Abstract:
  This paper provides a starting point for Software Engineering (SE)
researchers and practitioners faced with the problem of training machine
learning models on small datasets. Due to the high costs associated with
labeling data, in Software Engineering,there exist many small (< 1 000 samples)
and medium-sized (< 100 000 samples) datasets. While deep learning has set the
state of the art in many machine learning tasks, it is only recently that it
has proven effective on small-sized datasets, primarily thanks to pre-training,
a semi-supervised learning technique that leverages abundant unlabelled data
alongside scarce labelled data.In this work, we evaluate pre-trained
Transformer models on a selection of 13 smaller datasets from the SE
literature, covering both,source code and natural language. Our results suggest
that pre-trained Transformers are competitive and in some cases superior to
previous models, especially for tasks involving natural language; whereas for
source code tasks, in particular for very small datasets,traditional machine
learning methods often has the edge.In addition, we experiment with several
techniques that ought to aid training on small datasets, including active
learning, data augmentation, soft labels, self-training and intermediate-task
fine-tuning, and issue recommendations on when they are effective. We also
release all the data, scripts, and most importantly pre-trained models for the
community to reuse on their own datasets.


Abstract:
  Let $p$ be a prime number and $E_{p}$ denote the elliptic curve $y^2=x^3+px$.
It is known that for $p$ which is congruent to $1, 9$ modulo $16$, the rank of
$E_{p}$ over $\mathbb{Q}$ is equal to $0, 2$. Under the condition that the
Birch and Swinnerton-Dyer conjecture is true, we give a necessary and
sufficient condition that the rank is $2$ in terms of the constant term of some
polynomial that is defined by a recurrence formula.


Abstract:
  Peak estimation bounds extreme values of a function of state along
trajectories of a dynamical system. This paper focuses on extending peak
estimation to continuous and discrete settings with time-independent and
time-dependent uncertainty. Techniques from optimal control are used to
incorporate uncertainty into an existing occupation measure-based peak
estimation framework, which includes special consideration for handling
switching uncertainties. The resulting infinite-dimensional linear programs can
be solved approximately with Linear Matrix Inequalities arising from the
moment-SOS hierarchy.


Abstract:
  The share-of-choice product design (SOCPD) problem is to find the product, as
defined by its attributes, that maximizes market share arising from a
collection of customer types or segments. When customers follow a logit model
of choice, the market share is given by a weighted sum of logistic
probabilities, leading to the logit-based share-of-choice product design
problem. At first glance, this problem appears hopeless: one must optimize an
objective function that is neither convex nor concave, over an
exponentially-sized discrete set of attribute combinations. In this paper, we
develop an exact methodology for solving this problem based on modern integer,
convex and conic optimization. At the heart of our methodology is the
surprising result that the logit-based SOCPD problem can be exactly
reformulated as a mixed-integer convex program. Using both synthetic problem
instances and instances derived from real conjoint data sets, we show that our
methodology can solve large instances to provable optimality or near optimality
in operationally feasible time frames.


Abstract:
  Boom cranes are among the most used cranes to lift heavy loads. Although
fairly simple mechanically, from the control viewpoint this kind of crane is a
nonlinear underactuated system which presents several challenges, especially
when con-trolled in the presence of constraints. To solve this problem, we
propose an approach based on the Explicit Reference Governor (ERG), which does
not require any online optimization, thus making it computationally
inexpensive. The proposed control scheme is able to steer the crane to a
desired position ensuring the respect of limited joint ranges, maximum
oscillation angle, and the avoidance of static obstacles.


Abstract:
  Recent works have proved the existence of symmetry-protected edge states in
certain one-dimensional topological band insulators and superconductors at the
gap-closing points which define quantum phase transitions between two
topologically nontrivial phases. We show how this picture generalizes to
multiband critical models belonging to any of the chiral symmetry classes AIII,
BDI, or CII of noninteracting fermions in one dimension.


Abstract:
  We report on a combined far- and near-field study of surface plasmon
polaritons on freestanding perforated gold films. The samples are fabricated by
focused ion beam milling of {a periodic hole array into} a carbon membrane
followed by thermal evaporation of gold and plasma ashing of the carbon film.
Optical transmission spectra show a series of characteristic features, which
can be attributed to the excitation of surface plasmon modes via the periodic
nanohole array. The corresponding near-field distributions are mapped by
electron energy-loss spectroscopy. Besides the optically bright surface plasmon
modes, we observe in the near-field an additional dark plasmon mode, which is
absent in the normal incidence far-field spectra. Our experimental results are
in good agreement with numerical computations based on a discontinuous Galerkin
time-domain method.


Abstract:
  In steady state, the fuel cycle of a fusion plasma requires inward particle
fluxes of fuel ions. These particle flows are also accompanied by heating. In
the case of classical transport in a rotating cylindrical plasma, this heating
can proceed through several distinct channels depending on the physical
mechanisms involved. Some channels directly heat the fuel ions themselves,
whereas others heat electrons. Which channel dominates depends, in general, on
the details of the temperature, density, and rotation profiles of the plasma
constituents. However, remarkably, under relatively few assumptions concerning
these profiles, if the alpha particles, the byproducts of the fusion reaction,
can be removed directly by other means, a hot-ion mode tends to emerge
naturally.


Abstract:
  Orbiting a M dwarf 12 pc away, the transiting exoplanet GJ 1132b is a prime
target for transmission spectroscopy. With a mass of 1.7 Earth masses and
radius of 1.1 Earth radii, GJ 1132b's bulk density indicates that this planet
is rocky. Yet with an equilibrium temperature of 580 K, GJ 1132b may still
retain some semblance of an atmosphere. Understanding whether this atmosphere
exists and its composition will be vital for understanding how the atmospheres
of terrestrial planets orbiting M dwarfs evolve. We observe five transits of GJ
1132b with the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST).
We find a featureless transmission spectrum from 1.1--1.7 microns, ruling out
cloud-free atmospheres with metallicities <300x Solar with >4.8$\sigma$
confidence. We combine our WFC3 results with transit depths from TESS and
archival broadband and spectroscopic observations to find a featureless
spectrum from 0.7--4.5 microns. GJ 1132b has a high mean molecular weight
atmosphere, possesses a high-altitude aerosol layer, or has effectively no
atmosphere. Higher precision observations are required to differentiate between
these possibilities. We explore the impact of hot and cold starspots on the
observed transmission spectrum GJ 1132b, quantifying the amplitude of
spot-induced transit depth features. Using a simple Poisson model we estimate
spot temperature contrasts, spot covering fractions, and spot sizes for GJ
1132. These limits, and the modeling framework, may be useful for future
observations of GJ 1132b or other planets transiting similarly inactive M
dwarfs.


Abstract:
  We report distance measurements for the Norma cluster based on the
near-infrared $J$- and $K_s$-band Fundamental Plane (FP) relations. Our
simultaneous $J$ and $K_s$-band photometry analyses were performed using 31
early-type galaxies in the nearby Norma cluster obtained using the 1.4 m
InfraRed Survey Facility (IRSF) at the South African Astronomical Observatory.
Our final $K_s$-band FP sample consists of 41 early-type galaxies from the
Norma cluster observed using the IRSF and the New Technology Telescope (NTT) at
the European Southern Observatory. This is the largest cluster sample used for
peculiar velocity studies in the Great Attractor region to date. From the
$K_s$-band FP, we find a distance to the Norma cluster of $4915 \pm 121$ km
s$^{-1}$. The implied peculiar velocity for Norma is $44 \pm 151$ km s$^{-1}$
which further supports a small peculiar velocity for the Norma cluster.


Abstract:
  As deep neural networks require tremendous amount of computation and memory,
analog computing with emerging memory devices is a promising alternative to
digital computing for edge devices. However, because of the increasing
simulation time for analog computing system, it has not been explored. To
overcome this issue, analytically approximated simulators are developed, but
these models are inaccurate and narrow down the options for peripheral circuits
for multiply-accumulate operation (MAC). In this sense, we propose a
methodology, SEMULATOR (SiMULATOR by Emulating the analog computing block)
which uses a deep neural network to emulate the behavior of crossbar-based
analog computing system. With the proposed neural architecture, we
experimentally and theoretically shows that it emulates a MAC unit for neural
computation. In addition, the simulation time is incomparably reduced when it
compared to the circuit simulators such as SPICE.


Abstract:
  Compression is a standard procedure for making convolutional neural networks
(CNNs) adhere to some specific computing resource constraints. However,
searching for a compressed architecture typically involves a series of
time-consuming training/validation experiments to determine a good compromise
between network size and performance accuracy. To address this, we propose an
image complexity-guided network compression technique for biomedical image
segmentation. Given any resource constraints, our framework utilizes data
complexity and network architecture to quickly estimate a compressed model
which does not require network training. Specifically, we map the dataset
complexity to the target network accuracy degradation caused by compression.
Such mapping enables us to predict the final accuracy for different network
sizes, based on the computed dataset complexity. Thus, one may choose a
solution that meets both the network size and segmentation accuracy
requirements. Finally, the mapping is used to determine the convolutional
layer-wise multiplicative factor for generating a compressed network. We
conduct experiments using 5 datasets, employing 3 commonly-used CNN
architectures for biomedical image segmentation as representative networks. Our
proposed framework is shown to be effective for generating compressed
segmentation networks, retaining up to $\approx 95\%$ of the full-sized network
segmentation accuracy, and at the same time, utilizing $\approx 32x$ fewer
network trainable weights (average reduction) of the full-sized networks.


Abstract:
  We prove an optimal version of Wigner's unitary-antiunitary theorem. The main
tool in our proof is Gleason's theorem.


