title:
Automated Learning for Deformable Medical Image Registration by Jointly
  Optimizing Network Architectures and Objective Functions

abstract:
  Deformable image registration plays a critical role in various tasks of
medical image analysis. A successful registration algorithm, either derived
from conventional energy optimization or deep networks requires tremendous
efforts from computer experts to well design registration energy or to
carefully tune network architectures for the specific type of medical data. To
tackle the aforementioned problems, this paper proposes an automated learning
registration algorithm (AutoReg) that cooperatively optimizes both
architectures and their corresponding training objectives, enabling
non-computer experts, e.g., medical/clinical users, to conveniently find
off-the-shelf registration algorithms for diverse scenarios. Specifically, we
establish a triple-level framework to deduce registration network architectures
and objectives with an auto-searching mechanism and cooperating optimization.
We conduct image registration experiments on multi-site volume datasets and
various registration tasks. Extensive results demonstrate that our AutoReg may
automatically learn an optimal deep registration network for given volumes and
achieve state-of-the-art performance, also significantly improving computation
efficiency than the mainstream UNet architectures (from 0.558 to 0.270 seconds
for a 3D image pair on the same configuration).


title:
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive
  Semantic Segmentation

abstract:
  In this paper, we present a direct adaptation strategy (ADAS), which aims to
directly adapt a single model to multiple target domains in a semantic
segmentation task without pretrained domain-specific models. To do so, we
design a multi-target domain transfer network (MTDT-Net) that aligns visual
attributes across domains by transferring the domain distinctive features
through a new target adaptive denormalization (TAD) module. Moreover, we
propose a bi-directional adaptive region selection (BARS) that reduces the
attribute ambiguity among the class labels by adaptively selecting the regions
with consistent feature statistics. We show that our single MTDT-Net can
synthesize visually pleasing domain transferred images with complex driving
datasets, and BARS effectively filters out the unnecessary region of training
images for each target domain. With the collaboration of MTDT-Net and BARS, our
ADAS achieves state-of-the-art performance for multi-target domain adaptation
(MTDA). To the best of our knowledge, our method is the first MTDA method that
directly adapts to multiple domains in semantic segmentation.


title:
Analysis and application of multiplicative stochastic process with a
  sample-dependent lower bound

abstract:
  A multiplicative stochastic process with the lower bound lognormally
distributed is investigated. For the process, the model is constructed, and its
distribution function (involving four parameters) and the related statistical
properties are derived. By adjusting the parameters, it is confirmed that the
theoretical distribution is consistent with empirical distributions of some
real data.


title:
A Unified Theory of Free Energy Functionals and Applications to
  Diffusion

abstract:
  Free energy functionals of Ginzburg-Landau type lie at the heart of a broad
class of continuum dynamical models, such as the Cahn-Hilliard and
Swift-Hohenberg equations. Despite the wide use of such models, the assumptions
embodied in the free energy functionals are frequently either poorly justified
or lead to physically opaque parameters. Here, we introduce a mathematically
rigorous pathway for constructing free energy functionals that generalizes
beyond the constraints of Ginzburg-Landau gradient expansions. We show that the
new formalism unifies existing free energetic descriptions under a single
umbrella by establishing the criteria under which the generalized free energy
reduces to gradient-based representations. Consequently, we derive a precise
physical interpretation of the gradient energy parameter in the Cahn-Hilliard
model as the product of an interaction length scale and the free energy
curvature. The practical impact of our approach is demonstrated using both a
model free energy function and the silicon-germanium alloy system.


title:
CheckSel: Efficient and Accurate Data-valuation Through Online
  Checkpoint Selection

abstract:
  Data valuation and subset selection have emerged as valuable tools for
application-specific selection of important training data. However, the
efficiency-accuracy tradeoffs of state-of-the-art methods hinder their
widespread application to many AI workflows. In this paper, we propose a novel
2-phase solution to this problem. Phase 1 selects representative checkpoints
from an SGD-like training algorithm, which are used in phase-2 to estimate the
approximate training data values, e.g. decrease in validation loss due to each
training point. A key contribution of this paper is CheckSel, an Orthogonal
Matching Pursuit-inspired online sparse approximation algorithm for checkpoint
selection in the online setting, where the features are revealed one at a time.
Another key contribution is the study of data valuation in the domain
adaptation setting, where a data value estimator obtained using checkpoints
from training trajectory in the source domain training dataset is used for data
valuation in a target domain training dataset. Experimental results on
benchmark datasets show the proposed algorithm outperforms recent baseline
methods by up to 30% in terms of test accuracy while incurring a similar
computational burden, for both standalone and domain adaptation settings.


title: Stability of xenon-sodium compounds at moderately low pressures

abstract:
  A growing body of theoretical and experimental evidence suggests that inert
gases (He, Ne, Ar, Kr, Xe, Rn) become less and less inert under increasing
pressure. Here we use the ab initio evolutionary algorithm to predict stable
compounds of Xe and Na at pressures below 100 GPa, and find three stable
compounds, NaXe, NaXe$_3$ and NaXe$_4$. The NaXe belongs to a well-known cubic
CsCl structure type. The NaXe$_4$'s structure is common in amphiboles, whereas
the NaXe$_3$ has a unique structure, analogous to the "post-perovskite"
orthorhombic CaIrO$_3$-type structure with Ir atoms removed. This is the first
time that a cation-vacant version of the CaIrO$_3$ is found in any compound.
NaXe, NaXe$_3$ and NaXe$_4$ are found to be metallic.


title:
Accelerator and Beam Physics: Grand Challenges and Research
  Opportunities

abstract:
  Accelerator and beam physics (ABP) is the science of the motion, generation,
acceleration, manipulation, prediction, observation and use of charged particle
beams. It focuses on fundamental long-term accelerator and beam physics
research and development. Accelerator and beam physics research has resulted in
important advances in accelerator science, yet support for this research is
declining. NSF has terminated its program in accelerator Science and funding by
DOE through GARD and Accelerator Stewardship has been steady or declining. The
declining support for accelerator research will slow advances and threaten
student training and work-force development in accelerator science. We propose
a robust and scientifically challenging program in accelerator and beam
physics, which will position the field of US High Energy Physics to be
productive and competitive for decades to come.


title: Lattice fluctuation induced pseudogap in quasi-one-dimensional Ta2NiSe5

abstract:
  In conventional solid-state systems, the development of an energy gap is
often associated with a broken symmetry. However, strongly correlated materials
can exhibit energy gaps without any global symmetry breaking -- the so-called
pseudogap, most notably in the Mott insulating state and the fluctuating
superconducting or charge density wave states. To date, lattice induced
pseudogap remains elusive. Combining angle-resolved photoemission spectroscopy
(ARPES) and single crystal x-ray diffraction, we identify a pseudogap with
primary lattice origin instead of direct Coulomb interaction in the quasi-1D
excitonic insulator candidate Ta2NiSe5. With both thermal and nonthermal
tuning, strong lattice contribution is revealed by pervasive diffuse scattering
signal and negative electronic compressibility in the pseudogap state.
Meanwhile, by tuning the normal state dielectric environment, the Coulomb
interaction is found to be an order of magnitude too small to account for the
energy gap in the broken-symmetry state. Combining first-principles and
microscopic model calculations, we show that it is the interband
electron-phonon coupling that mediates the effective electron-hole
hybridization, causing a metal-to-insulator-like transition without breaking
the global symmetry in the pseudogap state. Our work establishes the precedence
of an electron-phonon coupling induced pseudogap, highlighting Ta2NiSe5 as a
highly lattice-tuneable platform to study low dimensional fluctuations.


title:
Minimizing state preparation times in pulse-level variational molecular
  simulations

abstract:
  Quantum simulation on NISQ devices is severely limited by short coherence
times. A variational pulse-shaping algorithm known as ctrl-VQE was recently
proposed to address this issue by eliminating the need for parameterized
quantum circuits, which lead to long state preparation times. Here, we find the
shortest possible pulses for ctrl-VQE to prepare target molecular wavefunctions
for a given device Hamiltonian describing coupled transmon qubits. We find that
the time-optimal pulses that do this have a bang-bang form consistent with
Pontryagin's maximum principle. We further investigate how the minimal state
preparation time is impacted by truncating the transmons to two versus more
levels. We find that leakage outside the computational subspace (something that
is usually considered problematic) speeds up the state preparation, further
reducing device coherence-time demands. This speedup is due to an enlarged
solution space of target wavefunctions and to the appearance of additional
channels connecting initial and target states.


title:
Measuring the CP properties of the Higgs sector at electron-positron
  colliders

abstract:
  The violation of CP symmetry at the electro-weak scale is one of the
essential ingredients for electro-weak baryogenesis. It is therefore of great
interest to map the CP properties of the Higgs sector in as much detail as
possible. A linear electron-positron Higgs factory collider will provide many
opportunities to probe the CP nature of the Higgs sector, thanks to access to
several Higgs production processes at a wide range of centre-of-mass energies.
In this paper we report on two studies based on full simulation of detector
response and realistic reconstruction algorithms: 1) a study of $h \to \tau
\tau$ at ILC-250, in which mixing of CP eigenstates can be measured to a
precision of 75~mrad; and 2) the current status of an ongoing study of the
$ZZ$--fusion process at 1.4 TeV CLIC and 1 TeV ILC, for which we expect to
achieve concrete results during the Snowmass study period.


title:
Edge Detection and Image Filter algorithms for Spectroscopic Analysis
  with Deep Learning Applications

abstract:
  Edge detection and image filters are commonly used in computer vision.
However, they have never been applied to the data analysis of angle-resolved
photoemission spectroscopy (ARPES) data before in a systematic fashion. In this
paper we will use the Sobel, Laplacian of a gaussian (LoG), Canny, Prewitt,
Roberts, and fuzzy logic methods for edge detection in the ARPES results of
HfP2, ZrSiS, and Hf2Te2P2. We find that the Canny filter is the best method for
edge detection of noisy data that is typical of ARPES measurements, while the
other edge detection techniques are not able to correctly detect ARPES bands.


