  As Deep learning (DL) systems continuously evolve and grow, assuring their
quality becomes an important yet challenging task. Compared to non-DL systems,
DL systems have more complex team compositions and heavier data dependency.
These inherent characteristics would potentially cause DL systems to be more
vulnerable to bugs and, in the long run, to maintenance issues. Code smells are
empirically tested as efficient indicators of non-DL systems. Therefore, we
took a step forward into identifying code smells, and understanding their
impact on maintenance in this comprehensive study. This is the first study on
investigating code smells in the context of DL software systems, which helps
researchers and practitioners to get a first look at what kind of maintenance
modification made and what code smells developers have been dealing with. Our
paper has three major contributions. First, we comprehensively investigated the
maintenance modifications that have been made by DL developers via studying the
evolution of DL systems, and we identified nine frequently occurred
maintenance-related modification categories in DL systems. Second, we
summarized five code smells in DL systems. Third, we validated the prevalence,
and the impact of our newly identified code smells through a mixture of
qualitative and quantitative analysis. We found that our newly identified code
smells are prevalent and impactful on the maintenance of DL systems from the
developer's perspective.


title:
Stable, accurate and efficient deep neural networks for inverse problems
  with analysis-sparse models

abstract:
  Solving inverse problems is a fundamental component of science, engineering
and mathematics. With the advent of deep learning, deep neural networks have
significant potential to outperform existing state-of-the-art, model-based
methods for solving inverse problems. However, it is known that current
data-driven approaches face several key issues, notably instabilities and
hallucinations, with potential impact in critical tasks such as medical
imaging. This raises the key question of whether or not one can construct
stable and accurate deep neural networks for inverse problems. In this work, we
present a novel construction of an accurate, stable and efficient neural
network for inverse problems with general analysis-sparse models. To construct
the network, we unroll NESTA, an accelerated first-order method for convex
optimization. Combined with a compressed sensing analysis, we prove accuracy
and stability. Finally, a restart scheme is employed to enable exponential
decay of the required network depth, yielding a shallower, and consequently
more efficient, network. We showcase this approach in the case of Fourier
imaging, and verify its stability and performance via a series of numerical
experiments. The key impact of this work is to provide theoretical guarantees
for computing and developing stable neural networks in practice.

categories: cs.LG cs.CV cs.IT cs.NA eess.IV math.IT math.NA


title: Impurity States and Indirect Exchange Interaction in Irradiated Graphene

abstract:
  We theoretically investigate the impurity levels and exchange interaction
between magnetic impurities in graphene driven by an off-resonant circularly
polarized light field. Our analysis captures the non-perturbative effects
resulting from scattering with magnetic impurities with a strong onsite
potential. Under irradiation, a dynamical band gap opens up at the Dirac point,
allowing impurity levels to exist inside the gap. These impurity levels are
shown to give rise to a resonance feature in the exchange energy for impurities
located either at the same or different sublattices. The exchange interaction
also shows a wider spatial range of antiferromagnetic behavior due to
irradiation. Our work demonstrates that the exchange energy of magnetic
impurities in graphene is extensively tunable by light irradiation in the
presence of strong potential scattering.


title: Dojo: A Differentiable Simulator for Robotics

abstract:
  We present a differentiable rigid-body-dynamics simulator for robotics that
prioritizes physical accuracy and differentiability: Dojo. The simulator
utilizes an expressive maximal-coordinates representation, achieves stable
simulation at low sample rates, and conserves energy and momentum by employing
a variational integrator. A nonlinear complementarity problem, with nonlinear
friction cones, models hard contact and is reliably solved using a custom
primal-dual interior-point method. The implicit-function theorem enables
efficient differentiation of an intermediate relaxed problem and computes
smooth gradients from the contact model. We demonstrate the usefulness of the
simulator and its gradients through a number of examples including: simulation,
trajectory optimization, reinforcement learning, and system identification.