Abstract:
  We aim to address the problem of Natural Language Video Localization
(NLVL)-localizing the video segment corresponding to a natural language
description in a long and untrimmed video. State-of-the-art NLVL methods are
almost in one-stage fashion, which can be typically grouped into two
categories: 1) anchor-based approach: it first pre-defines a series of video
segment candidates (e.g., by sliding window), and then does classification for
each candidate; 2) anchor-free approach: it directly predicts the probabilities
for each video frame as a boundary or intermediate frame inside the positive
segment. However, both kinds of one-stage approaches have inherent drawbacks:
the anchor-based approach is susceptible to the heuristic rules, further
limiting the capability of handling videos with variant length. While the
anchor-free approach fails to exploit the segment-level interaction thus
achieving inferior results. In this paper, we propose a novel Boundary Proposal
Network (BPNet), a universal two-stage framework that gets rid of the issues
mentioned above. Specifically, in the first stage, BPNet utilizes an
anchor-free model to generate a group of high-quality candidate video segments
with their boundaries. In the second stage, a visual-language fusion layer is
proposed to jointly model the multi-modal interaction between the candidate and
the language query, followed by a matching score rating layer that outputs the
alignment score for each candidate. We evaluate our BPNet on three challenging
NLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive
experiments and ablative studies on these datasets demonstrate that the BPNet
outperforms the state-of-the-art methods.


Abstract:
  In this paper, we consider the concentration property of solutions to the
dispersive Ginzburg-Landau (or Gross-Pitaevskii) equation in three dimensions.
On a spatial domain, it has long been conjectured that such a solution
concentrates near some curve evolving according to the binormal curvature flow,
and conversely, that a curve moving this way can be realized in a suitable
sense by some solution to the dispersive Ginzburg-Landau equation. Some partial
results are known with rather strong symmetry assumptions.
  Our main theorems here provide affirmative answer to both conjectures under
certain small curvature assumption. The results are valid for small but fixed
material parameter in the equation, in contrast to the general practice to take
this parameter to its zero limit. The advantage is that we can retain precise
description of the vortex filament structure. The results hold on a long but
finite time interval, depending on the curvature assumption.


Abstract:
  The late evolutionary stages of low- and intermediate-mass stars are
characterised by mass loss through a dust-driven stellar wind. Recent
observations reveal complex structures within these winds, that are believed to
be formed primarily via interaction with a companion. How these complexities
arise, and which structures are formed in which type of systems, is still
poorly understood. Particularly, there is a lack of studies investigating the
structure formation in eccentric systems. We aim to improve our understanding
of the wind morphology of eccentric AGB binary systems by investigating the
mechanism responsible for the different small-scale structures and global
morphologies that arise in a polytropic wind with different velocities. Using
the smoothed particle hydrodynamics (SPH) code Phantom, we generate nine
different high-resolution, 3D simulations of an AGB star with a solar-mass
companion with various wind velocity and eccentricity combinations. The models
assume a polytropic gas, with no additional cooling. We conclude that for
models with a high wind velocity, the short interaction with the companion
results in a regular spiral morphology, that is flattened. In the case of a
lower wind velocity, the stronger interaction results in the formation of a
high-energy region and bow-shock structure that can shape the wind into an
irregular morphology if instabilities arise. High-eccentricity models show a
complex, phase-dependent interaction leading to wind structures that are
irregular in three dimensions. However, the significant interaction with the
companion compresses matter into an equatorial density enhancement,
irrespective of eccentricity.


Abstract:
  Considering the huge computational resources required by smoothed particle
hydrodynamics (SPH) simulations and the overestimation of post-collision
materials from perfect merging, we develop a statistical method to deal with
collisions during the formation of planetary systems by introducing random
material loss. In this method the mass and water content lost by the sole
outcome from every merger vary randomly within a range dependent on the total
mass and water content of colliding bodies. The application of the random loss
method to the planet formation in the solar system shows a good consistency
with existing SPH results. We also apply this method to the extrasolar
planetary system 55 Cancri which hosts (at least) five planets and study the
formation of terrestrial planets between the outermost two planets. A disk with
500 Mars mass embryos in dynamically cold orbits before the late-stage
accretion phase is assumed. Scenarios with different amounts of planetary
embryos and different loss parameters are adopted in our simulations. The
statistical result from hundreds of simulations shows that an Earth-like planet
with water inventory of roughly 6 Earth ocean could form between 55 Cnc f and
d. It may reside between 1.0 and 2.6 AU but the most likely region extends from
1.5 to 2.1 AU. Thus the probability of this planet being in the potentially
habitable zone (0.59--1.43 AU) is relatively low, only around 10\%. Planets 55
Cnc f and d could also be shaped and gain some water from giant impacts and
consequently the orbits of them may also change accordingly.


Abstract:
  We investigate the effect of strong interaction in the dressed energy levels
of the two level emitters. Strong dipole-dipole interactions give rise to new
sidebands in the fluorescence spectrum due to specific couplings among the
collective dressed levels which in turn depends on the spatial configuration of
atoms. These couplings are the main responsible for the frequencies and variety
of sidebands. We explain the general method for finding the dressed energy
levels for a system of any number of strongly coupled atoms and we solve this
problem for two different spatial configurations of three coupled two-level
emitters. We show that the coupling among dressed levels and consequently
energies and number of sidebands in the fluorescence spectrum are different for
each configuration. Thus the fluorescence spectrum of strongly interacting
atoms contains information about the number and configuration of atoms.


Abstract:
  Multi-source domain adaptation aims at leveraging the knowledge from multiple
tasks for predicting a related target domain. Hence, a crucial aspect is to
properly combine different sources based on their relations. In this paper, we
analyzed the problem for aggregating source domains with different label
distributions, where most recent source selection approaches fail. Our proposed
algorithm differs from previous approaches in two key ways: the model
aggregates multiple sources mainly through the similarity of semantic
conditional distribution rather than marginal distribution; the model proposes
a \emph{unified} framework to select relevant sources for three popular
scenarios, i.e., domain adaptation with limited label on target domain,
unsupervised domain adaptation and label partial unsupervised domain adaption.
We evaluate the proposed method through extensive experiments. The empirical
results significantly outperform the baselines.


Abstract:
  Research has shown assistance can provide many benefits to novices lacking
the mental models needed for problem solving in a new domain. However, varying
approaches to assistance, such as subgoals and next-step hints, have been
implemented with mixed results. Next-Step hints are common in data-driven
tutors due to their straightforward generation from historical student data, as
well as research showing positive impacts on student learning. However, there
is a lack of research exploring the possibility of extending data-driven
methods to provide higher-level assistance. Therefore, we modified our
data-driven Next-Step hint generator to provide Waypoints, hints that are a few
steps ahead, representing problem-solving subgoals. We hypothesized that
Waypoints would benefit students with high prior knowledge, and that Next-Step
hints would most benefit students with lower prior knowledge. In this study, we
investigated the influence of data-driven hint type, Waypoints versus Next-Step
hints, on student learning in a logic proof tutoring system, Deep Thought, in a
discrete mathematics course. We found that Next-Step hints were more beneficial
for the majority of students in terms of time, efficiency, and accuracy on the
posttest. However, higher totals of successfully used Waypoints were correlated
with improvements in efficiency and time in the posttest. These results suggest
that Waypoint hints could be beneficial, but more scaffolding may be needed to
help students follow them.


Abstract:
  Online social media has been a popular source for people to consume and share
news content. More recently, the spread of misinformation online has caused
widespread concerns. In this work, we focus on a critical task of detecting
fauxtography on social media where the image and associated text together
convey misleading information. Many efforts have been made to mitigate
misinformation online, but we found that the fauxtography problem has not been
fully addressed by existing work. Solutions focusing on detecting fake images
or misinformed texts alone on social media often fail to identify the
misinformation delivered together by the image and the associated text of a
fauxtography post. In this paper, we develop FauxWard, a novel graph
convolutional neural network framework that explicitly explores the complex
information extracted from a user comment network of a social media post to
effectively identify fauxtography. FauxWard is content-free in the sense that
it does not analyze the visual or textual contents of the post itself, which
makes it robust against sophisticated fauxtography uploaders who intentionally
craft image-centric posts by editing either the text or image content. We
evaluate FauxWard on two real-world datasets collected from mainstream social
media platforms (i.e., Reddit and Twitter). The results show that FauxWard is
both effective and efficient in identifying fauxtography posts on social media.


Abstract:
  Deploying antenna arrays with an asymptotically large aperture will be
central to achieving the theoretical gains of massive MIMO in beyond-5G
systems. Such extra-large MIMO (XL-MIMO) systems experience propagation
conditions which are not typically observed in conventional massive MIMO
systems, such as spatial non-stationarities and near-field propagation.
Moreover, standard precoding schemes, such as zero-forcing (ZF), may not apply
to XL-MIMO transmissions due to the prohibitive complexity associated with such
a large-scale scenario. We propose two novel precoding schemes that aim at
reducing the complexity without losing much performance. The proposed schemes
leverage a plane-wave approximation and user grouping to obtain a
low-complexity approximation of the ZF precoder. Our simulation results show
that the proposed schemes offer a possibility for a performance and complexity
trade-off compared to the benchmark schemes.


Abstract:
  We consider a fermionic many body system in Zd with a short range interaction
and quasi-periodic disorder. In the strong disorder regime and assuming a
Diophantine condition on the frequencies and on the chemical potential, we
prove at $T=0$ the exponential decay of the correlations and the vanishing of
the Drude weight, signaling Anderson localization in the ground state. The
proof combines Ward Identities, Renormalization Group and KAM Lindstedt series
methods.


Abstract:
  This article presents a novel approach to incorporate visual cues from
video-data from a wide-angle stereo camera system mounted at an urban
intersection into the forecast of cyclist trajectories. We extract features
from image and optical flow (OF) sequences using 3D convolutional neural
networks (3D-ConvNet) and combine them with features extracted from the
cyclist's past trajectory to forecast future cyclist positions. By the use of
additional information, we are able to improve positional accuracy by about 7.5
% for our test dataset and by up to 22 % for specific motion types compared to
a method solely based on past trajectories. Furthermore, we compare the use of
image sequences to the use of OF sequences as additional information, showing
that OF alone leads to significant improvements in positional accuracy. By
training and testing our methods using a real-world dataset recorded at a
heavily frequented public intersection and evaluating the methods' runtimes, we
demonstrate the applicability in real traffic scenarios. Our code and parts of
our dataset are made publicly available.


Abstract:
  We present a photonic reservoir computing, relying on a non-linear
phase-to-amplitude mapping process, able to classify in real-time multi-Gbaud
time traces subject to transmission effects. This approach delivers an
all-optical, low-power neuromorphic dispersion compensator.