title:
SpectroLab: An Open Source Matlab Based Toolbox for High Throughput
  Spectroscopy Analysis

abstract:
  We present an open source software package SpectroLab a Matlab-based tool
developed in 2018 for the analysis of spectroscopic data. In this package,
there are tools for derivative analysis, stacked energy contours, stacked plots
for theory, 3D volumetric plots, core level analysis, and derivative analysis.
The package can currently be used for angle-resolved photoemission spectroscopy
(ARPES) data, with the ability to also be used for other spectroscopic data in
the future. We apply this program to the HfP2, ZrSiS, and Hf2Te2P systems to
demonstrate its robustness.


title:
Grounding Commands for Autonomous Vehicles via Layer Fusion with
  Region-specific Dynamic Layer Attention

abstract:
  Grounding a command to the visual environment is an essential ingredient for
interactions between autonomous vehicles and humans. In this work, we study the
problem of language grounding for autonomous vehicles, which aims to localize a
region in a visual scene according to a natural language command from a
passenger. Prior work only employs the top layer representations of a
vision-and-language pre-trained model to predict the region referred to by the
command. However, such a method omits the useful features encoded in other
layers, and thus results in inadequate understanding of the input scene and
command. To tackle this limitation, we present the first layer fusion approach
for this task. Since different visual regions may require distinct types of
features to disambiguate them from each other, we further propose the
region-specific dynamic (RSD) layer attention to adaptively fuse the multimodal
information across layers for each region. Extensive experiments on the
Talk2Car benchmark demonstrate that our approach helps predict more accurate
regions and outperforms state-of-the-art methods.


title:
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image
  Labels for Quantitative Clinical Evaluation

abstract:
  Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging.
However, long image acquisition times, the need for qualitative expert
analysis, and the lack of (and difficulty extracting) quantitative indicators
that are sensitive to tissue health have curtailed widespread clinical and
research studies. While recent machine learning methods for MRI reconstruction
and analysis have shown promise for reducing this burden, these techniques are
primarily validated with imperfect image quality metrics, which are discordant
with clinically-relevant measures that ultimately hamper clinical deployment
and clinician trust. To mitigate this challenge, we present the Stanford Knee
MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative
knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation
of MRI reconstruction and analysis tools. This 1.6TB dataset consists of
raw-data measurements of ~25,000 slices (155 patients) of anonymized patient
MRI scans, the corresponding scanner-generated DICOM images, manual
segmentations of four tissues, and bounding box annotations for sixteen
clinically relevant pathologies. We provide a framework for using qMRI
parameter maps, along with image reconstructions and dense image labels, for
measuring the quality of qMRI biomarker estimates extracted from MRI
reconstruction, segmentation, and detection techniques. Finally, we use this
framework to benchmark state-of-the-art baselines on this dataset. We hope our
SKM-TEA dataset and code can enable a broad spectrum of research for modular
image reconstruction and image analysis in a clinically informed manner.
Dataset access, code, and benchmarks are available at
https://github.com/StanfordMIMI/skm-tea.


title:
Low-dose CT reconstruction by self-supervised learning in the projection
  domain

abstract:
  In the intention of minimizing excessive X-ray radiation administration to
patients, low-dose computed tomography (LDCT) has become a distinct trend in
radiology. However, while lowering the radiation dose reduces the risk to the
patient, it also increases noise and artifacts, compromising image quality and
clinical diagnosis. In most supervised learning methods, paired CT images are
required, but such images are unlikely to be available in the clinic. We
present a self-supervised learning model (Noise2Projection) that fully exploits
the raw projection images to reduce noise and improve the quality of
reconstructed LDCT images. Unlike existing self-supervised algorithms, the
proposed method only requires noisy CT projection images and reduces noise by
exploiting the correlation between nearby projection images. We trained and
tested the model using clinical data and the quantitative and qualitative
results suggest that our model can effectively reduce LDCT image noise while
also drastically removing artifacts in LDCT images.


title:
Fairness Evaluation in Deepfake Detection Models using Metamorphic
  Testing

abstract:
  Fairness of deepfake detectors in the presence of anomalies are not well
investigated, especially if those anomalies are more prominent in either male
or female subjects. The primary motivation for this work is to evaluate how
deepfake detection model behaves under such anomalies. However, due to the
black-box nature of deep learning (DL) and artificial intelligence (AI)
systems, it is hard to predict the performance of a model when the input data
is modified. Crucially, if this defect is not addressed properly, it will
adversely affect the fairness of the model and result in discrimination of
certain sub-population unintentionally. Therefore, the objective of this work
is to adopt metamorphic testing to examine the reliability of the selected
deepfake detection model, and how the transformation of input variation places
influence on the output. We have chosen MesoInception-4, a state-of-the-art
deepfake detection model, as the target model and makeup as the anomalies.
Makeups are applied through utilizing the Dlib library to obtain the 68 facial
landmarks prior to filling in the RGB values. Metamorphic relations are derived
based on the notion that realistic perturbations of the input images, such as
makeup, involving eyeliners, eyeshadows, blushes, and lipsticks (which are
common cosmetic appearance) applied to male and female images, should not alter
the output of the model by a huge margin. Furthermore, we narrow down the scope
to focus on revealing potential gender biases in DL and AI systems.
Specifically, we are interested to examine whether MesoInception-4 model
produces unfair decisions, which should be considered as a consequence of
robustness issues. The findings from our work have the potential to pave the
way for new research directions in the quality assurance and fairness in DL and
AI systems.


title: Uncertainty Relation and Minimum Wave Packet on Circle

abstract:
  We discuss on the uncertainty relation (UR) for a closed one dimensional
system (circle). In such a system, we cannot use the length parameter along the
circle as a position variable. Otherwise we meet a difficulty about the
definiteness of the average position and the standard deviation (SD). From this
reason, we define the position variable in an external Euclidean space in which
our circle is embedded. In the same way we define a SD by using that position
variable. Then we obtain the UR which is similar to the usual one. We also
discuss the minimum wave packet (MWP) on the circle. MWP is expressed by von
Mises distribution function with a limit of its parameter.


title: A New Tool for Detecting BSM Physics in $B \to K^* \ell \ell$ Decays

abstract:
  Flavor-changing neutral current (FCNC) processes such as $b \to s\ell\ell$ do
not occur at tree-level in the Standard Model (SM), which makes them sensitive
probes of physics beyond the standard model (BSM). Intriguing BSM hints in $b
\to s$ transitions have been observed by multiple flavor physics experiments,
LHCb, Belle, BaBar, and Belle II. We have upgraded the widely used event
generator EvtGen to model $B\to K^* \ell^+ \ell^-$ with improved SM decay
amplitudes and amplitudes for possible BSM physics contributions, implemented
in the operator product expansion in terms of Wilson coefficients; this
upgraded event generator can then be used to investigate the experimental
sensitivity to the most general BSM signal resulting from dimension-six
operators. We describe the advantages and potential of the newly developed
'Sibidanov Physics Generator' in improving sensitivity of searches and
clarifying signatures. Advantages include properly simulating BSM scenarios,
interference between SM and BSM amplitudes, and correlations between different
BSM observables as well as acceptance bias. We show that exploiting such
correlations substantially improves experimental sensitivity. As a
demonstration of the proposed approach, we review the prospects for improved
measurements with $B \to K^* \ell \ell$ decays from the expected 50 ab$^{-1}$
dataset of the Belle II experiment with a four-dimensional unbinned maximum
likelihood fit. We describe new promising experimental signatures, and novel
ideas such as $\Delta$-observables, which reduce uncertainties in the SM
expectations due to QCD and resonance effects. The $\Delta$-observables appear
ideally suited for Belle II with the large data sets expected in the next
decade. Belle II also has excellent sensitivity to New Physics (NP) in the
Wilson coefficients $C_7$ and $C_7'$, which appear at low $q^2$ in the
di-electron channel.


title:
Large enhancement in thermal conductivity of solvent cast
  expanded-graphite/polyetherimide composites

abstract:
  We demonstrate in this work, that expanded graphite (EG) can lead to a very
large enhancement in thermal conductivity of polyetherimide-graphene and
epoxy-graphene nanocomposites prepared via solvent casting technique. A k value
of 6.56 Wm-1K-1 is achieved for 10 weight % composition sample, representing an
enhancement of ~2770% over pristine polyetherimide (k ~ 0.23 Wm-1K-1). This
extraordinary enhancement in thermal conductivity is shown to be due to a
network of continuous graphene sheets over long length scales, resulting in low
thermal contact resistance at bends/turns due to the graphene sheets being
covalently bonded at such junctions. Solvent casting offers the advantage of
preserving the porous structure of expanded graphite in the composite,
resulting in the above highly thermally conductive interpenetrating network of
graphene and polymer. Solvent casting also does not break down the expanded
graphite particles, due to minimal forces involved, allowing for efficient heat
transfer over long length scales, further enhancing overall composite thermal
conductivity. Comparisons with a recently introduced effective medium model
shows a very high value of predicted particle-particle interfacial conductance,
providing evidence for efficient interfacial thermal transport in expanded
graphite composites. Field Emission Environmental Scanning Electron Microscopy
(FE-ESEM) is used to provide detailed understanding of interpenetrating
graphene-polymer structure in the expanded graphite composite. These results
open up novel avenues for achieving high thermal conductivity polymer
composites.


title:
Stabilized exponential-SAV schemes preserving energy dissipation law and
  maximum bound principle for the Allen-Cahn type equations

abstract:
  It is well-known that the Allen-Cahn equation not only satisfies the energy
dissipation law but also possesses the maximum bound principle (MBP) in the
sense that the absolute value of its solution is pointwise bounded for all time
by some specific constant under appropriate initial/boundary conditions. In
recent years, the scalar auxiliary variable (SAV) method and many of its
variants have attracted much attention in numerical solution for gradient flow
problems due to their inherent advantage of preserving certain discrete
analogues of the energy dissipation law. However, existing SAV schemes usually
fail to preserve the MBP when applied to the Allen-Cahn equation. In this
paper, we develop and analyze new first- and second-order stabilized
exponential-SAV schemes for a class of Allen-Cahn type equations, which are
shown to simultaneously preserve the energy dissipation law and MBP in discrete
settings. In addition, optimal error estimates for the numerical solutions are
rigorously obtained for both schemes. Extensive numerical tests and comparisons
are also conducted to demonstrate the performance of the proposed schemes.


