Abstract:
  We have calculated coupling constants of a neutral pion with the lowest two
nucleon resonances. This includes both the diagonal as well as non-diagonal
coupling constant involving a nucleon resonance and a nucleon. For this, we
first calculate vacuum-to-pion correlation function of the interpolating fields
of two nucleons and then take its matrix elements with respect to nucleon
and/or nucleon resonance spinors. Using different QCD sum rules obtained from
different matrix elements we eliminate unwanted coupling constants and solve
for the desired ones.16


Abstract:
  We propose GraphMineSuite (GMS): the first benchmarking suite for graph
mining that facilitates evaluating and constructing high-performance graph
mining algorithms. First, GMS comes with a benchmark specification based on
extensive literature review, prescribing representative problems, algorithms,
and datasets. Second, GMS offers a carefully designed software platform for
seamless testing of different fine-grained elements of graph mining algorithms,
such as graph representations or algorithm subroutines. The platform includes
parallel implementations of more than 40 considered baselines, and it
facilitates developing complex and fast mining algorithms. High modularity is
possible by harnessing set algebra operations such as set intersection and
difference, which enables breaking complex graph mining algorithms into simple
building blocks that can be separately experimented with. GMS is supported with
a broad concurrency analysis for portability in performance insights, and a
novel performance metric to assess the throughput of graph mining algorithms,
enabling more insightful evaluation. As use cases, we harness GMS to rapidly
redesign and accelerate state-of-the-art baselines of core graph mining
problems: degeneracy reordering (by up to >2x), maximal clique listing (by up
to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x),
also obtaining better theoretical performance bounds.


Abstract:
  A novel multiple-input multiple-output (MIMO) dual-function radar
communication (DFRC) system is proposed. The system transmits wideband,
orthogonal frequency division multiplexing (OFDM) waveforms using a small
subset of the available antennas in each channel use. The proposed system
assigns most carriers to antennas in a shared fashion, thus efficiently
exploiting the available communication bandwidth, and a small set of
subcarriers to active antennas in an exclusive fashion (private subcarriers). A
novel target estimation approach is proposed to overcome the coupling of target
parameters introduced by subcarrier sharing. The obtained parameters are
further refined via an iterative approach, which formulates a sparse signal
recovery problem based on the data of the private subcarriers. The system is
endowed with beamforming capability, via waveform precoding and antenna
selection. The precoding and antenna selection matrices are optimally
co-designed to meet a joint sensing-communication system performance. The
sparsity of the transmit array is exploited at the communication receiver to
recover the transmitted information. The use of shared subcarriers enables high
communication rate, while the sparse transmit array maintains low system
hardware cost. The sensing problem is formulated by taking into account
frequency selective fading, and a method is proposed to estimate the channel
coefficients during the sensing process. The functionality of the proposed
system is demonstrated via simulations.


Abstract:
  Cyber Threat hunting is a proactive search for known attack behaviors in the
organizational information system. It is an important component to mitigate
advanced persistent threats (APTs). However, the attack behaviors recorded in
provenance data may not be completely consistent with the known attack
behaviors. In this paper, we propose DeepHunter, a graph neural network (GNN)
based graph pattern matching approach that can match provenance data against
known attack behaviors in a robust way. Specifically, we design a graph neural
network architecture with two novel networks: attribute embedding networks that
could incorporate Indicators of Compromise (IOCs) information, and graph
embedding networks that could capture the relationships between IOCs. To
evaluate DeepHunter, we choose five real and synthetic APT attack scenarios.
Results show that DeepHunter can hunt all attack behaviors, and the accuracy
and robustness of DeepHunter outperform the state-of-the-art method, Poirot.


Abstract:
  Let $G$ be a locally compact group and $p,q\in \mathbb{R}$ with $p>1$
$p\not=2$ and $q$ between $2$ and $p$ (if $p<2$ then $p<q<2$, if $p>2$ then
$2<q<p.$) The main result of the paper is that $A_q(G)$ multiplies $A_p(G)$,
more precisely we show that the Banach algebra $A_p(G)$is a Banach module on
$A_q(G).$


Abstract:
  In condensed matter systems, zero-dimensional or one-dimensional Majorana
modes can be realized respectively as the end and edge states of
one-dimensional and two-dimensional topological superconductors. In this
$\textit{top-down}$ approach, $(d-1)$-dimensional Majorana modes are obtained
as the boundary states of a topologically nontrivial $d$-dimensional bulk. In a
$\textit{bottom-up}$ approach instead, $d$-dimensional Majorana modes in a
$d$-dimensional system can be realized as the continuous limit of a periodic
lattice of coupled $(d-1)$-dimensional Majorana modes. We illustrate this idea
by considering one-dimensional proximitized superconductors with
spatially-modulated potential or magnetic fields. The ensuing inhomogenous
topological state exhibits one-dimensional counterpropagating Majorana modes
with finite dispersion, and with a Majorana gap which can be controlled by
external fields. In the massless case, the Majorana modes have opposite
Majorana polarizations and pseudospins, are conformally invariant, and realize
centrally extended quantum mechanical supersymmetry. The supersymmetry exhibits
spontaneous partial breaking. Consequently, the massless Majorana fermion can
be identified as a Goldstino, i.e., the Nambu-Goldstone fermion associated with
the spontaneously broken supersymmetry.


Abstract:
  We study rotating global AdS solutions in five-dimensional Einstein gravity
coupled to a multiplet complex scalar within a cohomogeneity-1 ansatz. The
onset of the gravitational and scalar field superradiant instabilities of the
Myers-Perry-AdS black hole mark bifurcation points to black resonators and
hairy Myers-Perry-AdS black holes, respectively. These solutions are subject to
the other (gravitational or scalar) instability, and result in hairy black
resonators which contain both gravitational and scalar hair. The hairy black
resonators have smooth zero-horizon limits that we call graviboson stars. In
the hairy black resonator and graviboson solutions, multiple scalar components
with different frequencies are excited, and hence these are multioscillating
solutions. The phase structure of the solutions are examined in the
microcanonical ensemble, i.e. at fixed energy and angular momenta. It is found
that the entropy of the hairy black resonator is never the largest among them.
We also find that hairy black holes with higher scalar wavenumbers are
entropically dominant and occupy more of phase space than those of lower
wavenumbers.


Abstract:
  The scattering of Dirac fermions in the background fields of topological
solitons of the $(2+1)$-dimensional nonlinear $O(3)$ $\sigma$-model is studied
using both analytical and numerical methods. General formulae describing
fermion scattering are obtained and the symmetry properties of the partial
scattering amplitudes and elements of the $S$-matrix are determined. Within the
framework of the Born approximation, the scattering amplitudes, differential
cross-sections, and total cross-sections of fermion-soliton scattering are
obtained in analytical forms, and their symmetry properties and asymptotic
behavior are investigated. The dependences of the first several partial
elements of the $S$-matrix on the momentum of the fermion are obtained using
numerical methods, and some properties of these dependences are ascertained and
discussed.


Abstract:
  In this paper, we calculate four geometric constants for discrete Morrey
spaces. The constants are generalized von Neumann-Jordan constant, modified von
Neumann-Jordan constant, von Neumann-Jordan type constant, and Zb\"{a}ganu
constant. The four constants measure uniformly nonsquareness of the above
spaces. We obtain that the value of each of the four constants for the above
spaces is two, which means that the spaces are NOT uniformly nonsquare.


Abstract:
  This work presents a new fine-grained transparent object segmentation
dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale
transparent object segmentation dataset. Unlike Trans10K-v1 that only has two
limited categories, our new dataset has several appealing benefits. (1) It has
11 fine-grained categories of transparent objects, commonly occurring in the
human domestic environment, making it more practical for real-world
application. (2) Trans10K-v2 brings more challenges for the current advanced
segmentation methods than its former version. Furthermore, a novel
transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly,
the transformer encoder of Trans2Seg provides the global receptive field in
contrast to CNN's local receptive field, which shows excellent advantages over
pure CNN architectures. Secondly, by formulating semantic segmentation as a
problem of dictionary look-up, we design a set of learnable prototypes as the
query of Trans2Seg's transformer decoder, where each prototype learns the
statistics of one category in the whole dataset. We benchmark more than 20
recent semantic segmentation methods, demonstrating that Trans2Seg
significantly outperforms all the CNN-based methods, showing the proposed
algorithm's potential ability to solve transparent object segmentation.


Abstract:
  "Changing-look" quasars are a new class of highly variable active galactic
nuclei that have changed their spectral type over surprisingly short timescales
of just a few years. The origin of this phenomenon is debated, but is likely to
reflect some change in the accretion flow. To investigate the disk-corona
systems in these objects, we measure optical/UV-X-ray spectral indices
($\alpha_{\rm OX}$) and Eddington ratios ($\lambda_{\rm Edd}$) of ten
previously-discovered changing-look quasars at two or more epochs. By comparing
these data with simulated results based on the behavior of X-ray binaries, we
find possible similarities in spectral indices below 1% Eddington ratio. We
further investigate the Eddington ratios of changing-look quasars before and
after their spectral type changes, and find that changing-look quasars cross
the 1% Eddington ratio boundary when their broad emission lines
disappear/emerge. This is consistent with the disk-wind model as the origin of
broad emission lines.


Abstract:
  Top-performing computer vision models are powered by convolutional neural
networks (CNNs). Training an accurate CNN highly depends on both the raw sensor
data and their associated ground truth (GT). Collecting such GT is usually done
through human labeling, which is time-consuming and does not scale as we wish.
This data labeling bottleneck may be intensified due to domain shifts among
image sensors, which could force per-sensor data labeling. In this paper, we
focus on the use of co-training, a semi-supervised learning (SSL) method, for
obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep
object detectors. In particular, we assess the goodness of multi-modal
co-training by relying on two different views of an image, namely, appearance
(RGB) and estimated depth (D). Moreover, we compare appearance-based
single-modal co-training with multi-modal. Our results suggest that in a
standard SSL setting (no domain shift, a few human-labeled data) and under
virtual-to-real domain shift (many virtual-world labeled data, no human-labeled
data) multi-modal co-training outperforms single-modal. In the latter case, by
performing GAN-based domain translation both co-training modalities are on
pair; at least, when using an off-the-shelf depth estimation model not
specifically trained on the translated images.


