title: Quantum supremacy with spin squeezed atomic ensembles

abstract:
  We propose a method to achieve quantum supremacy using ensembles of qubits,
using only spin squeezing, basis rotations, and Fock state measurements. Each
ensemble is assumed to be controllable only with its total spin. Using a
repeated sequence of random basis rotations followed by squeezing, we show that
the probability distribution of the final measurements quickly approaches a
Porter-Thomas distribution. We show that the sampling probability can be
related to a #P-hard problem with a complexity scaling as $(N+1)^M$, where $N$
is the number of qubits in an ensemble and $ M $ is the number of ensembles.
The scheme can be implemented with hot or cold atomic ensembles. Due to the
large number of atoms in typical atomic ensembles, this allows access to the
quantum supremacy regime with a modest number of ensembles or gate depth.


title: Metric decomposability theorems on sets of integers

abstract:
  A set $\mathcal{A}\subset \mathbb{N}$ is called additively decomposable
(resp. asymptotically additively decomposable) if there exist sets
$\mathcal{B},\mathcal{C}\subset \mathbb{N}$ of cardinality at least two each
such that $\mathcal{A}=\mathcal{B}+\mathcal{C}$ (resp. $\mathcal{A}\Delta
(\mathcal{B}+\mathcal{C})$ is finite). If none of these properties hold, the
set $\mathcal{A}$ is called totally primitive. We define
$\mathbb{Z}$-decomposability analogously with subsets $\mathcal{A,B,C}$ of
$\mathbb{Z}$. Wirsing showed that almost all subsets of $\mathbb{N}$ are
totally primitive. In this paper, in the spirit of Wirsing, we study
decomposability from a probabilistic viewpoint. First, we show that almost all
symmetric subsets of $\mathbb{Z}$ are $\mathbb{Z}$-decomposable. Then we show
that almost all small perturbations of the set of primes yield a totally
primitive set. Further, this last result still holds when the set of primes is
replaced by the set of sums of two squares, which is by definition
decomposable.


title:
Uniqueness results for inverse source problems of semilinear elliptic
  equations

abstract:
  We study inverse source problems associated to semilinear elliptic equations
of the form \[ \Delta u(x)+a(x,u)=F(x), \] on a bounded domain $\Omega\subset
\mathbb{R}^n$, $n\geq 2$. Unlike inverse source problems for linear elliptic
equations, we show that for a class of nonlinearities $a(x,u)$, including
certain polynomials, both $a(x,u)$ and $F(x)$ can be uniquely determined from
the corresponding Dirichlet-to-Neumann map. For general nonlinearities
$a(x,z)$, we show that we may recover $a(x,z)$, or a condition for its
derivatives $\partial_z^ka(x,z)$, and a source $F(x)$ and up to a gauge
symmetry. We also generalize results of [FO20,LLLS20] by not assuming that
$u\equiv 0$ is a solution and recovering lower order terms.


title: A quantum Fourier transform (QFT) based note detection algorithm

abstract:
  In quantum information processing (QIP), the quantum Fourier transform (QFT)
has a plethora of applications [1] [2] [3]: Shor's algorithm and phase
estimation are just a few well-known examples. Shor's quantum factorization
algorithm, one of the most widely quoted quantum algorithms [4] [5] [6] relies
heavily on the QFT and efficiently finds integer prime factors of large numbers
on quantum computers [4]. This seminal ground-breaking design for quantum
algorithms has triggered a cascade of viable alternatives to previously
unsolvable problems on a classical computer that are potentially superior and
can run in polynomial time. In this work we examine the QFT's structure and
implementation for the creation of a quantum music note detection algorithm
both on a simulated and a real quantum computer. Though formal approaches [7]
[1] [8] [9] exist for the verification of quantum algorithms, in this study we
limit ourselves to a simpler, symbolic representation which we validate using
the symbolic SymPy [10] [11] package which symbolically replicates quantum
computing processes. The algorithm is then implemented as a quantum circuit,
using IBM's qiskit [12] library and finally period detection is exemplified on
an actual single musical tone using a varying number of qubits.


title:
Blind Equalization and Channel Estimation in Coherent Optical
  Communications Using Variational Autoencoders

abstract:
  We investigate the potential of adaptive blind equalizers based on
variational inference for carrier recovery in optical communications. These
equalizers are based on a low-complexity approximation of maximum likelihood
channel estimation. We generalize the concept of variational autoencoder (VAE)
equalizers to higher order modulation formats encompassing probabilistic
constellation shaping (PCS), ubiquitous in optical communications, oversampling
at the receiver, and dual-polarization transmission. Besides black-box
equalizers based on convolutional neural networks, we propose a model-based
equalizer based on a linear butterfly filter and train the filter coefficients
using the variational inference paradigm. As a byproduct, the VAE also provides
a reliable channel estimation. We analyze the VAE in terms of performance and
flexibility over a classical additive white Gaussian noise (AWGN) channel with
inter-symbol interference (ISI) and over a dispersive linear optical
dual-polarization channel. We show that it can extend the application range of
blind adaptive equalizers by outperforming the state-of-the-art
constant-modulus algorithm (CMA) for PCS for both fixed but also time-varying
channels. The evaluation is accompanied with a hyperparameter analysis.


title:
Bayesian estimation of in-game home team win probability for college
  basketball

abstract:
  Two new Bayesian methods for estimating and predicting in-game home team win
probabilities are proposed. The first method has a prior that adjusts as a
function of lead differential and time elapsed. The second is an adjusted
version of the first, where the adjustment is a linear combination of the
Bayesian estimator with a time-weighted pre-game win probability. The proposed
methods are compared to existing methods, showing the new methods perform
better for both estimation and prediction. The utility is illustrated via an
application to the 2016 NCAA Division 1 Championship game.


title:
Analyze, Debug, Optimize: Real-Time Tracing for Perception and Mapping
  Systems in ROS 2

abstract:
  Perception and mapping systems are among the most computationally, memory,
and bandwidth intensive software components in robotics. Therefore, analysis,
debugging, and optimization are crucial to improve perception systems
performance in real-time applications. However, standard approaches often
depict a partial picture of the actual performance. Fortunately,
instrumentation and tracing offer a great opportunity for detailed performance
analysis of real-time systems. In this paper, we show how our novel open-source
tracing tools and techniques for ROS 2 enable us to identify delays,
bottlenecks and critical paths inside centralized, or distributed, perception
and mapping systems.


title: Asymptotic of the dissipative eigenvalues of Maxwell's equations

abstract:
  Let $\Omega = \mathbb R^3 \setminus \bar{K}$, where $K$ is an open bounded
domain with smooth boundary $\Gamma$. Let $V(t) = e^{tG_b},\: t \geq 0,$ be the
semigroup related to Maxwell's equations in $\Omega$ with dissipative boundary
condition $\nu \wedge (\nu \wedge E)+ \gamma(x) (\nu \wedge H) = 0, \gamma(x) >
0, \forall x \in \Gamma.$ We study the case when $\gamma(x) \neq 1, \: \forall
x \in \Gamma,$ and we establish a Weyl formula for the counting function of the
eigenvalues of $G_b$ in a polynomial neighbourhood of the negative real axis.


title: Sometimes size does not matter

abstract:
  Cosmological fine-tuning has traditionally been associated with the
narrowness of the intervals in which the parameters of the physical models must
be located to make life possible. A more thorough approach focuses on the
probability of the interval, not on its size. Most attempts to measure the
probability of the life-permitting interval for a given parameter rely on a
Bayesian statistical approach for which the prior distribution of the parameter
is uniform. However, the parameters in these models often take values in spaces
of infinite size, so that a uniformity assumption is not possible. This is
known as the normalization problem. This paper explains a framework to measure
tuning that, among others, deals with normalization, assuming that the prior
distribution belongs to a class of maximum entropy (maxent) distributions. By
analyzing an upper bound of the tuning probability for this class of
distributions the method solves the so-called weak anthropic principle, and
offer a solution, at least in this context, to the well-known lack of
invariance of maxent distributions. The implication of this approach is that,
since all mathematical models need parameters, tuning is not only a question of
natural science, but also a problem of mathematical modeling. Cosmological
tuning is thus a particular instantiation of a more general scenario.
Therefore, whenever a mathematical model is used to describe nature, not only
in physics but in all of science, tuning is present. And the question of
whether the tuning is fine or coarse for a given parameter -- if the interval
in which the parameter is located has low or high probability, respectively --
depends crucially not only on the interval but also on the assumed class of
prior distributions. Novel upper bounds for tuning probabilities are presented.


title: Bounded Fatou and Julia components of meromorphic functions

abstract:
  We completely characterise the bounded sets that arise as components of the
Fatou and Julia sets of meromorphic functions. On the one hand, we prove that a
bounded domain is a Fatou component of some meromorphic function if and only if
it is regular. On the other hand, we prove that a planar continuum is a Julia
component of some meromorphic function if and only if it has empty interior. We
do so by constructing meromorphic functions with wandering continua using
approximation theory.


title:
Effect of decay of the final states on the probabilities of the
  Landau-Zener transitions in multistate non-integrable models

abstract:
  For a Landau-Zener transition in a two-level system, the probability for a
particle, initially in the first level, to survive the transition and to remain
in the first level, does not depend on whether or not the second level is
broadened [V. M. Akulin and W. P. Schleicht, Phys. Rev. A {\bf 46}, 4110
(1992)]. In other words, the seminal Landau-Zener result applies regardless of
the broadening of the second level. The same question for the multistate
Landau-Zener transition is addressed in the present paper. While for integrable
multistate models, where the transition does not involve interference of the
virtual paths, it can be argued that the independence of the broadening
persists, we focus on non-integrable models involving interference. For a
simple four-state model, which allows an analytical treatment, we demonstrate
that the decay of the excited states affects the survival probability provided
that {\em the widths of the final states are different}.