Abstract:
  In this work we study $d$-dimensional majorant properties. We prove that a
set of frequencies in ${\mathbb Z}^d$ satisfies the strict majorant property on
$L^p([0,1]^d)$ for all $p> 0$ if and only if the set is affinely independent.
We further construct three types of violations of the strict majorant property.
Any set of at least $d+2$ frequencies in ${\mathbb Z}^d$ violates the strict
majorant property on $L^p([0,1]^d)$ for an open interval of $p \not\in 2
{\mathbb N}$ of length 2. Any infinite set of frequencies in ${\mathbb Z}^d$
violates the strict majorant property on $L^p([0,1]^d)$ for an infinite
sequence of open intervals of $p \not\in 2 {\mathbb N}$ of length $2$. Finally,
given any $p>0$ with $p \not\in 2{\mathbb N}$, we exhibit a set of $d+2$
frequencies on the moment curve in ${\mathbb R}^d$ that violate the strict
majorant property on $L^p([0,1]^d).$


Abstract:
  Quantum computing promises significant improvements of computation
capabilities in various fields such as machine learning and complex
optimization problems. Recent technological advancements suggest that the
adiabatic quantum computing ansatz may soon see practical applications. In this
work, we adopt this computation paradigm to develop a quantum computation based
solver of the well-known weapon target assignment problem, an NP-hard nonlinear
integer programming optimization task. The feasibility of the presented model
is demonstrated by numerical simulation of the adiabatic evolution of a system
of quantum bits towards the optimal solution encoded in the model Hamiltonian.
Over all, the described method is not limited to the context of weapon
management but is, with slight modifications to the model Hamiltonian,
applicable to worker-task allocation optimization in general.


Abstract:
  Let $G$ be a connected graph. For an ordered set $S=\{v_1,\ldots,
v_\ell\}\subseteq V(G)$, the vector $r_G(v|S) = (d_G(v_1,v), \ldots,
d_G(v_\ell,v))$ is called the metric $S$-representation of $v$. If for any pair
of different vertices $u,v\in V(G)$, the vectors $r(v|S)$ and $r(u|S)$ differ
in at least $k$ positions, then $S$ is a $k$-metric generator for $G$. A
smallest $k$-metric generator for $G$ is a $k$-{\em metric basis} for $G$, its
cardinality being the $k$-metric dimension of $G$. A sharp upper bound and a
closed formulae for the $k$-metric dimension of the hierarchical product of
graphs is proved. Also, sharp lower bounds for the $k$-metric dimension of the
splice and link products of graphs are presented. An integer linear programming
model for computing the $k$-metric dimension and a $k$-metric basis of a given
graph is proposed. These results are applied to bound or to compute the
$k$-metric dimension of some classes of graphs that are of interest in
mathematical chemistry.


Abstract:
  We present the low-resolution transmission spectra of the puffy hot Jupiter
HAT-P-65b (0.53 M$_\mathrm{Jup}$, 1.89 R$_\mathrm{Jup}$, $T_\mathrm{eq}=1930$
K), based on two transits observed using the OSIRIS spectrograph on the 10.4 m
Gran Telescopio CANARIAS (GTC). The transmission spectra of the two nights are
consistent, covering the wavelength range 517--938 nm and consisting of mostly
5 nm spectral bins. We perform equilibrium-chemistry spectral retrieval
analyses on the jointly fitted transmission spectrum and obtain an equilibrium
temperature of $1645^{+255}_{-244}$ K and a cloud coverage of
$36^{+23}_{-17}$%, revealing a relatively clear planetary atmosphere. Based on
free-chemistry retrieval, we report strong evidence for TiO. Additional
individual analyses in each night reveal weak-to-moderate evidence for TiO in
both nights, but moderate evidence for Na or VO only in one of the nights.
Future high-resolution Doppler spectroscopy as well as emission observations
will help confirm the presence of TiO and constrain its role in shaping the
vertical thermal structure of HAT-P-65b's atmosphere.


Abstract:
  We introduce a novel problem for diversity-aware clustering. We assume that
the potential cluster centers belong to a set of groups defined by protected
attributes, such as ethnicity, gender, etc. We then ask to find a minimum-cost
clustering of the data into $k$ clusters so that a specified minimum number of
cluster centers are chosen from each group. We thus require that all groups are
represented in the clustering solution as cluster centers, according to
specified requirements. More precisely, we are given a set of clients $C$, a
set of facilities $\pazocal{F}$, a collection $\mathcal{F}=\{F_1,\dots,F_t\}$
of facility groups $F_i \subseteq \pazocal{F}$, budget $k$, and a set of
lower-bound thresholds $R=\{r_1,\dots,r_t\}$, one for each group in
$\mathcal{F}$. The \emph{diversity-aware $k$-median problem} asks to find a set
$S$ of $k$ facilities in $\pazocal{F}$ such that $|S \cap F_i| \geq r_i$, that
is, at least $r_i$ centers in $S$ are from group $F_i$, and the $k$-median cost
$\sum_{c \in C} \min_{s \in S} d(c,s)$ is minimized. We show that in the
general case where the facility groups may overlap, the diversity-aware
$k$-median problem is \np-hard, fixed-parameter intractable, and inapproximable
to any multiplicative factor. On the other hand, when the facility groups are
disjoint, approximation algorithms can be obtained by reduction to the
\emph{matroid median} and \emph{red-blue median} problems. Experimentally, we
evaluate our approximation methods for the tractable cases, and present a
relaxation-based heuristic for the theoretically intractable case, which can
provide high-quality and efficient solutions for real-world datasets.


Abstract:
  Vehicular ad hoc networks is a modern technology that holds an important
aspect in the transportation domain due to its abilities to increase traffic
efficiency and safety. It is a another variant of Mobile ad-hoc networks that
provides Vehicles to Vehicles (V2V), Rode-side Unit to Road-side Unit(R2R) and
Vehicles to road-side Unit (V2R) communication.VANET is a multidimensional
network in which the vehicles ceaselessly alter their locations. Connected
vehicles broadcast sensitive information which must be communicated with the
neighbors in a safe and established environment. VANET may also contain
dishonest nodes such as man in -the-middle attackers that aim to distribute and
share malicious content with the vehicles, thus contaminating the network with
secure information. In this situation implementing a trust among connected
vehicles can raise security as every participating vehicle will create and
propagate authentic, accurate,and trusted content within the network.In this
paper we used a trust model to determine the trust level and eliminate the
malicious nodes.We created a simulation for calculating the trust level and
eliminating the malicious node in the wireless ad hoc networks using ns2
simulator and network animator(nam).The simulation results showed a better
bandwidth in communication between the nodes after the trust level is updated


Abstract:
  In this paper, we consider the linear programming (LP) formulation for deep
reinforcement learning. The number of the constraints depends on the size of
state and action spaces, which makes the problem intractable in large or
continuous environments. The general augmented Lagrangian method suffers the
double-sampling obstacle in solving the LP. Namely, the conditional
expectations originated from the constraint functions and the quadratic
penalties in the augmented Lagrangian function impose difficulties in sampling
and evaluation. Motivated from the updates of the multipliers, we overcome the
obstacles in minimizing the augmented Lagrangian function by replacing the
intractable conditional expectations with the multipliers. Therefore, a deep
parameterized augment Lagrangian method is proposed. Furthermore, the
replacement provides a promising breakthrough to integrate the two steps in the
augmented Lagrangian method into a single constrained problem. A general
theoretical analysis shows that the solutions generated from a sequence of the
constrained optimizations converge to the optimal solution of the LP if the
error is controlled properly. A theoretical analysis on the quadratic penalty
algorithm under neural tangent kernel setting shows the residual can be
arbitrarily small if the parameter in network and optimization algorithm is
chosen suitably. Preliminary experiments illustrate that our method is
competitive to other state-of-the-art algorithms.


Abstract:
  This paper explores Tadmor's minimum entropy principle for the relativistic
hydrodynamics (RHD) equations and incorporates this principle into the design
of robust high-order discontinuous Galerkin (DG) and finite volume schemes for
RHD on general meshes. The schemes are proven to preserve numerical solutions
in a global invariant region constituted by all the known intrinsic
constraints: minimum entropy principle, the subluminal constraint on fluid
velocity, and the positivity of pressure and rest-mass density. Relativistic
effects lead to some essential difficulties in the present study, which are not
encountered in the non-relativistic case. Most notably, in the RHD case the
specific entropy is a highly nonlinear implicit function of the conservative
variables, and, moreover, there is also no explicit formula of the flux in
terms of the conservative variables. In order to overcome the resulting
challenges, we first propose a novel equivalent form of the invariant region,
by skillfully introducing two auxiliary variables. As a notable feature, all
the constraints in the novel form are explicit and linear with respect to the
conservative variables. This provides a highly effective approach to
theoretically analyze the invariant-region-preserving (IRP) property of schemes
for RHD, without any assumption on the IRP property of the exact Riemann
solver. Based on this, we prove the convexity of the invariant region and
establish the generalized Lax--Friedrichs splitting properties via technical
estimates, lying the foundation for our IRP analysis. It is shown that the
first-order Lax--Friedrichs scheme for RHD satisfies a local minimum entropy
principle and is IRP under a CFL condition. Provably IRP high-order DG and
finite volume methods are developed for the RHD with the help of a simple
scaling limiter. Several numerical examples demonstrate the effectiveness of
the proposed schemes.


Abstract:
  Antenna technology is at the basis of ubiquitous wireless communication
systems and sensors. Radiation is typically sustained by conduction currents
flowing around resonant metallic objects that are optimized to enhance
efficiency and bandwidth. However, resonant conductors are prone to large
scattering of impinging waves, leading to challenges in crowded antenna
environments due to blockage and distortion. Metasurface cloaks have been
explored in the quest of addressing this challenge by reducing antenna
scattering, but with limited performance in terms of bandwidth, footprint and
overall scattering reduction. Here we introduce a different route towards
radio-transparent antennas, in which the cloak itself acts as the radiating
element, drastically reducing the overall footprint while enhancing scattering
suppression and bandwidth, without sacrificing other relevant radiation metrics
compared to conventional antennas. This technique offers a new application of
cloaking technology, with promising features for crowded wireless communication
platforms and noninvasive sensing.