Abstract:
  High entropy alloys offer a huge search space for new electrocatalysts.
Searching for a global property maximum in one quinary system could require,
depending on compositional resolution, the synthesis of up to 10E6 samples
which is impossible using conventional approaches. Co-sputtered materials
libraries address this challenge by synthesis of controlled composition
gradients of each element. However, even such a materials library covers less
than 1% of the composition space of a quinary system. We present a new strategy
using deposition source permutations optimized for highest improvement of the
covered new compositions. Using this approach, the composition space can be
sampled in different subsections allowing identification of the contribution of
individual elements and their combinations on electrochemical activity.
Unsupervised machine learning reveals that electrochemical activity is governed
by the complex interplay of chemical and structural factors. Out of 2394
measured compositions, a new highly active composition for the oxygen reduction
reaction around Ru17Rh5Pd19Ir29Pt30 was identified.


Abstract:
  In this work, we introduce a novel method for solving the set inversion
problem by formulating it as a binary classification problem. Aiming to develop
a fast algorithm that can work effectively with high-dimensional and
computationally expensive nonlinear models, we focus on active learning, a
family of new and powerful techniques which can achieve the same level of
accuracy with fewer data points compared to traditional learning methods.
Specifically, we propose OASIS, an active learning framework using Support
Vector Machine algorithms for solving the problem of set inversion. Our method
works well in high dimensions and its computational cost is relatively robust
to the increase of dimension. We illustrate the performance of OASIS by several
simulation studies and show that our algorithm outperforms VISIA, the
state-of-the-art method.


Abstract:
  Comprehensive calculations were performed to predict the phase behaviour of
large spherical colloids mixed with small spherical colloids that act as
depletant. To this end, the free volume theory (FVT) of Lekkerkerker et al.
[Europhys. Lett. 20 (1992) 559] is used as a basis and is extended to
explicitly include the hard-sphere character of colloidal depletants into the
expression for the free volume fraction. Taking the excluded volume of the
depletants into account in both the system and the reservoir provides a
relation between the depletant concentration in the reservoir and in the system
that accurately matches with computer simulation results of Dijkstra et al.
[Phys. Rev. E 59 (1999) 5744]. Moreover, the phase diagrams for highly
asymmetric mixtures with size ratios q . 0:2 obtained by using this new
approach corroborates simulation results significantly better than earlier FVT
applications to binary hard-sphere mixtures. The phase diagram of a binary
hard-sphere mixture with a size ratio of q = 0:4, where a binary interstitial
solid solution is formed at high densities, is investigated using a numerical
free volume approach. At this size ratio, the obtained phase diagram is
qualitatively different from previous FVT approaches for hard-sphere and
penetrable depletants, but again compares well with simulation predictions.


Abstract:
  We show that hermitian K-theory is universal among generalized motivic
cohomology theories with transfers along finite Gorenstein morphisms with
trivialized dualizing sheaf. As an application, we obtain a Hilbert scheme
model for hermitian K-theory as a motivic space. We also give an application to
computational complexity: we prove that 1-generic minimal border rank tensors
degenerate to the big Coppersmith-Winograd tensor.


Abstract:
  Event detection on social media has attracted a number of researches, given
the recent availability of large volumes of social media discussions. Previous
works on social media event detection either assume a specific type of event,
or assume certain behavior of observed variables. In this paper, we propose a
general method for event detection on social media that makes few assumptions.
The main assumption we make is that when an event occurs, affected semantic
aspects will behave differently from its usual behavior. We generalize the
representation of time units based on word embeddings of social media text, and
propose an algorithm to detect events in time series in a general sense. In the
experimental evaluation, we use a novel setting to test if our method and
baseline methods can exhaustively catch all real-world news in the test period.
The evaluation results show that when the event is quite unusual with regard to
the base social media discussion, it can be captured more effectively with our
method. Our method can be easily implemented and can be treated as a starting
point for more specific applications.


Abstract:
  We prove that in a $C^1$-open and $C^k$-dense set of some classes of $C^k$
Anosov flows all Lyapunov exponents have multiplicity 1 with respect to
appropriate measures. The classes are geodesic flows with equilibrium states of
H\"older-continuous potentials, volume-preserving flows, and all fiber-bunched
Anosov flows with equilibrium states of H\"older-continuous potentials. In the
proof, we use and prove perturbative results for jets of flows to modify
eigenvalues of certain Poincar\'e maps and, using a Markov partition, apply the
simplicity criterion of Avila and Viana.


Abstract:
  Pyserini is an easy-to-use Python toolkit that supports replicable IR
research by providing effective first-stage retrieval in a multi-stage ranking
architecture. Our toolkit is self-contained as a standard Python package and
comes with queries, relevance judgments, pre-built indexes, and evaluation
scripts for many commonly used IR test collections. We aim to support, out of
the box, the entire research lifecycle of efforts aimed at improving ranking
with modern neural approaches. In particular, Pyserini supports sparse
retrieval (e.g., BM25 scoring using bag-of-words representations), dense
retrieval (e.g., nearest-neighbor search on transformer-encoded
representations), as well as hybrid retrieval that integrates both approaches.
This paper provides an overview of toolkit features and presents empirical
results that illustrate its effectiveness on two popular ranking tasks. We also
describe how our group has built a culture of replicability through shared
norms and tools that enable rigorous automated testing.


Abstract:
  Keyword extraction is the task of identifying words (or multi-word
expressions) that best describe a given document and serve in news portals to
link articles of similar topics. In this work we develop and evaluate our
methods on four novel data sets covering less represented, morphologically-rich
languages in European news media industry (Croatian, Estonian, Latvian and
Russian). First, we perform evaluation of two supervised neural
transformer-based methods (TNT-KID and BERT+BiLSTM CRF) and compare them to a
baseline TF-IDF based unsupervised approach. Next, we show that by combining
the keywords retrieved by both neural transformer based methods and extending
the final set of keywords with an unsupervised TF-IDF based technique, we can
drastically improve the recall of the system, making it appropriate to be used
as a recommendation system in the media house environment.


Abstract:
  An expander code is a binary linear code whose parity-check matrix is the
bi-adjacency matrix of a bipartite expander graph. We provide a new formula for
the minimum distance of such codes. We also provide a new proof of the result
that $2(1-\varepsilon) \gamma n$ is a lower bound of the minimum distance of
the expander code given by a $(m,n,d,\gamma,1-\varepsilon)$ expander bipartite
graph.


Abstract:
  When we deploy machine learning models in high-stakes medical settings, we
must ensure these models make accurate predictions that are consistent with
known medical science. Inherently interpretable networks address this need by
explaining the rationale behind each decision while maintaining equal or higher
accuracy compared to black-box models. In this work, we present a novel
interpretable neural network algorithm that uses case-based reasoning for
mammography. Designed to aid a radiologist in their decisions, our network
presents both a prediction of malignancy and an explanation of that prediction
using known medical features. In order to yield helpful explanations, the
network is designed to mimic the reasoning processes of a radiologist: our
network first detects the clinically relevant semantic features of each image
by comparing each new image with a learned set of prototypical image parts from
the training images, then uses those clinical features to predict malignancy.
Compared to other methods, our model detects clinical features (mass margins)
with equal or higher accuracy, provides a more detailed explanation of its
prediction, and is better able to differentiate the classification-relevant
parts of the image.


Abstract:
  With recent advances in the development of more powerful quantum computers,
the research area of quantum software engineering is emerging, having the goal
to provide concepts, principles, and guidelines to develop high-quality quantum
applications. In classical software engineering, lifecycles are used to
document the process of designing, implementing, maintaining, analyzing, and
adapting software. Such lifecycles provide a common understanding of how to
develop and operate an application, which is especially important due to the
interdisciplinary nature of quantum computing. Since today`s quantum
applications are, in most cases, hybrid, consisting of quantum and classical
programs, the lifecycle for quantum applications must involve the development
of both kinds of programs. However, the existing lifecycles only target the
development of quantum or classical programs in isolation. Additionally, the
various programs must be orchestrated, e.g., using workflows. Thus, the
development of quantum applications also incorporates the workflow lifecycle.
In this chapter, we analyze the software artifacts usually comprising a quantum
application and present their corresponding lifecycles. Furthermore, we
identify the points of connection between the various lifecycles and integrate
them into the overall quantum software development lifecycle. Therefore, the
integrated lifecycle serves as a basis for the development and execution of
hybrid quantum applications.


Abstract:
  Deciding what constitutes a single module, what classes belong to which
module or the right set of modules for a specific software system has always
been a challenging task. The problem is even harder in large-scale software
systems composed of thousands of classes and hundreds of modules. Over the
years, researchers have been proposing different techniques to support
developers in re-modularizing their software systems. In particular, the
search-based software re-modularization is an active research topic within the
software engineering community for more than 20 years.
  This paper describes our efforts in applying search-based software
re-modularization approaches at Adyen, a large-scale payment company. Adyen's
code base has 5.5M+ lines of code, split into around hundreds of modules. We
leveraged the existing body of knowledge in the field to devise our own search
algorithm and applied it to our code base. Our results show that search-based
approaches scale to large code bases as ours. Our algorithm can find solutions
that improve the code base according to the metrics we optimize for, and
developers see value in the recommendations. Based on our experiences, we then
list a set of challenges and opportunities for future researchers, aiming at
making search-based software re-modularization more efficient for large-scale
software companies.


