Abstract:
  We present an approach for automated in-situ monitoring of phytoplankton and
zooplankton communities based on a dual magnification dark-field imaging
microscope/camera. We describe the Dual Scripps Plankton Camera (DSPC) system
and associated image processing, and assess its capabilities in detecting and
characterizing plankton species of different size and taxonomic categories, and
in measuring their abundances in both laboratory and field applications. In the
laboratory, body size and abundance estimates by the DSPC significantly and
robustly scale with the same measurements derived by traditional microscopy. In
the field, a DSPC installed permanently at 3 m depth in Lake Greifensee
(Switzerland), delivered images of plankton individuals, colonies, and
heterospecific aggregates without disrupting natural arrangements of
interacting organisms, their microenvironment or their behavior at hourly
timescales. The DSPC was able to track the dynamics of taxa in the size range
between ~10 $\mu$m to ~ 1 cm, covering virtually all the components of the
planktonic food web (including parasites and potentially toxic cyanobacteria).
Comparing data from the field-deployed DSPC to traditional sampling and
microscopy revealed a general overall agreement in estimates of plankton
diversity and abundances, despite imaging limitations in detecting small
phytoplankton species and rare and large zooplankton taxa (e.g. carnivorous
zooplankton). The most significant disagreements between traditional methods
and the DSPC resided in the measurements of community properties of
zooplankton, organisms that are heterogeneously distributed spatially and
temporally, and whose demography appeared to be better captured by automated
imaging. Time series collected by the DSPC depicted ecological succession
patterns, algal bloom dynamics and circadian fluctuations with a temporal
frequency and morphological [continues...]


Abstract:
  The recent COVID-19 pandemic has increased the focus on hygienic and
contactless identity verification methods. However, the pandemic led to the
wide use of face masks, essential to keep the pandemic under control. The
effect of wearing a mask on face recognition in a collaborative environment is
currently sensitive yet understudied issue. Recent reports have tackled this by
evaluating the masked probe effect on the performance of automatic face
recognition solutions. However, such solutions can fail in certain processes,
leading to performing the verification task by a human expert. This work
provides a joint evaluation and in-depth analyses of the face verification
performance of human experts in comparison to state-of-the-art automatic face
recognition solutions. This involves an extensive evaluation with 12 human
experts and 4 automatic recognition solutions. The study concludes with a set
of take-home messages on different aspects of the correlation between the
verification behavior of human and machine.


Abstract:
  We reveal limitations of several standard coupled-cluster (CC) methods with
perturbation-theorybased noniterative or approximate iterative treatments of
triple excitations when applied to thedetermination of highly accurate
potential energy curves (PECs) of ionic dimers, such as the X $^2\Sigma^+_g$
electronic ground state of Rb$_2^+$. Such computations are of current interest
for the understanding ofion-atom interactions in the ultracold regime. We
demonstrate that these coupled-cluster methodslead to an unphysical long-range
barrier for the Rb$_2^+$ system. The barrier is small but clearly spoilsthe
entire long-range behavior of the PEC. The effect is also found for other
X$_2^+$ systems, like X = Li, Na, and K. Calculations using a new flexible
framework for obtaining leading perturbativetriples corrections derived using
an analytic CC singles and doubles (CCSD) energy derivativeformulation
demonstrate that the origin of this problem lies in the use of $T_3$ amplitudes
obtained from approximate CC singles, doubles and triples (CCSDT) amplitude
equations. It is shown thatthe unphysical barrier is related to a symmetry
instability of the underlying Hartree-Fock mean-fieldsolution. Physically
meaningful perturbative corrections in the long-range tail of the PEC
mayinstead be obtained using symmetry-broken reference determinants.


Abstract:
  Word meaning is notoriously difficult to capture, both synchronically and
diachronically. In this paper, we describe the creation of the largest resource
of graded contextualized, diachronic word meaning annotation in four different
languages, based on 100,000 human semantic proximity judgments. We thoroughly
describe the multi-round incremental annotation process, the choice for a
clustering algorithm to group usages into senses, and possible - diachronic and
synchronic - uses for this dataset.


Abstract:
  The Astrometric Gravitation Probe mission is a modern version of the 1919
Dyson-Eddington-Davidson experiment, based on a space-borne telescope with a
permanent built-in eclipse, provided by a coronagraphic system. The expected
improvement on experimental bounds to General Relativity and competing
gravitation theories is by at least two orders of magnitude. The measurement
principle is reviewed, in particular the principle of Fizeau-like combination
of a set of individual inverted coronagraphs simultaneously feeding a common
high resolution telescope. Also, the payload has a dual field of view property,
in order to support simultaneous observations of stellar fields either very
close, or far away, from the Sun, i.e. fields affected by either high or low
light bending. We discuss a set of solutions introduced in the optical design
to improve on technical feasibility and robustness of the optical performance
against perturbations, in particular induced by manufacturing and alignment
tolerances, and launch stresses.


Abstract:
  In this essay I argue that the Hilbert space of states dual to a traversable
wormhole is smaller than the tensor product of the independent Hilbert spaces
of the boundary field theories. From the point of view of semiclassical
physics, the decrease in the number of states is perceived as an emergent,
non-local interaction stabilizing the wormhole. This presents new possibilities
for models of radiating black holes and raises questions about results
established under the spell of the tensor product.


Abstract:
  We study a generalization of the two-dimensional transverse-field Ising
model, combining both ferromagnetic and antiferromagnetic two-body
interactions, that hosts exact global and local Z2 gauge symmetries. Using
exact diagonalization and stochastic series expansion quantum Monte Carlo
methods, we confirm the existence of the topological phase in line with
previous theoretical predictions. Our simulation results show that the
transition between the confined topological phase and the deconfined
paramagnetic phase is of first-order, in contrast to the conventional Z2
lattice gauge model in which the transition maps onto that of the standard
Ising model and is continuous. We further generalize the model by replacing the
transverse field on the gauge spins with a ferromagnetic XX interaction while
keeping the local gauge symmetry intact. We find that the Z2 topological phase
remains stable, while the paramagnetic phase is replaced by a ferromagnetic
phase. The topological-ferromagnetic quantum phase transition is also of
first-order. For both models, we discuss the low-energy spinon and vison
excitations of the topological phase and their avoided level crossings
associated with the first-order quantum phase transitions.


Abstract:
  The sensibility and sensitivity of the environment play a decisive role in
the safe and secure operation of autonomous vehicles. This perception of the
surrounding is way similar to human visual representation. The human's brain
perceives the environment by utilizing different sensory channels and develop a
view-invariant representation model. Keeping in this context, different
exteroceptive sensors are deployed on the autonomous vehicle for perceiving the
environment. The most common exteroceptive sensors are camera, Lidar and radar
for autonomous vehicle's perception. Despite being these sensors have
illustrated their benefit in the visible spectrum domain yet in the adverse
weather conditions, for instance, at night, they have limited operation
capability, which may lead to fatal accidents. In this work, we explore thermal
object detection to model a view-invariant model representation by employing
the self-supervised contrastive learning approach. For this purpose, we have
proposed a deep neural network Self Supervised Thermal Network (SSTN) for
learning the feature embedding to maximize the information between visible and
infrared spectrum domain by contrastive learning, and later employing these
learned feature representation for the thermal object detection using
multi-scale encoder-decoder transformer network. The proposed method is
extensively evaluated on the two publicly available datasets: the FLIR-ADAS
dataset and the KAIST Multi-Spectral dataset. The experimental results
illustrate the efficacy of the proposed method.


Abstract:
  How can minorities of individuals overturn social conventions? The theory of
critical mass states that when a committed minority reaches a critical size a
cascade of behavioural changes can occur, overturning apparently stable social
norms. Evidence comes from several theoretical and empirical studies in which
minorities of very different sizes, including extremely small ones, manage to
bring a system to its tipping point. Here, we explore this diversity of
scenarios by introducing a crucial element of realism into a standard model for
social convention, namely, the possibility of group interactions. Indeed, usual
models adopt all-to-all or pairwise descriptions of the social structure, thus
neglecting the effects of groups. Instead, we investigate their role by
explicitly including many-body interactions with variable internal social
influence into the framework and leveraging real-world interaction data
including groups of different sizes. We find that the critical mass necessary
to trigger behaviour change can indeed be very small (in some cases down to
0.3%) if individuals are less prone to change their views. Higher-order (group)
interactions turn out to have a complex role: the ability of the committed
minority to overturn existing norms is non-monotonic in the group size. The
results, analytically confirmed within a simplified mean-field approach, hold
in a broad region of the parameter space. Our findings reconcile the different
sizes of critical mass found in previous empirical and theoretical
investigations by unveiling the critical role played by groups in modulating
the minority takeover. This further highlights the importance of the emerging
field of higher-order networks, beyond pairwise interactions.


