Abstract:
  Isolated mechanical systems -- e.g., those floating in space, in free-fall,
or on a frictionless surface -- are able to achieve net rotation by cyclically
changing their shape, even if they have no net angular momentum. Similarly,
swimmers immersed in "perfect fluids" are able to use cyclic shape changes to
both translate and rotate even if the swimmer-fluid system has no net linear or
angular momentum. Finally, systems fully constrained by direct nonholonomic
constraints (e.g., passive wheels) can push against these constraints to move
through the world. Previous work has demonstrated that the net displacement
induced by these shape changes corresponds to the amount of *constraint
curvature* that the gaits enclose.
  To properly assess or optimize the utility of a gait, however, we must also
consider the time or resources required to execute it: A gait that produces a
small displacement per cycle, but that can be executed in a short time, may
produce a faster average velocity than a gait that produces a large
displacement per cycle, but takes much longer to complete a cycle at the same
average instantaneous effort.
  In this paper, we consider two effort-based cost functions for assessing the
costs associated with executing these cycles. For each of these cost functions,
we demonstrate that fixing the average instantaneous cost to a unit value
allows us to transform the effort costs into time-to-execute costs for any
given gait cycle. We then illustrate how the interaction between the constraint
curvature and these costs leads to characteristic geometries for optimal
cycles, in which the gait trajectories resemble elastic hoops distended from
within by internal pressures.


Abstract:
  Snapshot hyperspectral imaging can capture the 3D hyperspectral image (HSI)
with a single 2D measurement and has attracted increasing attention recently.
Recovering the underlying HSI from the compressive measurement is an ill-posed
problem and exploiting the image prior is essential for solving this ill-posed
problem. However, existing reconstruction methods always start from modeling
image prior with the 1D vector or 2D matrix and cannot fully exploit the
structurally spectral-spatial nature in 3D HSI, thus leading to a poor
fidelity. In this paper, we propose an effective high-order tensor optimization
based method to boost the reconstruction fidelity for snapshot hyperspectral
imaging. We first build high-order tensors by exploiting the spatial-spectral
correlation in HSI. Then, we propose a weight high-order singular value
regularization (WHOSVR) based low-rank tensor recovery model to characterize
the structure prior of HSI. By integrating the structure prior in WHOSVR with
the system imaging process, we develop an optimization framework for HSI
reconstruction, which is finally solved via the alternating minimization
algorithm. Extensive experiments implemented on two representative systems
demonstrate that our method outperforms state-of-the-art methods.


Abstract:
  Sparse matrices, more specifically SpGEMM kernels, are commonly found in a
wide range of applications, spanning graph-based path-finding to machine
learning algorithms (e.g., neural networks). A particular challenge in
implementing SpGEMM kernels has been the pressure placed on DRAM memory. One
approach to tackle this problem is to use an inner product method for the
SpGEMM kernel implementation. While the inner product produces fewer
intermediate results, it can end up saturating the memory bandwidth, given the
high number of redundant fetches of the input matrix elements. Using an outer
product-based SpGEMM kernel can reduce redundant fetches, but at the cost of
increased overhead due to extra computation and memory accesses for
producing/managing partial products.
  In this thesis, we introduce a novel SpGEMM kernel implementation based on
the row-wise product approach. We leverage atomic instructions to merge
intermediate partial products as they are generated. The use of atomic
instructions eliminates the need to create partial product matrices.
  To evaluate our row-wise product approach, we map an optimized SpGEMM kernel
to a custom accelerator designed to accelerate graph-based applications. The
targeted accelerator is an experimental system named PIUMA, being developed by
Intel. PIUMA provides several attractive features, including fast context
switching, user-configurable caches, globally addressable memory, non-coherent
caches, and asynchronous pipelines. We tailor our SpGEMM kernel to exploit many
of the features of the PIUMA fabric.
  This thesis compares our SpGEMM implementation against prior solutions, all
mapped to the PIUMA framework. We briefly describe some of the PIUMA
architecture features and then delve into the details of our optimized SpGEMM
kernel. Our SpGEMM kernel can achieve 9.4x speedup as compared to competing
approaches.


Abstract:
  The Gamma Factory (GF) is an ambitious proposal, currently explored within
the CERN Physics Beyond Colliders program, for a source of photons with
energies up to $\approx 400\,$MeV and photon fluxes (up to $\approx 10^{17}$
photons per second) exceeding those of the currently available gamma sources by
orders of magnitude. The high-energy (secondary) photons are produced via
resonant scattering of the primary laser photons by highly relativistic
partially-stripped ions circulating in the accelerator. The secondary photons
are emitted in a narrow cone and the energy of the beam can be monochromatized,
eventually down to the $\approx1$ ppm level, via collimation, at the expense of
the photon flux. This paper surveys the new opportunities that may be afforded
by the GF in nuclear physics and related fields.


Abstract:
  The application of strain to 2D materials allows manipulating the electronic,
magnetic, and thermoelectric properties. These physical properties are
sensitive to slight variations induced by tensile and compressive strain and to
the uniaxial strain direction. Herein, we take advantage of the reversible
semiconductor-metal transition observed in certain monolayers to propose a
hetero-bilayer device. We propose to pill up phosphorene (layered black
phosphorus) and carbon monosulfide monolayers. In the first, such transition
appears for positive strain, while the second appears for negative strain. Our
first-principle calculations show that depending on the direction of the
applied uniaxial strain; it is possible to achieve reversible control in the
layer that behaves as an electronic conductor while the other layer remains as
a thermal conductor. The described strain-controlled selectivity could be used
in the design of novel devices.


Abstract:
  The recently proposed high-order TENO scheme [Fu et al., Journal of
Computational Physics, 305, pp.333-359] has shown great potential in predicting
complex fluids owing to the novel weighting strategy, which ensures the
high-order accuracy, the low numerical dissipation, and the sharp
shock-capturing capability. However, the applications are still restricted to
simple geometries with Cartesian or curvilinear meshes. In this work, a new
class of high-order shock-capturing TENO schemes for unstructured meshes are
proposed. Similar to the standard TENO schemes and some variants of WENO
schemes, the candidate stencils include one large stencil and several small
third-order stencils. Following a strong scale-separation procedure, a tailored
novel ENO-like stencil selection strategy is proposed such that the high-order
accuracy is restored in smooth regions by selecting the candidate
reconstruction on the large stencil while the ENO property is enforced near
discontinuities by adopting the candidate reconstruction from smooth small
stencils. The nonsmooth stencils containing genuine discontinuities are
explicitly excluded from the final reconstruction, leading to excellent
numerical stability. Different from the WENO concept, such unique sharp stencil
selection retains the low numerical dissipation without sacrificing the
shock-capturing capability. The newly proposed framework enables arbitrarily
high-order TENO reconstructions on unstructured meshes. For conceptual
verification, the TENO schemes with third- to sixth-order accuracy are
constructed. Without parameter tuning case by case, the performance of the
proposed TENO schemes is demonstrated by examining a set of benchmark cases
with broadband flow length scales.


Abstract:
  Reading is a complex process which requires proper understanding of texts in
order to create coherent mental representations. However, comprehension
problems may arise due to hard-to-understand sections, which can prove
troublesome for readers, while accounting for their specific language skills.
As such, steps towards simplifying these sections can be performed, by
accurately identifying and evaluating difficult structures. In this paper, we
describe our approach for the SemEval-2021 Task 1: Lexical Complexity
Prediction competition that consists of a mixture of advanced NLP techniques,
namely Transformer-based language models, pre-trained word embeddings, Graph
Convolutional Networks, Capsule Networks, as well as a series of hand-crafted
textual complexity features. Our models are applicable on both subtasks and
achieve good performance results, with a MAE below 0.07 and a Person
correlation of .73 for single word identification, as well as a MAE below 0.08
and a Person correlation of .79 for multiple word targets. Our results are just
5.46% and 6.5% lower than the top scores obtained in the competition on the
first and the second subtasks, respectively.


Abstract:
  Negative viscosity seems to be an impossible parameter for any thermodynamic
system. But for some special boundary conditions the viscosity of a fluid has
apparently become negative, like for secondary flow of a fluid or in a plasma
flow interacting with a dominant magnetic field. This work studied the effect
of negative viscosity for a fluid flow over a cylinder. Four different
viscosities are considered, in which the positive viscosities of Air and CO2
has been considered at 300 K temperature and their negative pair of viscosities
are considered in this work. The results show a vast difference in the vortex
formation and pattern. General incompressible Navier Stokes equation has been
employed for the analysis. The thermodynamic feasibility, vortex formation,
variation of X direction velocity, variation of the VA factor and variation of
drag coefficient has been studied subsequently in this work. SimFlow CFD
software has been used in this work, which uses the OpenFOAM solver.


Abstract:
  We introduce operational quantum tasks based on betting with risk-aversion --
or quantum betting tasks for short -- inspired by standard quantum state
discrimination and classical horse betting with risk-aversion and side
information. In particular, we introduce the operational tasks of quantum state
betting (QSB), noisy quantum state betting (nQSB), and quantum channel betting
(QCB) played by gamblers with different risk tendencies. We prove that the
advantage that informative measurements (non-constant channels) provide in QSB
(nQSB) is exactly characterised by Arimoto's $\alpha$-mutual information, with
the order $\alpha$ determining the risk aversion of the gambler. More
generally, we show that Arimoto-type information-theoretic quantities
characterise the advantage that resourceful objects offer at playing quantum
betting tasks when compared to resourceless objects, for general quantum
resource theories (QRTs) of measurements, channels, states, and
state-measurement pairs, with arbitrary resources. In limiting cases, we show
that QSB (QCB) recovers the known tasks of quantum state (channel)
discrimination when $\alpha \rightarrow \infty$, and quantum state (channel)
exclusion when $\alpha \rightarrow -\infty$. Inspired by these connections, we
also introduce new quantum R\'enyi divergences for measurements, and derive a
new family of resource monotones for the QRT of measurement informativeness.
This family of resource monotones recovers in the same limiting cases as above,
the generalised robustness and the weight of informativeness. Altogether, these
results establish a broad and continuous family of four-way correspondences
between operational tasks, mutual information measures, quantum R\'enyi
divergences, and resource monotones, that can be seen to generalise two
limiting correspondences that were recently discovered for the QRT of
measurement informativeness.