title:
Planning and Control of Multi-Robot-Object Systems under Temporal Logic
  Tasks and Uncertain Dynamics

abstract:
  We develop an algorithm for the motion and task planning of a system
comprised of multiple robots and unactuated objects under tasks expressed as
Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject
to uncertain dynamics in an obstacle-cluttered environment. The key part of the
proposed solution is the intelligent construction of a coupled transition
system that encodes the motion and tasks of the robots and the objects. We
achieve such a construction by designing appropriate adaptive control protocols
in the lower level, which guarantee the safe robot navigation/object
transportation in the environment while compensating for the dynamic
uncertainties. The transition system is efficiently interfaced with the
temporal logic specification via a sampling-based algorithm to output a
discrete path as a sequence of synchronized actions of the robots; such actions
satisfy the robots' as well as the objects' specifications. The robots execute
this discrete path by using the derived low level control protocol. Simulation
results verify the proposed framework.


title: High-order Unified Gas-kinetic Scheme

abstract:
  In this paper, we present a high-order unified gas-kinetic scheme (UGKS)
using the weighted essentially non-oscillatory with adaptive-order (WENO-AO)
method for spatial reconstruction and the two-stage fourth-order scheme for
time evolution. Since the UGKS updates both the macroscopic flow variables and
microscopic distribution function, and provides an adaptive flux function by
combining the equilibrium and non-equilibrium parts, it is possible to take
separate treatment of the equilibrium and non-equilibrium calculation in the
UGKS for the development of high-order scheme. Considering the fact that
high-order techniques are commonly required for continuum flow with complex
structures, and the rarefied flow structure are relatively simple and smooth in
the physical space, we apply the high-order techniques in the equilibrium part
of the UGKS for the capturing of macroscopic flow evolution, and retain the
calculation of distribution function as a second-order method, so that a
balance of computational cost and numerical accuracy could be well achieved.
The high-order UGKS has been validated by several numerical test cases,
including sine-wave accuracy test, sod-shock tube, Couette, oscillating
Couette, lid-driven cavity and oscillating cavity flow. It is shown that the
current method preserves the multiscale property of the original UGKS and
obtains more accurate solutions in several cases.


title: Some New Results on Efficient and Perfect Edge Domination of Graphs

abstract:
  A neighborhood star-free graph is one in which every vertex of degree at
least two is contained in a triangle. This class has been considered before, in
the context of edge domination. In the present paper, we study the complexity
of the dominating induced matching problem for the class, and prove that the
corresponding decision problem is NP-complete for several subclasses of
neighborhood star-free graphs. In addition, we show that the dominating induced
matching problem can be solved in polynomial time for neighborhood star-free
graphs, containing no crickets as induced subgraphs.


title:
Enable Deep Learning on Mobile Devices: Methods, Systems, and
  Applications

abstract:
  Deep neural networks (DNNs) have achieved unprecedented success in the field
of artificial intelligence (AI), including computer vision, natural language
processing and speech recognition. However, their superior performance comes at
the considerable cost of computational complexity, which greatly hinders their
applications in many resource-constrained devices, such as mobile phones and
Internet of Things (IoT) devices. Therefore, methods and techniques that are
able to lift the efficiency bottleneck while preserving the high accuracy of
DNNs are in great demand in order to enable numerous edge AI applications. This
paper provides an overview of efficient deep learning methods, systems and
applications. We start from introducing popular model compression methods,
including pruning, factorization, quantization as well as compact model design.
To reduce the large design cost of these manual solutions, we discuss the
AutoML framework for each of them, such as neural architecture search (NAS) and
automated pruning and quantization. We then cover efficient on-device training
to enable user customization based on the local data on mobile devices. Apart
from general acceleration techniques, we also showcase several task-specific
accelerations for point cloud, video and natural language processing by
exploiting their spatial sparsity and temporal/token redundancy. Finally, to
support all these algorithmic advancements, we introduce the efficient deep
learning system design from both software and hardware perspectives.


title: LOv-Calculus: A Graphical Language for Linear Optical Quantum Circuits

abstract:
  We introduce the LOv-calculus, a graphical language for reasoning about
linear optical quantum circuits with so-called vacuum state auxiliary inputs.
We present the axiomatics of the language and prove its soundness and
completeness: two LOv-circuits represent the same quantum process if and only
if one can be transformed into the other with the rules of the LOv-calculus. We
give a confluent and terminating rewrite system to rewrite any
polarisation-preserving LOv-circuit into a unique triangular normal form,
inspired by the universal decomposition of Reck et al. (1994) for linear
optical quantum circuits.


title:
Human-AI Collaboration via Conditional Delegation: A Case Study of
  Content Moderation

abstract:
  Despite impressive performance in many benchmark datasets, AI models can
still make mistakes, especially among out-of-distribution examples. It remains
an open question how such imperfect models can be used effectively in
collaboration with humans. Prior work has focused on AI assistance that helps
people make individual high-stakes decisions, which is not scalable for a large
amount of relatively low-stakes decisions, e.g., moderating social media
comments. Instead, we propose conditional delegation as an alternative paradigm
for human-AI collaboration where humans create rules to indicate trustworthy
regions of a model. Using content moderation as a testbed, we develop novel
interfaces to assist humans in creating conditional delegation rules and
conduct a randomized experiment with two datasets to simulate in-distribution
and out-of-distribution scenarios. Our study demonstrates the promise of
conditional delegation in improving model performance and provides insights
into design for this novel paradigm, including the effect of AI explanations.


title: Travel time optimization on multi-AGV routing by reverse annealing

abstract:
  Quantum annealing has been actively researched since D-Wave Systems produced
the first commercial machine in 2011. Controlling a large fleet of automated
guided vehicles is one of the real-world applications utilizing quantum
annealing. In this study, we propose a formulation to control the traveling
routes to minimize the travel time. We validate our formulation through
simulation in a virtual plant and authenticate the effectiveness for faster
distribution compared to a greedy algorithm that does not consider the overall
detour distance. Furthermore, we utilize reverse annealing to maximize the
advantage of the D-Wave's quantum annealer. Starting from relatively good
solutions obtained by a fast greedy algorithm, reverse annealing searches for
better solutions around them. Our reverse annealing method improves the
performance compared to standard quantum annealing alone and performs up to 10
times faster than the strong classical solver, Gurobi. This study extends a use
of optimization with general problem solvers in the application of multi-AGV
systems and reveals the potential of reverse annealing as an optimizer.

categories: quant-ph cs.MA cs.RO cs.SY eess.SY stat.CO


title: Can Rationalization Improve Robustness?

abstract:
  A growing line of work has investigated the development of neural NLP models
that can produce rationales--subsets of input that can explain their model
predictions. In this paper, we ask whether such rationale models can also
provide robustness to adversarial attacks in addition to their interpretable
nature. Since these models need to first generate rationales ("rationalizer")
before making predictions ("predictor"), they have the potential to ignore
noise or adversarially added text by simply masking it out of the generated
rationale. To this end, we systematically generate various types of 'AddText'
attacks for both token and sentence-level rationalization tasks, and perform an
extensive empirical evaluation of state-of-the-art rationale models across five
different tasks. Our experiments reveal that the rationale models show the
promise to improve robustness, while they struggle in certain scenarios--when
the rationalizer is sensitive to positional bias or lexical choices of attack
text. Further, leveraging human rationale as supervision does not always
translate to better performance. Our study is a first step towards exploring
the interplay between interpretability and robustness in the
rationalize-then-predict framework.


title: Evasive subspaces, generalized rank weights and near MRD codes

abstract:
  We revisit and extend the connections between $\mathbb{F}_{q^m}$-linear
rank-metric codes and evasive $\mathbb{F}_q$-subspaces of $\mathbb{F}_{q^m}^k$.
We give a unifying framework in which we prove in an elementary way how the
parameters of a rank-metric code are related to special geometric properties of
the associated evasive subspace, with a particular focus on the generalized
rank weights. In this way, we can also provide alternative and very short
proofs of known results on scattered subspaces. We then use this simplified
point of view in order to get a geometric characterization of near MRD codes
and a clear bound on their maximal length. Finally we connect the theory of
quasi-MRD codes with $h$-scattered subspaces of maximum dimension, extending to
all the parameters sets the already known results on MRD codes.


title: SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech

abstract:
  The recent progress in non-autoregressive text-to-speech (NAR-TTS) has made
fast and high-quality speech synthesis possible. However, current NAR-TTS
models usually use phoneme sequence as input and thus cannot understand the
tree-structured syntactic information of the input sequence, which hurts the
prosody modeling. To this end, we propose SyntaSpeech, a syntax-aware and
light-weight NAR-TTS model, which integrates tree-structured syntactic
information into the prosody modeling modules in PortaSpeech
\cite{ren2021portaspeech}. Specifically, 1) We build a syntactic graph based on
the dependency tree of the input sentence, then process the text encoding with
a syntactic graph encoder to extract the syntactic information. 2) We
incorporate the extracted syntactic encoding with PortaSpeech to improve the
prosody prediction. 3) We introduce a multi-length discriminator to replace the
flow-based post-net in PortaSpeech, which simplifies the training pipeline and
improves the inference speed, while keeping the naturalness of the generated
audio. Experiments on three datasets not only show that the tree-structured
syntactic information grants SyntaSpeech the ability to synthesize better audio
with expressive prosody, but also demonstrate the generalization ability of
SyntaSpeech to adapt to multiple languages and multi-speaker text-to-speech.
Ablation studies demonstrate the necessity of each component in SyntaSpeech.
Source code and audio samples are available at https://syntaspeech.github.io


title: Online Deep Learning from Doubly-Streaming Data

abstract:
  This paper investigates a new online learning problem with doubly-streaming
data, where the data streams are described by feature spaces that constantly
evolve, with new features emerging and old features fading away. The challenges
of this problem are two folds: 1) Data samples ceaselessly flowing in may carry
shifted patterns over time, requiring learners to update hence adapt
on-the-fly. 2) Newly emerging features are described by very few samples,
resulting in weak learners that tend to make error predictions. A plausible
idea to overcome the challenges is to establish relationship between the
pre-and-post evolving feature spaces, so that an online learner can leverage
the knowledge learned from the old features to better the learning performance
on the new features. Unfortunately, this idea does not scale up to
high-dimensional media streams with complex feature interplay, which suffers an
tradeoff between onlineness (biasing shallow learners) and
expressiveness(requiring deep learners). Motivated by this, we propose a novel
OLD^3S paradigm, where a shared latent subspace is discovered to summarize
information from the old and new feature spaces, building intermediate feature
mapping relationship. A key trait of OLD^3S is to treat the model capacity as a
learnable semantics, yields optimal model depth and parameters jointly, in
accordance with the complexity and non-linearity of the input data streams in
an online fashion. Both theoretical analyses and empirical studies substantiate
the viability and effectiveness of our proposal.


