Abstract:
  Advanced building control methods such as model predictive control (MPC)
offer significant potential benefits to both consumers and grid operators, but
the high computational requirements have acted as barriers to more widespread
adoption. Local control computation requires installation of expensive
computational hardware, while cloud computing introduces data security and
privacy concerns. In this paper, we drastically reduce the local computational
requirements of advanced building control through a reinforcement learning
(RL)-based approach called Behavioral Cloning, which represents the MPC policy
as a neural network that can be locally implemented and quickly computed on a
low-cost programmable logic controller. While previous RL and approximate MPC
methods must be specifically trained for each building, our key improvement is
that our controller can generalize to many buildings, electricity rates, and
thermostat setpoint schedules without additional, effort-intensive retraining.
To provide this versatility, we have adapted the traditional Behavioral Cloning
approach through (1) a constraint-informed parameter grouping (CIPG) method
that provides a more efficient representation of the training data; (2) an
MPC-Guided training data generation method using the DAgger algorithm that
improves stability and constraint satisfaction; and (3) a new deep learning
model-structure called reverse-time recurrent neural networks (RT-RNN) that
allows future information to flow backward in time to more effectively
interpret the temporal information in disturbance predictions. The result is an
easy-to-deploy, generalized behavioral clone of MPC that can be implemented on
a programmable logic controller and requires little building-specific
controller tuning, reducing the effort and costs associated with implementing
smart residential heat pump control.


Abstract:
  Let $\mathcal{G}^{(\lambda)}$ be a group scheme which deforms $\mathbb{G}_a$
to $\mathbb{G}_m$. We explicitly describe the Cartier dual of the $l$-th
Frobenius type kernel $N_l$ of the group scheme $\mathcal{E}^{(\lambda,\mu;D)}$
which is an extension of $\mathcal{G}^{(\lambda)}$ by $\mathcal{G}^{(\mu)}$.
Here we assume that the base ring $A$ is a $\mathbb{Z}_{(p)}$-algebra
containing some nilpotent elements. The obtained result generalizes a previous
result by N. Aki and M. Amano (Tsukuba J.Math. $\textbf{34}$ (2010)) which
assumes that $A$ is an $\mathbb{F}_p$-algebra.


Abstract:
  The competition of depletion attractions and longer-ranged repulsions between
colloidal particles in colloid-polymer mixtures leads to the formation of
heterogeneous gel-like structures. For instance, gel networks, i.e., states
where the colloids arrange in thin strands that span the whole system occur at
low packing fractions for attractions that are stronger than those at the
binodal line of equilibrium liquid-fluid phase separation. By using Brownian
dynamics simulations we explore the formation, structure, and ageing dynamics
of gel networks. We determine reduced network that focus on the essential
connections in a gel network. We compare the observed properties to those of
bulky gels or cluster fluids. Our results demonstrate that both the structure
as well as the (often slow) dynamics of the stable or meta-stable heterogenous
states in colloid-polymer mixtures possess distinct features on various length
and time scales and thus are richly divers.


Abstract:
  Recent studies revealed that the electric multipole moments of insulators
result in fractional electric charges localized to the hinges and corners of
the sample. We here explore the magnetic analog of this relation. We show that
a collinear antiferromagnet with spin $S$ defined on a $d$-dimensional cubic
lattice features fractionally quantized magnetization $M_{\text{c}}^z=S/2^d$ at
the corners. We find that the quantization is robust even in the presence of
gapless excitations originating from the spontaneous formation of the N\'eel
order, although the localization length diverges, suggesting a power-law
localization of the corner magnetization. When the spin rotational symmetry
about the $z$ axis is explicitly broken, the corner magnetization is no longer
sharply quantized. Even in this case, we numerically find that the deviation
from the quantized value is negligibly small based on quantum Monte Carlo
simulations.


Abstract:
  Video streaming platforms such as Youtube, Twitch, and DLive allow users to
live-stream video content for viewers who can optionally express their
appreciation through monetary donations. DLive is one of the smaller and
lesser-known streaming platforms, and historically has had fewer content
moderation practices. It has thus become a popular place for violent extremists
and other clandestine groups to earn money and propagandize. What is the
financial structure of the DLive streaming ecosystem and how much money is
changing hands? In the past it has been difficult to understand how far-right
extremists fundraise via podcasts and video streams because of the secretive
nature of the activity and because of the difficulty of getting data from
social media platforms. This paper describes a novel experiment to collect and
analyze data from DLive's publicly available ledgers of transactions in order
to understand the financial structure of the clandestine, extreme far-right
video streaming community. The main findings of this paper are, first, that the
majority of donors are using micropayments in varying frequencies, but a small
handful of donors spend large amounts of money to finance their favorite
streamers. Next, the timing of donations to high-profile far-right streamers
follows a fairly predictable pattern that is closely tied to a broadcast
schedule. Finally, the far-right video streaming financial landscape is divided
into separate cliques which exhibit very little crossover in terms of sizable
donations. This work will be important to technology companies, policymakers,
and researchers who are trying to understand how niche social media services,
including video platforms, are being exploited by extremists to propagandize
and fundraise.


Abstract:
  Charts often contain visually prominent features that draw attention to
aspects of the data and include text captions that emphasize aspects of the
data. Through a crowdsourced study, we explore how readers gather takeaways
when considering charts and captions together. We first ask participants to
mark visually prominent regions in a set of line charts. We then generate text
captions based on the prominent features and ask participants to report their
takeaways after observing chart-caption pairs. We find that when both the chart
and caption describe a high-prominence feature, readers treat the doubly
emphasized high-prominence feature as the takeaway; when the caption describes
a low-prominence chart feature, readers rely on the chart and report a
higher-prominence feature as the takeaway. We also find that external
information that provides context, helps further convey the caption's message
to the reader. We use these findings to provide guidelines for authoring
effective chart-caption pairs.


Abstract:
  In sorted range selection problem, the aim is to preprocess a given array
A[1: n] so as to answers queries of type: given two indices i,j ($1 \le i\le j
\le n$) and an integer k, report k smallest elements in sorted order present in
the sub-array A[i: j] Brodal et.al.[2] have shown that the problem can be
solved in O(k) time after O(n log n) preprocessing in linear space. In this
paper we discuss another tradeoff. We reduce preprocessing time to O(n), but
query time is O(k log k), again using linear space. Our method is very simple.


Abstract:
  Let n respondents rank order d items, and suppose that d << n. Our main task
is to uncover and display the structure of the observed rank data by an
exploratory riffle shuffling procedure which sequentially decomposes the n
voters into a finite number of coherent groups plus a noisy group : where the
noisy group represents the outlier voters and each coherent group is composed
of a finite number of coherent clusters. We consider exploratory riffle
shuffling of a set of items to be equivalent to optimal two blocks seriation of
the items with crossing of some scores between the two blocks. A riffle
shuffled coherent cluster of voters within its coherent group is essentially
characterized by the following facts : a) Voters have identical first TCA
factor score, where TCA designates taxicab correspondence analysis, an L1
variant of correspondence analysis ; b) Any preference is easily interpreted as
riffle shuffling of its items ; c) The nature of different riffle shuffling of
items can be seen in the structure of the contingency table of the first-order
marginals constructed from the Borda scorings of the voters ; d) The first TCA
factor scores of the items of a coherent cluster are interpreted as Borda scale
of the items. We also introduce a crossing index, which measures the extent of
crossing of scores of voters between the two blocks seriation of the items. The
novel approach is explained on the benchmarking SUSHI data set, where we show
that this data set has a very simple structure, which can also be communicated
in a tabular form.


Abstract:
  This paper proposes a novel machine-learning approach for predicting AC-OPF
solutions that features a fast and scalable training. It is motivated by the
two critical considerations: (1) the fact that topology optimization and the
stochasticity induced by renewable energy sources may lead to fundamentally
different AC-OPF instances; and (2) the significant training time needed by
existing machine-learning approaches for predicting AC-OPF. The proposed
approach is a 2-stage methodology that exploits a spatial decomposition of the
power network that is viewed as a set of regions. The first stage learns to
predict the flows and voltages on the buses and lines coupling the regions, and
the second stage trains, in parallel, the machine-learning models for each
region. Experimental results on the French transmission system (up to 6,700
buses and 9,000 lines) demonstrate the potential of the approach. Within a
short training time, the approach predicts AC-OPF solutions with very high
fidelity and minor constraint violations, producing significant improvements
over the state-of-the-art. The results also show that the predictions can seed
a load flow optimization to return a feasible solution within 0.03% of the
AC-OPF objective, while reducing running times significantly.


Abstract:
  With increasing automation in passenger vehicles, the study of safe and
smooth occupant-vehicle interaction and control transitions is key. In this
study, we focus on the development of contextual, semantically meaningful
representations of the driver state, which can then be used to determine the
appropriate timing and conditions for transfer of control between driver and
vehicle. To this end, we conduct a large-scale real-world controlled data study
where participants are instructed to take-over control from an autonomous agent
under different driving conditions while engaged in a variety of distracting
activities. These take-over events are captured using multiple driver-facing
cameras, which when labelled result in a dataset of control transitions and
their corresponding take-over times (TOTs). We then develop and train TOT
models that operate sequentially on mid to high-level features produced by
computer vision algorithms operating on different driver-facing camera views.
The proposed TOT model produces continuous predictions of take-over times
without delay, and shows promising qualitative and quantitative results in
complex real-world scenarios.


Abstract:
  In this paper, we improve speech translation (ST) through effectively
leveraging large quantities of unlabeled speech and text data in different and
complementary ways. We explore both pretraining and self-training by using the
large Libri-Light speech audio corpus and language modeling with CommonCrawl.
Our experiments improve over the previous state of the art by 2.6 BLEU on
average on all four considered CoVoST 2 language pairs via a simple recipe of
combining wav2vec 2.0 pretraining, a single iteration of self-training and
decoding with a language model. Different to existing work, our approach does
not leverage any other supervision than ST data. Code and models will be
publicly released.