Abstract:
  The success of Deep Artificial Neural Networks (DNNs) in many domains created
a rich body of research concerned with hardware accelerators for
compute-intensive DNN operators. However, implementing such operators
efficiently with complex hardware intrinsics such as matrix multiply is a task
not yet automated gracefully. Solving this task often requires joint program
and data layout transformations. First solutions to this problem have been
proposed, such as TVM, UNIT or ISAMIR, which work on a loop-level
representation of operators and specify data layout and possible program
transformations before the embedding into the operator is performed. This
top-down approach creates a tension between exploration range and search space
complexity, especially when also exploring data layout transformations such as
im2col, channel packing or padding.
  In this work, we propose a new approach to this problem. We created a
bottom-up method that allows the joint transformation of both compuation and
data layout based on the found embedding. By formulating the embedding as a
constraint satisfaction problem over the scalar dataflow, every possible
embedding solution is contained in the search space. Adding additional
constraints and optmization targets to the solver generates the subset of
preferable solutions.
  An evaluation using the VTA hardware accelerator with the Baidu DeepBench
inference benchmark shows that our approach can automatically generate code
competitive to reference implementations. Further, we show that dynamically
determining the data layout based on intrinsic and workload is beneficial for
hardware utilization and performance. In cases where the reference
implementation has low hardware utilization due to its fixed deployment
strategy, we achieve a geomean speedup of up to x2.813, while individual
operators can improve as much as x170.


Abstract:
  Future linear $e^+e^-$ colliders aim for extremely high precision
measurements. To achieve this, not only excellent detectors and well controlled
machine conditions are needed, but also the best possible estimate of
backgrounds. To avoid that lacking channels and too low statistics becomes a
major source of systematic errors in data-MC comparisons, all SM channels with
the potential to yield at least a few events under the full lifetime of the
projects need to be generated, with statistics largely exceeding that of the
real data. Also machine conditions need to be accurately taken into account.
This includes beam-polarisation, interactions due to the photons inevitably
present in the highly focused beams, and coherent interactions of whole
bunches. This endeavour has already been partly achieved in preparing design
documents for both the ILC and CLIC: Comprehensive samples of fully simulated
and reconstructed events are available for use. In this contribution, we
present how the generation of physics events at linear colliders is categorised
and organised, and the tools used. Also covered is how different aspects of
machine conditions, different sources of spurious interactions (such as
beam-induced backgrounds) are treated and the tools involved for these aspects.


Abstract:
  In this paper, we study the singularities of spacelike constant mean
curvature one (CMC 1) surfaces in the de Sitter 3-space. We prove the duality
between generalized conelike singular points and 5/2-cuspidal edges on
spacelike CMC 1 surfaces. To describe the duality between $A_{k+3}$
singularities and cuspidal $S_k$ singularities, we introduce two invariants,
called the $\alpha$-invariant and $\sigma$-invariant, of spacelike CMC 1
surfaces at their singular points. Moreover, we give a classification of
non-degenerate singular points on spacelike CMC 1 surfaces.


Abstract:
  Membership of stars in open clusters is one of the most crucial parameters in
studies of star clusters. Gaia opened a new window in the estimation of
membership because of its unprecedented 6-D data. In the present study, we used
published membership data of nine open star clusters as a training set to find
new members from Gaia DR2 data using a supervised random forest model with a
precision of around 90\%. The number of new members found is often double the
published number. Membership probability of a larger sample of stars in
clusters is a major benefit in determination of cluster parameters like
distance, extinction and mass functions. We also found members in the outer
regions of the cluster and found sub-structures in the clusters studied. The
color magnitude diagrams are more populated and enriched by the addition of new
members making their study more promising.


Abstract:
  Ultra-reliable low-latency Vehicle-to-Everything (V2X) communications are
needed to meet the extreme requirements of enhanced driving applications.
Millimeter-Wave (24.25-52.6 GHz) or sub-THz (>100 GHz) V2X communications are a
viable solution, provided that the highly collimated beams are kept aligned
during vehicles' maneuverings. In this work, we propose a sensor-assisted
dynamic Beamwidth and Power Control (BPC) system to counteract the detrimental
effect of vehicle dynamics, exploiting data collected by on-board inertial and
positioning sensors, mutually exchanged among vehicles over a parallel low-rate
link, e.g., 5G New Radio (NR) Frequency Range 1 (FR1). The proposed BPC
solution works on top of a sensor-aided Beam Alignment and Tracking (BAT)
system, overcoming the limitations of fixed-beamwidth systems and optimizing
the performance in challenging Vehicle-to-Vehicle (V2V) scenarios, even if
extensions to Vehicle-to-Infrastructure (V2I) use-cases are feasible. We
validate the sensor-assisted dynamic BPC on real trajectories and sensors' data
collected by a dedicated experimental campaign. The goal is to show the
advantages of the proposed BPC strategy in a high data-rate Line-Of-Sight (LOS)
V2V context, and to outline the requirements in terms of sensors' sampling time
and accuracy, along with the end-to-end latency on the control channel.


Abstract:
  In this paper, we propose an energy-efficient federated meta-learning
framework. The objective is to enable learning a meta-model that can be
fine-tuned to a new task with a few number of samples in a distributed setting
and at low computation and communication energy consumption. We assume that
each task is owned by a separate agent, so a limited number of tasks is used to
train a meta-model. Assuming each task was trained offline on the agent's local
data, we propose a lightweight algorithm that starts from the local models of
all agents, and in a backward manner using projected stochastic gradient ascent
(P-SGA) finds a meta-model. The proposed method avoids complex computations
such as computing hessian, double looping, and matrix inversion, while
achieving high performance at significantly less energy consumption compared to
the state-of-the-art methods such as MAML and iMAML on conducted experiments
for sinusoid regression and image classification tasks.


Abstract:
  The concept of gyrogroups is a generalization of groups which do not
explicitly have associativity. Recently, Atiponrat extended the idea of
topological (paratopological) groups to topological (paratopological)
gyrogroups. In this paper, we prove that every regular (Hausdorff) locally
gyroscopic invariant paratopological gyrogroup $G$ is completely regular
(function Hausdorff). These results improve theorems of Banakh and Ravsky for
paratopological groups. Also, we extend the Pontrjagin conditions of
(para)topological groups to (para)topological gyrogroups.


Abstract:
  In this work we present for the first time the general solution of the
temporal evolution equation arising from the matching of a conformally flat
interior to the Vaidya solution. This problem was first articulated by Banerjee
et al. (A. Banerjee, S. B. Dutta Choudhury, and Bidyut K. Bhui, Phys. Rev. D,
40 (670) 1989) in which they provided a particular solution of the temporal
equation. This simple, exact solution has been widely utilised in modeling
dissipative collapse with the most notable result being prediction of the
avoidance of the horizon as the collapse proceeds. We study the dynamics of
dissipative collapse arising from the general solution obtained via the method
of symmetries and of the singularity analysis. We show that the end-state of
collapse for our model is significantly different from the widely used linear
solution.


Abstract:
  We propose a learning framework based on stochastic Bregman iterations to
train sparse neural networks with an inverse scale space approach. We derive a
baseline algorithm called LinBreg, an accelerated version using momentum, and
AdaBreg, which is a Bregmanized generalization of the Adam algorithm. In
contrast to established methods for sparse training the proposed family of
algorithms constitutes a regrowth strategy for neural networks that is solely
optimization-based without additional heuristics. Our Bregman learning
framework starts the training with very few initial parameters, successively
adding only significant ones to obtain a sparse and expressive network. The
proposed approach is extremely easy and efficient, yet supported by the rich
mathematical theory of inverse scale space methods. We derive a statistically
profound sparse parameter initialization strategy and provide a rigorous
stochastic convergence analysis of the loss decay and additional convergence
proofs in the convex regime. Using only 3.4% of the parameters of ResNet-18 we
achieve 90.2% test accuracy on CIFAR-10, compared to 93.6% using the dense
network. Our algorithm also unveils an autoencoder architecture for a denoising
task. The proposed framework also has a huge potential for integrating sparse
backpropagation and resource-friendly training.


Abstract:
  We theoretically implement some hyperparallel optical elements, including
quantum single photon transistor, router, and dynamic random access memory
(DRAM). The inevitable side leakage and the imperfect birefringence of the
quantum dot (QD)-cavity mediates are taken into account, and unity fidelities
of our optical elements can be achieved. The hyperparallel constructions are
based on polarization and spatial degrees of freedom (DOFs) of the photon to
increase the parallel efficiency, improve the capacity of channel, save the
quantum resources, reduce the operation time, and decrease the environment
noises. Moreover, the practical schemes are robust against the side leakage and
the coupling strength limitation in the microcavities.


Abstract:
  In domains where computational resources and labeled data are limited, such
as in robotics, deep networks with millions of weights might not be the optimal
solution. In this paper, we introduce a connectivity scheme for pyramidal
architectures to increase their capacity for learning features. Experiments on
facial expression recognition of unseen people demonstrate that our approach is
a potential candidate for applications with restricted resources, due to good
generalization performance and low computational cost. We show that our
approach generalizes as well as convolutional architectures in this task but
uses fewer trainable parameters and is more robust for low-resolution faces.


Abstract:
  In this paper, we propose a new signal organization method to work in the
structure of the multi level coding (MLC). The transmit bits are divided into
opportunistic bit (OB) and conventional bit (CB), which are mapped to the lower
level- and higher level signal in parallel to the MLC, respectively. Because
the OB's mapping does not require signal power explicitly, the energy of the CB
modulated symbol can be doubled. As the result, the overall mutual information
of the proposed method is found higher than that of the conventional BPSK in
one dimensional case. Moreover, the extension of the method to the
two-complex-dimension shows the better performance over the QPSK. The numerical
results confirm this approach.


