title:
Dynamic Scheduling for Minimizing AoI in Resource-Constrained
  Multi-Source Relaying Systems with Stochastic Arrivals

abstract:
  We consider a multi-source relaying system where the independent sources
randomly generate status update packets which are sent to the destination with
the aid of a relay through unreliable links. We develop scheduling policies to
minimize the sum average age of information (AoI) subject to transmission
capacity and long-run average resource constraints. We formulate a stochastic
optimization problem and solve it under two different scenarios regarding the
knowledge of system statistics: known environment and unknown environment. For
the known environment, a constrained Markov decision process (CMDP) approach
and a drift-plus-penalty method are proposed. The CMDP problem is solved by
transforming it into an MDP problem using the Lagrangian relaxation method. We
theoretically analyze the structure of optimal policies for the MDP problem and
subsequently propose a structure-aware algorithm that returns a practical
near-optimal policy. By the drift-plus-penalty method, we devise a dynamic
near-optimal low-complexity policy. For the unknown environment, we develop a
deep reinforcement learning policy by employing the Lyapunov optimization
theory and a dueling double deep Q-network. Simulation results are provided to
assess the performance of our policies and validate the theoretical results.
The results show up to 91% performance improvement compared to a baseline
policy.


title: Anonymous Hyperlocal Communities: What do they talk about?

abstract:
  In this paper, we study what users talk about in a plethora of independent
hyperlocal and anonymous online communities in a single country: Saudi Arabia
(KSA). We base this perspective on performing a content classification of the
Jodel network in the KSA. To do so, we first contribute a content
classification schema that assesses both the intent (why) and the topic (what)
of posts. We use the schema to label 15k randomly sampled posts and further
classify the top 1k hashtags. We observe a rich set of benign (yet at times
controversial in conservative regimes) intents and topics that dominantly
address information requests, entertainment, or dating/flirting. By comparing
two large cities (Riyadh and Jeddah), we further show that hyperlocality leads
to shifts in topic popularity between local communities. By evaluating votes
(content appreciation) and replies (reactions), we show that the communities
react differently to different topics; e.g., entertaining posts are much
appreciated through votes, receiving the least replies, while beliefs &
politics receive similarly few replies but are controversially voted.


title:
Joint Survey Processing II: Stellar Proper Motions in the COSMOS Field,
  from Hubble Space Telescope ACS and Subaru Telescope HSC Observations

abstract:
  We analyze stellar proper motions in the COSMOS field to assess the presence
of bulk motions. At bright magnitudes (G-band 18.5--20.76 AB), we use the
proper motions of 1,010 stars in the Gaia DR2 catalog. At the faint end, we
computed proper motions of 11,519 point-like objects at i-band magnitudes
19--25 AB using Hubble ACS and Subaru HSC, which span two epochs about 11 years
apart. In order to measure these proper motions with unprecedented accuracy at
faint magnitudes, we developed a foundational set of astrometric tools which
will be required for Joint Survey Processing (JSP) of data from the next
generation of optical/infrared surveys. The astrometric grids of Hubble ACS and
Subaru HSC mosaics were corrected at the catalog level, using proper
motion-propagated and parallax-corrected Gaia DR2 sources. These astrometric
corrections were verified using compact extragalactic sources. Upon comparison
of our measured proper motions with Gaia DR2, we estimate the uncertainties in
our measurements to be ~2--3 mas/yr per axis, down to 25.5 AB mag. We corrected
proper motions for the mean motion of the Sun, and we find that late-type
main-sequence stars predominantly in the thin disk in the COSMOS field have
space velocities mainly towards the Galactic center. We detect candidate
high-velocity (> 220 km/s) stars, 6 of them at ~0.4-6 kpc from the Gaia sample,
and 5 of them at ~20 kpc from the faint star HSC and ACS sample. The sources
from the faint star sample may be candidate halo members of the Sangarius
stream.


title:
NELA-GT-2021: A Large Multi-Labelled News Dataset for The Study of
  Misinformation in News Articles

abstract:
  In this paper, we present the fourth installment of the NELA-GT datasets,
NELA-GT-2021. The dataset contains 1.8M articles from 367 outlets between
January 1st, 2021 and December 31st, 2021. Just as in past releases of the
dataset, NELA-GT-2021 includes outlet-level veracity labels from Media
Bias/Fact Check and tweets embedded in collected news articles. The
NELA-GT-2021 dataset can be found at: https://doi.org/10.7910/DVN/RBKVBM


title:
Refactoring Debt: Myth or Reality? An Exploratory Study on the
  Relationship Between Technical Debt and Refactoring

abstract:
  To meet project timelines or budget constraints, developers intentionally
deviate from writing optimal code to feasible code in what is known as
incurring Technical Debt (TD). Furthermore, as part of planning their
correction, developers document these deficiencies as comments in the code
(i.e., self-admitted technical debt or SATD). As a means of improving source
code quality, developers often apply a series of refactoring operations to
their codebase. In this study, we explore developers repaying this debt through
refactoring operations by examining occurrences of SATD removal in the code of
76 open-source Java systems. Our findings show that TD payment usually occurs
with refactoring activities and developers refactor their code to remove TD for
specific reasons. We envision our findings supporting vendors in providing
tools to better support developers in the automatic repayment of technical
debt.


title:
Empirical limb-darkening coefficients & transit parameters of known
  exoplanets from TESS

abstract:
  Although the main goal of the Transiting Exoplanet Survey Satellite
(\textit{TESS}) is to search for new transiting exoplanets, its data can also
be used to study in further detail already known systems. The \textit{TESS}
bandpass is particularly interesting to study the limb-darkening effect of the
stellar host which is imprinted in transit lightcurves, as the widely used
\textsc{phoenix} and \textsc{atlas} stellar models predict different
limb-darkening profiles. Here we study this effect by fitting the transit
lightcurves of 176 known exoplanetary systems observed by \textit{TESS}, which
allows us to extract empirical limb-darkening coefficients (LDCs) for the
widely used quadratic law, but also updated transit parameters (including
ephemerides refinements) as a byproduct. Comparing our empirically obtained
LDCs with theoretical predictions, we find significant offsets when using
tabulated \textit{TESS} LDCs. Specifically, the $u_2$ coefficients obtained
using \textsc{phoenix} models show the largest discrepancies depending on the
method used to derive them, with offsets that can reach up to $\Delta u_2
\approx 0.2$ on average. Most of those average offsets disappear, however, if
one uses the SPAM algorithm introduced by Howarth (2011) to calculate the LDCs
instead. Our results suggest, however, that for stars cooler than about 5000 K,
no methodology is good enough to explain the limb-darkening effect: we observe
a sharp deviation between measured and predicted LDCs on both quadratic LDCs of
order $\Delta u_1, \Delta u_2 \approx 0.2$ for those cool stars. We recommend
caution when assuming limb-darkening coefficients as perfectly known thus, in
particular for these cooler stars when analyzing \textit{TESS} transit
lightcurves.


title: Point Density-Aware Voxels for LiDAR 3D Object Detection

abstract:
  LiDAR has become one of the primary 3D object detection sensors in autonomous
driving. However, LiDAR's diverging point pattern with increasing distance
results in a non-uniform sampled point cloud ill-suited to discretized
volumetric feature extraction. Current methods either rely on voxelized point
clouds or use inefficient farthest point sampling to mitigate detrimental
effects caused by density variation but largely ignore point density as a
feature and its predictable relationship with distance from the LiDAR sensor.
Our proposed solution, Point Density-Aware Voxel network (PDV), is an
end-to-end two stage LiDAR 3D object detection architecture that is designed to
account for these point density variations. PDV efficiently localizes voxel
features from the 3D sparse convolution backbone through voxel point centroids.
The spatially localized voxel features are then aggregated through a
density-aware RoI grid pooling module using kernel density estimation (KDE) and
self-attention with point density positional encoding. Finally, we exploit
LiDAR's point density to distance relationship to refine our final bounding box
confidences. PDV outperforms all state-of-the-art methods on the Waymo Open
Dataset and achieves competitive results on the KITTI dataset. We provide a
code release for PDV which is available at https://github.com/TRAILab/PDV.


title:
On the detectability of gravitational waves from primordial black holes
  orbiting Sgr A*

abstract:
  In this work we characterize the expected gravitational wave signal
detectable by the planned space-borne interferometer LISA and the proposed next
generation space-borne interferometer $\mu$Ares arising from a population of
primordial black holes orbiting Sgr A*, the super-massive black hole at the
Galactic center. Assuming that such objects indeed form the entire diffuse mass
allowed by the observed orbit of S2 in the Galactic center, under the
simplified assumption of circular orbits and monochromatic mass function, we
assess the expected signal in gravitational waves, either from resolved and
non-resolved sources.


title:
Facilitating Federated Genomic Data Analysis by Identifying Record
  Correlations while Ensuring Privacy

abstract:
  With the reduction of sequencing costs and the pervasiveness of computing
devices, genomic data collection is continually growing. However, data
collection is highly fragmented and the data is still siloed across different
repositories. Analyzing all of this data would be transformative for genomics
research. However, the data is sensitive, and therefore cannot be easily
centralized. Furthermore, there may be correlations in the data, which if not
detected, can impact the analysis. In this paper, we take the first step
towards identifying correlated records across multiple data repositories in a
privacy-preserving manner. The proposed framework, based on random shuffling,
synthetic record generation, and local differential privacy, allows a trade-off
of accuracy and computational efficiency. An extensive evaluation on real
genomic data from the OpenSNP dataset shows that the proposed solution is
efficient and effective.


title: Distributed $\mathcal{H}^2$-matrices for boundary element methods

abstract:
  Standard discretization techniques for boundary integral equations, e.g., the
Galerkin boundary element method, lead to large densely populated matrices that
require fast and efficient compression techniques like the fast multipole
method or hierarchical matrices. If the underlying mesh is very large, running
the corresponding algorithms on a distributed computer is attractive, e.g.,
since distributed computers frequently are cost-effective and offer a high
accumulated memory bandwidth.
  Compared to the closely related particle methods, for which distributed
algorithms are well-established, the Galerkin discretization poses a challenge,
since the supports of the basis functions influence the block structure of the
matrix and therefore the flow of data in the corresponding algorithms. This
article introduces distributed $\mathcal{H}^2$-matrices, a class of
hierarchical matrices that is closely related to fast multipole methods and
particularly well-suited for distributed computing. While earlier efforts
required the global tree structure of the $\mathcal{H}^2$-matrix to be stored
in every node of the distributed system, the new approach needs only local
multilevel information that can be obtained via a simple distributed algorithm,
allowing us to scale to significantly larger systems.
  Experiments show that this approach can handle very large meshes with more
than $130$ million triangles efficiently.