Abstract:
  We present a perturbative approach to solving the three-nucleon continuum
Faddeev equation. This approach is particularly well suited to dealing with
variable strengths of contact terms in a chiral three-nucleon force. We use
examples of observables in the elastic nucleon-deuteron scattering as well as
in the deuteron breakup reaction to demonstrate high precision of the proposed
procedure and its capability to reproduce exact results. A significant
reduction of computer time achieved by the perturbative approach in comparison
to exact treatment makes this approach valuable for fine-tuning of the
three-nucleon Hamiltonian parameters.


Abstract:
  We study the limit behaviour of singularly-perturbed elliptic functionals of
the form \[ \mathcal F_k(u,v)=\int_A v^2\,f_k(x,\nabla
u)\.dx+\frac{1}{\varepsilon_k}\int_A g_k(x,v,\varepsilon_k\nabla v)\.dx\,, \]
where $u$ is a vector-valued Sobolev function, $v \in [0,1]$ a phase-field
variable, and $\varepsilon_k>0$ a singular-perturbation parameter, i.e.,
$\varepsilon_k \to 0$, as $k\to +\infty$.
  Under mild assumptions on the integrands $f_k$ and $g_k$, we show that if
$f_k$ grows superlinearly in the gradient-variable, then the functionals
$\mathcal F_k$ $\Gamma$-converge (up to subsequences) to a brittle
energy-functional, i.e., to a free-discontinuity functional whose surface
integrand does not depend on the jump-amplitude of $u$. This result is achieved
by providing explicit asymptotic formulas for the bulk and surface integrands
which show, in particular, that volume and surface term in $\mathcal F_k$
decouple in the limit.
  The abstract $\Gamma$-convergence analysis is complemented by a stochastic
homogenisation result for stationary random integrands.


Abstract:
  We characterize stable differential-algebraic equations (DAEs) using a
generalized Lyapunov inequality. The solution of this inequality is then used
to rewrite stable DAEs as dissipative Hamiltonian (dH) DAEs on the subspace
where the solutions evolve. Conversely, we give sufficient conditions
guaranteeing stability of dH DAEs. Further, for stabilizable descriptor systems
we construct solutions of generalized algebraic Bernoulli equations which can
then be used to rewrite these systems as pH descriptor systems. Furthermore, we
show how to describe the stable and stabilizable systems using Dirac and
Lagrange structures.


Abstract:
  We consider the decay of the false vacuum, realised within a quantum quench
into an anti-confining regime of the Ising spin chain with a magnetic field
opposite to the initial magnetisation. Although the effective linear potential
between the domain walls is repulsive, the time evolution of correlations still
shows a suppression of the light cone and a reduction of vacuum decay. The
suppressed decay is a lattice effect, and can be assigned to emergent Bloch
oscillations.


Abstract:
  For compact, isometrically embedded Riemannian manifolds $ N \hookrightarrow
\mathbb{R}^L$, we introduce a fourth-order version of the wave map equation. By
energy estimates, we prove an priori estimate for smooth local solutions in the
energy subcritical dimension $ n = 1,2$. The estimate excludes blow-up of a
Sobolev norm in finite existence times. In particular, combining this with
recent work of local well-posedness of the Cauchy problem, it follows that for
smooth initial data with compact support, there exists a (smooth) unique global
solution in dimension $n = 1,2$. We also give a proof of the uniqueness of
solutions that are bounded in these Sobolev norms.


Abstract:
  Hjorth, assuming ${\sf{AD+ZF+DC}}$, showed that there is no sequence of
length $\omega_2$ consisting of distinct $\Sigma^1_2$-sets. We show that the
same theory implies that for $n\geq 0$, there is no sequence of length
$\delta^1_{2n+2}$ consisting of distinct $\Sigma^1_{2n+2}$ sets. The theorem
settles Question 30.21 of Kanamori, which was also conjectured by Kechris.


Abstract:
  In this paper, we introduce a definition of Fenchel conjugate and Fenchel
biconjugate on Hadamard manifolds based on the tangent bundle. Our definition
overcomes the inconvenience that the conjugate depends on the choice of a
certain point on the manifold, as previous definitions required. On the other
hand, this new definition still possesses properties known to hold in the
Euclidean case. It even yields a broader interpretation of the Fenchel
conjugate in the Euclidean case itself. Most prominently, our definition of the
Fenchel conjugate provides a Fenchel-Moreau Theorem for geodesically convex,
proper, lower semicontinuous functions. In addition, this framework allows us
to develop a theory of separation of convex sets on Hadamard manifolds, and a
strict separation theorem is obtained.


Abstract:
  Several areas have been improved with Deep Learning during the past years.
For non-safety related products adoption of AI and ML is not an issue, whereas
in safety critical applications, robustness of such approaches is still an
issue. A common challenge for Deep Neural Networks (DNN) occur when exposed to
out-of-distribution samples that are previously unseen, where DNNs can yield
high confidence predictions despite no prior knowledge of the input.
  In this paper we analyse two supervisors on two well-known DNNs with varied
setups of training and find that the outlier detection performance improves
with the quality of the training procedure. We analyse the performance of the
supervisor after each epoch during the training cycle, to investigate
supervisor performance as the accuracy converges. Understanding the
relationship between training results and supervisor performance is valuable to
improve robustness of the model and indicates where more work has to be done to
create generalized models for safety critical applications.


Abstract:
  The temperature dependencies of the excess conductivity $\sigma'(T)$ and
possible pseudogap (PG), in a Dy$_{0.6}$Y$_{0.4}$Rh$_{3.85}$Ru$_{0.15}$B$_4$
polycrystal were studied for the first time. It was shown that $\sigma'(T)$
near T$_{c}$ is well described by the Aslamazov Larkin fluctuation theory,
demonstrating a crossover with increasing temperature. Using the crossover
temperature $T_0$, the coherence length along the c axis $\xi_c(0)$, was
determined. Above the level of $T_{2D}>T_{0}$, an unusual dependence
$\sigma'(T)$ was found, which is not described by the fluctuation theories in
the range from $T_{0}$ to $T_{FM}$, at which a ferromagnetic transition occurs.
The range in which superconducting fluctuations exist is apparently quite
narrow and amounts to $\Delta T_{fl}=2.8 K$. The resulting temperature
dependence of the PG parameter $\Delta^*(T)$ has the form typical of magnetic
superconductors with features at $T_{max}=154 K$ and the temperature of a
possible structural transition at $T_{s}=95 K$. Below $T_{s}$, dependence
$\Delta^*{T}$ has a shape typical for PG in cuprates, which suggests that the
PG state can be realized in Dy$_{0.6}$Y$_{0.4}$Rh$_{3.85}$Ru$_{0.15}$B$_4$ in
this temperature range. Comparison of $\Delta^*(T)$ with the Peters Bauer
theory made it possible to determine the density of local pairs ~0.35, near
T$_{c}$, which is 1.17 times greater than in optimally doped
YBa$_{2}$Cu$_{3}$O$_{7-\delta}$ single crystals.


Abstract:
  Tracking multiple objects in videos relies on modeling the spatial-temporal
interactions of the objects. In this paper, we propose a solution named
TransMOT, which leverages powerful graph transformers to efficiently model the
spatial and temporal interactions among the objects. TransMOT effectively
models the interactions of a large number of objects by arranging the
trajectories of the tracked objects as a set of sparse weighted graphs, and
constructing a spatial graph transformer encoder layer, a temporal transformer
encoder layer, and a spatial graph transformer decoder layer based on the
graphs. TransMOT is not only more computationally efficient than the
traditional Transformer, but it also achieves better tracking accuracy. To
further improve the tracking speed and accuracy, we propose a cascade
association framework to handle low-score detections and long-term occlusions
that require large computational resources to model in TransMOT. The proposed
method is evaluated on multiple benchmark datasets including MOT15, MOT16,
MOT17, and MOT20, and it achieves state-of-the-art performance on all the
datasets.


Abstract:
  The orthant model is a directed percolation model on $\mathbb{Z}^d$, in which
all clusters are infinite. We prove a sharp threshold result for this model: if
$p$ is larger than the critical value above which the cluster of $0$ is
contained in a cone, then the shift from $0$ that is required to contain the
cluster of $0$ in that cone is exponentially small. As a consequence, above
this critical threshold, a shape theorem holds for the cluster of $0$, as well
as ballisiticity of the random walk on this cluster.


Abstract:
  Having engaging and informative conversations with users is the utmost goal
for open-domain conversational systems. Recent advances in transformer-based
language models and their applications to dialogue systems have succeeded to
generate fluent and human-like responses. However, they still lack control over
the generation process towards producing contentful responses and achieving
engaging conversations. To achieve this goal, we present \textbf{DiSCoL}
(\textbf{Di}alogue \textbf{S}ystems through \textbf{Co}versational
\textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue
system that leverages conversational lines (briefly \textbf{convlines}) as
controllable and informative content-planning elements to guide the generation
model produce engaging and informative responses. Two primary modules in
DiSCoL's pipeline are conditional generators trained for 1) predicting relevant
and informative convlines for dialogue contexts and 2) generating high-quality
responses conditioned on the predicted convlines. Users can also change the
returned convlines to \textit{control} the direction of the conversations
towards topics that are more interesting for them. Through automatic and human
evaluations, we demonstrate the efficiency of the convlines in producing
engaging conversations.