Abstract:
  We address the novel task of jointly reconstructing the 3D shape, texture,
and motion of an object from a single motion-blurred image. While previous
approaches address the deblurring problem only in the 2D image domain, our
proposed rigorous modeling of all object properties in the 3D domain enables
the correct description of arbitrary object motion. This leads to significantly
better image decomposition and sharper deblurring results. We model the
observed appearance of a motion-blurred object as a combination of the
background and a 3D object with constant translation and rotation. Our method
minimizes a loss on reconstructing the input image via differentiable rendering
with suitable regularizers. This enables estimating the textured 3D mesh of the
blurred object with high fidelity. Our method substantially outperforms
competing approaches on several benchmarks for fast moving objects deblurring.
Qualitative results show that the reconstructed 3D mesh generates high-quality
temporal super-resolution and novel views of the deblurred object.


Abstract:
  HERMES (High Energy Rapid Modular Ensemble of Satellites) is an innovative
mission aiming to observe transient high-energy events such as gamma-ray bursts
(GRBs) through a constellation of CubeSats hosting a broadband X and gamma-ray
detector. The detector is based on a solid-state Silicon Drift Detector (SDD)
coupled to a scintillator crystal, and is sensitive in the 2 keV to 2 MeV band.
An accurate evaluation of the foreseen in-orbit instrumental background is
essential to assess the scientific performance of the experiment. An outline of
the Monte Carlo simulations of the HERMES payload will be provided, describing
the various contributions on the total background and the optimization
strategies followed in the instrument design. Moreover, the simulations were
used in order to derive the effective area and response matrices of the
instrument, also as a function of the source location with respect to the
detector frame of reference.


Abstract:
  Understanding and manipulation of the laser processing quality during the
ablation of solids have crucial importance from fundamental and industrial
perspectives. Here we have studied the effect of external magnetic field on the
micro-material processing of silicon by ultrashort laser pulses. It was found
experimentally that such a field directed along the laser beam improves the
quality and efficiency of the material removal. Additionally, we observe that
the formation of laser-induced periodic surface structures (LIPSS) in a
multi-pulse regime is affected by the external magnetic field. Our results open
a route towards efficient and controllable ultrafast laser micromachining.


Abstract:
  Distributed Nash equilibrium (NE) seeking problem for multi-coalition games
has attracted increasing attention in recent years, but the research mainly
focuses on the case without agreement demand within coalitions. This paper
considers a class of networked games among multiple coalitions where each
coalition contains multiple agents that cooperate to minimize the sum of their
costs, subject to the demand of reaching an agreement on their state values.
Furthermore, the underlying network topology among the agents does not need to
be balanced. To achieve the goal of NE seeking within such a context, two
estimates are constructed for each agent, namely, an estimate of partial
derivatives of the cost function and an estimate of global state values, based
on which, an iterative state updating law is elaborately designed. Linear
convergence of the proposed algorithm is demonstrated. It is shown that the
consistency-constrained multi-coalition games investigated in this paper put
the well-studied networked games among individual players and distributed
optimization in a unified framework, and the proposed algorithm can easily
degenerate into solutions to these problems.


Abstract:
  Mobile application security has been a major area of focus for security
research over the course of the last decade. Numerous application analysis
tools have been proposed in response to malicious, curious, or vulnerable apps.
However, existing tools, and specifically, static analysis tools, trade
soundness of the analysis for precision and performance and are hence soundy.
Unfortunately, the specific unsound choices or flaws in the design of these
tools is often not known or well-documented, leading to misplaced confidence
among researchers, developers, and users. This paper describes the
Mutation-based Soundness Evaluation ($\mu$SE) framework, which systematically
evaluates Android static analysis tools to discover, document, and fix flaws,
by leveraging the well-founded practice of mutation analysis. We implemented
$\mu$SE and applied it to a set of prominent Android static analysis tools that
detect private data leaks in apps. In a study conducted previously, we used
$\mu$SE to discover $13$ previously undocumented flaws in FlowDroid, one of the
most prominent data leak detectors for Android apps. Moreover, we discovered
that flaws also propagated to other tools that build upon the design or
implementation of FlowDroid or its components. This paper substantially extends
our $\mu$SE framework and offers an new in-depth analysis of two more major
tools in our 2020 study, we find $12$ new, undocumented flaws and demonstrate
that all $25$ flaws are found in more than one tool, regardless of any
inheritance-relation among the tools. Our results motivate the need for
systematic discovery and documentation of unsound choices in soundy tools and
demonstrate the opportunities in leveraging mutation testing in achieving this
goal.


Abstract:
  We describe a framework for reformulating and solving optimization problems
that generalizes the well-known framework originally introduced by Benders. We
discuss details of the application of the procedures to several classes of
optimization problems that fall under the umbrella of multilevel/multistage
mixed integer linear optimization problems. The application of this abstract
framework to this broad class of problems provides new insights and a broader
interpretation of the core ideas, especially as they relate to duality and the
value functions of optimization problems that arise in this context.


Abstract:
  We propose protocols for determining the distances in Hilbert space between
pure and mixed quantum states prepared on a quantum computer. In the case of
pure quantum states, the protocol is based on measuring the square of modulus
of scalar product between certain states. Determination of the distance between
mixed quantum states is reduced to measuring the squares of modules of scalar
products between all pure states included in the mixed states. In addition, we
develop a protocol that allows one to determine the speed of evolution of the
spin system simulated by a quantum computer. These protocols we apply to
measure distances and speeds of evolution of different quantum systems
implemented on the ibmq-santiago quantum computer.


Abstract:
  We study hydrodynamic limits of three kinds of one-dimensional stochastic
log-gases known as Dyson's Brownian motion model, its chiral version, and the
Bru--Wishart process studied in dynamical random matrix theory. We define the
measure-valued processes so that their Cauchy transforms solve the complex
Burgers-type equations. We show that applications of the method of
characteristic curves to these partial differential equations provide
functional equations relating the Cauchy transforms of measures at an arbitrary
time and those at the initial time. We rewrite the complex Burgers-like
equations and functional equations for the Cauchy transforms of the log-gas
measures to those for the $R$-transforms and the $S$-transforms of the measures
which play central roles in free probability theory. Some of the results are
discussed using the notion of free convolutions.


Abstract:
  Whilst lattice-based cryptosystems are believed to be resistant to quantum
attack, they are often forced to pay for that security with inefficiencies in
implementation. This problem is overcome by ring- and module-based schemes such
as Ring-LWE or Module-LWE, whose keysize can be reduced by exploiting its
algebraic structure, allowing for neater and faster computations. Many rings
may be chosen to define such cryptoschemes, but cyclotomic rings, due to their
cyclic nature allowing for easy multiplication, are the community standard.
However, there is still much uncertainty as to whether this structure may be
exploited to an adversary's benefit. In this paper, we show that the
decomposition group of a cyclotomic ring of arbitrary conductor may be utilised
in order to significantly decrease the dimension of the ideal (or module)
lattice required to solve a given instance of SVP. Moreover, we show that there
exist a large number of rational primes for which, if the prime ideal factors
of an ideal lie over primes of this form, give rise to an "easy" instance of
SVP. However, it is important to note that this work does not break Ring-LWE or
Module-LWE, since the security reduction is from worst case ideal or module SVP
to average case Ring-LWE or Module-LWE respectively, and is one way.


Abstract:
  We maximize the production of biogas in a gradostat at steady state. The
physical decision variables are the water, substrate, and biomass entering each
tank and the flows through the interconnecting pipes. Our main technical focus
is the nonconvex constraint describing microbial growth. We formulate a
relaxation and prove that it is exact when the gradostat is outflow connected,
its system matrix is irreducible, and the growth rate satisfies a simple
condition. The relaxation has second-order cone representations for the Monod
and Contois growth rates. We extend the steady state models to the case of
multiple time periods by replacing the derivatives with numerical
approximations instead of setting them to zero. The resulting optimizations are
second-order cone programs, which can be solved at large scales using standard
industrial software.


Abstract:
  Black-box optimization (BBO) algorithms are concerned with finding the best
solutions for problems with missing analytical details. Most classical methods
for such problems are based on strong and fixed a priori assumptions, such as
Gaussianity. However, the complex real-world problems, especially when the
global optimum is desired, could be very far from the a priori assumptions
because of their diversities, causing unexpected obstacles to these methods. In
this study, we propose a generative adversarial net-based broad-spectrum global
optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with
strategies to balance exploration-exploitation trade-off. It has potential to
better adapt to the regularity and structure of diversified landscapes than
other methods with fixed prior, e.g. Gaussian assumption or separability.
Experiments conducted on BBO benchmarking problems and several other benchmarks
with diversified landscapes exhibit that OPT-GAN outperforms other traditional
and neural net-based BBO algorithms.


Abstract:
  In computational material design, ionic radius is one of the most important
physical parameters used to predict material properties. Motivated by the
progress in computational materials science and material informatics, we extend
the renowned Shannon's table from 475 ions to 987 ions. Accordingly, a rigorous
Machine Learning (ML) approach is employed to extend the ionic radii table
using all possible combinations of Oxidation States (OS) and Coordination
Numbers (CN) available in crystallographic repositories. An ionic-radius
regression model for Shannon's database is developed as a function of the
period number, the valence orbital configuration, OS, CN, and Ionization
Potential. In the Gaussian Process Regression (GPR) model, the reached R-square
$R^2$ accuracy is 99\% while the root mean square error of radii is 0.0332 \AA.
The optimized GPR model is then employed for predicting a new set of ionic
radii for uncommon combinations of OS and CN extracted by harnessing crystal
structures from materials project databases. The generated data is consolidated
with the reputable Shannon's data and is made available online in a database
repository \url{https://cmd-ml.github.io/}.