title:
A Bayesian Precision Response-adaptive Phase II Clinical Trial Design
  for Radiotherapies with Competing Risk Survival Outcomes

abstract:
  Many phase II clinical trials have used survival outcomes as the primary
endpoints in recent decades. Suppose the radiotherapy is evaluated in a phase
II trial using survival outcomes. In that case, the competing risk issue often
arises because the time to disease progression can be censored by the time to
normal tissue complications, and vice versa. Besides, much literature has
examined that patients receiving the same radiotherapy dose may yield distinct
responses due to their heterogeneous radiation susceptibility statuses.
Therefore, the "one-dose-fit-all" strategy often fails, and it is more relevant
to evaluate the subgroup-specific treatment effect with the subgroup defined by
the radiation susceptibility status. In this paper, we propose a Bayesian
precision phase II trial design evaluating the subgroup-specific treatment
effects of radiotherapy. We use the cause-specific hazard approach to model the
competing risk survival outcomes. We propose restricting the candidate
radiation doses based on each patient's radiation susceptibility status. Only
the clinically feasible personalized dose will be considered, which enhances
the benefit for the patients in the trial. In addition, we propose a stratified
Bayesian adaptive randomization scheme such that more patients will be
randomized to the dose reporting more favorable survival outcomes. Numerical
studies have shown that the proposed design performed well and outperformed the
conventional design ignoring the competing risk issue.


title:
Enhanced-Fidelity Ultrafast Geometric Quantum Computation Using Strong
  Classical Drives

abstract:
  We propose a general approach to implement nonadiabatic geometric single- and
two-qubit gates beyond the rotating wave approximation (RWA). This protocol is
compatible with most optimal control methods used in previous RWA protocols;
thus, it is as robust as (or even more robust than) the RWA protocols. Using
counter-rotating effects allows us to apply strong drives. Therefore, we can
improve the gate speed by 5--10 times compared to the RWA counterpart for
implementing high-fidelity ($\geq 99.99\%$) gates. Such an ultrafast evolution
(nanoseconds, even picoseconds) significantly reduces the influence of
decoherence (e.g., the qubit dissipation and dephasing). Moreover, because the
counter-rotating effects no longer induce gate infidelities (in both the weak
and strong driving regimes), we can achieve a higher fidelity compared to the
RWA protocols. Therefore, in the presence of decoherence, one can implement
ultrafast geometric quantum gates with $\geq 99\%$ fidelities.


title: Semi-Discrete Normalizing Flows through Differentiable Tessellation

abstract:
  Mapping between discrete and continuous distributions is a difficult task and
many have had to resort to approximate or heuristical approaches. We propose a
tessellation-based approach that directly learns quantization boundaries on a
continuous space, complete with exact likelihood evaluations. This is done
through constructing normalizing flows on convex polytopes parameterized
through a differentiable Voronoi tessellation. Using a simple homeomorphism
with an efficient log determinant Jacobian, we can then cheaply parameterize
distributions on convex polytopes.
  We explore this approach in two application settings, mapping from discrete
to continuous and vice versa. Firstly, a Voronoi dequantization allows
automatically learning quantization boundaries in a multidimensional space. The
location of boundaries and distances between regions can encode useful
structural relations between the quantized discrete values. Secondly, a Voronoi
mixture model has constant computation cost for likelihood evaluation
regardless of the number of mixture components. Empirically, we show
improvements over existing methods across a range of structured data
modalities, and find that we can achieve a significant gain from just adding
Voronoi mixtures to a baseline model.


title: GraphTrack: A Graph-based Cross-Device Tracking Framework

abstract:
  Cross-device tracking has drawn growing attention from both commercial
companies and the general public because of its privacy implications and
applications for user profiling, personalized services, etc. One particular,
wide-used type of cross-device tracking is to leverage browsing histories of
user devices, e.g., characterized by a list of IP addresses used by the devices
and domains visited by the devices. However, existing browsing history based
methods have three drawbacks. First, they cannot capture latent correlations
among IPs and domains. Second, their performance degrades significantly when
labeled device pairs are unavailable. Lastly, they are not robust to
uncertainties in linking browsing histories to devices. We propose GraphTrack,
a graph-based cross-device tracking framework, to track users across different
devices by correlating their browsing histories. Specifically, we propose to
model the complex interplays among IPs, domains, and devices as graphs and
capture the latent correlations between IPs and between domains. We construct
graphs that are robust to uncertainties in linking browsing histories to
devices. Moreover, we adapt random walk with restart to compute similarity
scores between devices based on the graphs. GraphTrack leverages the similarity
scores to perform cross-device tracking. GraphTrack does not require labeled
device pairs and can incorporate them if available. We evaluate GraphTrack on
two real-world datasets, i.e., a publicly available mobile-desktop tracking
dataset (around 100 users) and a multiple-device tracking dataset (154K users)
we collected. Our results show that GraphTrack substantially outperforms the
state-of-the-art on both datasets.


title: Building Embedded Systems Like It's 1996

abstract:
  Embedded devices are ubiquitous. However, preliminary evidence shows that
attack mitigations protecting our desktops/servers/phones are missing in
embedded devices, posing a significant threat to embedded security. To this
end, this paper presents an in-depth study on the adoption of common attack
mitigations on embedded devices. Precisely, it measures the presence of
standard mitigations against memory corruptions in over 10k Linux-based
firmware of deployed embedded devices.
  The study reveals that embedded devices largely omit both user-space and
kernel-level attack mitigations. The adoption rates on embedded devices are
multiple times lower than their desktop counterparts. An equally important
observation is that the situation is not improving over time. Without changing
the current practices, the attack mitigations will remain missing, which may
become a bigger threat in the upcoming IoT era.
  Throughout follow-up analyses, we further inferred a set of factors possibly
contributing to the absence of attack mitigations. The exemplary ones include
massive reuse of non-protected software, lateness in upgrading outdated
kernels, and restrictions imposed by automated building tools. We envision
these will turn into insights towards improving the adoption of attack
mitigations on embedded devices in the future.


title: KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling

abstract:
  Currently, Medical Subject Headings (MeSH) are manually assigned to every
biomedical article published and subsequently recorded in the PubMed database
to facilitate retrieving relevant information. With the rapid growth of the
PubMed database, large-scale biomedical document indexing becomes increasingly
important. MeSH indexing is a challenging task for machine learning, as it
needs to assign multiple labels to each article from an extremely large
hierachically organized collection. To address this challenge, we propose
KenMeSH, an end-to-end model that combines new text features and a dynamic
\textbf{K}nowledge-\textbf{en}hanced mask attention that integrates document
features with MeSH label hierarchy and journal correlation features to index
MeSH terms. Experimental results show the proposed method achieves
state-of-the-art performance on a number of measures.


title:
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised
  Domain Adaptation

abstract:
  Unsupervised domain adaptation (UDA) is one of the prominent tasks of
transfer learning, and it provides an effective approach to mitigate the
distribution shift between the labeled source domain and the unlabeled target
domain. Prior works mainly focus on aligning the marginal distributions or the
estimated class-conditional distributions. However, the joint dependency among
the feature and the label is crucial for the adaptation task and is not fully
exploited. To address this problem, we propose the Bures Joint Distribution
Alignment (BJDA) algorithm which directly models the joint distribution shift
based on the optimal transport theory in the infinite-dimensional kernel
spaces. Specifically, we propose a novel alignment loss term that minimizes the
kernel Bures-Wasserstein distance between the joint distributions. Technically,
BJDA can effectively capture the nonlinear structures underlying the data. In
addition, we introduce a dynamic margin in contrastive learning phase to
flexibly characterize the class separability and improve the discriminative
ability of representations. It also avoids the cross-validation procedure to
determine the margin parameter in traditional triplet loss based methods.
Extensive experiments show that BJDA is very effective for the UDA tasks, as it
outperforms state-of-the-art algorithms in most experimental settings. In
particular, BJDA improves the average accuracy of UDA tasks by 2.8% on
Adaptiope, 1.4% on Office-Caltech10, and 1.1% on ImageCLEF-DA.


title: Beckmann's approach to multi-item multi-bidder auctions

abstract:
  We consider the problem of revenue-maximizing Bayesian auction design with
several i.i.d. bidders and several items. We show that the auction-design
problem can be reduced to the problem of continuous optimal transportation
introduced by Beckmann. We establish the strong duality between the two
problems and demonstrate the existence of solutions. We then develop a new
numerical approximation scheme that combines multi-to-single-agent reduction
and the majorization theory insights to characterize the solution.


title:
The dual spaces of variable anisotropic Hardy-Lorentz spaces and
  continuity of a class of linear operators

abstract:
  In this paper, the author obtain the continuity of a class of linear
operators on variable anisotropic Hardy-Lorentz spaces.
  In addition, the author also obtain that the dual space of variable
anisotropic Hardy-Lorentz spaces is the anisotropic BMO-type spaces with
variable exponents.
  This result is still new even when the exponent function $p(\cdot)$ is $p$.


title:
Room-temperature oxygen transport in nano-thin BixOySez enables
  precision modulation of 2D materials

abstract:
  Oxygen conductors and transporters are important to several consequential
renewable energy technologies, including fuel cells and syngas production.
Separately, monolayer transition metal dichalcogenides (TMDs) have demonstrated
significant promise for a range of applications, including quantum computing,
advanced sensors, valleytronics, and next-gen optoelectronics. Here, we
synthesize a few nanometer-thick BixOySez compound that strongly resembles a
rare R3m bismuth oxide (Bi2O3) phase, and combine it with monolayer TMDs, which
are highly sensitive to their environment. We use the resulting 2D
heterostructure to study oxygen transport through BixOySez into the interlayer
region, whereby the 2D material properties are modulated, finding
extraordinarily fast diffusion at room temperature under laser exposure. The
oxygen diffusion enables reversible and precise modification of the 2D material
properties by controllably intercalating and deintercalating oxygen. Changes
are spatially confined, enabling submicron features (e.g. pixels), and are
long-term stable for more than 221 days. Our work suggests few nanometer-thick
BixOySez is a promising unexplored room-temperature oxygen transporter.
Additionally, our findings suggest the mechanism can be applied to other 2D
materials as a generalized method to manipulate their properties with high
precision and submicron spatial resolution.

