Abstract:
  Total charge and energy evaluations for the electron beams generated in the
laser wakefield acceleration (LWFA) is the primary step in the determination of
the required target and laser parameters. Particle-in-cell (PIC) simulations is
an efficient numerical tool that can provide such evaluations unless the effect
of numerical dispersion is not diminished. The numerical dispersion, which is
specific for the PIC modeling, affects not only the dephasing lengths in LWFA
but also the total amount of the self-injected electrons. A numerical error of
the order of $10^{-4}-10^{-3}$ in the calculation of the speed of light results
in a significant error in the total injected charge and energy gain of the
accelerated electron bunches. In the standard numerical approach, the numerical
correction of the speed of light either requires infinitely small spatial grid
resolution (which needs large computation platform) or force to compromise with
the numerical accuracy. A simple and easy to implement numerical scheme is
shown to suppress the numerical dispersion of the electromagnetic pulse in PIC
simulations even with a modest spatial resolution, and without any special
treatments to the core structure of the numerical algorithm. Evaluated charges
of the self-injected electron bunches become essentially lower owing to the
better calculations of the wake phase velocity.


Abstract:
  We study the scheduling of jobs on a single parallel-batching machine with
non-identical job sizes and incompatible job families. Jobs from the same
family have the same processing time and can be loaded into a batch, as long as
the batch size respects the machine capacity. The objective is to minimize the
total weighted completion time. The problem combines two classic combinatorial
problems, namely bin packing and single machine scheduling. We develop three
new mixed-integer linear-programming formulations, namely an assignment-based
formulation, a time-indexed formulation (TIF), and a set-partitioning
formulation (SPF). We also propose a column generation (CG) algorithm for the
SPF, which is the basis for a branch-and-price (B&P) algorithm and a CG-based
heuristic. We develop a preprocessing method to reduce the formulation size. A
heuristic framework based on proximity search is also developed using the TIF.
The SPF and B&P can solve instances with non-unit and unit job durations to
optimality with up to 80 and 150 jobs within reasonable runtime limits,
respectively. The proposed heuristics perform better than the methods from the
literature.


Abstract:
  Implementation attacks like side-channel and fault attacks pose a
considerable threat to cryptographic devices that are physically accessible by
an attacker. As a consequence, devices like smart cards implement corresponding
countermeasures like redundant computation and masking. Recently, statistically
ineffective fault attacks (SIFA) were shown to be able to circumvent these
classical countermeasure techniques. We present a new approach for verifying
the SIFA protection of arbitrary masked implementations in both hardware and
software. The proposed method uses Boolean dependency analysis, factorization,
and known properties of masked computations to show whether the fault detection
mechanism of redundant masked circuits can leak information about the processed
secret values. We implemented this new method in a tool called Danira, which
can show the SIFA resistance of cryptographic implementations like AES S-Boxes
within minutes.


Abstract:
  We demonstrate theoretically and experimentally that a specifically designed
microcavity driven in the optical parametric oscillation regime exhibits
lighthouse-like emission, i.e., an emission focused around a single direction.
Remarkably, the emission direction of this micro-lighthouse is continuously
controlled by the linear polarization of the incident laser, and angular beam
steering over \unit{360}{\degree} is demonstrated. Theoretically, this
unprecedented effect arises from the interplay between the nonlinear optical
response of microcavity exciton-polaritons, the difference in the subcavities
forming the microcavity, and the rotational invariance of the device.


Abstract:
  Simulation-based Dynamic Traffic Assignment models have important
applications in real-time traffic management and control. The efficacy of these
systems rests on the ability to generate accurate estimates and predictions of
traffic states, which necessitates online calibration. A widely used solution
approach for online calibration is the Extended Kalman Filter (EKF), which --
although appealing in its flexibility to incorporate any class of parameters
and measurements -- poses several challenges with regard to calibration
accuracy and scalability, especially in congested situations for large-scale
networks. This paper addresses these issues in turn so as to improve the
accuracy and efficiency of EKF-based online calibration approaches for large
and congested networks. First, the concept of state augmentation is revisited
to handle violations of the Markovian assumption typically implicit in online
applications of the EKF. Second, a method based on graph-coloring is proposed
to operationalize the partitioned finite-difference approach that enhances
scalability of the gradient computations.
  Several synthetic experiments and a real world case study demonstrate that
application of the proposed approaches yields improvements in terms of both
prediction accuracy and computational performance. The work has applications in
real-world deployments of simulation-based dynamic traffic assignment systems.


Abstract:
  The ground state of an antiferromagnetic Heisenberg model on L X L clusters
joined by a single bond and balanced Bethe clusters are investigated with
quantum Monte Carlo and mean field theory. The improved Monte Carlo method of
Sandvik and Evertz is used and the observables include valence bond and loop
valence bond observables introduced by Lin and Sandvik as well as the valence
bond entropy and the second Renyi entropy. For the bisecting of the Bethe
cluster, in disagreement with our previous results and in agreement with mean
field theory, the valence loop entropy and the second Renyi entropy scale as
the logarithm of the number of sites in the cluster. For bisecting the L X L -
L X L clusters, the valence bond entropy scales as L, however, the loop entropy
and the entanglement entropy scale as the ln(L). The calculations suggest that
the area law is essentially correct and linking high entanglement objects will
not generate much more entanglement.


Abstract:
  The formation and presence of clathrate hydrates could influence the
composition and stability of planetary ices and comets; they are at the heart
of the development of numerous complex planetary models, all of which include
the necessary condition imposed by their stability curves, some of which
include the cage occupancy or host-guest content and the hydration number, but
fewer take into account the kinetics aspects. We measure the
temperature-dependent-diffusion-controlled formation of the carbon dioxide
clathrate hydrate in the 155-210~K range in order to establish the clathrate
formation kinetics at low temperature. We exposed thin water ice films of a few
microns in thickness deposited in a dedicated infrared transmitting closed cell
to gaseous carbon dioxide maintained at a pressure of a few times the pressure
at which carbon dioxide clathrate hydrate is thermodynamically stable. The time
dependence of the clathrate formation was monitored with the recording of
specific infrared vibrational modes of CO2 with a Fourier Transform InfraRed
(FTIR) spectrometer. These experiments clearly show a two-step clathrate
formation, particularly at low temperature, within a relatively simple
geometric configuration. We satisfactorily applied a model combining surface
clathration followed by a bulk diffusion-relaxation growth process to the
experiments and derived the temperature-dependent-diffusion coefficient for the
bulk spreading of clathrate. The derived apparent activation energy
corresponding to this temperature-dependent-diffusion coefficient in the
considered temperature range is E_a = 24.7 +/- 9.7 kJ/mol. The kinetics
parameters favour a possible carbon dioxide clathrate hydrate nucleation mainly
in planets or satellites.


Abstract:
  The quantum Cram\'er-Rao bound is a cornerstone of modern quantum metrology,
as it provides the ultimate precision in parameter estimation. In the
multiparameter scenario, this bound becomes a matrix inequality, which can be
cast to a scalar form with a properly chosen weight matrix. Multiparameter
estimation thus elicits tradeoffs in the precision with which each parameter
can be estimated. We show that, if the information is encoded in a unitary
transformation, we can naturally choose the weight matrix as the metric tensor
linked to the geometry of the underlying algebra $\mathfrak{su}(n)$, with
applications in numerous fields. This ensures an intrinsic bound that is
independent of the choice of parametrization.


Abstract:
  The constant growth in the number of malware - software or code fragment
potentially harmful for computers and information networks - and the use of
sophisticated evasion and obfuscation techniques have seriously hindered
classic signature-based approaches. On the other hand, malware detection
systems based on machine learning techniques started offering a promising
alternative to standard approaches, drastically reducing analysis time and
turning out to be more robust against evasion and obfuscation techniques. In
this paper, we propose a malware taxonomic classification pipeline able to
classify Windows Portable Executable files (PEs). Given an input PE sample, it
is first classified as either malicious or benign. If malicious, the pipeline
further analyzes it in order to establish its threat type, family, and
behavior(s). We tested the proposed pipeline on the open source dataset EMBER,
containing approximately 1 million PE samples, analyzed through static
analysis. Obtained malware detection results are comparable to other academic
works in the current state of art and, in addition, we provide an in-depth
classification of malicious samples. Models used in the pipeline provides
interpretable results which can help security analysts in better understanding
decisions taken by the automated pipeline.


Abstract:
  As an analogy of superalgebra of multivector fields with the Schounte
bracket, we introduce a non-trivial superbracket on differential forms of
manifold. We show properties of this new superalgebra. We extend this
superalgebra by adding one factor. The new extended superalgebra should be
studied more widely and in deep. We study Betti numbers of double weighted
homology groups by the Euler vector field. In appendix, we explain our bracket
is produced like as the Schouten bracket.


Abstract:
  The charge, spin, and composition degrees of freedom in high-entropy alloy
endow it with tunable valence and spin states, infinite combinations and
excellent mechanical performance. Meanwhile, the stacking, interlayer, and
angle degrees of freedom in van der Waals material bring it with exceptional
features and technological applications. Integration of these two distinct
material categories while keeping their merits would be tempting. Based on this
heuristic thinking, we design and explore a new range of materials (i.e.,
dichalcogenides, halides and phosphorus trisulfides) with multiple metallic
constitutions and intrinsic layered structure, which are coined as high-entropy
van der Waals materials. Millimeter-scale single crystals with homogeneous
element distribution can be efficiently acquired and easily exfoliated or
intercalated in this materials category. Multifarious physical properties like
superconductivity, magnetic ordering, metal-insulator transition and corrosion
resistance have been exploited. Further research based on the concept of
high-entropy van der Waals materials will enrich the high-throughput design of
new systems with intriguing properties and practical applications.


Abstract:
  In this paper, we study the Sobolev extension property of Lp-quasidisks which
are the generalizations of the classical quasidisks. After that, we also find
some applications of their Sobolev extension property.