title:
On solutions to the continuum version of the Kuramoto model with
  identical oscillators

abstract:
  The Kuramoto model provides a concrete mathematical realization of emergent
synchrony in a population of phase-coupled oscillators. Since Kuramoto's
publication, \textit{Oscillations, Waves, and Turbulence}, researchers have
worked to better characterize solution dynamics. In this paper, we combine the
method of characteristics with an iterative technique to prove existence and
uniqueness of solutions to the continuum version of the Kuramoto model in the
special case where oscillators are identical and characterize the oscillator
density in terms of a limiting projected characteristic. The model
characterization, in turn, provides for global asymptotic analysis of the
system via sub and super solutions. We believe the unique approach to model
analysis developed here has the potential to yield novel results on other more
complex versions of the extensively studied Kuramoto model.


title: 3D Nanoscale Mapping of Short-Range Order in GeSn Alloys

abstract:
  GeSn on Si has attracted much research interest due to its tunable direct
bandgap for mid-infrared applications. Recently, short-range order (SRO) in
GeSn alloys has been theoretically predicted, which profoundly impacts the band
structure. However, characterizing SRO in GeSn is challenging. Guided by
physics-informed Poisson statistical analyses of Kth-nearest neighbors (KNN) in
atom probe tomography, a new approach is demonstrated here for 3D nanoscale SRO
mapping and semi-quantitative strain mapping in GeSn. For GeSn with ~14 at.%
Sn, the SRO parameters of Sn-Sn 1NN in 10x10x10 nm$^{3}$ nanocubes can deviate
from that of the random alloys by $\pm$15%. The relatively large fluctuation of
the SRO parameters contributes to band-edge softening observed optically. Sn-Sn
1NN also tends to be more favored towards the surface, less favored under
strain relaxation or tensile strain, while almost independent of local Sn
composition. An algorithm based on least square fit of atomic positions further
verifies this Poisson-KNN statistical method. Compared to existing macroscopic
spectroscopy or electron microscopy techniques, this new APT statistical
analysis uniquely offers 3D SRO mapping at nanoscale resolution in a relatively
large volume with millions of atoms. It can also be extended to investigate SRO
in other alloy systems.


title:
Probing the Jaynes-Cummings ladder with spin circuit quantum
  electrodynamics

abstract:
  Circuit quantum electrodynamics with electron spins (spin circuit QED)
enables long-range interaction and single-shot readout of spin qubits, which
pave the way to large-scale spin qubit processors. Recent experimental work
reported an additional feature in the vacuum Rabi splitting of peaks in the
resonator transmission spectrum, which has remained unexplained until now. In
this work, we show that this feature originates from transitions between
excited states in the Jaynes-Cummings ladder, which are not included in
commonly used input-output models for spin circuit QED. We present an
input-output framework that does include these effects and is based on a
numerical solution of a Lindblad master equation in the stationary limit. In
new experiments, we first reproduce previous observations and then reveal both
excited-state transitions and multi-photon transitions by increasing the probe
power and using two-tone spectroscopy. This ability to probe the
Jaynes-Cummings ladder in spin circuit QED is an important new step in the
development of the platform. In future work, our input-output framework can be
straightforwardly extended to accurately describe resonator-mediated
interactions between two remote spins.


title:
Enhancing $T_{\mathrm{c}}$ in a composite superconductor/metal bilayer
  system: a dynamical cluster approximation study

abstract:
  It has been proposed that the superconducting transition temperature
$T_{\mathrm{c}}$ of an unconventional superconductor with a large pairing scale
but strong phase fluctuations can be enhanced by coupling it to a metal.
However, the general efficacy of this approach across different parameter
regimes remains an open question. Using the dynamical cluster approximation, we
study this question in a system composed of an attractive Hubbard layer in the
intermediate coupling regime, where the magnitude of the attractive Coulomb
interaction $|U|$ is slightly larger than the bandwidth $W$, hybridized with a
noninteracting metallic layer. We find that while the superconducting
transition becomes more mean-field-like with increasing interlayer hopping, the
superconducting transition temperature $T_{\mathrm{c}}$ exhibits a nonmonotonic
dependence on the strength of the hybridization $t_{\perp}$. This behavior
arises from a reduction of the effective pairing interaction in the correlated
layer that out-competes the growth in the intrinsic pair-field susceptibility
induced by the coupling to the metallic layer. We find that the largest
$T_{\mathrm{c}}$ inferred here for the composite system is below the maximum
value currently estimated for the isolated negative-$U$ Hubbard model.


title:
Full-scale validation of CFD simulations of buoyancy-driven ventilation
  in a three-story office building

abstract:
  Computational fluid dynamics (CFD) is frequently used to support the design
of naturally ventilated buildings; however, the model accuracy should be
thoroughly assessed, ideally through validation with full-scale measurements.
The present study aims to (1) validate transient CFD simulations with
uncertainty quantification (UQ) for buoyancy-driven natural ventilation against
full-scale experiments in an operational atrium building, and (2) quantify the
sensitivity of the CFD results to the thermal boundary conditions. The UQ and
sensitivity analysis consider uncertainties in the initial and boundary
conditions for the temperatures. Considering the volume-averaged air
temperature on each floor, the predictions and measurements agree well with
discrepancies less than 0.3 {\deg}C. When considering the temperature averaged
over smaller zones on each floor, two trends can be observed. First, in zones
not adjacent to windows, the discrepancies between the CFD and measurements can
be explained by uncertainty in the boundary conditions and the measurements.
Second, in zones adjacent to windows, higher discrepancies are observed due to
oscillations in the inflow jets just downstream of the windows, and due to
geometrical simplifications in the CFD model. The sensitivity analysis
demonstrates that the boundary conditions for the thermal mass surface
temperature and the outdoor temperature have a dominant effect on the indoor
air temperature predictions, with their relative importance varying as function
of proximity to the windows.


title:
Analytical Methods for Superresolution Dislocation Identification in
  Dark-Field X-ray Microscopy

abstract:
  In this work, we develop several inference methods to estimate the position
of dislocations from images generated using dark-field X-ray microscopy (DFXM)
-- achieving superresolution accuracy and principled uncertainty
quantification. Using the framework of Bayesian inference, we incorporate
models of the DFXM contrast mechanism and detector measurement noise, along
with initial position estimates, into a statistical model coupling DFXM images
with the dislocation position of interest. We motivate several position
estimation and uncertainty quantification algorithms based on this model. We
then demonstrate the accuracy of our primary estimation algorithm on synthetic
realistic DFXM images of edge dislocations in single crystal aluminum. We
conclude with a discussion of our methods' impact on future dislocation studies
and possible future research avenues.


title: Physical and Thermoelectric Properties of 2D B$_4$C Nanosheets

abstract:
  Boron carbide (B$_4$C) has been well studied both theoretically and
experimentally in its bulk form due to its exceptional hardness and use as a
high temperature thermoelectric. However, the properties of its two-dimensional
nanosheets are not well established. In this paper, using van der Waals
corrected density-functional theory (DFT) simulations, we show that the bulk
B$_4$C can be cleaved along different directions to form B$_4$C nanosheets with
low formation energies. We find that there is minimal dependence of the
formation energies on the cleavage planes and surface terminations. Whilst the
density of states of the bulk B$_4$C indicate that it is a semiconductor, the
B$_4$C nanosheets are found to be predominantly metallic. We attribute this
metallic behaviour to a redistribution of charges on the surface B-C bonds of
the films. The Seebeck coefficients of the the B$_4$C films remain comparable
to those of the bulk, and are nearly constant as a function of temperature. Our
results provide guidance for experimental synthesis efforts and future
application of B$_4$C nanosheets in nanoelectronic and thermoelectric
applications.


title:
Fusion of Sentiment and Asset Price Predictions for Portfolio
  Optimization

abstract:
  The fusion of public sentiment data in the form of text with stock price
prediction is a topic of increasing interest within the financial community.
However, the research literature seldom explores the application of investor
sentiment in the Portfolio Selection problem. This paper aims to unpack and
develop an enhanced understanding of the sentiment aware portfolio selection
problem. To this end, the study uses a Semantic Attention Model to predict
sentiment towards an asset. We select the optimal portfolio through a
sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for
price prediction and a mean-variance strategy. Our sentiment portfolio
strategies achieved on average a significant increase in revenue above the
non-sentiment aware models. However, the results show that our strategy does
not outperform traditional portfolio allocation strategies from a stability
perspective. We argue that an improved fusion of sentiment prediction with a
combination of price prediction and portfolio optimization leads to an enhanced
portfolio selection strategy.


title: Particle Swarm Optimization based on Novelty Search

abstract:
  In this paper we propose a Particle Swarm Optimization algorithm combined
with Novelty Search. Novelty Search finds novel place to search in the search
domain and then Particle Swarm Optimization rigorously searches that area for
global optimum solution. This method is never blocked in local optima because
it is controlled by Novelty Search which is objective free. For those functions
where there are many more local optima and second global optimum is far from
true optimum, the present method works successfully. The present algorithm
never stops until it searches entire search area. A series of experimental
trials prove the robustness and effectiveness of the present algorithm on
complex optimization test functions.


title: Immersive Virtual Reality Simulations of Bionic Vision

abstract:
  Bionic vision uses neuroprostheses to restore useful vision to people living
with incurable blindness. However, a major outstanding challenge is predicting
what people 'see' when they use their devices. The limited field of view of
current devices necessitates head movements to scan the scene, which is
difficult to simulate on a computer screen. In addition, many computational
models of bionic vision lack biological realism. To address these challenges,
we present VR-SPV, an open-source virtual reality toolbox for simulated
prosthetic vision that uses a psychophysically validated computational model to
allow sighted participants to 'see through the eyes' of a bionic eye user. To
demonstrate its utility, we systematically evaluated how clinically reported
visual distortions affect performance in a letter recognition and an immersive
obstacle avoidance task. Our results highlight the importance of using an
appropriate phosphene model when predicting visual outcomes for bionic vision.