categories: cond-mat.mtrl-sci cond-mat.mes-hall cond-mat.other physics.app-ph physics.optics


title:
Renormalization of One-Pion Exchange in Chiral Effective Field Theory
  for Antinucleon-Nucleon Scattering

abstract:
  The renormalization of iterated one-pion exchange (OPE) is studied in Chiral
Effective Field Theory ($\chi$EFT) for the antinucleon-nucleon
($\overline{N}\!N$) system. The OPE potential is cut off at a certain distance
and contact interactions are represented by a complex spherical well with the
same radius. We investigate the dependence on the cutoff radius of the phase
shifts, inelasticities, and mixing angles for the low partial waves in
$\overline{N}\!N$ scattering. We show that renormalization requires additional
contact interactions compared to the expectation based on naive dimensional
analysis. Results after renormalization are compared with the state-of-the-art
energy-dependent partial-wave analysis of $\overline{N}\!N$ data. We compare
our conclusions with applications of $\chi$EFT to the nucleon-nucleon system.


title:
STDAN: Deformable Attention Network for Space-Time Video
  Super-Resolution

abstract:
  The target of space-time video super-resolution (STVSR) is to increase the
spatial-temporal resolution of low-resolution (LR) and low frame rate (LFR)
videos. Recent approaches based on deep learning have made significant
improvements, but most of them only use two adjacent frames, that is,
short-term features, to synthesize the missing frame embedding, which suffers
from fully exploring the information flow of consecutive input LR frames. In
addition, existing STVSR models hardly exploit the temporal contexts explicitly
to assist high-resolution (HR) frame reconstruction. To address these issues,
in this paper, we propose a deformable attention network called STDAN for
STVSR. First, we devise a long-short term feature interpolation (LSTFI) module,
which is capable of excavating abundant content from more neighboring input
frames for the interpolation process through a bidirectional RNN structure.
Second, we put forward a spatial-temporal deformable feature aggregation
(STDFA) module, in which spatial and temporal contexts in dynamic video frames
are adaptively captured and aggregated to enhance SR reconstruction.
Experimental results on several datasets demonstrate that our approach
outperforms state-of-the-art STVSR methods.


title: Feasible Newton's methods for symmetric tensor Z-eigenvalue problems

abstract:
  Finding a Z-eigenpair of a symmetric tensor is equivalent to finding a KKT
point of a sphere constrained minimization problem. Based on this equivalency,
in this paper, we first propose a class of iterative methods to get a
Z-eigenpair of a symmetric tensor. Each method can generate a sequence of
feasible points such that the sequence of function evaluations is decreasing.
These methods can be regarded as extensions of the descent methods for
unconstrained optimization problems. We pay particular attention to the
Newton's method. We show that under appropriate conditions, the Newton's method
is globally and quadratically convergent. Moreover, after finitely many
iterations, the unit steplength will always be accepted. We also propose a
nonlinear equations based Newton's method and establish its global and
quadratic convergence. In the end, we do several numerical experiments to test
the proposed Newton's methods. The results show that both Newton's methods are
very efficient.


title:
Deploying in-network caches in support of distributed scientific data
  sharing

abstract:
  The importance of intelligent data placement, management, and analysis has
become apparent as scientific data volumes across the network continue to
increase. To that end, we describe the use of in-network caching service
deployments as a means to improve application performance and preserve
available network bandwidth in a high energy physics data distribution
environment. Details of the software and hardware deployments, performance
considerations, and cache usage analysis will be described. We include thoughts
on possible future deployment models involving caching node installations at
the edge along with methods to scale our approach.


title: RecursiveMix: Mixed Learning with History

abstract:
  Mix-based augmentation has been proven fundamental to the generalization of
deep vision models. However, current augmentations only mix samples at the
current data batch during training, which ignores the possible knowledge
accumulated in the learning history. In this paper, we propose a recursive
mixed-sample learning paradigm, termed "RecursiveMix" (RM), by exploring a
novel training strategy that leverages the historical input-prediction-label
triplets. More specifically, we iteratively resize the input image batch from
the previous iteration and paste it into the current batch while their labels
are fused proportionally to the area of the operated patches. Further, a
consistency loss is introduced to align the identical image semantics across
the iterations, which helps the learning of scale-invariant feature
representations. Based on ResNet-50, RM largely improves classification
accuracy by $\sim$3.2\% on CIFAR100 and $\sim$2.8\% on ImageNet with negligible
extra computation/storage costs. In the downstream object detection task, the
RM pretrained model outperforms the baseline by 2.1 AP points and surpasses
CutMix by 1.4 AP points under the ATSS detector on COCO. In semantic
segmentation, RM also surpasses the baseline and CutMix by 1.9 and 1.1 mIoU
points under UperNet on ADE20K, respectively. Codes and pretrained models are
available at \url{https://github.com/megvii-research/RecursiveMix}.


title: Lower bounds for negative moments of quadratic Dirichlet $L$-functions

abstract:
  We establish lower bounds for the $2k$-th moment of families of quadratic
Dirichlet $L$-functions at the central point for all real $k<0$, assuming a
conjecture of S. Chowla on the non-vanishing of these $L$-values.


title: Dielectric continuum methods for quantum chemistry

abstract:
  This review describes the theory and implementation of implicit solvation
models based on continuum electrostatics. Within quantum chemistry this
formalism is sometimes synonymous with the polarizable continuum model, a
particular boundary-element approach to the problem defined by the Poisson or
Poisson-Boltzmann equation, but that moniker belies the diversity of available
methods. This work reviews the current state-of-the art, with emphasis on
theory and methods rather than applications. The basics of continuum
electrostatics are described, including the nonequilibrium polarization
response upon excitation or ionization of the solute. Nonelectrostatic
interactions, which must be included in the model in order to obtain accurate
solvation energies, are described as well. Numerical techniques for
implementing the equations are discussed, including linear-scaling algorithms
that can be used in classical or mixed quantum/classical biomolecular
electrostatics calculations. Anisotropic models that can describe interfacial
solvation are briefly described.


title:
Real-Time Electric Vehicle Smart Charging at Workplaces: A Real-World
  Case Study

abstract:
  We study a real-time smart charging algorithm for electric vehicles (EVs) at
a workplace parking lot in order to minimize electricity cost from time-of-use
electricity rates and demand charges while ensuring that the owners of the EVs
receive adequate levels of charge. Notably, due to real-world constraints, our
algorithm is agnostic to both the state-of-charge and the departure time of the
EVs and uses scenario generation to account for each EV's unknown future
departure time as well as certainty equivalent control to account for the
unknown EV arrivals in the future. Real-world charging data from a Google
campus in California allows us to build realistic models of charging demand for
each day of the week. We then compare various results from our smart charging
algorithm to the status quo for a two week period at a Google parking location.


title: A Comparative Study on Forecasting of Retail Sales

abstract:
  Predicting product sales of large retail companies is a challenging task
considering volatile nature of trends, seasonalities, events as well as unknown
factors such as market competitions, change in customer's preferences, or
unforeseen events, e.g., COVID-19 outbreak. In this paper, we benchmark
forecasting models on historical sales data from Walmart to predict their
future sales. We provide a comprehensive theoretical overview and analysis of
the state-of-the-art timeseries forecasting models. Then, we apply these models
on the forecasting challenge dataset (M5 forecasting by Kaggle). Specifically,
we use a traditional model, namely, ARIMA (Autoregressive Integrated Moving
Average), and recently developed advanced models e.g., Prophet model developed
by Facebook, light gradient boosting machine (LightGBM) model developed by
Microsoft and benchmark their performances. Results suggest that ARIMA model
outperforms the Facebook Prophet and LightGBM model while the LightGBM model
achieves huge computational gain for the large dataset with negligible
compromise in the prediction accuracy.


title:
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark
  for Semantic and Generative Capabilities

abstract:
  Transfer learning has proven to be crucial in advancing the state of speech
and natural language processing research in recent years. In speech, a model
pre-trained by self-supervised learning transfers remarkably well on multiple
tasks. However, the lack of a consistent evaluation methodology is limiting
towards a holistic understanding of the efficacy of such models. SUPERB was a
step towards introducing a common benchmark to evaluate pre-trained models
across various speech tasks. In this paper, we introduce SUPERB-SG, a new
benchmark focused on evaluating the semantic and generative capabilities of
pre-trained models by increasing task diversity and difficulty over SUPERB. We
use a lightweight methodology to test the robustness of representations learned
by pre-trained models under shifts in data domain and quality across different
types of tasks. It entails freezing pre-trained model parameters, only using
simple task-specific trainable heads. The goal is to be inclusive of all
researchers, and encourage efficient use of computational resources. We also
show that the task diversity of SUPERB-SG coupled with limited task supervision
is an effective recipe for evaluating the generalizability of model
representation.


title: Efficient Language Modeling with Sparse all-MLP

abstract:
  All-MLP architectures have attracted increasing interest as an alternative to
attention-based models. In NLP, recent work like gMLP shows that all-MLPs can
match Transformers in language modeling, but still lag behind in downstream
tasks. In this work, we analyze the limitations of MLPs in expressiveness, and
propose sparsely activated MLPs with mixture-of-experts (MoEs) in both feature
and input (token) dimensions. Such sparse all-MLPs significantly increase model
capacity and expressiveness while keeping the compute constant. We address
critical challenges in incorporating conditional computation with two routing
strategies. The proposed sparse all-MLP improves language modeling perplexity
and obtains up to 2$\times$ improvement in training efficiency compared to both
Transformer-based MoEs (GShard, Switch Transformer, Base Layers and HASH
Layers) as well as dense Transformers and all-MLPs. Finally, we evaluate its
zero-shot in-context learning performance on six downstream tasks, and find
that it surpasses Transformer-based MoEs and dense Transformers.


title: QCD effects in non-QCD theories

abstract:
  It is shown that, in some non-QCD theories, there are effects shared by QCD:
(i)~in SU(2) Yang-Mills theory containing a nonlinear spinor field, there is a
mass gap; (ii)~in SU(3) Proca-Higgs theory, there are flux tube solutions with
a longitudinal electric field required for producing a force binding quarks;
(iii)~in non-Abelian Proca-Higgs theories, there exist flux tube solutions with
a momentum directed along the tube axis and particlelike solutions with a
nonvanishing total angular momentum created by crossed color electric and
magnetic fields; in QCD, such configurations may contribute to the proton spin.
We discuss the assumption that such non-QCD theories might be a consequence of
approximate solution of an infinite set of Dyson-Schwinger equations describing
the procedure of nonperturbative quantization. The phenomenon of dimensional
transmutation for nonperturbative quantization and the analogy between
nonperturbative quantization and turbulence modeling are also discussed.