title: InCloud: Incremental Learning for Point Cloud Place Recognition

abstract:
  Place recognition is a fundamental component of robotics, and has seen
tremendous improvements through the use of deep learning models in recent
years. Networks can experience significant drops in performance when deployed
in unseen or highly dynamic environments, and require additional training on
the collected data. However naively fine-tuning on new training distributions
can cause severe degradation of performance on previously visited domains, a
phenomenon known as catastrophic forgetting. In this paper we address the
problem of incremental learning for point cloud place recognition and introduce
InCloud, a structure-aware distillation-based approach which preserves the
higher-order structure of the network's embedding space. We introduce several
challenging new benchmarks on four popular and large-scale LiDAR datasets
(Oxford, MulRan, In-house and KITTI) showing broad improvements in point cloud
place recognition performance over a variety of network architectures. To the
best of our knowledge, this work is the first to effectively apply incremental
learning for point cloud place recognition.


title: Free Bol loops of exponent two

abstract:
  A Bol loop is a loop that satisfies the identity $x((yz)y)=((xy)z)y$. In this
paper, we give a construction of the free Bol loops of exponent two. We define
a canonical form of all their elements and describe their multiplication law
based on this form.


title:
Instance-aware multi-object self-supervision for monocular depth
  prediction

abstract:
  This paper proposes a self-supervised monocular image-to-depth prediction
framework that is trained with an end-to-end photometric loss that handles not
only 6-DOF camera motion but also 6-DOF moving object instances.
Self-supervision is performed by warping the images across a video sequence
using depth and scene motion including object instances. One novelty of the
proposed method is the use of a multi-head attention of the transformer network
that matches moving objects across time and models their interaction and
dynamics. This enables accurate and robust pose estimation for each object
instance. Most image-to-depth predication frameworks make the assumption of
rigid scenes, which largely degrades their performance with respect to dynamic
objects. Only a few SOTA papers have accounted for dynamic objects. The
proposed method is shown to largely outperform these methods on standard
benchmarks and the impact of the dynamic motion on these benchmarks is exposed.
Furthermore, the proposed image-to-depth prediction framework is also shown to
outperform SOTA video-to-depth prediction frameworks.


title:
Robust Seatbelt Detection and Usage Recognition for Driver Monitoring
  Systems

abstract:
  Wearing a seatbelt appropriately while driving can reduce serious
crash-related injuries or deaths by about half. However, current seatbelt
reminder system has multiple shortcomings, such as can be easily fooled by a
"Seatbelt Warning Stopper", and cannot recognize incorrect usages for example
seating in front of a buckled seatbelt or wearing a seatbelt under the arm.
General seatbelt usage recognition has many challenges, to name a few, lacking
of color information in Infrared (IR) cameras, strong distortion caused by wide
Field of View (FoV) fisheye lens, low contrast between belt and its background,
occlusions caused by hands or hair, and imaging blurry. In this paper, we
introduce a novel general seatbelt detection and usage recognition framework to
resolve the above challenges. Our method consists of three components: a local
predictor, a global assembler, and a shape modeling process. Our approach can
be applied to the driver in the Driver Monitoring System (DMS) or general
passengers in the Occupant Monitoring System (OMS) for various camera
modalities. Experiment results on both DMS and OMS are provided to demonstrate
the accuracy and robustness of the proposed approach.


title: The quantum low-rank approximation problem

abstract:
  We consider a quantum version of the famous low-rank approximation problem.
Specifically, we consider the distance $D(\rho,\sigma)$ between two normalized
quantum states, $\rho$ and $\sigma$, where the rank of $\sigma$ is constrained
to be at most $R$. For both the trace distance and Hilbert-Schmidt distance, we
analytically solve for the optimal state $\sigma$ that minimizes this distance.
For the Hilbert-Schmidt distance, the unique optimal state is $\sigma = \tau_R
+N_R$, where $\tau_R = \Pi_R \rho \Pi_R$ is given by projecting $\rho$ onto its
$R$ principal components with projector $\Pi_R$, and $N_R$ is a normalization
factor given by $N_R = \frac{1- \text{Tr}(\tau_R)}{R}\Pi_R$. For the trace
distance, this state is also optimal but not uniquely optimal, and we provide
the full set of states that are optimal. We briefly discuss how our results
have application for performing principal component analysis (PCA) via
variational optimization on quantum computers.