Abstract:
  The ongoing COVID-19 pandemic is being responded with various methods,
applying vaccines, experimental treatment options, total lockdowns or partial
curfews. Weekend curfews is one of the methods to reduce the amount of infected
persons and this method is practically applied in some countries such as
Turkey. In this study, the effect of weekend curfews on reducing the spread of
a contagious disease, such as COVID-19, is modeled using a Monte Carlo
algorithm with a hybrid lattice model. In the simulation setup, a fictional
country with three towns and 26,610 citizens were used as a model. Results
indicate that applying a weekend curfew reduces the active cases significantly
and is one of the efficient ways to fight the epidemic. The results also show
that applying personal precautions such as social distancing is important for
reducing the number of cases and deaths.


Abstract:
  Daniely and Schacham recently showed that gradient descent finds adversarial
examples on random undercomplete two-layers ReLU neural networks. The term
"undercomplete" refers to the fact that their proof only holds when the number
of neurons is a vanishing fraction of the ambient dimension. We extend their
result to the overcomplete case, where the number of neurons is larger than the
dimension (yet also subexponential in the dimension). In fact we prove that a
single step of gradient descent suffices. We also show this result for any
subexponential width random neural network with smooth activation function.


Abstract:
  We present a short introduction to a non-standard cosmological scenario
motivated by the duality symmetries of string theory, in which the big bang
singularity is replaced with a "big bounce" at high but finite curvature. The
bouncing epoch is prepared by a long (possibly infinitely extended) phase of
cosmic evolution, starting from an initial state asymptotically approaching the
string perturbative vacuum.


Abstract:
  Agrivoltaic systems represent a key technology for reaching sustainable
development goals reducing the completion of land for food versus land for
energy. Moreover, agrivoltaic systems are at the centre of the nexus between
electricity production, crop production, and irrigation water saving. In this
study, an optimization model for vertically mounted agrivoltaic systems with
bifacial photovoltaic models is developed. The model combines three main
submodels: solar radiation and shadings, photovoltaic, and crop yield.
Validation of the submodels is performed showing good agreement with measured
data and commercial software. The optimization model is set as multi objective
to explore the trade-offs between competing agrivoltaic key performance
indicators. The results shows that the row distance between bifacial
photovoltaic modules structure affects significantly the photosyntetically
active radiation distribution by reducing the crop yield of potato and oat of
about 50% by passing from 20 m to 5 m. The implementation of agrivoltaic system
for the investigated crops at the chosen location shows a land equivalent ratio
above 1.2 that justify the technology for reaching the country sustainability
goals.


Abstract:
  Linear optical multiports are widely used in photonic quantum information
processing. Naturally, these devices are directionally-biased since photons
always propagate from the input ports toward the output ports. Recently, the
concept of directionally-unbiased linear optical multiports was proposed. These
directionally-unbiased multiports allow photons to propagate along a reverse
direction, which can greatly reduce the number of required linear optical
elements for complicated linear optical quantum networks. Here, we report an
experimental demonstration of a 3 x 3 directionally-unbiased linear optical
fiber multiport using an optical tritter and mirrors. Compared to the previous
demonstration using bulk optical elements which works only with light sources
with a long coherence length, our experimental directionally-unbiased 3 x 3
optical multiport does not require a long coherence length since it provides
negligible optical path length differences among all possible optical
trajectories. It can be a useful building block for implementing large-scale
quantum walks on complex graph networks.


Abstract:
  Artificial intelligence (AI) will play an increasing role in cellular network
deployment, configuration and management. This paper examines the security
implications of AI-driven 6G radio access networks (RANs). While the expected
timeline for 6G standardization is still several years out, pre-standardization
efforts related to 6G security are already ongoing and will benefit from
fundamental and experimental research. The Open RAN (O-RAN) describes an
industry-driven open architecture and interfaces for building next generation
RANs with AI control. Considering this architecture, we identify the critical
threats to data driven network and physical layer elements, the corresponding
countermeasures, and the research directions.


Abstract:
  This paper is concerned with the relativistic Boltzmann equation without
angular cutoff. We establish the global-in-time existence, uniqueness and
asymptotic stability for solutions nearby the relativistic Maxwellian to the
special relativistic Boltzmann equation without any angular cutoff. We work in
the case of a spatially periodic box. We assume the generic hard-interaction
and soft-interaction conditions on the collision kernel that were derived by
Dudy\'nski and Ekiel-Je$\dot{\text{z}}$ewska (Comm. Math. Phys. 115(4):607-629,
1985), and our assumptions include the case of Israel particles (J. Math. Phys.
4:1163-1181, 1963). In this physical situation, the angular function in the
collision kernel is not locally integrable, and the collision operator behaves
like a fractional diffusion operator. We further derive the relativistic
analogue of the Carleman dual representation of Boltzmann collision operator.
This is the first global existence and stability result for relativistic
Boltzmann equation without angular cutoff and this resolves the open question
of perturbative global existence without the Grad's angular cut-off assumption.


Abstract:
  We study the problem of online peak-demand minimization under energy storage
constraints. It is motivated by an increasingly popular scenario where
large-load customers utilize energy storage to reduce the peak procurement from
the grid, which accounts for up to $90\%$ of their electric bills. The problem
is uniquely challenging due to (i) the coupling of online decisions across time
imposed by the inventory constraints and (ii) the noncumulative nature of the
peak procurement. In this paper, we develop an optimal online algorithm for the
problem, attaining the best possible competitive ratio (CR) among all
deterministic and randomized algorithms. We show that the optimal CR can be
computed in polynomial time, by solving a linear number of linear-fractional
problems. More importantly, we generalize our approach to develop an
\emph{anytime-optimal} online algorithm that achieves the best possible CR at
any epoch, given the inputs and online decisions so far. The algorithm retains
the optimal worst-case performance and achieves adaptive average-case
performance. Simulation results based on real-world traces show that, under
typical settings, our algorithms improve peak reduction by over $19\%$ as
compared to baseline alternatives.


Abstract:
  Titan's surface was revealed by Cassini's instruments, showing the presence
of liquid hydrocarbons in lakes, and features like dry riverbed. In order to
study the sediment transport in Titan's channels and to map distribution of the
water-ice signature in these terrains, we use a radiative transfer model to
retrieve the surface albedo, after we estimated VIMS error via an original
method. We also establish a criteria related to the intensity of the water ice
signature. The tuning of the radiative transfer model shows that the fractal
dimension of Titan's aerosols is higher than previously thought, around 2.3 -
2.4. We find spots of increasing signal of water ice downstream, at the margins
of Tui Regio, that could correspond to alluvial fans, deltas or crater rims. We
also observe a very low water ice signal on Tui Regio, with a positive gradient
between the central region and the boundary of the area, possibly due to the
thickness variation of an evaporitic layer. The riverbeds show within the error
bars a decreasing grain size from the top to the bottom of the channels.


Abstract:
  There has been a surge in remote sensing machine learning applications that
operate on data from active or passive sensors as well as multi-sensor
combinations (Ma et al. (2019)). Despite this surge, however, there has been
relatively little study on the comparative value of 3D surface information for
machine learning classification tasks. Adding 3D surface information to RGB
imagery can provide crucial geometric information for semantic classes such as
buildings, and can thus improve out-of-sample predictive performance. In this
paper, we examine in-sample and out-of-sample classification performance of
Fully Convolutional Neural Networks (FCNNs) and Support Vector Machines (SVMs)
trained with and without 3D normalized digital surface model (nDSM)
information. We assess classification performance using multispectral imagery
from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D
Semantic Labeling contest and the United States Special Operations Command
(USSOCOM) Urban 3D Challenge. We find that providing RGB classifiers with
additional 3D nDSM information results in little increase in in-sample
classification performance, suggesting that spectral information alone may be
sufficient for the given classification tasks. However, we observe that
providing these RGB classifiers with additional nDSM information leads to
significant gains in out-of-sample predictive performance. Specifically, we
observe an average improvement in out-of-sample all-class accuracy of 14.4% on
the ISPRS dataset and an average improvement in out-of-sample F1 score of 8.6%
on the USSOCOM dataset. In addition, the experiments establish that nDSM
information is critical in machine learning and classification settings that
face training sample scarcity.


Abstract:
  In this paper, we present an attention-guided deformable convolutional
network for hand-held multi-frame high dynamic range (HDR) imaging, namely
ADNet. This problem comprises two intractable challenges of how to handle
saturation and noise properly and how to tackle misalignments caused by object
motion or camera jittering. To address the former, we adopt a spatial attention
module to adaptively select the most appropriate regions of various exposure
low dynamic range (LDR) images for fusion. For the latter one, we propose to
align the gamma-corrected images in the feature-level with a Pyramid, Cascading
and Deformable (PCD) alignment module. The proposed ADNet shows
state-of-the-art performance compared with previous methods, achieving a
PSNR-$l$ of 39.4471 and a PSNR-$\mu$ of 37.6359 in NTIRE 2021 Multi-Frame HDR
Challenge.


Abstract:
  Semiconducting Transition Metal Dichalcogenides (TMDs) have significant
nonlinear optical effects. In this work we have used second-harmonic generation
(SHG) and the four-wave mixing (FWM) spectroscopy in resonance with the
excitons in MoS2, MoSe2, and WS2 monolayers to characterize the nonlinear
optical properties of these materials. We show that trions and excitons are
responsible for enhancing the nonlinear optical response, and determine the
exciton and trion energies by comparing with the photoluminescence spectra.
Moreover, we extract the second and third order optical sheet susceptibility
near exciton energies and compare with values found in the literature. We also
demonstrate the ability to generate different nonlinear effects in a wide
spectral range in the visible region for monolayer MoS2, opening the
possibility of using two-dimensional materials for nonlinear optoelectronic and
photonic applications.