Abstract:
  Leakage outside of the qubit computational subspace poses a threatening
challenge to quantum error correction (QEC). We propose a scheme using two
leakage-reduction units (LRUs) that mitigate these issues for a transmon-based
surface code, without requiring an overhead in terms of hardware or QEC-cycle
time as in previous proposals. For data qubits we consider a microwave drive to
transfer leakage to the readout resonator, where it quickly decays, ensuring
that this negligibly affects the coherence within the computational subspace
for realistic system parameters. For ancilla qubits we apply a
$|1\rangle\leftrightarrow|2\rangle$ $\pi$ pulse conditioned on the measurement
outcome. Using density-matrix simulations of the distance-3 surface code we
show that the average leakage lifetime is reduced to almost 1 QEC cycle, even
when the LRUs are implemented with limited fidelity. Furthermore, we show that
this leads to a significant reduction of the logical error rate. This LRU
scheme opens the prospect for near-term scalable QEC demonstrations.


Abstract:
  For the exactly solvable model of exponential last passage percolation on
$\mathbb{Z}^2$, consider the geodesic $\Gamma_n$ joining $(0,0)$ and $(n,n)$
for large $n$. It is well known that the transversal fluctuation of $\Gamma_n$
around the line $x=y$ is $n^{2/3+o(1)}$ with high probability. We obtain the
exponent governing the decay of the small ball probability for $\Gamma_{n}$ and
establish that for small $\delta$, the probability that $\Gamma_{n}$ is
contained in a strip of width $\delta n^{2/3}$ around the diagonal is $\exp
(-\Theta(\delta^{-3/2}))$ uniformly in high $n$. We also obtain optimal small
deviation estimates for the one point distribution of the geodesic showing that
for $\frac{t}{2n}$ bounded away from $0$ and $1$, we have
$\mathbb{P}(|x(t)-y(t)|\leq \delta n^{2/3})=\Theta(\delta)$ uniformly in high
$n$, where $(x(t),y(t))$ is the unique point where $\Gamma_{n}$ intersects the
line $x+y=t$. Our methods are expected to go through for other exactly solvable
models of planar last passage percolation and, upon taking the $n\to \infty$
limit, provide analogous estimates for geodesics in the directed landscape.


Abstract:
  We propose a modified quantum teleportation scheme to increase the
teleportation accuracy by applying a cubic phase gate to the displaced squeezed
state. We have described the proposed scheme in Heisenberg's language,
evaluating it from the point of view of adding an error in teleportation, and
have shown that it allows achieving less error than the original scheme.
Repeating the description in the language of wave functions, we have found the
range of the displacement values, at which our conclusions will be valid. Using
the example of teleportation of the vacuum state, we have shown that the scheme
allows one to achieve high fidelity values.


Abstract:
  Non-Hermitian skin effects and exceptional points are topological phenomena
characterized by integer winding numbers. In this study, we give methods to
theoretically detect skin effects and exceptional points by generalizing
inversion symmetry. The generalization of inversion symmetry is unique to
non-Hermitian systems. We show that parities of the winding numbers can be
determined from energy eigenvalues on the inversion-invariant momenta when
generalized inversion symmetry is present. The simple expressions for the
winding numbers allow us to easily analyze skin effects and exceptional points
in non-Hermitian bands. We also demonstrate the methods for (second-order) skin
effects and exceptional points by using lattice models.


Abstract:
  Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition
syndrome that involves the central and peripheral nervous systems. Accurate
detection and segmentation of neurofibromas are essential for assessing tumor
burden and longitudinal tumor size changes. Automatic convolutional neural
networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical
location and heterogeneous appearance on MRI. In this study, we propose deep
interactive networks (DINs) to address the above limitations. User interactions
guide the model to recognize complicated tumors and quickly adapt to
heterogeneous tumors. We introduce a simple but effective Exponential Distance
Transform (ExpDT) that converts user interactions into guide maps regarded as
the spatial and appearance prior. Comparing with popular Euclidean and geodesic
distances, ExpDT is more robust to various image sizes, which reserves the
distribution of interactive inputs. Furthermore, to enhance the tumor-related
features, we design a deep interactive module to propagate the guides into
deeper layers. We train and evaluate DINs on three MRI data sets from NF1
patients. The experiment results yield significant improvements of 44% and 14%
in DSC comparing with automated and other interactive methods, respectively. We
also experimentally demonstrate the efficiency of DINs in reducing user burden
when comparing with conventional interactive methods. The source code of our
method is available at \url{https://github.com/Jarvis73/DINs}.


Abstract:
  Medical terminology normalization aims to map the clinical mention to
terminologies come from a knowledge base, which plays an important role in
analyzing Electronic Health Record(EHR) and many downstream tasks. In this
paper, we focus on Chinese procedure terminology normalization. The expression
of terminologies are various and one medical mention may be linked to multiple
terminologies. Previous study explores some methods such as multi-class
classification or learning to rank(LTR) to sort the terminologies by literature
and semantic information. However, these information is inadequate to find the
right terminologies, particularly in multi-implication cases. In this work, we
propose a combined recall and rank framework to solve the above problems. This
framework is composed of a multi-task candidate generator(MTCG), a keywords
attentive ranker(KAR) and a fusion block(FB). MTCG is utilized to predict the
mention implication number and recall candidates with semantic similarity. KAR
is based on Bert with a keywords attentive mechanism which focuses on keywords
such as procedure sites and procedure types. FB merges the similarity come from
MTCG and KAR to sort the terminologies from different perspectives. Detailed
experimental analysis shows our proposed framework has a remarkable improvement
on both performance and efficiency.


Abstract:
  Manipulating valley-dependent Berry phase effects provides remarkable
opportunities for both fundamental research and practical applications. Here,
by referring to effective model analysis, we propose a general scheme for
realizing topological magneto-valley phase transitions. More importantly, by
using valley-half-semiconducting VSi2N4 as an outstanding example, we
investigate sign change of valley-dependent Berry phase effects which drive the
change-in-sign valley anomalous transport characteristics via external means
such as biaxial strain, electric field, and correlation effects. As a result,
this gives rise to quantized versions of valley anomalous transport phenomena.
Our findings not only uncover a general framework to control valley degree of
freedom, but also motivate further research in the direction of multifunctional
quantum devices in valleytronics and spintronics.


Abstract:
  The rise of algorithmic decision-making has spawned much research on fair
machine learning (ML). Financial institutions use ML for building risk
scorecards that support a range of credit-related decisions. Yet, the
literature on fair ML in credit scoring is scarce. The paper makes three
contributions. First, we revisit statistical fairness criteria and examine
their adequacy for credit scoring. Second, we catalog algorithmic options for
incorporating fairness goals in the ML model development pipeline. Last, we
empirically compare different fairness processors in a profit-oriented credit
scoring context using real-world data. The empirical results substantiate the
evaluation of fairness measures, identify suitable options to implement fair
credit scoring, and clarify the profit-fairness trade-off in lending decisions.
We find that multiple fairness criteria can be approximately satisfied at once
and recommend separation as a proper criterion for measuring the fairness of a
scorecard. We also find fair in-processors to deliver a good balance between
profit and fairness and show that algorithmic discrimination can be reduced to
a reasonable level at a relatively low cost. The codes corresponding to the
paper are available on GitHub.


Abstract:
  Complex singularities have been suggested in propagators of confined
particles, e.g., the Landau-gauge gluon propagator. We rigorously reconstruct
Minkowski propagators from Euclidean propagators with complex singularities. As
a result, the analytically continued Wightman function is holomorphic in the
tube, and the Lorentz symmetry and locality are kept intact, whereas the
reconstructed Wightman function violates the temperedness and the positivity
condition. Moreover, we argue that complex singularities correspond to confined
zero-norm states in an indefinite metric state space.


Abstract:
  For a fixed positive $\epsilon$, we show the existence of a constant
$C_\epsilon$ with the following property: Given a $\pm1$-edge-labeling
$c:E(K_n)\to \{ -1,1\}$ of the complete graph $K_n$ with $c(E(K_n))=0$, and a
spanning forest $F$ of $K_n$ of maximum degree $\Delta$, one can determine in
polynomial time an isomorphic copy $F'$ of $F$ in $K_n$ with $|c(E(F'))|\leq
\left(\frac{3}{4}+\epsilon\right)\Delta+C_\epsilon.$ Our approach is based on
the method of conditional expectation.


Abstract:
  Current perception systems often carry multimodal imagers and sensors such as
2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream
perception tasks, robust and accurate calibration of the multimodal sensor data
is essential. We propose a novel deep learning-driven technique (CalibDNN) for
accurate calibration among multimodal sensor, specifically LiDAR-Camera pairs.
The key innovation of the proposed work is that it does not require any
specific calibration targets or hardware assistants, and the entire processing
is fully automatic with a single model and single iteration. Results comparison
among different methods and extensive experiments on different datasets
demonstrates the state-of-the-art performance.


Abstract:
  In the present study, we propose to implement a new framework for estimating
generative models via an adversarial process to extend an existing GAN
framework and develop a white-box controllable image cartoonization, which can
generate high-quality cartooned images/videos from real-world photos and
videos. The learning purposes of our system are based on three distinct
representations: surface representation, structure representation, and texture
representation. The surface representation refers to the smooth surface of the
images. The structure representation relates to the sparse colour blocks and
compresses generic content. The texture representation shows the texture,
curves, and features in cartoon images. Generative Adversarial Network (GAN)
framework decomposes the images into different representations and learns from
them to generate cartoon images. This decomposition makes the framework more
controllable and flexible which allows users to make changes based on the
required output. This approach overcomes any previous system in terms of
maintaining clarity, colours, textures, shapes of images yet showing the
characteristics of cartoon images.


Abstract:
  The time average expected age of information (AoI) is studied for status
updates sent over an error-prone channel from an energy-harvesting transmitter
with a finite-capacity battery. Energy cost of sensing new status updates is
taken into account as well as the transmission energy cost better capturing
practical systems. The optimal scheduling policy is first studied under the
hybrid automatic repeat request (HARQ) protocol when the channel and energy
harvesting statistics are known, and the existence of a threshold-based optimal
policy is shown. For the case of unknown environments, average-cost
reinforcement-learning algorithms are proposed that learn the system parameters
and the status update policy in real-time. The effectiveness of the proposed
methods is demonstrated through numerical results.


