title: The Transferability Limits of Static Benchmarks

abstract:
  Every practical method to solve the Schr\"odinger equation for interacting
many-particle systems introduces approximations. Such methods are therefore
plagued by systematic errors. For computational chemistry, it is decisive to
quantify the specific error for some system under consideration. Traditionally,
the primary resource for such an error assessment have been benchmarking
results, usually taken from the literature. However, their transferability to a
specific molecular system, and hence, the reliability of the traditional
approach always remains uncertain to some degree. In this communication, we
elaborate on the shortcomings of this traditional way of static benchmarking by
exploiting statistical analyses at the example of one of the largest quantum
chemical benchmark sets available. We demonstrate the uncertainty of error
estimates in the light of the choice of reference data selected for a benchmark
study. To alleviate the issues with static benchmarks, we advocate to rely
instead on a rolling and system-focused approach for rigorously quantifying the
uncertainty of a quantum chemical result.


title:
Second Order Regret Bounds Against Generalized Expert Sequences under
  Partial Bandit Feedback

abstract:
  We study the problem of expert advice under partial bandit feedback setting
and create a sequential minimax optimal algorithm. Our algorithm works with a
more general partial monitoring setting, where, in contrast to the classical
bandit feedback, the losses can be revealed in an adversarial manner. Our
algorithm adopts a universal prediction perspective, whose performance is
analyzed with regret against a general expert selection sequence. The regret we
study is against a general competition class that covers many settings (such as
the switching or contextual experts settings) and the expert selection
sequences in the competition class are determined by the application at hand.
Our regret bounds are second order bounds in terms of the sum of squared losses
and the normalized regret of our algorithm is invariant under arbitrary affine
transforms of the loss sequence. Our algorithm is truly online and does not use
any preliminary information about the loss sequences.

categories: cs.LG cs.IT eess.SP math.IT math.OC stat.ML


title:
ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP): Evidence for
  a Molecular Jet Launched at an Unprecedented Early Phase of Protostellar
  evolution

abstract:
  Protostellar outflows and jets play a vital role in star formation as they
carry away excess angular momentum from the inner disk surface, allowing the
material to be transferred toward the central protostar. Theoretically, low
velocity and poorly collimated outflows appear from the beginning of the
collapse, at the first hydrostatic core (FHSC) stage. With growing protostellar
core mass, high-density jets are launched which entrain an outflow from the
infalling envelope. Until now, molecular jets have been observed at high
velocity ($\gtrsim$ 100 km/s) in early Class\,0 protostars. We, for the first
time, detect a dense molecular jet in SiO emission with small-velocity ($\sim$
4.2 km\,s$^{-1}$, deprojected $\sim$ 24 km\,s$^{-1}$) from source
G208.89-20.04Walma (hereafter, G208Walma) using ALMA Band\,6 observations. This
object has some characteristics of FHSCs, such as a small outflow/jet velocity,
extended 1.3\,mm continuum emission, and N$_2$D$^+$ line emission. Additional
characteristics, however, are typical of early protostars: collimated outflow
and SiO jet. The full extent of the outflow corresponds to a dynamical time
scale of $\sim$ 930$^{+200}_{-100}$ years. The spectral energy distribution
also suggests a very young source having an upper limit of T$_{bol}$ $\sim$ 31
K and L$_{bol}$ $\sim$ 0.8 L$_\sun$. We conclude that G208Walma is likely in
the transition phase from FHSC to protostar, and the molecular jet has been
launched within a few hundred years of initial collapse. Therefore, G208Walma
may be the earliest object discovered in the protostellar phase with a
molecular jet.


title:
Cosmic ray spectrum of protons plus helium nuclei between 6 TeV and 158
  TeV from HAWC data

abstract:
  A measurement with high statistics of the differential energy spectrum of
light elements in cosmic rays, in particular, of primary H plus He nuclei, is
reported. The spectrum is presented in the energy range from $6$ to $158$ TeV
per nucleus. Data was collected with the High Altitude Water Cherenkov (HAWC)
Observatory between June 2015 and June 2019. The analysis was based on a
Bayesian unfolding procedure, which was applied on a subsample of vertical HAWC
data that was enriched to $82\%$ of events induced by light nuclei. To achieve
the mass separation, a cut on the lateral age of air shower data was set guided
by predictions of CORSIKA/QGSJET-II-04 simulations. The measured spectrum is
consistent with a broken power-law spectrum and shows a kneelike feature at
around $E = 24.0^{+3.6}_{-3.1} $ TeV, with a spectral index $\gamma = -2.51 \pm
0.02$ before the break and with $\gamma = -2.83 \pm 0.02$ above it. The feature
has a statistical significance of $4.1 \, \sigma$. Within systematic
uncertainties, the significance of the spectral break is $0.8 \, \sigma$.


title:
Renormalized Area for Minimal Hypersurfaces of 5D Poincar\'e-Einstein
  Spaces

abstract:
  In this paper we derive a Gauss-Bonnet formula for the renormalized area of
Graham-Witten minimal hypersurfaces of 5-dimensional Poincar\'e-Einstein
spaces. The formula we derive expresses the renormalized area in terms of
integrals of local geometric quantities. We also prove a result which relates
the total integral of the Laplacian of the norm squared of the second
fundamental form to conformal geometry at infinity.


title:
Achieving Representative Data via Convex Hull Feasibility Sampling
  Algorithms

abstract:
  Sampling biases in training data are a major source of algorithmic biases in
machine learning systems. Although there are many methods that attempt to
mitigate such algorithmic biases during training, the most direct and obvious
way is simply collecting more representative training data. In this paper, we
consider the task of assembling a training dataset in which minority groups are
adequately represented from a given set of data sources. In essence, this is an
adaptive sampling problem to determine if a given point lies in the convex hull
of the means from a set of unknown distributions. We present adaptive sampling
methods to determine, with high confidence, whether it is possible to assemble
a representative dataset from the given data sources. We also demonstrate the
efficacy of our policies in simulations in the Bernoulli and a multinomial
setting.


title: On a System of Weakly Null Semilinear Equations

abstract:
  We develop a new method for addressing certain weakly null systems of wave
equations. This approach does not rely on Lorentz invariance nor on the use of
null foliations, both of which restrict applications to, e.g., multiple speed
systems. The proof uses a class of space-time Klainerman-Sobolev estimates of
the first author, Tataru, and Tohaneanu, which pair nicely with local energy
estimates that combine the $r^{p}$-weighted method of Dafermos and Rodnianski
with the ghost weight method of Alinhac. We further refine the standard local
energy estimate with a modification of the $\partial_{t} - \partial_{r}$
portion of the multiplier.


title: Explicit caching HYB: a new high-performance SpMV framework on GPGPU

abstract:
  Sparse Matrix-Vector Multiplication (SpMV) is a critical operation for the
iterative solver of Finite Element Methods on computer simulation. Since the
SpMV operation is a memory-bound algorithm, the efficiency of data movements
heavily influenced the performance of the SpMV on GPU. In recent years, many
research is conducted in accelerating the performance of SpMV on the graphic
processing units (GPU). The performance optimization methods used in existing
studies focus on the following areas: improve the load balancing between GPU
processors, and reduce the execution divergence between GPU threads. Although
some studies have made preliminary optimization on the input vector fetching,
the effect of explicitly caching the input vector on GPU base SpMV has not been
studied in depth yet. In this study, we are trying to minimize the data
movements cost for GPU-based SpMV using a new framework named "explicit caching
Hybrid (EHYB)". The EHYB framework achieved significant performance improvement
by using the following methods: 1. Improve the speed of data movements by
partitioning and explicitly caching the input vector to the shared memory of
the CUDA kernel. 2. Reduce the volume of data movements by storing the major
part of the column index with a compact format. We tested our implementation
with sparse matrices derived from FEM applications in different areas. The
experiment results show that our implementation can overperform the
state-of-the-arts implementation with significant speedup, and leads to higher
FLOPs than the theoryperformance up-boundary of the existing GPU-based SpMV
implementations.


title: CMS contribution to Hadron Spectroscopy (Snowmass 2021 White Paper)

abstract:
  In this short White Paper we glance over the studies carried out by the CMS
Collaboration so far in the field of conventional and exotic hadron
spectroscopy, by highlighting the relevant scientific achievements as well.
Moreover, we briefly discuss the future perspectives of this scientific
engagement.


title:
High-precision half-life determination of $^{14}$O via direct $\beta$
  counting

abstract:
  The half-life of the superallowed Fermi $\beta^+$ emitter $^{14}$O was
determined to high precision via a direct $\beta$ counting experiment performed
at the Isotope Separator and Accelerator (ISAC) facility at TRIUMF. The result,
$T_{1/2}$($^{14}$O) = 70619.2(76) ms, is consistent with, but is more precise
than, the world average obtained from 11 previous measurements. Combining the
$^{14}$O half-life deduced in the present work with the previous most precise
measurements of this quantity leads to a reduction in the overall uncertainty,
by nearly a factor of 2. The new world average is $T_{1/2}$($^{14}$O) =
70619.6(63) ms with a reduced $\chi^2$ value of 0.87 obtained from 8 degrees of
freedom.


title: Percolation in a triangle on a square lattice

abstract:
  Percolation on a plane is usually associated with clusters spanning two
opposite sides of a rectangular system. Here we investigate three-leg clusters
generated on a square lattice and spanning the three sides of equilateral
triangles. If the position and orientation of the triangles relative to the
lattice are uniformly randomized, one obtains an efficient method of
determining the percolation threshold, on par with the most advanced Monte
Carlo methods developed for the rectangular geometry. The universal crossing
probability for three-leg clusters is geometry-independent, which opens a way
for further improvements of the method.