Abstract:
  In constrained reinforcement learning (RL), a learning agent seeks to not
only optimize the overall reward but also satisfy the additional safety,
diversity, or budget constraints. Consequently, existing constrained RL
solutions require several new algorithmic ingredients that are notably
different from standard RL. On the other hand, reward-free RL is independently
developed in the unconstrained literature, which learns the transition dynamics
without using the reward information, and thus naturally capable of addressing
RL with multiple objectives under the common dynamics. This paper bridges
reward-free RL and constrained RL. Particularly, we propose a simple
meta-algorithm such that given any reward-free RL oracle, the approachability
and constrained RL problems can be directly solved with negligible overheads in
sample complexity. Utilizing the existing reward-free RL solvers, our framework
provides sharp sample complexity results for constrained RL in the tabular MDP
setting, matching the best existing results up to a factor of horizon
dependence; our framework directly extends to a setting of tabular two-player
Markov games, and gives a new result for constrained RL with linear function
approximation.


Abstract:
  Knowledge distillation can lead to deploy-friendly networks against the
plagued computational complexity problem, but previous methods neglect the
feature hierarchy in detectors. Motivated by this, we propose a general
framework for detection distillation. Our method, called Hands-on Guidance
Distillation, distills the latent knowledge of all stage features for imposing
more comprehensive supervision, and focuses on the essence simultaneously for
promoting more intense knowledge absorption. Specifically, a series of novel
mechanisms are designed elaborately, including correspondence establishment for
consistency, hands-on imitation loss measure and re-weighted optimization from
both micro and macro perspectives. We conduct extensive evaluations with
different distillation configurations over VOC and COCO datasets, which show
better performance on accuracy and speed trade-offs. Meanwhile, feasibility
experiments on different structural networks further prove the robustness of
our HGD.


Abstract:
  The biomechanical energy harvester is expected to harvest the electric
energies from human motions. A tradeoff between harvesting energy and keeping
the user's natural movements should be balanced via optimization techniques. In
previous studies, the hardware itself has been specialized in advance for a
single task like walking with constant speed on a flat. A key ingredient is
Continuous Variable Transmission (CVT) to extend it applicable for multiple
tasks. CVT could continuously adjust its gear ratio to balance the tradeoff for
each task; however, such gear-ratio optimization problem remains open yet since
its optimal solution may depend on the user, motion, and environment.
Therefore, this paper focuses on a framework for data-driven optimization of a
gear ratio in a CVT-equipped biomechanical energy harvester. Since the data
collection requires a heavy burden on the user, we have to optimize the gear
ratio for each task in the shortest possible time. To this end, our framework
is designed sample-efficiently based on the fact that the user encounters
multiple tasks, which are with similarities with each other. Specifically, our
framework employs multi-task Bayesian optimization to reuse the optimization
results of the similar tasks previously optimized by finding their
similarities. Through experiments, we confirmed that, for each task, the
proposed framework could achieve the optimal gear ratio of around 50~\% faster
than one by random search, and that takes only around 20~minutes. Experimental
results also suggested that the optimization can be accelerated by actively
exploiting similarities with previously optimized tasks.


Abstract:
  Self-attention models have been successfully applied in end-to-end speech
recognition systems, which greatly improve the performance of recognition
accuracy. However, such attention-based models cannot be used in online speech
recognition, because these models usually have to utilize a whole acoustic
sequences as inputs. A common method is restricting the field of attention
sights by a fixed left and right window, which makes the computation costs
manageable yet also introduces performance degradation. In this paper, we
propose Memory-Self-Attention (MSA), which adds history information into the
Restricted-Self-Attention unit. MSA only needs localtime features as inputs,
and efficiently models long temporal contexts by attending memory states.
Meanwhile, recurrent neural network transducer (RNN-T) has proved to be a great
approach for online ASR tasks, because the alignments of RNN-T are local and
monotonic. We propose a novel network structure, called Memory-Self-Attention
(MSA) Transducer. Both encoder and decoder of the MSA Transducer contain the
proposed MSA unit. The experiments demonstrate that our proposed models improve
WER results than Restricted-Self-Attention models by $13.5 on WSJ and $7.1 on
SWBD datasets relatively, and without much computation costs increase.


Abstract:
  Online gaming is growing faster than ever before, with increasing challenges
of providing better user experience. Recommender systems (RS) for online games
face unique challenges since they must fulfill players' distinct desires, at
different user levels, based on their action sequences of various action types.
Although many sequential RS already exist, they are mainly single-sequence,
single-task, and single-user-level. In this paper, we introduce a new
sequential recommendation model for multiple sequences, multiple tasks, and
multiple user levels (abbreviated as M$^3$Rec) in Tencent Games platform, which
can fully utilize complex data in online games. We leverage Graph Neural
Network and multi-task learning to design M$^3$Rec in order to model the
complex information in the heterogeneous sequential recommendation scenario of
Tencent Games. We verify the effectiveness of M$^3$Rec on three online games of
Tencent Games platform, in both offline and online evaluations. The results
show that M$^3$Rec successfully addresses the challenges of recommendation in
online games, and it generates superior recommendations compared with
state-of-the-art sequential recommendation approaches.


Abstract:
  We show that robustly transitive endomorphisms of a closed manifolds must
have a non-trivial dominated splitting or be a local diffeomorphism. This
allows to get some topological obstructions for the existence of robustly
transitive endomorphisms. To obtain the result we must understand the structure
of the kernel of the differential and the recurrence to the critical set of the
endomorphism after perturbation.


Abstract:
  Message authentication guarantees the integrity of messages exchanged over
untrusted channels. Yet, the required per-message authentication tags
considerably expand packet sizes, which is especially problematic in
constrained environments. To address this issue, progressive message
authentication aggregates and distributes integrity protection over multiple
messages, promising to reduce overheads while upholding strong security of
traditional integrity protection. However, as we show in this paper, existing
progressive message authentication schemes are susceptible to packet drops: By
interfering with just two selected packets, an attacker can remove integrity
protection from a complete sequence of messages.
  Revisiting the security of progressive message authentication, we consider it
imperative to thwart such attacks by rethinking how authentication tags depend
on the successful reception of packets. To this end, we propose R2-D2, which
relies on randomized dependencies with parameterized security guarantees to
mitigate network-level attacks inherent to current progressive authentication
schemes. To deploy our approach to resource-constrained devices, we introduce
SP-MAC, which implements R2-D2 using efficient XOR operations. Our evaluation
shows that SP-MAC protects against sophisticated network-layer attacks, and
still operates as resources-conscious and fast as existing insecure schemes.


Abstract:
  Anomaly detection from a single image is challenging since anomaly data is
always rare and can be with highly unpredictable types. With only anomaly-free
data available, most existing methods train an AutoEncoder to reconstruct the
input image and find the difference between the input and output to identify
the anomalous region. However, such methods face a potential problem - a coarse
reconstruction generates extra image differences while a high-fidelity one may
draw in the anomaly. In this paper, we solve this contradiction by proposing a
two-stage approach, which generates high-fidelity yet anomaly-free
reconstructions. Our Unsupervised Two-stage Anomaly Detection (UTAD) relies on
two technical components, namely the Impression Extractor (IE-Net) and the
Expert-Net. The IE-Net and Expert-Net accomplish the two-stage anomaly-free
image reconstruction task while they also generate intuitive intermediate
results, making the whole UTAD interpretable. Extensive experiments show that
our method outperforms state-of-the-arts on four anomaly detection datasets
with different types of real-world objects and textures.


Abstract:
  The prediction of material properties through electronic-structure
simulations based on density-functional theory has become routinely common,
thanks, in part, to the steady increase in the number and robustness of
available simulation packages. This plurality of codes and methods aiming to
solve similar problems is both a boon and a burden. While providing great
opportunities for cross-verification, these packages adopt different methods,
algorithms, and paradigms, making it challenging to choose, master, and
efficiently use any one for a given task. Leveraging recent advances in
managing reproducible scientific workflows, we demonstrate how developing
common interfaces for workflows that automatically compute material properties
can tackle the challenge mentioned above, greatly simplifying interoperability
and cross-verification. We introduce design rules for reproducible and reusable
code-agnostic workflow interfaces to compute well-defined material properties,
which we implement for eleven different quantum engines and use to compute
three different material properties. Each implementation encodes carefully
selected simulation parameters and workflow logic, making the implementer's
expertise of the quantum engine directly available to non-experts. Full
provenance and reproducibility of the workflows is guaranteed through the use
of the AiiDA infrastructure. All workflows are made available as open-source
and come pre-installed with the Quantum Mobile virtual machine, making their
use straightforward.


Abstract:
  We investigate population and individual firing behaviors in sparsely
synchronized rhythms (SSRs) in a spiking neural network of the hippocampal
dentate gyrus (DG). The main encoding granule cells (GCs) are grouped into
lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell
(BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all
competition, leading to sparse activation of the GCs, occurs in each GC
cluster. Such sparsity has been thought to enhance pattern separation performed
in the DG. During the winner-take-all competition, SSRs are found to appear in
each population of the GCs and the BCs through interaction of excitation of the
GCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear
successively with the population frequency $f_p~ (= 13$ Hz) in the raster plots
of spikes. We also note that excitatory hilar mossy cells (MCs) control the
firing activity of the GC-BC loop by providing excitation to both the GCs and
the BCs. SSR also appears in the population of MCs via interaction with the GCs
(i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively
characterized in terms of the synchronization measures. In addition, we
investigate individual firing activity of GCs, BCs, and MCs in the SSRs.
Individual GCs exhibit random spike skipping, leading to a multi-peaked
inter-spike-interval histogram, which is well characterized in terms of the
random phase-locking degree. On the other hand, both BCs and MCs show
"intrastripe" burstings within stripes, together with "interstripe" random
spike skipping. MC loss may occur during epileptogenesis. With decreasing the
fraction of the MCs, changes in the population and individual firings in the
SSRs are also studied. Finally, quantitative association between the
population/individual firing behaviors in the SSRs and the winner-take-all
competition is discussed.