Abstract:
  We identify potential early markets for fusion energy and their projected
cost targets, based on analysis and synthesis of many relevant, recent studies
and reports. Because private fusion companies aspire to start commercial
deployment before 2040, we examine cost requirements for fusion-generated
electricity, process heat, and hydrogen production based on today's market
prices but with various adjustments relating to possible scenarios in 2035,
such as "business-as-usual," high renewables penetration, and carbon pricing up
to 100 \$/tCO$_2$. Key findings are that fusion developers should consider
focusing initially on high-priced global electricity markets and including
integrated thermal storage in order to maximize revenue and compete in markets
with high renewables penetration. Process heat and hydrogen production will be
tough early markets for fusion, but may open up to fusion as markets evolve and
if fusion's levelized cost of electricity falls below 50 \$/MWh$_\mathrm{e}$.
Finally, we discuss potential ways for a fusion plant to increase revenue via
cogeneration (e.g., desalination, direct air capture, or district heating) and
to lower capital costs (e.g., by minimizing construction times and interest or
by retrofitting coal plants).


Abstract:
  We study synchronizing partial DFAs, which extend the classical concept of
synchronizing complete DFAs and are a special case of synchronizing unambiguous
NFAs. A partial DFA is called synchronizing if it has a word (called a reset
word) whose action brings a non-empty subset of states to a unique state and is
undefined for all other states. While in the general case the problem of
checking whether a partial DFA is synchronizing is PSPACE-complete, we show
that in the strongly connected case this problem can be efficiently reduced to
the same problem for a complete DFA. Using combinatorial, algebraic, and formal
languages methods, we develop techniques that relate main synchronization
problems for strongly connected partial DFAs with the same problems for
complete DFAs. In particular, this includes the \v{C}ern\'{y} and the rank
conjectures, the problem of finding a reset word, and upper bounds on the
length of the shortest reset words of literal automata of finite prefix codes.
We conclude that solving fundamental synchronization problems is equally hard
in both models, as an essential improvement of the results for one model
implies an improvement for the other.


Abstract:
  In this work, the antibacterial activity of the polymeric precursor
dicarbonyldichlororuthenium has been studied against Escherichia coli and
Staphylococcus aureus. This Ru carbonyl precursor shows minimum inhibitory
concentration at nanogram per millilitre, which renders it a novel
antimicrobial polymer without any organic ligands. Besides,
dicarbonyldichlororuthenium antimicrobial activity is markedly boosted under
photoirradiation, which can be ascribed to the enhanced generation of reactive
oxygen species under UV irradiation. This compound has been able to inhibit
bacterial growth via the disruption of bacterial membranes and triggering
upregulation of stress responses as shown in microscopic measurements. The
activity of polymeric ruthenium as an antibacterial material is significant
even at very low concentrations while remaining biocompatible to the mammalian
cells at much higher concentrations. This study proves that this simple Ru
carbonyl precursor can be used as an antimicrobial compound with high activity
and a low toxicity profile in the context of need for new antimicrobial agents
to fight bacterial infections.


Abstract:
  The radio galaxy 1321+045 is a rare example of a young, compact steep
spectrum source located in the center of a z=0.263 galaxy cluster. Using a
combination of Chandra, VLBA, VLA, MERLIN and IRAM 30m observations, we
investigate the conditions which have triggered this outburst. We find that the
previously identified 5 kpc scale radio lobes are probably no longer powered by
the AGN, which seems to have launched a new ~20 pc jet on a different axis,
likely within the last few hundred years. We estimate the enthalpy of the lobes
to be 8.48 [+6.04,-3.56] x10^57 erg, only sufficient to balance cooling in the
surrounding 16 kpc for ~9 Myr. The cluster ICM properties are similar to those
of rapidly cooling nearby clusters, with a low central entropy (8.6 [+2.2,-1.4]
kev cm^2 within 8 kpc), short central cooling time (390 [+170,-150] Myr), and
t_cool/t_ff and t_cool/t_eddy ratios indicative of thermal instability out to
~45 kpc. Despite previous detection of Halpha emission from the BCG, our IRAM
30m observations do not detect CO emission in either the (1-0) or (3-2)
transitions. We place 3sigma limits on the molecular gas mass of M_mol
<=7.7x10^9 Msol and <=5.6x10^9 Msol from the two lines respectively. We find
indications of a recent minor cluster merger which has left a ~200 kpc tail of
stripped gas in the ICM, and probably induced sloshing motions.


Abstract:
  Recent spectroscopic observations by sensitive radio telescopes require
accurate molecular spectral line frequencies to identify molecular species in a
forest of lines detected. To measure rest frequencies of molecular spectral
lines in the laboratory, an emission-type millimeter and submillimeter-wave
spectrometer utilizing state-of-the-art radio-astronomical technologies is
developed. The spectrometer is equipped with a 200 cm glass cylinder cell, a
two sideband (2SB) Superconductor-Insulator-Superconductor (SIS) receiver in
the 230 GHz band, and wide-band auto-correlation digital spectrometers. By
using the four 2.5 GHz digital spectrometers, a total instantaneous bandwidth
of the 2SB SIS receiver of 8 GHz can be covered with a frequency resolution of
88.5 kHz. Spectroscopic measurements of CH$_3$CN and HDO are carried out in the
230 GHz band so as to examine frequency accuracy, stability, sensitivity, as
well as intensity calibration accuracy of our system. As for the result of
CH$_3$CN, we confirm that the frequency accuracy for lines detected with
sufficient signal to noise ratio is better than 1 kHz, when the high resolution
spectrometer having a channel resolution of 17.7 kHz is used. In addition, we
demonstrate the capability of this system by spectral scan measurement of
CH$_3$OH from 216 GHz to 264 GHz. We assign 242 transitions of CH$_3$OH, 51
transitions of $^{13}$CH$_3$OH, and 21 unidentified emission lines for 295
detected lines. Consequently, our spectrometer demonstrates sufficient
sensitivity, spectral resolution, and frequency accuracy for in-situ
experimental-based rest frequency measurements of spectral lines on various
molecular species.


Abstract:
  Achieving human-level performance on some of Machine Reading Comprehension
(MRC) datasets is no longer challenging with the help of powerful Pre-trained
Language Models (PLMs). However, it is necessary to provide both answer
prediction and its explanation to further improve the MRC system's reliability,
especially for real-life applications. In this paper, we propose a new
benchmark called ExpMRC for evaluating the explainability of the MRC systems.
ExpMRC contains four subsets, including SQuAD, CMRC 2018, RACE$^+$, and C$^3$
with additional annotations of the answer's evidence. The MRC systems are
required to give not only the correct answer but also its explanation. We use
state-of-the-art pre-trained language models to build baseline systems and
adopt various unsupervised approaches to extract evidence without a
human-annotated training set. The experimental results show that these models
are still far from human performance, suggesting that the ExpMRC is
challenging. Resources will be available through
https://github.com/ymcui/expmrc


Abstract:
  We consider thermal effects in the propagation of gravitational waves on a
cosmological background. In particular, we consider scalar field cosmologies
and study gravitational modes near cosmological singularities. We point out
that the contribution of thermal radiation can heavily affect the dynamics of
gravitational waves giving enhancement or dissipation effects both at quantum
and classical level.These effects are considered both in General Relativity and
in modified theories like $F(R)$ gravity which can be easily reduced to
scalar-tensor cosmology. The possible detection and disentanglement of standard
and scalar gravitational modes on the stochastic background are also discussed.


Abstract:
  Exceptional points (EPs), i.e., non-Hermitian degeneracies at which
eigenvalues and eigenvectors coalesce, can be realized by tuning the gain/loss
contrast of different modes in non-Hermitian systems or by engineering the
asymmetric coupling of modes. Here we demonstrate a mechanism that can achieve
EPs of arbitrary order by employing the non-reciprocal coupling of spinning
cylinders sitting on a dielectric waveguide. The spinning motion breaks the
time-reversal symmetry and removes the degeneracy of opposite chiral modes of
the cylinders. Under the excitation of a linearly polarized plane wave, the
chiral mode of one cylinder can unidirectionally couple to the same mode of the
other cylinder via the spin-orbit interaction associated with the evanescent
wave of the waveguide. The structure can give rise to arbitrary-order EPs that
are robust against spin-flipping perturbations, in contrast to conventional
systems relying on spin-selective excitations. In addition, we show that
higher-order EPs in the proposed system are accompanied by enhanced optical
isolation, which may find applications in designing novel optical isolators,
nonreciprocal optical devices, and topological photonics.


Abstract:
  The design of the cross-section of an FRP-reinforced concrete beam is an
iterative process of estimating both its dimensions and the reinforcement
ratio, followed by the check of the compliance of a number of strength and
serviceability constraints. The process continues until a suitable solution is
found. Since there are infinite solutions to the problem, it appears convenient
to define some optimality criteria so as to measure the relative goodness of
the different solutions. This paper intends to develop a preliminary least-cost
section design model that follows the recommendations in the ACI 440.1 R-06,
and uses a relatively new artificial intelligence technique called particle
swarm optimization (PSO) to handle the optimization tasks. The latter is based
on the intelligence that emerges from the low-level interactions among a number
of relatively non-intelligent individuals within a population.


Abstract:
  We study the problem of testing the null hypothesis that X and Y are
conditionally independent given Z, where each of X, Y and Z may be functional
random variables. This generalises testing the significance of X in a
regression model of scalar response Y on functional regressors X and Z. We show
however that even in the idealised setting where additionally (X, Y, Z) has a
Gaussian distribution, the power of any test cannot exceed its size. Further
modelling assumptions are needed and we argue that a convenient way of
specifying these is based on choosing methods for regressing each of X and Y on
Z. We propose a test statistic involving inner products of the resulting
residuals that is simple to compute and calibrate: type I error is controlled
uniformly when the in-sample prediction errors are sufficiently small. We show
this requirement is met by ridge regression in functional linear model settings
without requiring any eigen-spacing conditions or lower bounds on the
eigenvalues of the covariance of the functional regressor. We apply our test in
constructing confidence intervals for truncation points in truncated functional
linear models and testing for edges in a functional graphical model for EEG
data.