title:
Continual Learning for Multivariate Time Series Tasks with Variable
  Input Dimensions

abstract:
  We consider a sequence of related multivariate time series learning tasks,
such as predicting failures for different instances of a machine from time
series of multi-sensor data, or activity recognition tasks over different
individuals from multiple wearable sensors. We focus on two under-explored
practical challenges arising in such settings: (i) Each task may have a
different subset of sensors, i.e., providing different partial observations of
the underlying 'system'. This restriction can be due to different manufacturers
in the former case, and people wearing more or less measurement devices in the
latter (ii) We are not allowed to store or re-access data from a task once it
has been observed at the task level. This may be due to privacy considerations
in the case of people, or legal restrictions placed by machine owners.
Nevertheless, we would like to (a) improve performance on subsequent tasks
using experience from completed tasks as well as (b) continue to perform better
on past tasks, e.g., update the model and improve predictions on even the first
machine after learning from subsequently observed ones. We note that existing
continual learning methods do not take into account variability in input
dimensions arising due to different subsets of sensors being available across
tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In
this work, we address this shortcoming of existing methods. To this end, we
learn task-specific generative models and classifiers, and use these to augment
data for target tasks. Since the input dimensions across tasks vary, we propose
a novel conditioning module based on graph neural networks to aid a standard
recurrent neural network. We evaluate the efficacy of the proposed approach on
three publicly available datasets corresponding to two activity recognition
tasks (classification) and one prognostics task (regression).


title: Electron Scattering and Neutrino Physics

abstract:
  A thorough understanding of neutrino-nucleus scattering physics is crucial
for the successful execution of the entire US neutrino physics program.
Neutrino-nucleus interaction constitutes one of the biggest systematic
uncertainties in neutrino experiments - both at intermediate energies affecting
long-baseline Deep Underground Neutrino Experiment (DUNE), as well as at low
energies affecting coherent scattering neutrino program - and could well be the
difference between achieving or missing discovery level precision. To this end,
electron-nucleus scattering experiments provide vital information to test,
assess and validate different nuclear models and event generators intended to
be used in neutrino experiments.
  In this white paper, we highlight connections between electron- and
neutrino-nucleus scattering physics at energies ranging from 10s of MeV to a
few GeV, review the status of ongoing and planned electron scattering
experiments, identify gaps, and layout a path forward that benefits the
neutrino community. We also highlight the systemic challenges with respect to
the divide between the nuclear and high-energy physics communities and funding
that presents additional hurdle in mobilizing these connections to the benefit
of neutrino programs.


title:
Line bundles on the moduli space of parabolic connections over a compact
  Riemann surface

abstract:
  Let $X$ be a compact Riemann surface of genus $g \geq 3$ and $S$ a finite
subset of $X$. Let $\xi$ be fixed a holomorphic line bundle over $X$ of degree
$d$. Let $\mathcal{M}_{pc}(r, d, \alpha)$ (respectively, $\mathcal{M}_{pc}(r,
\alpha, \xi)$ ) denote the moduli space of parabolic connections of rank $r$,
degree $d$ and full flag rational generic weight system $\alpha$,
(respectively, with the fixed determinant $\xi$) singular over the parabolic
points $S \subset X$.
  Let $\mathcal{M}'_{pc}(r, d, \alpha)$ (respectively, $\mathcal{M}'_{pc}(r,
\alpha, \xi)$) be the Zariski dense open subset of $\mathcal{M}_{pc}(r, d,
\alpha)$ (respectively, $\mathcal{M}_{pc}(r, \alpha, \xi)$ )parametrizing all
parabolic connections such that the underlying parabolic bundle is stable. We
show that there is a natural compactification of the moduli spaces
$\mathcal{M}'_{pc}(r, d, \alpha)$, and $\mathcal{M}'_{pc}(r, \alpha, \xi)$ by
smooth divisors.
  We describe the numerically effectiveness of these divisors at infinity. We
determine the Picard group of the moduli spaces $\mathcal{M}_{pc}(r, d,
\alpha)$, and $\mathcal{M}_{pc}(r, \alpha, \xi)$. Let $\mathcal{C}(L)$ denote
the space of holomorphic connections on an ample line bundle $L$ over the
moduli space $\mathcal{M}(r, d, \alpha)$ of parabolic bundles. We show that
$\mathcal{C}(L)$ does not admit any non-constant algebraic function.


title: DIAS: A Domain-Independent Alife-Based Problem-Solving System

abstract:
  A domain-independent problem-solving system based on principles of Artificial
Life is introduced. In this system, DIAS, the input and output dimensions of
the domain are laid out in a spatial medium. A population of actors, each
seeing only part of this medium, solves problems collectively in it. The
process is independent of the domain and can be implemented through different
kinds of actors. Through a set of experiments on various problem domains, DIAS
is shown able to solve problems with different dimensionality and complexity,
to require no hyperparameter tuning for new problems, and to exhibit lifelong
learning, i.e. adapt rapidly to run-time changes in the problem domain, and do
it better than a standard non-collective approach. DIAS therefore demonstrates
a role for Alife in building scalable, general, and adaptive problem-solving
systems.


title:
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object
  Manipulation

abstract:
  Manipulating volumetric deformable objects in the real world, like plush toys
and pizza dough, bring substantial challenges due to infinite shape variations,
non-rigid motions, and partial observability. We introduce ACID, an
action-conditional visual dynamics model for volumetric deformable objects
based on structured implicit neural representations. ACID integrates two new
techniques: implicit representations for action-conditional dynamics and
geodesics-based contrastive learning. To represent deformable dynamics from
partial RGB-D observations, we learn implicit representations of occupancy and
flow-based forward dynamics. To accurately identify state change under large
non-rigid deformations, we learn a correspondence embedding field through a
novel geodesics-based contrastive loss. To evaluate our approach, we develop a
simulation framework for manipulating complex deformable shapes in realistic
scenes and a benchmark containing over 17,000 action trajectories with six
types of plush toys and 78 variants. Our model achieves the best performance in
geometry, correspondence, and dynamics predictions over existing approaches.
The ACID dynamics models are successfully employed to goal-conditioned
deformable manipulation tasks, resulting in a 30% increase in task success rate
over the strongest baseline. For more results and information, please visit
https://b0ku1.github.io/acid/ .


title: System of kinematical conservation laws (KCL) a review article

abstract:
  In a wide range of physical phenomena, we find propagating surfaces {\Omega}t
which need mathematical treatment. In this article, we review the theory of the
system of kinematical conservation laws (KCL), which govern the evolution of
these surfaces and have been developed by the second author and his
collaborators. KCL are the most general equations in conservation form,
governing the evolution of {\Omega}t with physically realistic singularities. A
very special type of singularity is a kink, which is a point on {\Omega}t when
{\Omega}t is a curve in R2 andisacurveon{\Omega}t when{\Omega}t isasurfaceinR3.
Acrossakinkthenormalnto{\Omega}t and the normal velocity m on {\Omega}t are
discontinuous. The main aim of this article is to identify density of the
conserved variable and the flux for the KCL which we did not do earlier. The
presentation of this article is like that in a popular article, which which
aims at non-experts in the field.


title: A meridian lemma for fully alternating links in thickened surfaces

abstract:
  Menasco showed that a closed surface in the complement of a non-split prime
alternating link in $S^3$ contains a circle isotopic in the link complement to
a meridian of the links. This result is known as the meridian lemma for
alternating links. We give a meridian lemma for the class of fully alternating
links in the thickened orientable surfaces of positive genus.


title:
Snowmass2021 Cosmic Frontier White Paper: Puzzling Excesses in Dark
  Matter Searches and How to Resolve Them

abstract:
  Intriguing signals with excesses over expected backgrounds have been observed
in many astrophysical and terrestrial settings, which could potentially have a
dark matter origin. Astrophysical excesses include the Galactic Center GeV
gamma-ray excess detected by the Fermi Gamma-Ray Space Telescope, the AMS
antiproton and positron excesses, and the 511 and 3.5 keV X-ray lines. Direct
detection excesses include the DAMA/LIBRA annual modulation signal, the XENON1T
excess, and low-threshold excesses in solid state detectors. We discuss avenues
to resolve these excesses, with actions the field can take over the next
several years.


title:
Stochastic integral representation of solutions to Hodge theoretic
  Poisson's equations on Graphs, and cooperative value allocation of Shapley
  and Nash

abstract:
  The fundamental connection between stochastic differential equations (SDEs)
and partial differential equations (PDEs) has found numerous applications in
diverse fields. We explore a similar link between stochastic calculus and
combinatorial PDEs on graphs with Hodge structure, by showing that the solution
to the Hodge-theoretic Poisson's equation on graphs allows for a stochastic
integral representation driven by a canonical time-reversible Markov chain.
When the underlying graph has a hypercube structure, we further show that the
solution to the Poisson's equation can be fully characterized by five
properties, which can be thought of as a completion of the Lloyd Shapley's four
axioms.


title:
Let's Collaborate: Regret-based Reactive Synthesis for Robotic
  Manipulation

abstract:
  As robots gain capabilities to enter our human-centric world, they require
formalism and algorithms that enable smart and efficient interactions. This is
challenging, especially for robotic manipulators with complex tasks that may
require collaboration with humans. Prior works approach this problem through
reactive synthesis and generate strategies for the robot that guarantee task
completion by assuming an adversarial human. While this assumption gives a
sound solution, it leads to an "unfriendly" robot that is agnostic to the human
intentions. We relax this assumption by formulating the problem using the
notion of regret. We identify an appropriate definition for regret and develop
regret-minimizing synthesis framework that enables the robot to seek
cooperation when possible while preserving task completion guarantees. We
illustrate the efficacy of our framework via various case studies.