title: SkiQL: A Unified Schema Query Language

abstract:
  Most NoSQL systems are schema-on-read: data can be stored without first
having to declare a Schema that imposes a structure. This schemaless feature
offers flexibility to evolve data-intensive applications when data frequently
change. However, freeing from declaring schemas does not mean their absence,
but rather that they are implicit in data and code. Therefore, diagramming
tools similar to those available for relational systems are also needed to help
developers and administrators understanding NoSQL schemas.
  Visualizing diagrams is not practical if schemas contain hundreds of database
entities, and exploration or query facilities are then needed. In schemaless
NoSQL stores, data of the same entity can be stored with different structure
which can increase the difficulty of having readable diagrams.
  NoSQL schema management tools should therefore have three main components:
schema extraction, schema visualization, and schema query. Since that there
exist four main NoSQL data models, it is convenient that such tools can be
built on a generic data model that provide platform-independence to query and
visualize schemas. With the aim of favoring the creation of generic database
tools, the authors of this paper defined the U-Schema unified data model that
integrates the four main NoSQL data models and the relational model.
  This paper is focused on querying NoSQL and relational schemas which are
represented as U-Schema models. We present the SkiQL language designed on
U-Schema to achieve a platform-independent schema query service. SkiQL provides
two constructs: schema-query and relationship-query. The former allows to
obtain information of entity or relationship types, and the latter that of the
aggregations or references (relations among types). We will show how SkiQL was
evaluated by calculating well-known metrics for languages and using a survey.


title:
A deep learning algorithm for reducing false positives in screening
  mammography

abstract:
  Screening mammography improves breast cancer outcomes by enabling early
detection and treatment. However, false positive callbacks for additional
imaging from screening exams cause unnecessary procedures, patient anxiety, and
financial burden. This work demonstrates an AI algorithm that reduces false
positives by identifying mammograms not suspicious for breast cancer. We
trained the algorithm to determine the absence of cancer using 123,248 2D
digital mammograms (6,161 cancers) and performed a retrospective study on
14,831 screening exams (1,026 cancers) from 15 US and 3 UK sites. Retrospective
evaluation of the algorithm on the largest of the US sites (11,592 mammograms,
101 cancers) a) left the cancer detection rate unaffected (p=0.02,
non-inferiority margin 0.25 cancers per 1000 exams), b) reduced callbacks for
diagnostic exams by 31.1% compared to standard clinical readings, c) reduced
benign needle biopsies by 7.4%, and d) reduced screening exams requiring
radiologist interpretation by 41.6% in the simulated clinical workflow. This
work lays the foundation for semi-autonomous breast cancer screening systems
that could benefit patients and healthcare systems by reducing false positives,
unnecessary procedures, patient anxiety, and expenses.


title:
Effective Field Theory of Gravitating Continuum: Solids, Fluids, and
  Aether Unified

abstract:
  We investigate the relativistic effective field theory (EFT) describing a
non-dissipative gravitating continuum. In addition to ordinary continua, namely
solids and fluids, we find an extraordinary more symmetric continuum, aether.
In particular, the symmetry of the aether concludes that a homogeneous and
isotropic state behaves like a cosmological constant. We formulate the EFT in
the unitary/comoving gauge in which the dynamical degrees of freedom of the
continuum (phonons) are eaten by the spacetime metric. This gauge choice, which
is interpreted as the Lagrangian description in hydrodynamics, offers a neat
geometrical understanding of continua. We examine a thread-based spacetime
decomposition with respect to the four-velocity of the continuum which is
different from the foliation-based Arnowitt-Deser-Misner one. Our thread-based
decomposition respects the symmetries of the continua and, therefore, makes it
possible to systematically find invariant building blocks of the EFT for each
continuum even at higher orders in the derivative expansion. We also discuss
the linear dynamics of the system and show that both gravitons and phonons
acquire "masses" in a gravitating background.


title:
Adaptive Modulation for Wobbling UAV Air-to-Ground Links in
  Millimeter-wave Bands

abstract:
  The emerging millimeter-wave (mm-wave) unmanned aerial vehicle (UAV)
air-to-ground (A2G) communications are facing the Doppler effect problem that
arises from the inevitable wobbling of the UAV. The fast time-varying channel
for UAV A2G communications may lead to the outdated channel state information
(CSI) from the channel estimation. In this paper, we introduce two detectors to
demodulate the received signal and get the instantaneous bit error probability
(BEP) of a mm-wave UAV A2G link under imperfect CSI. Based on the designed
detectors, we propose an adaptive modulation scheme to maximize the average
transmission rate under imperfect CSI by optimizing the data transmission time
subject to the maximum tolerable BEP. A power control policy is in conjunction
with adaptive modulation to minimize the transmission power while maintaining
both the BEP under the threshold and the maximized average transmission rate.
Numerical results show that the proposed adaptive modulation scheme in
conjunction with the power control policy could maximize the temporally
averaged transmission rate, while saves as much as 50\% energy.


title:
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text
  Generation

abstract:
  Recent improvements in KG-to-text generation are due to additional auxiliary
pre-trained tasks designed to give the fine-tune task a boost in performance.
These tasks require extensive computational resources while only suggesting
marginal improvements. Here, we demonstrate that by fusing graph-aware elements
into existing pre-trained language models, we are able to outperform
state-of-the-art models and close the gap imposed by additional pre-train
tasks. We do so by proposing a mask structure to capture neighborhood
information and a novel type encoder that adds a bias to the graph-attention
weights depending on the connection type. Experiments on two KG-to-text
benchmark datasets show these models to be superior in quality while involving
fewer parameters and no additional pre-trained tasks. By formulating the
problem as a framework, we can interchange the various proposed components and
begin interpreting KG-to-text generative models based on the topological and
type information found in a graph.


title: Geometric Understanding of Sketches

abstract:
  Sketching is used as a ubiquitous tool of expression by novices and experts
alike. In this thesis I explore two methods that help a system provide a
geometric machine-understanding of sketches, and in-turn help a user accomplish
a downstream task.
  The first work deals with interpretation of a 2D-line drawing as a graph
structure, and also illustrates its effectiveness through its physical
reconstruction by a robot. We setup a two-step pipeline to solve the problem.
Formerly, we estimate the vertices of the graph with sub-pixel level accuracy.
We achieve this using a combination of deep convolutional neural networks
learned under a supervised setting for pixel-level estimation followed by the
connected component analysis for clustering. Later we follow it up with a
feedback-loop-based edge estimation method. To complement the
graph-interpretation, we further perform data-interchange to a robot legible
ASCII format, and thus teach a robot to replicate a line drawing.
  In the second work, we test the 3D-geometric understanding of a sketch-based
system without explicit access to the information about 3D-geometry. The
objective is to complete a contour-like sketch of a 3D-object, with
illumination and texture information. We propose a data-driven approach to
learn a conditional distribution modelled as deep convolutional neural networks
to be trained under an adversarial setting; and we validate it against a
human-in-the-loop. The method itself is further supported by synthetic data
generation using constructive solid geometry following a standard graphics
pipeline. In order to validate the efficacy of our method, we design a
user-interface plugged into a popular sketch-based workflow, and setup a simple
task-based exercise, for an artist. Thereafter, we also discover that
form-exploration is an additional utility of our application.


title:
DRAGON : A suite of Hardware Simulation and Optimization tools for
  Modern Workloads

abstract:
  We introduce DRAGON, an open-source, fast and explainable toolchain hardware
simulation and optimization enables hardware architects to simulate hardware
designs, and to optimize hardware designs to efficiently execute workloads.
  The DRAGON toolchain provides the following tools: Hardware Model Generator
(DGen), Hardware Simulator (DSim) and Hardware Optimizer (DOpt).
  DSim provides the simulation of running algorithms (represented as data-flow
graphs) on hardware described. DGen describes the hardware in detail, with user
input architectures/technology (represented in a custom description language).
A novel methodology of gradient descent from the simulation allows us optimize
the hardware model (giving the directions for improvements in technology
parameters and design parameters), provided by Dopt.
  DRAGON framework (DSim) is much faster than previously avaible works for
simulation, which is possible through performance-first code writing practices,
mathematical formulas for common computing operations to avoid cycle-accurate
simulation steps, efficient algorithms for mapping, and data-structure
representations for hardware state. DRAGON framework (Dopt) generates
performance optimized architectures for both AI and Non-AI Workloads, and
provides technology improvement directions for 100x-1000x better future
computing systems.


title:
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State
  Tracking

abstract:
  Dialogue State Tracking (DST) aims to keep track of users' intentions during
the course of a conversation. In DST, modelling the relations among domains and
slots is still an under-studied problem. Existing approaches that have
considered such relations generally fall short in: (1) fusing prior slot-domain
membership relations and dialogue-aware dynamic slot relations explicitly, and
(2) generalizing to unseen domains. To address these issues, we propose a novel
\textbf{D}ynamic \textbf{S}chema \textbf{G}raph \textbf{F}usion
\textbf{Net}work (\textbf{DSGFNet}), which generates a dynamic schema graph to
explicitly fuse the prior slot-domain membership relations and dialogue-aware
dynamic slot relations. It also uses the schemata to facilitate knowledge
transfer to new domains. DSGFNet consists of a dialogue utterance encoder, a
schema graph encoder, a dialogue-aware schema graph evolving network, and a
schema graph enhanced dialogue state decoder. Empirical results on benchmark
datasets (i.e., SGD, MultiWOZ2.1, and MultiWOZ2.2), show that DSGFNet
outperforms existing methods.


title: Curve Shortening Flow on $\mathbb{T}^2$

abstract:
  We present a characterization for the initial data of a soliton solution for
the curve shortening flow (CSF) on the torus of revolution. Furthermore, we
describe the behavior of such solutions by showing that the two ends of each
curve are asymptotic to the equator.


title: Weighted homological regularities

abstract:
  Let $A$ be a noetherian connected graded algebra. We introduce and study
homological invariants that are weighted sums of the homological and internal
degrees of cochain complexes of graded $A$-modules, providing weighted versions
of Castelnuovo--Mumford regularity, Tor-regularity, Artin--Schelter regularity,
and concavity. In some cases an invariant (such as Tor-regularity) that is
infinite can be replaced with a weighted invariant that is finite, and several
homological invariants of complexes can be expressed as weighted homological
regularities. We prove a few weighted homological identities some of which
unify different classical homological identities and produce interesting new
ones.


title: Double version of the Rashba and Dresselhaus spin-orbit coupling

abstract:
  The Rashba and Dresselhaus types of spin-orbit coupling are two typical
linear coupling forms. We establish the fundamental physics of a model which
can be viewed as the double version of the Rashba and Dresselhaus spin-orbit
coupling. This model describes the low energy physics of a class of massless
Dirac fermions in spin-orbit systems. The physical properties of the massless
Dirac fermions are determined by the mathematical relations of spin-orbit
coefficients. For equal Rashba and Dresselhauss coupling constants,
k-independent eigenspinors and a persistent spin helix combined with massless
birefringent Dirac fermions emerge in this model. The spin-orbit coupled
systems described by this model have potential technological applications from
spintronics to quantum computation.