Abstract:
  Multifidelity simulation methodologies are often used in an attempt to
judiciously combine low-fidelity and high-fidelity simulation results in an
accuracy-increasing, cost-saving way. Candidates for this approach are
simulation methodologies for which there are fidelity differences connected
with significant computational cost differences. Physics-informed Neural
Networks (PINNs) are candidates for these types of approaches due to the
significant difference in training times required when different fidelities
(expressed in terms of architecture width and depth as well as optimization
criteria) are employed. In this paper, we propose a particular multifidelity
approach applied to PINNs that exploits low-rank structure. We demonstrate that
width, depth, and optimization criteria can be used as parameters related to
model fidelity, and show numerical justification of cost differences in
training due to fidelity parameter choices. We test our multifidelity scheme on
various canonical forward PDE models that have been presented in the emerging
PINNs literature.


Abstract:
  Recent work has shown that many problems of satisfiability and resiliency in
workflows may be viewed as special cases of the authorization policy existence
problem (APEP), which returns an authorization policy if one exists and 'No'
otherwise. However, in many practical settings it would be more useful to
obtain a 'least bad' policy than just a 'No', where 'least bad' is
characterized by some numerical value indicating the extent to which the policy
violates the base authorization relation and constraints. Accordingly, we
introduce the Valued APEP, which returns an authorization policy of minimum
weight, where the (non-negative) weight is determined by the constraints
violated by the returned solution. We then establish a number of results
concerning the parameterized complexity of Valued APEP. We prove that the
problem is fixed-parameter tractable (FPT) if the set of constraints satisfies
two restrictions, but is intractable if only one of these restrictions holds.
(Most constraints known to be of practical use satisfy both restrictions.) We
also introduce a new type of resiliency for workflow satisfiability problem,
show how it can be addressed using Valued APEP and use this to build a set of
benchmark instances for Valued APEP. Following a set of computational
experiments with two mixed integer programming (MIP) formulations, we
demonstrate that the Valued APEP formulation based on the user profile concept
has FPT-like running time and usually significantly outperforms a naive
formulation.


Abstract:
  Sample efficiency and risk-awareness are central to the development of
practical reinforcement learning (RL) for complex decision-making. The former
can be addressed by transfer learning and the latter by optimizing some utility
function of the return. However, the problem of transferring skills in a
risk-aware manner is not well-understood. In this paper, we address the problem
of risk-aware policy transfer between tasks in a common domain that differ only
in their reward functions, in which risk is measured by the variance of reward
streams. Our approach begins by extending the idea of generalized policy
improvement to maximize entropic utilities, thus extending policy improvement
via dynamic programming to sets of policies and levels of risk-aversion. Next,
we extend the idea of successor features (SF), a value function representation
that decouples the environment dynamics from the rewards, to capture the
variance of returns. Our resulting risk-aware successor features (RaSF)
integrate seamlessly within the RL framework, inherit the superior task
generalization ability of SFs, and incorporate risk-awareness into the
decision-making. Experiments on a discrete navigation domain and control of a
simulated robotic arm demonstrate the ability of RaSFs to outperform
alternative methods including SFs, when taking the risk of the learned policies
into account.


Abstract:
  Using Langevin dynamics simulations, we study the hysteresis in unzipping of
longer double stranded DNA chains whose ends are subjected to a time dependent
periodic force with frequency $\omega$ and amplitude $G$ keeping the other end
fixed. We find that the area of the hysteresis loop, $A_{loop}$, scales as
$1/\omega$ at higher frequencies, whereas it scales as
$(G-G_c)^{\alpha}\omega^{\beta}$ with exponents $\alpha=1$ and $\beta=1.25$ in
the low frequency regime. These values are same as the exponents obtained in
Monte Carlo simulation studies of a directed self avoiding walk model of a
homopolymer DNA [R. Kapri, Phys. Rev. E 90, 062719 (2014)], and the block
copolymer DNA [R. K. Yadav and R. Kapri, Phys. Rev. E 103, 012413 (2021)] on a
square lattice, and differs from the values reported earlier using Langevin
dynamics simulation studies on a much shorter DNA hairpins.


Abstract:
  In spoken conversational question answering (SCQA), the answer to the
corresponding question is generated by retrieving and then analyzing a fixed
spoken document, including multi-part conversations. Most SCQA systems have
considered only retrieving information from ordered utterances. However, the
sequential order of dialogue is important to build a robust spoken
conversational question answering system, and the changes of utterances order
may severely result in low-quality and incoherent corpora. To this end, we
introduce a self-supervised learning approach, including incoherence
discrimination, insertion detection, and question prediction, to explicitly
capture the coreference resolution and dialogue coherence among spoken
documents. Specifically, we design a joint learning framework where the
auxiliary self-supervised tasks can enable the pre-trained SCQA systems towards
more coherent and meaningful spoken dialogue learning. We also utilize the
proposed self-supervised learning tasks to capture intra-sentence coherence.
Experimental results demonstrate that our proposed method provides more
coherent, meaningful, and appropriate responses, yielding superior performance
gains compared to the original pre-trained language models. Our method achieves
state-of-the-art results on the Spoken-CoQA dataset.


Abstract:
  Chalcogenide phase change materials (PCMs) have been extensively applied in
data storage, and they are now being proposed for high resolution displays,
holographic displays, reprogrammable photonics, and all-optical neural
networks. These wide-ranging applications all exploit the radical property
contrast between the PCMs different structural phases, extremely fast switching
speed, long-term stability, and low energy consumption. Designing PCM photonic
devices requires an accurate model to predict the response of the device during
phase transitions. Here, we describe an approach that accurately predicts the
microstructure and optical response of phase change materials during laser
induced heating. The framework couples the Gillespie Cellular Automata approach
for modelling phase transitions with effective medium theory and Fresnel
equations. The accuracy of the approach is verified by comparing the PCM
optical response and microstructure evolution with the results of nanosecond
laser switching experiments. We anticipate that this approach to simulating the
switching response of PCMs will become an important component for designing and
simulating programmable photonics devices. The method is particularly important
for predicting the multi-level optical response of PCMs, which is important for
all-optical neural networks and PCM-programmable perceptrons.


Abstract:
  We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice
images. In this framework, the slice images of a CT-scan volume are first
proprocessed using segmentation techniques to filter out images of closed lung,
and to remove the useless background. Then a resampling method is used to
select one or multiple sets of a fixed number of slice images for training and
validation. A 3D CNN network with BERT is used to classify this set of selected
slice images. In this network, an embedding feature is also extracted. In cases
where there are more than one set of slice images in a volume, the features of
all sets are extracted and pooled into a global feature vector for the whole
CT-scan volume. A simple multiple-layer perceptron (MLP) network is used to
further classify the aggregated feature vector. The models are trained and
evaluated on the provided training and validation datasets. On the validation
dataset, the accuracy is 0.9278 and the F1 score is 0.9261.


Abstract:
  We review the status of the anomalies in $b\to s \ell\ell$ transitions, and
comment on the impact of the most recent measurements in 2019 and 2020 on the
global fits. We also discuss a few developments in the theory calculation of
local and non-local form factors.


Abstract:
  In this paper, we introduce the new task of controllable text edition, in
which we take as input a long text, a question, and a target answer, and the
output is a minimally modified text, so that it fits the target answer. This
task is very important in many situations, such as changing some conditions,
consequences, or properties in a legal document, or changing some key
information of an event in a news text. This is very challenging, as it is hard
to obtain a parallel corpus for training, and we need to first find all text
positions that should be changed and then decide how to change them. We
constructed the new dataset WikiBioCTE for this task based on the existing
dataset WikiBio (originally created for table-to-text generation). We use
WikiBioCTE for training, and manually labeled a test set for testing. We also
propose novel evaluation metrics and a novel method for solving the new task.
Experimental results on the test set show that our proposed method is a good
fit for this novel NLP task.


Abstract:
  A mixture preorder is a preorder on a mixture space (such as a convex set)
that is compatible with the mixing operation. In decision theoretic terms, it
satisfies the central expected utility axiom of strong independence. We
consider when a mixture preorder has a multi-representation that consists of
real-valued, mixture-preserving functions. If it does, it must satisfy the
mixture continuity axiom of Herstein and Milnor (1953). Mixture continuity is
sufficient for a mixture-preserving multi-representation when the dimension of
the mixture space is countable, but not when it is uncountable. Our strongest
positive result is that mixture continuity is sufficient in conjunction with a
novel axiom we call countable domination, which constrains the order complexity
of the mixture preorder in terms of its Archimedean structure. We also consider
what happens when the mixture space is given its natural weak topology.
Continuity (having closed upper and lower sets) and closedness (having a closed
graph) are stronger than mixture continuity. We show that continuity is
necessary but not sufficient for a mixture preorder to have a
mixture-preserving multi-representation. Closedness is also necessary; we leave
it as an open question whether it is sufficient. We end with results concerning
the existence of mixture-preserving multi-representations that consist entirely
of strictly increasing functions, and a uniqueness result.