Abstract:
  It is desirable to include more controllable attributes to enhance the
diversity of generated responses in open-domain dialogue systems. However,
existing methods can generate responses with only one controllable attribute or
lack a flexible way to generate them with multiple controllable attributes. In
this paper, we propose a Progressively trained Hierarchical Encoder-Decoder
(PHED) to tackle this task. More specifically, PHED deploys Conditional
Variational AutoEncoder (CVAE) on Transformer to include one aspect of
attributes at one stage. A vital characteristic of the CVAE is to separate the
latent variables at each stage into two types: a global variable capturing the
common semantic features and a specific variable absorbing the attribute
information at that stage. PHED then couples the CVAE latent variables with the
Transformer encoder and is trained by minimizing a newly derived ELBO and
controlled losses to produce the next stage's input and produce responses as
required. Finally, we conduct extensive evaluations to show that PHED
significantly outperforms the state-of-the-art neural generation models and
produces more diverse responses as expected.


Abstract:
  In this paper, we give some observation of applying modern optimization
methods for functionals describing digital predistortion (DPD) of signals with
orthogonal frequency division multiplexing (OFDM) modulation. The considered
family of model functionals is determined by the class of cascade
Wiener--Hammerstein models, which can be represented as a computational graph
consisting of various nonlinear blocks. To assess optimization methods with the
best convergence depth and rate as a properties of this models family we
multilaterally consider modern techniques used in optimizing neural networks
and numerous numerical methods used to optimize non-convex multimodal
functions. The research emphasizes the most effective of the considered
techniques and describes several useful observations about the model properties
and optimization methods behavior.


Abstract:
  We present a general, efficient technique for providing contextual
predictions that are "multivalid" in various senses, against an online sequence
of adversarially chosen examples $(x,y)$. This means that the resulting
estimates correctly predict various statistics of the labels $y$ not just
marginally -- as averaged over the sequence of examples -- but also
conditionally on $x \in G$ for any $G$ belonging to an arbitrary intersecting
collection of groups $\mathcal{G}$.
  We provide three instantiations of this framework. The first is mean
prediction, which corresponds to an online algorithm satisfying the notion of
multicalibration from Hebert-Johnson et al. The second is variance and higher
moment prediction, which corresponds to an online algorithm satisfying the
notion of mean-conditioned moment multicalibration from Jung et al. Finally, we
define a new notion of prediction interval multivalidity, and give an algorithm
for finding prediction intervals which satisfy it. Because our algorithms
handle adversarially chosen examples, they can equally well be used to predict
statistics of the residuals of arbitrary point prediction methods, giving rise
to very general techniques for quantifying the uncertainty of predictions of
black box algorithms, even in an online adversarial setting. When instantiated
for prediction intervals, this solves a similar problem as conformal
prediction, but in an adversarial environment and with multivalidity guarantees
stronger than simple marginal coverage guarantees.


Abstract:
  This work introduces ParAMS -- a versatile Python package that aims to make
parameterization workflows in computational chemistry and physics more
accessible, transparent and reproducible. We demonstrate how ParAMS facilitates
the parameter optimization for potential energy surface (PES) models, which can
otherwise be a tedious specialist task. Because of the package's modular
structure, various functionality can be easily combined to implement a
diversity of parameter optimization protocols. For example, the choice of PES
model and the parameter optimization algorithm can be selected independently.
An illustration of ParAMS' strengths is provided in two case studies: i) a
density functional-based tight binding (DFTB) repulsive potential for the
inorganic ionic crystal ZnO, and ii) a ReaxFF force field for the simulation of
organic disulfides.


Abstract:
  Even though Afaan Oromo is the most widely spoken language in the Cushitic
family by more than fifty million people in the Horn and East Africa, it is
surprisingly resource-scarce from a technological point of view. The increasing
amount of various useful documents written in English language brings to
investigate the machine that can translate those documents and make it easily
accessible for local language. The paper deals with implementing a translation
of English to Afaan Oromo and vice versa using Neural Machine Translation. But
the implementation is not very well explored due to the limited amount and
diversity of the corpus. However, using a bilingual corpus of just over 40k
sentence pairs we have collected, this study showed a promising result. About a
quarter of this corpus is collected via Community Engagement Platform (CEP)
that was implemented to enrich the parallel corpus through crowdsourcing
translations.


Abstract:
  We introduce the notion of a bicocycle double cross product (resp. sum) Lie
group (resp. Lie algebra), and a bicocycle double cross product bialgebra,
generalizing the unified products. On the level of Lie groups the construction
yields a Lie group on the product space of two pointed manifolds, none of which
being necessarily a subgroup. On the level of Lie algebras, similarly, a Lie
algebra is obtained on the direct sum of two vector spaces, none of which is
required to be a subalgebra. Finally, on the quantum level the theory presents
a bialgebra, on the tensor product of two (co)algebras that are not necessarily
sub-bialgebras, the semidual of which being a cocycle bicrossproduct bialgebra.


Abstract:
  The present study deals a scientometric analysis of 8486 bibliometric
publications retrieved from the Web of Science database during the period 2008
to 2017. Data is collected and analyzed using Bibexcel software. The study
focuses on various aspect of the quantitative research such as growth of papers
(year wise), Collaborative Index (CI), Degree of Collaboration (DC),
Co-authorship Index (CAI), Collaborative Co-efficient (CC), Modified
Collaborative Co-Efficient (MCC), Lotka's Exponent value, Kolmogorov-Smirnov
test (K-S Test).


Abstract:
  The spontaneous breaking of parity-time ($\mathcal{PT}$) symmetry, which
yields rich critical behavior in non-Hermitian systems, has stimulated much
interest. Whereas most previous studies were performed within the
single-particle or mean-field framework, exploring the interplay between
$\mathcal{PT}$ symmetry and quantum fluctuations in a many-body setting is a
burgeoning frontier. Here, by studying the collective excitations of a Fermi
superfluid under an imaginary spin-orbit coupling, we uncover an emergent
$\mathcal{PT}$-symmetry breaking in the Anderson-Bogoliubov (AB) modes, whose
quasiparticle spectra undergo a transition from being completely real to
completely imaginary, even though the superfluid ground state retains an
unbroken $\mathcal{PT}$ symmetry. The critical point of the transition is
marked by a non-analytic kink in the speed of sound, as the latter completely
vanishes at the critical point where the system is immune to low-frequency
perturbations.These critical phenomena derive from the presence of a spectral
point gap in the complex quasiparticle dispersion, and are therefore
topological in origin.


Abstract:
  The application machine learning (ML) algorithms to turbulence modeling has
shown promise over the last few years, but their application has been
restricted to eddy viscosity based closure approaches. In this article we
discuss rationale for the application of machine learning with high-fidelity
turbulence data to develop models at the level of Reynolds stress transport
modeling. Based on these rationale we compare different machine learning
algorithms to determine their efficacy and robustness at modeling the different
transport processes in the Reynolds Stress Transport Equations. Those data
driven algorithms include Random forests, gradient boosted trees and neural
networks. The direct numerical simulation (DNS) data for flow in channels is
used both as training and testing of the ML models. The optimal
hyper-parameters of the ML algorithms are determined using Bayesian
optimization. The efficacy of above mentioned algorithms is assessed in the
modeling and prediction of the terms in the Reynolds stress transport
equations. It was observed that all the three algorithms predict the turbulence
parameters with acceptable level of accuracy. These ML models are then applied
for prediction of the pressure strain correlation of flow cases that are
different from the flows used for training, to assess their robustness and
generalizability. This explores the assertion that ML based data driven
turbulence models can overcome the modeling limitations associated with the
traditional turbulence models and ML models trained with large amounts data
with different classes of flows can predict flow field with reasonable accuracy
for unknown flows with similar flow physics. In addition to this verification
we carry out validation for the final ML models by assessing the importance of
different input features for prediction.


Abstract:
  We investigate the effects of field temperature $T^{(f)}$ on the entanglement
harvesting between two uniformly accelerated detectors. For their parallel
motion, the thermal nature of fields does not produce any entanglement, and
therefore, the outcome is the same as the non-thermal situation. On the
contrary, $T^{(f)}$ affects entanglement harvesting when the detectors are in
anti-parallel motion, i.e., when detectors $A$ and $B$ are in the right and
left Rindler wedges, respectively. While for $T^{(f)}=0$ entanglement
harvesting is possible for all values of $A$'s acceleration $a_A$, in the
presence of temperature, it is possible only within a narrow range of $a_A$. In
$(1+1)$ dimensions, the range starts from specific values and extends to
infinity, and as we increase $T^{(f)}$, the minimum required value of $a_A$ for
entanglement harvesting increases. Moreover, above a critical value $a_A=a_c$
harvesting increases as we increase $T^{(f)}$, which is just opposite to the
accelerations below it. There are several critical values in $(1+3)$ dimensions
when they are in different accelerations. Contrary to the single range in
$(1+1)$ dimensions, here harvesting is possible within several discrete ranges
of $a_A$. Interestingly, for equal accelerations, one has a single critical
point, with nature quite similar to $(1+1)$ dimensional results. We also
discuss the dependence of mutual information among these detectors on $a_A$ and
$T^{(f)}$.


Abstract:
  We study thermodynamics and critical behaviors of higher-dimensional Lovelock
black holes with non-maximally symmetric horizons in the canonical ensemble of
extended phase space. The effects from non-constancy of the horizon of the
black hole via appearing two chargelike parameters in thermodynamic quantities
of third-order Lovelock black holes are investigated. We find that Ricci flat
black holes with nonconstant curvature horizon show critical behavior. This is
an interesting feature that is not seen for any kind of black hole in Einstein
or Lovelock gravity in the literature. We examine how various interesting
thermodynamic phenomena such as standard first-order small-large black hole
phase transition, a reentrant phase transition, or zeroth order phase
transition happens for Ricci flat, spherical, or hyperbolic black holes with
nonconstant curvature horizon depending on the values of Lovelock coefficient
and chargelike parameters. While for a spherical black hole of third order
Lovelock gravity with constant curvature horizon phase transition is observed
only for $7\leq d \leq11$, for our solution criticality and phase transition
exist in every dimension. With a proper choice of the free parameters, a
large-small-large black hole phase transition occurs. This process is
accompanied by a finite jump of the Gibbs free energy referred to as a
zeroth-order phase transition. For the case $\kappa=-1$ a novel behavior is
found for which three critical points could exist.