Abstract:
  There are multiple mappings that can be used to generate what we call the
'edge geometry' of a regular N-gon, but they are all based on piecewise
isometries acting on the extended edges of N to form a 'singularity' set W.
This singularity set is also known as the 'web' because it is connected and
consists of rays or line segments, with possible accumulation points in the
limit. We will use three such maps here, all of which appear to share the same
local geometry of W. These mappings are the outer-billiards map Tau, the
digital-filter map Df and the 'dual-center' map Dc. In 'Outer-billiards,
digital filters and kicked Hamiltonians' (arXiv:1206.5223) we show that the Df
and Dc maps are equivalent to a 'shear and rotation' in a toral space and in
the complex plane respectively, and in 'First Families of Regular Polygons and
their Mutations' (arXiv:1612.09295) we show that the web for Tau can also be
reduced to a shear and rotation. This equivalence of maps supports the premise
that this web geometry is inherent in the N-gon. Here we describe the edge
geometry up to N = 25 and in Part 2 this will be extended to N = 50. In all
cases this geometry defines an invariant region local to N. Typically this
region contains multiple S[k] 'tiles' from the First Family of N, but our
emphasis is on the S[1] and S[2] tiles adjacent to N. Since the web evolves in
a multi-step fashion, it is possible to make predictions about the
'next-generation' tiles which will survive in the early web of S[1] and S[2].
The Edge Conjecture defines just 8 classes of N-gons based on this edge
geometry. Since the webs are recursive these predictions have long-term
implications.


Abstract:
  When solving a complex task, humans will spontaneously form teams and to
complete different parts of the whole task, respectively. Meanwhile, the
cooperation between teammates will improve efficiency. However, for current
cooperative MARL methods, the cooperation team is constructed through either
heuristics or end-to-end blackbox optimization. In order to improve the
efficiency of cooperation and exploration, we propose a structured
diversification emergence MARL framework named {\sc{Rochico}} based on
reinforced organization control and hierarchical consensus learning.
{\sc{Rochico}} first learns an adaptive grouping policy through the
organization control module, which is established by independent multi-agent
reinforcement learning. Further, the hierarchical consensus module based on the
hierarchical intentions with consensus constraint is introduced after team
formation. Simultaneously, utilizing the hierarchical consensus module and a
self-supervised intrinsic reward enhanced decision module, the proposed
cooperative MARL algorithm {\sc{Rochico}} can output the final diversified
multi-agent cooperative policy. All three modules are organically combined to
promote the structured diversification emergence. Comparative experiments on
four large-scale cooperation tasks show that {\sc{Rochico}} is significantly
better than the current SOTA algorithms in terms of exploration efficiency and
cooperation strength.


Abstract:
  Reducing the complexity of the pipeline of instance segmentation is crucial
for real-world applications. This work addresses this issue by introducing an
anchor-box free and single-shot instance segmentation framework, termed
PolarMask, which reformulates the instance segmentation problem as predicting
the contours of objects in the polar coordinate, with several appealing
benefits. (1) The polar representation unifies instance segmentation (masks)
and object detection (bounding boxes) into a single framework, reducing the
design and computational complexity. (2) Two modules are carefully designed
(i.e. soft polar centerness and polar IoU loss) to sample high-quality center
examples and optimize polar contour regression, making the performance of
PolarMask does not depend on the bounding box prediction results and thus
becomes more efficient in training. (3) PolarMask is fully convolutional and
can be easily embedded into most off-the-shelf detection methods. To further
improve the accuracy of the framework, a Refined Feature Pyramid is introduced
to further improve the feature representation at different scales, termed
PolarMask++. Extensive experiments demonstrate the effectiveness of both
PolarMask and PolarMask++, which achieve competitive results on instance
segmentation in the challenging COCO dataset with single-model and single-scale
training and testing, as well as new state-of-the-art results on rotate text
detection and cell segmentation. We hope the proposed polar representation can
provide a new perspective for designing algorithms to solve single-shot
instance segmentation. The codes and models are available at:
github.com/xieenze/PolarMask.


Abstract:
  Ultra intense lasers are a promising source of energetic ions for various
applications. An interesting approach described in Ferri et al. 2019 argues
from Particle-in-Cell simulations that using two laser pulses of half energy
(half intensity) arriving with close to 45 degrees angle of incidence is
significantly more effective at accelerating ions than one pulse at full energy
(full intensity). For a variety of reasons, at the time of this writing there
has not yet been a true experimental confirmation of this enhancement. In this
paper we perform 2D Particle-in-Cell simulations to examine if a milliJoule
class, 5x10^18 W cm^-2 peak intensity laser system could be used for such a
demonstration experiment. Laser systems in this class can operate at a kHz rate
which should be helpful for addressing some of the challenges of performing
this experiment. Despite investigating a 3.5 times lower intensity than Ferri
et al. 2019 did, we find that the double pulse approach enhances the peak
proton energy and the energy conversion to protons by a factor of about three
compared to a single laser pulse with the same total laser energy. We also
comment on the nature of the enhancement and describe simulations that examine
how the enhancement may depend on the spatial or temporal alignment of the two
pulses.


Abstract:
  Ensuring performance robustness for a variety of situations that can occur in
real-world environments is one of the challenging tasks in sound event
classification. One of the unpredictable and detrimental factors in
performance, especially in indoor environments, is reverberation. To alleviate
this problem, we propose a conditioning method that provides room impulse
response (RIR) information to help the network become less sensitive to
environmental information and focus on classifying the desired sound.
Experimental results show that the proposed method successfully reduced
performance degradation caused by the reverberation of the room. In particular,
our proposed method works even with similar RIR that can be inferred from the
room type rather than the exact one, which has the advantage of potentially
being used in real-world applications.


Abstract:
  The aims of this paper are: 1) to identify "worst smells", i.e., bad smells
that never have a good reason to exist, 2) to determine the frequency,
change-proneness, and severity associated with worst smells, and 3) to identify
the "worst reasons", i.e., the reasons for introducing these worst smells in
the first place. To achieve these aims we ran a survey with 71 developers. We
learned that 80 out of 314 catalogued code smells are "worst"; that is,
developers agreed that these 80 smells should never exist in any code base. We
then checked the frequency and change-proneness of these worst smells on 27
large Apache open-source projects. Our results show insignificant differences,
in both frequency and change proneness, between worst and non-worst smells.
That is to say, these smells are just as damaging as other smells, but there is
never any justifiable reason to introduce them. Finally, in follow-up phone
interviews with five developers we confirmed that these smells are indeed
worst, and the interviewees proposed seven reasons for why they may be
introduced in the first place. By explicitly identifying these seven reasons,
project stakeholders can, through quality gates or reviews, ensure that such
smells are never accepted in a code base, thus improving quality without
compromising other goals such as agility or time to market.


Abstract:
  Content-based image retrieval (CBIR) systems on pixel domain use low-level
features, such as colour, texture and shape, to retrieve images. In this
context, two types of image representations i.e. local and global image
features have been studied in the literature. Extracting these features from
pixel images and comparing them with images from the database is very
time-consuming. Therefore, in recent years, there has been some effort to
accomplish image analysis directly in the compressed domain with lesser
computations. Furthermore, most of the images in our daily transactions are
stored in the JPEG compressed format. Therefore, it would be ideal if we could
retrieve features directly from the partially decoded or compressed data and
use them for retrieval. Here, we propose a unified model for image retrieval
which takes DCT coefficients as input and efficiently extracts global and local
features directly in the JPEG compressed domain for accurate image retrieval.
The experimental findings indicate that our proposed model performed similarly
to the current DELG model which takes RGB features as an input with reference
to mean average precision while having a faster training and retrieval speed.


Abstract:
  End-to-end (E2E) spoken language understanding (SLU) can infer semantics
directly from speech signal without cascading an automatic speech recognizer
(ASR) with a natural language understanding (NLU) module. However, paired
utterance recordings and corresponding semantics may not always be available or
sufficient to train an E2E SLU model in a real production environment. In this
paper, we propose to unify a well-optimized E2E ASR encoder (speech) and a
pre-trained language model encoder (language) into a transformer decoder. The
unified speech-language pre-trained model (SLP) is continually enhanced on
limited labeled data from a target domain by using a conditional masked
language model (MLM) objective, and thus can effectively generate a sequence of
intent, slot type, and slot value for given input speech in the inference. The
experimental results on two public corpora show that our approach to E2E SLU is
superior to the conventional cascaded method. It also outperforms the present
state-of-the-art approaches to E2E SLU with much less paired data.


Abstract:
  The mission statement (MS) is the most used organizational strategic planning
tool worldwide. The relationship between an MS and an organizations financial
performance has been shown to be significantly positive, albeit small. However,
an MSs relationship to the macroeconomic environment and to organizational
innovation has not been investigated. We implemented a Structural Equation
Modeling using the SCImago Institutional Ranking (SIR) as a global baseline
sample and assessment of organizational research and innovation (RandI), an
automated MS content analysis, and the Economic Complexity Index (ECI) as a
comprehensive macroeconomic environment measure. We found that the median
performance of organizations that do not report an MS is significantly higher
than that of reporting organizations, and that a path-dependence driven by the
State's long-term view and investment is a better explanatory variable for
organizational RandI performance than the MS construct or the intermediate-term
macroeconomic environment.


Abstract:
  Neural architecture search (NAS) and hyperparameter optimization (HPO) make
deep learning accessible to non-experts by automatically finding the
architecture of the deep neural network to use and tuning the hyperparameters
of the used training pipeline. While both NAS and HPO have been studied
extensively in recent years, NAS methods typically assume fixed hyperparameters
and vice versa - there exists little work on joint NAS + HPO. Furthermore, NAS
has recently often been framed as a multi-objective optimization problem, in
order to take, e.g., resource requirements into account. In this paper, we
propose a set of methods that extend current approaches to jointly optimize
neural architectures and hyperparameters with respect to multiple objectives.
We hope that these methods will serve as simple baselines for future research
on multi-objective joint NAS + HPO. To facilitate this, all our code is
available at https://github.com/automl/multi-obj-baselines.