title: Casimir preserving spectrum of two-dimensional turbulence

abstract:
  We present predictions of the energy spectrum of forced two-dimensional
turbulence obtained by employing a structure-preserving integrator. In
particular, we construct a finite-mode approximation of the Navier-Stokes
equations on the unit sphere, which, in the limit of vanishing viscosity,
preserves the Lie-Poisson structure. As a result, integrated powers of
vorticity are conserved in the inviscid limit. We obtain robust evidence for
the existence of the double energy cascade, including the formation of the -3
scaling of the inertial range of the direct cascade. We show that this can be
achieved at modest resolutions compared to those required by traditional
numerical methods.


title:
Performer: A Novel PPG to ECG Reconstruction Transformer For a Digital
  Biomarker of Cardiovascular Disease Detection

abstract:
  Cardiovascular diseases (CVDs) have become the top one cause of death;
three-quarters of these deaths occur in lower-income communities.
Electrocardiography (ECG), an electrical measurement capturing the cardiac
activities, is a gold-standard to diagnose CVDs. However, ECG is infeasible for
continuous cardiac monitoring due to its requirement for user participation.
Meanwhile, photoplethysmography (PPG) is easy to collect, but the limited
accuracy constrains its clinical usage. In this research, a novel
Transformer-based architecture, Performer, is invented to reconstruct ECG from
PPG and to create a novel digital biomarker, PPG along with its reconstructed
ECG, as multiple modalities for CVD detection. This architecture, for the first
time, performs Transformer sequence to sequence translation on biomedical
waveforms, while also utilizing the advantages of the easily accessible PPG and
the well-studied base of ECG. Shifted Patch-based Attention (SPA) is created to
maximize the signal features by fetching the various sequence lengths as
hierarchical stages into the training while also capturing cross-patch
connections through the shifted patch mechanism. This architecture generates a
state-of-the-art performance of 0.29 RMSE for reconstructing ECG from PPG,
achieving an average of 95.9% diagnosis for CVDs on the MIMIC III dataset and
75.9% for diabetes on the PPG-BP dataset. Performer, along with its novel
digital biomarker, offers a low-cost and non-invasive solution for continuous
cardiac monitoring, only requiring the easily extractable PPG data to
reconstruct the not-as-accessible ECG data. As a prove of concept, an earring
wearable, named PEARL (prototype), is designed to scale up the point-of-care
(POC) healthcare system.


title: High powers of general random elements of compact Lie groups

abstract:
  For a Haar-distributed element $H$ of a compact Lie group, Eric Rains proved
that there is a natural number $D$ such that, for all $d\ge D$, the eigenvalue
distribution of $H^d$ is fixed, and Rains described this fixed distribution
explicitly. In the present paper we consider random elements $U$ of a compact
Lie group that are not necessarily Haar-distributed. In particular, we
introduce a type of absolute continuity condition and prove that, under this
condition, the eigenvalue distribution of powers of $U$ converges to that of
$H^D$. We then consider the limiting distribution of $U^d$ itself.


title: PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution

abstract:
  3D neural networks are widely used in real-world applications (e.g., AR/VR
headsets, self-driving cars). They are required to be fast and accurate;
however, limited hardware resources on edge devices make these requirements
rather challenging. Previous work processes 3D data using either voxel-based or
point-based neural networks, but both types of 3D models are not
hardware-efficient due to the large memory footprint and random memory access.
In this paper, we study 3D deep learning from the efficiency perspective. We
first systematically analyze the bottlenecks of previous 3D methods. We then
combine the best from point-based and voxel-based models together and propose a
novel hardware-efficient 3D primitive, Point-Voxel Convolution (PVConv). We
further enhance this primitive with the sparse convolution to make it more
effective in processing large (outdoor) scenes. Based on our designed 3D
primitive, we introduce 3D Neural Architecture Search (3D-NAS) to explore the
best 3D network architecture given a resource constraint. We evaluate our
proposed method on six representative benchmark datasets, achieving
state-of-the-art performance with 1.8-23.7x measured speedup. Furthermore, our
method has been deployed to the autonomous racing vehicle of MIT Driverless,
achieving larger detection range, higher accuracy and lower latency.


title:
Generalizable Neural Performer: Learning Robust Radiance Fields for
  Human Novel View Synthesis

abstract:
  This work targets at using a general deep learning framework to synthesize
free-viewpoint images of arbitrary human performers, only requiring a sparse
number of camera views as inputs and skirting per-case fine-tuning. The large
variation of geometry and appearance, caused by articulated body poses, shapes
and clothing types, are the key bottlenecks of this task. To overcome these
challenges, we present a simple yet powerful framework, named Generalizable
Neural Performer (GNR), that learns a generalizable and robust neural body
representation over various geometry and appearance. Specifically, we compress
the light fields for novel view human rendering as conditional implicit neural
radiance fields from both geometry and appearance aspects. We first introduce
an Implicit Geometric Body Embedding strategy to enhance the robustness based
on both parametric 3D human body model and multi-view images hints. We further
propose a Screen-Space Occlusion-Aware Appearance Blending technique to
preserve the high-quality appearance, through interpolating source view
appearance to the radiance fields with a relax but approximate geometric
guidance.
  To evaluate our method, we present our ongoing effort of constructing a
dataset with remarkable complexity and diversity. The dataset GeneBody-1.0,
includes over 360M frames of 370 subjects under multi-view cameras capturing,
performing a large variety of pose actions, along with diverse body shapes,
clothing, accessories and hairdos. Experiments on GeneBody-1.0 and ZJU-Mocap
show better robustness of our methods than recent state-of-the-art
generalizable methods among all cross-dataset, unseen subjects and unseen poses
settings. We also demonstrate the competitiveness of our model compared with
cutting-edge case-specific ones. Dataset, code and model will be made publicly
available.


title: Reachability in Bidirected Pushdown VASS

abstract:
  A pushdown vector addition system with states (PVASS) extends the model of
vector addition systems with a pushdown store. A PVASS is said to be
\emph{bidirected} if every transition (pushing/popping a symbol or modifying a
counter) has an accompanying opposite transition that reverses the effect.
Bidirectedness arises naturally in many models; it can also be seen as a
overapproximation of reachability. We show that the reachability problem for
\emph{bidirected} PVASS is decidable in Ackermann time and primitive recursive
for any fixed dimension. For the special case of one-dimensional bidirected
PVASS, we show reachability is in $\mathsf{PSPACE}$, and in fact in polynomial
time if the stack is polynomially bounded. Our results are in contrast to the
\emph{directed} setting, where decidability of reachability is a long-standing
open problem already for one dimensional PVASS, and there is a
$\mathsf{PSPACE}$-lower bound already for one-dimensional PVASS with bounded
stack.
  The reachability relation in the bidirected (stateless) case is a congruence
over $\mathbb{N}^d$. Our upper bounds exploit saturation techniques over
congruences. In particular, we show novel elementary-time constructions of
semilinear representations of congruences generated by finitely many vector
pairs. In the case of one-dimensional PVASS, we employ a saturation procedure
over bounded-size counters.
  We complement our upper bound with a $\mathsf{TOWER}$-hardness result for
arbitrary dimension and $k$-$\mathsf{EXPSPACE}$ hardness in dimension $2k+6$
using a technique by Lazi\'{c} and Totzke to implement iterative
exponentiations.


title: $\mathfrak{m}$-Baer and $\mathfrak{m}$-Rickart lattices

abstract:
  In this paper we introduce the notions of Rickart and Baer lattices and their
duals. We show that part of the theory of Rickart and Baer modules can be
understood just using techniques from the theory of lattices. For, we use
linear morphisms introduced by T. Albu and M. Iosif. We focus on a submonoid
with zero $\mathfrak{m}$ of the monoid of all linear endomorphism of a lattice
$\mathcal{L}$ in order to give a more general approach and apply our results in
the theory of modules. We also show that $\mathfrak{m}$-Rickart and
$\mathfrak{m}$-Baer lattices can be characterized by the annihilators in
$\mathfrak{m}$ generated by idempotents as in the case of modules.


title:
Complete collection of one-loop triple-collinear splitting operators for
  dimensionally-regulated QCD

abstract:
  We provide results for the one-loop triple-collinear color/spin-space
splitting operators for the five possible processes, $q \to qq'\bar{q}'$, $q
\to qq\bar{q}$, $q \to qgg$, $g \to gq\bar{q}$ and $g \to ggg$. The expressions
are exact in dimensionally-regulated massless QCD up to a single integral,
which we expand to second order in the dimensional-regularisation parameter. We
also evaluate the related splitting functions. Our results are both sufficient
and indispensable for the construction of subtraction and
integrated-subtraction terms for triple-collinear singularities of one-loop
double-real-emission cross-section contributions as part of a
next-to-next-to-next-to leading order subtraction scheme.


title:
Coordination and Discoordination in Linear Algebra, Linear Information
  Theory, and Coded Caching

abstract:
  In the first part of this paper we develop some theorems in linear algebra
applicable to information theory when all random variables involved are linear
functions of the individual bits of a source of independent bits.
  We say that a collection of subspaces of a vector space are "coordinated" if
the vector space has a basis such that each subspace is spanned by its
intersection with the basis. We measure the failure of a collection of
subspaces to be coordinated by an invariant that we call the "discoordination"
of the family. We develop some foundational results regarding discoordination.
In particular, these results give a number of new formulas involving three
subspaces of a vector space.
  We then apply a number of our results, along with a method of Tian to obtain
some new lower bounds in a special case of the basic coded caching problem. In
terms of the usual notation for these problems, we show that for $N=3$
documents and $K=3$ caches, we have $6M+5R\ge 11$ for a scheme that achieves
the memory-rate pair $(M,R)$, assuming the scheme is linear. We also give a new
caching scheme for $N=K=3$ that achieves the pair $(M,R) = (1/2,5/3)$.