title:
Structural physical approximation of partial transposition makes
  possible to distinguish SLOCC inequivalent classes of three-qubit system

abstract:
  Detection and classification of entanglement properties of a multi-qubit
system is a topic of great interest. This topic has been studied extensively
and thus we found different approaches for the detection and classification of
multi-qubit entangled states. We have applied partial transposition operation
on one of the qubit of the three-qubit system and then studied the entanglement
properties of the three-qubit system, which is under investigation. Since the
partial transposition operation is not a quantum operation so we have
approximated partial transposition operation in such a way so that it represent
a completely positive map. The approximated partial transposition operation is
also known as structural physical approximation of partial transposition
(SPA-PT). We have studied in detail the application of SPA-PT on a three qubit
system and provided explicitly the matrix elements of the density matrix
describing SPA-PT of a three qubit system. Moreover, we propose criterion to
classify all possible stochastic local operations and classical
communication(SLOCC) inequivalent classes of a pure as well as mixed three
qubit state through SPA-PT map, which makes our criterion experimentally
realizable. We have illustrated our criterion for detection and classification
of three-qubit entangled states by considering few examples.


title:
Improving results by improving densities: Density-corrected density
  functional theory

abstract:
  DFT calculations have become widespread in both chemistry and materials,
because they usually provide useful accuracy at much lower computational cost
than wavefunction-based methods. All practical DFT calculations require an
approximation to the unknown exchange-correlation energy, which is then used
self-consistently in the Kohn-Sham scheme to produce an approximate energy from
an approximate density. Density-corrected DFT is simply the study of the
relative contributions to the total energy error. In the vast majority of DFT
calculations, the error due to the approximate density is negligible. But with
certain classes of functionals applied to certain classes of problems, the
density error is sufficiently large as to contribute to the energy noticeably,
and its removal leads to much better results. These problems include reaction
barriers, torsional barriers involving $\pi$-conjugation, halogen bonds,
radicals and anions, most stretched bonds, etc. In all such cases, use of a
more accurate density significantly improves performance, and often the simple
expedient of using the Hartree-Fock density is enough. This article explains
what DC-DFT is, where it is likely to improve results, and how DC-DFT can
produce more accurate functionals. We also outline challenges and prospects for
the field.


title:
Beam dynamics challenges in linear colliders based on laser-plasma
  accelerators

abstract:
  In this paper we discuss design considerations and beam dynamics challenges
associated with laser-driven plasma-based accelerators as applied to
multi-TeV-scale linear colliders. Plasma accelerators provide ultra-high
gradients and ultra-short bunches, offering the potential for compact linacs
and reduced power requirements. We show that stable, efficient acceleration
with beam quality preservation is possible in the nonlinear bubble regime of
laser-plasma accelerators using beam shaping. Ion motion, naturally occuring
for dense beams (i.e., low emittance and high energy) severely damps transverse
beam instabilities. Coulomb scattering by the background ions is considered and
it is shown that the strong focusing in the plasma strongly suppresses
scattering-induced emittance growth. Betatron radiation emission from the
transverse motion of the beam in the plasma will result in beam power loss and
energy spread growth; however for sub-100 nm emittances, the beam power loss
and energy spread growth will be sub-percent for multi-TeV-class plasma linacs.


title:
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement
  Learning Perspective

abstract:
  One of the most fundamental questions in quantitative finance is the
existence of continuous-time diffusion models that fit market prices of a given
set of options. Traditionally, one employs a mix of intuition, theoretical and
empirical analysis to find models that achieve exact or approximate fits. Our
contribution is to show how a suitable game theoretical formulation of this
problem can help solve this question by leveraging existing developments in
modern deep multi-agent reinforcement learning to search in the space of
stochastic processes. More importantly, we hope that our techniques can be
leveraged and extended by the community to solve important problems in that
field, such as the joint SPX-VIX calibration problem. Our experiments show that
we are able to learn local volatility, as well as path-dependence required in
the volatility process to minimize the price of a Bermudan option. In one
sentence, our algorithm can be seen as a particle method \`{a} la Guyon et
Henry-Labordere where particles, instead of being designed to ensure
$\sigma_{loc}(t,S_t)^2 = \mathbb{E}[\sigma_t^2|S_t]$, are learning RL-driven
agents cooperating towards more general calibration targets. This is the first
work bridging reinforcement learning with the derivative calibration problem.


title:
Modeling of Dynamic Networks based on Egocentric Data with Durational
  Information

abstract:
  Modeling of dynamic networks -- networks that evolve over time -- has
manifold applications in many fields. In epidemiology in particular, there is a
need for data-driven modeling of human sexual relationship networks for the
purpose of modeling and simulation of the spread of sexually transmitted
disease. Dynamic network data about such networks are extremely difficult to
collect, however, and much more readily available are egocentrically sampled
data of a network at a single time point, with some attendant information about
the sexual history of respondents.
  Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM)
framework, which facilitates separable modeling of the tie duration
distributions and the structural dynamics of tie formation. In this work, we
apply this modeling framework to this problem, by studying the long-run
properties of STERGM processes, developing methods for fitting STERGMs to
egocentrically sampled data, and extending the network size adjustment method
of Krivitsky, Handcock, and Morris (2011) to dynamic models.


title:
The influence of the medium physical conditions and atomic constants on
  the Stokes profiles of absorption lines in the solar spectrum

abstract:
  The Stokes profiles of Fe I lines in the photosphere of the Sun are
calculated within the Unno-Beckers-Landi-Dagl`Innocenti theory. Estimates of
the magnetic strengthening of the lines were obtained. The changes in the
Stokes profiles depending on the excitation potential, wavelength, equivalent
width, Lande factor, micro-macroturbulent velocities, radial velocity, damping
constant, atmospheric model, magnetic field strength and direction are
considered. The graphically presented variations of the Stokes profiles make it
possible to determine the initial values of the input parameters for solving
the problems of magnetic field vector reconstruction by the inversion method.
The presented dependencies of the magnetic strengthening on the line parameters
will help to correctly select magnetically sensitive lines for the
investigation of sunspots, flux tubes, plages, and other magnetic features.


title: Topological asymptotic dimension

abstract:
  We initiate a study of asymptotic dimension for locally compact groups. This
is shown to be finite for residually compact groups. We show that
polycyclic-by-compact groups and compactly generated, topologically virtually
nilpotent groups are residually compact, and show that compactly generated
nilpotent groups are polycyclic-by-compact. Along the way, the notion of Hirsch
length is extended to topological groups and classical results of Hirsch and
Malcev are extended using a topological version of the Poincar\'{e} lemma. We
prove that for compactly generated, solvable-by-compact groups the asymptotic
dimension is majorized by the Hirsch length, and equality holds for
polycyclic-by-compact groups. We extend the class of elementary amenable groups
beyond the discrete case and show that topologically elementary amenable groups
with finite Hirsch length have finite asymptotic dimension. We prove that a
topologically elementary amenable group of finite Hirsch length with no
nontrivial locally elliptic normal closed subgroup is solvable-by-compact.
Finally, we show that a totally disconnected, locally compact, second countable
group has finite asymptotic dimension, if all of its discrete quotients are so.


title:
Magnetic-field and pressure phase diagram of the triangular lattice
  antiferromagnet CsCuCl$_3$ clarified by magnetic susceptibility measured with
  a proximity detector oscillator

abstract:
  The effect of pressure ($P$) on magnetic susceptibility of CsCuCl$_3$ was
examined in magnetic fields ($\rm \mu_0$$H$) of up to 51 T using a proximity
detector oscillator (PDO), and the $H$-$P$ phase diagram of CsCuCl$_3$ was
constructed over the saturation field ($H_{\rm sat}$). We found that, with
increasing $P$, $H_{\rm sat}$ increases and the uud-phase that appeared at $P$
= 0.7 GPa widened. Based on comparison between the experimental and calculated
$H$-$P$ phase diagrams, the Y-phase was predicted to appear above 1.7 GPa. The
interchain antiferromagnetic exchange interaction in the $ab$-plane was
evaluated and found to increase with increasing $P$, which is consistent with a
previous study under high pressure [D. Yamamoto {\it et al.}, Nat. Commun. {\bf
12}, 4263 (2021).]. Moreover, an anomaly was observed below $P$ = 0.6 GPa just
below $H_{\rm sat}$ and might be a new phase transition derived from nonlinear
response caused by the PDO technique.


title: The Role of Interactivity in Structured Estimation

abstract:
  We study high-dimensional sparse estimation under three natural constraints:
communication constraints, local privacy constraints, and linear measurements
(compressive sensing). Without sparsity assumptions, it has been established
that interactivity cannot improve the minimax rates of estimation under these
information constraints. The question of whether interactivity helps with
natural inference tasks has been a topic of active research. We settle this
question in the affirmative for the prototypical problems of high-dimensional
sparse mean estimation and compressive sensing, by demonstrating a gap between
interactive and noninteractive protocols. We further establish that the gap
increases when we have more structured sparsity: for block sparsity this gap
can be as large as polynomial in the dimensionality. Thus, the more structured
the sparsity is, the greater is the advantage of interaction. Proving the lower
bounds requires a careful breaking of a sum of correlated random variables into
independent components using Baranyai's theorem on decomposition of
hypergraphs, which might be of independent interest.

categories: cs.DS cs.DM cs.IT cs.LG math.IT math.ST stat.TH


title:
Block-STM: Scaling Blockchain Execution by Turning Ordering Curse to a
  Performance Blessing

abstract:
  Block-STM is a parallel execution engine for smart contracts, built around
the principles of Software Transactional Memory. Transactions are grouped in
blocks, and every execution of the block must yield the same deterministic
outcome. Block-STM further enforces that the outcome is consistent with
executing transactions according to a preset order, leveraging this order to
dynamically detect dependencies and avoid conflicts during speculative
transaction execution. At the core of Block-STM is a novel, low-overhead
collaborative scheduler of execution and validation tasks.
  Block-STM is implemented on the main branch of the Diem Blockchain code-base.
The evaluation demonstrates that Block-STM is adaptive to workloads with
different conflict rates and utilizes the inherent parallelism therein.
Block-STM achieves up to 140k tps in the benchmarks, which is a 20x improvement
over the sequential baseline with 32 threads. The throughput on a contended
workload is up to 60k tps.