title: The Overlooked Classifier in Human-Object Interaction Recognition

abstract:
  Human-Object Interaction (HOI) recognition is challenging due to two factors:
(1) significant imbalance across classes and (2) requiring multiple labels per
image. This paper shows that these two challenges can be effectively addressed
by improving the classifier with the backbone architecture untouched. Firstly,
we encode the semantic correlation among classes into the classification head
by initializing the weights with language embeddings of HOIs. As a result, the
performance is boosted significantly, especially for the few-shot subset.
Secondly, we propose a new loss named LSE-Sign to enhance multi-label learning
on a long-tailed dataset. Our simple yet effective method enables
detection-free HOI classification, outperforming the state-of-the-arts that
require object detection and human pose by a clear margin. Moreover, we
transfer the classification model to instance-level HOI detection by connecting
it with an off-the-shelf object detector. We achieve state-of-the-art without
additional fine-tuning.


title: Optimal Quantum State Tomography with Noisy Gates

abstract:
  Quantum state tomography (QST) represents an essential tool for the
characterization, verification, and validation (QCVV) of quantum processors.
Only for a few idealized scenarios, there are analytic results for the optimal
measurement set for QST. E.g., in a setting of non-degenerate measurements, an
optimal minimal set of measurement operators for QST has eigenbases which are
mutually unbiased. However, in other set-ups, dependent on the rank of the
projection operators and the size of the quantum system, the optimal choice of
measurements for efficient QST needs to be numerically approximated. We have
generalized this problem by introducing the framework of customized efficient
QST. Here we extend customized QST and look for the optimal measurement set for
QST in the case where some of the quantum gates applied in the measurement
process are noisy. To achieve this, we use two distinct noise models: first,
the depolarizing channel, and second, over- and under-rotation in single-qubit
and to two-qubit gates (for further information, please see Methods). We
demonstrate the benefit of using entangling gates for the efficient QST
measurement schemes for two qubits at realistic noise levels, by comparing the
fidelity of reconstruction of our optimized QST measurement set to the
state-of-the-art scheme using only product bases.


title: A Systematic Literature Review on Trust in the Software Ecosystem

abstract:
  We conduct a systematic literature review on the concept of trust in the
worldwide software ecosystem. We acknowledge that trust is something between
two actors in the software ecosystem, and we examine what role trust plays in
the relationships between end-users and (1) software products, (2) package
managers, (3) software producing organizations, and (4) software engineers. Two
major findings emerged from the systematic literature review. To begin, we
provide a definition of trust in the software ecosystem, including a
theoretical framework that decomposes and signifies a theoretical understanding
of trust. Second, we provide a list of trust factors that can be used to
assemble an overview of software trust.


title: A note on estimating Bass model parameters

abstract:
  Bass (1969) proposed a model (the Bass model) for the timing of adoptions of
a new product, where the adoption rate increases with the number of past
adoptions. This model has been widely used in marketing and operations
management literature to model new product demand over time. In this note, we
provide a simple approach to estimate the Bass model parameters and prove the
convergence rate.


title: A characterization of the individual maximum and anti-maximum principle

abstract:
  Abstract approaches to maximum and anti-maximum principles for differential
operators typically rely on the condition that all vectors in the domain of the
operator are dominated by the leading eigenfunction of the operator. We study
the necessity of this condition. In particular, we show that under a number of
natural assumptions, so-called individual versions of both the maximum and the
anti-maximum principle simultaneously hold if and only if the aforementioned
domination condition is satisfied.
  Consequently, we are able to show that a variety of concrete differential
operators do not satisfy an anti-maximum principle.


title: State-Machine Replication Scalability Made Simple (Extended Version)

abstract:
  Consensus, state-machine replication (SMR) and total order broadcast (TOB)
protocols are notorious for being poorly scalable with the number of
participating nodes. Despite the recent race to reduce overall message
complexity of leader-driven SMR/TOB protocols, scalability remains poor and the
throughput is typically inversely proportional to the number of nodes. We
present Insanely Scalable State-Machine Replication, a generic construction to
turn leader-driven protocols into scalable multi-leader ones. For our scalable
SMR construction we use a novel primitive called Sequenced (Total Order)
Broadcast (SB) which we wrap around PBFT, HotStuff and Raft leader-driven
protocols to make them scale. Our construction is general enough to accommodate
most leader-driven ordering protocols (BFT or CFT) and make them scale. Our
implementation improves the peak throughput of PBFT, HotStuff, and Raft by 37x,
56x, and 55x, respectively, at a scale of 128 nodes.


title:
Leveraging Labeling Representations in Uncertainty-based Semi-supervised
  Segmentation

abstract:
  Semi-supervised segmentation tackles the scarcity of annotations by
leveraging unlabeled data with a small amount of labeled data. A prominent way
to utilize the unlabeled data is by consistency training which commonly uses a
teacher-student network, where a teacher guides a student segmentation. The
predictions of unlabeled data are not reliable, therefore, uncertainty-aware
methods have been proposed to gradually learn from meaningful and reliable
predictions. Uncertainty estimation, however, relies on multiple inferences
from model predictions that need to be computed for each training step, which
is computationally expensive. This work proposes a novel method to estimate the
pixel-level uncertainty by leveraging the labeling representation of
segmentation masks. On the one hand, a labeling representation is learnt to
represent the available segmentation masks. The learnt labeling representation
is used to map the prediction of the segmentation into a set of plausible
masks. Such a reconstructed segmentation mask aids in estimating the
pixel-level uncertainty guiding the segmentation network. The proposed method
estimates the uncertainty with a single inference from the labeling
representation, thereby reducing the total computation. We evaluate our method
on the 3D segmentation of left atrium in MRI, and we show that our uncertainty
estimates from our labeling representation improve the segmentation accuracy
over state-of-the-art methods.


title: Deep Multimodal Guidance for Medical Image Classification

abstract:
  Medical imaging is a cornerstone of therapy and diagnosis in modern medicine.
However, the choice of imaging modality for a particular theranostic task
typically involves trade-offs between the feasibility of using a particular
modality (e.g., short wait times, low cost, fast acquisition, reduced
radiation/invasiveness) and the expected performance on a clinical task (e.g.,
diagnostic accuracy, efficacy of treatment planning and guidance). In this
work, we aim to apply the knowledge learned from the less feasible but
better-performing (superior) modality to guide the utilization of the
more-feasible yet under-performing (inferior) modality and steer it towards
improved performance. We focus on the application of deep learning for
image-based diagnosis. We develop a light-weight guidance model that leverages
the latent representation learned from the superior modality, when training a
model that consumes only the inferior modality. We examine the advantages of
our method in the context of two clinical applications: multi-task skin lesion
classification from clinical and dermoscopic images and brain tumor
classification from multi-sequence magnetic resonance imaging (MRI) and
histopathology images. For both these scenarios we show a boost in diagnostic
performance of the inferior modality without requiring the superior modality.
Furthermore, in the case of brain tumor classification, our method outperforms
the model trained on the superior modality while producing comparable results
to the model that uses both modalities during inference.


title:
PC-SwinMorph: Patch Representation for Unsupervised Medical Image
  Registration and Segmentation

abstract:
  Medical image registration and segmentation are critical tasks for several
clinical procedures. Manual realisation of those tasks is time-consuming and
the quality is highly dependent on the level of expertise of the physician. To
mitigate that laborious task, automatic tools have been developed where the
majority of solutions are supervised techniques. However, in medical domain,
the strong assumption of having a well-representative ground truth is far from
being realistic. To overcome this challenge, unsupervised techniques have been
investigated. However, they are still limited in performance and they fail to
produce plausible results. In this work, we propose a novel unified
unsupervised framework for image registration and segmentation that we called
PC-SwinMorph. The core of our framework is two patch-based strategies, where we
demonstrate that patch representation is key for performance gain. We first
introduce a patch-based contrastive strategy that enforces locality conditions
and richer feature representation. Secondly, we utilise a 3D
window/shifted-window multi-head self-attention module as a patch stitching
strategy to eliminate artifacts from the patch splitting. We demonstrate,
through a set of numerical and visual results, that our technique outperforms
current state-of-the-art unsupervised techniques.


title: Data-driven geometric scale detection via Delaunay interpolation

abstract:
  Accurate approximation of a real-valued function depends on two aspects of
the available data: the density of inputs within the domain of interest and the
variation of the outputs over that domain. There are few methods for assessing
whether the density of inputs is \textit{sufficient} to identify the relevant
variations in outputs -- i.e., the "geometric scale" of the function -- despite
the fact that sampling density is closely tied to the success or failure of an
approximation method. In this paper, we introduce a general purpose,
computational approach to detecting the geometric scale of real-valued
functions over a fixed domain using a deterministic interpolation technique
from computational geometry. Our algorithm is based on the observation that a
sequence of piecewise linear interpolants will converge to a continuous
function at a quadratic rate (in $L^2$ norm) if and only if the data are
sampled densely enough to distinguish the feature from noise. We present
numerical experiments demonstrating how our method can identify feature scale,
estimate uncertainty in feature scale, and assess the sampling density for
fixed (i.e. static) datasets of input-output pairs. In addition, we include
analytical results in support of our numerical findings and will release
lightweight code that can be adapted for use in a variety of data science
settings.


title: Linear Quadratic Mean-Field Games with Communication Constraints

abstract:
  In this paper, we study a large population game with heterogeneous dynamics
and cost functions solving a consensus problem. Moreover, the agents have
communication constraints which appear as: (1) an Additive-White Gaussian Noise
(AWGN) channel, and (2) asynchronous data transmission via a fixed scheduling
policy. Since the complexity of solving the game increases with the number of
agents, we use the Mean-Field Game paradigm to solve it. Under standard
assumptions on the information structure of the agents, we prove that the
control of the agent in the MFG setting is free of the dual effect. This allows
us to obtain an equilibrium control policy for the generic agent, which is a
function of only the local observation of the agent. Furthermore, the
equilibrium mean-field trajectory is shown to follow linear dynamics, hence
making it computable. We show that in the finite population game, the
equilibrium control policy prescribed by the MFG analysis constitutes an
$\epsilon$-Nash equilibrium, where $\epsilon$ tends to zero as the number of
agents goes to infinity. The paper is concluded with simulations demonstrating
the performance of the equilibrium control policy.