Abstract:
  Widespread processes in nature and technology are governed by the dynamical
transition whereby a material in an initially solid-like state then yields
plastically. Major unresolved questions concern whether any material will yield
smoothly and gradually (ductile behaviour) or fail abruptly and
catastrophically (brittle behaviour); the roles of sample annealing, disorder
and shear band formation in the onset of yielding and failure; and, most
importantly from a practical viewpoint, whether any impending catastrophic
failure can be anticipated before it happens. We address these questions by
studying the yielding of slowly sheared athermal amorphous materials, within a
minimal mesoscopic lattice elastoplastic model. Our contributions are fourfold.
First, we elucidate whether yielding will be ductile or brittle, for any given
level of sample annealing. Second, we show that yielding comprises two distinct
stages: a pre-failure stage, in which small levels of strain heterogeneity
slowly accumulate, followed by a catastrophic brittle failure event, in which a
crack quickly propagates across the sample via a cooperating line of plastic
events. Third, we provide an expression for the slowly growing level of strain
heterogeneity in the pre-failure stage, expressed in terms of the macroscopic
stress-strain curve and the sample size, and in excellent agreement with our
simulation results. Fourth, we elucidate the basic mechanism via which a crack
then nucleates and provide an approximate expression for the probability
distribution of shear strains at which failure occurs, as determined by the
disorder inherent in the sample, expressed in terms of a single annealing
parameter, and the system size. Importantly, this indicates a possible route to
predicting sudden material failure, before it occurs.


Abstract:
  When a three-dimensional material is constructed by stacking different
two-dimensional layers into an ordered structure, new and unique physical
properties can emerge. An example is the delafossite PdCoO2, which consists of
alternating layers of metallic Pd and Mott-insulating CoO2 sheets. To
understand the nature of the electronic coupling between the layers that gives
rise to the unique properties of PdCoO2, we revealed its layer-resolved
electronic structure combining standing-wave X-ray photoemission spectroscopy
and ab initio many-body calculations. Experimentally, we have decomposed the
measured valence band spectrum into contributions from Pd and CoO2 layers.
Computationally, we find that many-body interactions in Pd and CoO2 layers are
highly different. Holes in the CoO2 layer interact strongly with
charge-transfer excitons in the same layer, whereas holes in the Pd layer
couple to plasmons in the Pd layer. Interestingly, we find that holes in states
hybridized across both layers couple to both types of excitations
(charge-transfer excitons or plasmons), with the intensity of photoemission
satellites being proportional to the projection of the state onto a given
layer. This establishes satellites as a sensitive probe for inter-layer
hybridization. These findings pave the way towards a better understanding of
complex many-electron interactions in layered quantum materials.


Abstract:
  Recent query explanation systems help users understand anomalies in
aggregation results by proposing predicates that describe input records that,
if deleted, would resolve the anomalies. However, it can be difficult for users
to understand how a predicate was chosen, and these approaches are limited to
errors that can be resolved through deletion. In contrast, data errors may be
due to group-wise errors, such as missing records or systematic value errors.
This paper presents Reptile, an explanation system for hierarchical data. Given
an anomalous aggregate query result, Reptile recommends the next drill-down
attribute,and ranks the drill-down groups based on the extent repairing the
group's statistics to its expected values resolves the anomaly. Reptile
efficiently trains a multi-level model that leverages the data's hierarchy to
estimate the expected values, and uses a factorised representation of the
feature matrix to remove redundancies due to the data's hierarchical structure.
We further extend model training to support factorised data, and develop a
suite of optimizations that leverage the data's hierarchical structure. Reptile
reduces end-to-end runtimes by more than 6 times compared to a Matlab-based
implementation, correctly identifies 21/30 data errors in John Hopkin's
COVID-19 data, and correctly resolves 20/22 complaints in a user study using
data and researchers from Columbia University's Financial Instruments Sector
Team.


Abstract:
  We designed Si-based all-dielectric 1 $\times$ 2 TE and TM power splitters
with various splitting ratios and simulated them using the inverse design of
adjoint and numerical 3D finite-difference time-domain methods. The proposed
devices exhibit ultra-high transmission efficiency above 98 and 99\%, and
excess losses below 0.1 and 0.035 dB, for TE and TM splitters, respectively.
The merits of these devices include a minor footprint of 2.2 $\times$ 2.2
$\mu$m $\times$ $\mu$m and a broad operating bandwidth of 200 nm with a center
wavelength of $\lambda$ = 1.55 $\mu$ m.


Abstract:
  Numerical continuation in the context of optimization can be used to mitigate
convergence issues due to a poor initial guess. In this work, we extend this
idea to Riemannian optimization problems, that is, the minimization of a target
function on a Riemannian manifold. For this purpose, a suitable homotopy is
constructed between the original problem and a problem that admits an easy
solution. We develop and analyze a path-following numerical continuation
algorithm on manifolds for solving the resulting parameter-dependent equation.
To illustrate our developments, we consider two typical classical applications
of Riemannian optimization: the computation of the Karcher mean and low-rank
matrix completion. We demonstrate that numerical continuation can yield
improvements for challenging instances of both problems.


Abstract:
  Iso-edge domains are a variant of the iso-Delaunay decomposition introduced
by Voronoi. They were introduced by Baranovskii & Ryshkov in order to solve the
covering problem in dimension $5$.
  In this work we revisit this decomposition and prove the following new
results:
  $\bullet$ We review the existing theory and give a general mass-formula for
the iso-edge domains.
  $\bullet$ We prove that the associated toroidal compactification of the
moduli space of principally polarized abelian varieties is projective.
  $\bullet$ We prove the Conway--Sloane conjecture in dimension $5$.
  $\bullet$ We prove that the quadratic forms for which the conorms are
non-negative are exactly the matroidal ones in dimension $5$.


Abstract:
  In this paper we study exact boundary controllability for a linear wave
equation with strong and weak interior degeneration of the coefficient in the
principle part of the elliptic operator. The objective is to provide a
well-posedness analysis of the corresponding system and derive conditions for
its controllability through boundary actions. Passing to a relaxed version of
the original problem, we discuss existence and uniqueness of solutions, and
using the HUM method we derive conditions on the rate of degeneracy for both
exact boundary controllability and the lack thereof.


Abstract:
  Modularity is a quantity which has been introduced in the context of complex
networks in order to quantify how close a network is to an ideal modular
network in which the nodes form small interconnected communities that are
joined together with relatively few edges. In this paper, we consider this
quantity on a recent probabilistic model of complex networks introduced by
Krioukov et al. (Phys. Rev. E 2010).
  This model views a complex network as an expression of hidden hierarchies,
encapsulated by an underlying hyperbolic space. For certain parameters, this
model was proved to have typical features that are observed in complex networks
such as power law degree distribution, bounded average degree, clustering
coefficient that is asymptotically bounded away from zero, and ultra-small
typical distances. In the present work, we investigate its modularity and we
show that, in this regime, it converges to 1 in probability.


Abstract:
  Setting an effective reserve price for strategic bidders in repeated auctions
is a central question in online advertising. In this paper, we investigate how
to set an anonymous reserve price in repeated auctions based on historical bids
in a way that balances revenue and incentives to misreport. We propose two
simple and computationally efficient methods to set reserve prices based on the
notion of a clearing price and make them robust to bidder misreports. The first
approach adds random noise to the reserve price, drawing on techniques from
differential privacy. The second method applies a smoothing technique by adding
noise to the training bids used to compute the reserve price. We provide
theoretical guarantees on the trade-offs between the revenue performance and
bid-shading incentives of these two mechanisms. Finally, we empirically
evaluate our mechanisms on synthetic data to validate our theoretical findings.


Abstract:
  This paper explores the genotype-phenotype relationship. It outlines
conditions under which the dependence of quantitative trait on the genome might
be predictable, based on measurement of a limited subset of genotypes. It uses
the theory of real-valued Boolean functions in a systematic way to translate
trait data into the Fourier domain. Important trait features, such as the
roughness of the trait landscape or the modularity of a trait have a simple
Fourier interpretation. Roughness at a gene location corresponds to high
sensitivity to mutation, while a modular organization of gene activity reduces
such sensitivity.
  Traits where rugged loci are rare will naturally compress gene data in the
Fourier domain, leading to a sparse representation of trait data, concentrated
in identifiable, low-level coefficients. This Fourier representation of a trait
organizes epistasis in a form which is isometric to the trait data. As Fourier
matrices are known to be maximally incoherent with the standard basis, this
permits employing compressive sensing techniques to work from data sets that
are relatively small -- sometimes even polynomial -- compared to the
exponentially large sets of possible genomes.
  This theory provides a theoretical underpinning for systematic use of Boolean
function machinery to dissect the dependency of a trait on the genome and
environment.


Abstract:
  One of the open questions in the field of interaction design is "what inputs
or interaction techniques should be used with augmented reality devices?" The
transition from a touchpad and a keyboard to a multi-touch device was
relatively small. The transition from a multi-touch device to an HMD with no
controllers or clear surface to interact with is more complicated. This book is
a guide for how to figure out what interaction techniques and modalities people
prefer when interacting with those devices. The name of the technique covered
here is Elicitation. Elicitation is a form of participatory design, meaning
design with direct end-user involvement. By running an elicitation study
researchers can observe unconstrained human interactions with emerging
technologies to help guide input design.


Abstract:
  We use a sample of 14 massive, dynamically relaxed galaxy clusters to
constrain the Hubble Constant, $H_0$, by combining X-ray and Sunyaev-Zel'dovich
(SZ) effect signals measured with Chandra, Planck and Bolocam. This is the
first such analysis to marginalize over an empirical, data-driven prior on the
overall accuracy of X-ray temperature measurements, while our restriction to
the most relaxed, massive clusters also minimizes astrophysical systematics.
For a cosmological-constant model with $\Omega_m = 0.3$ and $\Omega_{\Lambda} =
0.7$, we find $H_0 = 67.3^{+21.3}_{-13.3}$ km/s/Mpc, limited by the temperature
calibration uncertainty (compared to the statistically limited constraint of
$H_0 = 72.3^{+7.6}_{-7.6}$ km/s/Mpc). The intrinsic scatter in the X-ray/SZ
pressure ratio is found to be $13 \pm 4$ per cent ($10 \pm 3$ per cent when two
clusters with significant galactic dust emission are removed from the sample),
consistent with being primarily due to triaxiality and projection. We discuss
the prospects for reducing the dominant systematic limitation to this analysis,
with improved X-ray calibration and/or precise measurements of the relativistic
SZ effect providing a plausible route to per cent level constraints on $H_0$.