title: Testing High-Energy Emission Models for Blazars with X-ray Polarimetry

abstract:
  Both leptonic and hadronic emission processes may contribute to blazar jet
emission; which dominates in blazars's high energy emission component remains
an open question. Some intermediate synchrotron peaked blazars transition from
their low to high energy emission components in the X-ray band making them
excellent laboratories to probe both components simultaneously, and good
targets for the newly launched Imaging X-ray Polarimetry Explorer. We
characterize the spectral energy distributions for three such blazars:
CGRaBS~J0211+1051, TXS~0506+056, and S5~0716+714, predicting their X-ray
polarization behavior by fitting a multizone polarized leptonic jet model. We
find that a significant detection of electron synchrotron dominated
polarization is possible with a 300~ks observation for S5~0716+714 and
CGRaBS~J0211+1051 in their flaring states, while even 500~ks observations are
unlikely to measure synchrotron self-Compton polarization. Importantly,
non-leptonic emission processes like proton synchrotron are marginally
detectable for our brightest ISP, S5~0716+714, during a flaring state. Improved
{\it IXPE} data reduction methods or next generation telescopes like {\it eXTP}
are needed to confidently measure SSC polarization.


title: Online Simulation Reduction

abstract:
  We study the problem of simultaneously performing reachability analysis and
simulation reduction of transition systems, called online minimization by Lee
and Yannakakis [1992] who settled the question for bisimulation. We call this
problem online simulation reduction to reflect some significant dissimilarities
w.r.t. Lee and Yannakakis' online bisimulation minimization. Indeed, by means
of a reduction to an undecidable problem and of its relationship with graph
st-connectivity, we show that by moving from bisimilarity to similarity this
online reduction problem becomes fundamentally different. Then, we put forward
a symbolic (i.e., processing state partitions) algorithm that performs online
reduction in a complete way for the simulation quasiorder while yielding a
sound over-approximation for simulation equivalence, and a second symbolic
algorithm which is complete for simulation equivalence but, due to the
undecidability result, reveals unavoidable limitations on its termination.


title: Corner the Empress

abstract:
  Wythoff Nim aka Corner the Lady is a classic combinatorial game. A Queen is
placed on an infinite chess board and two players take alternate turns, moving
the Queen closer to the corner. The first player that corners the Queen wins.
What happens if the Queen gets superior powers and is able to go off diagonal?
In this paper we study the intriguing patterns that emerge from such games.


title: Parallel Synthesis for Autoregressive Speech Generation

abstract:
  Autoregressive models have achieved outstanding performance in neural speech
synthesis tasks. Though they can generate highly natural human speech, the
iterative generation inevitably makes the synthesis time proportional to the
utterance's length, leading to low efficiency. Many works were dedicated to
generating the whole speech time sequence in parallel and then proposed
GAN-based, flow-based, and score-based models. This paper proposed a new
thought for the autoregressive generation. Instead of iteratively predicting
samples in a time sequence, the proposed model performs frequency-wise
autoregressive generation (FAR) and bit-wise autoregressive generation (BAR) to
synthesize speech. In FAR, a speech utterance is first split into different
frequency subbands. The proposed model generates a subband conditioned on the
previously generated one. A full band speech can then be reconstructed by using
these generated subbands and a synthesis filter bank. Similarly, in BAR, an
8-bit quantized signal is generated iteratively from the first bit. By
redesigning the autoregressive method to compute in domains other than the time
domain, the number of iterations in the proposed model is no longer
proportional to the utterance's length but the number of subbands/bits. The
inference efficiency is hence significantly increased. Besides, a post-filter
is employed to sample audio signals from output posteriors, and its training
objective is designed based on the characteristics of the proposed
autoregressive methods. The experimental results show that the proposed model
is able to synthesize speech faster than real-time without GPU acceleration.
Compared with the baseline autoregressive and non-autoregressive models, the
proposed model achieves better MOS and shows its good generalization ability
while synthesizing 44 kHz speech or utterances from unseen speakers.


title: "Colliders or Bust"

abstract:
  Charged-particle colliders have proven key instruments of discovery in
high-energy physics. Pushing the frontiers of our knowledge ever further has
relied on, and still keeps calling for, ever better performance and novel
techniques. During more than four decades, Swapan Chattopadhyay has made
numerous essential contributions to this endeavour. Often far ahead of his
time, he helped advance many areas of collider development, notably in the
domains of stochastic cooling, the development of asymmetric B factories, the
design of next- and next-next-generation of high-energy colliders, and the
harnessing of energy-recovery for particle colliders.


title: Loop counting matters in SMEFT

abstract:
  We show that, in addition to the counting of canonical dimensions, a counting
of loop orders is necessary to fully specify the power counting of Standard
Model Effective Field Theory (SMEFT). Using concrete examples, we demonstrate
that considering the canonical dimensions of operators alone may lead to
inconsistent results. The counting of both, canonical dimensions and loop
orders, establishes a clear hierarchy of the terms in SMEFT. In practice, this
serves to identify, and focus on, the potentially dominating effects in any
given high-energy process in a meaningful way. Additionally, this will lead to
a consistent limitation of free parameters in SMEFT applications.


title: Simulating bulk viscosity in neutron stars I: formalism

abstract:
  The faithful inclusion of the effects of bulk viscosity induced by the
presence of chemical reactions is an important issue for simulations of
core-collapse supernovae, binary neutron star mergers and neutron star
oscillations, where particle abundances are locally pushed out of chemical
equilibrium by rarefaction and compression of the fluid elements. In this work,
we discuss three different approaches that can be used to implement bulk
viscosity in general relativistic hydrodynamic simulations of neutron stars:
the exact multi-component reacting fluid, and two M\"uller-Israel-Stewart
theories, namely the second order Hiscock-Lindblom model and its linear limit,
the Maxwell-Cattaneo model. After discussing the theory behind the three
approaches, we specialize their dynamics equations to spherical symmetry in the
radial gauge-polar slicing (i.e., Schwarzschild) coordinates. We also discuss a
particular choice for the equation of state of the fluid and the associated
neutrino emission rates, which are used in a companion paper for the numerical
comparison of the three frameworks, and we obtain the effective sound speed for
the Hiscock-Lindblom theory in the non-linear regime.


title:
Simulating bulk viscosity in neutron stars II: evolution in spherical
  symmetry

abstract:
  Out-of-equilibrium reactions between different particle species are the main
process contributing to bulk viscosity in neutron stars. In this work, we
numerically compare three different approaches to the modeling of bulk
viscosity: the multi-component fluid with reacting particle species and two
bulk stress formalism based on the M\"uller-Israel-Stewart theory, namely the
Hiscock-Lindblom and the Maxwell-Cattaneo models, whose flux-conservative
formulation in radial gauge-polar slicing coordinates and spherical symmetry is
derived in a companion paper. In our knowledge, this is the first time that a
neutron star is simulated with the complete Hiscock-Lindblom model of bulk
viscosity. We find that the Hiscock-Lindblom and Maxwell-Cattaneo models are
good approximations of the multi-component fluid for small perturbations and
when the non-equilibrium equation of state of the fluid depends on only one
independent particle fraction. For more than one independent particle fraction
and for large perturbations, the bulk stress approximation is still valid but
less accurate. In addition, we include the energy loss due to the luminosity of
the reactions in the bulk stress formulation. We find that the energy loss due
to bulk viscosity has a larger effect on the dynamics than the bulk stress or
the variation in particle composition per se. The new one-dimensional,
general-relativistic hydrodynamic code developed for this work, hydro-bulk-1D,
is publicly available.


title: Tensor Electromagnetism and Emergent Elasticity in Jammed Solids

abstract:
  The theory of mechanical response and stress transmission in disordered,
jammed solids poses several open questions of how non-periodic networks --
apparently indistinguishable from a snapshot of a fluid -- sustain shear. We
present a stress-only theory of emergent elasticity for a non-thermal amorphous
assembly of grains in a jammed solid, where each grain is subjected to
mechanical constraints of force and torque balance. These grain-level
constraints lead to the Gauss's law of an emergent $U(1)$ tensor
electromagnetism, which then accounts for the mechanical response of such
solids. This formulation of amorphous elasticity has several immediate
consequences. The mechanical response maps exactly to the static, dielectric
response of this tensorial electromagnetism with the polarizability of the
medium mapping to emergent elastic moduli. External forces act as vector
electric charges whereas the tensorial magnetic fields are sourced by momentum
density. The dynamics in the electric and magnetic sectors, naturally translate
into the dynamics of the rigid jammed network and ballistic particle motion
respectively. The theoretical predictions for both stress-stress correlations
and responses are borne out by the results of numerical simulations of
frictionless granular packings in the static limit of the theory in both 2D and
3D.


title:
Evolution of Electronic and Magnetic Properties in Topological Semimetal
  SmSbxTe2-x

abstract:
  The ZrSiS-type materials have attracted intensive attention due to the
existence of various topological fermions. The magnetic version of the
ZrSiS-type materials, LnSbTe (Ln = lanthanides), is an ideal candidate to
explore novel exotic states due to the interaction between magnetism and
topology. In this work, we report the experimental study on structural,
magnetic, thermodynamic, and electronic properties for SmSbxTe2-x with various
Sb content. The revealed evolutions of these properties with tuning the
compositions would provide useful insights for the fundamental topological
physics and the future applications.


title:
Thermodynamic modeling with uncertainty quantification in the Nb-Ni
  system using the upgraded PyCalphad and ESPEI

abstract:
  The Nb-Ni system has been remodeled with uncertainty quantification (UQ) by
using the presently upgraded software tools of PyCalphad and ESPEI that contain
the new capability to model site occupancy of Wyckoff position for the phases
of interest. Specifically, the five- and three-sublattice models are used to
model the topologically close pack (TCP) phases of {\mu}-Nb7Ni6 and
{\delta}-NbNi3, respectively, according to exactly their Wyckoff positions;
where the inputs for CALPHAD-based modeling include the presently predicted
thermochemical data as a function of temperature by density functional theory
(DFT) based first-principles and phonon calculations together with both phase
equilibrium and site occupancy data in the literature. Besides phase diagram
and thermodynamic properties, the present CALPHAD predictions of site
occupancies are also agreed well with experimental data such as the measured Nb
sites in {\mu}-Nb7Ni6. In addition, the predicted UQ values using the Markov
Chain Monte Carlo (MCMC) method as implemented in ESPEI make it possible to
quantify uncertainties in the Nb-Ni system, such as site occupancies in
{\mu}-Nb7Ni6 and enthalpy of mixing in liquid.