Abstract:
  The emission mechanism for hard $\gamma$-ray spectra from supernova remnants
(SNRs) is still a matter of debate. Recent multi-wavelength observations of TeV
source HESS J1912+101 show that it is associated with an SNR with an age of
$\sim 100$ kyrs, making it unlikely produce the TeV $\gamma$-ray emission via
leptonic processes. We analyzed Fermi observations of it and found an extended
source with a hard spectrum. HESS J1912+101 may represent a peculiar stage of
SNR evolution that dominates the acceleration of TeV cosmic rays. By fitting
the multi-wavelength spectra of 13 SNRs with hard GeV $\gamma$-ray spectra with
simple emission models with a density ratio of GeV electrons to protons of
$\sim 10^{-2}$, we obtain reasonable mean densities and magnetic fields with a
total energy of $\sim 10^{50}$ ergs for relativistic ions in each SNR. Among
these sources, only two of them, namely SN 1006 and RCW 86, favor a leptonic
origin for the $\gamma$-ray emission. The magnetic field energy is found to be
comparable to that of the accelerated relativistic ions and their ratio has a
tendency of increase with the age of SNRs. These results suggest that TeV
cosmic rays mainly originate from SNRs with hard $\gamma$-ray spectra.


Abstract:
  "No-till" and cover cropping are often identified as the leading simple, best
management practices for carbon sequestration in agriculture. However, the root
of the problem is more complex, with the potential benefits of these approaches
depending on numerous factors including a field's soil type(s), topography, and
management history. Instead of using computer vision approaches to simply
classify a field a still vs. no-till, we instead seek to identify the degree of
residue coverage across afield through a probabilistic deep learning
segmentation approach to enable more accurate analysis of carbon holding
potential and realization. This approach will not only provide more precise
insights into currently implemented practices, but also enable a more accurate
identification process of fields with the greatest potential for adopting new
practices to significantly impact carbon sequestration in agriculture.


Abstract:
  It was recently pointed out that so-called "superhydrides", hydrogen-rich
materials that appear to become superconducting at high temperatures and
pressures, exhibit physical properties that are different from both
conventional and unconventional standard type I and type II superconductors
[1,2]. Here we consider magnetic field expulsion in the first material in this
class discovered in 2015, sulfur hydride [3]. A nuclear resonant scattering
experiment has been interpreted as demonstration that the Meissner effect takes
place in this material [4,5]. Here we point out that the observed effect, under
the assumption that the system is in thermodynamic equilibrium, implies a
Meissner pressure [6] in this material that is {\it much larger} than that of
standard superconductors. This suggests that hydride superconductors are
qualitatively different from the known standard superconductors {\it if} they
are superconductors.


Abstract:
  Scaling arguments provide valuable analysis tools across physics and complex
systems yet are often employed as one generic method, without explicit
reference to the various mathematical concepts underlying them. A careful
understanding of these concepts empowers us to unlock their full potential.


Abstract:
  The number of units of a network dynamical system, its size, arguably
constitutes its most fundamental property. Many units of a network, however,
are typically experimentally inaccessible such that the network size is often
unknown. Here we introduce a \emph{detection matrix }that suitably arranges
multiple transient time series from the subset of accessible units to detect
network size via matching rank constraints. The proposed method is model-free,
applicable across system types and interaction topologies and applies to
non-stationary dynamics near fixed points, as well as periodic and chaotic
collective motion. Even if only a small minority of units is perceptible and
for systems simultaneously exhibiting nonlinearities, heterogeneities and
noise, \emph{exact} size detection is feasible. We illustrate applicability for
a paradigmatic class of biochemical reaction networks.


Abstract:
  Delay differential equations are of great importance in science, engineering,
medicine and biological models. These type of models include time delay
phenomena which is helpful for characterising the real-world applications in
machine learning, mechanics, economics, electrodynamics and so on. Besides,
special classes of functional differential equations have been investigated in
many researches. In this study, a numerical investigation of retarded type of
these models together with initial conditions are introduced. The technique is
based on a polynomial approach along with collocation points which maintains an
approximated solutions to the problem. Besides, an error analysis of the
approximate solutions is given. Accuracy of the method is shown by the results.
Consequently, illustrative examples are considered and detailed analysis of the
problem is acquired. Consequently, the future outlook is discussed in
conclusion.


Abstract:
  We report the results of the analyses of the cosmic ray data collected with a
4 tonne (3$\times$1$\times$1~m$^3$) active mass (volume) Liquid Argon
Time-Projection Chamber (TPC) operated in a dual-phase mode. We present a
detailed study of the TPC's response, its main detector parameters and
performance. The results are important for the understanding and further
developments of the dual-phase technology, thanks to the verification of key
aspects, such as the extraction of electrons from liquid to gas and their
amplification through the entire one square metre readout plain, gain
stability, purity and charge sharing between readout views.


Abstract:
  Modern vehicles are complex cyber-physical systems made of hundreds of
electronic control units (ECUs) that communicate over controller area networks
(CANs). This inherited complexity has expanded the CAN attack surface which is
vulnerable to message injection attacks. These injections change the overall
timing characteristics of messages on the bus, and thus, to detect these
malicious messages, time-based intrusion detection systems (IDSs) have been
proposed. However, time-based IDSs are usually trained and tested on
low-fidelity datasets with unrealistic, labeled attacks. This makes difficult
the task of evaluating, comparing, and validating IDSs. Here we detail and
benchmark four time-based IDSs against the newly published ROAD dataset, the
first open CAN IDS dataset with real (non-simulated) stealthy attacks with
physically verified effects. We found that methods that perform hypothesis
testing by explicitly estimating message timing distributions have lower
performance than methods that seek anomalies in a distribution-related
statistic. In particular, these "distribution-agnostic" based methods
outperform "distribution-based" methods by at least 55% in area under the
precision-recall curve (AUC-PR). Our results expand the body of knowledge of
CAN time-based IDSs by providing details of these methods and reporting their
results when tested on datasets with real advanced attacks. Finally, we develop
an after-market plug-in detector using lightweight hardware, which can be used
to deploy the best performing IDS method on nearly any vehicle.


Abstract:
  A graph $ G $ is said to be $ (H;k) $-vertex stable if $ G $ contains
a~subgraph isomorphic to $ H $ even after removing any $ k $ of its vertices
alongside with their incident edges. We will denote by $ \text{stab}(H;k) $ the
minimum size among sizes of all $ (H;k) $-vertex stable graphs. In this paper
we consider a~case where the structure $ H $ is a~star graph $ K_{1,r} $ and
the the number of vertices in $ G $ is exact, \ie equal to $ 1 + r + k $. We
will show that under the above assumptions $ \text{stab}(K_{1,r};k) $ equals
either $ \frac{1}{2}(k + 1)(2r + k) $, $ \frac{1}{2}\big((r + k)^{2} - 1\big) $
or $ \frac{1}{2}(r + k)^{2} $. Moreover, we will characterize all the extremal
graphs.


Abstract:
  We present a novel method for reliably explaining the predictions of neural
networks. We consider an explanation reliable if it identifies input features
relevant to the model output by considering the input and the neighboring data
points. Our method is built on top of the assumption of smooth landscape in a
loss function of the model prediction: locally consistent loss and gradient
profile. A theoretical analysis established in this study suggests that those
locally smooth model explanations are learned using a batch of noisy copies of
the input with the L1 regularization for a saliency map. Extensive experiments
support the analysis results, revealing that the proposed saliency maps
retrieve the original classes of adversarial examples crafted against both
naturally and adversarially trained models, significantly outperforming
previous methods. We further demonstrated that such good performance results
from the learning capability of this method to identify input features that are
truly relevant to the model output of the input and the neighboring data
points, fulfilling the requirements of a reliable explanation.


Abstract:
  We study certain physically-relevant subgeometries of binary symplectic polar
spaces $W(2N-1,2)$ of small rank $N$, when the points of these spaces
canonically encode $N$-qubit observables. Key characteristics of a subspace of
such a space $W(2N-1,2)$ are: the number of its negative lines, the
distribution of types of observables, the character of the geometric hyperplane
the subspace shares with the distinguished (non-singular) quadric of
$W(2N-1,2)$ and the structure of its Veldkamp space. In particular, we classify
and count polar subspaces of $W(2N-1,2)$ whose rank is $N-1$. $W(3,2)$ features
three negative lines of the same type and its $W(1,2)$'s are of five different
types. $W(5,2)$ is endowed with 90 negative lines of two types and its
$W(3,2)$'s split into 13 types. 279 out of 480 $W(3,2)$'s with three negative
lines are composite, i.\,e. they all originate from the two-qubit $W(3,2)$.
Given a three-qubit $W(3,2)$ and any of its geometric hyperplanes, there are
three other $W(3,2)$'s possessing the same hyperplane. The same holds if a
geometric hyperplane is replaced by a `planar' tricentric triad. A hyperbolic
quadric of $W(5,2)$ is found to host particular sets of seven $W(3,2)$'s, each
of them being uniquely tied to a Conwell heptad with respect to the quadric.
There is also a particular type of $W(3,2)$'s, a representative of which
features a point each line through which is negative. Finally, $W(7,2)$ is
found to possess 1908 negative lines of five types and its $W(5,2)$'s fall into
as many as 29 types. 1524 out of 1560 $W(5,2)$'s with 90 negative lines
originate from the three-qubit $W(5,2)$. Remarkably, the difference in the
number of negative lines for any two distinct types of four-qubit $W(5,2)$'s is
a multiple of four.


Abstract:
  In this paper we studied a broader type of generalized balls which are
domains on the complex projective with possibly Levi-degenerate boundaries. We
proved rigidity theorems for proper holomorphic mappings among them by
exploring the structure of the moduli spaces of projective linear subspaces,
which generalized some earlier results for the ordinary generalized balls with
Levi-nondegenerate boundaries.


Abstract:
  Decentralized data storage systems like the Interplanetary Filesystem (IPFS)
are becoming increasingly popular, e. g., as a data layer in blockchain
applications and for sharing content in a censorship-resistant manner. In IPFS,
data is hosted by an open set of peers, requests to which are broadcast to all
directly connected peers and routed via a distributed hash table (DHT). In this
paper, we showcase how the monitoring of said data requests allows for profound
insights about the IPFS network while simultaneously breaching individual
users' privacy. To this end, we present a passive monitoring methodology that
enables us to collect data requests of a significant and upscalable portion of
the total IPFS node population. Using a measurement setup implementing our
approach and data collected over a period of fifteen months, we demonstrate the
estimation of, among other things: the size of the IPFS network, activity
levels and structure, and content popularity distributions. We furthermore
present how our methodology can be abused for attacks on users' privacy. As a
demonstration, we identify and successfully surveil public IPFS/HTTP gateways,
thereby also uncovering their (normally hidden) node identifiers. We find that
the number of requests by public gateways is substantial, suggesting
substantial usage of these gateways. We give a detailed analysis of the
mechanics and reasons behind implied privacy threats and discuss possible
countermeasures.