Abstract:
  We show that any $m\times m$ matrix $M$ with integer entries and $\det M
=\Delta \neq 0$ can be equipped by a finite digit set
$\mathcal{D}\subset\mathbb{Z}^m$ such that any integer $m$-dimensional vector
belongs to the set $$ {\rm Fin}_{\mathcal{D}}(M)= \Bigl\{\sum_{k\in I}M^k {d}_k
: \emptyset\neq I \text{ finite subset of } \mathbb{Z} \text{ and } {d}_k \in
\mathcal{D} \text{ for each } k \in I\Bigr\} \subset \bigcup\limits_{k\in
\mathbb{N}} \frac{1}{\Delta^k}\mathbb{Z}^{m} \,. $$ We also characterize the
matrices $M$ for which the sets $ {\rm Fin}_{\mathcal{D}}(M)$ and $
\bigcup\limits_{k\in \mathbb{N}} \frac{1}{\Delta^k}\mathbb{Z}^{m}$ coincide.


Abstract:
  Feynman-Vernon influence functional (IF) was originally introduced to
describe the effect of a quantum environment on the dynamics of an open quantum
system. We apply the IF approach to describe quantum many-body dynamics in
isolated spin systems, viewing the system as an environment for its local
subsystems. While the IF can be computed exactly only in certain many-body
models, it generally satisfies a self-consistency equation, provided the
system, or an ensemble of systems, are translationally invariant. We view the
IF as a fictitious wavefunction in the temporal domain, and approximate it
using matrix-product states (MPS). This approach is efficient provided the
temporal entanglement of the IF is sufficiently low. We illustrate the
versatility of the IF approach by analyzing several models that exhibit a range
of dynamical behaviors, from thermalizing to many-body localized. In
particular, we study the non-equilibrium dynamics in the quantum Ising model in
both Floquet and Hamiltonian settings. We find that temporal entanglement
entropy may be significantly lower compared to the spatial entanglement and
analyze the IF in the continuous-time limit. We simulate the
thermodynamic-limit evolution of local observables in various regimes,
including the relaxation of impurities embedded in an infinite-temperature
chain, and the long-lived oscillatory dynamics of the magnetization associated
with the confinement of excitations. By incorporating disorder-averaging into
the formalism, we analyze discrete time-crystalline response using the IF
method. In this case, we find that the temporal entanglement entropy scales
logarithmically with evolution time. The IF approach offers a new lens on
many-body non-equilibrium phenomena, both in ergodic and non-ergodic regimes,
connecting the theory of open quantum systems theory to quantum statistical
physics.


Abstract:
  Recent advances in automatic music transcription (AMT) have achieved highly
accurate polyphonic piano transcription results by incorporating onset and
offset detection. The existing literature, however, focuses mainly on the
leverage of deep and complex models to achieve state-of-the-art (SOTA)
accuracy, without understanding model behaviour. In this paper, we conduct a
comprehensive examination of the Onsets-and-Frames AMT model, and pinpoint the
essential components contributing to a strong AMT performance. This is achieved
through exploitation of a modified additive attention mechanism. The
experimental results suggest that the attention mechanism beyond a moderate
temporal context does not benefit the model, and that rule-based
post-processing is largely responsible for the SOTA performance. We also
demonstrate that the onsets are the most significant attentive feature
regardless of model complexity. The findings encourage AMT research to weigh
more on both a robust onset detector and an effective post-processor.


Abstract:
  The Neural Tangent Kernel (NTK) is the wide-network limit of a kernel defined
using neural networks at initialization, whose embedding is the gradient of the
output of the network with respect to its parameters. We study the "after
kernel", which is defined using the same embedding, except after training, for
neural networks with standard architectures, on binary classification problems
extracted from MNIST and CIFAR-10, trained using SGD in a standard way. For
some dataset-architecture pairs, after a few epochs of neural network training,
a hard-margin SVM using the network's after kernel is much more accurate than
when the network's initial kernel is used. For networks with an architecture
similar to VGG, the after kernel is more "global", in the sense that it is less
invariant to transformations of input images that disrupt the global structure
of the image while leaving the local statistics largely intact. For fully
connected networks, the after kernel is less global in this sense. The after
kernel tends to be more invariant to small shifts, rotations and zooms; data
augmentation does not improve these invariances. The (finite approximation to
the) conjugate kernel, obtained using the last layer of hidden nodes,
sometimes, but not always, provides a good approximation to the NTK and the
after kernel.
  Training a network with a larger learning rate (while holding the training
error constant) produces a better kernel, as measured by the test error of a
hard-margin SVM. The after kernels of networks trained with larger learning
rates tend to be more global, and more invariant to small shifts, rotations and
zooms.


Abstract:
  Solar twins are key in different areas of astrophysics, however only just
over a hundred were identified and well-studied in the last two decades. In
this work, we take advantage of the very precise \textit{Gaia} (DR2/EDR3),
Tycho and 2MASS photometric systems to create the Inti survey of new solar
twins in the Northern Hemisphere. The spectra of our targets were initially
obtained with spectrographs of moderate resolution (ARCES and Goodman
spectrographs with $R$ = 31500 and 11930, respectively) to find the best solar
twin candidates and then observed at McDonald Observatory with higher resolving
power (TS23, $R$ = 60000) and signal-to-noise ratio (SNR $\sim$ 300-500). The
stellar parameters were estimated through the differential spectroscopic
equilibrium relative to the Sun, which allow us to achieve a high internal
precision ($\sigma(T_{\rm{eff}})$ = 15 K, $\sigma(\log g)$ = 0.03 dex,
$\sigma$([Fe/H]) = 0.01 dex, and $\sigma(v_{t})$ = 0.03 km s$^{-1}$). We
propose a new class of stars with evolution similar to the Sun: \textit{solar
proxy}, which is useful to perform studies related to the evolution of the Sun,
such as its rotational and magnetic evolution. Its definition is based on
metallicity ($-$0.15 dex $\leq$ [Fe/H] $\leq$ +0.15 dex) and mass (0.95
M$_{\odot}$ $\leq$ M $\leq$ 1.05 M$_{\odot}$) constraints, thus assuring that
the star follows a similar evolutionary path as the Sun along the main
sequence. Based on this new definition, we report 70 newly identified solar
proxies, 46 solar analogs and 13 solar-type stars. In addition, we identified 9
\textit{close solar twins} whose stellar parameters are the most similar to
those of the Sun.


Abstract:
  Python has become a popular programming language because of its excellent
programmability. Many modern software packages utilize Python for high-level
algorithm design and depend on native libraries written in C/C++/Fortran for
efficient computation kernels. Interaction between Python code and native
libraries introduces performance losses because of the abstraction lying on the
boundary of Python and native libraries. On the one side, Python code,
typically run with interpretation, is disjoint from its execution behavior. On
the other side, native libraries do not include program semantics to understand
algorithm defects.
  To understand the interaction inefficiencies, we extensively study a large
collection of Python software packages and categorize them according to the
root causes of inefficiencies. We extract two inefficiency patterns that are
common in interaction inefficiencies. Based on these patterns, we develop
PieProf, a lightweight profiler, to pinpoint interaction inefficiencies in
Python applications. The principle of PieProf is to measure the inefficiencies
in the native execution and associate inefficiencies with high-level Python
code to provide a holistic view. Guided by PieProf, we optimize 17 real-world
applications, yielding speedups up to 6.3$\times$ on application level.


Abstract:
  Neighborhood search is a cornerstone of state-of-the-art traveling salesman
and vehicle routing metaheuristics. While neighborhood exploration procedures
are well developed for problems with individual services, their counterparts
for one-to-one pickup-and-delivery problems have been more scarcely studied. A
direct extension of classic neighborhoods is often inefficient or complex due
to the necessity of jointly considering service pairs. To circumvent these
issues, we introduce major improvements to existing neighborhood searches for
the pickup-and-delivery traveling salesman problem and new large neighborhoods.
We show that the classical Relocate-Pair neighborhood can be fully explored in
$O(n^2)$ instead of $O(n^3)$ time. We adapt the 4-Opt and Balas-Simonetti
neighborhoods to consider precedence constraints. Moreover, we introduce an
exponential-size neighborhood called 2k-Opt, which includes all solutions
generated by multiple nested 2-Opts and can be searched in $O(n^2)$ time using
dynamic programming. We conduct extensive computational experiments,
highlighting the significant contribution of these new neighborhoods and
speed-up strategies within two classical metaheuristics. Notably, our approach
permits to repeatedly solve small pickup-and-delivery problem instances to
optimality or near-optimality within milliseconds, and therefore it represents
a valuable tool for time-critical applications such as meal delivery or
mobility of demand.


Abstract:
  Coupled map lattices (CMLs) are prototypical dynamical systems on
networks/graphs. They exhibit complex patterns generated via the interplay of
diffusive/Laplacian coupling and nonlinear reactions modelled by a single
iterated map at each node; the maps are often taken as unimodal, e.g., logistic
or tent maps. In this letter, we propose a class of higher-order coupled
dynamical systems involving the hypergraph Laplacian, which we call coupled
hypergraph maps (CHMs). By combining linearized (in-)stability analysis of
synchronized states, hypergraph spectral theory, and numerical methods, we
detect robust regions of chaotic cluster synchronization occurring in parameter
space upon varying coupling strength and the main bifurcation parameter of the
unimodal map. Furthermore, we find key differences between Laplacian and
hypergraph Laplacian coupling and detect various other classes of periodic and
quasi-periodic patterns. The results show the high complexity of coupled graph
maps and indicate that they might be an excellent universal model class to
understand the similarities and differences between dynamics on classical
graphs and dynamics on hypergraphs.