Abstract:
  Understanding the fracture toughness of glasses is of prime importance for
science and technology. We study it here using extensive atomistic simulations
in which the interaction potential, glass transition cooling rate and loading
geometry are systematically varied, mimicking a broad range of experimentally
accessible properties. Glasses' nonequilibrium mechanical disorder is
quantified through $A_{\rm g}$, the dimensionless prefactor of the universal
spectrum of nonphononic excitations, which measures the abundance of soft
glassy defects that affect plastic deformability. We show that while a
brittle-to-ductile transition might be induced by reducing the cooling rate,
leading to a reduction in $A_{\rm g}$, iso-$\!A_{\rm g}$ glasses are either
brittle or ductile depending on the degree of Poisson contraction under
unconstrained uniaxial tension. Eliminating Poisson contraction using
constrained tension reveals that iso-$\!A_{\rm g}$ glasses feature similar
toughness, and that varying $A_{\rm g}$ under these conditions results in
significant toughness variation. Our results highlight the roles played by both
soft defects and loading geometry (which affects the activation of defects) in
the toughness of glasses.


Abstract:
  We consider a multiuser diffusion-based molecular communication (MC) system
where multiple spatially distributed transmitter (TX)-receiver (RX) pairs
establish point-to-point communication links employing the same type of
signaling molecules. To realize the full potential of such a system, an
in-depth understanding of the interplay between the spatial user density and
inter-user interference (IUI) and its impact on system performance in an
asymptotic regime with large numbers of users is needed. In this paper, we
adopt a three-dimensional (3-D) system model with multiple independent and
spatially distributed point-to-point transmission links, where both the TXs and
RXs are positioned according to a regular hexagonal grid, respectively. Based
on this model, we first derive an expression for the channel impulse responses
(CIRs) of all TX-RX links in the system. Then, we provide the maximum
likelihood (ML) decision rule for the RXs and show that it reduces to a
threshold-based detector. We derive an analytical expression for the
corresponding detection threshold which depends on the statistics of the MC
channel and the statistics of the IUI. Furthermore, we derive an analytical
expression for the bit error rate (BER) and the achievable rate of a single
transmission link. Finally, we propose a new performance metric, which we refer
to as area rate efficiency (ARE), that captures the tradeoff between the user
density and IUI. The ARE characterizes how efficiently given TX and RX areas
are used for information transmission and is given in terms of bits per area
unit. We show that there exists an optimal user density for maximization of the
ARE. Results from particle-based and Monte Carlo simulations validate the
accuracy of the expressions derived for the CIR, optimal detection threshold,
BER, and ARE.


Abstract:
  Intermediate mass ratio inspiral (IMRI) binaries -- containing stellar-mass
black holes coalescing into intermediate-mass black holes ($M>100M_{\odot}$) --
are a highly anticipated source of gravitational waves (GWs) for Advanced
LIGO/Virgo. Their detection and source characterization would provide a unique
probe of strong-field gravity and stellar evolution. Due to the asymmetric
component masses and the large primary, these systems generically excite
subdominant modes while reducing the importance of the dominant quadrupole
mode. Including higher order harmonics can also result in a $10\%-25\%$
increase in signal-to-noise ratio for IMRIs, which may help to detect these
systems. We show that by including subdominant GW modes into the analysis we
can achieve a precise characterization of IMRI source properties. For example,
we find that the source properties for IMRIs can be measured to within
$2\%-15\%$ accuracy at a fiducial signal-to-noise ratio of 25 if subdominant
modes are included. When subdominant modes are neglected, the accuracy degrades
to $9\%-44\%$ and significant biases are seen in chirp mass, mass ratio,
primary spin and luminosity distances. We further demonstrate that including
subdominant modes in the waveform model can enable an informative measurement
of both individual spin components and improve the source localization by a
factor of $\sim$10. We discuss some important astrophysical implications of
high-precision source characterization enabled by subdominant modes such as
constraining the mass gap and probing formation channels.


Abstract:
  In this paper, we report about a large-scale online discussion with 1099
citizens on the Afghanistan Sustainable Development Goals.


Abstract:
  We make a spectral analysis of the massive Dirac operator in a tubular
neighborhood of an unbounded planar curve,subject to infinite mass boundary
conditions. Under general assumptions on the curvature, we locate the essential
spectrum and derive an effective Hamiltonian on the base curve which
approximates the original operator in the thin-strip limit. We also investigate
the existence of bound states in the non-relativistic limit and give a
geometric quantitative condition for the bound states to exist.


Abstract:
  Current neuroimaging techniques provide paths to investigate the structure
and function of the brain in vivo and have made great advances in understanding
Alzheimer's disease (AD). However, the group-level analyses prevalently used
for investigation and understanding of the disease are not applicable for
diagnosis of individuals. More recently, deep learning, which can efficiently
analyze large-scale complex patterns in 3D brain images, has helped pave the
way for computer-aided individual diagnosis by providing accurate and automated
disease classification. Great progress has been made in classifying AD with
deep learning models developed upon increasingly available structural MRI data.
The lack of scale-matched functional neuroimaging data prevents such models
from being further improved by observing functional changes in pathophysiology.
Here we propose a potential solution by first learning a
structural-to-functional transformation in brain MRI, and further synthesizing
spatially matched functional images from large-scale structural scans. We
evaluated our approach by building computational models to discriminate
patients with AD from healthy normal subjects and demonstrated a performance
boost after combining the structural and synthesized functional brain images
into the same model. Furthermore, our regional analyses identified the temporal
lobe to be the most predictive structural-region and the parieto-occipital lobe
to be the most predictive functional-region of our model, which are both in
concordance with previous group-level neuroimaging findings. Together, we
demonstrate the potential of deep learning with large-scale structural and
synthesized functional MRI to impact AD classification and to identify AD's
neuroimaging signatures.


Abstract:
  Since most industrial control applications use PID controllers, PID tuning
and anti-windup measures are significant problems. This paper investigates
tuning the feedback gains of a PID controller via back-calculation and
automatic differentiation tools. In particular, we episodically use a cost
function to generate gradients and perform gradient descent to improve
controller performance. We provide a theoretical framework for analyzing this
non-convex optimization and establish a relationship between back-calculation
and disturbance feedback policies. We include numerical experiments on linear
systems with actuator saturation to show the efficacy of this approach.


Abstract:
  Risk is traditionally described as the expected likelihood of an undesirable
outcome, such as collisions for autonomous vehicles. Accurately predicting risk
or potentially risky situations is critical for the safe operation of
autonomous vehicles. In our previous work, we showed that risk could be
characterized by two components: 1) the probability of an undesirable outcome
and 2) an estimate of how undesirable the outcome is (loss). This paper is an
extension to our previous work. In this paper, using our trained deep
reinforcement learning model for navigating around crowds, we developed a
risk-based decision-making framework for the autonomous vehicle that integrates
the high-level risk-based path planning with the reinforcement learning-based
low-level control. We evaluated our method in a high-fidelity simulation such
as CARLA. This work can improve safety by allowing an autonomous vehicle to one
day avoid and react to risky situations.


Abstract:
  Inelastic Cooper pair tunneling across a voltage-biased Josephson junction in
series with one or more microwave cavities can generate photons via resonant
processes in which the energy lost by the Cooper pair matches that of the
photon(s) produced. We generalise previous theoretical treatments of such
systems to analyse cases where two or more different photon generation
processes are resonant simultaneously. We also explore in detail a specific
case where generation of a single photon in one cavity mode is simultaneously
resonant with the generation of two photons in a second mode. We find that the
coexistence of the two resonances leads to effective couplings between the
modes which in turn generate entanglement.


Abstract:
  Quantile regression is a field with steadily growing importance in
statistical modeling. It is a complementary method to linear regression, since
computing a range of conditional quantile functions provides a more accurate
modelling of the stochastic relationship among variables, especially in the
tails. We introduce a non-restrictive and highly flexible nonparametric
quantile regression approach based on C- and D-vine copulas. Vine copulas allow
for separate modeling of marginal distributions and the dependence structure in
the data, and can be expressed through a graph theoretical model given by a
sequence of trees. This way we obtain a quantile regression model, that
overcomes typical issues of quantile regression such as quantile crossings or
collinearity, the need for transformations and interactions of variables. Our
approach incorporates a two-step ahead ordering of variables, by maximizing the
conditional log-likelihood of the tree sequence, while taking into account the
next two tree levels. Further, we show that the nonparametric conditional
quantile estimator is consistent. The performance of the proposed methods is
evaluated in both low- and high-dimensional settings using simulated and real
world data. The results support the superior prediction ability of the proposed
models.


Abstract:
  We revisit the problem of local normality of Kraus-Polley-Reents infravacuum
representations and provide a straightforward proof based on the Araki-Yamagami
criterion. We apply this result to the theory of superselection sectors.
Namely, we extend the novel formalism of second conjugate classes and relative
normalizers to the local relativistic setting.


Abstract:
  In this essay we give a general picture about the evolution of Grohendieck's
ideas regarding the notion of space. Starting with his fundamental work in
algebraic geometry, where he introduces schemes and toposes as generalizations
of classical notions of spaces, passing through tame topology and ending with
the formulation of a geometry of forms, we show how the ideas of Grothendieck
evolved from pure mathematical considerations to physical and philosophical
questions about the nature and structure of space and its mathematical models.


Abstract:
  Manipulating and cooling small particles with light are long-standing
challenges in many areas of science, from the foundations of physics to
applications in biology and nano-technology. Light fields can, in particular,
be used to isolate mesoscopic particles from their environment by levitating
them optically. These levitated particles of micron size and smaller exhibit
pristine mechanical resonances and can be cooled down to their motional quantum
ground state. Significant roadblocks on the way to scale up levitation from a
single to multiple particles in close proximity are the requirements to
constantly monitor the particles' positions as well as to engineer light fields
that react fast and appropriately to their displacements. Given the complexity
of light scattering between particles, each of these two challenges currently
seems insurmountable already in itself. Here, we present an approach that
solves both problems at once by forgoing any local information on the
particles. Instead, our procedure is based on the far-field information stored
in the scattering matrix and its changes with time. We demonstrate how to
compose from these ingredients a linear energy-shift operator, whose maximal or
minimal eigenstates are identified as the incoming wavefronts that implement
the most efficient heating or cooling of a moving ensemble of
arbitrarily-shaped levitated particles, respectively. We expect this optimal
approach to be a game-changer for the collective manipulation of multiple
particles on-the-fly, i.e., without the necessity to track them. An
experimental implementation is suggested based on stroboscopic scattering
matrix measurements and a time-adaptive injection of the optimal light fields.