Abstract:
  Machine learning systems are often trained using data collected from
historical decisions. If past decisions were biased, then automated systems
that learn from historical data will also be biased. We propose a black-box
approach to identify and remove biased training data. Machine learning models
trained on such debiased data (a subset of the original training data) have low
individual discrimination, often 0%. These models also have greater accuracy
and lower statistical disparity than models trained on the full historical
data. We evaluated our methodology in experiments using 6 real-world datasets.
Our approach outperformed seven previous approaches in terms of individual
discrimination and accuracy.


Abstract:
  Convolutional neural networks (CNNs) learn to extract representations of
complex features, such as object shapes and textures to solve image recognition
tasks. Recent work indicates that CNNs trained on ImageNet are biased towards
features that encode textures and that these alone are sufficient to generalize
to unseen test data from the same distribution as the training data but often
fail to generalize to out-of-distribution data. It has been shown that
augmenting the training data with different image styles decreases this texture
bias in favor of increased shape bias while at the same time improving
robustness to common corruptions, such as noise and blur. Commonly, this is
interpreted as shape bias increasing corruption robustness. However, this
relationship is only hypothesized. We perform a systematic study of different
ways of composing inputs based on natural images, explicit edge information,
and stylization. While stylization is essential for achieving high corruption
robustness, we do not find a clear correlation between shape bias and
robustness. We conclude that the data augmentation caused by style-variation
accounts for the improved corruption robustness and increased shape bias is
only a byproduct.


Abstract:
  We investigate the photon pumping effect in a topological model consisting of
a periodically driven spin-1/2 coupled to a quantum cavity mode out of the
adiabatic limit. In the strong-drive adiabatic limit, a quantized frequency
conversion of photons is expected as the temporal analog of the Hall current.
We numerically establish a novel photon pumping phenomenon in the
experimentally accessible nonadiabatic driving regime for a broad region of the
parameter space. The photon frequency conversion efficiency exhibits strong
fluctuations and high efficiency that can reach up 80% of the quantized value
for commensurate frequency combinations. We link the pumping properties to the
delocalization of the corresponding Floquet states which display multifractal
behavior as the result of hybridization between localized and delocalized
sectors. Finally we demonstrate that the quantum coherence properties of the
initial state are preserved during the frequency conversion process in both the
strong and ultra-weak-drive limit.


Abstract:
  We study the optimal investment policy of a firm facing both technological
and cash-flow uncertainty. At any point in time, the firm can decide to invest
in a standalone technology or to wait for a technological breakthrough.
Breakthroughs occur when market conditions become favorable enough, exceeding a
certain threshold value that is ex-ante unknown to the firm. A microfoundation
for this assumption is that a breakthrough occurs when the share of the surplus
from the new technology accruing to its developer is high enough to cover her
privately observed cost. We show that the relevant Markov state variables for
the firm's optimal investment policy are the current market conditions and
their current historic maximum, and that the firm optimally invests in the
stand-alone technology only when market conditions deteriorate enough after
reaching a maximum. Empirically, investments in new technologies requiring the
active cooperation of developers should thus take place in booms, whereas
investments in state-of-the-art technologies should take place in busts.
Moreover, the required return for investing in the stand-alone technology is
always higher than if this were the only available technology and can take
arbitrarily large values following certain histories. Finally, a decrease in
development costs, or an increase in the value of the new technology, makes the
firm more prone to bear downside risk and to delay investment in the
stand-alone technology.


Abstract:
  In this paper, we present the XMUSPEECH system for Task 1 of 2020
Personalized Voice Trigger Challenge (PVTC2020). Task 1 is a joint wake-up word
detection with speaker verification on close talking data. The whole system
consists of a keyword spotting (KWS) sub-system and a speaker verification (SV)
sub-system. For the KWS system, we applied a Temporal Depthwise Separable
Convolution Residual Network (TDSC-ResNet) to improve the system's performance.
For the SV system, we proposed a multi-task learning network, where phonetic
branch is trained with the character label of the utterance, and speaker branch
is trained with the label of the speaker. Phonetic branch is optimized with
connectionist temporal classification (CTC) loss, which is treated as an
auxiliary module for speaker branch. Experiments show that our system gets
significant improvements compared with baseline system.


Abstract:
  In order to transmit electrical energy in a continuous and quality manner, it
is necessary to control it from the point of production to the point of
consumption. Therefore, protection of transmission and distribution lines is
essential at every stage from production to consumption. The main function of
the protection relays in electrical installations should be deactivated as soon
as possible in the event of short circuits in the system. The most important
part of the system is energy transmission lines and distance protection relays
that protect these lines. An accurate error location technique is required to
make fast and efficient work. Transformer neutral point grounding in
transmission lines affects the operation of the zero component current during
the single phase to ground short circuit failure of a power system. Considering
the relationship between the grounding system and protection systems, an
appropriate grounding choice should be made. Artificial neural network (ANN)
has been used in order to accurately locate short circuit faults in different
grounding systems in transmission lines. Compared with support vector machines
(SVM) for testing inside ANN The transmission line model is made in the
PSCAD-EMTDC simulation program. Data sets were created by recording the image
of the impedance change of the R-X impedance diagram of the distance protection
relay in short circuit faults created in different grounding systems. The
related focal points in the images are given as an introduction to different
ANN models using feature extraction and image processing techniques and the ANN
model with the highest fault location estimation accuracy was chosen.


Abstract:
  Two intermetallic FeAl compounds with Al content of 70.68 and 72.17 at.pct
were studied using M\"ossbauer spectroscopy (5 to 296 K) and X-ray diffraction
(15 to 300 K). The compounds were found to crystallize in the orthorhombic Cmcm
space group (eta-phase). The collected data revealed that dynamics of the Fe
atoms (harmonic in entire temperature range) is significantly different that Al
atoms. For the latter strong anharmonicity was evidenced. Moreover, it was
found that partial filling of the different Al sites leads to occurrence of low
and high symmetry coordination of Fe atoms, which was reflected in occurrence
of two distinct doublets in M\"ossbauer spectra. All spectral parameters of the
doublets as well as the Debye temperature, force constant, kinetic and
potential energies of vibrations were determined. Those results revealed
significant differences between both alloys, likely originating from
approaching the stability boundary of the eta-phase for Fe-Al 72.17 at.pct
alloy.


Abstract:
  In clinical practice, medical image interpretation often involves
multi-labeled classification, since the affected parts of a patient tend to
present multiple symptoms or comorbidities. Recently, deep learning based
frameworks have attained expert-level performance on medical image
interpretation, which can be attributed partially to large amounts of accurate
annotations. However, manually annotating massive amounts of medical images is
impractical, while automatic annotation is fast but imprecise (possibly
introducing corrupted labels). In this work, we propose a new regularization
approach, called Flow-Mixup, for multi-labeled medical image classification
with corrupted labels. Flow-Mixup guides the models to capture robust features
for each abnormality, thus helping handle corrupted labels effectively and
making it possible to apply automatic annotation. Specifically, Flow-Mixup
decouples the extracted features by adding constraints to the hidden states of
the models. Also, Flow-Mixup is more stable and effective comparing to other
known regularization methods, as shown by theoretical and empirical analyses.
Experiments on two electrocardiogram datasets and a chest X-ray dataset
containing corrupted labels verify that Flow-Mixup is effective and insensitive
to corrupted labels.


Abstract:
  Multi-scale biomedical knowledge networks are expanding with emerging
experimental technologies that generates multi-scale biomedical big data. Link
prediction is increasingly used especially in bipartite biomedical networks to
identify hidden biological interactions and relationshipts between key entities
such as compounds, targets, gene and diseases. We propose a Graph Neural
Networks (GNN) method, namely Graph Pair based Link Prediction model (GPLP),
for predicting biomedical network links simply based on their topological
interaction information. In GPLP, 1-hop subgraphs extracted from known network
interaction matrix is learnt to predict missing links. To evaluate our method,
three heterogeneous biomedical networks were used, i.e. Drug-Target Interaction
network (DTI), Compound-Protein Interaction network (CPI) from NIH Tox21, and
Compound-Virus Inhibition network (CVI). Our proposed GPLP method significantly
outperforms over the state-of-the-art baselines. In addition, different network
incompleteness is analysed with our devised protocol, and we also design an
effective approach to improve the model robustness towards incomplete networks.
Our method demonstrates the potential applications in other biomedical
networks.


Abstract:
  Assuming Lehmer's conjecture, we estimate the degree of the trace field
  $K(M_{p/q})$ of a hyperbolic Dehn-filling $M_{p/q}$ of a 1-cusped
  hyperbolic 3-manifold $M$ by \begin{equation*}
  \dfrac{1}{C}(\max\;\{|p|,|q|\})\leq \text{deg }K(M_{p/q})
  \leq C(\max\;\{|p|,|q|\}) \end{equation*} where $C=C_M$ is a constant that
depends on $M$.


Abstract:
  The calculation of the MP2 correlation energy for extended systems can be
viewed as a multi-dimensional integral in the thermodynamic limit, and the
standard method for evaluating the MP2 energy can be viewed as a trapezoidal
quadrature scheme. We demonstrate that existing analysis neglects certain
contributions due to the non-smoothness of the integrand, and may significantly
underestimate finite-size errors. We propose a new staggered mesh method, which
uses two staggered Monkhorst-Pack meshes for occupied and virtual orbitals,
respectively, to compute the MP2 energy. The staggered mesh method circumvents
a significant error source in the standard method, in which certain quadrature
nodes are always placed on points where the integrand is discontinuous. One
significant advantage of the proposed method is that there are no tunable
parameters, and the additional numerical effort needed can be negligible
compared to the standard MP2 calculation. Numerical results indicate that the
staggered mesh method can be particularly advantageous for quasi-1D systems, as
well as quasi-2D and 3D systems with certain symmetries.


Abstract:
  Unsupervised concept identification through clustering, i.e., identification
of semantically related words and phrases, is a common approach to identify
contextual primitives employed in various use cases, e.g., text dimension
reduction, i.e., replace words with the concepts to reduce the vocabulary size,
summarization, and named entity resolution. We demonstrate the first results of
an unsupervised approach for the identification of groups of persons as actors
extracted from a set of related articles. Specifically, the approach clusters
mentions of groups of persons that act as non-named entity actors in the texts,
e.g., "migrant families" = "asylum-seekers." Compared to our baseline, the
approach keeps the mentions of the geopolitical entities separated, e.g., "Iran
leaders" != "European leaders," and clusters (in)directly related mentions with
diverse wording, e.g., "American officials" = "Trump Administration."