title:
Mechanics, Energetics, Entropy and Kinetics of a Binary Mechanical Model
  System

abstract:
  With the formal construction of a thermodynamic spring, I describe the
mechanics, energetics, entropy, and kinetics of a binary mechanical model
system. A protein that transitions between two metastable structural states
behaves as a molecular switch, and an ensemble of molecular switches that
displace compliant elements equilibrated with a system force constitutes a
binary mechanical model system. In biological systems, many protein switches
equilibrate with cellular forces, yet the statistical mechanical problem
relevant to this system has remained unsolved. A binary mechanical model system
establishes a limited number of macroscopic parameters into which structural
and mechanistic details must be fit. Novel advances include a non-equilibrium
kinetic and energetic equivalence; scalable limits on kinetics and energetics;
and entropic effects on kinetics and mechanics. The model unifies disparate
models of molecular motor mechanochemistry, accounts for the mechanical
performance of muscle in both transient and steady states, and provides a new
perspective on biomechanics with a focus here on how muscle and molecular motor
ensembles work.


title:
TSR-DSAW: Table Structure Recognition via Deep Spatial Association of
  Words

abstract:
  Existing methods for Table Structure Recognition (TSR) from camera-captured
or scanned documents perform poorly on complex tables consisting of nested rows
/ columns, multi-line texts and missing cell data. This is because current
data-driven methods work by simply training deep models on large volumes of
data and fail to generalize when an unseen table structure is encountered. In
this paper, we propose to train a deep network to capture the spatial
associations between different word pairs present in the table image for
unravelling the table structure. We present an end-to-end pipeline, named
TSR-DSAW: TSR via Deep Spatial Association of Words, which outputs a digital
representation of a table image in a structured format such as HTML. Given a
table image as input, the proposed method begins with the detection of all the
words present in the image using a text-detection network like CRAFT which is
followed by the generation of word-pairs using dynamic programming. These
word-pairs are highlighted in individual images and subsequently, fed into a
DenseNet-121 classifier trained to capture spatial associations such as
same-row, same-column, same-cell or none. Finally, we perform post-processing
on the classifier output to generate the table structure in HTML format. We
evaluate our TSR-DSAW pipeline on two public table-image datasets -- PubTabNet
and ICDAR 2013, and demonstrate improvement over previous methods such as
TableNet and DeepDeSRT.


title: Chemical Relaxation of a Binary Mechanical Model System

abstract:
  With potential relevance to biomechanics, an interesting problem in
statistical mechanics not previously solved is a binary mechanical model
system. Discrete chemical states of proteins are often associated with discrete
metastable structural states, such that with a change in state a protein acts
as a molecular switch. An ensemble of molecular switches that displace
compliant elements equilibrated with an external force, F, constitutes a binary
mechanical model system. As one in a series of publications developing this
model, here I consider the mechanical performance of this system. Four
processes naturally emerge from a transient analysis which are consistent with
the four phases observed in a muscle force transient.


title: Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings

abstract:
  The performance of sentence representation has been remarkably improved by
the framework of contrastive learning. However, recent works still require full
fine-tuning, which is quite inefficient for large-scaled pre-trained language
models. To this end, we present a novel method which freezes the whole language
model and only optimizes the prefix deep continuous prompts. It not only tunes
around 0.1% parameters of the original language model, but avoids the
cumbersome computation of searching handcrafted prompts. Experimental results
show that our proposed DCPCSE outperforms the state-of-the-art method SimCSE by
a large margin. We raise the performance of unsupervised BERT$_{base}$ and
supervised RoBERTa$_{large}$ by 2.24 and 1.00 points, respectively. Our code is
publicly avaliable at https://github.com/YJiangcm/DCPCSE


title: Diagrammatic structures of the Nielsen identity

abstract:
  The $\Gamma$-function, or the effective potential of a gauge field theory
should comply with the Nielsen identity, which implies how the effective
potential evolves as we shift the gauge-fixing term. In this paper, relying on
an abelian toy model, we aim at proving this identity in a diagrammatic form
with the $\overline{R}_{\xi}$ gauge. The basic idea is to find out the ghost
chain after partially differentiating the diagram by the $\xi$ parameter, and
shrink the waists of the diagram into points to separate the bulk-part and
$C$-part of the diagrams. The calculations can be generalized to the models
implemented with non-abelian groups, multiple Higgs and fermion multiplets, and
to the finite temperature cases. Inspired by this, we also suggest that when
resumming the super-daisy diagrams, one can deduct some irrelevant terms at the
connections between the daisy ringlets to fit the Nielsen identity up to
arbitrary $\hbar$ orders.


title: Rethinking Stability for Attribution-based Explanations

abstract:
  As attribution-based explanation methods are increasingly used to establish
model trustworthiness in high-stakes situations, it is critical to ensure that
these explanations are stable, e.g., robust to infinitesimal perturbations to
an input. However, previous works have shown that state-of-the-art explanation
methods generate unstable explanations. Here, we introduce metrics to quantify
the stability of an explanation and show that several popular explanation
methods are unstable. In particular, we propose new Relative Stability metrics
that measure the change in output explanation with respect to change in input,
model representation, or output of the underlying predictor. Finally, our
experimental evaluation with three real-world datasets demonstrates interesting
insights for seven explanation methods and different stability metrics.


title:
Contribution of the Weinberg-type Operator to atomic and nuclear
  electric dipole moments

abstract:
  The contribution of the CP violating three-gluon Weinberg operator,
$\frac{1}{3!} w f^{abc} \epsilon^{\nu \rho \alpha \beta} G^a_{\mu \nu}
G^b_{\alpha \beta} G^{c \mu}_{\rho}$, to the atomic and nuclear EDMs is
estimated using QCD sum rules. After calculating the transition matrix element
between the pion and the vacuum through the Weinberg operator, we obtain the
long-range CP-odd nuclear force by determining the isovector CP-odd
pion-nucleon vertex, using chiral perturbation theory at NLO. The EDMs of
$^{199}$Hg, $^{129}$Xe, $^{225}$Ra, $^2$H, and $^3$He are finally given
including comprehensive uncertainty analysis. While the leading contribution of
the $^{199}$Hg EDM is given by the intrinsic nucleon EDM, that of $^{129}$Xe
atom may be dominated by the one-pion exchange CP-odd nuclear force generated
by the Weinberg operator. From current experimental data of the $^{199}$Hg
atomic EDM, we obtain an upper limit on the Weinberg operator magnitude of
$\left|w \right| < 4 \times 10^{-10} {\rm GeV}^{-2}$ if we assume that it is
the only source of CP violation at the scale $\mu =1$ TeV.


title:
Planckian Dissipation and non-Gingzburg-Landau Type Upper Critical Field
  in Bi2201

abstract:
  Resistivity and Hall effect measurements have been carried out on a
micro-fabricated bridge of Bi2201 single crystal at low temperatures down to
0.3 K under high magnetic fields. When superconductivity is crashed by a high
magnetic field, the recovered "normal state" resistivity still shows a linear
temperature dependence in low temperature region. Combining with the effective
mass and the charge carrier density, we get a linear scattering rate $1/\tau =
\alpha k_{B} T/\hbar$ with $0.95 < \alpha < 1.42$, which gives a strong
evidence of the Planckian dissipation. Furthermore, our results reveal a new
type of temperature dependence of upper critical field,
$H_{c2}(T)=H^*\sqrt{(1-t)/(t+0.135)}$, which is totally different from the
expectation of the Ginzburg-Landau theory, and suggests uncondensed Cooper
pairs above $T_c$.


title: Aspects of 4d supersymmetric dynamics and geometry

abstract:
  In this set of five lectures we present a basic toolbox to discuss the
dynamics of four dimensional supersymmetric quantum field theories. In
particular we overview the program of geometrically engineering the four
dimensional supersymmetric models as compactifications of six dimensional
SCFTs. We discuss how strong coupling phenomena in four dimensions, such as
duality and emergence of symmetry, can be naturally imbedded in the geometric
constructions. The lectures mostly review results which previously appeared in
the literature but also contain some unpublished derivations.


title: Local solubility for a family of quadrics over a split quadric surface

abstract:
  We study the density of everywhere locally soluble diagonal quadric surfaces,
parameterised by rational points that lie on a split quadric surface.


title:
Robust Event Triggering Control for Lateral Dynamics of Intelligent
  Vehicles with Designable Inter-event Times

abstract:
  In this brief, an improved event-triggered update mechanism (ETM) for the
linear quadratic regulator is proposed to solve the lateral motion control
problem of intelligent vehicle under bounded disturbances. Based on a novel
event function using a clock-like variable to determine the triggering time, we
further introduce two new design parameters to improve control performance.
Distinct from existing event-based control mechanisms, the inter-event times
(IETs) derived from the above control framework are designable, meaning that
the proposed ETM can be deployed on practical vehicle more easily and
effectively. In addition, the improved IETs-designable ETM features a global
robust event-separation property that is extremely required for practical
lateral motion control of vehicle subject to diverse disturbances. Theoretical
analysis proves the feasibility and stability of the proposed control strategy
for trajectory tracking under bounded disturbances. Finally, simulation results
verify the theoretical results and show the advantages of the proposed control
strategy.


title: Accelerating DETR Convergence via Semantic-Aligned Matching

abstract:
  The recently developed DEtection TRansformer (DETR) establishes a new object
detection paradigm by eliminating a series of hand-crafted components. However,
DETR suffers from extremely slow convergence, which increases the training cost
significantly. We observe that the slow convergence is largely attributed to
the complication in matching object queries with target features in different
feature embedding spaces. This paper presents SAM-DETR, a
Semantic-Aligned-Matching DETR that greatly accelerates DETR's convergence
without sacrificing its accuracy. SAM-DETR addresses the convergence issue from
two perspectives. First, it projects object queries into the same embedding
space as encoded image features, where the matching can be accomplished
efficiently with aligned semantics. Second, it explicitly searches salient
points with the most discriminative features for semantic-aligned matching,
which further speeds up the convergence and boosts detection accuracy as well.
Being like a plug and play, SAM-DETR complements existing convergence solutions
well yet only introduces slight computational overhead. Extensive experiments
show that the proposed SAM-DETR achieves superior convergence as well as
competitive detection accuracy. The implementation codes are available at
https://github.com/ZhangGongjie/SAM-DETR.