title: MINSU (Mobile Inventory And Scanning Unit):Computer Vision and AI

abstract:
  The MINSU(Mobile Inventory and Scanning Unit) algorithm uses the
computational vision analysis method to record the residual quantity/fullness
of the cabinet. To do so, it goes through a five-step method: object detection,
foreground subtraction, K-means clustering, percentage estimation, and
counting. The input image goes through the object detection method to analyze
the specific position of the cabinets in terms of coordinates. After doing so,
it goes through the foreground subtraction method to make the image more
focus-able to the cabinet itself by removing the background (some manual work
may have to be done such as selecting the parts that were not grab cut by the
algorithm). In the K-means clustering method, the multi-colored image turns
into a 3 colored monotonous image for quicker and more accurate analysis. At
last, the image goes through percentage estimation and counting. In these two
methods, the proportion that the material inside the cabinet is found in
percentage which then is used to approximate the number of materials inside.
Had this project been successful, the residual quantity management could solve
the problem addressed earlier in the introduction.


title: GM-TOuNN: Graded Multiscale Topology Optimization using Neural Networks

abstract:
  Multiscale topology optimization (M-TO) entails generating an optimal global
topology, and an optimal set of microstructures at a smaller scale, for a
physics-constrained problem. With the advent of additive manufacturing, M-TO
has gained significant prominence. However, generating optimal microstructures
at various locations can be computationally very expensive. As an alternate,
graded multiscale topology optimization (GM-TO) has been proposed where one or
more pre-selected and graded (parameterized) microstructural topologies are
used to fill the domain optimally. This leads to a significant reduction in
computation while retaining many of the benefits of M-TO.
  A successful GM-TO framework must: (1) be capable of efficiently handling
numerous pre-selected microstructures, (2) be able to continuously switch
between these microstructures during optimization, (3) ensure that the
partition of unity is satisfied, and (4) discourage microstructure mixing at
termination.
  In this paper, we propose to meet these requirements by exploiting the unique
classification capacity of neural networks. Specifically, we propose a graded
multiscale topology optimization using neural-network (GM-TOuNN) framework with
the following features: (1) the number of design variables is only weakly
dependent on the number of pre-selected microstructures, (2) it guarantees
partition of unity while discouraging microstructure mixing, and (3) it
supports automatic differentiation, thereby eliminating manual sensitivity
analysis. The proposed framework is illustrated through several examples.


title: Revisiting Transformer-based Models for Long Document Classification

abstract:
  The recent literature in text classification is biased towards short text
sequences (e.g., sentences or paragraphs). In real-world applications,
multi-page multi-paragraph documents are common and they cannot be efficiently
encoded by vanilla Transformer-based models. We compare different
Transformer-based Long Document Classification (TrLDC) approaches that aim to
mitigate the computational overhead of vanilla transformers to encode much
longer text, namely sparse attention and hierarchical encoding methods. We
examine several aspects of sparse attention (e.g., size of local attention
window, use of global attention) and hierarchical (e.g., document splitting
strategy) transformers on four document classification datasets covering
different domains. We observe a clear benefit from being able to process longer
text, and, based on our results, we derive practical advice of applying
Transformer-based models on long document classification tasks.


title:
Multifidelity deep neural operators for efficient learning of partial
  differential equations with application to fast inverse design of nanoscale
  heat transport

abstract:
  Deep neural operators can learn operators mapping between
infinite-dimensional function spaces via deep neural networks and have become
an emerging paradigm of scientific machine learning. However, training neural
operators usually requires a large amount of high-fidelity data, which is often
difficult to obtain in real engineering problems. Here, we address this
challenge by using multifidelity learning, i.e., learning from multifidelity
datasets. We develop a multifidelity neural operator based on a deep operator
network (DeepONet). A multifidelity DeepONet includes two standard DeepONets
coupled by residual learning and input augmentation. Multifidelity DeepONet
significantly reduces the required amount of high-fidelity data and achieves
one order of magnitude smaller error when using the same amount of
high-fidelity data. We apply a multifidelity DeepONet to learn the phonon
Boltzmann transport equation (BTE), a framework to compute nanoscale heat
transport. By combining a trained multifidelity DeepONet with genetic algorithm
or topology optimization, we demonstrate a fast solver for the inverse design
of BTE problems.


title: Hard X-ray helical dichroism of disordered molecular media

abstract:
  Chirality is a structural property of molecules lacking mirror symmetry that
has strong implications in diverse fields, ranging from life to materials
sciences. Established spectroscopic methods that are sensitive to chirality,
such as circular dichroism (CD), exhibit weak signal contributions on an
achiral background. Helical dichroism (HD), which is based on the orbital
angular momentum (OAM) of light, offers a new approach to probe molecular
chirality, but it has never been demonstrated on disordered samples.
Furthermore, in the optical domain the challenge lies in the need to transfer
the OAM of the photon to an electron that is localized on an {\AA}-size
orbital. Here, we overcome this challenge using hard X-rays with spiral Fresnel
zone, which can induce an OAM. We present the first HD spectra of a disordered
powder sample of enantiopure molecular complexes of [Fe(4,4'-diMebpy)3]2+ at
the iron K-edge (7.1 keV) with OAM-carrying beams. The HD spectra exhibit the
expected inversions of signs switching from a left to a right helical wave
front or from an enantiomer to the other. The asymmetry ratios for the HD
spectra are within one to five percent for OAM beams with topological charges
of one and three. These results open a new window into the studies of molecular
chirality and its interaction with the orbital angular momentum of light.


title: Isoperimetric Inequalities Made Simpler

abstract:
  We give an alternative, simple method to prove isoperimetric inequalities
over the hypercube. In particular, we show:
  1. An elementary proof of classical isoperimetric inequalities of Talagrand,
as well as a stronger isoperimetric result conjectured by Talagrand and
recently proved by Eldan and Gross.
  2. A strengthening of the Friedgut junta theorem, asserting that if the
$p$-moment of the sensitivity of a function is constant for some $p\geq 1/2 +
\varepsilon$, then the function is close to a junta. In this language,
Friedgut's theorem is the special case that $p=1$.


title: Designing Experiments Toward Shrinkage Estimation

abstract:
  We consider how increasingly available observational data can be used to
improve the design of randomized controlled trials (RCTs). We seek to design a
prospective RCT, with the intent of using an Empirical Bayes estimator to
shrink the causal estimates from our trial toward causal estimates obtained
from an observational study. We ask: how might we design the experiment to
better complement the observational study in this setting?
  We propose using an estimator that shrinks each component of the RCT causal
estimator toward its observational counterpart by a factor proportional to its
variance. First, we show that the risk of this estimator can be computed
efficiently via numerical integration. We then propose algorithms for
determining the best allocation of units to strata (the best "design"). We
consider three options: Neyman allocation; a "naive" design assuming no
unmeasured confounding in the observational study; and a "defensive" design
accounting for the imperfect parameter estimates we would obtain from the
observational study with unmeasured confounding.
  We also incorporate results from sensitivity analysis to establish guardrails
on the designs, so that our experiment could be reasonably analyzed with and
without shrinkage. We demonstrate the superiority of these experimental designs
with a simulation study involving causal inference on a rare, binary outcome.


title:
Time Series of Non-Additive Metrics: Identification and Interpretation
  of Contributing Factors of Variance by Linear Decomposition

abstract:
  The research paper addresses linear decomposition of time series of
non-additive metrics that allows for the identification and interpretation of
contributing factors (input features) of variance. Non-additive metrics, such
as ratios, are widely used in a variety of domains. It commonly requires
preceding aggregations of underlying variables that are used to calculate the
metric of interest. The latest poses a dimensionality challenge when the input
features and underlying variables are formed as two-dimensional arrays along
elements, such as account or customer identifications, and time points. It
rules out direct modeling of the time series of a non-additive metric as a
function of input features. The article discusses a five-step approach: (1)
segmentations of input features and the underlying variables of the metric that
are supported by unsupervised autoencoders, (2) univariate or joint fittings of
the metric by the aggregated input features on the segmented domains, (3)
transformations of pre-screened input features according to the fitted models,
(4) aggregation of the transformed features as time series, and (5) modelling
of the metric time series as a sum of constrained linear effects of the
aggregated features. Alternatively, approximation by numerical differentiation
has been considered to linearize the metric. It allows for element level
univariate or joint modeling of step (2). The process of these analytical steps
allows for a backward-looking explanatory decomposition of the metric as a sum
of time series of the survived input features. The paper includes a synthetic
example that studies loss-to-balance monthly rates of a hypothetical retail
credit portfolio. To validate that no latent factors other than the survived
input features have significant impacts on the metric, Statistical Process
Control has been introduced for the residual time series.


title:
Experimental quantum state discrimination using the optimal fixed rate
  of inconclusive outcomes strategy

abstract:
  The problem of non-orthogonal state discrimination underlies crucial quantum
information tasks, such as cryptography and computing protocols. Therefore, it
is decisive to find optimal scenarios for discrimination among quantum states.
We experimentally investigate the strategy for the optimal discrimination of
two non-orthogonal states considering a fixed rate of inconclusive outcomes
(FRIO). The main advantage of the FRIO strategy is to interpolate between
unambiguous and minimum error discrimination by solely adjusting the rate of
inconclusive outcomes. We present a versatile experimental scheme that performs
the optimal FRIO measurement for any pair of generated non-orthogonal states
with arbitrary a priori probabilities and for any fixed rate of inconclusive
outcomes. Considering different values of the free parameters in the FRIO
protocol, we implement it upon qubit states encoded in the polarization mode of
single photons generated in the spontaneous parametric down-conversion process.
Moreover, we resort to a newfangled double-path Sagnac interferometer to
perform a three-outcome non-projective measurement required for the
discrimination task, showing excellent agreement with the theoretical
prediction. This experiment provides a practical toolbox for a wide range of
quantum state discrimination strategies using the FRIO scheme, which can
greatly benefit quantum information applications and fundamental studies in
quantum theory.


title:
Creep tide theory. Equations for differentiated bodies with aligned
  layers

abstract:
  The creep tide theory is used to establish the basic equations of the tidal
evolution of differentiated bodies formed by aligned homogeneous layers in
co-rotation. The mass concentration of the body is given by the fluid Love
number $k_f$. The formulas are given by series expansions valid for high
eccentricity systems. They are equivalent to Darwin's equations, but formally
more compact. An application to the case of Enceladus, with $k_f=0.942$, is
discussed.