Abstract:
  The Kerr rotating black hole metric has unstable photon orbits that orbit
around the hole at fixed values of the Boyer-Lindquist coordinate $r$ that
depend on the axial angular momentum of the orbit, as well as on the parameters
of the hole. For zero orbital axial angular momentum, these orbits cross the
rotational axes at a fixed value of $r$ that depends on the mass $M$ and
angular momentum $J$ of the black hole. Nonzero angular momentum of the hole
causes the photon orbit to rotate so that its direction when crossing the north
polar axis changes from one crossing to the next by an angle I shall call
$\Delta\phi$, which depends on the black hole dimensionless rotation parameter
$a/M = cJ/(GM^2)$ by an equation involving a complete elliptic integral of the
first kind. When the black hole has $a/M \approx 0.994\,341\,179\,923\,26$,
which is nearly maximally rotating, a photon sent out in a constant-$r$
direction from the north polar axis at $r \approx 2.423\,776\,210\,035\,73\,
GM/c^2$ returns to the north polar axis in precisely the opposite direction (in
a frame nonrotating with respect to the distant stars), a photon boomerang.


Abstract:
  Machine-learned potential energy surfaces (PESs) for molecules with more than
10 atoms are typically forced to use lower-level electronic structure methods
such as density functional theory and second-order Moller-Plesset perturbation
theory (MP2). While these are efficient and realistic, they fall short of the
accuracy of the ``gold standard'' coupled-cluster method, especially with
respect to reaction and isomerization barriers. We report a major step forward
in applying a $\Delta$-machine learning method to the challenging case of
acetylacetone, whose MP2 barrier height for H-atom transfer is low by roughly
1.5 kcal/mol relative to the benchmark CCSD(T) barrier of 3.2 kcal/mol. From a
database of 2151 local CCSD(T) energies, and training with as few as 430
energies, we obtain a new PES with a barrier of 3.49 kcal/mol in agreement with
the LCCSD(T) one of 3.54 kcal/mol and close to the benchmark value. Tunneling
splittings due to H-atom transfer are calculated using this new PES, providing
improved estimates over previous ones obtained using an MP2-based PES.


Abstract:
  Numerical solutions to high-dimensional partial differential equations (PDEs)
based on neural networks have seen exciting developments. This paper derives
complexity estimates of the solutions of $d$-dimensional second-order elliptic
PDEs in the Barron space, that is a set of functions admitting the integral of
certain parametric ridge function against a probability measure on the
parameters. We prove under some appropriate assumptions that if the
coefficients and the source term of the elliptic PDE lie in Barron spaces, then
the solution of the PDE is $\epsilon$-close with respect to the $H^1$ norm to a
Barron function. Moreover, we prove dimension-explicit bounds for the Barron
norm of this approximate solution, depending at most polynomially on the
dimension $d$ of the PDE. As a direct consequence of the complexity estimates,
the solution of the PDE can be approximated on any bounded domain by a
two-layer neural network with respect to the $H^1$ norm with a
dimension-explicit convergence rate.


Abstract:
  Layout designs are encountered in a variety of fields. For problems with many
design degrees of freedom, efficiency of design methods becomes a major
concern. In recent years, machine learning methods such as artificial neural
networks have been used increasingly to speed up the design process. A main
issue of many such approaches is the need for a large corpus of training data
that are generated using high-dimensional simulations. The high computational
cost associated with training data generation largely diminishes the efficiency
gained by using machine learning methods. In this work, an adaptive artificial
neural network-based generative design approach is proposed and developed. This
method uses a generative adversarial network to generate design candidates and
thus the number of design variables is greatly reduced. To speed up the
evaluation of the objective function, a convolutional neural network is
constructed as the surrogate model for function evaluation. The inverse design
is carried out using the genetic algorithm in conjunction with two neural
networks. A novel adaptive learning and optimization strategy is proposed,
which allows the design space to be effectively explored for the search for
optimal solutions. As such the number of training data needed is greatly
reduced. The performance of the proposed design method is demonstrated on two
heat source layout design problems. In both problems, optimal designs have been
obtained. Compared with several existing approaches, the proposed approach has
the best performance in terms of accuracy and efficiency.


Abstract:
  Accountability is widely understood as a goal for well governed computer
systems, and is a sought-after value in many governance contexts. But how can
it be achieved? Recent work on standards for governable artificial intelligence
systems offers a related principle: traceability. Traceability requires
establishing not only how a system worked but how it was created and for what
purpose, in a way that explains why a system has particular dynamics or
behaviors. It connects records of how the system was constructed and what the
system did mechanically to the broader goals of governance, in a way that
highlights human understanding of that mechanical operation and the decision
processes underlying it. We examine the various ways in which the principle of
traceability has been articulated in AI principles and other policy documents
from around the world, distill from these a set of requirements on software
systems driven by the principle, and systematize the technologies available to
meet those requirements. From our map of requirements to supporting tools,
techniques, and procedures, we identify gaps and needs separating what
traceability requires from the toolbox available for practitioners. This map
reframes existing discussions around accountability and transparency, using the
principle of traceability to show how, when, and why transparency can be
deployed to serve accountability goals and thereby improve the normative
fidelity of systems and their development processes.


Abstract:
  Recent works have shown that a rich set of semantic directions exist in the
latent space of Generative Adversarial Networks (GANs), which enables various
facial attribute editing applications. However, existing methods may suffer
poor attribute variation disentanglement, leading to unwanted change of other
attributes when altering the desired one. The semantic directions used by
existing methods are at attribute level, which are difficult to model complex
attribute correlations, especially in the presence of attribute distribution
bias in GAN's training set. In this paper, we propose a novel framework (IALS)
that performs Instance-Aware Latent-Space Search to find semantic directions
for disentangled attribute editing. The instance information is injected by
leveraging the supervision from a set of attribute classifiers evaluated on the
input images. We further propose a Disentanglement-Transformation (DT) metric
to quantify the attribute transformation and disentanglement efficacy and find
the optimal control factor between attribute-level and instance-specific
directions based on it. Experimental results on both GAN-generated and
real-world images collectively show that our method outperforms
state-of-the-art methods proposed recently by a wide margin. Code is available
at https://github.com/yxuhan/IALS.


Abstract:
  We discuss the recent results on the muon anomalous magnetic moment in the
context of new physics models with light scalars. We propose a model in which
the one-loop contributions to g-2 of a scalar and a pseudoscalar naturally
cancel in the massless limit due to the symmetry structure of the model. This
model allows to interpolate between two possible interpretations. In the first
interpretation, the results provide a strong evidence of the existence of new
physics, dominated by the positive contribution of a CP-even scalar. In the
second one, supported by the recent lattice result, the data provides a strong
upper bound on new physics, specifically in the case of (negative) pseudoscalar
contributions. We emphasize that tree-level signatures of the new degrees of
freedom of the model are enhanced relative to conventional explanations of the
discrepancy. As a result, this model can be tested in the near future with
accelerator-based experiments and possibly also at the precision frontier.


Abstract:
  Probabilistic point cloud registration methods are becoming more popular
because of their robustness. However, unlike point-to-plane variants of
iterative closest point (ICP) which incorporate local surface geometric
information such as surface normals, most probabilistic methods (e.g., coherent
point drift (CPD)) ignore such information and build Gaussian mixture models
(GMMs) with isotropic Gaussian covariances. This results in sphere-like GMM
components which only penalize the point-to-point distance between the two
point clouds. In this paper, we propose a novel method called CPD with Local
Surface Geometry (LSG-CPD) for rigid point cloud registration. Our method
adaptively adds different levels of point-to-plane penalization on top of the
point-to-point penalization based on the flatness of the local surface. This
results in GMM components with anisotropic covariances. We formulate point
cloud registration as a maximum likelihood estimation (MLE) problem and solve
it with the Expectation-Maximization (EM) algorithm. In the E step, we
demonstrate that the computation can be recast into simple matrix manipulations
and efficiently computed on a GPU. In the M step, we perform an unconstrained
optimization on a matrix Lie group to efficiently update the rigid
transformation of the registration. The proposed method outperforms
state-of-the-art algorithms in terms of accuracy and robustness on various
datasets captured with range scanners, RGBD cameras, and LiDARs. Also, it is
significantly faster than modern implementations of CPD. The source code is
available at https://github.com/ChirikjianLab/LSG-CPD.git.


Abstract:
  Recent work has proven the effectiveness of transformers in many computer
vision tasks. However, the performance of transformers in gaze estimation is
still unexplored. In this paper, we employ transformers and assess their
effectiveness for gaze estimation. We consider two forms of vision transformer
which are pure transformers and hybrid transformers. We first follow the
popular ViT and employ a pure transformer to estimate gaze from images. On the
other hand, we preserve the convolutional layers and integrate CNNs as well as
transformers. The transformer serves as a component to complement CNNs. We
compare the performance of the two transformers in gaze estimation. The Hybrid
transformer significantly outperforms the pure transformer in all evaluation
datasets with less parameters. We further conduct experiments to assess the
effectiveness of the hybrid transformer and explore the advantage of
self-attention mechanism. Experiments show the hybrid transformer can achieve
state-of-the-art performance in all benchmarks with pre-training.To facilitate
further research, we release codes and models in
https://github.com/yihuacheng/GazeTR.


Abstract:
  When fitting N-body models to astronomical data - including transit times,
radial velocity, and astrometric positions at observed times - the derivatives
of the model outputs with respect to the initial conditions can help with model
optimization and posterior sampling. Here we describe a general-purpose
symplectic integrator for arbitrary orbital architectures, including those with
close encounters, which we have recast to maintain numerical stability and
precision for small step sizes. We compute the derivatives of the N-body
coordinates and velocities as a function of time with respect to the initial
conditions and masses by propagating the Jacobian along with the N-body
integration. For the first time we obtain the derivatives of the transit times
with respect to the initial conditions and masses using the chain rule, which
is quicker and more accurate than using finite differences or automatic
differentiation. We implement this algorithm in an open source package,
NbodyGradient.jl, written in the Julia language, which has been used in the
optimization and error analysis of transit-timing variations in the TRAPPIST-1
system. We present tests of the accuracy and precision of the code, and show
that it compares favorably in speed to other integrators which are written in
C.