title:
A Holistic Approach to Predicting Top Quark Kinematic Properties with
  the Covariant Particle Transformer

abstract:
  Precise reconstruction of top quark properties is a challenging task at the
Large Hadron Collider due to combinatorial backgrounds and missing information.
We introduce a physics-informed neural network architecture called the
Covariant Particle Transformer (CPT) for directly predicting the top quark
kinematic properties from reconstructed final state objects. This approach is
permutation invariant and partially Lorentz covariant and can account for a
variable number of input objects. In contrast to previous machine
learning-based reconstruction methods, CPT is able to predict top quark
four-momenta regardless of the jet multiplicity in the event. Using
simulations, we show that the CPT performs favorably compared with other
machine learning top quark reconstruction approaches.


title: Thermodynamic principle for quantum metrology

abstract:
  The heat dissipation in quantum metrology represents not only an unavoidable
problem towards practical applications of quantum sensing devices but also a
fundamental relationship between thermodynamics and quantum metrology. However,
a general thermodynamic principle which governs the rule of energy consumption
in quantum metrology, similar to Landauer's principle for heat dissipation in
computations, has remained elusive. Here, we establish such a physical
principle for energy consumption in order to achieve a certain level of
measurement precision in quantum metrology, and show that it is intrinsically
determined by the erasure of quantum Fisher information. The principle provides
a powerful tool to investigate the advantage of quantum resources, not only in
measurement precision but also in energy efficiency. It also serves as a bridge
between thermodynamics and various fundamental physical concepts related in
quantum physics and quantum information theory.


title:
IoT Coverage Enhancement using Repetition in Energy Constrained Devices:
  an Analytic Approach

abstract:
  Novel Internet-of-Things (IoT) access technologies are emerging as part of
the next generation cellular networks. These technologies are specifically
oriented towards energy-limited IoT devices that are scattered far away from
their serving base station. One of the key methods of achieving deep coverage
is via repeated transmissions of data. However, repetition leads to higher
energy consumption and reduces the device battery-lifetime. Thus, a trade-off
between coverage and energy consumption exists and requires careful
investigation. This paper evaluates the effects of transmission repetition on
enhancing the probability of coverage and the cost of the incurred energy.
Using an empirical repetition profile based on traffic load and device distance
from the base station, we derive the coverage probability and energy profile
models for IoT links that utilize two different diversity combining techniques.
In particular, we focus on two common diversity combining that are; (i)
Selection Combining (SC), and (ii) Maximal Ratio Combining (MRC). We utilize
tools from stochastic geometry to formulate an analytic framework that compares
these two combining methods. This framework can aid network designers in
jointly maximizing the network coverage while minimizing the energy expenditure
of devices.


title: Completing the $\mathrm{A}_2$ Andrews-Schilling-Warnaar identities

abstract:
  We study the Andrews-Schilling-Warnaar sum-sides for the principal characters
of standard (i.e., integrable, highest weight) modules of $\mathrm{A}_2^{(1)}$.
These characters have been studied recently by various subsets of Corteel,
Dousse, Foda, Uncu, Warnaar and Welsh. We prove complete sets of identities for
moduli $5$ through $8$ and $10$, in Andrews-Schilling-Warnaar form. The cases
of moduli $6$ and $10$ are new. Our methods depend on the Corteel-Welsh
recursions governing the cylindric partitions and on certain relations
satisfied by the Andrews-Schilling-Warnaar sum-sides. We speculate on the role
of the latter in the proofs of higher modulus identities. Further, we provide a
complete set of conjectures for modulus $9$. In fact, we show that at any given
modulus, a complete set of conjectures may be deduced using a subset of "seed"
conjectures. These seed conjectures are obtained by appropriately truncating
conjectures for the "infinite" level. Additionally, for moduli $3k$, we use an
identity of Weierstrass to deduce new sum-product identities starting from the
results of Andrews-Schilling-Warnaar.

categories: math.CO math-ph math.MP math.NT math.QA math.RT


title:
Improving IoT-over-Satellite Connectivity using Frame Repetition
  Technique

abstract:
  Through Non-Terrestrial Networks (NTN), a global coverage connecting areas
with minimal or no terrestrial services is envisaged using satellite and
air-borne platforms. However, one of the challenges to delivering NTN
communications is the increased levels of path-loss, relative to typical
terrestrial scenarios, due to the vast communication distance towards satellite
platforms. Typically, cellular IoT technologies adopt frame repetitions where
many redundant data transmissions are included to enhance its success rate.
This can be also extended to satellite links to counter the effect of long
distance communications on the link budget. In this paper, we put forth an
analytic framework that captures the repetition behavior based on the
probability of line-of-sight (LoS), as it heavily influences the propagation
conditions. We analyze the coverage performance in terms of the frame success
rate, which allows us to tune the repetitions behavior to suit a given
satellite admittance region. Furthermore, we obtain an optimal global average
success rate by considering the availability of satellites. The presented
framework can facilitate coverage enhancements for IoT-over-Satellite networks.


title:
Lifelong Adaptive Machine Learning for Sensor-based Human Activity
  Recognition Using Prototypical Networks

abstract:
  Continual learning, also known as lifelong learning, is an emerging research
topic that has been attracting increasing interest in the field of machine
learning. With human activity recognition (HAR) playing a key role in enabling
numerous real-world applications, an essential step towards the long-term
deployment of such recognition systems is to extend the activity model to
dynamically adapt to changes in people's everyday behavior. Current research in
continual learning applied to HAR domain is still under-explored with
researchers exploring existing methods developed for computer vision in HAR.
Moreover, analysis has so far focused on task-incremental or class-incremental
learning paradigms where task boundaries are known. This impedes the
applicability of such methods for real-world systems since data is presented in
a randomly streaming fashion. To push this field forward, we build on recent
advances in the area of continual machine learning and design a lifelong
adaptive learning framework using Prototypical Networks, LAPNet-HAR, that
processes sensor-based data streams in a task-free data-incremental fashion and
mitigates catastrophic forgetting using experience replay and continual
prototype adaptation. Online learning is further facilitated using contrastive
loss to enforce inter-class separation. LAPNet-HAR is evaluated on 5 publicly
available activity datasets in terms of the framework's ability to acquire new
information while preserving previous knowledge. Our extensive empirical
results demonstrate the effectiveness of LAPNet-HAR in task-free continual
learning and uncover useful insights for future challenges.


title: The spectrum of the Grigoriev-Laurent pseudomoments

abstract:
  Grigoriev (2001) and Laurent (2003) independently showed that the
sum-of-squares hierarchy of semidefinite programs does not exactly represent
the hypercube $\{\pm 1\}^n$ until degree at least $n$ of the hierarchy. Laurent
also observed that the pseudomoment matrices her proof constructs appear to
have surprisingly simple and recursively structured spectra as $n$ increases.
While several new proofs of the Grigoriev-Laurent lower bound have since
appeared, Laurent's observations have remained unproved. We give yet another,
representation-theoretic proof of the lower bound, which also yields exact
formulae for the eigenvalues of the Grigoriev-Laurent pseudomoments. Using
these, we prove and elaborate on Laurent's observations.
  Our arguments have two features that may be of independent interest. First,
we show that the Grigoriev-Laurent pseudomoments are a special case of a Gram
matrix construction of pseudomoments proposed by Bandeira and Kunisky (2020).
Second, we find a new realization of the irreducible representations of the
symmetric group corresponding to Young diagrams with two rows, as spaces of
multivariate polynomials that are multiharmonic with respect to an equilateral
simplex.


title: Internal mode-induced growth in $3$d nonlinear Klein-Gordon equations

abstract:
  This note complements the paper \cite{LP} by proving a scattering statement
for solutions of nonlinear Klein-Gordon equations with an internal mode in
$3$d. We show that small solutions exhibit growth around a one-dimensional set
in frequency space and become of order one in $L^{\infty}$ after a short
transient time. The dynamics are driven by the feedback of the internal mode
into the equation for the field (continuous spectral) component.
  The main part of the proof consists of showing suitable smallness for a
"good" component of the radiation field. This is done in two steps: first,
using the machinery developed in \cite{LP}, we reduce the problem to bounding a
certain quadratic normal form correction. Then we control this latter by
establishing some refined estimates for certain bilinear operators with
singular kernels.


title:
How to Train a (Bad) Algorithmic Caseworker: A Quantitative
  Deconstruction of Risk Assessments in Child-Welfare

abstract:
  Child welfare (CW) agencies use risk assessment tools as a means to achieve
evidence-based, consistent, and unbiased decision-making. These risk
assessments act as data collection mechanisms and have further evolved into
algorithmic systems in recent years. Moreover, several of these algorithms have
reinforced biased theoretical constructs and predictors because of the easy
availability of structured assessment data. In this study, we critically
examine the Washington Assessment of Risk Model (WARM), a prominent risk
assessment tool that has been adopted by over 30 states in the United States
and has been repurposed into more complex algorithms. We compared WARM against
the narrative coding of casenotes written by caseworkers who used WARM. We
found significant discrepancies between the casenotes and WARM data where WARM
scores did not not mirror caseworkers' notes about family risk. We provide the
SIGCHI community with some initial findings from the quantitative
de-construction of a child-welfare algorithm.


title: Toward Efficient Hyperspectral Image Processing inside Camera Pixels

abstract:
  Hyperspectral cameras generate a large amount of data due to the presence of
hundreds of spectral bands as opposed to only three channels (red, green, and
blue) in traditional cameras. This requires a significant amount of data
transmission between the hyperspectral image sensor and a processor used to
classify/detect/track the images, frame by frame, expending high energy and
causing bandwidth and security bottlenecks. To mitigate this problem, we
propose a form of processing-in-pixel (PIP) that leverages advanced CMOS
technologies to enable the pixel array to perform a wide range of complex
operations required by the modern convolutional neural networks (CNN) for
hyperspectral image recognition (HSI). Consequently, our PIP-optimized custom
CNN layers effectively compress the input data, significantly reducing the
bandwidth required to transmit the data downstream to the HSI processing unit.
This reduces the average energy consumption associated with pixel array of
cameras and the CNN processing unit by 25.06x and 3.90x respectively, compared
to existing hardware implementations. Our custom models yield average test
accuracies within 0.56% of the baseline models for the standard HSI benchmarks.