title:
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster
  Evolution

abstract:
  Clustering aims to group unlabeled objects based on similarity inherent among
them into clusters. It is important for many tasks such as anomaly detection,
database sharding, record linkage, and others. Some clustering methods are
taken as batch algorithms that incur a high overhead as they cluster all the
objects in the database from scratch or assume an incremental workload. In
practice, database objects are updated, added, and removed from databases
continuously which makes previous results stale. Running batch algorithms is
infeasible in such scenarios as it would incur a significant overhead if
performed continuously. This is particularly the case for high-velocity
scenarios such as ones in Internet of Things applications.
  In this paper, we tackle the problem of clustering in high-velocity dynamic
scenarios, where the objects are continuously updated, inserted, and deleted.
Specifically, we propose a generally dynamic approach to clustering that
utilizes previous clustering results. Our system, DynamicC, uses a machine
learning model that is augmented with an existing batch algorithm. The DynamicC
model trains by observing the clustering decisions made by the batch algorithm.
After training, the DynamicC model is usedin cooperation with the batch
algorithm to achieve both accurate and fast clustering decisions. The
experimental results on four real-world and one synthetic datasets show that
our approach has a better performance compared to the state-of-the-art method
while achieving similarly accurate clustering results to the baseline batch
algorithm.


title:
An Accelerated Stochastic Algorithm for Solving the Optimal Transport
  Problem

abstract:
  We propose a novel accelerated stochastic algorithm -- primal-dual
accelerated stochastic gradient descent with variance reduction (PDASGD) -- for
solving the optimal transport (OT) problem between two discrete distributions.
PDASGD can also be utilized to compute for the Wasserstein barycenter (WB) of
multiple discrete distributions. In both the OT and WB cases, the proposed
algorithm enjoys the best-known convergence rate (in the form of order of
computational complexity) in the literature. PDASGD is easy to implement in
nature, due to its stochastic property: computation per iteration can be much
faster than other non-stochastic counterparts. We carry out numerical
experiments on both synthetic and real data; they demonstrate the improved
efficiency of PDASGD.


title:
Stacking Faults Assist Lithium-Ion Conduction in a Halide-Based
  Superionic Conductor

abstract:
  In the pursuit of urgently-needed, energy dense solid-state batteries for
electric vehicle and portable electronics applications, halide solid
electrolytes offer a promising path forward with exceptional compatibility
against high-voltage oxide electrodes, tunable ionic conductivities, and facile
processing. For this family of compounds, synthesis protocols strongly affect
cation site disorder and modulate Li+ mobility. In this work, we reveal the
presence of a high concentration of stacking faults in the superionic conductor
Li3YCl6 and demonstrate a method of controlling its Li+ conductivity by tuning
the defect concentration with synthesis and heat treatments at select
temperatures. Leveraging complementary insights from variable temperature
synchrotron X-ray diffraction, neutron diffraction, cryogenic transmission
electron microscopy, solid-state nuclear magnetic resonance, density functional
theory, and electrochemical impedance spectroscopy, we identify the nature of
planar defects and the role of nonstoichiometry in lowering Li+ migration
barriers and increasing Li site connectivity in mechanochemically-synthesized
Li3YCl6. We harness paramagnetic relaxation enhancement to enable 89Y
solid-state NMR, and directly contrast the Y cation site disorder resulting
from different preparation methods, demonstrating a potent tool for other
researchers studying Y-containing compositions. With heat treatments at
temperatures as low as 333 K (60{\deg}C), we decrease the concentration of
planar defects, demonstrating a simple method for tuning the Li+ conductivity.
Findings from this work are expected to be generalizable to other halide solid
electrolyte candidates and provide an improved understanding of defect-enabled
Li+ conduction in this class of Li-ion conductors.