Abstract:
  Given a class $\mathcal P$ of Banach spaces, a locally convex space (LCS) $E$
is called {\em multi-$\mathcal P$} if $E$ can be isomorphically embedded into a
product of spaces that belong to $\mathcal P$. We investigate the question
whether the free locally convex space $L(X)$ is strongly nuclear, nuclear,
Schwartz, multi-Hilbert or multi-reflexive.
  If $X$ is a Tychonoff space containing an infinite compact subset then, as it
follows from the results of \cite{Aus}, $L(X)$ is not nuclear. We prove that
for such $X$ the free LCS $L(X)$ has the stronger property of not being
multi-Hilbert. We deduce that if $X$ is a $k$-space, then the following
properties are equivalent: (1) $L(X)$ is strongly nuclear; (2) $L(X)$ is
nuclear; (3) $L(X)$ is multi-Hilbert; (4) $X$ is countable and discrete. On the
other hand, we show that $L(X)$ is strongly nuclear for every projectively
countable $P$-space (in particular, for every Lindel\"of $P$-space) $X$.
  We observe that every Schwartz LCS is multi-reflexive. It is known that if
$X$ is a $k_\omega$-space, then $L(X)$ is a Schwartz LCS \cite{Chasco}, hence
$L(X)$ is multi-reflexive. We show that for any first-countable paracompact
  (in particular, metrizable) space $X$ the converse is true, so $L(X)$ is
multi-reflexive if and only if $X$ is a $k_\omega$-space, equivalently, if $X$
is a locally compact and $\sigma$-compact space.
  Similarly, we show that for any first-countable paracompact space $X$ the
free abelian topological group $A(X)$ is a Schwartz group if and only if $X$ is
a locally compact space such that the set $X^{(1)}$ of all non-isolated points
of $X$ is $\sigma$-compact.


Abstract:
  By lowering the number of vertical connections in fully connected 3D
networks-on-chip (NoCs), partially connected 3D NoCs (PC-3DNoCs) help alleviate
reliability and fabrication issues. This paper proposes a novel, adaptive
congestion- and energy-aware elevator-selection scheme called AdEle to improve
the traffic distribution in PC-3DNoCs. AdEle employs an offline multi-objective
simulated-annealing-based algorithm to find good elevator subsets and an online
elevator selection policy to enhance elevator selection during routing.
Compared to the state-of- the-art techniques under different real-application
traffics and configuration scenarios, AdEle improves the network latency by
10.9% on average (up to 14.6%) with less than 6.9% energy consumption overhead.


Abstract:
  In this paper, we demonstrate a domain inversion crystal structure to study
the cascaded three-wave mixing process in a two peak Stark-chirped rapid
adiabatic passage. We have achieved efficient wavelength conversion, which can
be performed in intuitive order and counterintuitive order. The requirement of
crystal is reduced and the flexibility of structure design is improved. When
the conversion wavelength is fixed, increasing the coupling coefficient between
the two peaks can reduce the intensity of the intermediate wavelength while
maintaining high conversion efficiency. Compared with the cascaded wavelength
conversion process based on stimulated Raman rapid adiabatic passage, the
two-peak Stark-chirped rapid adiabatic passage has a larger convertible
wavelength bandwidth. This scheme provides a theoretical basis for obtaining
mid-infrared laser source via flexible crystal structure.


Abstract:
  Photonic quantum technologies$^1$, with applications in quantum
communication, sensing as well as quantum simulation and computing, are on the
verge of becoming commercially available. One crucial building block are
tailored nanoscale integratable quantum light sources, matching the specific
needs of use-cases. Several different approaches to realize solid-state quantum
emitters$^2$ with high performance$^3$ have been pursued. However, the
properties of the emitted single photons are always defined by the individual
quantum light source and despite numerous quantum emitter tuning
techniques$^{4-7}$, scalability is still a major challenge. Here we show an
emitter-independent method to tailor and control the properties of the single
photon emission. We demonstrate a laser-controlled down-conversion process from
an excited state of a quantum three-level system$^8$. Starting from a biexciton
state, a tunable control laser field defines a virtual state in a stimulated
process. From there, spontaneous emission to the ground state leads to
optically controlled single photon emission. Based on this concept, we
demonstrate energy tuning of the single photon emission with a control laser
field. The nature of the involved quantum states furthermore provides a unique
basis for the future control of polarization and bandwidth, as predicted by
theory$^{9,10}$. Our demonstration marks an important step towards tailored
single photon emission from a photonic quantum system based on quantum optical
principles.


Abstract:
  In recent years, the existence of a hadronically stable $\bar{b} \bar{b} u d$
tetraquark with quantum numbers $I(J^P) = 0(1^+)$ was confirmed by first
principles lattice QCD computations. In this work we use lattice QCD to compare
two frequently discussed competing structures for this tetraquark by
considering meson-meson as well as diquark-antidiquark creation operators. We
use the static-light approximation, where the two $\bar{b}$ quarks are assumed
to be infinitely heavy with frozen positions, while the light $u$ and $d$
quarks are fully relativistic. By minimizing effective energies and by solving
generalized eigenvalue problems we determine the importance of the meson-meson
and the diquark-antidiquark creation operators with respect to the ground
state. It turns out, that the diquark-antidiquark structure dominates for
$\bar{b} \bar{b}$ separations $r < 0.25 \, \text{fm}$, whereas it becomes
increasingly more irrelevant for larger separations, where the $I(J^P) =
0(1^+)$ tetraquark is mostly a meson-meson state. We also estimate the
meson-meson to diquark-antidiquark ratio of this tetraquark and find around
$60\% / 40\%$.


Abstract:
  Gapless Luttinger liquid is conventionally viewed as topologically trivial,
unless it hosts degenerate ground states and or entanglement spectrum, which
necessitates partial bulk degree of freedom to be gapped out. Here we predict
an emergent gapless topological Luttinger liquid which is beyond the
conventional scenarios and is characterized by the nontrivial many-body bulk
spin texture, and propose feasible scheme for experimental observation. We
consider a one-dimensional spin-orbit coupled Fermi-Hubbard model with
fractional filling, whose low-energy physics is effectively described by a
spinless Luttinger liquid and is trivial in the conventional characterization.
We show that, as being tuned by the filling factor and interaction strength,
the many-body ground state may exhibit nontrivial winding in its bulk spin
texture in the projected momentum space, manifesting an emergent topological
phase. A topological transition occurs when the projected spin-state at a high
symmetry momentum becomes fully mixed one, resulting from competing processes
of particle scattering to the lower and higher subbands, for which the spin
texture at such momentum point is ill-defined, but the Luttinger liquid keeps
gapless through the transition. Surprisingly, at relatively small filling the
Luttinger liquid remains topologically nontrivial even at infinitely strong
interaction. The results can be generalized to finite temperature which
facilitates the real experimental detection. This work shows a novel gapless
topological Luttinger liquid whose characterization is beyond the low-energy
effective theory, and can be verified based on current experiments.


Abstract:
  The traditional phase sensitivity function (PSF) has manifested its efficacy
in investigating synchronization behaviors for limit-cycle oscillators.
However, some subtle details may be ignored when the phase value is accumulated
in space or the perturbation is space-dependent. In this paper, we compared
spatial PSF with the traditional PSF for oscillators close to the saddle-node
homoclinic (SNH) bifurcation, also known as saddle-node on invariant circle
(SNIC) bifurcation. It is found that the spatial phase sensitivity function
could reveal the phase accumulation feature on the limit cycle. Moreover, it is
proved that for any two-dimensional smooth dynamical system, type II phase
response curve is the only possible type. Finally, the synchronization
distributions of uncoupled SNH oscillator driven by common and independent
noises are studied, which shows the space-dependent coupling function of common
noise could have significant influences on the synchronization behavior.


Abstract:
  We theoretically consider spin pumping in a junction between a ferromagnetic
insulator (FI) and a two-dimensional electron gas (2DEG) in which the Rashba
and Dresselhaus spin-orbit interactions coexist. Using second-order
perturbation theory, we derive an increase in linewidth in the case of an
interfacial exchange coupling in a ferromagnetic resonance (FMR) experiment. We
clarify how the enhancement of Gilbert damping depends on the resonant
frequency and spin orientation of the FI. We show that this setup of an FMR
experiment can provide information on the spin texture of 2DEG at the Fermi
surface.


Abstract:
  The Shapley value has become popular in the Explainable AI (XAI) literature,
thanks, to a large extent, to a solid theoretical foundation, including four
"favourable and fair" axioms for attribution in transferable utility games. The
Shapley value is provably the only solution concept satisfying these axioms. In
this paper, we introduce the Shapley value and draw attention to its recent
uses as a feature selection tool. We call into question this use of the Shapley
value, using simple, abstract "toy" counterexamples to illustrate that the
axioms may work against the goals of feature selection. From this, we develop a
number of insights that are then investigated in concrete simulation settings,
with a variety of Shapley value formulations, including SHapley Additive
exPlanations (SHAP) and Shapley Additive Global importancE (SAGE).


Abstract:
  Concept learning approaches based on refinement operators explore partially
ordered solution spaces to compute concepts, which are used as binary
classification models for individuals. However, the number of concepts explored
by these approaches can easily grow to the millions for complex learning
problems. This often leads to impractical runtimes. We propose to alleviate
this problem by predicting the length of target concepts before the exploration
of the solution space. By these means, we can prune the search space during
concept learning. To achieve this goal, we compare four neural architectures
and evaluate them on four benchmarks. Our evaluation results suggest that
recurrent neural network architectures perform best at concept length
prediction with a macro F-measure ranging from 38% to 92%. We then extend the
CELOE algorithm--which learns ALC concepts--with our concept length predictor.
Our extension yields the algorithm CLIP. In our experiments, CLIP is at least
7.5 times faster than other state-of-the-art concept learning algorithms for
ALC--including CELOE--and achieves significant improvements in the F-measure of
the concepts learned on 3 out of 4 datasets. For reproducibility, we provide
our implementation in the public GitHub repository at
https://github.com/ConceptLengthLearner/LearnLengths


Abstract:
  Large Transformer-based language models can aid human authors by suggesting
plausible continuations of text written so far. However, current interactive
writing assistants do not allow authors to guide text generation in desired
topical directions. To address this limitation, we design a framework that
displays multiple candidate upcoming topics, of which a user can select a
subset to guide the generation. Our framework consists of two components: (1) a
method that produces a set of candidate topics by predicting the centers of
word clusters in the possible continuations, and (2) a text generation model
whose output adheres to the chosen topics. The training of both components is
self-supervised, using only unlabeled text. Our experiments demonstrate that
our topic options are better than those of standard clustering approaches, and
our framework often generates fluent sentences related to the chosen topics, as
judged by automated metrics and crowdsourced workers.