title:
A Universal Formulation of Uncertainty Relation for Error-Disturbance
  and Local Representability of Quantum Observables

abstract:
  A universal formulation of the quantum uncertainty regarding quantum
indeterminacy, quantum measurement, and its inevitable observer effect is
presented with additional focus on the representability of quantum observables
over a given state. The operational tangibility of the framework assures that
the resultant general relations admit natural operational interpretations and
characterisations, and thereby perhaps most importantly, their experimental
verifiability. In view of the universal formulation, Heisenberg's original
philosophy of the uncertainty principle, most typically exemplified in his
famous gamma-ray microscope Gedankenexperiment, is revisited; it is
reformulated and restated as a refined no-go theorem, albeit perhaps in a
weaker form than was originally intended. The relations entail, in essence as
corollaries to their special cases, several previously known relations,
including most notably the standard Kennard-Robertson relation, the
Arthurs-Kelly-Goodman relations for joint measurement as well as the Ozawa and
the Watanabe-Sagawa-Ueda relations for error and error-disturbance.


title:
Automatic Parameter Selection for Electron Ptychography via Bayesian
  Optimization

abstract:
  Electron ptychography provides new opportunities to resolve atomic structures
with deep sub-angstrom spatial resolution and studying electron-beam sensitive
materials with high dose efficiency. In practice, obtaining accurate
ptychography images requires simultaneously optimizing multiple parameters that
are often selected based on trial-and-error, resulting in low-throughput
experiments and preventing wider adoption. Here, we develop an automatic
parameter selection framework to circumvent this problem using Bayesian
optimization with Gaussian processes. With minimal prior knowledge, the
workflow efficiently produces ptychographic reconstructions that are superior
than the ones processed by experienced experts. The method also facilitates
better experimental designs by exploring optimized experimental parameters from
simulated data.


title: Optimal cold atom thermometry using adaptive Bayesian strategies

abstract:
  Precise temperature measurements on systems of few ultracold atoms is of
paramount importance in quantum technology, but can be very resource-intensive.
Here, we put forward an adaptive Bayesian framework that substantially boosts
the performance of cold atom temperature estimation. Specifically, we process
data from release--recapture thermometry experiments on few potassium atoms
cooled down to the microkelvin range in an optical tweezer. We demonstrate that
adaptively choosing the release--recapture times to maximise information gain
does substantially reduce the number of measurements needed for the estimate to
converge to a final reading. Unlike conventional methods, our proposal
systematically avoids capturing and processing uninformative measurements.
Furthermore, we are able to produce much more reliable estimates, especially
when the measured data are scarce and noisy. Likewise, the resulting estimates
converge faster to the real temperature in the asymptotic limit. Our method can
be adapted to enhance the precision and resource-efficiency of many other
techniques running on different experimental setups, thus opening new avenues
in quantum thermometry.


title: Translation between Molecules and Natural Language

abstract:
  Joint representations between images and text have been deeply investigated
in the literature. In computer vision, the benefits of incorporating natural
language have become clear for enabling semantic-level control of images. In
this work, we present $\textbf{MolT5}-$a self-supervised learning framework for
pretraining models on a vast amount of unlabeled natural language text and
molecule strings. $\textbf{MolT5}$ allows for new, useful, and challenging
analogs of traditional vision-language tasks, such as molecule captioning and
text-based de novo molecule generation (altogether: translation between
molecules and language), which we explore for the first time. Furthermore,
since $\textbf{MolT5}$ pretrains models on single-modal data, it helps overcome
the chemistry domain shortcoming of data scarcity. Additionally, we consider
several metrics, including a new cross-modal embedding-based metric, to
evaluate the tasks of molecule captioning and text-based molecule generation.
By interfacing molecules with natural language, we enable a higher semantic
level of control over molecule discovery and understanding--a critical task for
scientific domains such as drug discovery and material design. Our results show
that $\textbf{MolT5}$-based models are able to generate outputs, both molecule
and text, which in many cases are high quality and match the input modality. On
molecule generation, our best model achieves 30% exact matching test accuracy
(i.e., it generates the correct structure for about one-third of the captions
in our held-out test set).


title: Majorana bound states with chiral magnetic textures

abstract:
  The aim of this Tutorial is to give a pedagogical introduction into
realizations of Majorana fermions, usually termed as Majorana bound states
(MBS), in condensed matter systems with magnetic textures. We begin by
considering the Kitaev chain model of 'spinless' fermions and show how two
'half' fermions can appear at chain ends due to interactions. By considering
this model and its two-dimensional generalization, we emphasize intricate
relation between topological superconductivity and possible realizations of
MBS. We further discuss how 'spinless' fermions can be realized in more
physical systems, e.g., by employing the spin-momentum locking. Next, we
demonstrate how magnetic textures can be used to induce synthetic or fictitious
spin-orbit interactions, and, thus, stabilize MBS. We describe a general
approach that works for arbitrary textures and apply it to skyrmions. We show
how MBS can be stabilized by elongated skyrmions, certain higher order
skyrmions, and chains of skyrmions. We also discuss how braiding operations can
be performed with MBS stabilized on magnetic skyrmions. This Tutorial is aimed
at students at graduate level.


title: A new preferential model with homophily for recommender systems

abstract:
  "Rich-get-richer" and "homophily" are two important phenomena in evolving
social networks. "Rich-get-richer" means people with higher followings are more
likely to attract new fans, and "homophily" means people prefer to bond with
others of the same social group or who have some other attribute in common. To
formalize the phenomena simultaneously in the context of an evolving social
network, we consider a K-groups preferential attachment (KPA) network model,
which is helpful for the social networks recommender system. The main
contribution of this paper is to propose a new evolving social network model
with the mechanisms of rich-get-richer and homophily. We show that the KPA
model exhibits a power-law degree distribution for each group and prove the
central limit theorem (CLT) for the maximum likelihood estimation (MLE) of the
parameters in the KPA model. We illustrate our results through simulated data
and explore the usage of this model with real data examples.


title: Real-Time Neural Character Rendering with Pose-Guided Multiplane Images

abstract:
  We propose pose-guided multiplane image (MPI) synthesis which can render an
animatable character in real scenes with photorealistic quality. We use a
portable camera rig to capture the multi-view images along with the driving
signal for the moving subject. Our method generalizes the image-to-image
translation paradigm, which translates the human pose to a 3D scene
representation -- MPIs that can be rendered in free viewpoints, using the
multi-views captures as supervision. To fully cultivate the potential of MPI,
we propose depth-adaptive MPI which can be learned using variable exposure
images while being robust to inaccurate camera registration. Our method
demonstrates advantageous novel-view synthesis quality over the
state-of-the-art approaches for characters with challenging motions. Moreover,
the proposed method is generalizable to novel combinations of training poses
and can be explicitly controlled. Our method achieves such expressive and
animatable character rendering all in real time, serving as a promising
solution for practical applications.


title: Exoplanet Cartography using Convolutional Neural Networks

abstract:
  In the near-future, dedicated telescopes observe Earth-like exoplanets in
reflected light, allowing their characterization. Because of the huge
distances, every exoplanet will be a single pixel, but temporal variations in
its spectral flux hold information about the planet's surface and atmosphere.
We test convolutional neural networks for retrieving a planet's rotation axis,
surface and cloud map from simulated single-pixel flux and polarization
observations. We investigate the assumption that the planets reflect Lambertian
in the retrieval while their actual reflection is bidirectional, and of
including polarization in retrievals. We simulate observations along a planet's
orbit using a radiative transfer algorithm that includes polarization and
bidirectional reflection by vegetation, desert, oceans, water clouds, and
Rayleigh scattering in 6 spectral bands from 400 to 800 nm, at various photon
noise levels. The surface-types and cloud patterns of the facets covering a
model planet are based on probability distributions. Our networks are trained
with simulated observations of millions of planets before retrieving maps of
test planets. The neural networks can constrain rotation axes with a mean
squared error (MSE) as small as 0.0097, depending on the orbital inclination.
On a bidirectionally reflecting planet, 92% of ocean and 85% of vegetation,
desert, and cloud facets are correctly retrieved, in the absence of noise. With
realistic noise, it should still be possible to retrieve the main map features
with a dedicated telescope. Except for face-on orbits, a network trained with
Lambertian reflecting planets, yields significant retrieval errors when given
observations of bidirectionally reflecting planets, in particular, brightness
artefacts around a planet's pole. Including polarization improves retrieving
the rotation axis and the accuracy of the retrieval of ocean and cloud facets.


title: Zero-Shot Logit Adjustment

abstract:
  Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses
challenges in recognizing novel classes in the test phase. The development of
generative models enables current GZSL techniques to probe further into the
semantic-visual link, culminating in a two-stage form that includes a generator
and a classifier. However, existing generation-based methods focus on enhancing
the generator's effect while neglecting the improvement of the classifier. In
this paper, we first analyze of two properties of the generated pseudo unseen
samples: bias and homogeneity. Then, we perform variational Bayesian inference
to back-derive the evaluation metrics, which reflects the balance of the seen
and unseen classes. As a consequence of our derivation, the aforementioned two
properties are incorporated into the classifier training as seen-unseen priors
via logit adjustment. The Zero-Shot Logit Adjustment further puts
semantic-based classifiers into effect in generation-based GZSL. Our
experiments demonstrate that the proposed technique achieves state-of-the-art
when combined with the basic generator, and it can improve various generative
Zero-Shot Learning frameworks. Our codes are available on
https://github.com/cdb342/IJCAI-2022-ZLA.