Abstract:
  The electroweak symmetry breaking (EWSB) mechanism is still an undecided
question in particle physics. We propose to utilize the single top quark and
Higgs associated production ($th$), $Zh$ production via gluon fusion at the LHC
to probe the couplings between the Higgs and the gauge bosons and further to
test the EWSB. We demonstrate that the $th$ and $gg\to Zh$ productions are
sensitive to the relative sign of couplings ($ht\bar{t}$, $hWW$) and
($ht\bar{t}$, $hZZ$), respectively. We find that the relative sign between
$hWW$ and $hZZ$ couplings could be fully determined after combining the present
measurements from $gg\to h$, $t\bar{t}h$ and the $th$, $Zh$ channels, as well
as $tZj$ and $Zt\bar{t}$ production at the 13 TeV LHC, and this conclusion is
not sensitive to the possible new physics contribution induced by $Zt\bar{t}$
couplings in the $gg\to Zh$ production.


Abstract:
  A single transverse mode high pulse-energy VECSEL was developed. The GaSb
based VECSEL emits at a wavelength of 2.04 microns with a peak power exceeding
500W while maintaining good beam quality. The cavity employs a Pockels cell
combined with a low-loss thin film polarizer to selectively dump the
intracavity energy into a 10ns pulse. The laser has promise for incoherent
LiDAR, materials processing, gas sensing, and nonlinear optics.


Abstract:
  Deep learning-based video manipulation methods have become widely accessible
to the masses. With little to no effort, people can quickly learn how to
generate deepfake (DF) videos. While deep learning-based detection methods have
been proposed to identify specific types of DFs, their performance suffers for
other types of deepfake methods, including real-world deepfakes, on which they
are not sufficiently trained. In other words, most of the proposed deep
learning-based detection methods lack transferability and generalizability.
Beyond detecting a single type of DF from benchmark deepfake datasets, we focus
on developing a generalized approach to detect multiple types of DFs, including
deepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW)
videos. To better cope with unknown and unseen deepfakes, we introduce a
Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model
training strategy and explores spatial as well as the temporal information in
deepfakes. Through extensive experiments, we show that existing defense methods
are not ready for real-world deployment. Whereas our defense method (CLRNet)
achieves far better generalization when detecting various benchmark deepfake
methods (97.57% on average). Furthermore, we evaluate our approach with a
high-quality DeepFake-in-the-Wild dataset, collected from the Internet
containing numerous videos and having more than 150,000 frames. Our CLRNet
model demonstrated that it generalizes well against high-quality DFW videos by
achieving 93.86% detection accuracy, outperforming existing state-of-the-art
defense methods by a considerable margin.


Abstract:
  Colonies of bacterial cells endowed with a pili-based self-propulsion
machinery represent an ideal system for studying how active adhesion forces,
mediated by dynamic, bond-like interactions, affect structure and dynamics of
many-particle systems. We introduce a molecular-dynamics-simulation-based
approach to study Neisseria gonorrhoeae colonies. A generic, adaptable
simulation method for particle systems with fluctuating bond-like interactions
is devised. The simulations are employed to investigate growth of bacterial
colonies and the dependence of the colony structure on cell-cell interactions.
In colonies consisting only of wild-type cells, active pilus retraction is
found to enhance local ordering. For mixed colonies consisting of different
types of cells, the simulations show a segregation depending on the
pili-mediated interactions among different cells. Both results are in good
qualitative agreement with experimental observations. By representing an
experimental setup in silico, we study the power-spectral density of
colony-shape fluctuations and the fluctuation-response relation. Simulations
predict a strong violation of the equilibrium fluctuation-response relation
across the measurable frequency range. Furthermore, we show that active force
generation enables colonies to spread on adhesive surfaces and to invade narrow
channels. Our work presents a foundation for quantitative studies of the
physics of many-particle systems with active adhesion forces in complex
geometries.


Abstract:
  We investigate in detail the spectrum of gravitational waves induced by a
peaked primordial curvature power spectrum generated in single field
inflationary models. We argue that the $f_{\rm NL}$ parameter can be inferred
by measuring the high frequency spectral tilt of the induced gravitational
waves. We also show that the intrinsically non-Gaussian impact of $f_{\rm NL}$
in $\Omega_{\rm GW}$ is to broaden its peak, although at a negligible level in
order not to overproduce primordial black holes. We discuss possible
degeneracies in the high frequency spectral tilt between $f_{\rm NL}$ and a
general equation of state of the universe $w$. Finally, we discuss the
constraints on the amplitude, peak and slope (or equivalently, $f_{\rm NL}$) of
the primordial power spectrum by combining current and future gravitational
wave experiments with limits on $\mu$ distortions from the cosmic microwave
background.


Abstract:
  Using the Markovian master equation for quantum quasiparticles, we show that
convection in the stellar photosphere generates plasma waves by an irreversible
process akin to Zeldovich superradiance and sonic booms. In the Sun, this
mechanism is most efficient in quiet regions with magnetic fields of order one
gauss. Most energy is carried by Alfven waves with megahertz frequencies, which
travel upwards until they reach a height at which they dissipate via mode
conversion. This gives the right power flux for the observed energy transport
from the colder photosphere to the hotter corona.


Abstract:
  We study high-dimensional Bayesian linear regression with product priors.
Using the nascent theory of non-linear large deviations (Chatterjee and
Dembo,2016), we derive sufficient conditions for the leading-order correctness
of the naive mean-field approximation to the log-normalizing constant of the
posterior distribution. Subsequently, assuming a true linear model for the
observed data, we derive a limiting infinite dimensional variational formula
for the log normalizing constant of the posterior. Furthermore, we establish
that under an additional "separation" condition, the variational problem has a
unique optimizer, and this optimizer governs the probabilistic properties of
the posterior distribution. We provide intuitive sufficient conditions for the
validity of this "separation" condition. Finally, we illustrate our results on
concrete examples with specific design matrices.


Abstract:
  Objective: Deep learning-based neural decoders have emerged as the prominent
approach to enable dexterous and intuitive control of neuroprosthetic hands.
Yet few studies have materialized the use of deep learning in clinical settings
due to its high computational requirements. Methods: Recent advancements of
edge computing devices bring the potential to alleviate this problem. Here we
present the implementation of a neuroprosthetic hand with embedded deep
learning-based control. The neural decoder is designed based on the recurrent
neural network (RNN) architecture and deployed on the NVIDIA Jetson Nano - a
compacted yet powerful edge computing platform for deep learning inference.
This enables the implementation of the neuroprosthetic hand as a portable and
self-contained unit with real-time control of individual finger movements.
Results: The proposed system is evaluated on a transradial amputee using
peripheral nerve signals (ENG) with implanted intrafascicular microelectrodes.
The experiment results demonstrate the system's capabilities of providing
robust, high-accuracy (95-99%) and low-latency (50-120 msec) control of
individual finger movements in various laboratory and real-world environments.
Conclusion: Modern edge computing platforms enable the effective use of deep
learning-based neural decoders for neuroprosthesis control as an autonomous
system. Significance: This work helps pioneer the deployment of deep neural
networks in clinical applications underlying a new class of wearable biomedical
devices with embedded artificial intelligence.


Abstract:
  For $G = \mathrm{GL}_2, \mathrm{SL}_2, \mathrm{PGL}_2$ we compute the
intersection E-polynomials and the intersection Poincar\'e polynomials of the
$G$-character variety of a compact Riemann surface $C$ and of the moduli space
of $G$-Higgs bundles on $C$ of degree zero. We derive several results
concerning the P=W conjectures for these singular moduli spaces.


Abstract:
  Getting a robust time-series clustering with best choice of distance measure
and appropriate representation is always a challenge. We propose a novel
mechanism to identify the clusters combining learned compact representation of
time-series, Auto Encoded Compact Sequence (AECS) and hierarchical clustering
approach. Proposed algorithm aims to address the large computing time issue of
hierarchical clustering as learned latent representation AECS has a length much
less than the original length of time-series and at the same time want to
enhance its performance.Our algorithm exploits Recurrent Neural Network (RNN)
based under complete Sequence to Sequence(seq2seq) autoencoder and
agglomerative hierarchical clustering with a choice of best distance measure to
recommend the best clustering. Our scheme selects the best distance measure and
corresponding clustering for both univariate and multivariate time-series. We
have experimented with real-world time-series from UCR and UCI archive taken
from diverse application domains like health, smart-city, manufacturing etc.
Experimental results show that proposed method not only produce close to
benchmark results but also in some cases outperform the benchmark.


Abstract:
  The all-optical synchronization systems used in various X-ray free-electron
lasers (XFEL) such as the European XFEL observe the transient fields of passing
electron bunches coupled into one or more pickups in the Bunch Arrival Time
Monitors (BAM). The extracted signal is then amplitude modulated on reference
laser pulses in a Mach-Zehnder type electro-optical modulator. With the
emerging demand for future experiments with ultra-short FEL shots, fs precision
is required for the synchronization systems even with 1 pC bunches. Since the
sensitivity of the BAM depends in particular on the slope of the bipolar signal
at the zero-crossing and thus, also on the bunch charge, a redesign with the
aim of a significant increase by optimized geometry and bandwidth is
inevitable. In this contribution the theoretical foundations of the pickup
signal are aggregated and treated with a focus on ultra-short bunches as well
as a general formulation. A possible new pickup concept is simulated and its
performance is compared to the previous concept. A significant improvement of
slope and voltage is found. The improvement is mainly achieved by the reduced
distance to the beam and a higher bandwidth.