title:
Computerization of Clinical Pathways: A Literature Review and Directions
  for Future Research

abstract:
  Clinical Pathways (CP) are medical management plans developed to standardize
patient treatment activities, optimize resource usage, reduce expenses, and
improve the quality of healthcare services. Most CPs currently in use are
paper-based documents (i.e., not computerized). CP computerization has been an
active research topic since the inception of CP use in hospitals. This
literature review research aims to examine studies that focused on CP
computerization and offers recommendations for future research in this
important research area. Some critical research suggestions include
centralizing computerized CPs in Healthcare Information Systems (HIS), CP term
standardization using international medical terminology systems, developing a
global CP-specific digital coding system, creating a unified CP meta-ontology,
developing independent Clinical Pathway Management Systems (CPMS), and
supporting CPMSs with machine learning sub-systems.


title: Mutually orthogonal cycle systems

abstract:
  An ${\ell}$-cycle system ${\mathcal F}$ of a graph $\Gamma$ is a set of
${\ell}$-cycles which partition the edge set of $\Gamma$. Two such cycle
systems ${\mathcal F}$ and ${\mathcal F}'$ are said to be {\em orthogonal} if
no two distinct cycles from ${\mathcal F}\cup {\mathcal F}'$ share more than
one edge. Orthogonal cycle systems naturally arise from face $2$-colourable
polyehdra and in higher genus from Heffter arrays with certain orderings. A set
of pairwise orthogonal $\ell$-cycle systems of $\Gamma$ is said to be a set of
mutually orthogonal cycle systems of $\Gamma$.
  Let $\mu(\ell,n)$ (respectively, $\mu'(\ell,n)$) be the maximum integer $\mu$
such that there exists a set of $\mu$ mutually orthogonal (cyclic) $\ell$-cycle
systems of the complete graph $K_n$. We show that if $\ell\geq 4$ is even and
$n\equiv 1\pmod{2\ell}$, then $\mu'(\ell,n)$, and hence $\mu(\ell,n)$, is
bounded below by a constant multiple of $n/\ell^2$. In contrast, we obtain the
following upper bounds: $\mu(\ell,n)\leq n-2$; $\mu(\ell,n)\leq
(n-2)(n-3)/(2(\ell-3))$ when $\ell \geq 4$; $\mu(\ell,n)\leq 1$ when
$\ell>n/\sqrt{2}$; and $\mu'(\ell,n)\leq n-3$ when $n \geq 4$. We also obtain
computational results for small values of $n$ and $\ell$.


title: Polaronic Conductivity in Cr$_2$Ge$_2$Te$_6$ Single Crystals

abstract:
  Intrinsic, two-dimensional (2D) ferromagnetic semiconductors are an important
class of materials for spin-charge conversion applications. Cr$_2$Ge$_2$Te$_6$
retains long-range magnetic order in bilayer at cryogenic temperatures and
shows complex magnetic interactions with considerable magnetic anisotropy.
Here, we performed a series of structural, magnetic, X-ray scattering,
electronic, thermal transport and first-principles calculation studies which
reveal that localized electronic charge carriers in Cr$_2$Ge$_2$Te$_6$ are
dressed by surrounding lattice and are involved in polaronic transport via
hopping that is sensitive on details of magnetocrystalline anisotropy. This
opens possibility for manipulation of charge transport in Cr$_2$Ge$_2$Te$_6$ -
based devices by electron-phonon- and spin-orbit coupling-based tailoring of
polaron properties.


title:
Emergence of insulating ferrimagnetism and perpendicular magnetic
  anisotropy in 3d-5d perovskite oxide composite films for insulator spintronic