title: StyleGAN-Human: A Data-Centric Odyssey of Human Generation

abstract:
  Unconditional human image generation is an important task in vision and
graphics, which enables various applications in the creative industry. Existing
studies in this field mainly focus on "network engineering" such as designing
new components and objective functions. This work takes a data-centric
perspective and investigates multiple critical aspects in "data engineering",
which we believe would complement the current practice. To facilitate a
comprehensive study, we collect and annotate a large-scale human image dataset
with over 230K samples capturing diverse poses and textures. Equipped with this
large dataset, we rigorously investigate three essential factors in data
engineering for StyleGAN-based human generation, namely data size, data
distribution, and data alignment. Extensive experiments reveal several valuable
observations w.r.t. these aspects: 1) Large-scale data, more than 40K images,
are needed to train a high-fidelity unconditional human generation model with
vanilla StyleGAN. 2) A balanced training set helps improve the generation
quality with rare face poses compared to the long-tailed counterpart, whereas
simply balancing the clothing texture distribution does not effectively bring
an improvement. 3) Human GAN models with body centers for alignment outperform
models trained using face centers or pelvis points as alignment anchors. In
addition, a model zoo and human editing applications are demonstrated to
facilitate future research in the community.


title: Retrieval-Augmented Diffusion Models

abstract:
  Generative image synthesis with diffusion models has recently achieved
excellent visual quality in several tasks such as text-based or
class-conditional image synthesis. Much of this success is due to a dramatic
increase in the computational capacity invested in training these models. This
work presents an alternative approach: inspired by its successful application
in natural language processing, we propose to complement the diffusion model
with a retrieval-based approach and to introduce an explicit memory in the form
of an external database. During training, our diffusion model is trained with
similar visual features retrieved via CLIP and from the neighborhood of each
training instance. By leveraging CLIP's joint image-text embedding space, our
model achieves highly competitive performance on tasks for which it has not
been explicitly trained, such as class-conditional or text-image synthesis, and
can be conditioned on both text and image embeddings. Moreover, we can apply
our approach to unconditional generation, where it achieves state-of-the-art
performance. Our approach incurs low computational and memory overheads and is
easy to implement. We discuss its relationship to concurrent work and will
publish code and pretrained models soon.


title: Exogenous delivery of water to Mercury

abstract:
  Radar and spacecraft observations show the permanently shadowed regions
around Mercury's North Pole to contain water ice and complex organic material.
One possible source of this material are impacts by interplanetary dust
particles (IDPs), asteroids, and comets.
  We have performed numerical simulations of the dynamical evolution of
asteroids and comets over the few Myr and checked for their impacts with
Mercury. We use the N-body integrator RMVS/Swifter to propagate the Sun and the
eight planets from their current positions. We add comets and asteroids to the
simulations as massless test particles, based on their current orbital
distributions. Asteroid impactors are assigned a probability of being
water-rich (C-class) based on the measured distribution of taxonomic types. For
comets, we assume a constant water fraction. For IDPs, we use a dynamical
meteoroid model to compute the dust flux on Mercury. Relative to previous work
on asteroid and comet impacts (Moses et al. 1999), we leverage 20 years of
progress in minor body surveys.
  Immediate post-impact ejection of impactor material into outer space is taken
into account as is the migration efficiency of water across Mercury's surface
to the polar cold traps.
  We find that asteroids deliver $\sim 1 \times 10^{3}$ kg/yr of water to
Mercury, comets deliver $\sim 1 \times 10^{3}$ kg/yr and IDPs deliver $\sim 16
\times 10^{3}$ kg/yr within a factor of several. Over a timescale of $\sim 1$
Gyr, this is enough to deliver the minimum amount of water required by the
radar and MESSENGER observations.
  While other sources of water on Mercury are not ruled out by our analysis, we
show that they are not required to explain the currently available
observational lower limits.


title: Personality Traits in Game Development

abstract:
  Existing work on personality traits in software development excludes game
developers as a discrete group. Whilst games are software, game development has
unique considerations, so game developers may exhibit different personality
traits from other software professionals. We assessed responses from 123 game
developers on an International Personality Item Pool Five Factor Model scale
and demographic questionnaire using factor analysis. Programmers reported lower
Extraversion than designers, artists and production team members; lower
Openness than designers and production, and reported higher Neuroticism than
production -- potentially linked to burnout and crunch time. Compared to
published norms of software developers, game developers reported lower
Openness, Conscientiousness, Extraversion and Agreeableness, but higher
Neuroticism. These personality differences have many practical implications:
differences in Extraversion among roles may precipitate communication
breakdowns; differences in Openness may induce conflict between programmers and
designers. Understanding the relationship between personality traits and roles
can help recruiters steer new employees into appropriate roles, and help
managers apply appropriate stress management techniques. To realise these
benefits, individuals must be distinguished from roles: just because an
individual occupies a role does not mean they possess personality traits
associated with that role.


title: Task-Induced Representation Learning

abstract:
  In this work, we evaluate the effectiveness of representation learning
approaches for decision making in visually complex environments. Representation
learning is essential for effective reinforcement learning (RL) from
high-dimensional inputs. Unsupervised representation learning approaches based
on reconstruction, prediction or contrastive learning have shown substantial
learning efficiency gains. Yet, they have mostly been evaluated in clean
laboratory or simulated settings. In contrast, real environments are visually
complex and contain substantial amounts of clutter and distractors.
Unsupervised representations will learn to model such distractors, potentially
impairing the agent's learning efficiency. In contrast, an alternative class of
approaches, which we call task-induced representation learning, leverages task
information such as rewards or demonstrations from prior tasks to focus on
task-relevant parts of the scene and ignore distractors. We investigate the
effectiveness of unsupervised and task-induced representation learning
approaches on four visually complex environments, from Distracting DMControl to
the CARLA driving simulator. For both, RL and imitation learning, we find that
representation learning generally improves sample efficiency on unseen tasks
even in visually complex scenes and that task-induced representations can
double learning efficiency compared to unsupervised alternatives. Code is
available at https://clvrai.com/tarp.


title: Skill-based Meta-Reinforcement Learning

abstract:
  While deep reinforcement learning methods have shown impressive results in
robot learning, their sample inefficiency makes the learning of complex,
long-horizon behaviors with real robot systems infeasible. To mitigate this
issue, meta-reinforcement learning methods aim to enable fast learning on novel
tasks by learning how to learn. Yet, the application has been limited to
short-horizon tasks with dense rewards. To enable learning long-horizon
behaviors, recent works have explored leveraging prior experience in the form
of offline datasets without reward or task annotations. While these approaches
yield improved sample efficiency, millions of interactions with environments
are still required to solve complex tasks. In this work, we devise a method
that enables meta-learning on long-horizon, sparse-reward tasks, allowing us to
solve unseen target tasks with orders of magnitude fewer environment
interactions. Our core idea is to leverage prior experience extracted from
offline datasets during meta-learning. Specifically, we propose to (1) extract
reusable skills and a skill prior from offline datasets, (2) meta-train a
high-level policy that learns to efficiently compose learned skills into
long-horizon behaviors, and (3) rapidly adapt the meta-trained policy to solve
an unseen target task. Experimental results on continuous control tasks in
navigation and manipulation demonstrate that the proposed method can
efficiently solve long-horizon novel target tasks by combining the strengths of
meta-learning and the usage of offline datasets, while prior approaches in RL,
meta-RL, and multi-task RL require substantially more environment interactions
to solve the tasks.


title: Demonstration of the Two-Fluxonium Cross-Resonance Gate

abstract:
  The superconducting fluxonium qubit has a great potential for high-fidelity
quantum gates with its long coherence times and strong anharmonicity at the
half flux quantum sweet spot. However, current implementations of two-qubit
gates compromise fluxonium's coherence properties by requiring either a
temporary population of the non-computational states or tuning the magnetic
flux off the sweet spot. Here we realize a fast all-microwave cross-resonance
gate between two capacitively-coupled fluxoniums with the qubit dynamics well
confined to the computational space. We demonstrate a direct CNOT gate in 70 ns
with fidelity up to $\mathcal{F}=0.9949(6)$ despite the limitations of a
sub-optimal measurement setup and device coherence. Our results project a
possible pathway towards reducing the two-qubit error rate below $10^{-4}$ with
present-day technologies.


title: Proto2Proto: Can you recognize the car, the way I do?

abstract:
  Prototypical methods have recently gained a lot of attention due to their
intrinsic interpretable nature, which is obtained through the prototypes. With
growing use cases of model reuse and distillation, there is a need to also
study transfer of interpretability from one model to another. We present
Proto2Proto, a novel method to transfer interpretability of one prototypical
part network to another via knowledge distillation. Our approach aims to add
interpretability to the "dark" knowledge transferred from the teacher to the
shallower student model. We propose two novel losses: "Global Explanation" loss
and "Patch-Prototype Correspondence" loss to facilitate such a transfer. Global
Explanation loss forces the student prototypes to be close to teacher
prototypes, and Patch-Prototype Correspondence loss enforces the local
representations of the student to be similar to that of the teacher. Further,
we propose three novel metrics to evaluate the student's proximity to the
teacher as measures of interpretability transfer in our settings. We
qualitatively and quantitatively demonstrate the effectiveness of our method on
CUB-200-2011 and Stanford Cars datasets. Our experiments show that the proposed
method indeed achieves interpretability transfer from teacher to student while
simultaneously exhibiting competitive performance.


title: Persistent homology in cosmic shear II: A tomographic analysis of DES-Y1

abstract:
  We demonstrate how to use persistent homology for cosmological parameter
inference in a tomographic cosmic shear survey. We obtain the first
cosmological parameter constraints from persistent homology by applying our
method to the first-year data of the Dark Energy Survey.
  To obtain these constraints, we analyse the topological structure of the
matter distribution by extracting persistence diagrams from signal-to-noise
maps of aperture masses. This presents a natural extension to the widely used
peak count statistics. Extracting the persistence diagrams from the
cosmo-SLICS, a suite of $N$-body simulations with variable cosmological
parameters, we interpolate the signal using Gaussian Processes and marginalise
over the most relevant systematic effects, including intrinsic alignments and
baryonic effects.
  We find for the structure growth parameter $S_8=0.747^{+0.025}_{-0.031}$,
which is in full agreement with other late-time probes. We also constrain the
intrinsic alignment parameter to $A=1.54\pm 0.52$, ruling out the case of no
intrinsic alignments at a $3\sigma$-level.