title: Gas and Dust Shadows in the TW Hydrae Disk

abstract:
  We present new observations of CO J=2-1 emission from the protoplanetary disk
around TW Hya. Emission is detected out to 240 au (4") and found to exhibit
azimuthal variations up to 20% beyond 180 au (3"), with the west side of the
disk brighter than the east. This asymmetry is interpreted as tracing the
shadow previously seen in scattered light. A reanalysis of the multi-epoch
observations of the dust shadow in scattered light from Debes et al. (2017)
suggests that an oscillatory motion would provide a better model of the
temporal evolution of the dust shadow rather than orbital motion. Both models
predict an angular offset between the dust shadow and the gas shadow of up to
~100 deg. We attribute this offset to the finite rate at which dust grains and
gas molecules can exchange heat, dominated by the collisional rate between gas
molecules and dust grains, $t_{\rm coll}$. The angular offsets derived are
equivalent to collisional timescales that range from the near instantaneous up
to $t_{\rm coll}$ ~ 10 years, depending on whether a straight or a curved dust
shadow, as suggested by HST observations reported by Debes et al. (2017), is
adopted. The inferred range of $t_{\rm coll}$ are consistent with those
predictions based on representative gas densities, temperatures, gas-to-dust
ratios and grain sizes. These results represent the first time empirical
constraints can be placed on $t_{\rm coll}$.


title:
Stability of China's Stock Market: Measure and Forecast by Ricci
  Curvature on Network

abstract:
  The systemic stability of a stock market is one of the core issues in the
financial field. The market can be regarded as a complex network whose nodes
are stocks connected by edges that signify their correlation strength. Since
the market is a strongly nonlinear system, it is difficult to measure the
macroscopic stability and depict market fluctuations in time. In this paper, we
use a geometric measure derived from discrete Ricci curvature to capture the
higher-order nonlinear architecture of financial networks. In order to confirm
the effectiveness of our method, we use it to analyze the CSI 300 constituents
of China's stock market from 2005--2020 and the systemic stability of the
market is quantified through the network's Ricci type curvatures. Furthermore,
we use a hybrid model to analyze the curvature time series and predict the
future trends of the market accurately. As far as we know, this is the first
paper to apply Ricci curvature to forecast the systemic stability of domestic
stock market, and our results show that Ricci curvature has good explanatory
power for the market stability and can be a good indicator to judge the future
risk and volatility of the domestic market.


title:
Learning fixed-complexity polyhedral Lyapunov functions from
  counterexamples

abstract:
  In this paper, we present an algorithm for synthesizing polyhedral Lyapunov
functions for hybrid systems with multiple modes, each with linear dynamics and
state-based switching between these modes. The problem of proving global
asymptotic stability (GAS) for such systems is quite challenging. In this
paper, we present an algorithm to synthesize a fixed-complexity polyhedral
Lyapunov function to prove that such a system is GAS. Such functions are
defined as the piecewise maximum over a fixed number of linear functions.
Previous work on this problem reduces to a system of \emph{bilinear}
constraints that are well-known to be computationally hard to solve precisely.
Therefore, heuristic approaches such as alternating descent have been employed.
In this paper, we first prove that deciding if there exists a polyhedral
Lyapunov function for a given piecewise linear system is a NP-hard problem. We
then present a counterexample-guided algorithm for solving this problem. Our
approach alternates between choosing candidate Lyapunov functions based on a
finite set of counterexamples, and verifying if these candidates satisfy the
Lyapunov conditions. If the verification of a given candidate fails, we find a
new counterexample that is added back to our set. We prove that if this
algorithm terminates, it discovers a valid Lyapunov function or concludes that
no such Lyapunov function exists. However, our initial algorithm can be
non-terminating. We modify our algorithm to provide a terminating version based
on the so-called cutting-plane argument from nonsmooth optimization. We
demonstrate our algorithm on small numerical examples.


title:
Feasibility of the experimental study of $D_{s}^{\ast}$ ${\to}$
  ${\phi}{\pi}$ decay

abstract:
  The current knowledge on the $D_{s}^{\ast}$ meson are very limited. Besides
the dominant electromagnetic decays, the $D_{s}^{\ast}$ weak decays are legal
and offer the valuable opportunities to explore the wanted $D_{s}^{\ast}$
meson. In this paper, the $D_{s}^{\ast}$ ${\to}$ ${\phi}{\pi}$ decay was
studied with the PQCD approach. It is found that the branching ratio ${\cal
B}(D_{s}^{\ast}{\to}{\phi}{\pi})$ ${\sim}$ ${\cal O}(10^{-7})$, which
corresponds to several thousands of events at the $e^{+}e^{-}$ collider
experiments including STCF, SuperKEKB, CEPC and FCC-ee, and several millions of
events at the hadron collider experiments, such as LHCb@HL-LHC. It is feasible
to experimentally study the $D_{s}^{\ast}$ ${\to}$ ${\phi}{\pi}$ weak decay in
the future.


title:
Breakdown voltage and linear temperature drift in a single-molecule
  junction

abstract:
  Using first-principles calculations based on density functional theory
combined with the non-equilibrium Green's function approach, the transport
behaviors of a single-molecule junction formed by benzenedithiol connected to
gold electrodes are investigated. The breakdown voltage for the model of
benzenedithiol plus gold electrodes is 0.7 V, which is close to the
experimental value. A linear response between the conductance and temperature
(known as linear temperature drift) is found in the molecular device, which
indicates that it could be used to maintain the stability of molecular
circuits. Meanwhile, input and output with the same accuracies would be useful
for designing multi-level circuits, which would be used to improve the
resolution ratio in analog-to-digital converters. The present findings indicate
that benzenedithiol-based single-molecule junctions would be promising
functional units for molecular sensors.


title:
Dendrite formation in rechargeable lithium-metal batteries: Phase-field
  modeling using open-source finite element library

abstract:
  We describe a phase-field model for the electrodeposition process that forms
dendrites within metal-anode batteries. We derive the free energy functional
model, arriving at a system of partial differential equations that describe the
evolution of a phase field, the lithium-ion concentration, and an electric
potential. We formulate, discretize, and solve the set of partial differential
equations describing the coupled electrochemical interactions during a battery
charge cycle using an open-source finite element library. The open-source
library allows us to use parallel solvers and time-marching adaptivity. We
describe two- and three-dimensional simulations; these simulations agree with
experimentally-observed dendrite growth rates and morphologies reported in the
literature.


title:
HASA: Hybrid Architecture Search with Aggregation Strategy for
  Echinococcosis Classification and Ovary Segmentation in Ultrasound Images

abstract:
  Different from handcrafted features, deep neural networks can automatically
learn task-specific features from data. Due to this data-driven nature, they
have achieved remarkable success in various areas. However, manual design and
selection of suitable network architectures are time-consuming and require
substantial effort of human experts. To address this problem, researchers have
proposed neural architecture search (NAS) algorithms which can automatically
generate network architectures but suffer from heavy computational cost and
instability if searching from scratch. In this paper, we propose a hybrid NAS
framework for ultrasound (US) image classification and segmentation. The hybrid
framework consists of a pre-trained backbone and several searched cells (i.e.,
network building blocks), which takes advantage of the strengths of both NAS
and the expert knowledge from existing convolutional neural networks.
Specifically, two effective and lightweight operations, a mixed depth-wise
convolution operator and a squeeze-and-excitation block, are introduced into
the candidate operations to enhance the variety and capacity of the searched
cells. These two operations not only decrease model parameters but also boost
network performance. Moreover, we propose a re-aggregation strategy for the
searched cells, aiming to further improve the performance for different vision
tasks. We tested our method on two large US image datasets, including a 9-class
echinococcosis dataset containing 9566 images for classification and an ovary
dataset containing 3204 images for segmentation. Ablation experiments and
comparison with other handcrafted or automatically searched architectures
demonstrate that our method can generate more powerful and lightweight models
for the above US image classification and segmentation tasks.


title:
Leveraging convergence behavior to balance conflicting tasks in
  multi-task learning

abstract:
  Multi-Task Learning is a learning paradigm that uses correlated tasks to
improve performance generalization. A common way to learn multiple tasks is
through the hard parameter sharing approach, in which a single architecture is
used to share the same subset of parameters, creating an inductive bias between
them during the training process. Due to its simplicity, potential to improve
generalization, and reduce computational cost, it has gained the attention of
the scientific and industrial communities. However, tasks often conflict with
each other, which makes it challenging to define how the gradients of multiple
tasks should be combined to allow simultaneous learning. To address this
problem, we use the idea of multi-objective optimization to propose a method
that takes into account temporal behaviour of the gradients to create a dynamic
bias that adjust the importance of each task during the backpropagation. The
result of this method is to give more attention to the tasks that are diverging
or that are not being benefited during the last iterations, allowing to ensure
that the simultaneous learning is heading to the performance maximization of
all tasks. As a result, we empirically show that the proposed method
outperforms the state-of-art approaches on learning conflicting tasks. Unlike
the adopted baselines, our method ensures that all tasks reach good
generalization performances.


title:
SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide
  Association Study

abstract:
  Self-supervised pre-training methods have brought remarkable breakthroughs in
the understanding of text, image, and speech. Recent developments in genomics
has also adopted these pre-training methods for genome understanding. However,
they focus only on understanding haploid sequences, which hinders their
applicability towards understanding genetic variations, also known as single
nucleotide polymorphisms (SNPs), which is crucial for genome-wide association
study. In this paper, we introduce SNP2Vec, a scalable self-supervised
pre-training approach for understanding SNP. We apply SNP2Vec to perform
long-sequence genomics modeling, and we evaluate the effectiveness of our
approach on predicting Alzheimer's disease risk in a Chinese cohort. Our
approach significantly outperforms existing polygenic risk score methods and
all other baselines, including the model that is trained entirely with haploid
sequences. We release our code and dataset on
https://github.com/HLTCHKUST/snp2vec.