abstract:
  Magnetic insulators with strong perpendicular magnetic anisotropy (PMA) play
a key role in exploring pure spin current phenomena and developing
ultralow-dissipation spintronic devices, thereby it is highly desirable to
develop new material platforms. Here we report epitaxial growth of
La2/3Sr1/3MnO3 (LSMO)-SrIrO3 (SIO) composite oxide films (LSMIO) with different
crystalline orientations fabricated by sequential two-target ablation process
using pulsed laser deposition. The LSMIO films exhibit high crystalline quality
with homogeneous mixture of LSMO and SIO at atomic level. Ferrimagnetic and
insulating transport characteristics are observed, with the
temperature-dependent electric resistivity well fitted by Mott
variable-range-hopping model. Moreover, the LSMIO films show strong PMA.
Through further constructing all perovskite oxide heterostructures of the
ferrimagnetic insulator LSMIO and a strong spin-orbital coupled SIO layer,
pronounced spin Hall magnetoresistance (SMR) and spin Hall-like anomalous Hall
effect (SH-AHE) were observed. These results illustrate the potential
application of the ferrimagnetic insulator LSMIO in developing all-oxide
ultralow-dissipation spintronic devices.


title: TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

abstract:
  Causal Emotion Entailment (CEE) aims to discover the potential causes behind
an emotion in a conversational utterance. Previous works formalize CEE as
independent utterance pair classification problems, with emotion and speaker
information neglected. From a new perspective, this paper considers CEE in a
joint framework. We classify multiple utterances synchronously to capture the
correlations between utterances in a global view and propose a Two-Stream
Attention Model (TSAM) to effectively model the speaker's emotional influences
in the conversational history. Specifically, the TSAM comprises three modules:
Emotion Attention Network (EAN), Speaker Attention Network (SAN), and
interaction module. The EAN and SAN incorporate emotion and speaker information
in parallel, and the subsequent interaction module effectively interchanges
relevant information between the EAN and SAN via a mutual BiAffine
transformation. Experimental results on a benchmark dataset demonstrate that
our model achieves new State-Of-The-Art (SOTA) performance and outperforms
baselines remarkably.


title: Partial Likelihood Thompson Sampling

abstract:
  We consider the problem of deciding how best to target and prioritize
existing vaccines that may offer protection against new variants of an
infectious disease. Sequential experiments are a promising approach; however,
challenges due to delayed feedback and the overall ebb and flow of disease
prevalence make available method inapplicable for this task. We present a
method, partial likelihood Thompson sampling, that can handle these challenges.
Our method involves running Thompson sampling with belief updates determined by
partial likelihood each time we observe an event. To test our approach, we ran
a semi-synthetic experiment based on 200 days of COVID-19 infection data in the
US.


title:
Asymptotic Normality of Log Likelihood Ratio and Fundamental Limit of
  the Weak Detection for Spiked Wigner Matrices

abstract:
  We consider the problem of detecting the presence of a signal in a rank-one
spiked Wigner model. Assuming that the signal is drawn from the Rademacher
prior, we prove that the log likelihood ratio of the spiked model against the
null model converges to a Gaussian when the signal-to-noise ratio is below a
certain threshold. From the mean and the variance of the limiting Gaussian, we
also compute the limit of the sum of the Type-I error and the Type-II error of
the likelihood ratio test.


title:
Keeping Minimal Experience to Achieve Efficient Interpretable Policy
  Distillation

abstract:
  Although deep reinforcement learning has become a universal solution for
complex control tasks, its real-world applicability is still limited because
lacking security guarantees for policies. To address this problem, we propose
Boundary Characterization via the Minimum Experience Retention (BCMER), an
end-to-end Interpretable Policy Distillation (IPD) framework. Unlike previous
IPD approaches, BCMER distinguishes the importance of experiences and keeps a
minimal but critical experience pool with almost no loss of policy similarity.
Specifically, the proposed BCMER contains two basic steps. Firstly, we propose
a novel multidimensional hyperspheres intersection (MHI) approach to divide
experience points into boundary points and internal points, and reserve the
crucial boundary points. Secondly, we develop a nearest-neighbor-based model to
generate robust and interpretable decision rules based on the boundary points.
Extensive experiments show that the proposed BCMER is able to reduce the amount
of experience to 1.4%~19.1% (when the count of the naive experiences is 10k)
and maintain high IPD performance. In general, the proposed BCMER is more
suitable for the experience storage limited regime because it discovers the
critical experience and eliminates redundant experience.