Abstract:
  Reconstructing under-sampled k-space measurements in Compressed Sensing MRI
(CS-MRI) is classically solved with regularized least-squares. Recently, deep
learning has been used to amortize this optimization by training reconstruction
networks on a dataset of under-sampled measurements. Here, a crucial design
choice is the regularization function(s) and corresponding weight(s). In this
paper, we explore a novel strategy of using a hypernetwork to generate the
parameters of a separate reconstruction network as a function of the
regularization weight(s), resulting in a regularization-agnostic reconstruction
model. At test time, for a given under-sampled image, our model can rapidly
compute reconstructions with different amounts of regularization. We analyze
the variability of these reconstructions, especially in situations when the
overall quality is similar. Finally, we propose and empirically demonstrate an
efficient and data-driven way of maximizing reconstruction performance given
limited hypernetwork capacity. Our code is publicly available at
https://github.com/alanqrwang/RegAgnosticCSMRI.


Abstract:
  One of the challenges in a task oriented natural language application like
the Google Assistant, Siri, or Alexa is to localize the output to many
languages. This paper explores doing this by applying machine translation to
the English output. Using machine translation is very scalable, as it can work
with any English output and can handle dynamic text, but otherwise the problem
is a poor fit. The required quality bar is close to perfection, the range of
sentences is extremely narrow, and the sentences are often very different than
the ones in the machine translation training data. This combination of
requirements is novel in the field of domain adaptation for machine
translation. We are able to reach the required quality bar by building on
existing ideas and adding new ones: finetuning on in-domain translations,
adding sentences from the Web, adding semantic annotations, and using automatic
error detection. The paper shares our approach and results, together with a
distillation model to serve the translation models at scale.


Abstract:
  For an ordinal $\alpha$, an $\alpha$-ITRM is a machine model of transfinite
computability that operates on finitely many registers, each of which can
contain an ordinal $\rho<\alpha$; they were introduced by Koepke in \cite{KM}.
  In \cite{alpha itrms}, it was shown that the $\alpha$-ITRM-computable subsets
of $\alpha$ are exactly those in a level $L_{\beta(\alpha)}$ of the
constructible hierarchy. It was conjectured in \cite{alpha itrms} that
$\beta(\alpha)$ is the first limit of admissible ordinals above $\alpha$. Here,
we show that this is false; in particular, even the computational strength of
$\omega^{\omega}$-ITRMs goes far beyond $\omega_{\omega}^{\text{CK}}$.


Abstract:
  Flow over a surface can be stratified by imposing a fixed mean vertical
temperature (density) gradient profile throughout or via cooling at the
surface. These distinct mechanisms can act simultaneously to establish a stable
stratification in a flow. Here, we perform a series of direct numerical
simulations of open-channel flows to study adaptation of a neutrally stratified
turbulent flow under the combined or independent action of the aforementioned
mechanisms. We force the fully developed flow with a constant mass flow rate.
This flow forcing technique enables us to keep the bulk Reynolds number
constant throughout our investigation and avoid complications arising from the
acceleration of the bulk flow when a constant pressure gradient approach were
to be adopted to force the flow instead. When both stratification mechanisms
are active, the dimensionless stratification perturbation number emerges as an
external flow control parameter, in addition to the Reynolds, Froude, and
Prandtl numbers. We demonstrate that significant deviations from the
Monin-Obukhov similarity formulation are possible when both types of
stratification mechanisms are active within an otherwise weakly stable flow,
even when the flux Richardson number is well below 0.2. An extended version of
the similarity theory due to Zilitinkevich and Calanca shows promise in
predicting the dimensionless shear for cases where both types of stratification
mechanisms are active, but the extended theory is less accurate for gradients
of scalar. The degree of deviation from neutral dimensionless shear as a
function of the vertical coordinate emerges as a qualitative measure of the
strength of stable stratification for all the cases investigated in this study.


Abstract:
  Articulatory-to-acoustic (forward) mapping is a technique to predict speech
using various articulatory acquisition techniques as input (e.g. ultrasound
tongue imaging, MRI, lip video). The advantage of lip video is that it is
easily available and affordable: most modern smartphones have a front camera.
There are already a few solutions for lip-to-speech synthesis, but they mostly
concentrate on offline training and inference. In this paper, we propose a
system built from a backend for deep neural network training and inference and
a fronted as a form of a mobile application. Our initial evaluation shows that
the scenario is feasible: a top-5 classification accuracy of 74% is combined
with feedback from the mobile application user, making sure that the speaking
impaired might be able to communicate with this solution.


Abstract:
  We investigate the hypothesis that within a combination of a 'number concept'
plus a 'substantive concept', such as 'eleven animals,' the identity and
indistinguishability present on the level of the concepts, i.e., all eleven
animals are identical and indistinguishable, gives rise to a statistical
structure of the Bose-Einstein type similar to how Bose-Einstein statistics is
present for identical and indistinguishable quantum particles. We proceed by
identifying evidence for this hypothesis by extracting the statistical data
from the World-Wide-Web utilizing the Google Search tool. By using the
Kullback-Leibler divergence method, we then compare the obtained distribution
with the Maxwell-Boltzmann as well as with the Bose-Einstein distributions and
show that the Bose-Einstein's provides a better fit as compared to the
Maxwell-Boltzmanns.


Abstract:
  We present an application of invariant polynomials in machine learning. Using
the methods developed in previous work, we obtain two types of generators of
the Lorentz- and permutation-invariant polynomials in particle momenta; minimal
algebra generators and Hironaka decompositions. We discuss and prove some
approximation theorems to make use of these invariant generators in machine
learning algorithms in general and in neural networks specifically. By
implementing these generators in neural networks applied to regression tasks,
we test the improvements in performance under a wide range of hyperparameter
choices and find a reduction of the loss on training data and a significant
reduction of the loss on validation data. For a different approach on
quantifying the performance of these neural networks, we treat the problem from
a Bayesian inference perspective and employ nested sampling techniques to
perform model comparison. Beyond a certain network size, we find that networks
utilising Hironaka decompositions perform the best.


Abstract:
  Developing state-machine replication protocols for practical use is a complex
and labor-intensive process because of the myriad of essential tasks (e.g.,
deployment, communication, recovery) that need to be taken into account in an
implementation. In this paper, we show how this problem can be addressed with
stream-based replication, a novel approach that implements a replication
protocol as application on top of a data-stream processing framework. With such
framework already handling most essential tasks and furthermore providing means
for debugging and monitoring, this technique has the key benefit of
significantly minimizing overhead for both programmers as well as system
operators. Our first stream-based protocol Tara tolerates crashes and comprises
full-fledged mechanisms for request handling, checkpointing, and view changes.
Still, Tara's prototype implementation, which is based on Twitter's Heron
framework, consists of fewer than 1,500 lines of application-level code.


Abstract:
  Evaluating Software testability can assist software managers in optimizing
testing budgets and identifying opportunities for refactoring. In this paper,
we abandon the traditional approach of pursuing testability measurements based
on the correlation between software metrics and test characteristics observed
on past projects, e.g., the size, the organization or the code coverage of the
test cases. We propose a radically new approach that exploits automatic test
generation and mutation analysis to quantify the amount of evidence about the
relative hardness of identifying effective test cases. We introduce two novel
evidence-based testability metrics, describe a prototype to compute them, and
discuss initial findings on whether our measurements can reflect actual
testability issues.


Abstract:
  The high reflect beamforming gain of the intelligent reflecting surface (IRS)
makes it appealing not only for wireless information transmission but also for
wireless power transfer. In this letter, we consider an IRS-assisted wireless
powered communication network, where a base station (BS) transmits energy to
multiple users grouped into multiple clusters in the downlink, and the
clustered users transmit information to the BS in the manner of hybrid
non-orthogonal multiple access and time division multiple access in the uplink.
We investigate optimizing the reflect beamforming of the IRS and the time
allocation among the BS's power transfer and different user clusters'
information transmission to maximize the throughput of the network, and we
propose an efficient algorithm based on the block coordinate ascent,
semidefinite relaxation, and sequential rank-one constraint relaxation
techniques to solve the resultant problem. Simulation results have verified the
effectiveness of the proposed algorithm and have shown the impact of user
clustering setup on the throughput performance of the network.


Abstract:
  We prove normalization for (univalent, Cartesian) cubical type theory,
closing the last major open problem in the syntactic metatheory of cubical type
theory. Our normalization result is reduction-free, in the sense of yielding a
bijection between equivalence classes of terms in context and a tractable
language of $\beta/\eta$-normal forms. As corollaries we obtain both
decidability of judgmental equality and the injectivity of type constructors.


Abstract:
  This paper presents a simple unsupervised visual representation learning
method with a pretext task of discriminating all images in a dataset using a
parametric, instance-level classifier. The overall framework is a replica of a
supervised classification model, where semantic classes (e.g., dog, bird, and
ship) are replaced by instance IDs. However, scaling up the classification task
from thousands of semantic labels to millions of instance labels brings
specific challenges including 1) the large-scale softmax computation; 2) the
slow convergence due to the infrequent visiting of instance samples; and 3) the
massive number of negative classes that can be noisy. This work presents
several novel techniques to handle these difficulties. First, we introduce a
hybrid parallel training framework to make large-scale training feasible.
Second, we present a raw-feature initialization mechanism for classification
weights, which we assume offers a contrastive prior for instance discrimination
and can clearly speed up converge in our experiments. Finally, we propose to
smooth the labels of a few hardest classes to avoid optimizing over very
similar negative pairs. While being conceptually simple, our framework achieves
competitive or superior performance compared to state-of-the-art unsupervised
approaches, i.e., SimCLR, MoCoV2, and PIC under ImageNet linear evaluation
protocol and on several downstream visual tasks, verifying that full instance
classification is a strong pretraining technique for many semantic visual
tasks.


Abstract:
  Oxygen defective cerium oxides exhibits a non classical giant
electromechanical response that is superior to lead based electrostrictors. In
this work, we report the key role of acceptor dopants, with different size and
valence Mg2+, Sc3+, Gd3+, and La3+, on polycrystalline bulk ceria. Different
dopants tune the electrostrictive properties by changing the electrosteric
dopant defect interactions. We find two distinct electromechanical behaviors
when the interaction is weak dopant vacancy binding energy 0.3 eV,
electrostriction displays high coefficient, up to 10-17 m2V-2, with strongly
time dependent effects. In contrast, we observe no time dependent effects when
the interaction becomes strong 0.6 eV.