Abstract:
  This paper proposes a parallel computation strategy and a posterior-based
lattice expansion algorithm for efficient lattice rescoring with neural
language models (LMs) for automatic speech recognition. First, lattices from
first-pass decoding are expanded by the proposed posterior-based lattice
expansion algorithm. Second, each expanded lattice is converted into a minimal
list of hypotheses that covers every arc. Each hypothesis is constrained to be
the best path for at least one arc it includes. For each lattice, the neural LM
scores of the minimal list are computed in parallel and are then integrated
back to the lattice in the rescoring stage. Experiments on the Switchboard
dataset show that the proposed rescoring strategy obtains comparable
recognition performance and generates more compact lattices than a competitive
baseline method. Furthermore, the parallel rescoring method offers more
flexibility by simplifying the integration of PyTorch-trained neural LMs for
lattice rescoring with Kaldi.


Abstract:
  Motivated by the appearance of fractional powers of line bundles in studies
of vector-like spectra in 4d F-theory compactifications, we analyze the
structure and origin of these bundles. Fractional powers of line bundles are
also known as root bundles and can be thought of as generalizations of spin
bundles. We explain how these root bundles are linked to inequivalent F-theory
gauge potentials of a $G_4$-flux.
  While this observation is interesting in its own right, it is particularly
valuable for F-theory Standard Model constructions. In aiming for MSSMs, it is
desired to argue for the absence of vector-like exotics. We work out the root
bundle constraints on all matter curves in the largest class of currently-known
F-theory Standard Model constructions without chiral exotics and gauge coupling
unification. On each matter curve, we conduct a systematic "bottom"-analysis of
all solutions to the root bundle constraints and all spin bundles. Thereby, we
derive a lower bound for the number of combinations of root bundles and spin
bundles whose cohomologies satisfy the physical demand of absence of
vector-like pairs.
  On a technical level, this systematic study is achieved by a well-known
diagrammatic description of root bundles on nodal curves. We extend this
description by a counting procedure, which determines the cohomologies of
so-called limit root bundles on full blow-ups of nodal curves. By use of
deformation theory, these results constrain the vector-like spectra on the
smooth matter curves in the actual F-theory geometry.


Abstract:
  The Bayesian approach is effective for inverse problems. The posterior
density distribution provides useful information of the unknowns. However, for
problems with non-unique solutions, the classical estimators such as the
maximum a posterior (MAP) and conditional mean (CM) are not enough. We
introduce two new estimators, the local maximum a posterior (LMAP) and local
conditional mean (LCM). Their applications are demonstrated by three inverse
problems: an inverse spectral problem, an inverse source problem, and an
inverse medium problem.


Abstract:
  We derive the planar limit of 2- and 3-point functions of single-trace chiral
primary operators of ${\cal N}=2$ SQCD on $S^4$, to all orders in the 't Hooft
coupling. In order to do so, we first obtain a combinatorial expression for the
planar free energy of a hermitian matrix model with an infinite number of
arbitrary single and double trace terms in the potential; this solution might
have applications in many other contexts. We then use these results to evaluate
the analogous planar correlation functions on ${\mathbb R}^4$. Specifically, we
compute all the terms with a single value of the $\zeta$ function for a few
planar 2- and 3-point functions, and conjecture general formulas for these
terms for all 2- and 3-point functions on ${\mathbb R}^4$.


Abstract:
  The rotational sublevels of the key (000) and (010) vibrational states of the
H2S molecule were modeled with an accuracy close to experimental uncertainty
using the generating function and Euler approaches. The predictive ability of
the Hamiltonian parameters derived is tested against variational calculations.
Comparison of transitions wavenumbers obtained from the presently calculated
set of the H2S (000) and (010) energy levels with simulated (000)-(000),
(010)-(010) transitions included in HITRAN 2016 database revealed a large
discrepancy up to 44 cm-1. Large sets of accurate rotational sublevels of the
(000) and (010) states are calculated.


Abstract:
  The work is devoted to ways of modeling street traffic on a street layout
without traffic lights of an established topology. The behavior of traffic
participants takes into account the individual inclinations of drivers to
creatively interpret traffic rules. Participant interactions describe game
theory models that provide information for simulation algorithms based on
cellular automata. Driver diversification comes down to two types often
considered in such research: DE(fective)-agent and CO(operative)-agent. Various
ways of using the description of traffic participants to examine the impact of
behavior on street traffic dynamics were shown. Directions for the further
detailed analysis were indicated, which requires basic research in the field of
game theory models.


Abstract:
  We study the processes $\gamma \gamma \to \eta_c \to \eta' K^+ K^-$, $\eta'
\pi^+ \pi^-$, and $\eta \pi^+ \pi^-$ using a data sample of 519 $fb^{-1}$
recorded with the BaBar detector operating at the SLAC PEP-II asymmetric-energy
$e^+e^-$ collider at center-of-mass energies at and near the $\Upsilon(nS)$ ($n
= 2,3,4$) resonances. This is the first observation of the decay $\eta_c \to
\eta' K^+ K^-$ and we measure the branching fraction $\Gamma(\eta_c \to \eta'
K^+ K^-)/(\Gamma(\eta_c \to \eta' \pi^+ \pi^-)=0.644\pm 0.039_{\rm stat}\pm
0.032_{\rm sys}$. Significant interference is observed between $\gamma \gamma
\to \eta_c\to \eta \pi^+ \pi^-$ and the non-resonant two-photon process $\gamma
\gamma \to \eta \pi^+ \pi^-$. A Dalitz plot analysis is performed of $\eta_c$
decays to $\eta' K^+ K^-$, $\eta' \pi^+ \pi^-$, and $\eta \pi^+ \pi^-$.
Combined with our previous analysis of $\eta_c \to K \bar K \pi$, we measure
the $K^*_0(1430)$ parameters and the ratio between its $\eta' K$ and $\pi K$
couplings. The decay $\eta_c \to \eta' \pi^+ \pi^-$ is dominated by the
$f_0(2100)$ resonance, also observed in $J/\psi$ radiative decays. A new
$a_0(1700) \to \eta \pi$ resonance is observed in the $\eta_c \to \eta \pi^+
\pi^-$ channel. We also compare $\eta_c$ decays to $\eta$ and $\eta'$ final
states in association with scalar mesons as they relate to the identification
of the scalar glueball.


Abstract:
  Colloidal self-assembly -- the spontaneous organization of colloids into
ordered structures -- has been considered key to produce next-generation
materials. However, the present-day staggering variety of colloidal building
blocks and the limitless number of thermodynamic conditions make a systematic
exploration intractable. The true challenge in this field is to turn this logic
around, and to develop a robust, versatile algorithm to inverse design colloids
that self-assemble into a target structure. Here, we introduce a generic
inverse design method to efficiently reverse-engineer crystals, quasicrystals,
and liquid crystals by targeting their diffraction patterns. Our algorithm
relies on the synergetic use of an evolutionary strategy for parameter
optimization, and a convolutional neural network as an order parameter, and
provides a new way forward for the inverse design of experimentally feasible
colloidal interactions, specifically optimized to stabilize the desired
structure.


Abstract:
  There has been recently a growing interest in studying adversarial examples
on natural language models in the black-box setting. These methods attack
natural language classifiers by perturbing certain important words until the
classifier label is changed. In order to find these important words, these
methods rank all words by importance by querying the target model word by word
for each input sentence, resulting in high query inefficiency. A new
interesting approach was introduced that addresses this problem through
interpretable learning to learn the word ranking instead of previous expensive
search. The main advantage of using this approach is that it achieves
comparable attack rates to the state-of-the-art methods, yet faster and with
fewer queries, where fewer queries are desirable to avoid suspicion towards the
attacking agent. Nonetheless, this approach sacrificed the useful information
that could be leveraged from the target classifier for that sake of query
efficiency. In this paper we study the effect of leveraging the target model
outputs and data on both attack rates and average number of queries, and we
show that both can be improved, with a limited overhead of additional queries.


Abstract:
  A growing number of eclipsing binary systems of the "HW Vir" kind (i. e.,
composed by a subdwarf-B/O primary star and an M dwarf secondary) show
variations in their orbital period, also called Eclipse Time Variations (ETVs).
Their physical origin is not yet known with certainty: while some ETVs have
been claimed to arise from dynamical perturbations due to the presence of
circumbinary planetary companions, other authors suggest that the Applegate
effect or other unknown stellar mechanisms could be responsible for them. In
this work, we present twenty-eight unpublished high-precision light curves of
one of the most controversial of these systems, the prototype HW Virginis. We
homogeneously analysed the new eclipse timings together with historical data
obtained between 1983 and 2012, demonstrating that the planetary models
previously claimed do not fit the new photometric data, besides being
dynamically unstable. In an effort to find a new model able to fit all the
available data, we developed a new approach based on a global-search genetic
algorithm and eventually found two new distinct families of solutions that fit
the observed timings very well, yet dynamically unstable at the 10^5-year time
scale. This serves as a cautionary tale on the existence of formal solutions
that apparently explain ETVs but are not physically meaningful, and on the need
of carefully testing their stability. On the other hand, our data confirm the
presence of an ETV on HW Vir that known stellar mechanisms are unable to
explain, pushing towards further observing and modelling efforts.


Abstract:
  The electricity sector has tended to be one of the first industries to face
technology change motivated by sustainability concerns. Whilst efficient market
designs for electricity have tended to focus upon market power concerns,
environmental externalities pose extra challenges for efficient solutions.
Thus, we show that ad hoc remedies for market power alongside administered
carbon prices are inefficient unless they are integrated. Accordingly, we
develop an incentive-based market clearing design that can include
externalities as well as market power mitigation. A feature of the solution is
that it copes with incomplete information of the system operator regarding
generation costs. It is uses a network representation of the power system and
the proposed incentive mechanism holds even with energy limited technologies
having temporal constraints, e.g., storage. The shortcomings of price caps to
mitigate market power, in the context of sustainability externalities, are
overcome under the proposed incentive mechanism.