Abstract:
  After recalling some basic facts about F-wound commutative unipotent
algebraic groups over an imperfect field F we study their regular integral
models over Dedekind schemes of positive characteristic and compute the group
of isomorphisms classes of torsors of one-dimensional groups.


Abstract:
  Multilingual pretrained representations generally rely on subword
segmentation algorithms to create a shared multilingual vocabulary. However,
standard heuristic algorithms often lead to sub-optimal segmentation,
especially for languages with limited amounts of data. In this paper, we take
two major steps towards alleviating this problem. First, we demonstrate
empirically that applying existing subword regularization methods(Kudo, 2018;
Provilkov et al., 2020) during fine-tuning of pre-trained multilingual
representations improves the effectiveness of cross-lingual transfer. Second,
to take full advantage of different possible input segmentations, we propose
Multi-view Subword Regularization (MVR), a method that enforces the consistency
between predictions of using inputs tokenized by the standard and probabilistic
segmentations. Results on the XTREME multilingual benchmark(Hu et al., 2020)
show that MVR brings consistent improvements of up to 2.5 points over using
standard segmentation algorithms.


Abstract:
  Mixed Integer Linear Programs (MILP) are well known to be NP-hard problems in
general. Even though pure optimization-based methods, such as constraint
generation, are guaranteed to provide an optimal solution if enough time is
given, their use in online applications is still a great challenge due to their
usual excessive time requirements. To alleviate their computational burden,
some machine learning techniques have been proposed in the literature, using
the information provided by previously solved MILP instances. Unfortunately,
these techniques report a non-negligible percentage of infeasible or suboptimal
instances.
  By linking mathematical optimization and machine learning, this paper
proposes a novel approach that speeds up the traditional constraint generation
method, preserving feasibility and optimality guarantees. In particular, we
first identify offline the so-called invariant constraint set of past MILP
instances. We then train (also offline) a machine learning method to learn an
invariant constraint set as a function of the problem parameters of each
instance. Next, we predict online an invariant constraint set of the new unseen
MILP application and use it to initialize the constraint generation method.
This warm-started strategy significantly reduces the number of iterations to
reach optimality, and therefore, the computational burden to solve online each
MILP problem is significantly reduced. Very importantly, the proposed
methodology inherits the feasibility and optimality guarantees of the
traditional constraint generation method. The computational performance of the
proposed approach is quantified through synthetic and real-life MILP
applications.


Abstract:
  We continue the study dilation of linear maps on vector spaces introduced by
Bhat, De, and Rakshit. This notion is a variant of vector space dilation
introduced by Han, Larson, Liu, and Liu. We derive vector space versions of
Wold decomposition, Halmos dilation, N-dilation, inter-twining lifting theorem
and a variant of Ando dilation. It is noted further that unlike a kind of
uniqueness of Halmos dilation of strict contractions on Hilbert spaces, vector
space version of Halmos dilation can not be characterized.


Abstract:
  We introduce a novel amortised resource analysis couched in a type-and-effect
system. Our analysis is formulated in terms of the physicist's method of
amortised analysis, and is potential-based. The type system makes use of
logarithmic potential functions and is the first such system to exhibit
*logarithmic amortised complexity*. With our approach we target the automated
analysis of self-adjusting data structures, like splay trees, which so far have
only manually been analysed in the literature. In particular, we have
implemented a semi-automated prototype, which successfully analyses the zig-zig
case of *splaying*, once the type annotations are fixed.


Abstract:
  We investigate the relation of black hole mass versus host stellar mass and
that of mass accretion rate versus star formation rate (SFR) in moderately
luminous ($\log L_{\rm bol} \sim 44.5-46.5\ {\rm erg\ s^{-1}}$), X-ray selected
broad-line active galactic nuclei (AGNs) at $z=1.18-1.68$ in the
Subaru/XMM-Newton Deep Field. The far-infrared to far-ultraviolet spectral
energy distributions of 85 AGNs are reproduced with the latest version of Code
Investigating GALaxy Emission ({\tt CIGALE}), where the AGN clumpy torus model
{\tt SKIRTOR} is implemented. Most of their hosts are confirmed to be
main-sequence star-forming galaxies. We find that the mean ratio of the black
hole mass ($M_{\rm BH}$) to the total stellar mass ($M_{\rm stellar}$) is $\log
M_{\rm BH}/M_{\rm stellar} = -2.2$, which is similar to the local black
hole-to-bulge mass ratio. This suggests that if the host galaxies of these
moderately luminous AGNs at $z\sim1.4$ are dominated by bulges, they already
established the local black hole mass-bulge mass relation; if they are disk
dominant, their black holes are overmassive relative to the bulges. AGN
bolometric luminosities and SFR show a good correlation with ratios higher than
that expected from the local black hole-to-bulge mass relation, suggesting that
these AGNs are in a SMBH-growth dominant phase.


Abstract:
  We present high angular resolution imaging observations of 517 host stars of
TESS exoplanet candidates using the `Alopeke and Zorro speckle cameras at
Gemini North and South. The sample consists mainly of bright F, G, K stars at
distances of less than 500 pc. Our speckle observations span angular
resolutions of ~20 mas out to 1.2 arcsec, yielding spatial resolutions of <10
to 500 AU for most stars, and our contrast limits can detect companion stars
5-9 magnitudes fainter than the primary at optical wavelengths. We detect 102
close stellar companions and determine the separation, magnitude difference,
mass ratio, and estimated orbital period for each system. Our observations of
exoplanet host star binaries reveal that they have wider separations than field
binaries, with a mean orbital semi-major axis near 100 AU. Other imaging
studies have suggested this dearth of very closely separated binaries in
systems which host exoplanets, but incompleteness at small separations makes it
difficult to disentangle unobserved companions from a true lack of companions.
With our improved angular resolution and sensitivity, we confirm that this lack
of close exoplanet host binaries is indeed real. We also search for a
correlation between planetary orbital radii vs. binary star separation, but
given the very short orbital periods of the TESS planets, we do not find any
clear trend. We do note that in exoplanet systems containing binary host stars,
there is an observational bias against detecting Earth-size planet transits due
to transit depth dilution caused by the companion star.


Abstract:
  Recent development in AI has enabled the expansion of its application to
multiple domains. From medical treatment, gaming, manufacturing to daily
business processes. A huge amount of money has been poured into AI research due
to its exciting discoveries. Technology giants like Google, Facebook, Amazon,
and Baidu are the driving forces in the field today. But the rapid growth and
excitement that the technology offers obscure us from looking at the impact it
brings on our society. This short paper gives a brief history of AI and
summarizes various social, economic and ethical issues that are impacting our
society today. We hope that this work will provide a useful starting point and
perhaps reference for newcomers and stakeholders of the field.


Abstract:
  In this work, we provide robust bounds on the tail probabilities and the tail
index of heavy-tailed distributions in the context of model misspecification.
They are defined as the optimal value when computing the worst-case tail
behavior over all models within some neighborhood of the reference model. The
choice of the discrepancy between the models used to build this neighborhood
plays a crucial role in assessing the size of the asymptotic bounds. We
evaluate the robust tail behavior in ambiguity sets based on the Wasserstein
distance and Csisz\'ar $f$-divergence and obtain explicit expressions for the
corresponding asymptotic bounds. In an application to Danish fire insurance
claims we compare the difference between these bounds and show the importance
of the choice of discrepancy measure.


Abstract:
  When aggregating logically interconnected judgments from $n$ agents, the
result might be inconsistent with the logical connection. This inconsistency is
known as the doctrinal paradox, which plays a central role in the field of
judgment aggregation. Despite a large body of literature on the worst-case
analysis of the doctrinal paradox, little is known about its likelihood under
natural statistical models, except for a few i.i.d. distributions [List, 2005].
  In this paper, we characterize the likelihood of the doctrinal paradox under
a much more general and realistic model called the smoothed social choice
framework [Xia, 2020b], where agents' ground truth judgments are arbitrarily
correlated while the noises are independent. Our main theorem states that under
mild conditions, the smoothed likelihood of the doctrinal paradox is either
$0$, $\exp(-\Theta(n))$, $\Theta(n^{-1/2})$ or $\Theta(1)$. This not only
answers open questions by List [2005] for i.i.d. distributions but also draws
clear lines between situations with frequent and with vanishing paradoxes.


Abstract:
  Multi-object tracking (MOT) is an essential task in the computer vision
field. With the fast development of deep learning technology in recent years,
MOT has achieved great improvement. However, some challenges still remain, such
as sensitiveness to occlusion, instability under different lighting conditions,
non-robustness to deformable objects, etc. To address such common challenges in
most of the existing trackers, in this paper, a tracklet booster algorithm is
proposed, which can be built upon any other tracker. The motivation is simple
and straightforward: split tracklets on potential ID-switch positions and then
connect multiple tracklets into one if they are from the same object. In other
words, the tracklet booster consists of two parts, i.e., Splitter and
Connector. First, an architecture with stacked temporal dilated convolution
blocks is employed for the splitting position prediction via label smoothing
strategy with adaptive Gaussian kernels. Then, a multi-head self-attention
based encoder is exploited for the tracklet embedding, which is further used to
connect tracklets into larger groups. We conduct sufficient experiments on
MOT17 and MOT20 benchmark datasets, which demonstrates promising results.
Combined with the proposed tracklet booster, existing trackers usually can
achieve large improvements on the IDF1 score, which shows the effectiveness of
the proposed method.