Abstract:
  We performed photometric and spectroscopic investigations of NSVS 5029961 for
the first time. The new BV(RI)$_c$-band light curves were obtained with the
1.0-m telescope at Weihai Observatory of Shandong University. Applying the
Wilson-Devinney program, we found that NSVS 5029961 is an A-subtype shallow
contact binary with extremely low mass ratio (q = 0.1515, f = 19.1\%). Six
spectra have been obtained by LAMOST, and many chromospheric activity emission
line indicators were detected in the spectra, revealing that the target
exhibits strong chromospheric activity. We calculated the absolute parameters
with the photometric solutions and Gaia distance, and estimated the initial
masses of the two components and the age of the binary. The evolutionary status
was discussed by using the mass-radius and mass-luminosity diagrams. The result
shows the primary component is a little evolved star and the secondary
component has evolved away from the main sequence. The formation and evolution
investigations of NSVS 5029661 indicate that it may have evolved from a
detached binary with short period and low mass ratio by angular momentum loss
via magnetic braking and case A mass transfer, and is in a stable contact stage
at present.


Abstract:
  Federated learning is an emerging research paradigm for enabling
collaboratively training deep learning models without sharing patient data.
However, the data from different institutions are usually heterogeneous across
institutions, which may reduce the performance of models trained using
federated learning. In this study, we propose a novel heterogeneity-aware
federated learning method, SplitAVG, to overcome the performance drops from
data heterogeneity in federated learning. Unlike previous federated methods
that require complex heuristic training or hyper parameter tuning, our SplitAVG
leverages the simple network split and feature map concatenation strategies to
encourage the federated model training an unbiased estimator of the target data
distribution. We compare SplitAVG with seven state-of-the-art federated
learning methods, using centrally hosted training data as the baseline on a
suite of both synthetic and real-world federated datasets. We find that the
performance of models trained using all the comparison federated learning
methods degraded significantly with the increasing degrees of data
heterogeneity. In contrast, SplitAVG method achieves comparable results to the
baseline method under all heterogeneous settings, that it achieves 96.2% of the
accuracy and 110.4% of the mean absolute error obtained by the baseline in a
diabetic retinopathy binary classification dataset and a bone age prediction
dataset, respectively, on highly heterogeneous data partitions. We conclude
that SplitAVG method can effectively overcome the performance drops from
variability in data distributions across institutions. Experimental results
also show that SplitAVG can be adapted to different base networks and
generalized to various types of medical imaging tasks.


Abstract:
  We investigate the use of programmable optical lattices for quantum
simulation of Hubbard models, determining analytic expressions for the hopping
and Hubbard U, finding that they are suitable for emulating strongly correlated
systems with arbitrary structures, including those with multiple site basis and
impurities. Programmable potentials are highly flexible, with the ability to
control the depth and shape of individual sites in the optical lattice
dynamically. Quantum simulators of Hubbard models with (1) arbitrary basis are
required to represent many real materials of contemporary interest, (2) broken
translational symmetry are needed to study impurity physics, and (3) dynamical
lattices are needed to investigate strong correlation out of equilibrium. We
derive analytic expressions for Hubbard Hamiltonians in programmable potential
systems. We find experimental parameters for quantum simulation of Hubbard
models with arbitrary basis, concluding that programmable optical lattices are
suitable for this purpose. We discuss how programmable optical lattices can be
used for quantum simulation of dynamical multi-band Hubbard models that
represent complicated compounds, impurities, and non-equilibrium physics.


Abstract:
  The superconducting TMD 4Hb-TaS$_2$ consists of alternating layers of H and T
structures, which in their bulk form are metallic and Mott-insulating,
respectively. Recently, this compound has been proposed as a candidate chiral
superconductor, due to an observed enhancement of the muon spin relaxation at
$T_c$. 4Hb-TaS$_2$ also exhibits a puzzling $T$-linear specific heat at low
temperatures, which is unlikely to be caused by disorder. Elucidating the
origin of this behavior is an essential step in discerning the true nature of
the superconducting ground state. Here, we propose a simple model that
attributes the $T$-linear specific heat to the emergence of a robust multi-band
gapless superconducting state. We show that an extended regime of gapless
superconductivity naturally appears when the pair-breaking scattering rate on
distinct Fermi-surface pockets differs significantly, and the pairing
interaction is predominantly intra-pocket. Using a tight-binding model derived
from first-principle calculations, we show that the pair-breaking scattering
rate promoted by slow magnetic fluctuations on the T layers, which arise from
proximity to a Mott transition, can be significantly different in the various
H-layer dominated Fermi pockets depending on their hybridization with T-layer
states. Thus, our results suggest that the ground state of 4Hb-TaS$_2$ consists
of Fermi pockets displaying gapless superconductivity, which are shunted by
superconducting Fermi pockets that are nearly decoupled from the T-layers.


Abstract:
  Boosting Trees are one of the most successful statistical learning approaches
that involve sequentially growing an ensemble of simple regression trees (i.e.,
"weak learners"). However, gradient boosted trees are not yet available for
spatially correlated data. This paper proposes a new gradient Boosted Trees
algorithm for Spatial Data (Boost-S) with covariate information. Boost-S
integrates the spatial correlation structure into the classical framework of
gradient boosted trees. Each tree is grown by solving a regularized
optimization problem, where the objective function involves two penalty terms
on tree complexity and takes into account the underlying spatial correlation. A
computationally-efficient algorithm is proposed to obtain the ensemble trees.
The proposed Boost-S is applied to the spatially-correlated FDG-PET
(fluorodeoxyglucose-positron emission tomography) imaging data collected during
cancer chemoradiotherapy. Our numerical investigations successfully demonstrate
the advantages of the proposed Boost-S over existing approaches for this
particular application.


Abstract:
  We prove that strength and slice rank of homogeneous polynomials of degree $d
\geq 5$ over an algebraically closed field of characteristic zero coincide
generically. To show this, we establish a conjecture of Catalisano, Geramita,
Gimigliano, Harbourne, Migliore, Nagel and Shin concerning dimensions of secant
varieties of the varieties of reducible homogeneous polynomials. These
statements were already known in degrees $2\leq d\leq 7$ and $d=9$.


Abstract:
  Burr and Erd\H{o}s in 1975 conjectured, and Chv\'atal, R\"odl, Szemer\'edi
and Trotter later proved, that the Ramsey number of any bounded degree graph is
linear in the number of vertices. In this paper, we disprove the natural
directed analogue of the Burr--Erd\H{o}s conjecture, answering a question of
Buci\'c, Letzter, and Sudakov. If $H$ is an acyclic digraph, the oriented
Ramsey number of $H$, denoted $\overrightarrow{r_{1}}(H)$, is the least $N$
such that every tournament on $N$ vertices contains a copy of $H$. We show that
for any $\Delta \geq 2$ and any sufficiently large $n$, there exists an acyclic
digraph $H$ with $n$ vertices and maximum degree $\Delta$ such that \[
\overrightarrow{r_{1}}(H)\ge n^{\Omega(\Delta^{2/3}/ \log^{5/3} \Delta)}. \]
This proves that $\overrightarrow{r_{1}}(H)$ is not always linear in the number
of vertices for bounded-degree $H$. On the other hand, we show that
$\overrightarrow{r_{1}}(H)$ is nearly linear in the number of vertices for
typical bounded-degree acyclic digraphs $H$, and obtain linear or nearly linear
bounds for several natural families of bounded-degree acyclic digraphs.
  For multiple colors, we prove a quasipolynomial upper bound
$\overrightarrow{r_{k}}(H)=2^{(\log n)^{O_{k}(1)}}$ for all bounded-degree
acyclic digraphs $H$ on $n$ vertices, where $\overrightarrow{r_k}(H)$ is the
least $N$ such that every $k$-edge-colored tournament on $N$ vertices contains
a monochromatic copy of $H$. For $k\geq 2$ and $n\geq 4$, we exhibit an acyclic
digraph $H$ with $n$ vertices and maximum degree $3$ such that
$\overrightarrow{r_{k}}(H)\ge n^{\Omega(\log n/\log\log n)}$, showing that
these Ramsey numbers can grow faster than any polynomial in the number of
vertices.


Abstract:
  We explore data reduction and correction steps and processed data
reproducibility in the emerging single crystal total scattering based technique
of three-dimensional differential atomic pair distribution function
(3D-$\Delta$PDF) analysis. All steps from sample measurement to data-processing
are outlined in detail using a CuIr$_2$S$_4$ example crystal studied in a setup
equipped with a high-energy x-ray beam and a flat panel area detector.
Computational overhead as it pertains to data-sampling and the associated data
processing steps is also discussed. Various aspects of the final 3D-$\Delta$PDF
reproducibility are explicitly tested by varying data-processing order and
included steps, and by carrying out a crystal-to-crystal data comparison. We
identify situations in which the 3D-$\Delta$PDF is robust, and caution against
a few particular cases which can lead to inconsistent 3D-$\Delta$PDFs. Although
not all the approaches applied here-in will be valid across all systems, and a
more in-depth analysis of some of the effects of the data processing steps may
still needed, the methods collected here-in represent the start of a more
systematic discussion about data processing and corrections in this field.


Abstract:
  Machine learning models that offer excellent predictive performance often
lack the interpretability necessary to support integrated human machine
decision-making. In clinical medicine and other high-risk settings, domain
experts may be unwilling to trust model predictions without explanations. Work
in explainable AI must balance competing objectives along two different axes:
1) Explanations must balance faithfulness to the model's decision-making with
their plausibility to a domain expert. 2) Domain experts desire local
explanations of individual predictions and global explanations of behavior in
aggregate. We propose to train a proxy model that mimics the behavior of the
trained model and provides fine-grained control over these trade-offs. We
evaluate our approach on the task of assigning ICD codes to clinical notes to
demonstrate that explanations from the proxy model are faithful and replicate
the trained model behavior.


Abstract:
  We obtain a Fourier dimension estimate for sets of exact approximation order
introduced by Bugeaud for certain approximation functions $\psi$. This Fourier
dimension estimate implies that these sets of exact approximation order contain
normal numbers.