Abstract:
  In continuous variable systems, it is often advantageous to study the
system's covariance matrix rather than the more complex density operator.
However, the time evolution of the covariance matrix is known to be
self-contained only in the quadratic case, i.e. when the generator of the
evolution is at most second-order in the position and momentum operators. Here,
we derive an explicit model of covariance matrix evolution of infinite order,
which proves useful in scenarios as varied as random scattering and engineered
dissipation. Our findings have broader implications for contemporary measures
of non-Gaussianity.


Abstract:
  We consider the problem of the extraction of semantic attributes, supervised
only with classification labels. For example, when learning to classify images
of birds into species, we would like to observe the emergence of features that
zoologists use to classify birds. To tackle this problem, we propose training a
neural network with discrete features in the last layer, which is followed by
two heads: a multi-layered perceptron (MLP) and a decision tree. Since decision
trees utilize simple binary decision stumps we expect those discrete features
to obtain semantic meaning. We present a theoretical analysis as well as a
practical method for learning in the intersection of two hypothesis classes.
Our results on multiple benchmarks show an improved ability to extract a set of
features that are highly correlated with the set of unseen attributes.


Abstract:
  Dislocations govern the properties of any crystals. Yet, how dislocation of
pentagonheptagon (5|7) in grain boundaries (GBs) affects the mechanical
properties of two-dimensional MoS2 crystals remains poorly known. Using
atomistic simulations and continuum disclination dipole model, we show that,
depending on the tilt angle and 5|7 dislocation arrangement, MoS2 GB strength
can be enhanced or reduced with tilt angle. For zigzag-tilt GBs primarily
composed of Mo5|7+S5|7 dislocations, GB strength monotonically increases as the
square of tilt angle. For armchair-tilt GBs with Mo5|7 or S5|7 dislocations,
however, the trend of GB strength breaks down as 5|7 dislocations are
non-evenly spaced. Moreover, mechanical failure initiates at the bond shared by
5|7 rings, in contrast to graphene where failure occurs at the bond shared by
6|7 rings. This work provides new insights into mechanical design of synthetic
transition metal dichalcogenide crystals via dislocation engineering.


Abstract:
  Magnetic carbon nanostructures are currently under scrutiny for a wide
spectrum of applications. Here, we theoretically investigate armchair graphene
nanoribbons patterned with asymmetric edge extensions consisting of laterally
fused naphtho groups, as recently fabricated via on-surface synthesis. We show
that an individual edge extension acts as a spin-$\frac{1}{2}$ center and
develops a sizable spin-polarization of the conductance around the band edges.
The Heisenberg exchange coupling between a pair of edge extensions is dictated
by the position of the second naphtho group in the carbon backbone, thus
enabling ferromagnetic, antiferromagnetic, or non-magnetic states. The periodic
arrangement of edge extensions yields full spin-polarization at the band
extrema, and the accompanying ferromagnetic ground state can be driven into
non-magnetic or antiferromagnetic phases through external stimuli. Overall, our
work reveals precise tunability of the ${\pi}$-magnetism in graphene
nanoribbons induced by naphtho groups, thereby establishing these
one-dimensional architectures as suitable platforms for logic spintronics.


Abstract:
  Cloud applications are increasingly shifting from large monolithic services
to complex graphs of loosely-coupled microservices. Despite the advantages of
modularity and elasticity microservices offer, they also complicate cluster
management and performance debugging, as dependencies between tiers introduce
backpressure and cascading QoS violations.
  We present Sage, a machine learning-driven root cause analysis system for
interactive cloud microservices. Sage leverages unsupervised ML models to
circumvent the overhead of trace labeling, captures the impact of dependencies
between microservices to determine the root cause of unpredictable performance
online, and applies corrective actions to recover a cloud service's QoS. In
experiments on both dedicated local clusters and large clusters on Google
Compute Engine we show that Sage consistently achieves over 93% accuracy in
correctly identifying the root cause of QoS violations, and improves
performance predictability.


Abstract:
  In this paper we deal with the heat equation with drift in $L_{d+1}$.
Basically, we prove that, if the free term is in $L_{q}$ with high enough $q$,
then the equation is uniquely solvable in a rather unusual class of functions
such that $\partial_{t}u, D^{2}u\in L_{p}$ with $p<d+1$ and $Du\in L_{q}$.


Abstract:
  Tikhonov regularization with square-norm penalty for linear forward operators
has been studied extensively in the literature. However, the results on
convergence theory are based on technical proofs and difficult to interpret. It
is also often not clear how those results translate into the discrete,
numerical setting. In this paper we present a new strategy to study the
properties of a regularization method on the example of Tikhonov
regularization. The technique is based on the observation that Tikhonov
regularization approximates the unknown exact solution in the range of the
adjoint of the forward operator. This is closely related to the concept of
approximate source conditions, which we generalize to describe not only the
approximation of the unknown solution, but also noise-free and noisy data; all
from the same source space. Combining these three approximation results we
derive the well-known convergence results in a concise way and improve the
understanding by tightening the relation between concepts such as convergence
rates, parameter choice, and saturation. The new technique is not limited to
Tikhonov regularization, it can be applied also to iterative regularization,
which we demonstrate by relating Tikhonov regularization and Landweber
iteration. Because the Tikhonov functional is no longer the centrepiece of the
analysis, we can show that Tikhonov regularization can be used for
oversmoothing regularization. All results are accompanied by numerical
examples.


Abstract:
  The question why the solar corona is much hotter than the visible solar
surface still puzzles solar researchers. Most theories of the coronal heating
involve a tight coupling between the coronal magnetic field and the associated
thermal structure. This coupling is based on two facts: (i) the magnetic field
is the main source of the energy in the corona and (ii) the heat transfer
preferentially happens along the magnetic field, while is suppressed across it.
However, most of the information about the coronal heating is derived from
analysis of EUV or soft X-ray emissions, which are not explicitly sensitive to
the magnetic field. This paper employs another electromagnetic channel -- the
sunspot-associated microwave gyroresonant emission, which is explicitly
sensitive to both the magnetic field and thermal plasma. We use nonlinear
force-free field reconstructions of the magnetic skeleton dressed with a
thermal structure as prescribed by a field-aligned hydrodynamics to constrain
the coronal heating model. We demonstrate that the microwave gyroresonant
emission is extraordinarily sensitive to details of the coronal heating. We
infer heating model parameters consistent with observations.


Abstract:
  The exact critical Casimir amplitude is derived for anisotropic systems
within the $d=2$ Ising universality class by combining conformal field theory
(CFT) with anisotropic $\varphi^4$ theory. Explicit results are presented for
the general anisotropic scalar $\varphi^4$ model and for the fully anisotropic
triangular-lattice Ising model in finite rectangular and infinite strip
geometries with periodic boundary conditions (PBC). These results demonstrate
the validity of multiparameter universality for confined anisotropic systems
and the nonuniversality of the critical Casimir amplitude. We find an
unexpected complex form of self-similarity of the anisotropy effects near the
instability where weak anisotropy breaks down. This can be traced back to the
property of modular invariance of isotropic CFT for $d=2$. More generally, for
$d>2$ we predict the existence of self-similar structures of the finite-size
scaling functions of $O(n)$-symmetric systems with planar anisotropies and PBC
both in the critical region for $ n \geq 1$ as well as in the
Goldstone-dominated low-temperature region for $ n \geq 2$.


Abstract:
  Semantic segmentation is an extensively studied task in computer vision, with
numerous methods proposed every year. Thanks to the advent of deep learning in
semantic segmentation, the performance on existing benchmarks is close to
saturation. A natural question then arises: Does the superior performance on
the closed (and frequently re-used) test sets transfer to the open visual world
with unconstrained variations? In this paper, we take steps toward answering
the question by exposing failures of existing semantic segmentation methods in
the open visual world under the constraint of very limited human labeling
effort. Inspired by previous research on model falsification, we start from an
arbitrarily large image set, and automatically sample a small image set by
MAximizing the Discrepancy (MAD) between two segmentation methods. The selected
images have the greatest potential in falsifying either (or both) of the two
methods. We also explicitly enforce several conditions to diversify the exposed
failures, corresponding to different underlying root causes. A segmentation
method, whose failures are more difficult to be exposed in the MAD competition,
is considered better. We conduct a thorough MAD diagnosis of ten PASCAL VOC
semantic segmentation algorithms. With detailed analysis of experimental
results, we point out strengths and weaknesses of the competing algorithms, as
well as potential research directions for further advancement in semantic
segmentation. The codes are publicly available at
\url{https://github.com/QTJiebin/MAD_Segmentation}.


Abstract:
  Distance-based dynamic texture recognition is an important research field in
multimedia processing with applications ranging from retrieval to segmentation
of video data. Based on the conjecture that the most distinctive characteristic
of a dynamic texture is the appearance of its individual frames, this work
proposes to describe dynamic textures as kernelized spaces of frame-wise
feature vectors computed using the Scattering transform. By combining these
spaces with a basis-invariant metric, we get a framework that produces
competitive results for nearest neighbor classification and state-of-the-art
results for nearest class center classification.


Abstract:
  The introduction of new tools in people's workflow has always been promotive
of new creative paths. This paper discusses the impact of using computational
tools in the performance of creative tasks, especially focusing on graphic
design. The study was driven by a grounded theory methodology, applied to a set
of semi-structured interviews, made to twelve people working in the areas of
graphic design, data science, computer art, music and data visualisation. Among
other questions, the results suggest some scenarios in which it is or it is not
worth investing in the development of new intelligent creativity-aiding tools.