title:
Magnetism in four-layered Aurivillius Bi$_5$FeTi$_3$O$_{15}$ at high
  pressures : A nuclear forward scattering study

abstract:
  We report the structural and magnetic properties of four-layer Aurivillius
compound Bi$_5$FeTi$_3$O$_{15}$ (BFTO) at high hydrostatic pressure conditions.
The high-pressure XRD data does not explicitly show structural phase
transitions with hydrostatic pressure, however the observed changes in lattice
parameters indicate structural modifications at different pressure values. In
the initial pressure region values, the lattice parameters $\textit{a}$- and
$\textit{b}$- are nearly equal implying a quasi-tetragonal structure, however
as the pressure increases $\textit{a}$- and $\textit{b}$- diverges apart and
exhibits complete orthorhombic phase at pressure values of about $\geq$8 GPa.
Principal component analysis of high pressure Raman measurements point out an
evident change in the local structure at about 5.5 GPa indicating that the
evolution of the local structure under applied pressure seems to not follow
crystallographic changes (long range order). Nuclear forward scattering (NFS)
measurement reveal the development of magnetic ordering in BFTO at 5K with high
pressures. A progressive increase in magnetic order is observed with increase
in pressure at 5K. Further, NFS measurements carried out at constant pressure
(6.4GPa) and different temperatures indicate that the developed magnetism
disappears at higher temperatures (20K). It is attempted to explain these
observations in terms of the observed structural parameter variation with
pressure.


title: Learning-based Localizability Estimation for Robust LiDAR Localization

abstract:
  LiDAR-based localization and mapping is one of the core components in many
modern robotic systems due to the direct integration of range and geometry,
allowing for precise motion estimation and generation of high quality maps in
real-time. Yet, as a consequence of insufficient environmental constraints
present in the scene, this dependence on geometry can result in localization
failure, happening in self-symmetric surroundings such as tunnels. This work
addresses precisely this issue by proposing a neural network-based estimation
approach for detecting (non-)localizability during robot operation. Special
attention is given to the localizability of scan-to-scan registration, as it is
a crucial component in many LiDAR odometry estimation pipelines. In contrast to
previous, mostly traditional detection approaches, the proposed method enables
early detection of failure by estimating the localizability on raw sensor
measurements without evaluating the underlying registration optimization.
Moreover, previous approaches remain limited in their ability to generalize
across environments and sensor types, as heuristic-tuning of degeneracy
detection thresholds is required. The proposed approach avoids this problem by
learning from a corpus of different environments, allowing the network to
function over various scenarios. Furthermore, the network is trained
exclusively on simulated data, avoiding arduous data collection in challenging
and degenerate, often hard-to-access, environments. The presented method is
tested during field experiments conducted across challenging environments and
on two different sensor types without any modifications. The observed detection
performance is on par with state-of-the-art methods after environment-specific
threshold tuning.


title: A new class of holographic dark energy models in LRS Bianchi Type-I

abstract:
  In this paper, we examine the (LRS) Bianchi-type-I cosmological model with
holographic dark energy. The exact solutions to the corresponding field
equations are obtained by using generalized hybrid expansion law (HEL). The EoS
parameter $\omega$ for DE is found to be time dependent and redshift dependent
and its exiting range for derived model is agreeing well with the current
observations. Here we likewise apply two mathematical diagnostics, the
statefinder ({r, s}) and $\omega_{d}-\omega^{'}_{d}$ plane to segregate HDE
model from the $\Lambda CDM$ model. Here the $\omega_{d}-\omega^{'}_{d}$
diagnostic trajectories is the good tool to classifying the dynamical DE model.
We found that our model lies in both thawing region and freezing region. We
have also construct the potential as well as dynamics of the quintessence and
tachyon scalar field. Some physical and geometric properties of this model
along with the physical acceptability of cosmological solution have been
discussed in detail.


title: Generalized Tracially Approximated C*-algebras

abstract:
  In this paper, we introduce some classes of generalized tracial approximation
${\rm C^*}$-algebras. Let $\Omega$ be the class of unital ${\rm C^*}$-algebras
which are tracially $\mathcal{Z}$-absorbing (or have tracial nuclear dimension
at most $n$, or have the property $\rm SP$, or are $m$-almost divisible). Then
$A$ is tracially $\mathcal{Z}$-absorbing (has tracial nuclear dimension at most
$n$, has the property $\rm SP$, is weakly $m$-almost divisible) for any simple
unital ${\rm C^*}$-algebra $A$ in the class of those generalized tracial
approximation ${\rm C^*}$-algebras. As an application, let $A$ be an infinite
dimensional unital simple ${\rm C^*}$-algebra, and let $B$ be a centrally large
subalgebra of $A$. If $B$ is tracially $\mathcal{Z}$-absorbing, then $A$ is
tracially $\mathcal{Z}$-absorbing. This result was obtained by Archey, Buck,
and Phillips in \cite{AJN}.


title:
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An
  Accessible Dataset and Benchmark

abstract:
  We present a new dataset for 6-DoF pose estimation of known objects, with a
focus on robotic manipulation research. We propose a set of toy grocery
objects, whose physical instantiations are readily available for purchase and
are appropriately sized for robotic grasping and manipulation. We provide 3D
scanned textured models of these objects, suitable for generating synthetic
training data, as well as RGBD images of the objects in challenging, cluttered
scenes exhibiting partial occlusion, extreme lighting variations, multiple
instances per image, and a large variety of poses. Using semi-automated
RGBD-to-model texture correspondences, the images are annotated with ground
truth poses that were verified empirically to be accurate to within a few
millimeters. We also propose a new pose evaluation metric called {ADD-H} based
upon the Hungarian assignment algorithm that is robust to symmetries in object
geometry without requiring their explicit enumeration. We share pre-trained
pose estimators for all the toy grocery objects, along with their baseline
performance on both validation and test sets. We offer this dataset to the
community to help connect the efforts of computer vision researchers with the
needs of roboticists.


title:
Vertiport Selection in Hybrid Air-Ground Transportation Networks via
  Mathematical Programs with Equilibrium Constraints

abstract:
  Urban Air Mobility is a concept that promotes aerial modes of transport in
urban areas. In these areas, the location and capacity of the vertiports--where
the travelers embark and disembark the aircraft--not only affect the flight
delays of the aircraft, but can also aggravate the congestion of ground
vehicles by creating extra ground travel demands. We introduce a mathematical
model for selecting the vertiports location and capacity that minimizes the
traffic congestion in hybrid air-ground transportation networks. Our model is
based on a mathematical program with bilinear equilibrium constraints.
Furthermore, we show how to compute a global optimal solution of this
mathematical program by solving a mixed integer linear program. We demonstrate
our results via the Anaheim transportation network model, which contains more
than 400 nodes and 900 links.


title:
Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition
  Model Pretraining

abstract:
  Palmprints are private and stable information for biometric recognition. In
the deep learning era, the development of palmprint recognition is limited by
the lack of sufficient training data. In this paper, by observing that palmar
creases are the key information to deep-learning-based palmprint recognition,
we propose to synthesize training data by manipulating palmar creases.
Concretely, we introduce an intuitive geometric model which represents palmar
creases with parameterized B\'ezier curves. By randomly sampling B\'ezier
parameters, we can synthesize massive training samples of diverse identities,
which enables us to pretrain large-scale palmprint recognition models.
Experimental results demonstrate that such synthetically pretrained models have
a very strong generalization ability: they can be efficiently transferred to
real datasets, leading to significant performance improvements on palmprint
recognition. For example, under the open-set protocol, our method improves the
strong ArcFace baseline by more than 10\% in terms of TAR@1e-6. And under the
closed-set protocol, our method reduces the equal error rate (EER) by an order
of magnitude.


title: Deep Binary Reinforcement Learning for Scalable Verification

abstract:
  The use of neural networks as function approximators has enabled many
advances in reinforcement learning (RL). The generalization power of neural
networks combined with advances in RL algorithms has reignited the field of
artificial intelligence. Despite their power, neural networks are considered
black boxes, and their use in safety-critical settings remains a challenge.
Recently, neural network verification has emerged as a way to certify safety
properties of networks. Verification is a hard problem, and it is difficult to
scale to large networks such as the ones used in deep reinforcement learning.
We provide an approach to train RL policies that are more easily verifiable. We
use binarized neural networks (BNNs), a type of network with mostly binary
parameters. We present an RL algorithm tailored specifically for BNNs. After
training BNNs for the Atari environments, we verify robustness properties.


title: DNN Training Acceleration via Exploring GPGPU Friendly Sparsity

abstract:
  The training phases of Deep neural network~(DNN) consumes enormous processing
time and energy. Compression techniques utilizing the sparsity of DNNs can
effectively accelerate the inference phase of DNNs. However, it is hardly used
in the training phase because the training phase involves dense
matrix-multiplication using General-Purpose Computation on Graphics Processors
(GPGPU), which endorse the regular and structural data layout. In this paper,
we first propose the Approximate Random Dropout that replaces the conventional
random dropout of neurons and synapses with a regular and online generated
row-based or tile-based dropout patterns to eliminate the unnecessary
computation and data access for the multilayer perceptron~(MLP) and long
short-term memory~(LSTM). We then develop a SGD-based Search Algorithm that
produces the distribution of row-based or tile-based dropout patterns to
compensate for the potential accuracy loss. Moreover, aiming at the convolution
neural network~(CNN) training acceleration, we first explore the importance and
sensitivity of input feature maps; and then propose the sensitivity-aware
dropout method to dynamically drop the input feature maps based on their
sensitivity so as to achieve greater forward and backward training acceleration
while reserving better NN accuracy. To facilitate DNN programming, we build a
DNN training computation framework that unifies the proposed techniques in the
software stack. As a result, the GPGPU only needs to support the basic operator
-- matrix multiplication and can achieve significant performance improvement
regardless of DNN model.