Abstract:
  There is growing interest in ASR systems that can recognize phones in a
language-independent fashion. There is additionally interest in building
language technologies for low-resource and endangered languages. However, there
is a paucity of realistic data that can be used to test such systems and
technologies. This paper presents a publicly available, phonetically
transcribed corpus of 2255 utterances (words and short phrases) in the
endangered Tangkhulic language East Tusom (no ISO 639-3 code), a Tibeto-Burman
language variety spoken mostly in India. Because the dataset is transcribed in
terms of phones, rather than phonemes, it is a better match for universal phone
recognition systems than many larger (phonemically transcribed) datasets. This
paper describes the dataset and the methodology used to produce it. It further
presents basic benchmarks of state-of-the-art universal phone recognition
systems on the dataset as baselines for future experiments.


Abstract:
  We proposed a simple and efficient modular single-source surface integral
equation (SS-SIE) formulation for electromagnetic analysis of arbitrarily
connected penetrable and perfectly electrical conductor (PEC) objects in
two-dimensional space. In this formulation, a modular equivalent model for each
penetrable object consisting of the composite structure is first independently
constructed through replacing it by the background medium, no matter whether it
is surrounded by the background medium, other media, or partially connected
objects, and enforcing an equivalent electric current density on the boundary
to remain fields in the exterior region unchanged. Then, by combining all the
modular models and any possible PEC objects together, an equivalent model for
the composite structure can be derived. The troublesome junction handling
techniques are not needed and non-conformal meshes are intrinsically supported.
The proposed SS-SIE formulation is simple to implement, efficient, and
flexible, which shows significant performance improvement in terms of CPU time
compared with the original SS-SIE formulation and the
Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) formulation. Several numerical
examples including the coated dielectric cuboid, the large lossy objects, the
planar layered dielectric structure, and the partially connected dielectric and
PEC structure are carried out to validate its accuracy, efficiency and
robustness.


Abstract:
  This research quantitatively and qualitatively analyzes the factors
responsible for the water level variations in Lake Toba, North Sumatra
Province, Indonesia. According to several studies carried out from 1993 to
2020, changes in the water level were associated with climate variability,
climate change, and human activities. Furthermore, these studies stated that
reduced rainfall during the rainy season due to the El Nino Southern
Oscillation (ENSO) and the continuous increase in the maximum and average
temperatures were some of the effects of climate change in the Lake Toba
catchment area. Additionally, human interventions such as industrial
activities, population growth, and damage to the surrounding environment of the
Lake Toba watershed had significant impacts in terms of decreasing the water
level. However, these studies were unable to determine the factor that had the
most significant effect, although studies on other lakes worldwide have shown
these factors are the main causes of fluctuations or decreases in water levels.
A simulation study of Lake Toba's water balance showed the possibility of
having a water surplus until the mid-twenty-first century. The input discharge
was predicted to be greater than the output; therefore, Lake Toba could be
optimized without affecting the future water level. However, the climate
projections depicted a different situation, with scenarios predicting the
possibility of extreme climate anomalies, demonstrating drier climatic
conditions in the future. This review concludes that it is necessary to conduct
an in-depth, comprehensive, and systematic study to identify the most dominant
factor among the three that is causing the decrease in the Lake Toba water
level and to describe the future projected water level.


Abstract:
  This paper presents the CUHK-EE voice cloning system for ICASSP 2021 M2VoC
challenge. The challenge provides two Mandarin speech corpora: the AIShell-3
corpus of 218 speakers with noise and reverberation and the MST corpus
including high-quality speech of one male and one female speakers. 100 and 5
utterances of 3 target speakers in different voice and style are provided in
track 1 and 2 respectively, and the participants are required to synthesize
speech in target speaker's voice and style. We take part in the track 1 and
carry out voice cloning based on 100 utterances of target speakers. An
end-to-end voicing cloning system is developed to accomplish the task, which
includes: 1. a text and speech front-end module with the help of forced
alignment, 2. an acoustic model combining Tacotron2 and DurIAN to predict
melspectrogram, 3. a Hifigan vocoder for waveform generation. Our system
comprises three stages: multi-speaker training stage, target speaker adaption
stage and target speaker synthesis stage. Our team is identified as T17. The
subjective evaluation results provided by the challenge organizer demonstrate
the effectiveness of our system. Audio samples are available at our demo page:
https://daxintan-cuhk.github.io/CUHK-EE-system-M2VoC-challenge/ .


Abstract:
  The use of cumulative incidence functions for characterizing the risk of one
type of event in the presence of others has become increasingly popular over
the past decade. The problems of modeling, estimation and inference have been
treated using parametric, nonparametric and semi-parametric methods. Efforts to
develop suitable extensions of machine learning methods, such as regression
trees and related ensemble methods, have begun comparatively recently. In this
paper, we propose a novel approach to estimating cumulative incidence curves in
a competing risks setting using regression trees and associated ensemble
estimators. The proposed methods employ augmented estimators of the Brier score
risk as the primary basis for building and pruning trees, and lead to methods
that are easily implemented using existing R packages. Data from the Radiation
Therapy Oncology Group (trial 9410) is used to illustrate these new methods.


Abstract:
  The two years experience with Active Metal Casting flat bonds shows that this
technology is suitable for the heat fluxes expected in Tore Supra (10
MW/m${}^2$). Tests were pursued up to 3330 cycles, with elements still
functional. At higher heat fluxes, fatigue damage is observed, but the bond
resists remarkably well with no tile detachment. Examination of such
deliberately damaged bonds showed distributed cracking, proving the absence of
any weak link. The limitations to those higher heat fluxes are more related to
the design and the base materials than to the bond itself.


Abstract:
  The class of gross substitutes (GS) set functions plays a central role in
Economics and Computer Science. GS belongs to the hierarchy of {\em complement
free} valuations introduced by Lehmann, Lehmann and Nisan, along with other
prominent classes: $GS \subsetneq Submodular \subsetneq XOS \subsetneq
Subadditive$. The GS class has always been more enigmatic than its counterpart
classes, both in its definition and in its relation to the other classes. For
example, while it is well understood how closely the Submodular, XOS and
Subadditive classes (point-wise) approximate one another, approximability of
these classes by GS remained wide open.
  Our main result is the existence of a submodular valuation (one that is also
budget additive) that cannot be approximated by GS within a ratio better than
$\Omega(\frac{\log m}{\log\log m})$, where $m$ is the number of items. En
route, we uncover a new symmetrization operation that preserves GS, which may
be of independent interest.
  We show that our main result is tight with respect to budget additive
valuations. Additionally, for a class of submodular functions that we refer to
as {\em concave of Rado} valuations (this class greatly extends budget additive
valuations), we show approximability by GS within an $O(\log^2m)$ factor.


Abstract:
Two-dimensional infrared spectroscopy experiments have presented new results
regarding the dynamics of the hydrated excess proton (aka <q>hydronium</q>
cation solvated in water). It has been suggested by these experiments that the
hydrated excess proton has an anisotropy reorientation timescale of 2.5 ps,
which can be viewed as being somewhat long lived. Through the use of both the
reactive molecular dynamics Multistate-Empirical Valence Bond method and
Experiment Directed Simulation Ab Initio Molecular Dynamics we show that
timescales of the same magnitude are obtained that correspond to proton
transport, while also involving structural reorientations of the hydrated
proton structure that correspond to the so-called <q>special pair dance</q>.
The latter is a process predicted by prior computational studies in which the
central hydrated hydronium in a distorted Eigen cation
(H9O4+) structure continually switches special
pair partners with its strongly hydrogen-bonded neighboring water molecules.
These dynamics are further characterized through the time-evolution of
instantaneous normal modes. It is concluded that the hydrated excess proton has
a spectral signature unique from the other protons in the hydrated proton
complex. However, the results call into question the use of a static picture
based on a simple effective one dimensional potential well to describe the
hydrated excess proton in water. Instead, they more conclusively point to a
distorted and dynamic Eigen cation as the most prevalent hydrated proton
species in acid solutions of dilute to moderate concentrations.


Abstract:
  We study solutions to Nahm's equations with continuous symmetries and, under
certain (mild) hypotheses, we classify the corresponding Ans\"atze. Using our
classification, we construct novel Nahm data, and prescribe methods for
generating further solutions. Finally, we use these results to construct new
BPS monopoles with spherical symmetry.


Abstract:
  This paper proposes a computational technique based on "deep unfolding" to
solving the finite-time maximum hands-off control problem for discrete-time
nonlinear stochastic systems. In particular, we seek a sparse control input
sequence that stabilizes the system such that the expected value of the square
of the final states is small by training a deep neural network. The proposed
technique is demonstrated by a numerical experiment.


Abstract:
  The data paper is an emerging academic genre that focuses on the description
of research data objects. However, there is a lack of empirical knowledge about
this rising genre in quantitative science studies, particularly from the
perspective of its linguistic features. To fill this gap, this research aims to
offer a first quantitative examination of which rhetorical moves-rhetorical
units performing a coherent narrative function-are used in data paper
abstracts, as well as how these moves are used. To this end, we developed a new
classification scheme for rhetorical moves in data paper abstracts by expanding
a well-received system that focuses on English-language research article
abstracts. We used this expanded scheme to classify and analyze rhetorical
moves used in two flagship data journals, Scientific Data and Data in Brief. We
found that data papers exhibit a combination of IMRaD- and data-oriented moves
and that the usage differences between the journals can be largely explained by
journal policies concerning abstract and paper structure. This research offers
a novel examination of how the data paper, a novel data-oriented knowledge
representation, is composed, which greatly contributes to a deeper
understanding of data and data publication in the scholarly communication
system.