Abstract:
  This paper presents a new framework for human body part segmentation based on
Deep Convolutional Neural Networks trained using only synthetic data. The
proposed approach achieves cutting-edge results without the need of training
the models with real annotated data of human body parts. Our contributions
include a data generation pipeline, that exploits a game engine for the
creation of the synthetic data used for training the network, and a novel
pre-processing module, that combines edge response maps and adaptive histogram
equalization to guide the network to learn the shape of the human body parts
ensuring robustness to changes in the illumination conditions. For selecting
the best candidate architecture, we perform exhaustive tests on manually
annotated images of real human body limbs. We further compare our method
against several high-end commercial segmentation tools on the body parts
segmentation task. The results show that our method outperforms the other
models by a significant margin. Finally, we present an ablation study to
validate our pre-processing module. With this paper, we release an
implementation of the proposed approach along with the acquired datasets.


Abstract:
  Research in action detection has grown in the recentyears, as it plays a key
role in video understanding. Modelling the interactions (either spatial or
temporal) between actors and their context has proven to be essential for this
task. While recent works use spatial features with aggregated temporal
information, this work proposes to use non-aggregated temporal information.
This is done by adding an attention based method that leverages spatio-temporal
interactions between elements in the scene along the clip.The main contribution
of this work is the introduction of two cross attention blocks to effectively
model the spatial relations and capture short range temporal
interactions.Experiments on the AVA dataset show the advantages of the proposed
approach that models spatio-temporal relations between relevant elements in the
scene, outperforming other methods that model actor interactions with their
context by +0.31 mAP.


Abstract:
  In lattice quantum field theory studies, parameters defining the lattice
theory must be tuned toward criticality to access continuum physics. Commonly
used Markov chain Monte Carlo (MCMC) methods suffer from critical slowing down
in this limit, restricting the precision of continuum extrapolations. Further
difficulties arise when measuring correlation functions of operators widely
separated in spacetime: for most correlation functions, an exponentially severe
signal-to-noise problem is encountered as the operators are taken to be widely
separated. This dissertation details two new techniques to address these
issues. First, we define a novel MCMC algorithm based on generative flow-based
models. Such models utilize machine learning methods to describe efficient
approximate samplers for distributions of interest. Independently drawn
flow-based samples are then used as proposals in an asymptotically exact
Metropolis-Hastings Markov chain. We address incorporating symmetries of
interest, including translational and gauge symmetries. We secondly introduce
an approach to "deform" Monte Carlo estimators based on contour deformations
applied to the domain of the path integral. The deformed estimators associated
with an observable give equivalent unbiased measurements of that observable,
but generically have different variances. We define families of deformed
manifolds for lattice gauge theories and introduce methods to efficiently
optimize the choice of manifold (the "observifold"), minimizing the deformed
observable variance. Finally, we demonstrate that flow-based MCMC can mitigate
critical slowing down and observifolds can exponentially reduce variance in
proof-of-principle applications to scalar $\phi^4$ theory and $\mathrm{U}(1)$
and $\mathrm{SU}(N)$ lattice gauge theories.


Abstract:
  Gradient methods have applications in multiple fields, including signal
processing, image processing, and dynamic systems. In this paper, we present a
nonlinear gradient method for solving convex supra-quadratic functions by
developing the search direction, that done by hybridizing between the two
conjugate coefficients HRM [2] and NHS [1]. The numerical results proved the
effectiveness of the presented method by applying it to solve standard problems
and reaching the exact solution if the objective function is quadratic convex.
Also presented in this article, an application to the problem of named entities
in the Arabic medical language, as it proved the stability of the proposed
method and its efficiency in terms of execution time.


Abstract:
  Animal pose estimation is an important field that has received increasing
attention in the recent years. The main challenge for this task is the lack of
labeled data. Existing works circumvent this problem with pseudo labels
generated from data of other easily accessible domains such as synthetic data.
However, these pseudo labels are noisy even with consistency check or
confidence-based filtering due to the domain shift in the data. To solve this
problem, we design a multi-scale domain adaptation module (MDAM) to reduce the
domain gap between the synthetic and real data. We further introduce an online
coarse-to-fine pseudo label updating strategy. Specifically, we propose a
self-distillation module in an inner coarse-update loop and a mean-teacher in
an outer fine-update loop to generate new pseudo labels that gradually replace
the old ones. Consequently, our model is able to learn from the old pseudo
labels at the early stage, and gradually switch to the new pseudo labels to
prevent overfitting in the later stage. We evaluate our approach on the TigDog
and VisDA 2019 datasets, where we outperform existing approaches by a large
margin. We also demonstrate the generalization ability of our model by testing
extensively on both unseen domains and unseen animal categories. Our code is
available at the project website.


Abstract:
  Machine Learning compilers like TVM allow a fast and flexible deployment on
embedded CPUs. This enables the use of non-standard operators, which are common
in ML compression techniques. However, it is necessary to understand the
limitations of typical compute-intense operators in ML workloads to design a
proper solution. This is the first in-detail analysis of dense and convolution
operators, generated with TVM, that compares to the fundamental hardware limits
of embedded ARM processors. Thereby it explains the gap between computational
peak performance, theoretical and measured, and real-world state-of-the-art
results, created with TVM and openBLAS. Instead, one can see that
single-precision general matrix multiply (GEMM) and convolutions are bound by
L1-cache-read bandwidth. Explorations of 8-bit and bit-serial quantized
operators show that quantization can be used to achieve relevant speedups
compared to cache-bound floating-point operators. However, the performance of
quantized operators highly depends on the interaction between data layout and
bit packing.


Abstract:
  Interpreting how does deep neural networks (DNNs) make predictions is a vital
field in artificial intelligence, which hinders wide applications of DNNs.
Visualization of learned representations helps we humans understand the vision
of DNNs. In this work, visualized images that can activate the neural network
to the target classes are generated by back-propagation method. Here, rotation
and scaling operations are applied to introduce the transformation invariance
in the image generating process, which we find a significant improvement on
visualization effect. Finally, we show some cases that such method can help us
to gain insight into neural networks.


Abstract:
  Adaptive systems react to changes in their environment by changing their
behavior. Identifying these needed adaptations is very difficult, but central
to requirements elicitation for adaptive systems. As the necessary or potential
adaptations are typically not obvious to the stakeholders, the problem is how
to effectively elicit adaptation-relevant information. One approach is to use
creativity techniques to support the systematic identification and elicitation
of adaptation requirements. In particular, here, we analyze a set of creativity
triggers defined for systematic exploration of potential adaptation
requirements. We compare these triggers with brainstorming as a baseline in a
controlled experiment with 85 master students. The results indicate that the
proposed triggers are suitable for the efficient elicitation of adaptive
requirements and that the 15 trigger questions produce significantly more
requirements fragments than solo brainstorming.


Abstract:
  In order to solve fractional variational problems, there exist two theorems
of necessary conditions: an Euler-Lagrange equation which involves Caputo and
Riemann-Liouville fractional derivatives, and other Euler-Lagrange equation
that involves only Caputo derivatives. In this article, we make a comparison
solving a particular fractional variational problem with both methods to obtain
some conclusions about which method gives the optimal solution.


Abstract:
  A fundamental ability of humans is to utilize commonsense knowledge in
language understanding and question answering. In recent years, many
knowledge-enhanced Commonsense Question Answering (CQA) approaches have been
proposed. However, it remains unclear: (1) How far can we get by exploiting
external knowledge for CQA? (2) How much potential of knowledge has been
exploited in current CQA models? (3) Which are the most promising directions
for future CQA? To answer these questions, we benchmark knowledge-enhanced CQA
by conducting extensive experiments on multiple standard CQA datasets using a
simple and effective knowledge-to-text transformation framework. Experiments
show that: (1) Our knowledge-to-text framework is effective and achieves
state-of-the-art performance on CommonsenseQA dataset, providing a simple and
strong knowledge-enhanced baseline for CQA; (2) The potential of knowledge is
still far from being fully exploited in CQA -- there is a significant
performance gap from current models to our models with golden knowledge; and
(3) Context-sensitive knowledge selection, heterogeneous knowledge
exploitation, and commonsense-rich language models are promising CQA
directions.


Abstract:
  Refining low-resolution (LR) spatial fields with high-resolution (HR)
information, often known as statistical downscaling, is challenging as the
diversity of spatial datasets often prevents direct matching of observations.
Yet, when LR samples are modeled as aggregate conditional means of HR samples
with respect to a mediating variable that is globally observed, the recovery of
the underlying fine-grained field can be framed as taking an "inverse" of the
conditional expectation, namely a deconditioning problem. In this work, we
propose a Bayesian formulation of deconditioning which naturally recovers the
initial reproducing kernel Hilbert space formulation from Hsu and Ramos (2019).
We extend deconditioning to a downscaling setup and devise efficient
conditional mean embedding estimator for multiresolution data. By treating
conditional expectations as inter-domain features of the underlying field, a
posterior for the latent field can be established as a solution to the
deconditioning problem. Furthermore, we show that this solution can be viewed
as a two-staged vector-valued kernel ridge regressor and show that it has a
minimax optimal convergence rate under mild assumptions. Lastly, we demonstrate
its proficiency in a synthetic and a real-world atmospheric field downscaling
problem, showing substantial improvements over existing methods.