title: Nonreciprocal and chiral single-photon scattering for giant atoms

abstract:
  In this work, we investigate the nontrivial single-photon scattering
properties of giant atoms coupled to waveguides that can be an effective
platform for realising nonreciprocal and chiral quantum optics. For the
two-level giant-atom setup, we identify the condition for nonreciprocal
transmission: the external atomic dissipation is further required other than
the breaking of time-reversal symmetry by local coupling phases. Especially, in
the non-Markovian regime, unconventional revival peaks periodically appear in
the refection spectrum of such a two-level giant-atom system. To explore more
interesting scattering behaviours, we further extend the two-level giant-atom
system to $\Delta$-type and $\nabla$-type three-level giant atoms coupled to
double waveguides without external atomic dissipation. We analyse the different
physical mechanisms for the nonreciprocal and chiral scattering properties of
the $\Delta$-type and $\nabla$-type giant atoms. Our proposed giant-atom
structures have potential applications of high-efficient single-photon targeted
router and circulator for quantum information precessing.


title: Signature of edge states in resonant wave scattering

abstract:
  Particle beam scattering is a conventional technique for detecting the nature
of matter. We studied the scattering problem of a cluster connected to multiple
leads. We established the connection between the eigenstate of the topological
scattering center and the transmission and reflection amplitudes for the
resonant scattering process. We discovered that as an application, this
approach enables the detection of the edge state in the band gap for both
Hermitian and non-Hermitian systems and the identification of the topology of a
system. We investigated two types of Su-Schrieffer-Heeger chains as examples.
In addition, we proposed a dynamic scheme through an evanescently
coupled-waveguide array to detect the edge state on the basis of measured
transmission intensity. Numerical simulation revealed that pattern visibility
can be the signature of the edge states.


title:
Towards Effective Resource Procurement in MEC: a Resource Re-selling
  Framework

abstract:
  On-demand and resource reservation pricing models have been widely used in
cloud computing, catering to different user requirements. Nevertheless, in
Multi-Access Edge Computing (MEC), as the edge has limited resources compared
to the cloud, on-demand users may not get their jobs served on time, or at all,
if too many resources were reserved by reservation plan users. Concurrently,
reservation plan users may possess excess un-utilized quota. To optimize this
resource mismatch scenario, we propose a Sharing Quota Model (SQM) where
reservation plan users can re-sell unused resource quota to on-demand users,
with the mobile network operator (MNO) taking a commission. To analyze the
user's aggregate behavior at equilibrium and investigate the MNO's incentive of
allowing re-selling, we formulate a 3-stage non-cooperative Stackelberg Game.
Solving this game, we characterize the optimal strategies of buyers and
re-sellers. We show that on aggregate, users' optimal strategies give rise to 4
disjoint regions, dependent on the MNO's prices and supply levels. Based on
this, we characterise the MNO's optimal prices for on-demand users. Numerical
results show that having both the sharing and on-demand pool gives the MNO an
optimal revenue when the on-demand pool's supply is low, and when the MNO's
commission is low.


title: Using Geographic Load Shifting to Reduce Carbon Emissions

abstract:
  An increasing focus on the electricity use and carbon emissions associated
with computing has lead to pledges by major cloud computing companies to lower
their carbon footprint. Data centers have a unique ability to shift computing
load between different geographical locations, giving rise to geographic load
flexibility that can be employed to reduce carbon emissions. In this paper, we
present a model where data centers shift load independently of the ISOs. We
first consider the impact of load shifting guided by locational marginal carbon
emissions, denoted by $\lambda_{\text{CO}_2}$, a sensitivity metric that
measures the impact of incremental load shifts. Relative to previous models for
data center load shifting, the presented model improves accuracy and include
more realistic assumptions regarding the operation of both data centers and the
electricity market. Further, we introduce a new benchmark model in which data
centers have access to the full information about the power system and can
identify optimal shifts for the current time period. We demonstrate the
efficacy of our model on the IEEE RTS GMLC system using 5 minute load and
generation data for an entire year. Our results show that the proposed accuracy
improvements for the shifting model based on $\lambda_{\text{CO}_2}$ are highly
effective, leading to results that outperform the benchmark model.