Abstract:
  We study from the proof complexity perspective the (informal) proof search
problem:
  Is there an optimal way to search for propositional proofs?
  We note that for any fixed proof system there exists a time-optimal proof
search algorithm. Using classical proof complexity results about reflection
principles we prove that a time-optimal proof search algorithm exists without
restricting proof systems iff a p-optimal proof system exists.
  To characterize precisely the time proof search algorithms need for
individual formulas we introduce a new proof complexity measure based on
algorithmic information concepts. In particular, to a proof system $P$ we
attach {\bf information-efficiency function} $i_P(\tau)$ assigning to a
tautology a natural number, and we show that:
  - $i_P(\tau)$ characterizes time any $P$-proof search algorithm has to use on
$\tau$ and that for a fixed $P$ there is such an information-optimal algorithm,
  - a proof system is information-efficiency optimal iff it is p-optimal,
  - for non-automatizable systems $P$ there are formulas $\tau$ with short
proofs but having large information measure $i_P(\tau)$.
  We isolate and motivate the problem to establish unconditional
super-logarithmic lower bounds for $i_P(\tau)$ where no super-polynomial size
lower bounds are known. We also point out connections of the new measure with
some topics in proof complexity other than proof search.


Abstract:
  Finely tuning MPI applications and understanding the influence of
keyparameters (number of processes, granularity, collective
operationalgorithms, virtual topology, and process placement) is critical
toobtain good performance on supercomputers. With the high consumptionof
running applications at scale, doing so solely to optimize theirperformance is
particularly costly. Havinginexpensive but faithful predictions of expected
performance could bea great help for researchers and system administrators.
Themethodology we propose decouples the complexity of the platform, whichis
captured through statistical models of the performance of its maincomponents
(MPI communications, BLAS operations), from the complexityof adaptive
applications by emulating the application and skippingregular non-MPI parts of
the code. We demonstrate the capability of our method with
High-PerformanceLinpack (HPL), the benchmark used to rank supercomputers in
theTOP500, which requires careful tuning. We briefly present (1) how
theopen-source version of HPL can be slightly modified to allow a fastemulation
on a single commodity server at the scale of asupercomputer. Then we present
(2) an extensive (in)validation studythat compares simulation with real
experiments and demonstrates our ability to predict theperformance of HPL
within a few percent consistently. This study allows us toidentify the main
modeling pitfalls (e.g., spatial and temporal nodevariability or network
heterogeneity and irregular behavior) that needto be considered. Last, we show
(3) how our ``surrogate'' allowsstudying several subtle HPL parameter
optimization problems whileaccounting for uncertainty on the platform.


Abstract:
  In 2010, the unified gas kinetic scheme (UGKS) was proposed by Xu et al . (A
unified gas-kinetic scheme for continuum and rarefied flows, Journal of
Computational Physics, 2010). In the past decade, many numerical techniques
have been developed to improve the capability of the UGKS in the aspects of
efficiency increment, memory reduction, and physical modeling. The methodology
of the direct modeling of the UGKS on discretization scale provides a general
framework for construction of multiscale method for multiscale transport
processes. This paper reviews the development and extension of the UGKS in its
first decade.


Abstract:
  An accurate description of electron correlation is one of the most
challenging problems in quantum chemistry. The exact electron correlation can
be obtained by means of full configuration interaction (FCI). A simple strategy
for approximating FCI at a reduced computational cost is selected CI (SCI),
which diagonalizes the Hamiltonian within only the chosen configuration space.
Recovery of the contributions of the remaining configurations is possible with
second-order perturbation theory. Here, we apply adaptive sampling
configuration interaction (ASCI) combined with molecular orbital optimizations
(ASCI-SCF) corrected with second-order perturbation theory (ASCI-SCF-PT2) for
geometry optimization by implementing the analytical nuclear gradient algorithm
for ASCI-PT2 with the Z-vector (Lagrangian) formalism. We demonstrate that for
phenalenyl radicals and anthracene, optimized geometries and the number of
unpaired electrons can be obtained at nearly the CASSCF accuracy by
incorporating PT2 corrections and extrapolating them. We demonstrate the
current algorithm's utility for optimizing the equilibrium geometries and
electronic structures of 6-ring-fused polycyclic aromatic hydrocarbons and
4-periacene.


Abstract:
  Deep neural networks (DNNs) are widely used in pattern-recognition tasks for
which a human comprehensible, quantitative description of the data-generating
process, e.g., in the form of equations, cannot be achieved. While doing so,
DNNs often produce an abstract (entangled and non-interpretable) representation
of the data-generating process. This is one of the reasons why DNNs are not
extensively used in physics-signal processing: physicists generally require
their analyses to yield quantitative information about the studied systems. In
this article we use DNNs to disentangle components of oscillating time series,
and recover meaningful information. We show that, because DNNs can find useful
abstract feature representations, they can be used when prior knowledge about
the signal-generating process exists, but is not complete, as it is
particularly the case in "new-physics" searches. To this aim, we train our DNN
on synthetic oscillating time series to perform two tasks: a regression of the
signal latent parameters and signal denoising by an Autoencoder-like
architecture. We show that the regression and denoising performance is similar
to those of least-square curve fittings (LS-fit) with true latent parameters'
initial guesses, in spite of the DNN needing no initial guesses at all. We then
explore applications in which we believe our architecture could prove useful
for time-series processing in physics, when prior knowledge is incomplete. As
an example, we employ DNNs as a tool to inform LS-fits when initial guesses are
unknown. We show that the regression can be performed on some latent
parameters, while ignoring the existence of others. Because the Autoencoder
needs no prior information about the physical model, the remaining unknown
latent parameters can still be captured, thus making use of partial prior
knowledge, while leaving space for data exploration and discoveries.


Abstract:
  The reliability of cardiovascular computational models depends on the
accurate solution of the hemodynamics, the realistic characterization of the
hyperelastic and electric properties of the tissues along with the correct
description of their interaction. The resulting
fluid-structure-electrophysiology interaction (FSEI) thus requires an immense
computational power, usually available in large supercomputing centers, and
requires long time to obtain results even if multi-CPU processors are used (MPI
acceleration). In recent years, graphics processing units (GPUs) have emerged
as a convenient platform for high performance computing, as they allow for
considerable reductions of the time-to-solution. This approach is particularly
appealing if the tool has to support medical decisions that require solutions
within reduced times and possibly obtained by local computational resources.
Accordingly, our multi-physics solver has been ported to GPU architectures
using CUDA Fortran to tackle fast and accurate hemodynamics simulations of the
human heart without resorting to large-scale supercomputers. This work
describes the use of CUDA to accelerate the FSEI on heterogeneous clusters,
where both the CPUs and GPUs are used in synergistically with minor
modifications of the original source code. The resulting GPU accelerated code
solves a single heartbeat within a few hours (from three to ten depending on
the grid resolution) running on premises computing facility made of few GPU
cards, which can be easily installed in a medical laboratory or in a hospital,
thus opening towards a systematic computational fluid dynamics (CFD) aided
diagnostic.


Abstract:
  The density matrix formalism is a fundamental tool in studying various
problems in quantum information processing. In the space of density matrices,
the most well-known and physically relevant measures are the Hilbert-Schmidt
ensemble and the Bures-Hall ensemble. In this work, we propose a generalized
ensemble of density matrices, termed quantum interpolating ensemble, which is
able to interpolate between these two seemingly unrelated ensembles. As a first
step to understand the proposed ensemble, we derive the exact mean formulas of
entanglement entropies over such an ensemble generalizing several recent
results in the literature. We also derive some key properties of the
corresponding orthogonal polynomials relevant to obtaining other statistical
information of the entropies. Numerical results demonstrate the usefulness of
the proposed ensemble in estimating the degree of entanglement of quantum
states.


Abstract:
  We study tight projective 2-designs in three different settings. In the
complex setting, Zauner's conjecture predicts the existence of a tight
projective 2-design in every dimension. Pandey, Paulsen, Prakash, and Rahaman
recently proposed an approach to make quantitative progress on this conjecture
in terms of the entanglement breaking rank of a certain quantum channel. We
show that this quantity is equal to the size of the smallest weighted
projective 2-design. Next, in the finite field setting, we introduce a notion
of projective 2-designs, we characterize when such projective 2-designs are
tight, and we provide a construction of such objects. Finally, in the
quaternionic setting, we show that every tight projective 2-design for H^d
determines an equi-isoclinic tight fusion frame of d(2d-1) subspaces of
R^d(2d+1) of dimension 3.


Abstract:
  Quantum key distribution (QKD) provides information theoretically secures key
exchange requiring authentication of the classic data processing channel via
pre-sharing of symmetric private keys. In previous studies, the lattice-based
post-quantum digital signature algorithm Aigis-Sig, combined with public-key
infrastructure (PKI) was used to achieve high-efficiency quantum security
authentication of QKD, and its advantages in simplifying the MAN network
structure and new user entry were demonstrated. This experiment further
integrates the PQC algorithm into the commercial QKD system, the Jinan field
metropolitan QKD network comprised of 14 user nodes and 5 optical switching
nodes. The feasibility, effectiveness and stability of the post-quantum
cryptography (PQC) algorithm and advantages of replacing trusted relays with
optical switching brought by PQC authentication large-scale metropolitan area
QKD network were verified. QKD with PQC authentication has potential in
quantum-secure communications, specifically in metropolitan QKD networks.


Abstract:
  Neural network training and validation rely on the availability of large
high-quality datasets. However, in many cases only incomplete datasets are
available, particularly in health care applications, where each patient
typically undergoes different clinical procedures or can drop out of a study.
Since the data to train the neural networks need to be complete, most studies
discard the incomplete datapoints, which reduces the size of the training data,
or impute the missing features, which can lead to artefacts. Alas, both
approaches are inadequate when a large portion of the data is missing. Here, we
introduce GapNet, an alternative deep-learning training approach that can use
highly incomplete datasets. First, the dataset is split into subsets of samples
containing all values for a certain cluster of features. Then, these subsets
are used to train individual neural networks. Finally, this ensemble of neural
networks is combined into a single neural network whose training is fine-tuned
using all complete datapoints. Using two highly incomplete real-world medical
datasets, we show that GapNet improves the identification of patients with
underlying Alzheimer's disease pathology and of patients at risk of
hospitalization due to Covid-19. By distilling the information available in
incomplete datasets without having to reduce their size or to impute missing
values, GapNet will permit to extract valuable information from a wide range of
datasets, benefiting diverse fields from medicine to engineering.