Abstract:
  Transition systems (TS) and Petri nets (PN) are important models of
computation ubiquitous in formal methods for modeling systems. An important
problem is how to extract from a given TS a PN whose reachability graph is
equivalent (with a suitable notion of equivalence) to the original TS.
  This paper addresses the decomposition of transition systems into
synchronizing state machines (SMs), which are a class of Petri nets where each
transition has one incoming and one outgoing arc and all markings have exactly
one token. This is an important case of the general problem of extracting a PN
from a TS. The decomposition is based on the theory of regions, and it is shown
that a property of regions called excitation-closure is a sufficient condition
to guarantee the equivalence between the original TS and a decomposition into
SMs.
  An efficient algorithm is provided which solves the problem by reducing its
critical steps to the maximal independent set problem (to compute a minimal set
of irredundant SMs) or to satisfiability (to merge the SMs). We report
experimental results that show a good trade-off between quality of results vs.
computation time.


Abstract:
  In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art
algorithms for analyzing non-euclidean graph data. By applying deep-learning to
extract high-level representations from graph structures, GNNs achieve
extraordinary accuracy and great generalization ability in various tasks.
However, with the ever-increasing graph sizes, more and more complicated GNN
layers, and higher feature dimensions, the computational complexity of GNNs
grows exponentially. How to inference GNNs in real time has become a
challenging problem, especially for some resource-limited edge-computing
platforms.
  To tackle this challenge, we propose BlockGNN, a software-hardware co-design
approach to realize efficient GNN acceleration. At the algorithm level, we
propose to leverage block-circulant weight matrices to greatly reduce the
complexity of various GNN models. At the hardware design level, we propose a
pipelined CirCore architecture, which supports efficient block-circulant
matrices computation. Basing on CirCore, we present a novel BlockGNN
accelerator to compute various GNNs with low latency. Moreover, to determine
the optimal configurations for diverse deployed tasks, we also introduce a
performance and resource model that helps choose the optimal hardware
parameters automatically. Comprehensive experiments on the ZC706 FPGA platform
demonstrate that on various GNN tasks, BlockGNN achieves up to $8.3\times$
speedup compared to the baseline HyGCN architecture and $111.9\times$ energy
reduction compared to the Intel Xeon CPU platform.


Abstract:
  Molecular phenotyping by gene expression profiling is common in contemporary
cancer research and in molecular diagnostics. However, molecular profiling
remains costly and resource intense to implement, and is just starting to be
introduced into clinical diagnostics. Molecular changes, including genetic
alterations and gene expression changes, occuring in tumors cause morphological
changes in tissue, which can be observed on the microscopic level. The
relationship between morphological patterns and some of the molecular
phenotypes can be exploited to predict molecular phenotypes directly from
routine haematoxylin and eosin (H&E) stained whole slide images (WSIs) using
deep convolutional neural networks (CNNs). In this study, we propose a new,
computationally efficient approach for disease specific modelling of
relationships between morphology and gene expression, and we conducted the
first transcriptome-wide analysis in prostate cancer, using CNNs to predict
bulk RNA-sequencing estimates from WSIs of H&E stained tissue. The work is
based on the TCGA PRAD study and includes both WSIs and RNA-seq data for 370
patients. Out of 15586 protein coding and sufficiently frequently expressed
transcripts, 6618 had predicted expression significantly associated with
RNA-seq estimates (FDR-adjusted p-value < 1*10-4) in a cross-validation. 5419
(81.9%) of these were subsequently validated in a held-out test set. We also
demonstrate the ability to predict a prostate cancer specific cell cycle
progression score directly from WSIs. These findings suggest that contemporary
computer vision models offer an inexpensive and scalable solution for
prediction of gene expression phenotypes directly from WSIs, providing
opportunity for cost-effective large-scale research studies and molecular
diagnostics.


Abstract:
  From superconductors to atomic nuclei, strongly-interacting many-body systems
are ubiquitous in nature. Measuring the microscopic structure of such systems
is a formidable challenge, often met by particle knockout scattering
experiments. While such measurements are fundamental for mapping the structure
of atomic nuclei, their interpretation is often challenged by quantum
mechanical initial- and final-state interactions (ISI/FSI) of the incoming and
scattered particles. Here we overcome this fundamental limitation by measuring
the quasi-free scattering of 48 GeV/c 12C ions from hydrogen. The distribution
of single protons is studied by detecting two protons at large angles in
coincidence with an intact 11B nucleus. The 11B detection is shown to select
the transparent part of the reaction and exclude the otherwise large ISI/FSI
that would break the 11B apart. By further detecting residual 10B and 10Be
nuclei, we also identified short-range correlated (SRC) nucleon-nucleon pairs,
and provide direct experimental evidence for the separation of the pair
wave-function from that of the residual many-body nuclear system. All measured
reactions are well described by theoretical calculations that do not contain
ISI/FSI distortions. Our results thus showcase a new ability to study the
short-distance structure of short-lived radioactive atomic nuclei at the
forthcoming FAIR and FRIB facilities. These studies will be pivotal for
developing a ground-breaking microscopic understanding of the structure and
properties of nuclei far from stability and the formation of visible matter in
the universe.


Abstract:
  In this paper aspects related to handling of intraday imbalances for hydro
and wind power are addressed. The definition of imbalance cost is established
and used to describe the potential benefits of shifting from plant-specific
schedules to a common load requirement for wind and hydropower units in the
same price area. The Nordpool intraday pay-as-bid market has been the basis for
evaluation of imbalances, and some main characteristics for this market has
been described. We consider how internal handling of complementary imbalances
within the same river system with high inflow uncertainty and constrained
reservoirs can reduce volatility in short-term marginal cost and risk compared
to trading in the intraday market. We have also shown the the imbalance cost
for a power producer with both wind and hydropower assets can be reduced by
internal balancing in combination with sales and purchase in a pay-as-bid
intraday market


Abstract:
  Elastic Riemannian metrics have been used successfully in the past for
statistical treatments of functional and curve shape data. However, this usage
has suffered from an important restriction: the function boundaries are assumed
fixed and matched. Functional data exhibiting unmatched boundaries typically
arise from dynamical systems with variable evolution rates such as COVID-19
infection rate curves associated with different geographical regions. In this
case, it is more natural to model such data with sliding boundaries and use
partial matching, i.e., only a part of a function is matched to another
function. Here, we develop a comprehensive Riemannian framework that allows for
partial matching, comparing, and clustering of functions under both phase
variability and uncertain boundaries. We extend past work by: (1) Forming a
joint action of the time-warping and time-scaling groups; (2) Introducing a
metric that is invariant to this joint action, allowing for a gradient-based
approach to elastic partial matching; and (3) Presenting a modification that,
while losing the metric property, allows one to control relative influence of
the two groups. This framework is illustrated for registering and clustering
shapes of COVID-19 rate curves, identifying essential patterns, minimizing
mismatch errors, and reducing variability within clusters compared to previous
methods.


Abstract:
  One way to assess a certain aspect of the value of scientific research is to
measure the attention it receives on social media. While previous research has
mostly focused on the "number of mentions" of scientific research on social
media, the current study applies "topic networks" to measure public attention
to scientific research on Twitter. Topic networks are the networks of
co-occurring author keywords in scholarly publications and networks of
co-occurring hashtags in the tweets mentioning those scholarly publications.
This study investigates which topics in opioid scholarly publications have
received public attention on Twitter. Additionally, it investigates whether the
topic networks generated from the publications tweeted by all accounts (bot and
non-bot accounts) differ from those generated by non-bot accounts. Our analysis
is based on a set of opioid scholarly publications from 2011 to 2019 and the
tweets associated with them. We use co-occurrence network analysis to generate
topic networks. Results indicated that Twitter users have mostly used generic
terms to discuss opioid publications, such as "Opioid," "Pain," "Addiction,"
"Treatment," "Analgesics," "Abuse," "Overdose," and "Disorders." Results
confirm that topic networks provide a legitimate method to visualize public
discussions of health-related scholarly publications and how Twitter users
discuss health-related scientific research differently from the scientific
community. There was a substantial overlap between the topic networks based on
the tweets by all accounts and non-bot accounts. This result indicates that it
might not be necessary to exclude bot accounts for generating topic networks as
they have a negligible impact on the results.


Abstract:
  Viewing a quantum thermal machine as a non-Hermitian quantum system, we
characterize in full generality its analytical time-dependent dynamics by
deriving the spectrum of its non-Hermitian Liouvillian for an arbitrary initial
state. We show that the thermal machine features a number of Liouvillian
exceptional points (EPs) for experimentally realistic parameters, in particular
a third-dorder exceptional point that leaves signatures both in short and
long-time regimes. Remarkably, we demonstrate that this EP corresponds to a
regime of critical decay for the quantum thermal machine towards its steady
state, bearing a striking resemblance with a critically damped harmonic
oscillator. These results open up exciting possibilities for the precise
dynamical control of quantum thermal machines exploiting exceptional points
from non-Hermitian physics and are amenable to state-of-the-art solid-state
platforms such as semiconducting and superconducting devices.