title:
$C^*$-isomorphisms associated with two projections on a Hilbert
  $C^*$-module

abstract:
  Motivated by two norm equations used to characterize the Friedrichs angle,
this paper studies $C^*$-isomorphisms associated with two projections by
introducing the matched triple and the semi-harmonious pair of projections. A
triple $(P,Q,H)$ is said to be matched if $H$ is a Hilbert $C^*$-module, $P$
and $Q$ are projections on $H$ such that their infimum $P\wedge Q$ exists as an
element of $\mathcal{L}(H)$, where $\mathcal{L}(H)$ denotes the set of all
adjointable operators on $H$. The $C^*$-subalgebras of $\mathcal{L}(H)$
generated by elements in $\{P-P\wedge Q, Q-P\wedge Q, I\}$ and $\{P,Q,P\wedge
Q,I\}$ are denoted by $i(P,Q,H)$ and $o(P,Q,H)$, respectively. It is proved
that each faithful representation $(\pi, X)$ of $o(P,Q,H)$ can induce a
faithful representation $(\widetilde{\pi}, X)$ of $i(P,Q,H)$ such that
\begin{align*}&\widetilde{\pi}(P-P\wedge Q)=\pi(P)-\pi(P)\wedge \pi(Q),\\
&\widetilde{\pi}(Q-P\wedge Q)=\pi(Q)-\pi(P)\wedge \pi(Q). \end{align*} When
$(P,Q)$ is semi-harmonious, that is, $\overline{\mathcal{R}(P+Q)}$ and
$\overline{\mathcal{R}(2I-P-Q)}$ are both orthogonally complemented in $H$, it
is shown that $i(P,Q,H)$ and $i(I-Q,I-P,H)$ are unitarily equivalent via a
unitary operator in $\mathcal{L}(H)$. A counterexample is constructed, which
shows that the same may be not true when $(P,Q)$ fails to be semi-harmonious.
Likewise, a counterexample is constructed such that $(P,Q)$ is semi-harmonious,
whereas $(P,I-Q)$ is not semi-harmonious. Some additional examples indicating
new phenomena of adjointable operators acting on Hilbert $C^*$-modules are also
provided.


title: 3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification

abstract:
  Although accurate and fast point cloud classification is a fundamental task
in 3D applications, it is difficult to achieve this purpose due to the
irregularity and disorder of point clouds that make it challenging to achieve
effective and efficient global discriminative feature learning. Lately, 3D
Transformers have been adopted to improve point cloud processing. Nevertheless,
massive Transformer layers tend to incur huge computational and memory costs.
This paper presents a novel hierarchical framework that incorporates
convolution with Transformer for point cloud classification, named 3D
Convolution-Transformer Network (3DCTN), to combine the strong and efficient
local feature learning ability of convolution with the remarkable global
context modeling capability of Transformer. Our method has two main modules
operating on the downsampling point sets, and each module consists of a
multi-scale local feature aggregating (LFA) block and a global feature learning
(GFL) block, which are implemented by using Graph Convolution and Transformer
respectively. We also conduct a detailed investigation on a series of
Transformer variants to explore better performance for our network. Various
experiments on ModelNet40 demonstrate that our method achieves state-of-the-art
classification performance, in terms of both accuracy and efficiency.


title: Personalized Federated Learning With Graph

abstract:
  Knowledge sharing and model personalization are two key components in the
conceptual framework of personalized federated learning (PFL). Existing PFL
methods focus on proposing new model personalization mechanisms while simply
implementing knowledge sharing by aggregating models from all clients,
regardless of their relation graph. This paper aims to enhance the
knowledge-sharing process in PFL by leveraging the graph-based structural
information among clients. We propose a novel structured federated learning
(SFL) framework to learn both the global and personalized models simultaneously
using client-wise relation graphs and clients' private data. We cast SFL with
graph into a novel optimization problem that can model the client-wise complex
relations and graph-based structural topology by a unified framework. Moreover,
in addition to using an existing relation graph, SFL could be expanded to learn
the hidden relations among clients. Experiments on traffic and image benchmark
datasets can demonstrate the effectiveness of the proposed method. All
implementation codes are available on Github