Abstract:
  Any-to-any voice conversion (VC) aims to convert the timbre of utterances
from and to any speakers seen or unseen during training. Various any-to-any VC
approaches have been proposed like AUTOVC, AdaINVC, and FragmentVC. AUTOVC, and
AdaINVC utilize source and target encoders to disentangle the content and
speaker information of the features. FragmentVC utilizes two encoders to encode
source and target information and adopts cross attention to align the source
and target features with similar phonetic content. Moreover, pre-trained
features are adopted. AUTOVC used dvector to extract speaker information, and
self-supervised learning (SSL) features like wav2vec 2.0 is used in FragmentVC
to extract the phonetic content information. Different from previous works, we
proposed S2VC that utilizes Self-Supervised features as both source and target
features for VC model. Supervised phoneme posteriororgram (PPG), which is
believed to be speaker-independent and widely used in VC to extract content
information, is chosen as a strong baseline for SSL features. The objective
evaluation and subjective evaluation both show models taking SSL feature CPC as
both source and target features outperforms that taking PPG as source feature,
suggesting that SSL features have great potential in improving VC.


Abstract:
  We consider a toy model for emergence of chaos in a quantum many-body
short-range-interacting system: two one-dimensional hard-core particles in a
box, with a small mass defect as a perturbation over an integrable system, the
latter represented by two equal mass particles. To that system, we apply a
quantum generalization of Chirikov's criterion for the onset of chaos, i.e. the
criterion of overlapping resonances. There, classical nonlinear resonances
translate almost verbatim to the quantum language. Quantum mechanics intervenes
at a later stage: the resonances occupying less than one Hamiltonian eigenstate
are excluded from the chaos criterion. Resonances appear as contiguous patches
of low purity unperturbed eigenstates, separated by the groups of undestroyed
states -- the quantum analogues of the classical KAM tori.


Abstract:
  We review recent numerical studies of two-dimensional (2D) Dirac fermion
theories that exhibit an unusual mechanism of topological protection against
Anderson localization. These describe surface-state quasiparticles of
time-reversal invariant, three-dimensional (3D) topological superconductors
(TSCs), subject to the effects of quenched disorder. Numerics reveal a
surprising connection between 3D TSCs in classes AIII, CI, and DIII, and 2D
quantum Hall effects in classes A, C, and D. Conventional arguments derived
from the non-linear $\sigma$-model picture imply that most TSC surface states
should Anderson localize for arbitrarily weak disorder (CI, AIII), or exhibit
weak antilocalizing behavior (DIII). The numerical studies reviewed here
instead indicate spectrum-wide surface quantum criticality, characterized by
robust eigenstate multifractality throughout the surface-state energy spectrum.
In other words, there is an "energy stack" of critical wave functions. For
class AIII, multifractal eigenstate and conductance analysis reveals identical
statistics for states throughout the stack, consistent with the class A integer
quantum-Hall plateau transition (QHPT). Class CI TSCs exhibit surface stacks of
class C spin QHPT states. Critical stacking of a third kind, possibly
associated to the class D thermal QHPT, is identified for nematic velocity
disorder of a single Majorana cone in class DIII. The Dirac theories studied
here can be represented as perturbed 2D Wess-Zumino-Novikov-Witten sigma
models; the numerical results link these to Pruisken models with the
topological angle $\vartheta = \pi$. Beyond applications to TSCs, all three
stacked Dirac theories (CI, AIII, DIII) naturally arise in the effective
description of dirty $d$-wave quasiparticles, relevant to the high-$T_c$
cuprates.


Abstract:
  We consider the effects of the heat balance on the structural stability of a
preflare current layer. The problem of small perturbations is solved in the
piecewise homogeneous MHD approximation taking into account the viscosity, the
electrical and thermal conductivity, and the radiative cooling. Solution of the
problem allows the formation of an instability of a thermal nature. There is no
external magnetic field inside the current layer in equilibrium state, but it
can penetrate inside when the current layer is disturbed. Formation of a
magnetic field perturbation inside the layer creates a dedicated frequency in a
broadband disturbance subject to thermal instability. In the linear phase, the
growth time of the instability is proportional to the characteristic time of
radiative cooling of plasma and depends on the logarithmic derivatives of the
radiative cooling function with respect to the plasma parameters. The
instability results in transverse fragmentation of the current layer with a
spatial period of 1-10 Mm along the layer in a wide range of coronal plasma
parameters. The role of that instability in the triggering for the primary
energy release in solar flares is discussed.


Abstract:
  In this article, we consider a class of functions on $\mathbb{R}^d$, called
positive homogeneous functions, which interact well with certain continuous
one-parameter groups of (generally anisotropic) dilations. Generalizing the
Euclidean norm, positive homogeneous functions appear naturally in the study of
convolution powers of complex-valued functions on $\mathbb{Z}^d$. As the
spherical measure is a Radon measure on the unit sphere which is invariant
under the symmetry group of the Euclidean norm, to each positive homogeneous
function $P$, we construct a Radon measure $\sigma_P$ on $S=\{\eta \in
\mathbb{R}^d:P(\eta)=1\}$ which is invariant under the symmetry group of $P$.
With this measure, we prove a generalization of the classical polar-coordinate
integration formula and deduce a number of corollaries in this setting. We then
turn to the study of convolution powers of complex functions on $\mathbb{Z}^d$
and certain oscillatory integrals which arise naturally in that context. Armed
with our integration formula and the Van der Corput lemma, we establish sup
norm-type estimates for convolution powers; this result is new and partially
extends results of [20] and [21].


Abstract:
  Cyber attacks pose crucial threats to computer system security, and put
digital treasuries at excessive risks. This leads to an urgent call for an
effective intrusion detection system that can identify the intrusion attacks
with high accuracy. It is challenging to classify the intrusion events due to
the wide variety of attacks. Furthermore, in a normal network environment, a
majority of the connections are initiated by benign behaviors. The class
imbalance issue in intrusion detection forces the classifier to be biased
toward the majority/benign class, thus leave many attack incidents undetected.
Spurred by the success of deep neural networks in computer vision and natural
language processing, in this paper, we design a new system named DeepIDEA that
takes full advantage of deep learning to enable intrusion detection and
classification. To achieve high detection accuracy on imbalanced data, we
design a novel attack-sharing loss function that can effectively move the
decision boundary towards the attack classes and eliminates the bias towards
the majority/benign class. By using this loss function, DeepIDEA respects the
fact that the intrusion mis-classification should receive higher penalty than
the attack mis-classification. Extensive experimental results on three
benchmark datasets demonstrate the high detection accuracy of DeepIDEA. In
particular, compared with eight state-of-the-art approaches, DeepIDEA always
provides the best class-balanced accuracy.


Abstract:
  We establish a convergence theorem for a certain type of stochastic gradient
descent, which leads to a convergent variant of the back-propagation algorithm


Abstract:
  Many applications require the robustness, or ideally the invariance, of a
neural network to certain transformations of input data. Most commonly, this
requirement is addressed by either augmenting the training data, using
adversarial training, or defining network architectures that include the
desired invariance automatically. Unfortunately, the latter often relies on the
ability to enlist all possible transformations, which make such approaches
largely infeasible for infinite sets of transformations, such as arbitrary
rotations or scaling. In this work, we propose a method for provably invariant
network architectures with respect to group actions by choosing one element
from a (possibly continuous) orbit based on a fixed criterion. In a nutshell,
we intend to 'undo' any possible transformation before feeding the data into
the actual network. We analyze properties of such approaches, extend them to
equivariant networks, and demonstrate their advantages in terms of robustness
as well as computational efficiency in several numerical examples. In
particular, we investigate the robustness with respect to rotations of images
(which can possibly hold up to discretization artifacts only) as well as the
provable rotational and scaling invariance of 3D point cloud classification.


Abstract:
  Reranking is attracting incremental attention in the recommender systems,
which rearranges the input ranking list into the final rank-ing list to better
meet user demands. Most existing methods greedily rerank candidates through the
rating scores from point-wise or list-wise models. Despite effectiveness,
neglecting the mutual influence between each item and its contexts in the final
ranking list often makes the greedy strategy based reranking methods
sub-optimal. In this work, we propose a new context-wise reranking framework
named Generative Rerank Network (GRN). Specifically, we first design the
evaluator, which applies Bi-LSTM and self-attention mechanism to model the
contextual information in the labeled final ranking list and predict the
interaction probability of each item more precisely. Afterwards, we elaborate
on the generator, equipped with GRU, attention mechanism and pointer network to
select the item from the input ranking list step by step. Finally, we apply
cross-entropy loss to train the evaluator and, subsequently, policy gradient to
optimize the generator under the guidance of the evaluator. Empirical results
show that GRN consistently and significantly outperforms state-of-the-art
point-wise and list-wise methods. Moreover, GRN has achieved a performance
improvement of 5.2% on PV and 6.1% on IPV metric after the successful
deployment in one popular recommendation scenario of Taobao application.


Abstract:
  The formation of the solar system's giant planets predated the ultimate epoch
of massive impacts that concluded the process of terrestrial planet formation.
Following their formation, the giant planets' orbits evolved through an episode
of dynamical instability. Several qualities of the solar system have recently
been interpreted as evidence of this event transpiring within the first ~100
Myr after the Sun's birth; around the same time as the final assembly of the
inner planets. In a series of recent papers we argued that such an early
instability could resolve several problems revealed in classic numerical
studies of terrestrial planet formation; namely the small masses of Mars and
the asteroid belt. In this paper, we revisit the early instability scenario
with a large suite of simulations specifically designed to understand the
degree to which Earth and Mars' formation are sensitive to the specific
evolution of Jupiter and Saturn's orbits. By deriving our initial terrestrial
disks directly from recent high-resolution simulations of planetesimal
accretion, our results largely confirm our previous findings regarding the
instability's efficiency of truncating the terrestrial disk outside of the
Earth-forming region in simulations that best replicate the outer solar system.
Moreover, our work validates the primordial 2:1 Jupiter-Saturn resonance within
the early instability framework as a viable evolutionary path for the solar
system. While our simulations elucidate the fragility of the terrestrial system
during the epoch of giant planet migration, many realizations yield outstanding
solar system analogs when scrutinized against a number of observational
constraints. Finally, we highlight the inability of models to form adequate
Mercury-analogs and the low eccentricities of Earth and Venus as the most
significant outstanding problems for future numerical studies to resolve.