Abstract:
  Full-field imaging through scattering media is fraught with many challenges.
Despite many achievements in recent years, current imaging methods are too slow
to deal with fast dynamics that occur for example in biomedical imaging. Here
we present an ultra-fast all-optical method, where the object to be imaged and
the scattering medium (diffuser) are inserted into a highly multimode
self-imaging laser cavity. We show that the intra-cavity laser light from the
object is mainly focused onto specific regions of the scattering medium where
the phase variations are low. Thus, round trip loss within the laser cavity is
minimized, thereby overcoming most of the scattering effects. The method is
exploited to image objects through scattering media whose diffusion angle is
lower than the numerical aperture of the laser cavity. As our method is based
on optical feedback inside a laser cavity, it can deal with temporal variations
that occur on timescales as short as several cavity round trips, with an upper
bound of 200 ns.


Abstract:
  High capacity end-to-end approaches for human motion (behavior) prediction
have the ability to represent subtle nuances in human behavior, but struggle
with robustness to out of distribution inputs and tail events. Planning-based
prediction, on the other hand, can reliably output decent-but-not-great
predictions: it is much more stable in the face of distribution shift (as we
verify in this work), but it has high inductive bias, missing important aspects
that drive human decisions, and ignoring cognitive biases that make human
behavior suboptimal. In this work, we analyze one family of approaches that
strive to get the best of both worlds: use the end-to-end predictor on common
cases, but do not rely on it for tail events / out-of-distribution inputs --
switch to the planning-based predictor there. We contribute an analysis of
different approaches for detecting when to make this switch, using an
autonomous driving domain. We find that promising approaches based on
ensembling or generative modeling of the training distribution might not be
reliable, but that there very simple methods which can perform surprisingly
well -- including training a classifier to pick up on tell-tale issues in
predicted trajectories.


Abstract:
  We prove the existence of immersed closed curves of constant geodesic
curvature in an arbitrary Riemannian 2-sphere for almost every prescribed
curvature. To achieve this, we develop a min-max scheme for a weighted length
functional.


Abstract:
  In image anomaly detection, Autoencoders are the popular methods that
reconstruct the input image that might contain anomalies and output a clean
image with no abnormalities. These Autoencoder-based methods usually calculate
the anomaly score from the reconstruction error, the difference between the
input image and the reconstructed image. On the other hand, the accuracy of the
reconstruction is insufficient in many of these methods, so it leads to
degraded accuracy of anomaly detection. To improve the accuracy of the
reconstruction, we consider defining loss function in the frequency domain. In
general, we know that natural images contain many low-frequency components and
few high-frequency components. Hence, to improve the accuracy of the
reconstruction of high-frequency components, we introduce a new loss function
named weighted frequency domain loss(WFDL). WFDL provides a sharper
reconstructed image, which contributes to improving the accuracy of anomaly
detection. In this paper, we show our method's superiority over the
conventional Autoencoder methods by comparing it with AUROC on the MVTec AD
dataset.


Abstract:
  Background: Poverty among the population of a country is one of the most
disputable topics in social studies. Many researchers devote their work to
identifying the factors that influence it most. Bulgaria is one of the EU
member states with the highest poverty levels. Regional facets of social
exclusion and risks of poverty among the population are a key priority of the
National Development Strategy for the third decade of 21st century. In order to
mitigate the regional poverty levels it is necessary for the social policy
makers to pay more attention to the various factors expected to influence these
levels. Results: Poverty reduction is observed in most areas of the country.
The regions with obviously favorable developments are Sofia district, Pernik,
Pleven, Lovech, Gabrovo, Veliko Tarnovo, Silistra, Shumen, Stara Zagora,
Smolyan, Kyustendil and others. Increased levels of poverty are found for
Razgrad and Montana districts. It was fond that the reduction in the risk of
poverty is associated to the increase in employment, investment, and housing.
Conclusion: The social policy making needs to be aware of the fact that the
degree of exposition to risk of poverty and social exclusion significantly
relates to the levels of regional employment, investment and housing.


Abstract:
  A natural way of increasing our understanding of NP-complete graph problems
is to restrict the input to a special graph class. Classes of $H$-free graphs,
that is, graphs that do not contain some graph $H$ as an induced subgraph, have
proven to be an ideal testbed for such a complexity study. However, if the
forbidden graph $H$ contains a cycle or claw, then these problems often stay
NP-complete. A recent complexity study on the $k$-Colouring problem shows that
we may still obtain tractable results if we also bound the diameter of the
$H$-free input graph. We continue this line of research by initiating a
complexity study on the impact of bounding the diameter for a variety of
classical vertex partitioning problems restricted to $H$-free graphs. We prove
that bounding the diameter does not help for Independent Set, but leads to new
tractable cases for problems closely related to 3-Colouring. That is, we show
that Near-Bipartiteness, Independent Feedback Vertex Set, Independent Odd Cycle
Transversal, Acyclic 3-Colouring and Star 3-Colouring are all polynomial-time
solvable for chair-free graphs of bounded diameter. To obtain these results we
exploit a new structural property of 3-colourable chair-free graphs.


Abstract:
  The aim of this paper is to present an elementary computable theory of random
variables, based on the approach to probability via valuations. The theory is
based on a type of lower-measurable sets, which are controlled limits of open
sets, and extends existing work in this area by providing a computable theory
of conditional random variables. The theory is based within the framework of
type-two effectivity, so has an explicit direct link with Turing computation,
and is expressed in a system of computable types and operations, so has a clean
mathematical description.


Abstract:
  As the field of superconducting quantum computing approaches maturity,
optimization of single-device performance is proving to be a promising avenue
towards large-scale quantum computers. However, this optimization is possible
only if performance metrics can be accurately compared among measurements,
devices, and laboratories. Currently such comparisons are inaccurate or
impossible due to understudied errors from a plethora of sources. In this
Perspective, we outline the current state of error analysis for qubits and
resonators in superconducting quantum circuits, and discuss what future
investigations are required before superconducting quantum device optimization
can be realized.


Abstract:
  We address the issues of clustering and non-global logarithms for jet shapes
in the process of production of a Higgs/vector boson associated with a single
hard jet at hadron colliders. We perform an analytical fixed-order calculation
up to second order in the coupling as well as an all-orders estimation for the
specific invariant mass distribution of the highest-$p_t$ jet, for various jet
algorithms. Our results are derived in the eikonal (soft) limit and are valid
up to next-to-leading logarithmic accuracy. We perform a matching of the
resummed distribution to next-to-leading order results from MCFM and compare
our findings with the outputs of the Monte Carlo event generators Pythia 8 and
Herwig 7. After accounting for non-perturbative effects we compare our results
with available experimental data from the CMS collaboration for the Z + jet
production. We find good agreement over a wide range of the observable.


Abstract:
  Bioenergy with Carbon Capture and Sequestration (BECCS) is critical for
stringent climate change mitigation, but is commercially and technologically
immature and resource-intensive. In California, state and federal fuel and
climate policies can drive first-markets for BECCS. We develop a spatially
explicit optimization model to assess niche markets for renewable natural gas
(RNG) production with carbon capture and sequestration (CCS) from waste biomass
in California. Existing biomass residues produce biogas and RNG and enable
low-cost CCS through the upgrading process and CO$_2$ truck transport. Under
current state and federal policy incentives, we could capture and sequester 2.9
million MT CO$_2$/year (0.7% of California's 2018 CO$_2$ emissions) and produce
93 PJ RNG/year (4% of California's 2018 natural gas demand) with a profit
maximizing objective. Existing federal and state policies produce profits of
\$11/GJ. Distributed RNG production with CCS potentially catalyzes markets and
technologies for CO$_2$ capture, transport, and storage in California.


Abstract:
  Current and future generations of intensity mapping surveys promise dramatic
improvements in our understanding of galaxy evolution and large-scale
structure. An intensity map provides a census of the cumulative emission from
all galaxies in a given region and redshift, including faint objects that are
undetectable individually. Furthermore, cross-correlations between line
intensity maps and galaxy redshift surveys are sensitive to the line intensity
and clustering bias without the limitation of cosmic variance. Using the Fisher
information matrix, we derive simple expressions describing sensitivities to
the intensity and bias obtainable for cross-correlation surveys, focusing on
cosmic variance evasion. Based on these expressions, we conclude that the
optimal sensitivity is obtained by matching the survey depth, defined by the
ratio of the clustering power spectrum to noise in a given mode, between the
two surveys. We find that mid- to far-infrared space telescopes could benefit
from this technique by cross-correlating with coming galaxy redshift surveys
such as those planned for the Nancy Grace Roman Space Telescope, allowing for
sensitivities beyond the cosmic variance limit. Our techniques can therefore be
applied to survey design and requirements development to maximize the
sensitivities of future intensity mapping experiments to tracers of galaxy
evolution and large-scale structure cosmology.


Abstract:
  We study real steady state varieties of the dynamics of chemical reaction
networks. The dynamics are derived using mass action kinetics with parametric
reaction rates. The models studied are not inherently parametric in nature.
Rather, our interest in parameters is motivated by parameter uncertainty, as
reaction rates are typically either measured with limited precision or
estimated. We aim at detecting toricity and shifted toricity, using a framework
that has been recently introduced and studied for the non-parametric case over
both the real and the complex numbers. While toricity requires that the variety
specifies a subgroup of the direct power of the multiplicative group of the
underlying field, shifted toricity requires only a coset. In the non-parametric
case these requirements establish real decision problems. In the presence of
parameters we must go further and derive necessary and sufficient conditions in
the parameters for toricity or shifted toricity to hold. Technically, we use
real quantifier elimination methods. Our computations on biological networks
here once more confirm shifted toricity as a relevant concept, while toricity
holds only for degenerate parameter choices.