Abstract:
  The construction of symmetry breaking differential operators, using invariant
pluri-harmonic polynomials, due to T. Ibukiyama in the context of the Siegel
upper half space, is extended for scalar representations to general Hermitian
symmetric spaces of tube-type. The new context is described in terms of
Euclidean Jordan algebras and their representations. As an example, new and
explicit differential operators are obtained for the restriction from the tube
domain over the light cone to the product of two upper half-planes.


Abstract:
  The structure of the electronic nonlinear optical conductivity is elucidated
in a detailed study of the time-reversal symmetric two-band model. The
nonlinear conductivity is decomposed as a sum of contributions related with
different regions of the First Brillouin Zone, defined by single or multiphoton
resonances. All contributions are written in terms of the same integrals, which
contain all information specific to the particular model under study. In this
way, ready-to-use formulas are provided that reduce the often tedious
calculations of the second and third order optical conductivity to the
evaluation of a small set of similar integrals. In the scenario where charge
carriers are present prior to optical excitation, Fermi surface contributions
must also be considered and are shown to have an universal frequency
dependence, tunable by doping. General characteristics are made evident in this
type of resonance-based analysis: the existence of step functions that
determine the chemical potential dependence of electron-hole symmetric
insulators; the determination of the imaginary part by Hilbert transforms,
simpler than those of the nonlinear Kramers-Kr\"{o}nig relations; the absence
of Drude peaks in the diagonal elements of the second order conductivity, among
others. As examples, analytical expressions are derived for the nonlinear
conductivities of some simple systems: a very basic model of direct gap
semiconductors and the Dirac fermions of monolayer graphene.


Abstract:
  The Particle-Particle-Particle-Mesh algorithm elegantly extends the standard
Particle-In-Cell scheme by direct summation of interaction that happens over
distances below or around mesh size. Generally, this allows for a more accurate
description of Coulomb interactions and improves precision in the prediction of
key observables. Nevertheless, most implementations neglect electrostatic
boundary conditions for the short-ranged interaction that are directly summed.
In this paper a variational description of the Particle-Particle-Particle-Mesh
algorithm will be developed for the first time and subsequently used to derive
temporally and spatially discrete equations of motion. We show that the error
committed by neglecting boundary conditions on the short scale is directly tied
to the discretization error induced by the computational grid.


Abstract:
  The objective speech quality assessment is usually conducted by comparing
received speech signal with its clean reference, while human beings are capable
of evaluating the speech quality without any reference, such as in the mean
opinion score (MOS) tests. Non-intrusive speech quality assessment has
attracted much attention recently due to the lack of access to clean reference
signals for objective evaluations in real scenarios. In this paper, we propose
a novel non-intrusive speech quality measurement model, MetricNet, which
leverages label distribution learning and joint speech reconstruction learning
to achieve significantly improved performance compared to the existing
non-intrusive speech quality measurement models. We demonstrate that the
proposed approach yields promisingly high correlation to the intrusive
objective evaluation of speech quality on clean, noisy and processed speech
data.


Abstract:
  Automated detection of breast tumor in early stages using B-Mode Ultrasound
image is crucial for preventing widespread breast cancer specially among women.
This paper is primarily focusing on the classification of breast tumors through
statistical modelling such as Rician inverse Gaussian (RiIG) pdf of contourlet
transformed B-Mode image of breast tumors which is not reported yet in other
earlier works. The suitability of RiIG distribution in modeling the contourlet
coefficients is illustrated and compared with that of Nakagami distribution.
The proposed method consists of pre-processing to remove the speckle noise,
segmentation of the lesion region, contourlet transform on the B-Mode
Ultrasound image and using the corresponding contourlet sub-band coefficients
and the RiIG parameters, production of contourlet parametric (CP) images and
weighted contourlet parametric (WCP) images. A number of geometrical,
statistical, and texture features are calculated from B-Mode and the contourlet
parametric images. In order to classify the features, seven different
classifiers are employed. The proposed approach is applied to two different
datasets (Mendeley Data and Dataset B) those are available publicly. It is
shown that with parametric images, accuracies in the range of 94-97% are
achieved for different classifiers. Specifically, with the support vector
machine and k-nearest-neighbor classifier, very high accuracies of 97.2% and
97.55% can be obtained for the Mendeley Data and Dataset B,respectively, using
the weighted contourlet parametric images.The reported classification
performance is also compared with that of other works using the datasets
employed in this paper. It is seen that the proposed approach using weighted
contourlet parametric images can provide a superior performance.


Abstract:
  We consider a Frenkel-Kontorova system of harmonic oscillators in a
two-dimensional Euclidean lattice and we obtain a quantitative estimate on the
angular function of the equilibria. The proof relies on a PDE method related to
a classical conjecture by E. De Giorgi, also in view of an elegant technique
based on complex variables that was introduced by A. Farina.
  In the discrete setting, a careful analysis of the reminders is needed to
exploit this type of methodologies inspired by continuum models.


Abstract:
  Methods for calculating lower bounds to the exact energy using the variance
of the upper bound energy are discussed and explored. All the matrix elements
of the Hamiltonian squared are collected and considered, and those for which no
known solutions could be found in the literature are derived for an explicitly
correlated Gaussian (ECG) basis set. Analytical Solutions are determined for
two-electron, mono-nuclear systems, in addition to a one-dimensional integral
expression which has use in polyatomic calculations. The newly derived integral
expressions have been implemented in the integral library of the QUANTEN
computer program.


Abstract:
  Digital text is increasing day by day on the internet. It is very challenging
to classify a large and heterogeneous collection of data, which require
improved information processing methods to organize text. To classify large
size of corpus, one common approach is to use hierarchical text classification,
which aims to classify textual data in a hierarchical structure. Several
approaches have been proposed to tackle classification of text but most of the
research has been done on English language. This paper proposes a deep learning
model for hierarchical text classification of news in Urdu language -
consisting of 51,325 sentences from 8 online news websites belonging to the
following genres: Sports; Technology; and Entertainment. The objectives of this
paper are twofold: (1) to develop a large human-annotated dataset of news in
Urdu language for hierarchical text classification; and (2) to classify Urdu
news hierarchically using our proposed model based on LSTM mechanism named as
Hierarchical Multi-layer LSTMs (HMLSTM). Our model consists of two modules:
Text Representing Layer, for obtaining text representation in which we use
Word2vec embedding to transform the words to vector and Urdu Hierarchical LSTM
Layer (UHLSTML) an end-to-end fully connected deep LSTMs network to perform
automatic feature learning, we train one LSTM layer for each level of the class
hierarchy. We have performed extensive experiments on our self created dataset
named as Urdu News Dataset for Hierarchical Text Classification (UNDHTC). The
result shows that our proposed method is very effective for hierarchical text
classification and it outperforms baseline methods significantly and also
achieved good results as compare to deep neural model.


Abstract:
  In this paper, we consider stochastic optimal control of systems driven by
stochastic differential equations with irregular drift coefficient. We
establish a necessary and sufficient stochastic maximum principle. To achieve
this, we first derive an explicit representation of the first variation process
(in Sobolev sense ) of the controlled diffusion. Since the drift coefficient is
not smooth, the representation is given in terms of the local time of the state
process. Then we construct a sequence of optimal control problems with smooth
coefficients by an approximation argument. Finally, we use Ekeland's
variational principle to obtain an approximating adjoint process from which we
derive the maximum principle by passing to the limit.


Abstract:
  Archaeologists, like other scientists, are experiencing a data-flood in their
discipline, fueled by a surge in computing power and devices that enable the
creation, collection, storage and transfer of an increasingly complex (and
large) amount of data, such as remotely sensed imagery from a multitude of
sources. In this paper, we pose the preliminary question if this increasing
availability of information actually needs new computerized techniques, and
Artificial Intelligence methods, to make new and deeper understanding into
archaeological problems. Simply said, while it is a fact that Deep Learning
(DL) has become prevalent as a type of machine learning design inspired by the
way humans learn, and utilized to perform automatic actions people might
describe as intelligent, we want to anticipate, here, a discussion around the
subject whether machines, trained following this procedure, can extrapolate,
from archaeological data, concepts and meaning in the same way that humans
would do. Even prior to getting to technical results, we will start our
reflection with a very basic concept: Is a collection of satellite images with
notable archaeological sites informative enough to instruct a DL machine to
discover new archaeological sites, as well as other potential locations of
interest? Further, what if similar results could be reached with less
intelligent machines that learn by having people manually program them with
rules? Finally: If with barrier of meaning we refer to the extent to which
human-like understanding can be achieved by a machine, where should be posed
that barrier in the archaeological data science?


Abstract:
  In this paper, we propose a new high order semi-implicit scheme for the all
Mach full Euler equations of gas dynamics. Material waves are treated
explicitly, while acoustic waves are treated implicitly, thus avoiding severe
CFL restrictions for low Mach flows. High order accuracy in time is obtained by
semi-implicit temporal integrator based on the IMEX Runge-Kutta (IMEX-RK)
framework. High order in space is achieved by finite difference WENO schemes
with characteristic-wise reconstructions adapted to the semi-implicit IMEX-RK
time discretization. Type A IMEX schemes are constructed to handle not
well-prepared initial conditions. Besides, these schemes are proven to be
asymptotic preserving and asymptotically accurate as the Mach number vanishes
for well-prepared initial conditions. Divergence-free property of the
time-discrete schemes is proved. The proposed scheme can also well capture
discontinuous solutions in the compressible regime, especially for two
dimensional Riemann problems. Numerical tests in one and two space dimensions
will illustrate the effectiveness of the proposed schemes.


Abstract:
  We quantize the Hamilton equations instead of the Hamilton condition. The
resulting equation has the simple form $-\D u=0$ in a fiber bundle, where the
Laplacian is the Laplacian of the Wheeler-DeWitt metric provided $n\not=4$.
Using then separation of variables the solutions $u$ can be expressed as
products of temporal and spatial eigenfunctions, where the spatial
eigenfunctions are eigenfunctions of the Laplacian in the symmetric space
$SL(n,\R[])/SO(n)$. Since one can define a Schwartz space and tempered
distributions in $SL(n,\R[])/SO(n)$ as well as a Fourier transform, Fourier
quantization can be applied such that the spatial eigenfunctions are
transformed to Dirac measures and the spatial Laplacian to a multiplication
operator.