Abstract:
  5G cellular networks are being deployed all over the world and this
architecture supports ultra-dense network (UDN) deployment. Small cells have a
very important role in providing 5G connectivity to the end users. Exponential
increases in devices, data and network demands make it mandatory for the
service providers to manage handovers better, to cater to the services that a
user desire. In contrast to any traditional handover improvement scheme, we
develop a 'Deep-Mobility' model by implementing a deep learning neural network
(DLNN) to manage network mobility, utilizing in-network deep learning and
prediction. We use network key performance indicators (KPIs) to train our model
to analyze network traffic and handover requirements. In this method, RF signal
conditions are continuously observed and tracked using deep learning neural
networks such as the Recurrent neural network (RNN) or Long Short-Term Memory
network (LSTM) and system level inputs are also considered in conjunction, to
take a collective decision for a handover. We can study multiple parameters and
interactions between system events along with the user mobility, which would
then trigger a handoff in any given scenario. Here, we show the fundamental
modeling approach and demonstrate usefulness of our model while investigating
impacts and sensitivities of certain KPIs from the user equipment (UE) and
network side.


Abstract:
  As machine learning systems become more powerful they also become
increasingly unpredictable and opaque. Yet, finding human-understandable
explanations of how they work is essential for their safe deployment. This
technical report illustrates a methodology for investigating the causal
mechanisms that drive the behaviour of artificial agents. Six use cases are
covered, each addressing a typical question an analyst might ask about an
agent. In particular, we show that each question cannot be addressed by pure
observation alone, but instead requires conducting experiments with
systematically chosen manipulations so as to generate the correct causal
evidence.


Abstract:
  In this paper, we investigate the model reference adaptive control approach
for uncertain piecewise affine systems with performance guarantees. The
proposed approach ensures the error metric, defined as the weighted Euclidean
norm of the state tracking error, to be confined within a user-defined
time-varying performance bound. We introduce an auxiliary performance function
to construct a barrier Lyapunov function. This auxiliary performance signal is
reset at each switching instant, which prevents the transgression of the
barriers caused by the jumps of the error metric at switching instants. The
dwell time constraints are derived based on the parameters of the user-defined
performance bound and the auxiliary performance function. We also prove that
the Lyapunov function is non-increasing even at the switching instants and thus
does not impose extra dwell time constraints. Furthermore, we propose the
robust modification of the adaptive controller for the uncertain piecewise
affine systems subject to unmatched disturbances. A Numerical example validates
the correctness of the proposed approach.


Abstract:
  For a commutative Noetherian ring $R$ and a module-finite $R$-algebra
$\Lambda$, we study the set $\mathsf{tors} \Lambda$ (respectively,
$\mathsf{torf}\Lambda$) of torsion (respectively, torsionfree) classes of the
category of finitely generated $\Lambda$-modules. We construct a bijection from
$\mathsf{torf}\Lambda$ to $\prod_{\mathfrak{p}} \mathsf{torf}(\Lambda\otimes_R
\kappa(\mathfrak{p}))$, and an embedding $\Phi_{\rm t}$ from $\mathsf{tors}
\Lambda$ to $\mathbb{T}_R(\Lambda):=\prod_{\mathfrak{p}}
\mathsf{tors}(\Lambda\otimes_R \kappa(\mathfrak{p}))$, where $\mathfrak{p}$
runs all prime ideals of $R$. When $\Lambda=R$, these give classifications of
torsionfree classes, torsion classes and Serre subcategories of $\mathsf{mod}
R$ due to Takahashi, Stanley-Wang and Gabriel. To give a description of
$\mathrm{Im} \Phi_{\rm t}$, we introduce the notion of compatible elements in
$\mathbb{T}_R(\Lambda)$, and prove that all elements in $\mathrm{Im} \Phi_{\rm
t}$ are compatible. We give a sufficient condition on $(R, \Lambda)$ such that
all compatible elements belong to $\mathrm{Im} \Phi_{\rm t}$ (we call $(R,
\Lambda)$ compatible in this case). For example, if $R$ is semi-local and $\dim
R \leq 1$, then $(R, \Lambda)$ is compatible. We also give a sufficient
condition in terms of silting $\Lambda$-modules. As an application, for a
Dynkin quiver $Q$, $(R, RQ)$ is compatible and we have a poset isomorphism
$\mathsf{tors} RQ \simeq \mathsf{Hom}_{\rm poset}(\mathsf{Spec} R,
\mathfrak{C}_Q)$ for the Cambrian lattice $\mathfrak{C}_Q$ of $Q$.


Abstract:
  Outflows driven by active galactic nuclei (AGN) are an important channel for
accreting supermassive black holes (SMBHs) to interact with their host galaxies
and clusters. Properties of the outflows are however poorly constrained due to
the lack of kinetically resolved data of the hot plasma that permeates the
circumgalactic and intracluster space. In this work, we use a single parameter,
outflow-to-accretion mass-loading factor $m=\dot{M}_{\rm out}/\dot{M}_{\rm
BH}$, to characterize the outflows that mediate the interaction between SMBHs
and their hosts. By modeling both M87 and Perseus, and comparing the simulated
thermal profiles with the X-ray observations of these two systems, we
demonstrate that $m$ can be constrained between $200-500$. This parameter
corresponds to a bulk flow speed between $4,000-7,000\,{\rm km\,s}^{-1}$ at
around 1 kpc, and a thermalized outflow temperature between
$10^{8.7}-10^{9}\,{\rm K}$. Our results indicate that the dominant outflow
speeds in giant elliptical galaxies and clusters are much lower than in the
close vicinity of the SMBH, signaling an efficient coupling with and
deceleration by the surrounding medium on length scales below 1 kpc.
Consequently, AGNs may be efficient at launching outflows $\sim10$ times more
massive than previously uncovered by measurements of cold, obscuring material.
We also examine the mass and velocity distribution of the cold gas, which
ultimately forms a rotationally supported disk in simulated clusters. The
rarity of such disks in observations indicates that further investigations are
needed to understand the evolution of the cold gas after it forms.


Abstract:
  Adversarial attacks have threatened the application of deep neural networks
in security-sensitive scenarios. Most existing black-box attacks fool the
target model by interacting with it many times and producing global
perturbations. However, global perturbations change the smooth and
insignificant background, which not only makes the perturbation more easily be
perceived but also increases the query overhead. In this paper, we propose a
novel framework to perturb the discriminative areas of clean examples only
within limited queries in black-box attacks. Our framework is constructed based
on two types of transferability. The first one is the transferability of model
interpretations. Based on this property, we identify the discriminative areas
of a given clean example easily for local perturbations. The second is the
transferability of adversarial examples. It helps us to produce a local
pre-perturbation for improving query efficiency. After identifying the
discriminative areas and pre-perturbing, we generate the final adversarial
examples from the pre-perturbed example by querying the targeted model with two
kinds of black-box attack techniques, i.e., gradient estimation and random
search. We conduct extensive experiments to show that our framework can
significantly improve the query efficiency during black-box perturbing with a
high attack success rate. Experimental results show that our attacks outperform
state-of-the-art black-box attacks under various system settings.


Abstract:
  Detection of visual anomalies refers to the problem of finding patterns in
different imaging data that do not conform to the expected visual appearance
and is a widely studied problem in different domains. Due to the nature of
anomaly occurrences and underlying generating processes, it is hard to
characterize them and obtain labeled data. Obtaining labeled data is especially
difficult in biomedical applications, where only trained domain experts can
provide labels, which often come in large diversity and complexity. Recently
presented approaches for unsupervised detection of visual anomalies approaches
omit the need for labeled data and demonstrate promising results in domains,
where anomalous samples significantly deviate from the normal appearance.
Despite promising results, the performance of such approaches still lags behind
supervised approaches and does not provide a one-fits-all solution. In this
work, we present an image-to-image translation-based framework that
significantly surpasses the performance of existing unsupervised methods and
approaches the performance of supervised methods in a challenging domain of
cancerous region detection in histology imagery.


Abstract:
  In this paper, we present two new families of spatially homogeneous black
hole solution for $z=4$ Ho\v{r}ava-Lifshitz Gravity equations in $(4+1)$
dimensions with general coupling constant $\lambda$ and the especial case
$\lambda=1$, considering $\beta=-1/3$. The three-dimensional horizons are
considered to have Bianchi types $II$ and $III$ symmetries, and hence the
horizons are modeled on two types of Thurston $3$-geometries, namely the Nil
geometry and $H^2\times R$. Being foliated by compact 3-manifolds, the horizons
are neither spherical, hyperbolic, nor toroidal, and therefore are not of the
previously studied topological black hole solutions in Ho\v{r}ava-Lifshitz
gravity. Using the Hamiltonian formalism, we establish the conventional
thermodynamics of the solutions defining the mass and entropy of the black hole
solutions for several classes of solutions. It turned out that for both horizon
geometries the area term in the entropy receives two non-logarithmic negative
corrections proportional to Ho\v{r}ava-Lifshitz parameters. Also, we show that
choosing some proper set of parameters the solutions can exhibit locally stable
or unstable behavior.


Abstract:
  In this paper, we obtain the $H^{p_1}\times H^{p_2}\times H^{p_3}\to H^p$
boundedness for trilinear Fourier multiplier operators, which is a trilinear
analogue of the multiplier theorem of Calder\'on and Torchinsky (Adv. Math. 24
: 101-171, 1977). Our result improves the trilinear estimate in the very recent
work of the authors, Lee, Heo, Hong, Park, and Yang (Math. Ann., to appear ) by
additionally assuming an appropriate vanishing moment condition, which is
natural in the boundedness into the Hardy space $H^p$ for $0<p\le 1$.