title:
Asymptotic Behavior of Bayesian Generalization Error in Multinomial
  Mixtures

abstract:
  Multinomial mixtures are widely used in the information engineering field,
however, their mathematical properties are not yet clarified because they are
singular learning models. In fact, the models are non-identifiable and their
Fisher information matrices are not positive definite. In recent years, the
mathematical foundation of singular statistical models are clarified by using
algebraic geometric methods. In this paper, we clarify the real log canonical
thresholds and multiplicities of the multinomial mixtures and elucidate their
asymptotic behaviors of generalization error and free energy.


title:
Rydberg electromagnetically induced transparency and absorption of
  strontium triplet states in a weak microwave field

abstract:
  We study theoretically laser excitation of Rydberg triplet states of
strontium atoms in the presence of weak microwave (MW) fields. Starting from
the ground state $5s^2\,^1S_0$, the Rydberg excitation is realized through the
metastable, triplet $5s5p\,^3P_1$ state, whose decay rate $\gamma_2$ is
$2\pi\times 7.5$ kHz, much smaller than the one in the singlet state or alkali
atoms. The influences of $\gamma_2$ on the transparency and absorption spectrum
in the electromagnetically induced transparency (EIT), and electromagnetically
induced absorption (EIA) regime are examined. Narrow transparent windows and
absorption peaks are found, and depend on the weak MW field, whose Rabi
frequency $\Omega_m$ is as low as $\gamma_2$. It is found that high contrast of
the spectrum can be obtained using Sr atoms. We moreover study spectrum of
resonance fluorescence modulated by the MW field. Multiple peaks are found in
the fluorescence spectrum. When the MW field is weak, spectrum location of
these peaks are linearly proportional to $\Omega_m$. Our study shows that the
Sr triplet state could be used to develop a Rydberg MW electrometry that gains
unique advantages.


title:
DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust
  Universal Lesion Detection

abstract:
  Incorporating data-specific domain knowledge in deep networks explicitly can
provide important cues beneficial for lesion detection and can mitigate the
need for diverse heterogeneous datasets for learning robust detectors. In this
paper, we exploit the domain information present in computed tomography (CT)
scans and propose a robust universal lesion detection (ULD) network that can
detect lesions across all organs of the body by training on a single dataset,
DeepLesion. We analyze CT-slices of varying intensities, generated using
heuristically determined Hounsfield Unit(HU) windows that individually
highlight different organs and are given as inputs to the deep network. The
features obtained from the multiple intensity images are fused using a novel
convolution augmented multi-head self-attention module and subsequently, passed
to a Region Proposal Network (RPN) for lesion detection. In addition, we
observed that traditional anchor boxes used in RPN for natural images are not
suitable for lesion sizes often found in medical images. Therefore, we propose
to use lesion-specific anchor sizes and ratios in the RPN for improving the
detection performance. We use self-supervision to initialize weights of our
network on the DeepLesion dataset to further imbibe domain knowledge. Our
proposed Domain Knowledge augmented Multi-head Attention based Universal Lesion
Detection Network DMKA-ULD produces refined and precise bounding boxes around
lesions across different organs. We evaluate the efficacy of our network on the
publicly available DeepLesion dataset which comprises of approximately 32K CT
scans with annotated lesions across all organs of the body. Results demonstrate
that we outperform existing state-of-the-art methods achieving an overall
sensitivity of 87.16%.


title:
A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS
  Summary Data

abstract:
  Mendelian randomization (MR) is a powerful method that uses genetic variants
as instrumental variables (IVs) to infer the causal effect of a modifiable
exposure on an outcome. Although recent years have seen many extensions of
basic MR methods to be robust to certain violations of assumptions, few methods
were proposed to infer bi-directional causal relationships, especially for
phenotypes with limited biological understandings. The presence of horizontal
pleiotropy adds another layer of complexity. In this article, we show that
assumptions for common MR methods are often impossible or too stringent in the
existence of bi-directional relationships. We then propose a new focusing
framework for testing bi-directional causal effects between two traits with
possibly pleiotropic genetic variants. Our proposal can be coupled with many
state-of-art MR methods. We provide theoretical guarantees on the Type I error
and power of the proposed methods. We demonstrate the robustness of the
proposed methods using several simulated and real datasets.


title:
On step sizes rules for the continuous stochastic gradient scheme --
  Convergence results for constant step sizes and an Armijo-type line search

abstract:
  The recently proposed continuous stochastic gradient descent (CSG) scheme
represents an efficient method for the solution of a large class of stochastic
optimization problems. During the course of the iterations, CSG uses a tailored
convex combination of previously drawn gradient and objective function samples
to approximate the full gradient and objective function value at the current
position. It was shown that the error in these approximations tends to zero
during the optimization process and thus, CSG more and more behaves like a full
gradient descent algorithm. In this contribution, we show that convergence
results for constant step sizes known from full gradient descent schemes carry
over to CSG. Furthermore, we augment CSG by an Armijo-type backtracking line
search based on the gradient and objective function approximations and provide
convergence results for this variant. The resulting novel methods are
applicable for a large class of optimization problems. Their performance is
demonstrated in comparison to other stochastic gradient schemes known from
literature.


title:
Lead-agnostic Self-supervised Learning for Local and Global
  Representations of Electrocardiogram

abstract:
  In recent years, self-supervised learning methods have shown significant
improvement for pre-training with unlabeled data and have proven helpful for
electrocardiogram signals. However, most previous pre-training methods for
electrocardiogram focused on capturing only global contextual representations.
This inhibits the models from learning fruitful representation of
electrocardiogram, which results in poor performance on downstream tasks.
Additionally, they cannot fine-tune the model with an arbitrary set of
electrocardiogram leads unless the models were pre-trained on the same set of
leads. In this work, we propose an ECG pre-training method that learns both
local and global contextual representations for better generalizability and
performance on downstream tasks. In addition, we propose random lead masking as
an ECG-specific augmentation method to make our proposed model robust to an
arbitrary set of leads. Experimental results on two downstream tasks, cardiac
arrhythmia classification and patient identification, show that our proposed
approach outperforms other state-of-the-art methods.


title: Attention based Memory video portrait matting

abstract:
  We proposed a novel trimap free video matting method based on the attention
mechanism. By the nature of the problem, most existing approaches use either
multiple computational expansive modules or complex algorithms to exploit
temporal information fully. We designed a temporal aggregation module to
compute the temporal coherence between the current frame and its two previous
frames.


title: Variational problem on a metric-affine almost product manifold

abstract:
  A smooth manifold $M$ with a decomposition $TM={\cal D}_1+\ldots+{\cal D}_k$
of the tangent bundle into $k>2$ subbundles appears in theories of nets and
webs of foliations and in studies of Einstein equations on twisted products. We
study a variational problem, which is the geometrical part of an
Einstein-Hilbert type action on an almost product manifold, namely, we consider
an integrated sum of mixed scalar curvatures of several distributions, as a
functional of a pseudo-Riemannian metric, keeping the pairwise orthogonality of
the distributions, and a linear connection. Unlike in the Einstein-Cartan
theory, metrics in critical pairs metric-contorsion are restricted to those
that make all distributions totally umbilical. We examine further some special
cases: twisted products with statistical connections and 3-Sasaki manifolds
with metric compatible connections. Considering variations of connection among
statistical connections allows us to obtain examples of critical pairs
metric-contorsion from critical adapted metrics of the action with zero
contorsion tensor.


title:
Beating the 3 dB Limit for Intracavity Squeezing and Its Application to
  Nondemolition Qubit Readout

abstract:
  While the squeezing of a propagating field can, in principle, be made
arbitrarily strong, the cavity-field squeezing is subject to the well-known 3
dB limit, and thus has limited applications. Here, we present a novel method to
beat this limit using a fully quantum degenerate parametric amplifier (DPA).
Specifically, we show that by {\it simply} applying a two-tone driving to the
signal mode, the pump mode can, {\it counterintuitively}, be driven by the
photon loss of the signal mode into a steady squeezed state with, in principle,
an {\it arbitrarily high} degree of squeezing. Furthermore, we demonstrate that
this intracavity squeezing can increase the signal-to-noise ratio of
longitudinal qubit readout {\it exponentially} with the degree of squeezing.
Correspondingly, an improvement of the measurement error by {\it many orders of
magnitude} can be achieved even for modest parameters. In stark contrast, using
intracavity squeezing of the semiclassical DPA {\it cannot} practically
increase the signal-to-noise ratio and thus improve the measurement error. Our
results extend the range of applications of DPAs and open up new opportunities
for modern quantum technologies.


title: Superconductor vortex spectrum from Fermi arc states in Weyl semimetals

abstract:
  The vortex core of a type-II superconductor hosts discrete energy levels that
carry critical information about the parent normal state. For example, the
levels are equally spaced with a zero-point energy equal to half the level
spacing if the parent state is an ordinary metal. If the parent state is a
massless Dirac fermion instead, the zero-point energy vanishes and an exotic
Majorana fermion emerges. Weyl semimetals, three-dimensional topological
materials defined by accidental bulk band intersections or Weyl nodes, host a
peculiar metal on their surface composed of open Fermi arcs whose end points
merge with the bulk Weyl nodes, rather than closed Fermi surfaces. This bizarre
structure places Fermi arcs beyond a conventional Hamiltonian description; at
the same time, it inspires the fundamental question, "what is the vortex
spectrum of a superconductor that descends from a Fermi arc metal?" Here, we
answer this question using a powerful semiclassical approach substantiated by
lattice numerics. We show that the vortex states form cyclotron-like closed
orbits consisting of Fermi arcs on opposite surfaces connected by one-way bulk
conduits. The resulting spectrum is governed by the total Berry phase acquired
by a wavepacket along this orbit and has characteristic dependences on the
Berry phases and penetration depth of the Fermi arcs, bulk Weyl node locations,
vortex orientation and sample thickness. Remarkably, eliminating the zero-point
energy by manipulating these parameters yields a pair of non-local Majorana
fermions, while the thickness dependence disappears at a certain vortex
orientation that we dub the "magic angle". Interestingly, under mild conditions
pertinent to many lattice models and materials, non-local Majorana fermions
exist precisely at the magic angle.