title:
Gallery D.C.: Auto-created GUI Component Gallery for Design Search and
  Knowledge Discovery

abstract:
  GUI design is an integral part of software development. The process of
designing a mobile application typically starts with the ideation and
inspiration search from existing designs. However, existing
information-retrieval based, and database-query based methods cannot
efficiently gain inspirations in three requirements: design practicality,
design granularity and design knowledge discovery. In this paper we propose a
web application, called \tool that aims to facilitate the process of user
interface design through real world GUI component search. Gallery D.C. indexes
GUI component designs using reverse engineering and deep learning based
computer vision techniques on millions of real world applications. To perform
an advanced design search and knowledge discovery, our approach extracts
information about size, color, component type, and text information to help
designers explore multi-faceted design space and distill higher-order of design
knowledge. Gallery D.C. is well received via an informal evaluation with 7
professional designers. Web Link: http://mui-collection.herokuapp.com/. Demo
Video Link: https://youtu.be/zVmsz_wY5OQ.


title:
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time
  Series Data

abstract:
  Anomaly detection for indoor air quality (IAQ) data has become an important
area of research as the quality of air is closely related to human health and
well-being. However, traditional statistics and shallow machine learning-based
approaches in anomaly detection in the IAQ area could not detect anomalies
involving the observation of correlations across several data points (i.e.,
often referred to as long-term dependences). We propose a hybrid deep learning
model that combines LSTM with Autoencoder for anomaly detection tasks in IAQ to
address this issue. In our approach, the LSTM network is comprised of multiple
LSTM cells that work with each other to learn the long-term dependences of the
data in a time-series sequence. Autoencoder identifies the optimal threshold
based on the reconstruction loss rates evaluated on every data across all
time-series sequences. Our experimental results, based on the Dunedin CO2
time-series dataset obtained through a real-world deployment of the schools in
New Zealand, demonstrate a very high and robust accuracy rate (99.50%) that
outperforms other similar models.


title:
Room-temperature printing of ultrathin Quasi-2D GaN semiconductor via
  liquid metal gallium surface confined nitridation reaction

abstract:
  Outstanding wide-bandgap semiconductor material such as gallium nitride (GaN)
has been extensively utilized in power electronics, radiofrequency amplifiers,
and harsh environment devices. Due to its quantum confinement effect in
enabling desired deep-ultraviolet emission, excitonic impact, and electronic
transport features, two-dimensional (2D) or ultrathin quasi-2D GaN
semiconductors have been one of the most remarkable candidates for future
growth of microelectronic devices. Here, for the first time, we reported a
large area, wide bandgap, and room-temperature quasi-2D GaN synthesis and
printing strategy through introducing the plasma medicated liquid metal gallium
surface-confined nitridation reaction mechanism. The developed direct
fabrication and compositional process is consistent with various electronics
manufacturing approaches and thus opens an easy going way for cost-effective
growth of the third-generation semiconductor. In particular, the fully printed
field-effect transistors relying on the GaN thus made show p-type switching
with an on/off ratio greater than 105, maximum field-effect hole mobility of 53
cm2/(V*s), and a small sub-threshold swing. As it was demonstrated, the present
method allows to produce at room temperature the GaN with thickness spanning
from 1nm to nanometers. This basic method can be further extended, generalized,
and utilized for making various electronic and photoelectronic devices in the
coming time.


title:
First-Principles investigation of the First-order Phase Transition in
  Kagome Na$_2$Ti$_3$Cl$_8$

abstract:
  The antiferromagnetic kagome material Na2Ti3Cl8 exhibits a first-order phase
transition; the low-temperature phase is characterized by the trimerizations of
Ti ions. In this work, we carry out first-principles calculations on the
crystal and electronic structure of Na$_2$Ti$_3$Cl$_8$ at the high- and
low-temperature phases. The crystal structures, including the lattice constant
and the trimerization of Ti ions, are well reproduced by taking account a small
Hubbard correction of 1 eV. The calculated total energy landscape reveals a
first-order phase transition with the total energy barrier of 80 meV per
formula unit. Analysis of the electronic structure indicates a direct metal
bonding among Ti in the trimierized low-temperature phase.


title: Detecting Anomalous LAN Activities under Differential Privacy

abstract:
  Anomaly detection has emerged as a popular technique for detecting malicious
activities in local area networks (LANs). Various aspects of LAN anomaly
detection have been widely studied. Nonetheless, the privacy concern about
individual users or their relationship in LAN has not been thoroughly explored
in the prior work. In some realistic cases, the anomaly detection analysis
needs to be carried out by an external party, located outside the LAN. Thus, it
is important for the LAN admin to release LAN data to this party in a private
way in order to protect privacy of LAN users; at the same time, the released
data must also preserve the utility of being able to detect anomalies. This
paper investigates the possibility of privately releasing ARP data that can
later be used to identify anomalies in LAN. We present four approaches and show
that they satisfy different levels of differential privacy - a rigorous and
provable notion for quantifying privacy loss in a system. Our real-world
experimental results confirm practical feasibility of our approaches. With a
proper privacy budget, all of our approaches preserve more than 75% utility of
detecting anomalies in the released data.


title:
Hierarchical-Absolute Reciprocity Calibration for Millimeter-wave Hybrid
  Beamforming Systems

abstract:
  In time-division duplexing (TDD) millimeter-wave (mmWave) massive
multiple-input multiple-output (MIMO) systems, the reciprocity mismatch
severely degrades the performance of the hybrid beamforming (HBF). In this
work, to mitigate the detrimental effect of the reciprocity mismatch, we
investigate reciprocity calibration for the mmWave-HBF system with a
fully-connected phase shifter network. To reduce the overhead and computational
complexity of reciprocity calibration, we first decouple digital radio
frequency (RF) chains and analog RF chains with beamforming design. Then, the
entire calibration problem of the HBF system is equivalently decomposed into
two subproblems corresponding to the digital-chain calibration and analog-chain
calibration. To solve the calibration problems efficiently, a closed-form
solution to the digital-chain calibration problem is derived, while an
iterative-alternating optimization algorithm for the analog-chain calibration
problem is proposed. To measure the performance of the proposed algorithm, we
derive the Cram\'er-Rao lower bound on the errors in estimating mismatch
coefficients. The results reveal that the estimation errors of mismatch
coefficients of digital and analog chains are uncorrelated, and that the
mismatch coefficients of receive digital chains can be estimated perfectly.
Simulation results are presented to validate the analytical results and to show
the performance of the proposed calibration approach.


title:
Long-term evolution of a supernova remnant hosting a double neutron star
  binary

abstract:
  An ultra-stripped supernova (USSN) is a type of core-collapse SN explosion
proposed to be a candidate formation site of a double neutron star (DNS)
binary. We investigate the dynamical evolution of an ultra-stripped supernova
remnant (USSNR), which should host a DNS at its center. By accounting for the
mass-loss history of the progenitor binary using a model developed by a
previous study, we construct the large-scale structure of the {circumstellar
medium (CSM)} up to a radius $\sim 100\,{\rm pc}$, and simulate the explosion
and subsequent evolution of a USSN surrounded by such a CSM environment. We
find that the CSM encompasses an extended region characterized by a hot plasma
with a temperature $\sim 10^8\,$K located around the termination shock of the
wind from the progenitor binary ($\sim 10\,$pc), and the USSNR blastwave is
drastically weakened while penetrating through this hot plasma. Radio continuum
emission from a young USSNR is sufficiently bright to be detectable if it
inhabits our Galaxy but faint compared to the observed Galactic SNRs, and
thereafter declines in luminosity through adiabatic cooling. Within our
parameter space, USSNRs typically exhibit a low radio luminosity and surface
brightness compared to the known Galactic SNRs. Due to the small event rate of
USSNe and their relatively short observable lifespan, we calculate that USSNRs
account for only $\sim 0.1$-$1$ % of the total SNR population. This is
consistent with the fact that no SNR hosting a DNS binary has been discovered
in the Milky Way so far.


title:
Particle acceleration by counter-propagating circularly polarized
  Alfv\'en waves

abstract:
  Particle acceleration in counter-propagating two circularly polarized
Alfv\'en waves is investigated. Phase transitions of the behavior of particles
trapped in a trough of magnetic envelope occur when wave amplitudes exceed two
critical values. Above the critical amplitudes, the numerical simulation shows
that any particles irreversibly gain relativistic energy within a short time
regardless of their initial position and energy once the coherent waveform is
established. Furthermore, the accelerated particles have spatial coherence.
Higher wave phase velocity requires smaller critical amplitudes, while the
maximum attainable energy increases as the wavenumber and the frequency
decrease. The results may be applicable in astrophysical phenomena as well as a
future experiment using high-power lasers.


title: HST /COS Lyman-alpha Absorbers in Cosmic Voids

abstract:
  We investigate the spatial distribution of Lyman-$\alpha$ (Ly $\alpha$)
absorbers within cosmic voids. We create a catalogue of cosmic voids in Sloan
Digital Sky Survey Data Release 7 (SDSS DR7) with the VoidFinder algorithm of
the Void Analysis Software Toolkit (VAST). Using the largest catalogue of
low-redshift (z $\leq$ 0.75) IGM absorbers to date, we identify 392 Ly $\alpha$
absorbers inside voids. The fraction of Ly $\alpha$ absorbers inside voids (65
per cent) is comparable to the volume filling fraction of voids (68 per cent),
and significantly greater than the fraction of galaxies inside voids (21 per
cent). Inside voids, the spatial distribution of Ly $\alpha$ absorbers differs
markedly from that of galaxies. Galaxy density rises sharply near void edges,
while Ly $\alpha$ absorber density is relatively uniform. The radial
distribution of Ly $\alpha$ absorbers inside voids differs marginally from a
random distribution. We find that lower column density Ly $\alpha$ absorbers
are more centrally concentrated inside voids than higher column density Ly
$\alpha$ absorbers. These results suggest the presence of two populations of Ly
$\alpha$ absorbers: low column density systems that are nearly uniformly
distributed in the interiors of voids and systems associated with galaxies at
the edges of voids.


title: K-stability of Fano threefolds of rank 2 and degree 14 as double covers

abstract:
  We prove that every smooth Fano threefold from the family No 2.8 is K-stable.
Such a Fano threefold is a double cover of the blow-up of $\mathbb{P}^3$ at one
point branched along an anti-canonical divisor.


title:
Classification of convex ancient free boundary mean curvature flows in
  the ball

abstract:
  We prove that there exists, in every dimension, a unique (modulo rotations
about the origin and time translations) convex ancient mean curvature flow in
the ball with free boundary on the sphere.