Abstract:
  We discuss tensor categories motivated by CFT, their unitarizability and
applications to various models including the affine VOAs. We discuss
classification of type A Verlinde fusion categories.
  We propose an approach to Kazhdan-Lusztig-Finkelberg theorem. This theorem
gives a ribbon equivalence between the fusion category associated to a quantum
group at a certain root of unity and that associated to a corresponding affine
vertex operator algebra at a suitable positive integer level. We develop ideas
by Wenzl.
  Our results rely on the notion of weak-quasi-Hopf algebra of
  Drinfeld-Mack-Schomerus. We were also guided by Drinfeld original proof, by
  Bakalov and Kirillov and Neshveyev and Tuset work for a generic parameter.
  Wenzl described a fusion tensor product in quantum group fusion categories,
and related it to the unitary structure. Given two irreducible objects, the
inner product of the fusion tensor product is induced by the braiding of
U_q(g), with q a suitable root of 1. Moreover, the paper suggests a suitable
untwisting procedure to make the unitary structure trivial. Then it also
describes a continuous path that intuitively connects objects of the quantum
group fusion category to representations of the simple Lie group defining the
affine Lie algebra. We study this procedure.
  One of our main results is the construction of a Hopf algebra in a weak sense
associated to quantum group fusion category and of a twist of it giving a wqh
structure on the Zhu algebra and a unitary modular fusion category structure on
the representation category of the affine Lie algebra, confirming an early view
by Frenkel and Zhu.
  We show that this modular fusion category structure is equivalent to that
obtained via the tensor product theory of VOAs by Huang and Lepowsky. This
gives a direct proof of FKL theorem.


Abstract:
  We present spatially resolved Hubble Space Telescope grism spectroscopy of 15
galaxies at $z\sim0.8$ drawn from the DEEP2 survey. We analyze H$\alpha$+[N
II], [S II] and [S III] emission on kpc scales to explore which mechanisms are
powering emission lines at high redshifts, testing which processes may be
responsible for the well-known offset of high redshift galaxies from the
$z\sim0$ locus in the [O III]/H$\beta$ versus [N II]/H$\alpha$ BPT
(Baldwin-Phillips-Terlevich) excitation diagram. We study spatially resolved
emission line maps to examine evidence for active galactic nuclei (AGN),
shocks, diffuse ionized gas (DIG), or escaping ionizing radiation, all of which
may contribute to the BPT offsets observed in our sample. We do not find
significant evidence of AGN in our sample and quantify that, on average, AGN
would need to contribute $\sim$25% of the H$\alpha$ flux in the central
resolution element in order to cause the observed BPT offsets. We find weak
($2\sigma$) evidence of DIG emission at low surface brightnesses, yielding an
implied total DIG emission fraction of $\sim$20%, which is not significant
enough to be the dominant emission line driver in our sample. In general we
find that the observed emission is dominated by star forming H II regions. We
discuss trends with demographic properties and the possible role of
$\alpha$-enhanced abundance patterns in the emission spectra of high redshift
galaxies. Our results indicate that photo-ionization modeling with stellar
population synthesis inputs is a valid tool to explore the specific star
formation properties which may cause BPT offsets, to be explored in future
work.


Abstract:
  We present properties and invariants of Hamiltonian circuits in rectangular
grids. It is proved that all circuits on a $2n \times 2n$ chessboard have at
least $4n$ turns and at least $2n$ straights if $n$ is even and $2n+2$
straights if $n$ is odd. The minimum number of turns and straights are
presented and proved for circuits on an $n \times (n+1)$ chessboard. For the
general case of an $n \times m$ chessboard similar results are stated but not
all proofs are given.


Abstract:
  In this paper, we propose a new approach to train Generative Adversarial
Networks (GANs) where we deploy a double-oracle framework using the generator
and discriminator oracles. GAN is essentially a two-player zero-sum game
between the generator and the discriminator. Training GANs is challenging as a
pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium
is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend
the double oracle framework to GANs. We first generalize the players'
strategies as the trained models of generator and discriminator from the best
response oracles. We then compute the meta-strategies using a linear program.
For scalability of the framework where multiple generators and discriminator
best responses are stored in the memory, we propose two solutions: 1) pruning
the weakly-dominated players' strategies to keep the oracles from becoming
intractable; 2) applying continual learning to retain the previous knowledge of
the networks. We apply our framework to established GAN architectures such as
vanilla GAN, Deep Convolutional GAN, Spectral Normalization GAN and Stacked
GAN. Finally, we conduct experiments on MNIST, CIFAR-10 and CelebA datasets and
show that DO-GAN variants have significant improvements in both subjective
qualitative evaluation and quantitative metrics, compared with their respective
GAN architectures.


Abstract:
  In-band full-duplex systems promise to further increase the throughput of
wireless systems, by simultaneously transmitting and receiving on the same
frequency band. However, concurrent transmission generates a strong
self-interference signal at the receiver, which requires the use of
cancellation techniques. A wide range of techniques for analog and digital
self-interference cancellation have already been presented in the literature.
However, their evaluation focuses on cases where the underlying physical
parameters of the full-duplex system do not vary significantly. In this paper,
we focus on adaptive digital cancellation, motivated by the fact that physical
systems change over time. We examine some of the different cancellation methods
in terms of their performance and implementation complexity, considering the
cost of both cancellation and training. We then present a comparative analysis
of all these methods to determine which perform better under different system
performance requirements. We demonstrate that with a neural network approach,
the reduction in arithmetic complexity for the same cancellation performance
relative to a state-of-the-art polynomial model is several orders of magnitude.


Abstract:
  The subject of this paper is to study the decay of solutions for two systems
of laminated Timoshenko beams with interfacial slip in the whole space R
subject to a thermal effect of type III acting only on one component. When the
thermal effect is acting via the second or third component of the laminated
Timoshenko beam (rotation angle displacement or dynamic of the slip), we prove
that both systems are polynomially stable and obtain stability estimates in the
L2 -norm of solutions and their higher order derivatives with respect of the
space variable. The decay rates, as well as the absence and presence of the
regularity-loss type property, depend on the regularity of the initial data and
the speeds of wave propagations. However, when the thermal effect is acting via
the first comoponent (transversal displacement), we introduce a new stability
number \c{hi} and prove that the stability of systems is equivalent to \c{hi}
6= 0. An application to a case of lower order coupling terms will be also
given. To prove our results, we use the energy method in Fourier space combined
with well chosen weight functions to build appropriate Lyapunov functionals.


Abstract:
  Evolution of 3D graphics and graphical worlds has brought issues like content
optimization, real-time processing, rendering, and shared storage limitation
under consideration. Generally, different simplification approaches are used to
make 3D meshes viable for rendering. However, many of these approaches ignore
vertex attributes for instanced 3D meshes. In this paper, we implement and
evaluate a simple and improved version to simplify instanced 3D textured
models. The approach uses different vertex attributes in addition to geometry
to simplify mesh instances. The resulting simplified models demonstrate
efficient time-space requirements and better visual quality.


Abstract:
  Composite fermions (CFs) are the particles underlying the novel phenomena
observed in partially filled Landau levels. Both microscopic wave functions and
semi-classical dynamics suggest that a CF is a dipole consisting of an electron
and a double $2h/e$ quantum vortex, and its motion is subject to a Berry
curvature that is uniformly distributed in the momentum space. Based on the
picture, we study the electromagnetic response of composite fermions. We find
that the response in the long-wavelength limit has a form identical to that of
the Dirac CF theory. To obtain the result, we show that the Berry curvature
contributes a half-quantized Hall conductance which, notably, is independent of
the filling factor of a Landau level and not altered by the presence of
impurities. The latter is because CFs undergo no side-jumps when scattered by
quenched impurities in a Landau-level with the particle-hole symmetry. The
remainder of the response is from an effective system that has the same Fermi
wavevector, effective density, Berry phase, and therefore long-wavelength
response to electromagnetic fields as a Dirac CF system. By interpreting the
half-quantized Hall conductance as a contribution from a redefined vacuum, we
can explicitly show the emergence of a Dirac CF effective description from the
dipole picture. We further determine corrections due to electric quadrupoles
and magnetic moments of CFs and show deviations from the Dirac CF theory when
moving away from the long wavelength limit.


Abstract:
  Temporal action localization is an important yet challenging task in video
understanding. Typically, such a task aims at inferring both the action
category and localization of the start and end frame for each action instance
in a long, untrimmed video.While most current models achieve good results by
using pre-defined anchors and numerous actionness, such methods could be
bothered with both large number of outputs and heavy tuning of locations and
sizes corresponding to different anchors. Instead, anchor-free methods is
lighter, getting rid of redundant hyper-parameters, but gains few attention. In
this paper, we propose the first purely anchor-free temporal localization
method, which is both efficient and effective. Our model includes (i) an
end-to-end trainable basic predictor, (ii) a saliency-based refinement module
to gather more valuable boundary features for each proposal with a novel
boundary pooling, and (iii) several consistency constraints to make sure our
model can find the accurate boundary given arbitrary proposals. Extensive
experiments show that our method beats all anchor-based and actionness-guided
methods with a remarkable margin on THUMOS14, achieving state-of-the-art
results, and comparable ones on ActivityNet v1.3. Code is available at
https://github.com/TencentYoutuResearch/ActionDetection-AFSD.


Abstract:
  We continue the study of rateless codes for transmission of information
across channels whose rate of erasure is unknown. In such a code, an infinite
stream of encoding symbols can be generated from the message and sent across
the erasure channel, and the decoder can decode the message after it has
successfully collected a certain number of encoding symbols. A rateless erasure
code is real-time oblivious if rather than collecting encoding symbols as they
are received, the receiver either immediately decodes or discards each symbol
it receives.
  Efficient real-time oblivious erasure correction uses a feedback channel in
order to maximize the probability that a received encoding symbol is decoded
rather than discarded. We construct codes which are real-time oblivious, but
require fewer feedback messages and have faster decoding compared to previous
work. Specifically, for a message of length $k'$, we improve the expected
complexity of the feedback channel from $O(\sqrt{k'})$ to $O(1)$, and the
expected decoding complexity from $O(k'\log(k'))$ to $O(k')$. Our method
involves using an appropriate block erasure code to first encode the $k'$
message symbols, and then using a truncated version of the real-time oblivious
erasure correction of Beimel et al (2007) to transmit the encoded message to
the receiver, which then uses the decoding algorithm for the outer code to
recover the message.