Abstract:
  In this article, we develop an arithmetic analogue of Fourier--Jacobi period
integrals for a pair of unitary groups of equal rank. We construct the
so-called Fourier--Jacobi cycles, which are algebraic cycles on the product of
unitary Shimura varieties and abelian varieties. We propose the arithmetic
Gan--Gross--Prasad conjecture for these cycles, which is related to central
derivatives of certain Rankin--Selberg $L$-functions, and develop a relative
trace formula approach toward this conjecture. As a necessary ingredient, we
propose the conjecture of the corresponding arithmetic fundamental lemma, and
confirm it for unitary groups of rank at most two and for the minuscule case.


Abstract:
  We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting
popular Machine Learning (ML) methods both in regression and classification
settings. Using the recent Stata/Python integration platform (sfi) of Stata 16,
these commands provide hyper-parameters' optimal tuning via K-fold
cross-validation using greed search. More specifically, they make use of the
Python Scikit-learn API to carry out both cross-validation and outcome/label
prediction.


Abstract:
  As wearable devices move toward the face (i.e. smart earbuds, glasses), there
is an increasing need to facilitate intuitive interactions with these devices.
Current sensing techniques can already detect many mouth-based gestures;
however, users' preferences of these gestures are not fully understood. In this
paper, we investigate the design space and usability of mouth-based
microgestures. We first conducted brainstorming sessions (N=16) and compiled an
extensive set of 86 user-defined gestures. Then, with an online survey (N=50),
we assessed the physical and mental demand of our gesture set and identified a
subset of 14 gestures that can be performed easily and naturally. Finally, we
conducted a remote Wizard-of-Oz usability study (N=11) mapping gestures to
various daily smartphone operations under a sitting and walking context. From
these studies, we develop a taxonomy for mouth gestures, finalize a practical
gesture set for common applications, and provide design guidelines for future
mouth-based gesture interactions.


Abstract:
  We study the Hawking radiation from the five-dimensional charged static
squashed Kaluza-Klein black hole by the tunneling of charged fermions and
charged scalar particles, including the phenomenological quantum gravity
effects predicted by the generalized uncertainty principle with the minimal
measurable length. We derive corrections of the Hawking temperature to general
relativity, which are related to the energy of the emitted particle, the size
of the compact extra dimension, the charge of the black hole and the existence
of the minimal length in the squashed Kaluza-Klein geometry. We show that the
quantum gravity effect may slow down the increase of the Hawking temperature
due to the radiation, which may lead to the thermodynamic stable remnant of the
order of the Planck mass after the evaporation of the squashed Kaluza-Klein
black hole. We also find that the sparsity of the Hawking radiation may become
infinite when the mass of the squashed Kaluza-Klein black hole approaches its
remnant mass.


Abstract:
  We study the existence of nontrivial solutions for a nonlinear fractional
elliptic equation in presence of logarithmic and critical exponential
nonlinearities. This problem extends [5] to fractional $N/s$-Laplacian
equations with logarithmic nonlinearity. We overcome the lack of compactness
due to the critical exponential nonlinearity by using the fractional
Trudinger-Moser inequality. The existence result is established via critical
point theory.


Abstract:
  The non-equilibrium dynamics of stochastic light in a coherently-driven
nonlinear cavity resembles the equilibrium dynamics of a Brownian particle in a
scalar potential. This resemblance has been known for decades, but the
correspondence between the two systems has never been properly assessed. Here
we demonstrate that this correspondence can be exact, approximate, or break
down, depending on the cavity nonlinear response and driving frequency. For
weak on-resonance driving, the nonlinearity vanishes and the correspondence is
exact: The cavity dissipation and driving amplitude define a scalar potential,
the noise variance defines an effective temperature, and the intra-cavity field
satisfies Boltzmann statistics. For moderately strong non-resonant driving, the
correspondence is approximate: We introduce a potential that approximately
captures the nonlinear dynamics of the intra-cavity field, and we quantify the
accuracy of this approximation via deviations from Boltzmann statistics. For
very strong non-resonant driving, the correspondence breaks down: The
intra-cavity field dynamics is governed by non-conservative forces which
preclude a description based on a scalar potential only. We furthermore show
that this breakdown is accompanied by a phase transition for the intra-cavity
field fluctuations, reminiscent of a non-Hermitian phase transition. Our work
establishes clear connections between optical and stochastic thermodynamic
systems, and suggests that many fundamental results for overdamped Langevin
oscillators may be used to understand and improve resonant optical
technologies.


Abstract:
  We consider the problem of consistently estimating the conditional
distribution $P(Y \in A |X)$ of a functional data object $Y=(Y(t): t\in[0,1])$
given covariates $X$ in a general space, assuming that $Y$ and $X$ are related
by a functional linear regression model. Two natural estimation methods are
proposed, based on either bootstrapping the estimated model residuals, or
fitting functional parametric models to the model residuals and estimating $P(Y
\in A |X)$ via simulation. Whether either of these methods lead to consistent
estimation depends on the consistency properties of the regression operator
estimator, and the space within which $Y$ is viewed. We show that under general
consistency conditions on the regression operator estimator, which hold for
certain functional principal component based estimators, consistent estimation
of the conditional distribution can be achieved, both when $Y$ is an element of
a separable Hilbert space, and when $Y$ is an element of the Banach space of
continuous functions. The latter results imply that sets $A$ that specify path
properties of $Y$, which are of interest in applications, can be considered.
The proposed methods are studied in several simulation experiments, and data
analyses of electricity price and pollution curves.


Abstract:
  The performance of face recognition system degrades when the variability of
the acquired faces increases. Prior work alleviates this issue by either
monitoring the face quality in pre-processing or predicting the data
uncertainty along with the face feature. This paper proposes MagFace, a
category of losses that learn a universal feature embedding whose magnitude can
measure the quality of the given face. Under the new loss, it can be proven
that the magnitude of the feature embedding monotonically increases if the
subject is more likely to be recognized. In addition, MagFace introduces an
adaptive mechanism to learn a wellstructured within-class feature distributions
by pulling easy samples to class centers while pushing hard samples away. This
prevents models from overfitting on noisy low-quality samples and improves face
recognition in the wild. Extensive experiments conducted on face recognition,
quality assessments as well as clustering demonstrate its superiority over
state-of-the-arts. The code is available at
https://github.com/IrvingMeng/MagFace.


Abstract:
  Using a fast and accurate neural network potential we are able to
systematically explore the energy landscape of large unit cells of bulk
magnesium oxide with the minima hopping method. The potential is trained with a
focus on the near-stoichiometric compositions, in particular on suboxides,
i.e., Mg$_x$O$_{1-x}$ with $0.50<x<0.60$. Our extensive exploration
demonstrates that for bulk stoichiometric compounds, there are several new
low-energy rocksalt-like structures in which Mg atoms are octahedrally
six--coordinated and form trigonal prismatic motifs with different stacking
sequences. Furthermore, we find a dense spectrum of novel non-stoichiometric
crystal phases of Mg$_x$O$_{1-x}$ for each composition of $x$. These structures
are mostly similar to the rock salt structure with octahedral coordination and
five--coordinated Mg atoms. Due to the removal of one oxygen atom, the energy
landscape becomes more glass-like with oxygen-vacancy type structures that all
lie very close to each other energetically. For the same number of magnesium
and oxygen atoms our oxygen-deficient structures are lower in energy if the
vacancies are aligned along lines or planes than rock salt structures with
randomly distributed oxygen vacancies. We also found the putative global minima
configurations for each composition of the non-stoichiometric suboxide
structures. These structures are predominantly composed of (111) slabs of the
rock salt structure which are terminated with Mg atoms at the top and bottom,
and are stacked in different sequences along the $z$-direction. Like other
Magn\'eli-type phases, these structures have properties that differ
considerably from their stoichiometric counterparts such as low lattice thermal
conductivity and high electrical conductivity.


Abstract:
  The macroscopic dynamics of a droplet impacting a solid is crucially
determined by the intricate air dynamics occurring at the vanishingly small
length scale between droplet and substrate prior to direct contact. Here we
investigate the inverse problem, namely the role of air for the impact of a
horizontal flat disk onto a liquid surface, and find an equally significant
effect. Using an in-house experimental technique, we measure the free surface
deflections just before impact, with a precision of a few micrometers. Whereas
stagnation pressure pushes down the surface in the center, we observe a lift-up
under the edge of the disk, which sets in at a later stage, and which we show
to be consistent with a Kelvin-Helmholtz instability of the water-air
interface.


Abstract:
  Reinforcement learning (RL) agents in human-computer interactions
applications require repeated user interactions before they can perform well.
To address this "cold start" problem, we propose a novel approach of using
cognitive models to pre-train RL agents before they are applied to real users.
After briefly reviewing relevant cognitive models, we present our general
methodological approach, followed by two case studies from our previous and
ongoing projects. We hope this position paper stimulates conversations between
RL, HCI, and cognitive science researchers in order to explore the full
potential of the approach.


Abstract:
  In this paper we investigate how gradient-based algorithms such as gradient
descent, (multi-pass) stochastic gradient descent, its persistent variant, and
the Langevin algorithm navigate non-convex loss-landscapes and which of them is
able to reach the best generalization error at limited sample complexity. We
consider the loss landscape of the high-dimensional phase retrieval problem as
a prototypical highly non-convex example. We observe that for phase retrieval
the stochastic variants of gradient descent are able to reach perfect
generalization for regions of control parameters where the gradient descent
algorithm is not. We apply dynamical mean-field theory from statistical physics
to characterize analytically the full trajectories of these algorithms in their
continuous-time limit, with a warm start, and for large system sizes. We
further unveil several intriguing properties of the landscape and the
algorithms such as that the gradient descent can obtain better generalization
properties from less informed initializations.