title: Asymptotic analysis for elliptic systems in a narrow region

abstract:
  This paper is concerned with investigating the asymptotic behavior of the
gradients of solutions to a class of elliptic systems with general boundary
data, especially covering the Lam\'{e} systems, in a narrow region. The novelty
of this paper lies in finding the correction term to improve the previous
gradient estimates to be a precise asymptotic formula. Through this
justification, we capture all singular terms and thus give a fairly sharp
characterization in terms of the singularities of the gradients.


title:
A comparison between higher-order nonclassicalities of superposition
  engineered coherent and thermal states

abstract:
  We consider an experimentally obtainable superposition (SUP) operator,
defined by using a generalized superposition of products of field annihilation
and creation operators. We apply this SUP operator on coherent and thermal
quantum states, and the states thus produced are referred to as SUP-operated
coherent state (SOCS) and SUP-operated thermal state (SOTS), respectively. In
the present work, we report a comparative study between the higher-order
nonclassical properties of SOCS and SOTS.


title:
Piezoelectric ferromagnetism in Janus monolayer YBrI: a first-principle
  prediction

abstract:
  Coexistence of intrinsic ferromagnetism and piezoelectricity, namely
piezoelectric ferromagnetism (PFM), is crucial to advance multifunctional
spintronic technologies. In this work, we demonstrate that Janus monolayer YBrI
is a PFM, which is dynamically, mechanically and thermally stable. Electronic
correlation effects on physical properties of YBrI are investigated by using
generalized gradient approximation plus $U$ (GGA+$U$) approach. For
out-of-plane magnetic anisotropy, YBrI is a ferrovalley (FV) material, and the
valley splitting is larger than 82 meV in considered $U$ range. The anomalous
valley Hall effect (AVHE) can be achieved under an in-plane electric field.
However, for in-plane magnetic anisotropy, YBrI is a common ferromagnetic (FM)
semiconductor. When considering intrinsic magnetic anisotropy, the easy axis of
YBrI is always in-plane with magnetic anisotropy energy (MAE) from 0.309 meV to
0.237 meV ($U$=0.0 eV to 3.0 eV). However, the magnetization can be adjusted
from the in-plane to off-plane direction by external magnetic field, and then
lead to the occurrence of valley polarization. Moreover, missing centrosymmetry
along with mirror symmetry breaking results in both in-plane and out-of-plane
piezoelectricity in YBrI monolayer. At a typical $U$=2.0 eV, the $d_{11}$ is
predicted to be -5.61 pm/V, which is higher than or compared with ones of other
two-dimensional (2D) known materials. The electronic and piezoelectric
properties of YBrI can be effectively tuned by applying a biaxial strain. For
example, tensile strain can enhance valley splitting and $d_{11}$ (absolute
value). The predicted Curie temperature of YBrI is higher than those of
experimentally synthesized 2D ferromagnetic materials $\mathrm{CrI_3}$ and
$\mathrm{Cr_2Ge_2Te_6}$.


title:
graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS
  Results with Integration of Functional Annotation Data

abstract:
  Genome-wide association studies (GWAS) have successfully identified a large
number of genetic variants associated with traits and diseases. However, it
still remains challenging to fully understand functional mechanisms underlying
many associated variants. This is especially the case when we are interested in
variants shared across multiple phenotypes. To address this challenge, we
propose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrate
GWAS datasets for multiple phenotypes and incorporate functional annotations
within a unified framework. We conducted simulation studies to evaluate GGPA
2.0. The results indicate that incorporating functional annotation data using
GGPA 2.0 does not only improve detection of disease-associated variants, but
also allows to identify more accurate relationships among diseases. We analyzed
five autoimmune diseases and five psychiatric disorders with the functional
annotations derived from GenoSkyline and GenoSkyline-Plus and the prior disease
graph generated by biomedical literature mining. For autoimmune diseases, GGPA
2.0 identified enrichment for blood, especially B cells and regulatory T cells
across multiple diseases. Psychiatric disorders were enriched for brain,
especially prefrontal cortex and inferior temporal lobe for bipolar disorder
(BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfully
identified the pleiotropy between BIP and SCZ. These results demonstrate that
GGPA 2.0 can be a powerful tool to identify associated variants associated with
each phenotype or those shared across multiple phenotypes, while also promoting
understanding of functional mechanisms underlying the associated variants.


title:
Huge out-of-plane piezoelectric response in ferromagnetic monolayer
  NiClI

abstract:
  The combination of piezoelectricity and ferromagnetic (FM) order in a
two-dimensional (2D) material, namely 2D piezoelectric ferromagnetism (PFM),
may open up unprecedented opportunities for novel device applications. Here, we
predict an in-plane FM semiconductor Janus monolayer NiClI with considerably
large magnetic anisotropy energy (MAE) of 1.439 meV, exhibiting dynamic,
mechanical and thermal stabilities. The NiClI monolayer possesses larger
in-plane piezoelectricity ($d_{11}$$=$5.21 pm/V) comparable to that of
$\mathrm{MoS_2}$. Furthermore, NiClI has huge out-of-plane piezoelectricity
($d_{31}$$=$1.89 pm/V), which is highly desirable for ultrathin piezoelectric
device application. It is proved that huge out-of-plane piezoelectricity is
robust against electronic correlation, which confirms reliability of huge
$d_{31}$. Finally, being analogous to NiClI, PFM with large out-of-plane
piezoelectricity can also be achieved in the Janus monolayers of NiClBr and
NiBrI, with the predicted $d_{31}$ of 0.73 pm/V and 1.15 pm/V, respectively.
The predicted huge out-of-plane piezoelectric response makes Janus monolayer
NiClI a good platform for multifunctional semiconductor spintronic
applications, which is also compatible with the bottom/top gate technologies of
conventional semiconductor nanoelectronic devices.


title: Control-oriented meta-learning

abstract:
  Real-time adaptation is imperative to the control of robots operating in
complex, dynamic environments. Adaptive control laws can endow even nonlinear
systems with good trajectory tracking performance, provided that any uncertain
dynamics terms are linearly parameterizable with known nonlinear features.
However, it is often difficult to specify such features a priori, such as for
aerodynamic disturbances on rotorcraft or interaction forces between a
manipulator arm and various objects. In this paper, we turn to data-driven
modeling with neural networks to learn, offline from past data, an adaptive
controller with an internal parametric model of these nonlinear features. Our
key insight is that we can better prepare the controller for deployment with
control-oriented meta-learning of features in closed-loop simulation, rather
than regression-oriented meta-learning of features to fit input-output data.
Specifically, we meta-learn the adaptive controller with closed-loop tracking
simulation as the base-learner and the average tracking error as the
meta-objective. With both fully-actuated and underactuated nonlinear planar
rotorcraft subject to wind, we demonstrate that our adaptive controller
outperforms other controllers trained with regression-oriented meta-learning
when deployed in closed-loop for trajectory tracking control.


title:
Gradient estimates for the insulated conductivity problem: The case of
  $m$-convex inclusions

abstract:
  We consider an insulated conductivity model with two neighboring inclusions
of $m$-convex shapes in $\mathbb{R}^{d}$ when $m\geq2$ and $d\geq3$. We
establish the pointwise gradient estimates for the insulated conductivity
problem and capture the gradient blow-up rate of order
$\varepsilon^{-1/m+\beta}$ with
$\beta=[-(d+m-3)+\sqrt{(d+m-3)^{2}+4(d-2)}]/(2m)\in(0,1/m)$, as the distance
$\varepsilon$ between these two insulators tends to zero. In particular, the
optimality of the blow-up rate is also demonstrated for a class of axisymmetric
$m$-convex inclusions. This extends the recent results of Dong-Li-Yang.


title: Learning Convolutional Neural Networks in the Frequency Domain

abstract:
  Convolutional neural network (CNN) has achieved impressive success in
computer vision during the past few decades. The image convolution operation
helps CNNs to get good performance on image-related tasks. However, the image
convolution has high computation complexity and hard to be implemented. This
paper proposes the CEMNet, which can be trained in the frequency domain. The
most important motivation of this research is that we can use the
straightforward element-wise multiplication operation to replace the image
convolution in the frequency domain based on the Cross-Correlation Theorem,
which obviously reduces the computation complexity. We further introduce a
Weight Fixation mechanism to alleviate the problem of over-fitting, and analyze
the working behavior of Batch Normalization, Leaky ReLU, and Dropout in the
frequency domain to design their counterparts for CEMNet. Also, to deal with
complex inputs brought by Discrete Fourier Transform, we design a two-branches
network structure for CEMNet. Experimental results imply that CEMNet achieves
good performance on MNIST and CIFAR-10 databases.


title: Normalization by Evaluation for the Lambek Calculus

abstract:
  The syntactic calculus of Lambek is a deductive system for the multiplicative
fragment of intuitionistic non-commutative linear logic. As a fine-grained
calculus of resources, it has many applications, mostly in formal computational
investigations of natural languages.
  This paper introduces a calculus of beta-eta-long normal forms for
derivations in the Lambek calculus with multiplicative unit and conjunction
among its logical connectives. Reduction to normal form follows the
normalization by evaluation (NbE) strategy: (i) evaluate a derivation in a
Kripke model of Lambek calculus; (ii) extract normal forms from the obtained
semantic values. The implementation of the NbE algorithm requires the presence
of a strong monad in the Kripke interpretation of positive formulae, in analogy
with the extension of intuitionistic propositional logic with falsity and
disjunction. The NbE algorithm can also be instantiated with minor variations
to calculi for related substructural logics, such as multiplicative and dual
intuitionistic linear logic (MILL and DILL).


title: A van Benthem Theorem for Atomic and Molecular Logics

abstract:
  After recalling the definitions of atomic and molecular logics, we show how
notions of bisimulation can be automatically defined from the truth conditions
of the connectives of any of these logics. Then, we prove a generalization of
van Benthem modal characterization theorem for molecular logics. Our molecular
connectives should be uniform and contain all conjunctions and disjunctions. We
use modal logic, the Lambek calculus and modal intuitionistic logic as case
study and compare in particular our work with Olkhovikov's work.


title: Non-Normal Super-Strict Implications

abstract:
  This paper introduces the logics of super-strict implications that are based
on C.I. Lewis' non-normal modal logics S2 and S3. The semantics of these logics
is based on Kripke's semantics for non-normal modal logics. This solves a
question we left open in a previous paper by showing that these logics are
weakly connexive.