title: Machine learning identification of organic compounds using visible light

abstract:
  Identifying chemical compounds is essential in several areas of science and
engineering. Laser-based techniques are promising for autonomous compound
detection because the optical response of materials encodes enough electronic
and vibrational information for remote chemical identification. This has been
exploited using the fingerprint region of infrared absorption spectra, which
involves a large number of absorption peaks that are unique to individual
molecules, thus facilitating chemical identification. However, optical
identification using visible light has not been realized. Using decades of
experimental refractive index data in the scientific literature of pure organic
compounds and polymers over a broad range of frequencies from the ultraviolet
to the far-infrared, we develop a machine learning classifier that can
accurately identify organic species based on a single-wavelength dispersive
measurement in the visible spectral region, away from absorption resonances.
The optical classifier proposed here could be applied to autonomous material
identification protocols or applications.


title:
Joint Learning of Reward Machines and Policies in Environments with
  Partially Known Semantics

abstract:
  We study the problem of reinforcement learning for a task encoded by a reward
machine. The task is defined over a set of properties in the environment,
called atomic propositions, and represented by Boolean variables. One
unrealistic assumption commonly used in the literature is that the truth values
of these propositions are accurately known. In real situations, however, these
truth values are uncertain since they come from sensors that suffer from
imperfections. At the same time, reward machines can be difficult to model
explicitly, especially when they encode complicated tasks. We develop a
reinforcement-learning algorithm that infers a reward machine that encodes the
underlying task while learning how to execute it, despite the uncertainties of
the propositions' truth values. In order to address such uncertainties, the
algorithm maintains a probabilistic estimate about the truth value of the
atomic propositions; it updates this estimate according to new sensory
measurements that arrive from the exploration of the environment. Additionally,
the algorithm maintains a hypothesis reward machine, which acts as an estimate
of the reward machine that encodes the task to be learned. As the agent
explores the environment, the algorithm updates the hypothesis reward machine
according to the obtained rewards and the estimate of the atomic propositions'
truth value. Finally, the algorithm uses a Q-learning procedure for the states
of the hypothesis reward machine to determine the policy that accomplishes the
task. We prove that the algorithm successfully infers the reward machine and
asymptotically learns a policy that accomplishes the respective task.


title: Accelerating Machine Learning via the Weber-Fechner Law

abstract:
  The Weber-Fechner Law observes that human perception scales as the logarithm
of the stimulus. We argue that learning algorithms for human concepts could
benefit from the Weber-Fechner Law. Specifically, we impose Weber-Fechner on
simple neural networks, with or without convolution, via the logarithmic power
series of their sorted output. Our experiments show surprising performance and
accuracy on the MNIST data set within a few training iterations and limited
computational resources, suggesting that Weber-Fechner can accelerate machine
learning of human concepts.


title:
A Novel Scalable Apache Spark Based Feature Extraction Approaches for
  Huge Protein Sequence and their Clustering Performance Analysis

abstract:
  Genome sequencing projects are rapidly increasing the number of
high-dimensional protein sequence datasets. Clustering a high-dimensional
protein sequence dataset using traditional machine learning approaches poses
many challenges. Many different feature extraction methods exist and are widely
used. However, extracting features from millions of protein sequences becomes
impractical because they are not scalable with current algorithms. Therefore,
there is a need for an efficient feature extraction approach that extracts
significant features. We have proposed two scalable feature extraction
approaches for extracting features from huge protein sequences using Apache
Spark, which are termed 60d-SPF (60-dimensional Scalable Protein Feature) and
6d-SCPSF (6-dimensional Scalable Co-occurrence-based Probability-Specific
Feature). The proposed 60d-SPF and 6d-SCPSF approaches capture the statistical
properties of amino acids to create a fixed-length numeric feature vector that
represents each protein sequence in terms of 60-dimensional and 6-dimensional
features, respectively. The preprocessed huge protein sequences are used as an
input in two clustering algorithms, i.e., Scalable Random Sampling with
Iterative Optimization Fuzzy c-Means (SRSIO-FCM) and Scalable Literal Fuzzy
C-Means (SLFCM) for clustering. We have conducted extensive experiments on
various soybean protein datasets to demonstrate the effectiveness of the
proposed feature extraction methods, 60d-SPF, 6d-SCPSF, and existing feature
extraction methods on SRSIO-FCM and SLFCM clustering algorithms. The reported
results in terms of the Silhouette index and the Davies-Bouldin index show that
the proposed 60d-SPF extraction method on SRSIO-FCM and SLFCM clustering
algorithms achieves significantly better results than the proposed 6d-SCPSF and
existing feature extraction approaches.


title:
Automated detection of dark patterns in cookie banners: how to do it
  poorly and why it is hard to do it any other way

abstract:
  Cookie banners, the pop ups that appear to collect your consent for data
collection, are a tempting ground for dark patterns. Dark patterns are design
elements that are used to influence the user's choice towards an option that is
not in their interest. The use of dark patterns renders consent elicitation
meaningless and voids the attempts to improve a fair collection and use of
data. Can machine learning be used to automatically detect the presence of dark
patterns in cookie banners? In this work, a dataset of cookie banners of 300
news websites was used to train a prediction model that does exactly that. The
machine learning pipeline we used includes feature engineering, parameter
search, training a Gradient Boosted Tree classifier and evaluation. The
accuracy of the trained model is promising, but allows a lot of room for
improvement. We provide an in-depth analysis of the interdisciplinary
challenges that automated dark pattern detection poses to artificial
intelligence. The dataset and all the code created using machine learning is
available at the url to repository removed for review.


title: A Mask-Based Adversarial Defense Scheme

abstract:
  Adversarial attacks hamper the functionality and accuracy of Deep Neural
Networks (DNNs) by meddling with subtle perturbations to their inputs.In this
work, we propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to
mitigate the negative effect from adversarial attacks. To be precise, our
method promotes the robustness of a DNN by randomly masking a portion of
potential adversarial images, and as a result, the %classification result
output of the DNN becomes more tolerant to minor input perturbations. Compared
with existing adversarial defense techniques, our method does not need any
additional denoising structure, nor any change to a DNN's design. We have
tested this approach on a collection of DNN models for a variety of data sets,
and the experimental results confirm that the proposed method can effectively
improve the defense abilities of the DNNs against all of the tested adversarial
attack methods. In certain scenarios, the DNN models trained with MAD have
improved classification accuracy by as much as 20% to 90% compared to the
original models that are given adversarial inputs.


title: Automating Neural Architecture Design without Search

abstract:
  Neural structure search (NAS), as the mainstream approach to automate deep
neural architecture design, has achieved much success in recent years. However,
the performance estimation component adhering to NAS is often prohibitively
costly, which leads to the enormous computational demand. Though a large number
of efforts have been dedicated to alleviating this pain point, no consensus has
been made yet on which is optimal. In this paper, we study the automated
architecture design from a new perspective that eliminates the need to
sequentially evaluate each neural architecture generated during algorithm
execution. Specifically, the proposed approach is built by learning the
knowledge of high-level experts in designing state-of-the-art architectures,
and then the new architecture is directly generated upon the knowledge learned.
We implemented the proposed approach by using a graph neural network for link
prediction and acquired the knowledge from NAS-Bench-101. Compared to existing
peer competitors, we found a competitive network with minimal cost. In
addition, we also utilized the learned knowledge from NAS-Bench-101 to automate
architecture design in the DARTS search space, and achieved 97.82% accuracy on
CIFAR10, and 76.51% top-1 accuracy on ImageNet consuming only $2\times10^{-4}$
GPU days. This also demonstrates the high transferability of the proposed
approach, and can potentially lead to a new, more computationally efficient
paradigm in this research direction.


title: AU-NN: ANFIS Unit Neural Network

abstract:
  In this paper is described the ANFIS Unit Neural Network, a deep neural
network where each neuron is an independent ANFIS. Two use cases of this
network are shown to test the capability of the network. (i) Classification of
five imagined words. (ii) Incremental learning in the task of detecting
Imagined Word Segments vs. Idle State Segments. In both cases, the proposed
network outperforms the conventional methods. Additionally, is described a
process of classification where instead of taking the whole instance as one
example, each instance is decomposed into a set of smaller instances, and the
classification is done by a majority vote over all the predictions of the set.
The codes to build the AU-NN used in this paper, are available on the github
repository https://github.com/tonahdztoro/AU_NN.


title:
Dynamic Ensemble Bayesian Filter for Robust Control of a Human
  Brain-machine Interface

abstract:
  Objective: Brain-machine interfaces (BMIs) aim to provide direct brain
control of devices such as prostheses and computer cursors, which have
demonstrated great potential for mobility restoration. One major limitation of
current BMIs lies in the unstable performance in online control due to the
variability of neural signals, which seriously hinders the clinical
availability of BMIs. Method: To deal with the neural variability in online BMI
control, we propose a dynamic ensemble Bayesian filter (DyEnsemble). DyEnsemble
extends Bayesian filters with a dynamic measurement model, which adjusts its
parameters in time adaptively with neural changes. This is achieved by learning
a pool of candidate functions and dynamically weighting and assembling them
according to neural signals. In this way, DyEnsemble copes with variability in
signals and improves the robustness of online control. Results: Online BMI
experiments with a human participant demonstrate that, compared with the
velocity Kalman filter, DyEnsemble significantly improves the control accuracy
(increases the success rate by 13.9% and reduces the reach time by 13.5% in the
random target pursuit task) and robustness (performs more stably over different
experiment days). Conclusion: Our results demonstrate the superiority of
DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and
flexible framework for robust neural decoding, which is beneficial to different
neural decoding applications.