title: Fast Object Inertial Parameter Identification for Collaborative Robots

abstract:
  Collaborative robots (cobots) are machines designed to work safely alongside
people in human-centric environments. Providing cobots with the ability to
quickly infer the inertial parameters of manipulated objects will improve their
flexibility and enable greater usage in manufacturing and other areas. To
ensure safety, cobots are subject to kinematic limits that result in low
signal-to-noise ratios (SNR) for velocity, acceleration, and force-torque data.
This renders existing inertial parameter identification algorithms
prohibitively slow and inaccurate. Motivated by the desire for faster model
acquisition, we investigate the use of an approximation of rigid body dynamics
to improve the SNR. Additionally, we introduce a mass discretization method
that can make use of shape information to quickly identify plausible inertial
parameters for a manipulated object. We present extensive simulation studies
and real-world experiments demonstrating that our approach complements existing
inertial parameter identification methods by specifically targeting the typical
cobot operating regime.


title:
Three-Dimensional Inhomogeneity of Electron-Temperature-Gradient
  Turbulence in the Edge of Tokamak Plasmas

abstract:
  Nonlinear multiscale gyrokinetic simulations of a Joint European Torus edge
pedestal are used to show that electron-temperature-gradient (ETG) turbulence
has a rich three-dimensional structure, varying strongly according to the local
magnetic-field configuration. In the plane normal to the magnetic field, the
steep pedestal electron temperature gradient gives rise to anisotropic
turbulence with a radial (normal) wavelength much shorter than in the binormal
direction. In the parallel direction, the location and parallel extent of the
turbulence are determined by the variation in the magnetic drifts and
finite-Larmor-radius (FLR) effects. The magnetic drift and FLR topographies
have a perpendicular-wavelength dependence, which permits turbulence intensity
maxima near the flux-surface top and bottom at longer binormal scales, but
constrains turbulence to the outboard midplane at shorter electron-gyroradius
binormal scales. Our simulations show that long-wavelength ETG turbulence does
not transport heat efficiently, and significantly decreases overall ETG
transport -- in our case by $\sim$40 \% -- through multiscale interactions.


title: To further understand graph signals

abstract:
  Graph signal processing (GSP) is a framework to analyze and process graph
structured data. Many research works focus on developing tools such as Graph
Fourier transforms (GFT), filters and neural network models to handle graph
signals. Such approaches have successfully taken care of "signal processing" in
many circumstances. In this paper, we want to put emphasis on "graph signals"
themselves. Although there are characterizations of graph signals using the
notion of bandwidth derived from GFT, we want to argue here that graph signals
may contain hidden geometric information of the network, independent of (graph)
Fourier theories. We shall provide a framework to understand such information,
and demonstrate how new knowledge on "graph signals" can help with "signal
processing".


title: GSC Loss: A Gaussian Score Calibrating Loss for Deep Learning

abstract:
  Cross entropy (CE) loss integrated with softmax is an orthodox component in
most classification-based frameworks, but it fails to obtain an accurate
probability distribution of predicted scores that is critical for further
decision-making of poor-classified samples. The prediction score calibration
provides a solution to learn the distribution of predicted scores which can
explicitly make the model obtain a discriminative representation. Considering
the entropy function can be utilized to measure the uncertainty of predicted
scores. But, the gradient variation of it is not in line with the expectations
of model optimization. To this end, we proposed a general Gaussian Score
Calibrating (GSC) loss to calibrate the predicted scores produced by the deep
neural networks (DNN). Extensive experiments on over 10 benchmark datasets
demonstrate that the proposed GSC loss can yield consistent and significant
performance boosts in a variety of visual tasks. Notably, our label-independent
GSC loss can be embedded into common improved methods based on the CE loss
easily.