title: Normalisation for Some Infectious Logics and Their Relatives

abstract:
  We consider certain infectious logics (Sfde, dSfde, K3w, and PWK) and several
their non-infectious modifications, including two new logics, reformulate
previously constructed natural deduction systems for them (or present such
systems from scratch for the case of new logics) in way such that the proof of
normalisation theorem becomes possible for these logics. We present such a
proof and establish the negation subformula property for the logics in
question.


title: Combining First-Order Classical and Intuitionistic Logic

abstract:
  This paper studies a first-order expansion of a combination C+J of
intuitionistic and classical propositional logic, which was studied by
Humberstone (1979) and del Cerro and Herzig (1996), from a proof-theoretic
viewpoint. While C+J has both classical and intuitionistic implications, our
first-order expansion adds classical and intuitionistic universal quantifiers
and one existential quantifier to C+J. This paper provides a multi-succedent
sequent calculus G(FOC+J) for our combination of the first-order intuitionistic
and classical logic. Our sequent calculus G(FOC+J) restricts contexts of the
right rules for intuitionistic implication and intuitionistic universal
quantifier to particular forms of formulas. The cut-elimination theorem is
established to ensure the subformula property. As a corollary, G(FOC+J) is
conservative over both first-order intuitionistic and classical logic. Strong
completeness of G(FOC+J) is proved via a canonical model argument.


title: Natural Deduction for Assertibility and Deniability

abstract:
  In this paper we split every basic propositional connective into two
versions, one is called extensional and the other one intensional. The
extensional connectives are semantically characterized by standard truth
conditions that are relative to possible worlds. Assertibility and deniability
of sentences built out of atomic sentences by extensional connectives are
defined in terms of the notion of truth. The intensional connectives are
characterized directly by assertibility and deniability conditions without the
notion of truth. We pay special attention to the deniability condition for
intensional implication. We characterize the logic of this mixed language by a
system of natural deduction that sheds some light on the inferential behaviour
of these two kinds of connectives and on the way they can interact.


title: Monadicity of Non-deterministic Logical Matrices is Undecidable

abstract:
  The notion of non-deterministic logical matrix (where connectives are
interpreted as multi-functions) preserves many good properties of traditional
semantics based on logical matrices (where connectives are interpreted as
functions) whilst finitely characterizing a much wider class of logics, and has
proven to be decisive in a myriad of recent compositional results in logic.
Crucially, when a finite non-deterministic matrix satisfies monadicity
(distinct truth-values can be separated by unary formulas) one can
automatically produce an axiomatization of the induced logic. Furthermore, the
resulting calculi are analytical and enable algorithmic proof-search and
symbolic counter-model generation.
  For finite (deterministic) matrices it is well known that checking monadicity
is decidable. We show that, in the presence of non-determinism, the property
becomes undecidable. As a consequence, we conclude that there is no algorithm
for computing the set of all multi-functions expressible in a given finite
Nmatrix. The undecidability result is obtained by reduction from the halting
problem for deterministic counter machines.


title: Puzzles of Existential Generalisation from Type-theoretic Perspective

abstract:
  The present paper addresses several puzzles related to the Rule of
Existential Generalization, (EG). In solution to these puzzles from the
viewpoint of simple type theory, I distinguish (EG) from a modified Rule of
Existential Quantifier Introduction which is derivable from (EG). Both these
rules are often confused and both are considered as primitive but I show that
(EG) itself is derivable from the proper Rule of Existential Quantifier
Introduction. Moreover, the latter rule must be primitive in logical systems
that treat both total and partial functions, for the universal and the
existential quantifiers are not interdefinable in them. An appropriate natural
deduction for such a system is deployed. The present logical system is simpler
than the system recently proposed and applied by the present author. It
utilises an adequate definition of substitution which is capable of handling
not only a higher-order quantification, but also (hyper)intensional contexts.


title: Proof Theory of Skew Non-Commutative MILL

abstract:
  Monoidal closed categories naturally model NMILL, non-commutative
multiplicative intuitionistic linear logic: the monoidal unit and tensor
interpret the multiplicative verum and conjunction; the internal hom interprets
linear implication. In recent years, the weaker notion of (left) skew monoidal
closed category has been proposed by Ross Street, where the three structural
laws of left and right unitality and associativity are not required to be
invertible, they are merely natural transformations with a specific
orientation. A question arises: is it possible to find a logic which is
naturally modelled by skew monoidal closed categories? We answer positively by
introducing a cut-free sequent calculus for a skew version of NMILL that is a
presentation of the free skew monoidal closed category. We study the
proof-theoretic semantics of the sequent calculus by identifying a calculus of
derivations in normal form, obtained from an adaptation of Andreoli's focusing
technique to the skew setting. The resulting focused sequent calculus
peculiarly employs a system of tags for keeping track of new formulae appearing
in the antecedent and appropriately reducing non-deterministic choices in proof
search. Focusing solves the coherence problem for skew monoidal closed
categories by exhibiting an effective procedure for deciding equality of maps
in the free such category.


title:
Decidability of Intuitionistic Sentential Logic with Identity via
  Sequent Calculus

abstract:
  The aim of our paper is twofold: firstly we present a sequent calculus for an
intuitionistic non-Fregean logic ISCI, which is based on the calculus presented
in the paper by Chlebowski and Leszczynska-Jasion, 'An Investigation into
Intuitionistic Logic with Identity' (Bulletin of the Section of Logic 48(4), p.
259-283, 2019) and, secondly, we discuss the problem of decidability of ISCI
via the obtained system. The original calculus from the mentioned paper did not
provide the decidability result for ISCI. There are two problems to be solved
in order to obtain this result: the so called loops characteristic for
intuitionistic logic and the lack of the subformula property due to the form of
the identity-dedicated rules. We discuss possible routes to overcome these
problems: we consider a weaker version of the subformula property, guarded by
the complexity of formulas which can be included within it; we also present a
proof-search procedure such that when it fails, then there exists a
countermodel (in Kripke semantics for ISCI).


title: Superconnexivity Reconsidered

abstract:
  We reconsider the idea of superconnexivity, an idea that has not received
much attention so far. We inspect more closely the problems with the proposal
that are responsible for this disregard. However, we also suggest a slight
modification of the idea that has a much better chance of delivering the
desired results, which we call super-bot-connexivity.


title: Another Combination of Classical and Intuitionistic Conditionals

abstract:
  On the one hand, classical logic is an extremely successful theory, even if
not being perfect. On the other hand, intuitionistic logic is, without a doubt,
one of the most important non-classical logics. But, how can proponents of one
logic view the other logic? In this paper, we focus on one of the directions,
namely how classicists can view intuitionistic logic. To this end, we introduce
an expansion of positive intuitionistic logic, both semantically and
proof-theoretically, and establish soundness and strong completeness. Moreover,
we discuss the interesting status of disjunction, and the possibility of
combining classical logic and minimal logic. We also compare our system with
the system of Caleiro and Ramos.


title: Mortensen Logics

abstract:
  Mortensen introduced a connexive logic commonly known as 'M3V'. M3V is
obtained by adding a special conditional to LP. Among its most notable
features, besides its being connexive, M3V is negation-inconsistent and it
validates the negation of every conditional. But Mortensen has also studied and
applied extensively other non-connexive logics, for example, closed set logic,
CSL, and a variant of Sette's logic, identified and called 'P2' by Marcos.
  In this paper, we analyze and compare systematically the connexive variants
of CSL and P2, obtained by adding the M3V conditional to them. Our main
observations are two. First, that the inconsistency of M3V is exacerbated in
the connexive variant of closed set logic, while it is attenuated in the
connexive variant of the Sette-like P2. Second, that the M3V conditional is,
unlike other conditionals, "connexively stable", meaning that it remains
connexive when combined with the main paraconsistent negations.


title: Bilateral Inversion Principles

abstract:
  This paper formulates a bilateral account of harmony that is an alternative
to one proposed by Francez. It builds on an account of harmony for unilateral
logic proposed by K\"urbis and the observation that reading the rules for the
connectives of bilateral logic bottom up gives the grounds and consequences of
formulas with the opposite speech act. I formulate a process I call 'inversion'
which allows the determination of assertive elimination rules from assertive
introduction rules, and rejective elimination rules from rejective introduction
rules, and conversely. It corresponds to Francez's notion of vertical harmony.
I also formulate a process I call 'conversion', which allows the determination
of rejective introduction rules from assertive elimination rules and
conversely, and the determination of assertive introduction rules from
rejective elimination rules and conversely. It corresponds to Francez's notion
of horizontal harmony. The account has a number of features that distinguishes
it from Francez's.


title: Cyclic Negations and Four-valuedness

abstract:
  We consider an example of four valued semantics partially inspired by quantum
computations and negation-like operations occurred therein. In particular we
consider a representation of so called square root of negation within this four
valued semantics as an operation which acts like a cycling negation. We define
two variants of logical matrices performing different orders over the set of
truth values. Purely formal logical result of our study consists in
axiomatizing the logics of defined matrices as the systems of binary
consequence relation and proving correctness and completeness theorems for
these deductive systems.


title: The Forms of Categorical Proposition

abstract:
  An exhaustive survey of categorical propositions is proposed in the present
paper, both with respect to their nature and the logical problems raised by
them. Through a comparative analysis of Term Logic and First-Order Logic, it is
shown that the famous problem of existential import may be solved in two ways:
with a model-adaptive strategy, in which the square of opposition is validated
by restricting the models; with a language-adaptive strategy, in which the
logical form of categorical propositions is extended in order to validate the
square in every model. The latter strategy is advocated in the name of logic,
which means truth in every model. Finally, the present paper needs some
automatic process in order to determine the nature of logical relations between
any pair of the available 256 categorical propositions. This requires the
implementation of a programming machine in the style of Prolog.


title: Algebraizability of the Logic of Quasi-N4-Lattices

abstract:
  The class of quasi-N4-lattices (QN4-lattices) was introduced as a common
generalization of quasi-Nelson algebras and N4-lattices, in such a way that
N4-lattices are precisely the QN4-lattices satisfying the double negation law
(~~x = x) and quasi-Nelson algebras are the QN4-lattices satisfying the
explosive law (x ^ ~x) -> y = ((x ^ ~x) -> y) -> ((x ^ ~x) -> y). In this paper
we introduce, via a Hilbert-style presentation, a logic (L_QN4) whose algebraic
semantics is a class of algebras that we show to be term-equivalent to
QN4-lattices. The result is obtained by showing that the calculus introduced by
us is algebraizable in the sense of Blok and Pigozzi, and its equivalent
algebraic semantics is term-equivalent to the class of QN4-lattices. As a
prospect for future investigation, we consider the question of how one could
place L_QN4 within the family of relevance logics.