Abstract:
  Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the
tasks of sentiment and emotion, as well as physiological-emotion and
emotion-based stress recognition through more comprehensively integrating the
audio-visual, language, and biological signal modalities. The purpose of MuSe
2021 is to bring together communities from different disciplines; mainly, the
audio-visual emotion recognition community (signal-based), the sentiment
analysis community (symbol-based), and the health informatics community. We
present four distinct sub-challenges: MuSe-Wilder and MuSe-Stress which focus
on continuous emotion (valence and arousal) prediction; MuSe-Sent, in which
participants recognise five classes each for valence and arousal; and
MuSe-Physio, in which the novel aspect of `physiological-emotion' is to be
predicted. For this years' challenge, we utilise the MuSe-CaR dataset focusing
on user-generated reviews and introduce the Ulm-TSST dataset, which displays
people in stressful depositions. This paper also provides detail on the
state-of-the-art feature sets extracted from these datasets for utilisation by
our baseline model, a Long Short-Term Memory-Recurrent Neural Network. For each
sub-challenge, a competitive baseline for participants is set; namely, on test,
we report a Concordance Correlation Coefficient (CCC) of .4616 CCC for
MuSe-Wilder; .4717 CCC for MuSe-Stress, and .4606 CCC for MuSe-Physio. For
MuSe-Sent an F1 score of 32.82 % is obtained.


Abstract:
  GRB 190114C was a bright burst that occurred in the local Universe (z=0.425).
It was the first gamma-ray burst (GRB) ever detected at TeV energies, thanks to
MAGIC. We characterize the ambient medium properties of the host galaxy through
the study of the absorbing X-ray column density. Joining Swift, XMM-Newton, and
NuSTAR observations, we find that the GRB X-ray spectrum is characterized by a
high column density that is well in excess of the expected Milky Way value and
decreases, by a factor of ~2, around ~$10^5$ s. Such a variability is not
common in GRBs. The most straightforward interpretation of the variability in
terms of photoionization of the ambient medium is not able to account for the
decrease at such late times, when the source flux is less intense. Instead, we
interpret the decrease as due to a clumped absorber, denser along the line of
sight and surrounded by lower-density gas. After the detection at TeV energies
of GRB 190114C, two other GRBs were promptly detected. They share a high value
of the intrinsic column density and there are hints for a decrease of the
column density, too. We speculate that a high local column density might be a
common ingredient for TeV-detected GRBs.


Abstract:
  Many commonsense reasoning NLP tasks involve choosing between one or more
possible answers to a question or prompt based on knowledge that is often
implicit. Large pretrained language models (PLMs) can achieve near-human
performance on such tasks, while providing little human-interpretable evidence
of the underlying reasoning they use. In this work, we show how to use these
same models to generate such evidence: inspired by the contrastive nature of
human explanations, we use PLMs to complete explanation prompts which contrast
alternatives according to the key attribute(s) required to justify the correct
answer (for example, peanuts are usually salty while raisins are sweet).
Conditioning model decisions on these explanations improves performance on two
commonsense reasoning benchmarks, as compared to previous non-contrastive
alternatives. These explanations are also judged by humans to be more relevant
for solving the task, and facilitate a novel method to evaluate explanation
faithfulfness.


Abstract:
  Motivated by dark coronal lanes in SOHO / EIT 284 {\AA} EUV observations we
construct and optimize an atmosphere model of the AR 8535 sunspot by adding a
cool and dense component in the volume of plasma along open field lines
determined using the Potential Field Source Surface (PFSS) extrapolation. Our
model qualitatively reproduces the observed reduced microwave brightness
temperature in the northern part of the sunspot in the VLA observations from 13
May 1999 and provides a physical explanation for the coronal dark lanes. We
propose application of this method to other sunspots with such observed dark
regions in EUV or soft X-rays and with concurrent microwave observations to
determine the significance of open field regions. The connection between open
fields and the resulting plasma temperature and density change is of relevance
for slow solar wind source investigations.


Abstract:
  Auxiliary information attracts more and more attention in the area of machine
learning. Attempts so far to include such auxiliary information in
state-of-the-art learning process have often been based on simply appending
these auxiliary features to the data level or feature level. In this paper, we
intend to propose a novel training method with new options and architectures.
Siamese labels, which were used in the training phase as auxiliary modules.
While in the testing phase, the auxiliary module should be removed. Siamese
label module makes it easier to train and improves the performance in testing
process. In general, the main contributions can be summarized as, 1) Siamese
Labels are firstly proposed as auxiliary information to improve the learning
efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary
Network (SilaNet), which is to assist the training of the model; 3) Siamese
Labels Auxiliary Network is applied to compress the model parameters by 50% and
ensure the high accuracy at the same time. For the purpose of comparison, we
tested the network on CIFAR-10 and CIFAR100 using some common models. The
proposed SilaNet performs excellent efficiency both on the accuracy and
robustness.


Abstract:
  In classical newsvendor model, piece-wise linear shortage and excess costs
are balanced out to determine the optimal order quantity. However, for critical
perishable commodities, severity of the costs may be much more than linear. In
this paper we discuss a generalisation of the newsvendor model with piece-wise
polynomial cost functions to accommodate their severity. In addition, the
stochastic demand has been assumed to follow a completely unknown probability
distribution. Subsequently, non-parametric estimator of the optimal order
quantity has been developed from a random polynomial type estimating equation
using a random sample on demand. Strong consistency of the estimator has been
proven when the true optimal order quantity is unique. The result has been
extended to the case where multiple solutions for optimal order quantity are
available. Probability of existence of the estimated optimal order quantity has
been studied through extensive simulation experiments. Simulation results
indicate that the non-parametric method provides robust yet efficient estimator
of the optimal order quantity in terms of mean square error.


Abstract:
  Backdoor attacks have been considered a severe security threat to deep
learning. Such attacks can make models perform abnormally on inputs with
predefined triggers and still retain state-of-the-art performance on clean
data. While backdoor attacks have been thoroughly investigated in the image
domain from both attackers' and defenders' sides, an analysis in the frequency
domain has been missing thus far.
  This paper first revisits existing backdoor triggers from a frequency
perspective and performs a comprehensive analysis. Our results show that many
current backdoor attacks exhibit severe high-frequency artifacts, which persist
across different datasets and resolutions. We further demonstrate these
high-frequency artifacts enable a simple way to detect existing backdoor
triggers at a detection rate of 98.50% without prior knowledge of the attack
details and the target model. Acknowledging previous attacks' weaknesses, we
propose a practical way to create smooth backdoor triggers without
high-frequency artifacts and study their detectability. We show that existing
defense works can benefit by incorporating these smooth triggers into their
design consideration. Moreover, we show that the detector tuned over stronger
smooth triggers can generalize well to unseen weak smooth triggers. In short,
our work emphasizes the importance of considering frequency analysis when
designing both backdoor attacks and defenses in deep learning.


Abstract:
  Machine learning models for the ad-hoc retrieval of documents and passages
have recently shown impressive improvements due to better language
understanding using large pre-trained language models. However, these
over-parameterized models are inherently non-interpretable and do not provide
any information on the parts of the documents that were used to arrive at a
certain prediction.
  In this paper we introduce the select and rank paradigm for document ranking,
where interpretability is explicitly ensured when scoring longer documents.
Specifically, we first select sentences in a document based on the input query
and then predict the query-document score based only on the selected sentences,
acting as an explanation. We treat sentence selection as a latent variable
trained jointly with the ranker from the final output. We conduct extensive
experiments to demonstrate that our inherently interpretable select-and-rank
approach is competitive in comparison to other state-of-the-art methods and
sometimes even outperforms them. This is due to our novel end-to-end training
approach based on weighted reservoir sampling that manages to train the
selector despite the stochastic sentence selection. We also show that our
sentence selection approach can be used to provide explanations for models that
operate on only parts of the document, such as BERT.


Abstract:
  In this paper, we present a thorough study of maximizing a regularized
non-monotone submodular function subject to various constraints, i.e., $\max \{
g(A) - \ell(A) : A \in \mathcal{F} \}$, where $g \colon 2^\Omega \to
\mathbb{R}_+$ is a non-monotone submodular function, $\ell \colon 2^\Omega \to
\mathbb{R}_+$ is a normalized modular function and $\mathcal{F}$ is the
constraint set. Though the objective function $f := g - \ell$ is still
submodular, the fact that $f$ could potentially take on negative values
prevents the existing methods for submodular maximization from providing a
constant approximation ratio for the regularized submodular maximization
problem. To overcome the obstacle, we propose several algorithms which can
provide a relatively weak approximation guarantee for maximizing regularized
non-monotone submodular functions. More specifically, we propose a continuous
greedy algorithm for the relaxation of maximizing $g - \ell$ subject to a
matroid constraint. Then, the pipage rounding procedure can produce an integral
solution $S$ such that $\mathbb{E} [g(S) - \ell(S)] \geq e^{-1}g(OPT) -
\ell(OPT) - O(\epsilon)$. Moreover, we present a much faster algorithm for
maximizing $g - \ell$ subject to a cardinality constraint, which can output a
solution $S$ with $\mathbb{E} [g(S) - \ell(S)] \geq (e^{-1} - \epsilon) g(OPT)
- \ell(OPT)$ using $O(\frac{n}{\epsilon^2} \ln \frac 1\epsilon)$ value oracle
queries. We also consider the unconstrained maximization problem and give an
algorithm which can return a solution $S$ with $\mathbb{E} [g(S) - \ell(S)]
\geq e^{-1} g(OPT) - \ell(OPT)$ using $O(n)$ value oracle queries.


Abstract:
  In this paper, we present an uncertainty-aware INVASE to quantify predictive
confidence of healthcare problem. By introducing learnable Gaussian
distributions, we lever-age their variances to measure the degree of
uncertainty. Based on the vanilla INVASE, two additional modules are proposed,
i.e., an uncertainty quantification module in the predictor, and a reward
shaping module in the selector. We conduct extensive experiments on UCI-WDBC
dataset. Notably, our method eliminates almost all predictive bias with only
about 20% queries, while the uncertainty-agnostic counterpart requires nearly
100% queries. The open-source implementation with a detailed tutorial is
available at
https://github.com/jx-zhong-for-academic-purpose/Uncertainty-aware-INVASE/blob/main/tutorialinvase%2B.ipynb.