title:
Propagating waves, bi-directional reflectionless, and coherent perfect
  absorber-laser in finite periodic PT-symmetric waveguide networks

abstract:
  We investigate the scattering behavior between a unit cell with PT-symmetry
and its integrated system composed of the N-cells. In particular, we discuss
the formations of propagating transmittance, bi-directional reflectionless, and
coherent perfect absorber-laser (CPAL) occurred at a finite periodic optical
waveguide network. Through the use of parametric space obtained from
considerations of PT-symmetric transfer matrix and Lorentz reciprocity theorem,
our outcome is regardless of operating frequency, system configurations, and
materials embedded, also valid for any two-port wave systems. Hence, we can
interpret the relationship among the Bloch phase and the consequent propagating
transmitted wave. We observe that when its building unit cell is operated at PT
broken symmetric phase or an exceptional point, the integrated system would
always have a propagating transmitted wave, independent of the number and
transmission phase of the unit cell. On the other hand, when the unit cell is
operated at PT-symmetric phase, the formation of propagating transmitted waves
would depend on the transmission phase of that. We find that even though the
unit cell is not operated at the exceptional point, with a proper number of
unit cells and operation of specific PT-phase, it can eventually achieve the
reflectionless with bi-directionality. To implement CPAL in an integrated
system, there are two approaches. One is by unit cells having CPAL, while its
construction number has to be odd. Another one is through operation of specific
broken symmetric phases and a proper number of unit cells, while the
transmission phase is required to be null. We believe this work could offer an
alternative means to observe extraordinary wave phenomena of PT-symmetric
photonics by a replacement of a finite periodic structure.


title:
Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score
  for Predicting Future Conversion to Alzheimer's Disease

abstract:
  Background: The increasing availability of databases containing both magnetic
resonance imaging (MRI) and genetic data allows researchers to utilize
multimodal data to better understand the characteristics of dementia of
Alzheimer's type (DAT). Objective: The goal of this study was to develop and
analyze novel biomarkers that can help predict the development and progression
of DAT. Methods: We used feature selection and ensemble learning classifier to
develop an image/genotype-based DAT score that represents a subject's
likelihood of developing DAT in the future. Three feature types were used: MRI
only, genetic only, and combined multimodal data. We used a novel data
stratification method to better represent different stages of DAT. Using a
pre-defined 0.5 threshold on DAT scores, we predicted whether or not a subject
would develop DAT in the future. Results: Our results on Alzheimer's Disease
Neuroimaging Initiative (ADNI) database showed that dementia scores using
genetic data could better predict future DAT progression for currently normal
control subjects (Accuracy=0.857) compared to MRI (Accuracy=0.143), while MRI
can better characterize subjects with stable mild cognitive impairment
(Accuracy=0.614) compared to genetics (Accuracy=0.356). Combining MRI and
genetic data showed improved classification performance in the remaining
stratified groups. Conclusion: MRI and genetic data can contribute to DAT
prediction in different ways. MRI data reflects anatomical changes in the
brain, while genetic data can detect the risk of DAT progression prior to the
symptomatic onset. Combining information from multimodal data in the right way
can improve prediction performance.


title:
Mechanism and Model of a Soft Robot for Head Stabilization in Cancer
  Radiation Therapy

abstract:
  We present a parallel robot mechanism and the constitutive laws that govern
the deformation of its constituent soft actuators. Our ultimate goal is the
real-time motion-correction of a patient's head deviation from a target pose
where the soft actuators control the position of the patient's cranial region
on a treatment machine. We describe the mechanism, derive the stress-strain
constitutive laws for the individual actuators and the inverse kinematics that
prescribes a given deformation, and then present simulation results that
validate our mathematical formulation. Our results demonstrate deformations
consistent with our radially symmetric displacement formulation under a finite
elastic deformation framework.


title:
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures
  and Beyond

abstract:
  U-Net, as an encoder-decoder architecture with forward skip connections, has
achieved promising results in various medical image analysis tasks. Many recent
approaches have also extended U-Net with more complex building blocks, which
typically increase the number of network parameters considerably. Such
complexity makes the inference stage highly inefficient for clinical
applications. Towards an effective yet economic segmentation network design, in
this work, we propose backward skip connections that bring decoded features
back to the encoder. Our design can be jointly adopted with forward skip
connections in any encoder-decoder architecture forming a recurrence structure
without introducing extra parameters. With the backward skip connections, we
propose a U-Net based network family, namely Bi-directional O-shape networks,
which set new benchmarks on multiple public medical imaging segmentation
datasets. On the other hand, with the most plain architecture (BiO-Net),
network computations inevitably increase along with the pre-set recurrence
time. We have thus studied the deficiency bottleneck of such recurrent design
and propose a novel two-phase Neural Architecture Search (NAS) algorithm,
namely BiX-NAS, to search for the best multi-scale bi-directional skip
connections. The ineffective skip connections are then discarded to reduce
computational costs and speed up network inference. The finally searched
BiX-Net yields the least network complexity and outperforms other
state-of-the-art counterparts by large margins. We evaluate our methods on both
2D and 3D segmentation tasks in a total of six datasets. Extensive ablation
studies have also been conducted to provide a comprehensive analysis for our
proposed methods.


title: An Index for Inclusions of Operator Systems

abstract:
  Inspired by a well-known characterization of the index of an inclusion of
II$_1$ factors due to Pimsner and Popa, we define an index-type invariant for
inclusions of operator systems. We compute examples of this invariant, show
that it is multiplicative under minimal tensor products, and explain how it
generalizes the quantum Lov\'asz theta invariant for a matricial system defined
by Duan, Severini, and Winter.


title:
Synopses of Movie Narratives: a Video-Language Dataset for Story
  Understanding

abstract:
  Despite recent advances of AI, story understanding remains an open and
under-investigated problem. We collect, preprocess, and publicly release a
video-language story dataset, Synopses of Movie Narratives(SyMoN), containing
5,193 video summaries of popular movies and TV series. SyMoN captures
naturalistic storytelling videos for human audience made by human creators, and
has higher story coverage and more frequent mental-state references than
similar video-language story datasets. Differing from most existing video-text
datasets, SyMoN features large semantic gaps between the visual and the textual
modalities due to the prevalence of reporting bias and mental state
descriptions. We establish benchmarks on video-text retrieval and zero-shot
alignment on movie summary videos. With SyMoN, we hope to lay the groundwork
for progress in multimodal story understanding.


title:
Towards Scale Consistent Monocular Visual Odometry by Learning from the
  Virtual World

abstract:
  Monocular visual odometry (VO) has attracted extensive research attention by
providing real-time vehicle motion from cost-effective camera images. However,
state-of-the-art optimization-based monocular VO methods suffer from the scale
inconsistency problem for long-term predictions. Deep learning has recently
been introduced to address this issue by leveraging stereo sequences or
ground-truth motions in the training dataset. However, it comes at an
additional cost for data collection, and such training data may not be
available in all datasets. In this work, we propose VRVO, a novel framework for
retrieving the absolute scale from virtual data that can be easily obtained
from modern simulation environments, whereas in the real domain no stereo or
ground-truth data are required in either the training or inference phases.
Specifically, we first train a scale-aware disparity network using both
monocular real images and stereo virtual data. The virtual-to-real domain gap
is bridged by using an adversarial training strategy to map images from both
domains into a shared feature space. The resulting scale-consistent disparities
are then integrated with a direct VO system by constructing a virtual stereo
objective that ensures the scale consistency over long trajectories.
Additionally, to address the suboptimality issue caused by the separate
optimization backend and the learning process, we further propose a mutual
reinforcement pipeline that allows bidirectional information flow between
learning and optimization, which boosts the robustness and accuracy of each
other. We demonstrate the effectiveness of our framework on the KITTI and
vKITTI2 datasets.


title:
Parabolically induced representations of p-adic $G_2$ distinguished by
  $SO_4$ I

abstract:
  We consider the parabolically induced representations of the symmetric space
$SO_4\backslash G_2$ over a p-adic field using the geometric lemma when the
inducing parabolic is $P_{\beta}$. Using an explicit description of the
embedding of $G_2$ in $GL_8$, we characterize precisely the induced
representations which are $(SO_4, \chi)$-distinguished, given a certain type of
involutions is chosen.


title:
Image-based Stroke Assessment for Multi-site Preclinical Evaluation of
  Cerebroprotectants

abstract:
  Ischemic stroke is a leading cause of death worldwide, but there has been
little success translating putative cerebroprotectants from preclinical trials
to patients. We investigated computational image-based assessment tools for
practical improvement of the quality, scalability, and outlook for large scale
preclinical screening for potential therapeutic interventions. We developed,
evaluated, and deployed a pipeline for image-based stroke outcome
quantification for the Stroke Prelinical Assessment Network (SPAN), which is a
multi-site, multi-arm, multi-stage study evaluating a suite of
cerebroprotectant interventions. Our fully automated pipeline combines
state-of-the-art algorithmic and data analytic approaches to assess stroke
outcomes from multi-parameter MRI data collected longitudinally from a rodent
model of middle cerebral artery occlusion (MCAO), including measures of infarct
volume, brain atrophy, midline shift, and data quality. We tested our approach
with 1,368 scans and report population level results of lesion extent and
longitudinal changes from injury. We validated our system by comparison with
manual annotations of coronal MRI slices and tissue sections from the same
brain, using crowdsourcing from blinded stroke experts from the network. Our
results demonstrate the efficacy and robustness of our image-based stroke
assessments. The pipeline may provide a promising resource for ongoing
preclinical studies conducted by SPAN and other networks in the future.


title: Unitary rotation of pixellated polychromatic images

abstract:
  Unitary rotations of polychromatic images on finite two-dimensional
pixellated screens provide invertibility, group composition, and thus
conservation of information. Rotations have been applied on monochromatic image
data sets, where we now examine closer the Gibbs-like oscillations that appear
due to discrete "discontinuities" of the input images under unitary
transformations. Extended to three-color images we examine here the display of
color at the pixels where, due to the oscillations, some pixel color values may
fall outside their required common numerical range [0, 1], between absence and
saturation of the red, green, and blue formant color images.