Abstract:
  We study the utility of the lexical translation model (IBM Model 1) for
English text retrieval, in particular, its neural variants that are trained
end-to-end. We use the neural Model1 as an aggregator layer applied to
context-free or contextualized query/document embeddings. This new approach to
design a neural ranking system has benefits for effectiveness, efficiency, and
interpretability. Specifically, we show that adding an interpretable neural
Model 1 layer on top of BERT-based contextualized embeddings (1) does not
decrease accuracy and/or efficiency; and (2) may overcome the limitation on the
maximum sequence length of existing BERT models. The context-free neural Model
1 is less effective than a BERT-based ranking model, but it can run efficiently
on a CPU (without expensive index-time precomputation or query-time operations
on large tensors). Using Model 1 we produced best neural and non-neural runs on
the MS MARCO document ranking leaderboard in late 2020.


Abstract:
  Teaching cases based on stories about real organizations are a powerful means
of storytelling. These cases closely parallel real-world situations and can
deliver on pedagogical objectives as writers can use their creative license to
craft a storyline that better focuses on the specific principles, concepts, and
challenges they want to address in their teaching. The method instigates
critical discussion, draws out relevant experiences from students, encourages
questioning of accepted practices, and creates dialogue between theory and
practice. We present Horizon, a case study of a firm that suffers a
catastrophic incident of Intellectual Property (IP) theft. The case study was
developed to teach information security management (ISM) principles in key
areas such as strategy, risk, policy and training to postgraduate Information
Systems and Information Technology students at the University of Melbourne,
Australia.


Abstract:
  This article constructs the Shiromizu-Maeda-Sasaki 3-brane in the context of
five-dimensional Einstein-Chern-Simons gravity. We started by considering
Israel's junction condition for Lovelock's theory to read the junctions
conditions for AdS-Chern-Simons gravity. Using the S-expansion procedure, we
mapped the AdS-Chern-Simons junction conditions to Einstein-Chern-Simons
gravity, allowing us to derive effective four-dimensional Einstein-Chern-Simons
field equations.


Abstract:
  In this paper we consider the existence and stability of multi-spike
solutions to the fractional Gierer-Meinhardt model with periodic boundary
conditions. In particular we rigorously prove the existence of symmetric and
asymmetric two-spike solutions using a Lyapunov-Schmidt reduction. The linear
stability of these two-spike solutions is then rigorously analyzed and found to
be determined by the eigenvalues of a certain $2\times 2$ matrix. Our rigorous
results are complemented by formal calculations of $N$-spike solutions using
the method of matched asymptotic expansions. In addition, we explicitly
consider examples of one- and two-spike solutions for which we numerically
calculate their relevant existence and stability thresholds. By considering a
one-spike solution we determine that the introduction of fractional diffusion
for the activator or inhibitor will respectively destabilize or stabilize a
single spike solution with respect to oscillatory instabilities. Furthermore,
when considering two-spike solutions we find that the range of parameter values
for which asymmetric two-spike solutions exist and for which symmetric
two-spike solutions are stable with respect to competition instabilities is
expanded with the introduction of fractional inhibitor diffusivity. However our
calculations indicate that asymmetric two-spike solutions are always linearly
unstable.


Abstract:
  We present a language model that combines a large parametric neural network
(i.e., a transformer) with a non-parametric episodic memory component in an
integrated architecture. Our model uses extended short-term context by caching
local hidden states -- similar to transformer-XL -- and global long-term memory
by retrieving a set of nearest neighbor tokens at each timestep. We design a
gating function to adaptively combine multiple information sources to make a
prediction. This mechanism allows the model to use either local context,
short-term memory, or long-term memory (or any combination of them) on an ad
hoc basis depending on the context. Experiments on word-based and
character-based language modeling datasets demonstrate the efficacy of our
proposed method compared to strong baselines.


Abstract:
  This paper considers mean field games in a multi-agent Markov decision
process (MDP) framework. Each player has a continuum state and binary action,
and benefits from the improvement of the condition of the overall population.
Based on an infinite horizon discounted individual cost, we show existence of a
stationary equilibrium, and prove its uniqueness under a positive externality
condition. We further analyze comparative statics of the stationary equilibrium
by quantitatively determining the impact of the effort cost.


Abstract:
  We present a new method, exact in $\alpha'$, to explicitly compute string
tree-level amplitudes involving one massive state and any number of massless
ones. This construction relies on the so-called twisted heterotic string, which
admits only gauge multiplets, a gravitational multiplet, and a single massive
supermultiplet in its spectrum. In this simplified model, we determine the
moduli-space integrand of all amplitudes with one massive state using
Berends-Giele currents of the gauge multiplet. These integrands are then
straightforwardly mapped to gravitational amplitudes in the twisted heterotic
string and to the corresponding massive amplitudes of the conventional type-I
and type-II superstrings.


Abstract:
  Context: It is not uncommon for a new team member to join an existing Agile
software development team, even after development has started. This new team
member faces a number of challenges before they are integrated into the team
and can contribute productively to team progress. Ideally, each newcomer should
be supported in this transition through an effective team onboarding program,
although prior evidence suggests that this is challenging for many
organisations. Objective: We seek to understand how Agile teams address the
challenge of team onboarding in order to inform future onboarding design.
Method: We conducted an interview survey of eleven participants from eight
organisations to investigate what onboarding activities are common across Agile
software development teams. We also identify common goals of onboarding from a
synthesis of literature. A repertory grid instrument is used to map the
contributions of onboarding techniques to onboarding goals. Results: Our study
reveals that a broad range of team onboarding techniques, both formal and
informal, are used in practice. It also shows that particular techniques that
have high contributions to a given goal or set of goals. Conclusions: In
presenting a set of onboarding goals to consider and an evidence-based
mechanism for selecting techniques to achieve the desired goals it is expected
that this study will contribute to better-informed onboarding design and
planning. An increase in practitioner awareness of the options for supporting
new team members is also an expected outcome.


Abstract:
  Immersive viewing is emerging as the next interface evolution for
human-computer interaction. A truly wireless immersive application necessitates
immense data delivery with ultra-low latency, raising stringent requirements
for next-generation wireless networks. A potential solution for addressing
these requirements is through the efficient usage of in-device storage and
computation capabilities. This paper proposes a novel location-based coded
cache placement and delivery scheme, which leverages the nested code modulation
(NCM) to enable multi-rate multicasting transmission. To provide a uniform
quality of experience in different network locations, we formulate a linear
programming cache allocation problem. Next, based on the users' spatial
realizations, we adopt an NCM based coded delivery algorithm to efficiently
serve a distinct group of users during each transmission. Numerical results
demonstrate that the proposed location-based delivery method significantly
increases transmission efficiency compared to state of the art.


Abstract:
  The discovery of the disentanglement properties of the latent space in GANs
motivated a lot of research to find the semantically meaningful directions on
it. In this paper, we suggest that the disentanglement property is closely
related to the geometry of the latent space. In this regard, we propose an
unsupervised method for finding the semantic-factorizing directions on the
intermediate latent space of GANs based on the local geometry. Intuitively, our
proposed method, called Local Basis, finds the principal variation of the
latent space in the neighborhood of the base latent variable. Experimental
results show that the local principal variation corresponds to the semantic
factorization and traversing along it provides strong robustness to image
traversal. Moreover, we suggest an explanation for the limited success in
finding the global traversal directions in the latent space, especially W-space
of StyleGAN2. We show that W-space is warped globally by comparing the local
geometry, discovered from Local Basis, through the metric on Grassmannian
Manifold. The global warpage implies that the latent space is not well-aligned
globally and therefore the global traversal directions are bound to show
limited success on it.


Abstract:
  Developers often encounter unfamiliar code during software maintenance which
consumes a significant amount of time for comprehension, especially for novice
programmers. Automated techniques that analyze a source code and present key
information to the developers can lead to an effective comprehension of the
code. Researchers have come up with automated code summarization techniques
that focus on code summarization by generating brief summaries rather than
aiding its comprehension. Existing debuggers represent the execution states of
the program but they do not show the complete execution at a single point.
Studies have revealed that the effort required for program comprehension can be
reduced if novice programmers are provided with worked examples. Hence, we
propose COSPEX (Comprehension using Summarization via Program Execution) - an
Atom plugin that dynamically extracts key information for every line of code
executed and presents it to the developers in the form of an interactive
example-like dynamic information instance. As a preliminary evaluation, we
presented 14 undergraduates having Python programming experience up to 1 year
with a code comprehension task in a user survey. We observed that COSPEX helped
novice programmers in program comprehension and improved their understanding of
the code execution. The source code and tool are available at:
https://bit.ly/3utHOBM, and the demo on Youtube is available at:
https://bit.ly/2Sp08xQ.


Abstract:
  To enable large-scale Internet of Things (IoT) deployment, Low-power
wide-area networking (LPWAN) has attracted a lot of research attention with the
design objectives of low-power consumption, wide-area coverage, and low cost.
In particular, long battery lifetime is central to these technologies since
many of the IoT devices will be deployed in hard-toaccess locations. Prediction
of the battery lifetime depends on the accurate modelling of power consumption.
This paper presents detailed power consumption models for two cellular IoT
technologies: Narrowband Internet of Things (NB-IoT) and Long Term Evolution
for Machines (LTE-M). A comprehensive power consumption model based on User
Equipment (UE) states and procedures for device battery lifetime estimation is
presented. An IoT device power measurement testbed has been setup and the
proposed model has been validated via measurements with different coverage
scenarios and traffic configurations, achieving the modelling inaccuracy within
5%. The resulting estimated battery lifetime is promising, showing that the
10-year battery lifetime requirement specified by 3GPP can be met with proper
configuration of traffic profile, transmission, and network parameters.