Abstract:
  Statistical preconditioning can be used to design fast methods for
distributed large-scale empirical risk minimization problems, for strongly
convex and smooth loss functions, allowing fewer communication rounds. Multiple
worker nodes compute gradients in parallel, which are then used by the central
node to update the parameter by solving an auxiliary (preconditioned)
smaller-scale optimization problem. However, previous works require an exact
solution of an auxiliary optimization problem by the central node at every
iteration, which may be impractical. This paper proposes a method that allows
the inexact solution of the auxiliary problem, reducing the total computation
time. Moreover, for loss functions with high-order smoothness, we exploit the
structure of the auxiliary problem and propose a hyperfast second-order method
with complexity $\tilde{O}(\kappa^{1/5})$, where $\kappa$ is the local
condition number. Combining these two building blocks (inexactness and
hyperfast methods), we show complexity estimates for the proposed algorithm,
which is provably better than classical variance reduction methods and has the
same convergence rate as statistical preconditioning with exact solutions.
Finally, we illustrate the proposed method's practical efficiency by performing
large-scale numerical experiments on logistic regression models.


Abstract:
  We study the problem of fair allocation of a set of indivisible items among
agents with additive valuations, under cardinality constraints. In this setting
the items are partitioned into categories, each with its own limit on the
number of items it may contribute to any bundle. One example of such a problem
is allocating seats in a multitrack conference. We consider the fairness
measure known as the maximin share (MMS) guarantee, and propose a novel
polynomial-time algorithm for finding $1/2$-approximate MMS allocations. We
extend the notions and algorithms related to ordered and reduced instances to
work with cardinality constraints, and combine these with a bag filling style
procedure. Our algorithm improves on that of Biswas and Barman (IJCAI-18), with
its approximation ratio of $1/3$. We also present an optimizing algorithm,
which for each instance, instead of fixing $\alpha = 1/2$, uses bisection to
find the largest $\alpha$ for which our algorithm obtains a valid
$\alpha$-approximate MMS allocation. Numerical tests show that our algorithm
finds strictly better approximations than the guarantee of $1/2$ for most
instances, in many cases surpassing $3/5$. The optimizing version of the
algorithm produces MMS allocations in a comparable number of instances to that
of Biswas and Barman's algorithm, on average achieving a better approximation
when MMS is not obtained.


Abstract:
  We are interested here in the program of reconstruction of quantum mechanics
of the German physicist and philosopher Carl Friedrich von Weizs\"{a}cker,
which still has some supporters today. In the major part of this article, we
limit ourselves to examining purely epistemological and philosophical
questions. The theory of the German physicist is often interpreted with
reference to Kant, but the most of the time in a rather vague way (categories
of understanding, theory of experience). We can afford to be more precise.
First we situate the theory of fundamental alternatives (or Ur-alternatives) of
von Weizs\"{a}cker in the lineage of the Kantian theory of the transcendental
Ideal. We then show that the physicist only substitutes for the classical
logic, on which Kant relied, a quantum logic allowing to generate, from the
alternatives, via the local isomorphism between certain spinor groups and
groups linked to space-time, the whole of physical reality to which we have
access. After examining some problems, we finally show how this perspective
leads to a quantum theory of information.


Abstract:
  In this paper we investigate the monotonicity properties related to the ratio
of gamma functions, from which some related asymptotics and inequalities are
established. Some special cases also confirm the conjectures of C.-P. Chen
[Monotonicity properties, inequalities, and asymptotic expansions associated
with the gamma function. Appl. Math. Comput. 283 (2016), 385--396.].


Abstract:
  Quantum spin liquids hosting Majorana excitations have recently experienced
renewed interest for potential applications to topological quantum computation.
Performing logical operations with reduced poisoning requires to localize such
quasiparticles at specific point of the system, with energies that are well
defined and inside the bulk energy gap. These are two defining features of
second order topological insulators (SOTIs). Here, we show two spin models that
support quantum spin liquid phases characterised by Majorana excitations and
that behave as SOTIs, one of which is analytically solvable thanks to a theorem
by Lieb. We show that, depending on the values of spin couplings, it is
possible to localize either fermions or Majorana particles at their corners.


Abstract:
  In this paper, we prove that many fractal sets generated by the associated
dynamical systems only contain irrationals. As an application, we explicitly
construct some overlapping self-similar sets which only consist of irrationals.


Abstract:
  We discuss resonance properties in three-body systems, with examples on
$^{12}{\rm C}$ and $^{24}{\rm Mg}$. We use a microscopic cluster model, where
the generator coordinate is defined in the hyperspherical formalism. The
$^{12}{\rm C}$ nucleus is described by an $\alpha+\alpha+\alpha$ structure,
whereas $^{24}{\rm Mg}$ is considered as an $^{16}{\rm O}+\alpha+\alpha$
system. We essentially pay attention to resonances. We review various
techniques which may extend variational methods to resonances. We consider
$0^+$ and $2^+$ states in $^{12}{\rm C}$ and $^{24}{\rm Mg}$. We show that the
r.m.s. radius of a resonance is strongly sensitive to the variational basis.
This has consequences for the Hoyle state ($0^+_2$ state in $^{12}{\rm C}$)
whose radius has been calculated or measured in several works. In $^{24}{\rm
Mg}$, we identify two $0^+$ resonances slightly below the three-body threshold.


Abstract:
  Python has become the de-facto language for training deep neural networks,
coupling a large suite of scientific computing libraries with efficient
libraries for tensor computation such as PyTorch or TensorFlow. However, when
models are used for inference they are typically extracted from Python as
TensorFlow graphs or TorchScript programs in order to meet performance and
packaging constraints. The extraction process can be time consuming, impeding
fast prototyping. We show how it is possible to meet these performance and
packaging constraints while performing inference in Python. In particular, we
present a way of using multiple Python interpreters within a single process to
achieve scalable inference and describe a new container format for models that
contains both native Python code and data. This approach simplifies the model
deployment story by eliminating the model extraction step, and makes it easier
to integrate existing performance-enhancing Python libraries. We evaluate our
design on a suite of popular PyTorch models on Github, showing how they can be
packaged in our inference format, and comparing their performance to
TorchScript. For larger models, our packaged Python models perform the same as
TorchScript, and for smaller models where there is some Python overhead, our
multi-interpreter approach ensures inference is still scalable.


Abstract:
  In many contexts Gaussian Mixtures (GM) are used to approximate probability
distributions, possibly time-varying. In some applications the number of GM
components exponentially increases over time, and reduction procedures are
required to keep them reasonably limited. The GM reduction (GMR) problem can be
formulated by choosing different measures of the dissimilarity of GMs before
and after reduction, like the Kullback-Leibler Divergence (KLD) and the
Integral Squared Error (ISE). Since in no case the solution is obtained in
closed form, many approximate GMR algorithms have been proposed in the past
three decades, although none of them provides optimality guarantees. In this
work we discuss the importance of the choice of the dissimilarity measure and
the issue of consistency of all steps of a reduction algorithm with the chosen
measure. Indeed, most of the existing GMR algorithms are composed by several
steps which are not consistent with a unique measure, and for this reason may
produce reduced GMs far from optimality. In particular, the use of the KLD, of
the ISE and normalized ISE is discussed and compared in this perspective.


Abstract:
  We present a robust control framework for time-critical systems in which
satisfying real-time constraints robustly is of utmost importance for the
safety of the system. Signal Temporal Logic (STL) provides a formal means to
express a large variety of real-time constraints over signals and is suited for
planning and control purposes as it allows us to reason about the time
robustness of such constraints. The time robustness of STL particularly
quantifies the extent to which timing uncertainties can be tolerated without
violating real-time specifications. In this paper, we first pose a control
problem in which we aim to find an optimal input sequence to a control system
that maximizes the time robustness of an STL constraint. We then propose a
Mixed Integer Linear Program (MILP) encoding and provide correctness guarantees
along with a complexity analysis of the encoding. We also show in two case
studies that maximizing STL time robustness allows to account for timing
uncertainties of the underlying control system.


Abstract:
  RTNeural is a neural inferencing library written in C++. RTNeural is designed
to be used in systems with hard real-time constraints, with additional emphasis
on speed, flexibility, size, and convenience. The motivation and design of the
library are described, as well as real-world use-cases, and performance
comparisons with other neural inferencing libraries.


Abstract:
  Mouse-tracking of computer system users represents a less expensive, but also
a far more applicable alternative to eye-tracking. The main disadvantage of
mouse-tracking are errors manifested as discrepancies between the actual
eye-gaze position and the mouse cursor position. This paper presents a method
for automated correction of errors arising in mouse-tracking. Our approach
draws upon information theory and employs Shannon entropy. The method is based
on calculating the entropy of a visual representation of a Web page, i.e., we
quantify information potential values of various positions. Information
obtained, thereby, is paired with cumulative time intervals, spent by the mouse
cursor in each position. In this way, we identify cursor positions that do not
match eye-gaze positions. To verify the effectiveness of our method, we
compared the eye gaze and mouse cursor heat maps in the following ways: We
calculated the coefficient of correlation between the two; we computed
Euclidean distance between their centers of gravity; and we performed visual
comparison.


Abstract:
  We study the minimal gap statistic for sequences of the form $\left( \alpha
x_n \right)_{n = 1}^{\infty}$ where $\left( x_n \right)_{n = 1}^{\infty}$ is a
sequence of real numbers, and its connection to the additive energy of $\left(
x_n \right)_{n = 1}^{\infty}$. Inspired by a recent paper of Aistleitner,
El-Baz and Munsch we show conditionally on the Lindel\"{o}f Hypothesis that if
the additive energy is of lowest possible order then for almost all $\alpha$,
the minimal gap $\delta_{\min}^{\alpha} (N) = \min \left\{ \alpha x_m - \alpha
x_n \bmod \ 1 : 1 \leq m \neq n \leq N \right\}$ is close to that of a random
sequence, a result Rudnick showed for integer-valued sequences. We also show
unconditional results in this direction, as well as some converse theorems
about sequences with large additive energy.