title:
On the Expressive Power of the Normal Form for Branching-Time Temporal
  Logics

abstract:
  With the emerging applications that involve complex distributed systems
branching-time specifications are specifically important as they reflect
dynamic and non-deterministic nature of such applications. We describe the
expressive power of a simple yet powerful branching-time specification
framework -- branching-time normal form (BNF), which has been developed as part
of clausal resolution for branching-time temporal logics. We show the encoding
of Buchi Tree Automata in the language of the normal form, thus representing,
syntactically, tree automata in a high-level way. Thus we can treat BNF as a
normal form for the latter. These results enable us (1) to translate given
problem specifications into the normal form and apply as a verification method
a deductive reasoning technique -- the clausal temporal resolution; (2) to
apply one of the core components of the resolution method -- the loop searching
to extract, syntactically, hidden invariants in a wide range of complex
temporal specifications.


title: A Logic for Paraconsistent Transition Systems

abstract:
  Modelling complex information systems often entails the need for dealing with
scenarios of inconsistency in which several requirements either reinforce or
contradict each other. In this kind of scenarios, arising e.g. in knowledge
representation, simulation of biological systems, or quantum computation,
inconsistency has to be addressed in a precise and controlled way. This paper
generalises Belnap-Dunn four-valued logic, introducing paraconsistent
transition systems (PTS), endowed with positive and negative accessibility
relations, and a metric space over the lattice of truth values, and their modal
logic.


title: Routley Star in Information-Based Semantics

abstract:
  It is common in various non-classical logics, especially in relevant logics,
to characterize negation semantically via the operation known as Routley star.
This operation works well within relational semantic frameworks based on prime
theories. We study this operation in the context of "information-based"
semantics for which it is characteristic that sets of formulas supported by
individual information states are theories that do not have to be prime. We
will show that, somewhat surprisingly, the incorporation of Routley star into
the information-based semantics does not lead to a collapse or a trivialization
of the whole semantic system. On the contrary, it leads to a technically
elegant though quite restricted semantic framework that determines a particular
logic. We study some basic properties of this semantics. For example, we show
that within this framework double negation law is valid only in involutive
linear frames. We characterize axiomatically the logic of all linear frames and
show that the logic of involutive linear frames coincides with a system that
Mike Dunn coined Kalman logic. This logic is the fragment (for the language
restricted to conjunction, disjunction and negation) of the "semi-relevant"
logic known as R-mingle. Finally, we characterize by a deductive system the
logic of all information frames equipped with Routley star.


title: Dunn Semantics for Contra-Classical Logics

abstract:
  In this paper I show, with a rich and systematized diet of examples, that
many contra-classical logics can be presented as variants of FDE, obtained by
modifying at least one of the truth or falsity conditions of some connective.
Then I argue that using Dunn semantics provides a clear understanding of the
source of contra-classicality, namely, connectives that have either the
classical truth or the classical falsity condition of another connective. This
requires a fine-grained analysis of the sorts of modifications that can be made
to an evaluation condition, analysis which I offer here as well.


title: The Fate of Causal Structure under Time Reversal

abstract:
  What happens to the causal structure of a world when time is reversed? At
first glance it seems there are two possible answers: the causal relations are
reversed, or they are not. I argue that neither of these answers is correct: we
should either deny that time-reversed worlds have causal relations at all, or
deny that causal concepts developed in the actual world are reliable guides to
the causal structure of time-reversed worlds. The first option is motivated by
the instability under intervention of time-reversed dynamical evolutions. The
second option is motivated by a recognition of how contingent structural
features of the actual world shape, and license the application of, our causal
concepts and reasoning strategies.


title: Kraus-Like Decompositions

abstract:
  We show that for certain semigroups acting on a group algebra, Kraus
decompositions of the semigroup in terms of left-multiplication Kraus operators
cannot exist. This motivates us to introduce Kraus-like decompositions, which
are decompositions of a semigroup into a linear combination of
left-multiplication operators. Working in $\mathbb{Z}_N$, we then show that the
notion of \textit{convex} Kraus-like decompositions (in which the coefficients
in the sum decomposition are nonnegative and satisfy a sum rule) corresponds
naturally to the notion of a conditionally negative length. We show that for a
general finite group, the convexity of the Kraus-like decomposition is
equivalent to a set of semidefinite constraints on the length function, which
agree with the notion of conditional negativity when the group is
$\mathbb{Z}_N$. For a general finite group $G$, we prove a stability condition
which shows that if the semigroup associated with a length has a convex
Kraus-like decomposition for all $t>0$ small enough, then it has a convex
Kraus-like decomposition for all time $t>0$.


title: Entanglement, Complexity, and Causal Asymmetry in Quantum Theories

abstract:
  It is often claimed that one cannot locate a notion of causation in
fundamental physical theories. The reason most commonly given is that the
dynamics of those theories do not support any distinction between the past and
the future, and this vitiates any attempt to locate a notion of causal
asymmetry -- and thus of causation -- in fundamental physical theories. I argue
that this is incorrect: the ubiquitous generation of entanglement between
quantum systems grounds a relevant asymmetry in the dynamical evolution of
quantum systems. I show that by exploiting a connection between the amount of
entanglement in a quantum state and the algorithmic complexity of that state,
one can use recently developed tools for causal inference to identify a causal
asymmetry -- and a notion of causation -- in the dynamical evolution of quantum
systems. https://doi.org/10.1007/s10701-022-00562-0


title:
Learning high-order spatial discretisations of PDEs with
  symmetry-preserving iterative algorithms

abstract:
  Common techniques for the spatial discretisation of PDEs on a macroscale grid
include finite difference, finite elements and finite volume methods. Such
methods typically impose assumed microscale structures on the subgrid fields,
so without further tailored analysis are not suitable for systems with
subgrid-scale heterogeneity or nonlinearities. We provide a new algebraic route
to systematically approximate, in principle exactly, the macroscale closure of
the spatially-discrete dynamics of a general class of heterogeneous
non-autonomous reaction-advection-diffusion PDEs. This holistic discretisation
approach, developed through rigorous theory and verified with computer algebra,
systematically constructs discrete macroscale models through physics informed
by the PDE out-of-equilibrium dynamics, thus relaxing many assumptions
regarding the subgrid structure. The construction is analogous to recent
gray-box machine learning techniques in that predictions are directed by
iterative layers (as in neural networks), but informed by the subgrid physics
(or 'data') as expressed in the PDEs. A major development of the holistic
methodology, presented herein, is novel inter-element coupling between subgrid
fields which preserve self-adjointness of the PDE after macroscale
discretisation, thereby maintaining the spectral structure of the original
system. This holistic methodology also encompasses homogenisation of microscale
heterogeneous systems, as shown here with the canonical examples of
heterogeneous 1D waves and diffusion.


title:
The Uhlenbeck-Ford model: Exact virial coefficients and application as a
  reference system in fluid-phase free-energy calculations

abstract:
  The Uhlenbeck-Ford (UF) model was originally proposed for the theoretical
study of imperfect gases, given that all its virial coefficients can be
evaluated exactly, in principle. Here, in addition to computing the previously
unknown coefficients B11 through B13, we assess its applicability as a
reference system in fluid-phase free-energy calculations using molecular
simulation techniques. Our results demonstrate that, although the UF model
itself is too soft, appropriately scaled Uhlenbeck- Ford (sUF) models provide
robust reference systems that allow accurate fluid-phase free-energy
calculations without the need for an intermediate reference model. Indeed, in
addition to the accuracy with which their free energies are known and their
convenient scaling properties, the fluid is the only thermodynamically stable
phase for a wide range of sUF models. This set of favorable properties may
potentially put the sUF fluid-phase reference systems on par with the standard
role that harmonic and Einstein solids play as reference systems for
solid-phase free-energy calculations.


title: GPT-NeoX-20B: An Open-Source Autoregressive Language Model

abstract:
  We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language
model trained on the Pile, whose weights will be made freely and openly
available to the public through a permissive license. It is, to the best of our
knowledge, the largest dense autoregressive model that has publicly available
weights at the time of submission. In this work, we describe \model{}'s
architecture and training and evaluate its performance on a range of
language-understanding, mathematics, and knowledge-based tasks. We find that
GPT-NeoX-20B is a particularly powerful few-shot reasoner and gains far more in
performance when evaluated five-shot than similarly sized GPT-3 and FairSeq
models. We open-source the training and evaluation code, as well as the model
weights, at https://github.com/EleutherAI/gpt-neox.


title:
Information fusion approach for biomass estimation in a plateau
  mountainous forest using a synergistic system comprising UAS-based digital
  camera and LiDAR

abstract:
  Forest land plays a vital role in global climate, ecosystems, farming and
human living environments. Therefore, forest biomass estimation methods are
necessary to monitor changes in the forest structure and function, which are
key data in natural resources research. Although accurate forest biomass
measurements are important in forest inventory and assessments, high-density
measurements that involve airborne light detection and ranging (LiDAR) at a low
flight height in large mountainous areas are highly expensive. The objective of
this study was to quantify the aboveground biomass (AGB) of a plateau
mountainous forest reserve using a system that synergistically combines an
unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to
leverage their complementary advantages. In this study, we utilized digital
aerial photogrammetry (DAP), which has the unique advantages of speed, high
spatial resolution, and low cost, to compensate for the deficiency of forestry
inventory using UAS-based LiDAR that requires terrain-following flight for
high-resolution data acquisition. Combined with the sparse LiDAR points
acquired by using a high-altitude and high-speed UAS for terrain extraction,
dense normalized DAP point clouds can be obtained to produce an accurate and
high-resolution canopy height model (CHM). Based on the CHM and spectral
attributes obtained from multispectral images, we estimated and mapped the AGB
of the region of interest with considerable cost efficiency. Our study supports
the development of predictive models for large-scale wall-to-wall AGB mapping
by leveraging the complementarity between DAP and LiDAR measurements. This work
also reveals the potential of utilizing a UAS-based digital camera and LiDAR
synergistically in a plateau mountainous forest area.