title: A Closer Look at Personalization in Federated Image Classification

abstract:
  Federated Learning (FL) is developed to learn a single global model across
the decentralized data, while is susceptible when realizing client-specific
personalization in the presence of statistical heterogeneity. However, studies
focus on learning a robust global model or personalized classifiers, which
yield divergence due to inconsistent objectives. This paper shows that it is
possible to achieve flexible personalization after the convergence of the
global model by introducing representation learning. In this paper, we first
analyze and determine that non-IID data harms representation learning of the
global model. Existing FL methods adhere to the scheme of jointly learning
representations and classifiers, where the global model is an average of
classification-based local models that are consistently subject to
heterogeneity from non-IID data. As a solution, we separate representation
learning from classification learning in FL and propose RepPer, an independent
two-stage personalized FL framework.We first learn the client-side feature
representation models that are robust to non-IID data and aggregate them into a
global common representation model. After that, we achieve personalization by
learning a classifier head for each client, based on the common representation
obtained at the former stage. Notably, the proposed two-stage learning scheme
of RepPer can be potentially used for lightweight edge computing that involves
devices with constrained computation power.Experiments on various datasets
(CIFAR-10/100, CINIC-10) and heterogeneous data setup show that RepPer
outperforms alternatives in flexibility and personalization on non-IID data.


title: Adaptive Online Value Function Approximation with Wavelets

abstract:
  Using function approximation to represent a value function is necessary for
continuous and high-dimensional state spaces. Linear function approximation has
desirable theoretical guarantees and often requires less compute and samples
than neural networks, but most approaches suffer from an exponential growth in
the number of functions as the dimensionality of the state space increases. In
this work, we introduce the wavelet basis for reinforcement learning. Wavelets
can effectively be used as a fixed basis and additionally provide the ability
to adaptively refine the basis set as learning progresses, making it feasible
to start with a minimal basis set. This adaptive method can either increase the
granularity of the approximation at a point in state space, or add in
interactions between different dimensions as necessary. We prove that wavelets
are both necessary and sufficient if we wish to construct a function
approximator that can be adaptively refined without loss of precision. We
further demonstrate that a fixed wavelet basis set performs comparably against
the high-performing Fourier basis on Mountain Car and Acrobot, and that the
adaptive methods provide a convenient approach to addressing an oversized
initial basis set, while demonstrating performance comparable to, or greater
than, the fixed wavelet basis.


title:
A Computational Theory of Learning Flexible Reward-Seeking Behavior with
  Place Cells

abstract:
  An important open question in computational neuroscience is how various
spatially tuned neurons, such as place cells, are used to support the learning
of reward-seeking behavior of an animal. Existing computational models either
lack biological plausibility or fall short of behavioral flexibility when
environments change. In this paper, we propose a computational theory that
achieves behavioral flexibility with better biological plausibility. We first
train a mixture of Gaussian distributions to model the ensemble of firing
fields of place cells. Then we propose a Hebbian-like rule to learn the
synaptic strength matrix among place cells. This matrix is interpreted as the
transition rate matrix of a continuous time Markov chain to generate the
sequential replay of place cells. During replay, the synaptic strengths from
place cells to medium spiny neurons (MSN) are learned by a temporal-difference
like rule to store place-reward associations. After replay, the activation of
MSN will ramp up when an animal approaches the rewarding place, so the animal
can move along the direction where the MSN activation is increasing to find the
rewarding place. We implement our theory into a high-fidelity virtual rat in
the MuJoCo physics simulator. In a complex maze, the rat shows significantly
better learning efficiency and behavioral flexibility than a rat that
implements a neuroscience-inspired reinforcement learning algorithm, deep
Q-network.


title: From Monolith to Microservices: Static and Dynamic Analysis Comparison

abstract:
  One of the most challenging problems in the migration of a monolith to a
microservices architecture is the identification of the microservices
boundaries. Several approaches have been recently proposed for the automatic
identification of microservices, which, even though following the same basic
steps, diverge on how data of the monolith system is collected and analysed. In
this paper, we compare the decompositions generated for two monolith systems
into a set of candidate microservices, when static and dynamic analysis data
collection techniques are used. The decompositions are generated using a
combination of similarity measures and are evaluated according to a complexity
metric to answer the following research question: which collection of monolith
data, static or dynamic analysis, allows to generate better decompositions? As
result of the analysis we conclude that neither of the analysis techniques,
static nor dynamic, outperforms the other, but the dynamic collection of data
requires more effort.


title: Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings

abstract:
  The fault diagnosis of rolling bearings is a critical technique to realize
predictive maintenance for mechanical condition monitoring. In real industrial
systems, the main challenges for the fault diagnosis of rolling bearings
pertain to the accuracy and real-time requirements. Most existing methods focus
on ensuring the accuracy, and the real-time requirement is often neglected. In
this paper, considering both requirements, we propose a novel fast fault
diagnosis method for rolling bearings, based on extreme learning machine (ELM)
and logistic mapping, named logistic-ELM. First, we identify 14 kinds of
time-domain features from the original vibration signals according to
mechanical vibration principles and adopt the sequential forward selection
(SFS) strategy to select optimal features from them to ensure the basic
predictive accuracy and efficiency. Next, we propose the logistic-ELM for fast
fault classification, where the biases in ELM are omitted and the random input
weights are replaced by the chaotic logistic mapping sequence which involves a
higher uncorrelation to obtain more accurate results with fewer hidden neurons.
We conduct extensive experiments on the rolling bearing vibration signal
dataset of the Case Western Reserve University (CWRU) Bearing Data Centre. The
experimental results show that the proposed approach outperforms existing SOTA
comparison methods in terms of the predictive accuracy, and the highest
accuracy is 100% in seven separate sub data environments. The relevant code is
publicly available at https://github.com/TAN-OpenLab/logistic-ELM.


title: Graphical Residual Flows

abstract:
  Graphical flows add further structure to normalizing flows by encoding
non-trivial variable dependencies. Previous graphical flow models have focused
primarily on a single flow direction: the normalizing direction for density
estimation, or the generative direction for inference. However, to use a single
flow to perform tasks in both directions, the model must exhibit stable and
efficient flow inversion. This work introduces graphical residual flows, a
graphical flow based on invertible residual networks. Our approach to
incorporating dependency information in the flow, means that we are able to
calculate the Jacobian determinant of these flows exactly. Our experiments
confirm that graphical residual flows provide stable and accurate inversion
that is also more time-efficient than alternative flows with similar task
performance. Furthermore, our model provides performance competitive with other
graphical flows for both density estimation and inference tasks.


title:
SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian
  Networks

abstract:
  Initial work on variational autoencoders assumed independent latent variables
with simple distributions. Subsequent work has explored incorporating more
complex distributions and dependency structures: including normalizing flows in
the encoder network allows latent variables to entangle non-linearly, creating
a richer class of distributions for the approximate posterior, and stacking
layers of latent variables allows more complex priors to be specified for the
generative model. This work explores incorporating arbitrary dependency
structures, as specified by Bayesian networks, into VAEs. This is achieved by
extending both the prior and inference network with graphical residual flows -
residual flows that encode conditional independence by masking the weight
matrices of the flow's residual blocks. We compare our model's performance on
several synthetic datasets and show its potential in data-sparse settings.


title:
On Leveraging Variational Graph Embeddings for Open World Compositional
  Zero-Shot Learning

abstract:
  Humans are able to identify and categorize novel compositions of known
concepts. The task in Compositional Zero-Shot learning (CZSL) is to learn
composition of primitive concepts, i.e. objects and states, in such a way that
even their novel compositions can be zero-shot classified. In this work, we do
not assume any prior knowledge on the feasibility of novel compositions
i.e.open-world setting, where infeasible compositions dominate the search
space. We propose a Compositional Variational Graph Autoencoder (CVGAE)
approach for learning the variational embeddings of the primitive concepts
(nodes) as well as feasibility of their compositions (via edges). Such
modelling makes CVGAE scalable to real-world application scenarios. This is in
contrast to SOTA method, CGE, which is computationally very expensive. e.g.for
benchmark C-GQA dataset, CGE requires 3.94 x 10^5 nodes, whereas CVGAE requires
only 1323 nodes. We learn a mapping of the graph and image embeddings onto a
common embedding space. CVGAE adopts a deep metric learning approach and learns
a similarity metric in this space via bi-directional contrastive loss between
projected graph and image embeddings. We validate the effectiveness of our
approach on three benchmark datasets.We also demonstrate via an image retrieval
task that the representations learnt by CVGAE are better suited for
compositional generalization.


title:
Heterogeneous Information Network based Default Analysis on Banking
  Micro and Small Enterprise Users

abstract:
  Risk assessment is a substantial problem for financial institutions that has
been extensively studied both for its methodological richness and its various
practical applications. With the expansion of inclusive finance, recent
attentions are paid to micro and small-sized enterprises (MSEs). Compared with
large companies, MSEs present a higher exposure rate to default owing to their
insecure financial stability. Conventional efforts learn classifiers from
historical data with elaborate feature engineering. However, the main obstacle
for MSEs involves severe deficiency in credit-related information, which may
degrade the performance of prediction. Besides, financial activities have
diverse explicit and implicit relations, which have not been fully exploited
for risk judgement in commercial banks. In particular, the observations on real
data show that various relationships between company users have additional
power in financial risk analysis. In this paper, we consider a graph of banking
data, and propose a novel HIDAM model for the purpose. Specifically, we attempt
to incorporate heterogeneous information network with rich attributes on
multi-typed nodes and links for modeling the scenario of business banking
service. To enhance feature representation of MSEs, we extract interactive
information through meta-paths and fully exploit path information. Furthermore,
we devise a hierarchical attention mechanism respectively to learn the
importance of contents inside each meta-path and the importance of different
metapahs. Experimental results verify that HIDAM outperforms state-of-the-art
competitors on real-world banking data.


title: Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging

abstract:
  Photoacoustic imaging (PAI) can image high-resolution structures of clinical
interest such as vascularity in cancerous tumor monitoring. When imaging human
subjects, geometric restrictions force limited-view data retrieval causing
imaging artifacts. Iterative physical model based approaches reduce artifacts
but require prohibitively time consuming PDE solves. Machine learning (ML) has
accelerated PAI by combining physical models and learned networks. However, the
depth and overall power of ML methods is limited by memory intensive training.
We propose using invertible neural networks (INNs) to alleviate memory
pressure. We demonstrate INNs can image 3D photoacoustic volumes in the setting
of limited-view, noisy, and subsampled data. The frugal constant memory usage
of INNs enables us to train an arbitrary depth of learned layers on a consumer
GPU with 16GB RAM.