title:
Evaluating U-net Brain Extraction for Multi-site and Longitudinal
  Preclinical Stroke Imaging

abstract:
  Rodent stroke models are important for evaluating treatments and
understanding the pathophysiology and behavioral changes of brain ischemia, and
magnetic resonance imaging (MRI) is a valuable tool for measuring outcome in
preclinical studies. Brain extraction is an essential first step in most
neuroimaging pipelines; however, it can be challenging in the presence of
severe pathology and when dataset quality is highly variable. Convolutional
neural networks (CNNs) can improve accuracy and reduce operator time,
facilitating high throughput preclinical studies. As part of an ongoing
preclinical stroke imaging study, we developed a deep-learning mouse brain
extraction tool by using a U-net CNN. While previous studies have evaluated
U-net architectures, we sought to evaluate their practical performance across
data types. We ask how performance is affected with data across: six imaging
centers, two time points after experimental stroke, and across four MRI
contrasts. We trained, validated, and tested a typical U-net model on 240
multimodal MRI datasets including quantitative multi-echo T2 and apparent
diffusivity coefficient (ADC) maps, and performed qualitative evaluation with a
large preclinical stroke database (N=1,368). We describe the design and
development of this system, and report our findings linking data
characteristics to segmentation performance. We consistently found high
accuracy and ability of the U-net architecture to generalize performance in a
range of 95-97% accuracy, with only modest reductions in performance based on
lower fidelity imaging hardware and brain pathology. This work can help inform
the design of future preclinical rodent imaging studies and improve their
scalability and reliability.


title: Single Loop Gaussian Homotopy Method for Non-convex Optimization

abstract:
  The Gaussian homotopy (GH) method is a popular approach to finding better
local minima for non-convex optimization problems by gradually changing the
problem to be solved from a simple one to the original target one. Existing
GH-based methods consisting of a double loop structure incur high computational
costs, which may limit their potential for practical application. We propose a
novel single loop framework for GH methods (SLGH) for both deterministic and
stochastic settings. For those applications in which the convolution
calculation required to build a GH function is difficult, we present
zeroth-order SLGH algorithms with gradient-free oracles. The convergence rate
of (zeroth-order) SLGH depends on the decreasing speed of a smoothing
hyperparameter, and when the hyperparameter is chosen appropriately, it becomes
consistent with the convergence rate of (zeroth-order) gradient descent. In
experiments that included artificial highly non-convex examples and black-box
adversarial attacks, we have demonstrated that our algorithms converge much
faster than an existing double loop GH method while outperforming gradient
descent-based methods in terms of finding a better solution.


title: Formation of Colloidal Chains and Driven Clusters with Optical Binding

abstract:
  We study the effects of the optical binding force on wavelength sized
colloidal particles free to move in a counter-propagating beam. This work is
motivated by the concept of using optical binding to direct the assembly of
large numbers of colloidal particles; previous work has used small numbers of
particles and/or 1D or 2D restricted geometries. Utilizing a novel experimental
scheme, we describe the general static and dynamic self-organization behaviors
for 20--100 particles free to move in 3-dimensional space. We observe the
self-organization of the colloids into large optically bound structures along
with the formation of driven particle clusters. Furthermore we show that the
structure and behavior of these optically bound systems can be tuned using the
refractive index of the particles and properties of the binding light. In
particular, we show that the driven behavior originates from $N$-body
interactions, which has significant implications for future work on optically
bound clusters of more than 2 particles.


title:
Analytical Pricing of 2 Factor Structural PDE model for a Puttable Bond
  with Credit Risk

abstract:
  In this paper is proposed a 2 factor structural PDE model of pricing puttable
bond with credit risk and derived the analytical pricing formula. To this end,
first, a 2 factor structural (PDE) model of pricing zero coupon bond with
credit risk is provided, the analytical pricing formula is derived under some
conditions for default boundary and default recovery, and the strict
monotonicity of the bond price function with respect to the firm value variable
is proved. Then a (2 factor) pricing model of the option on zero coupon bond
with credit risk is provided and under some condition on the exercise price its
analytical pricing formula is derived by transforming the 2 factor model into a
terminal boundary value problem for Black-Scholes equation with time dependent
coefficient using zero coupon bond as numeraire. Using it, we provide the
pricing formulae of the puttable and callable bonds with credit risk.


title: Braneworlds in $f(Q)$ gravity

abstract:
  We propose a braneworld scenario in a modified symmetric teleparallel
gravitational theory, where the dynamics for the gravitational field is encoded
in the nonmetricity tensor rather than in the curvature. Assuming a single real
scalar field with a sine-Gordon self-interaction, the generalized quadratic
nonmetricity invariant $\mathbb{Q}$ controls the brane width while keeping the
shape of the energy density. By considering power corrections of the invariant
$\mathbb{Q}$ in the gravitational Lagrangian, the sine-Gordon potential is
modified exhibiting new barriers and false vacuum. As a result, the domain wall
brane obtains an inner structure, and it undergoes a splitting process. In
addition, we also propose a non-minimal coupling between a bulk fermion field
and the nonmetricity invariant $\mathbb{Q}$. Such geometric coupling leads to a
massless chiral fermion bound to the 3-brane and a stable tower of
non-localized massive states.


title:
Fully polarized nonlinear Breit-Wheeler pair production in pulsed plane
  waves

abstract:
  Fully polarized nonlinear Breit-Wheeler pair production from a beam of
polarized seed photons is investigated in pulsed plane-wave backgrounds. The
particle (electron and positron) spin and photon polarization are
comprehensively described with the theory of the density matrix. The compact
expressions for the energy spectrum and spin polarization of the produced
particles, depending on the initial polarization of the seed photon beam, are
derived and discussed in both linearly and circularly polarized laser
backgrounds. The numerical results suggest an appreciable improvement of the
positron yield by orthogonalizing the photon polarization to the laser
polarization, and the generation of a highly polarized positron beam from a
beam of circularly polarized seed photons. The locally monochromatic
approximation and the locally constant field approximation are derived and
benchmarked with the full quantum electrodynamic calculations.


title:
Self-interfering dynamics in Bose-Einstein condensates with engineered
  dispersions

abstract:
  Optical lattice and spin-orbit coupling are typical experimental approaches
to engineer dispersion. We reveal a self-interfering dynamics in a
noninteracting Bose-Einstein condensate with the engineered dispersion by
optical lattice or spin-orbit coupling. The self-interference results from the
co-occupation of positive and negative effect mass regimes in the engineered
dispersion. The physical origination of the self-interference is explained by
the Wigner distribution function of the self-interfering wave-packet. We
characterize detail features of the self-interference pattern.


title: Bayesian inference via sparse Hamiltonian flows

abstract:
  A Bayesian coreset is a small, weighted subset of data that replaces the full
dataset during Bayesian inference, with the goal of reducing computational
cost. Although past work has shown empirically that there often exists a
coreset with low inferential error, efficiently constructing such a coreset
remains a challenge. Current methods tend to be slow, require a secondary
inference step after coreset construction, and do not provide bounds on the
data marginal evidence. In this work, we introduce a new method -- sparse
Hamiltonian flows -- that addresses all three of these challenges. The method
involves first subsampling the data uniformly, and then optimizing a
Hamiltonian flow parametrized by coreset weights and including periodic
momentum quasi-refreshment steps. Theoretical results show that the method
enables an exponential compression of the dataset in a representative model,
and that the quasi-refreshment steps reduce the KL divergence to the target.
Real and synthetic experiments demonstrate that sparse Hamiltonian flows
provide accurate posterior approximations with significantly reduced runtime
compared with competing dynamical-system-based inference methods.


title: Information-Theoretic Odometry Learning

abstract:
  In this paper, we propose a unified information theoretic framework for
learning-motivated methods aimed at odometry estimation, a crucial component of
many robotics and vision tasks such as navigation and virtual reality where
relative camera poses are required in real time. We formulate this problem as
optimizing a variational information bottleneck objective function, which
eliminates pose-irrelevant information from the latent representation. The
proposed framework provides an elegant tool for performance evaluation and
understanding in information-theoretic language. Specifically, we bound the
generalization errors of the deep information bottleneck framework and the
predictability of the latent representation. These provide not only a
performance guarantee but also practical guidance for model design, sample
collection, and sensor selection. Furthermore, the stochastic latent
representation provides a natural uncertainty measure without the needs for
extra structures or computations. Experiments on two well-known odometry
datasets demonstrate the effectiveness of our method.


title: Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI

abstract:
  Purpose: To introduce a dual-domain reconstruction network with V-Net and
K-Net for accurate MR image reconstruction from undersampled k-space data.
Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded
U-Nets in image domain and/or k-space domain. Nevertheless, these methods have
following problems: (1) Directly applying U-Net in k-space domain is not
optimal for extracting features in k-space domain; (2) Classical image-domain
oriented U-Net is heavy-weight and hence is inefficient to be cascaded many
times for yielding good reconstruction accuracy; (3) Classical image-domain
oriented U-Net does not fully make use information of encoder network for
extracting features in decoder network; and (4) Existing methods are
ineffective in simultaneously extracting and fusing features in image domain
and its dual k-space domain. To tackle these problems, we propose in this paper
(1) an image-domain encoder-decoder sub-network called V-Net which is more
light-weight for cascading and effective in fully utilizing features in the
encoder for decoding, (2) a k-space domain sub-network called K-Net which is
more suitable for extracting hierarchical features in k-space domain, and (3) a
dual-domain reconstruction network where V-Nets and K-Nets are parallelly and
effectively combined and cascaded. Results: Extensive experimental results on
the challenging fastMRI dataset demonstrate that the proposed KV-Net can
reconstruct high-quality images and outperform current state-of-the-art
approaches with fewer parameters. Conclusions: To reconstruct images
effectively and efficiently from incomplete k-space data, we have presented a
parallel dual-domain KV-Net to combine K-Nets and V-Nets. The KV-Net is more
lightweight than state-of-the-art methods but achieves better reconstruction
performance.