Abstract:
  Most of the existing video self-supervised methods mainly leverage temporal
signals of videos, ignoring that the semantics of moving objects and
environmental information are all critical for video-related tasks. In this
paper, we propose a novel self-supervised method for video representation
learning, referred to as Video 3D Sampling (V3S). In order to sufficiently
utilize the information (spatial and temporal) provided in videos, we
pre-process a video from three dimensions (width, height, time). As a result,
we can leverage the spatial information (the size of objects), temporal
information (the direction and magnitude of motions) as our learning target. In
our implementation, we combine the sampling of the three dimensions and propose
the scale and projection transformations in space and time respectively. The
experimental results show that, when applied to action recognition, video
retrieval and action similarity labeling, our approach improves the
state-of-the-arts with significant margins.


Abstract:
  Quantum secure data transfer is an important topic for quantum cyber
security. We propose a scheme to realize quantum secure data transfer in the
basis of quantum secure direct communication (QSDC). In this proposal, the
transmitted data is encoded in the pulse shape of a single optical qubit, which
is emitted from a trapped atom owned by the sender and received by the receiver
with another trapped atom. The encoding process can be implemented with high
fidelity by controlling the time-dependent driving pulse on the trapped atom to
manipulate the Rabi frequency in accordance with the target pulse shape of the
emitted photons. In the receiving process, we prove that, the single photon can
be absorbed with arbitrary probability by selecting appropriate driving pulse.
We also show that, based on the QSDC protocol, the data transfer process is
immune to the individual attacks.


Abstract:
  Rate-splitting multiple access (RSMA) has been recognized as a promising
physical layer strategy for 6G. Motivated by ever increasing popularity of
cache-enabled content delivery in wireless communications, this paper proposes
an innovative multigroup multicast transmission scheme based on RSMA for
cache-aided cloud-radio access networks (C-RAN). Our proposed scheme not only
exploits the properties of content-centric communications and local caching at
the base stations (BSs), but also incorporates RSMA to better manage
interference in multigroup multicast transmission with statistical channel
state information (CSI) known at the central processor (CP) and the BSs. At the
RSMA-enabled cloud CP, the message of each multicast group is split into a
private and a common part with the former private part being decoded by all
users in the respective group and the latter common part being decoded by
multiple users from other multicast groups. Common message decoding is done for
the purpose of mitigating the interference. In this work, we jointly optimize
the clustering of BSs and the precoding with the aim of maximizing the minimum
rate among all multicast groups to guarantee fairness serving all groups. The
problem is a mixed-integer non-linear stochastic program (MINLSP), which is
solved by a practical algorithm we proposed including a heuristic clustering
algorithm for assigning a set of BSs to serve each user followed by an
efficient iterative algorithm that combines the sample average approximation
(SAA) and weighted minimum mean square error (WMMSE) to solve the stochastic
non-convex sub-problem of precoder design. Numerical results show the explicit
max-min rate gain of our proposed transmission scheme compared to the
state-of-the-art trivial interference processing methods. Therefore, we
conclude that RSMA is a promising technique for cache-aided C-RAN.


Abstract:
  In this paper, we investigate statistics on alternating words under
correspondence between ``possible reflection paths within several layers of
glass'' and ``alternating words''. For
$v=(v_1,v_2,\cdots,v_n)\in\mathbb{Z}^{n}$, we say $P$ is a path within $n$
glass plates corresponding to $v$, if $P$ has exactly $v_i$ reflections
occurring at the $i^{\rm{th}}$ plate for all $i\in\{1,2,\cdots,n\}$. We give a
recursion for the number of paths corresponding to $v$ satisfying $v \in
\mathbb{Z}^n$ and $\sum_{i\geq 1} v_i=m$. Also, we establish recursions for
statistics around the number of paths corresponding to a given vector
$v\in\mathbb{Z}^n$ and a closed form for $n=3$. Finally, we give a equivalent
condition for the existence of path corresponding to a given vector $v$.


Abstract:
  Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Units
(GRUs) and transformers have been developed to classify time series data with
the assumption that events in a sequence are ordered. On the other hand, fewer
models have been developed for set based inputs, where order does not matter.
There are several use cases where data is given as partially-ordered sequences
because of the granularity or uncertainty of time stamps. We introduce a novel
transformer based model for such prediction tasks, and benchmark against
extensions of existing order invariant models. We also discuss how transition
probabilities between events in a sequence can be used to improve model
performance. We show that the transformer-based equal-time model outperforms
extensions of existing set models on three data sets.


Abstract:
  In this paper, we show that a simple generalization of the holographic axion
model can realize spontaneous breaking of translational symmetry by considering
a special gauge-axion higher derivative term. The finite real part and
imaginary part of the stress tensor imply that the dual boundary system is a
viscoelastic solid. By calculating quasi-normal modes and making a comparison
with predictions from the elasticity theory, we verify the existence of phonons
and pseudo-phonons, where the latter is realized by introducing a weak explicit
breaking of translational symmetry, in the transverse channel. Finally, we
discuss how the phonon dynamics affects the charge transport.


Abstract:
  In the past decades, many graph drawing techniques have been proposed for
generating aesthetically pleasing graph layouts. However, it remains a
challenging task since different layout methods tend to highlight different
characteristics of the graphs. Recently, studies on deep learning based graph
drawing algorithm have emerged but they are often not generalizable to
arbitrary graphs without re-training. In this paper, we propose a Convolutional
Graph Neural Network based deep learning framework, DeepGD, which can draw
arbitrary graphs once trained. It attempts to generate layouts by compromising
among multiple pre-specified aesthetics considering a good graph layout usually
complies with multiple aesthetics simultaneously. In order to balance the
trade-off, we propose two adaptive training strategies which adjust the weight
factor of each aesthetic dynamically during training. The quantitative and
qualitative assessment of DeepGD demonstrates that it is capable of drawing
arbitrary graphs effectively, while being flexible at accommodating different
aesthetic criteria.


Abstract:
  One-shot anonymous unselfishness in economic games is commonly explained by
social preferences, which assume that people care about the monetary payoffs of
others. However, during the last ten years, research has shown that different
types of unselfish behaviour, including cooperation, altruism, truth-telling,
altruistic punishment, and trustworthiness are in fact better explained by
preferences for following one's own personal norms - internal standards about
what is right or wrong in a given situation. Beyond better organising various
forms of unselfish behaviour, this moral preference hypothesis has recently
also been used to increase charitable donations, simply by means of
interventions that make the morality of an action salient. Here we review
experimental and theoretical work dedicated to this rapidly growing field of
research, and in doing so we outline mathematical foundations for moral
preferences that can be used in future models to better understand selfless
human actions and to adjust policies accordingly. These foundations can also be
used by artificial intelligence to better navigate the complex landscape of
human morality.


Abstract:
  Safety is a critical concern when deploying reinforcement learning agents for
realistic tasks. Recently, safe reinforcement learning algorithms have been
developed to optimize the agent's performance while avoiding violations of
safety constraints. However, few studies have addressed the non-stationary
disturbances in the environments, which may cause catastrophic outcomes. In
this paper, we propose the context-aware safe reinforcement learning (CASRL)
method, a meta-learning framework to realize safe adaptation in non-stationary
environments. We use a probabilistic latent variable model to achieve fast
inference of the posterior environment transition distribution given the
context data. Safety constraints are then evaluated with uncertainty-aware
trajectory sampling. The high cost of safety violations leads to the rareness
of unsafe records in the dataset. We address this issue by enabling prioritized
sampling during model training and formulating prior safety constraints with
domain knowledge during constrained planning. The algorithm is evaluated in
realistic safety-critical environments with non-stationary disturbances.
Results show that the proposed algorithm significantly outperforms existing
baselines in terms of safety and robustness.


Abstract:
  In 2019 P. Patak and M. Tancer obtained the following higher-dimensional
generalization of the Heawood inequality on embeddings of graphs into surfaces.
We expose this result in a short well-structured way accessible to
non-specialists in the field.
  Let $\Delta_n^k$ be the union of $k$-dimensional faces of the $n$-dimensional
simplex.
  Theorem. (a) If $\Delta_n^k$ PL embeds into the connected sum of $g$ copies
of the Cartesian product $S^k\times S^k$ of two $k$-dimensional spheres, then
$g\ge\dfrac{n-2k}{k+2}$.
  (b) If $\Delta_n^k$ PL embeds into a closed $(k-1)$-connected PL
$2k$-manifold $M$, then $(-1)^k(\chi(M)-2)\ge\dfrac{n-2k}{k+1}$.


Abstract:
  We theoretically study the correlated insulator states, quantum anomalous
Hall (QAH) states, and field-induced topological transitions between different
correlated states in twisted multilayer graphene systems. Taking twisted
bilayer-monolayer graphene and twisted double-bilayer graphene as examples, we
show that both systems stay in spin polarized, $C_{3z}$-broken insulator states
with zero Chern number at 1/2 filling of the flat bands under finite
displacement fields. In some cases these spin polarized, nematic insulator
states are in the quantum valley Hall phase by virtue of the nontrivial band
topology of the systems. The spin polarized insulator state is quasi-degenerate
with the valley polarized state if only the dominant intra-valley Coulomb
interaction is included. Such quasi-degeneracy can be split by atomic on-site
interactions such that the spin polarized, nematic state become the unique
ground state. Such a scenario applies to various twisted multilayer graphene
systems at 1/2 filling, thus can be considered as a universal mechanism.
Moreover, under vertical magnetic fields, the orbital Zeeman splittings and the
field-induced change of charge density in twisted multilayer graphene systems
would compete with the atomic Hubbard interactions, which can drive transitions
from spin polarized zero-Chern-number states to valley-polarized QAH states
with small onset magnetic fields.