Abstract:
  Many-body localization of a disorder interacting boson system in one
dimension is studied numerically at the filling factor being one-half, in terms
of level statistics, local compressibility, correlation function and
entanglement entropies. The von Neumann entanglement entropy is decoupled into
a particle number entropy and a configuration entropy. An anomalous volume-law
behavior is found for the configuration entanglement entropy to confirm a
recent experimental observation [A. Lukin, M. Rispoli, R. Schittko, et al.,
Science 364, 256 (2019)] for sufficient strong disorder, while the particle
number entropy fulfills an area-law corresponding to the total entropy for
disordered spin chain. The localization length are extracted from a two-body
correlation function for many-body localization states and corresponding
time-evolutions states as well. A phase diagrams is established with consisting
of an ergodic thermalized region and a many-body-localization region in a
parameter space of the disorder strength and the energy density. Two regions
are separated by a many-body mobility edge deducted from the standard deviation
of the particle-number entanglement entropy, which appears consistent with that
based on the localization length. Slow dynamics characterized by a logarithmic
time-dependence is explicitly shown for both the particle number entropy and
the configuration entropy in an intermediate regime of their time-evolutions,
which does not show up in the Anderson localization case, i.e. non-interacting
disorder systems.


Abstract:
  Existing work in counterfactual Learning to Rank (LTR) has focussed on
optimizing feature-based models that predict the optimal ranking based on
document features. LTR methods based on bandit algorithms often optimize
tabular models that memorize the optimal ranking per query. These types of
model have their own advantages and disadvantages. Feature-based models provide
very robust performance across many queries, including those previously unseen,
however, the available features often limit the rankings the model can predict.
In contrast, tabular models can converge on any possible ranking through
memorization. However, memorization is extremely prone to noise, which makes
tabular models reliable only when large numbers of user interactions are
available. Can we develop a robust counterfactual LTR method that pursues
memorization-based optimization whenever it is safe to do? We introduce the
Generalization and Specialization (GENSPEC) algorithm, a robust feature-based
counterfactual LTR method that pursues per-query memorization when it is safe
to do so. GENSPEC optimizes a single feature-based model for generalization:
robust performance across all queries, and many tabular models for
specialization: each optimized for high performance on a single query. GENSPEC
uses novel relative high-confidence bounds to choose which model to deploy per
query. By doing so, GENSPEC enjoys the high performance of successfully
specialized tabular models with the robustness of a generalized feature-based
model. Our results show that GENSPEC leads to optimal performance on queries
with sufficient click data, while having robust behavior on queries with little
or noisy data.


Abstract:
  Common domain shift problem formulations consider the integration of multiple
source domains, or the target domain during training. Regarding the
generalization of machine learning models between different car interiors, we
formulate the criterion of training in a single vehicle: without access to the
target distribution of the vehicle the model would be deployed to, neither with
access to multiple vehicles during training. We performed an investigation on
the SVIRO dataset for occupant classification on the rear bench and propose an
autoencoder based approach to improve the transferability. The autoencoder is
on par with commonly used classification models when trained from scratch and
sometimes out-performs models pre-trained on a large amount of data. Moreover,
the autoencoder can transform images from unknown vehicles into the vehicle it
was trained on. These results are corroborated by an evaluation on real
infrared images from two vehicle interiors.


Abstract:
  Since its beginning in the 1950s, the field of artificial intelligence has
cycled several times between periods of optimistic predictions and massive
investment ("AI spring") and periods of disappointment, loss of confidence, and
reduced funding ("AI winter"). Even with today's seemingly fast pace of AI
breakthroughs, the development of long-promised technologies such as
self-driving cars, housekeeping robots, and conversational companions has
turned out to be much harder than many people expected. One reason for these
repeating cycles is our limited understanding of the nature and complexity of
intelligence itself. In this paper I describe four fallacies in common
assumptions made by AI researchers, which can lead to overconfident predictions
about the field. I conclude by discussing the open questions spurred by these
fallacies, including the age-old challenge of imbuing machines with humanlike
common sense.


Abstract:
  Wildfire is one of the biggest disasters that frequently occurs on the west
coast of the United States. Many efforts have been made to understand the
causes of the increases in wildfire intensity and frequency in recent years. In
this work, we propose static and dynamic prediction models to analyze and
assess the areas with high wildfire risks in California by utilizing a
multitude of environmental data including population density, Normalized
Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree
mortality area, tree mortality number, and altitude. Moreover, we focus on a
better understanding of the impacts of different factors so as to inform
preventive actions. To validate our models and findings, we divide the land of
California into 4,242 grids of 0.1 degrees $\times$ 0.1 degrees in latitude and
longitude, and compute the risk of each grid based on spatial and temporal
conditions. To verify the generalizability of our models, we further expand the
scope of wildfire risk assessment from California to Washington without any
fine tuning. By performing counterfactual analysis, we uncover the effects of
several possible methods on reducing the number of high risk wildfires. Taken
together, our study has the potential to estimate, monitor, and reduce the
risks of wildfires across diverse areas provided that such environment data is
available.


Abstract:
  Among the versatile forms of dynamical patterns of activity exhibited by the
brain, oscillations are one of the most salient and extensively studied, yet
are still far from being well understood. In this paper, we provide various
structural characterizations of the existence of oscillatory behavior in neural
networks using a classical neural mass model of mesoscale brain activity called
linear-threshold dynamics. Exploiting the switched-affine nature of this
dynamics, we obtain various necessary and/or sufficient conditions on the
network structure and its external input for the existence of oscillations in
(i) two-dimensional excitatory-inhibitory networks (E-I pairs), (ii) networks
with one inhibitory but arbitrary number of excitatory nodes, (iii) purely
inhibitory networks with an arbitrary number of nodes, and (iv) networks of E-I
pairs. Throughout our treatment, and given the arbitrary dimensionality of the
considered dynamics, we rely on the lack of stable equilibria as a system-based
proxy for the existence of oscillations, and provide extensive numerical
results to support its tight relationship with the more standard, signal-based
definition of oscillations in computational neuroscience.


Abstract:
  Thorstensen (2020) recently argued that the cataclysmic variable (CV) LAMOST
J024048.51+195226.9 may be a twin to the unique magnetic propeller system AE
Aqr. If this is the case, two predictions are that it should display a short
period white dwarf spin modulation, and that it should be a bright radio
source. We obtained follow-up optical and radio observations of this CV, in
order to see if this holds true. Our optical high-speed photometry does not
reveal a white dwarf spin signal, but lacks the sensitivity to detect a
modulation similar to the 33-s spin signal seen in AE Aqr. We detect the source
in the radio, and measure a radio luminosity similar to that of AE Aqr and
close to the highest so far reported for a CV. We also find good evidence for
radio variability on a time scale of tens of minutes. Optical polarimetric
observations produce no detection of linear or circular polarization. While we
are not able to provide compelling evidence, our observations are all
consistent with this object being a propeller system.


Abstract:
  With the growing prevalence of psychological interventions, it is vital to
have measures which rate the effectiveness of psychological care to assist in
training, supervision, and quality assurance of services. Traditionally,
quality assessment is addressed by human raters who evaluate recorded sessions
along specific dimensions, often codified through constructs relevant to the
approach and domain. This is however a cost-prohibitive and time-consuming
method that leads to poor feasibility and limited use in real-world settings.
To facilitate this process, we have developed an automated competency rating
tool able to process the raw recorded audio of a session, analyzing who spoke
when, what they said, and how the health professional used language to provide
therapy. Focusing on a use case of a specific type of psychotherapy called
Motivational Interviewing, our system gives comprehensive feedback to the
therapist, including information about the dynamics of the session (e.g.,
therapist's vs. client's talking time), low-level psychological language
descriptors (e.g., type of questions asked), as well as other high-level
behavioral constructs (e.g., the extent to which the therapist understands the
clients' perspective). We describe our platform and its performance using a
dataset of more than 5,000 recordings drawn from its deployment in a real-world
clinical setting used to assist training of new therapists. Widespread use of
automated psychotherapy rating tools may augment experts' capabilities by
providing an avenue for more effective training and skill improvement,
eventually leading to more positive clinical outcomes.


Abstract:
  Starting from a recently proposed comprehensive theory for the high-Tc
superconductivity in cuprates, we derive a general analytic expression for the
planar resistivity, in the presence of an applied external magnetic field
$\textbf{H}$ and explore its consequences in the different phases of these
materials. As an initial probe of our result, we show it compares very well
with experimental data for the resistivity of LSCO at different values of the
applied field. We also apply our result to Bi2201 and show that the
magnetoresistivity in the strange metal phase of this material, exhibits the
$H^2$ to $H$ crossover, as we move from the weak to the strong field regime.
Yet, despite of that, the magnetoresistivity does not present a quadrature
scaling. Remarkably, the resistivity H-field derivative does scale as a
function of $\frac{H}{T}$, in complete agreement with recent magneto-transport
measurements made in the strange metal phase of cuprates \cite{Hussey2020}. We,
finally, address the issue of the $T$-power-law dependence of the resistivity
of overdoped cuprates and compare our results with experimental data for
Tl2201. We show that this provides a simple method to determine whether the
quantum critical point associated to the pseudogap temperature $T^*(x)$ belongs
to the SC dome or not.


Abstract:
  Schooling fish provide a spectacular example of self-organization in Nature.
The most remarkable patterns they form are giant rotating clusters such as
balls, tori, and rings, but the underlying mechanism remains largely unknown.
Here we propose an agent-based model that limits the number of agents that can
interact with each other. We incorporate the characteristic behaviors of fish
by (i) attraction that is weakened in a dense cluster of fish, and (ii)
acceleration with finite duration ("fast-start") when the fish is out of the
cluster. By three-dimensional numerical simulations, we show emergence of giant
rotating clusters (balls, tori, and rings) that are much larger than the radius
of interaction. We present a phase diagram of patterns including polarized
schools and swarms, and propose a physical mechanism that determines the
cluster shape in terms of the interaction capacity and strength of attraction.
Our model also indicates that each fish randomly moves back and forth between
the inner and outer regions of a vortex on a large time-scale. These results
show that fish without inherent chirality form giant rotating clusters
spontaneously and only by short-ranged interactions.