title:
General properties of the Solutions to Moving Boundary Problems for
  Black-Sholes Equations

abstract:
  We study general properties such as the solution representation of a moving
boundary value problem of the Black-Scholes equation, its min-max estimation,
lower and upper gradient estimates, and strict monotonicity with respect to the
spatial variables of the solution. These results are used in the study of a
structural model of pricing puttable bond with credit risk. We first prove the
solution representation of a special fixed boundary value problem of the
Black-Scholes equation, the min-max estimate, the lower and upper gradient
estimates, and the strict monotonicity with respect to the spatial variables of
the solution. Then, these results are applied to give the solution
representation of a moving boundary value problem of the Black-Scholes equation
with moving boundary in the form of an exponential function, the min-max
estimate, the lower and upper gradient estimates, and the strict monotonicity
results on the spatial variables of the solution. Finally, we illustrate how
these results can be used in the derivation of analytical pricing formulae and
financial analysis of price functions of puttable bonds with credit risk
(corporate bonds with one early redemption date). Our results can be used for
the derivation and analysis of the analytical pricing formulae of the
one-factor structural model of a more general puttable bonds with credit risk
(corporate bond with several early redemption dates).


title: Tracking Dynamical Features via Continuation and Persistence

abstract:
  Multivector fields and combinatorial dynamical systems have recently become a
subject of interest due to their potential for use in computational methods. In
this paper, we develop a method to track an isolated invariant set -- a salient
feature of a combinatorial dynamical system -- across a sequence of multivector
fields. This goal is attained by placing the classical notion of the
"continuation" of an isolated invariant set in the combinatorial setting. In
particular, we give a "Tracking Protocol" that, when given a seed isolated
invariant set, finds a canonical continuation of the seed across a sequence of
multivector fields. In cases where it is not possible to continue, we show how
to use zigzag persistence to track homological features associated with the
isolated invariant sets. This construction permits viewing continuation as a
special case of persistence.


title: Snowmass2021 CMB-HD White Paper

abstract:
  CMB-HD is a proposed millimeter-wave survey over half the sky that would be
ultra-deep (0.5 uK-arcmin) and have unprecedented resolution (15 arcseconds at
150 GHz). Such a survey would answer many outstanding questions about the
fundamental physics of the Universe. Major advances would be 1.) the use of
gravitational lensing of the primordial microwave background to map the
distribution of matter on small scales (k~10 h Mpc^(-1)), which probes dark
matter particle properties. It will also allow 2.) measurements of the thermal
and kinetic Sunyaev-Zel'dovich effects on small scales to map the gas density
and velocity, another probe of cosmic structure. In addition, CMB-HD would
allow us to cross critical thresholds: 3.) ruling out or detecting any new,
light (< 0.1 eV) particles that were in thermal equilibrium with known
particles in the early Universe, 4.) testing a wide class of multi-field models
that could explain an epoch of inflation in the early Universe, and 5.) ruling
out or detecting inflationary magnetic fields. CMB-HD would also provide
world-leading constraints on 6.) axion-like particles, 7.) cosmic
birefringence, 8.) the sum of the neutrino masses, and 9.) the dark energy
equation of state. The CMB-HD survey would be delivered in 7.5 years of
observing 20,000 square degrees of sky, using two new 30-meter-class off-axis
crossed Dragone telescopes to be located at Cerro Toco in the Atacama Desert.
Each telescope would field 800,000 detectors (200,000 pixels), for a total of
1.6 million detectors.


title: Tree-level Interference in VBF production of $Vh$

abstract:
  We study the production of the Higgs in a association with a vector ($V=W,Z$)
via the VBF process, VBF-VH. In the Standard Model (SM), this process exhibits
tree-level destructive interference between between $W$ and $Z$ mediated
processes and is thus very sensitive to deviations in Higgs couplings to vector
bosons. We study this process at both the HL-LHC as well as future high energy
lepton colliders. We show in particular that the scenario where Higgs couplings
have the same magnitude but opposite relative sign as in the SM, a scenario
that is very difficult to distinguish without interference, can be probed with
this process at either collider.


title:
Asymptotics of quantum invariants of surface diffeomorphisms II: The
  figure-eight knot complement

abstract:
  In earlier work, the authors introduced a conjecture which, for an
orientation-preserving diffeomorphism $\varphi \colon S \to S$ of a surface,
connects a certain quantum invariant of $\varphi$ with the hyperbolic volume of
its mapping torus $M_\varphi$. This article provides a proof of this conjecture
in the simplest case where it applies, namely when the surface $S$ is the
one-puncture torus and the mapping torus $M_\varphi$ is the complement of the
figure-eight knot.


title: Beam background expectations for Belle II at SuperKEKB

abstract:
  The Belle II experiment at the SuperKEKB electron-positron collider aims to
collect an unprecedented data set of $\rm 50~{\rm ab}^{-1}$ to study
$CP$-violation in the $B$-meson system and to search for Physics beyond the
Standard Model (BSM). SuperKEKB is already the world's highest-luminosity
collider. In order to collect the planned data set within approximately one
decade, the target is to reach a peak luminosity of $\rm 6.3 \times
10^{35}~cm^{-2}s^{-1}$ by further increasing the beam currents and reducing the
beam-size at the interaction point by squeezing the betatron function down to
$\beta^{*}_{\rm y}=\rm 0.3~mm$. Beam backgrounds are a key challenge in this
context. We estimate the expected background evolution in the next ten years
and discuss potential challenges and background mitigation strategies. We find
that backgrounds will remain high but acceptable until a luminosity of at least
$\rm 2.8\times 10^{35}~cm^{-2}s^{-1}$ is reached at $\beta^{*}_{\rm y}=\rm
0.6~mm$. Beyond this luminosity, predictions are highly uncertain, owing to a
planned redesign of the interaction region. Improved background estimates with
reduced uncertainties for the final, maximum-luminosity operation will require
completion of this redesign.


title:
Great Balls of FIRE I: The formation of star clusters across cosmic time
  in a Milky Way-mass galaxy

abstract:
  The properties of young star clusters formed within a galaxy are thought to
vary in different interstellar medium (ISM) conditions, but the details of this
mapping from galactic to cluster scales are poorly understood due to the large
dynamic range involved in galaxy and star cluster formation. We introduce a new
method for modeling cluster formation in galaxy simulations: mapping giant
molecular clouds (GMCs) formed self-consistently in a FIRE-2 MHD galaxy
simulation onto a cluster population according to a GMC-scale cluster formation
model calibrated to higher-resolution simulations, obtaining detailed
properties of the galaxy's star clusters in mass, metallicity, space, and time.
We find $\sim 10\%$ of all stars formed in the galaxy originate in
gravitationally-bound clusters overall, and this fraction increases in regions
with elevated $\Sigma_{\rm gas}$ and $\Sigma_{\rm SFR}$, because such regions
host denser GMCs with higher star formation efficiency. These quantities vary
systematically over the history of the galaxy, driving variations in cluster
formation. The mass function of bound clusters varies -- no single
Schechter-like or power-law distribution applies at all times. In the most
extreme episodes, clusters as massive as $7\times 10^6 M_\odot$ form in
massive, dense clouds with high star formation efficiency. The initial
mass-radius relation of young star clusters is consistent with an
environmentally-dependent 3D density that increases with $\Sigma_{\rm gas}$ and
$\Sigma_{\rm SFR}$. The model does not reproduce the age and metallicity
statistics of old ($>11\rm Gyr$) globular clusters found in the Milky Way,
possibly because it forms stars more slowly at $z>3$.


title:
A Survey of Surface Defect Detection of Industrial Products Based on A
  Small Number of Labeled Data

abstract:
  The surface defect detection method based on visual perception has been
widely used in industrial quality inspection. Because defect data are not easy
to obtain and the annotation of a large number of defect data will waste a lot
of manpower and material resources. Therefore, this paper reviews the methods
of surface defect detection of industrial products based on a small number of
labeled data, and this method is divided into traditional image
processing-based industrial product surface defect detection methods and deep
learning-based industrial product surface defect detection methods suitable for
a small number of labeled data. The traditional image processing-based
industrial product surface defect detection methods are divided into
statistical methods, spectral methods and model methods. Deep learning-based
industrial product surface defect detection methods suitable for a small number
of labeled data are divided into based on data augmentation, based on transfer
learning, model-based fine-tuning, semi-supervised, weak supervised and
unsupervised.


title: Pico-photonics: Anomalous Atomistic Waves in Silicon

abstract:
  The concept of photonic frequency $(\omega)$ - momentum $(q)$ dispersion has
been extensively studied in artificial dielectric structures such as photonic
crystals and metamaterials. However, the $\omega-q$ dispersion of
electrodynamic excitations hosted in natural materials at the atomistic level
is far less explored. Here, we develop a Maxwell Hamiltonian theory of matter
combined with the quantum theory of atomistic polarization to obtain the
electrodynamic dispersion of natural materials interacting with the photon
field. We apply this theory to silicon and discover the existence of anomalous
atomistic waves. These waves occur in the spectral region where propagating
waves are conventionally forbidden in a macroscopic theory. Our findings
demonstrate that natural media can host a variety of yet to be discovered waves
with sub-nano-meter effective wavelengths in the pico-photonics regime.


title:
An Efficient Video Streaming Architecture with QoS Control for Virtual
  Desktop Infrastructure in Cloud Computing

abstract:
  In virtual desktop infrastructure (VDI) environments, the remote display
protocol has a big responsibility to transmit video data from a data
center-hosted desktop to the endpoint. The protocol must ensure a high level of
client perceived end-to-end quality of service (QoS) under heavy work load
conditions. Each remote display protocol works differently depending on the
network and which applications are being delivered. In healthcare applications,
doctors and nurses can use mobile devices directly to monitor patients.
Moreover, the ability to implement tasks requiring high consumption of CPU and
other resources is applicable to a variety of applications including research
and cloud gaming. Such computer games and complex processes will run on
powerful cloud servers and the screen contents will be transmitted to the
client. TO enable such applications, remote display technology requires further
enhancements to meet more stringent requirements on bandwidth and QoS, an to
allow realtime operation. In this paper, we present an architecture including
flexible QoS control to improve the user quality of experience (QoE). The QoS
control is developed based on linear regression modeling using historical
network data. Additionally, the architecture includes a novel compression
algorithm of 2D images, designed to guarantee the best image quality and to
reduce video delay; this algorithm is based on k-means clustering and can
satisfy the requirements of realtime onboard processing. Through simulations
with a real work dataset collected by the MIT Computer Science and Artificial
Lab, we present experimental as well as explain the performance of the QoS
system.