Abstract:
  We study the asymptotic behavior of solutions to the defocusing
mass-subcritical Hartree NLS $iu_t + \Delta u = F(u) = (|x|^{-\gamma}*|u|^2)u$
on $\mathbb{R}^d$, $d\geq 2$, $\frac{4}{3} < \gamma < 2$. We show that the
scattering problem associated to this equation is analytically well-posed in
the weighted spaces $\Sigma = H^1\cap\mathcal{F}H^1$ and $\mathcal{F}H^1$.
Furthermore, we show that the same problem fails to be analytically well-posed
for data in $L^2$. This constitutes an infinite loss of regularity between the
scattering problems in weighted spaces and in $L^2$. This further develops an
earlier investigation initiated by the author in which a finite breakdown of
regularity was proved for the $L^2$ scattering problem for the mass-subcritical
NLS with power nonlinearity $F(u) = |u|^pu$.


Abstract:
  We present the study of dynamical status of the globular cluster NGC 1851. A
combination of multiwavelength space and ground-based data sets are used for
the present analysis. In order to select the genuine cluster members, we used
the astro-photometric data available from HST and GAIA-DR2 catalogs. The BSS
radial distribution of the cluster is plotted from the center of the cluster to
the outskirts. The radial distribution of BSS shows a central peak, followed by
a dip at the intermediate radii (rmin ~ 90'') and a rising trend in the
outskirts. We also estimated A+rh parameter as 0.391 +/- 0.006 to validate the
findings of the radial distribution study. On the basis of the minima in the
BSS radial distribution and the value of A+rh parameter, we conclude that NGC
1851 belongs to Family II classification and is an intermediate dynamical state
cluster.


Abstract:
  In this work we study the formation of $N^*$'s as a consequence of the
dynamics involved in the $ND\bar D^*-N\bar D D^*$ system when the $D\bar
D^*-\bar D D^*$ subsystem generates $X(3872)$ in isospin 0 and $Z_c(3900)$ in
isospin 1. States with isospin $I=1/2$ and mass in the energy region
$4400-4600$ MeV are obtained with spin-parity $J^P=1/2^+$ and $3/2^+$,
predicting in this way the existence of $N^*$ resonances with hidden charm and
a three-body nature.


Abstract:
  Given any (not necessarily connected) finite graph in the combinatorial
sense, we construct calibrated 3-dimensional homologically area minimizing
oriented surface on a $7$-dimensional closed compact Riemannian manifold, so
that the singular set of the surface consists of precisely this finite graph.
The constructions are based on some unpublished ideas of Professor Camillo De
Lellis and Professor Robert Bryant.


Abstract:
  In the scientific literature, the term emergent phenomena is invoked in the
context of a collective behavior observed in a complex adaptive system that
exhibits no correspondence with the behavior of the system constituents.
Although this description is too generic to be termed a definition, it alludes
to the fact that emergent phenomena are closely related to esoteric
observations and neoteric developments in science. With these impressions, we
aim to investigate a variety of observed behavior from the perspective of an
emergentist. Starting with a few familiar portrayals of emergence, we devote a
large body of this narrative review to explain instances of emergence in
condensed matter systems, namely the phase transition phenomena, spontaneous
symmetry breaking, and macroscopic quantum phenomena. At the same time, to
present a broader perspective, we cross the domain boundaries to also provide a
succinct description of emergent phenomena prevailing in social sciences,
economics, computing environments, and in biological systems, as well.


Abstract:
  When designing plans in engineering, it is often necessary to consider
attributes associated to objects, e.g. the location of a robot. Our aim in this
paper is to incorporate attributes into existing categorical formalisms for
planning, namely those based on symmetric monoidal categories and string
diagrams. To accomplish this, we define a notion of a "symmetric monoidal
category with attributes." This is a symmetric monoidal category in which
objects are equipped with retrievable information and where the interactions
between objects and information are governed by an "attribute structure." We
discuss examples and semantics of such categories in the context of robotics to
illustrate our definition.


Abstract:
  We study the shapes of all Newton-Okounkov bodies $\Delta_{v}(D)$ of a given
big divisor $D$ on a surface $S$ with respect to all rank 2 valuations $v$ of
$K(S)$. We obtain upper bounds for, and in many cases we determine exactly, the
possible numbers of vertices of the bodies $\Delta_{v}(D)$. The upper bounds
are expressed in terms of Picard numbers and they are birationally invariant,
as they do not depend on the model $\tilde{S}$ where the valuation $v$ becomes
a flag valuation. We also conjecture that the set of all Newton-Okounkov bodies
of a single ample divisor $D$ determines the Picard number of $S$, and prove
that this is the case for Picard number 1, by an explicit characterization of
surfaces of Picard number 1 in terms of Newton-Okounkov bodies.


Abstract:
  Embedded systems acquire information about the real world from sensors and
process it to make decisions and/or for transmission. In some situations, the
relationship between the data and the decision is complex and/or the amount of
data to transmit is large (e.g. in biologgers). Artificial Neural Networks
(ANNs) can efficiently detect patterns in the input data which makes them
suitable for decision making or compression of information for data
transmission. However, ANNs require a substantial amount of energy which
reduces the lifetime of battery-powered devices. Therefore, the use of Spiking
Neural Networks can improve such systems by providing a way to efficiently
process sensory data without being too energy-consuming. In this work, we
introduce a low-powered neuron model called Integrate-and-Fire which exploits
the charge and discharge properties of the capacitor. Using parallel and series
RC circuits, we developed a trainable neuron model that can be expressed in a
recurrent form. Finally, we trained its simulation with an artificially
generated dataset of dog postures and implemented it as hardware that showed
promising energetic properties. This paper is the full text of the research,
presented at the 20th International Conference on Artificial Intelligence and
Soft Computing Web System (ICAISC 2021)


Abstract:
  3D morphable models are widely used for the shape representation of an object
class in computer vision and graphics applications. In this work, we focus on
deep 3D morphable models that directly apply deep learning on 3D mesh data with
a hierarchical structure to capture information at multiple scales. While great
efforts have been made to design the convolution operator, how to best
aggregate vertex features across hierarchical levels deserves further
attention. In contrast to resorting to mesh decimation, we propose an attention
based module to learn mapping matrices for better feature aggregation across
hierarchical levels. Specifically, the mapping matrices are generated by a
compatibility function of the keys and queries. The keys and queries are
trainable variables, learned by optimizing the target objective, and shared by
all data samples of the same object class. Our proposed module can be used as a
train-only drop-in replacement for the feature aggregation in existing
architectures for both downsampling and upsampling. Our experiments show that
through the end-to-end training of the mapping matrices, we achieve
state-of-the-art results on a variety of 3D shape datasets in comparison to
existing morphable models.


Abstract:
  We define a class of transversal slices in spaces which are quasi-Poisson for
the action of a complex semisimple group G. This is a multiplicative analogue
of Whittaker reduction. One example is the multiplicative universal centralizer
of G, which is equipped with the usual symplectic structure in this way. We
construct a smooth relative compactification by taking the closure of each
centralizer fiber in the wonderful compactification of G. By realizing this
partial compactification as a transversal in a larger quasi-Poisson variety, we
show that it is smooth and log-symplectic.


Abstract:
  Storage and retrieval of data in a computer memory plays a major role in
system performance. Traditionally, computer memory organization is static -
i.e., they do not change based on the application-specific characteristics in
memory access behaviour during system operation. Specifically, the association
of a data block with a search pattern (or cues) as well as the granularity of a
stored data do not evolve. Such a static nature of computer memory, we observe,
not only limits the amount of data we can store in a given physical storage,
but it also misses the opportunity for dramatic performance improvement in
various applications. On the contrary, human memory is characterized by
seemingly infinite plasticity in storing and retrieving data - as well as
dynamically creating/updating the associations between data and corresponding
cues. In this paper, we introduce Neural Storage (NS), a brain-inspired
learning memory paradigm that organizes the memory as a flexible neural memory
network. In NS, the network structure, strength of associations, and
granularity of the data adjust continuously during system operation, providing
unprecedented plasticity and performance benefits. We present the associated
storage/retrieval/retention algorithms in NS, which integrate a formalized
learning process. Using a full-blown operational model, we demonstrate that NS
achieves an order of magnitude improvement in memory access performance for two
representative applications when compared to traditional content-based memory.


Abstract:
  Application Specific Integrated Circuits (ASICs) are commonly used to
efficiently process the signals from sensors and detectors in space. Wire
bonding is a space qualified technique of making interconnections between ASICs
and their substrate packaging board for power, control and readout of the
ASICs. Wire bonding is nearly ubiquitous in modern space programs, but their
exposed wires can be prone to damage during assembly and subject to electric
interference during operations. Additional space around the ASICs needed for
wire bonding also impedes efficient packaging of large arrays of detectors.
Here we introduce the Through Silicon Vias (TSV) technology that replaces wire
bonds and eliminates their shortcomings. We have successfully demonstrated the
feasibility of implementing TSVs to existing ASIC wafers (a.k.a. a via-last
process) developed for processing the X-ray signals from the X-ray imaging
CdZnTe detectors on the Nuclear Spectroscopic Telescope Array (NuSTAR) Small
Explorer mission that was launched in 2012. While TSVs are common in the
semiconductor industry, this is the first (to our knowledge) successful
application for Astrophysics imaging instrumentation. We expect that the TSV
technology will simplify the detector assembly, and thus will enable
significant cost and schedule savings in assembly of large area CdZnTe
detectors.


Abstract:
  In recent years, we have witnessed a surge of interests in learning a
suitable distance metric from weakly supervised data. Most existing methods aim
to pull all the similar samples closer while push the dissimilar ones as far as
possible. However, when some classes of the dataset exhibit multimodal
distribution, these goals conflict and thus can hardly be concurrently
satisfied. Additionally, to ensure a valid metric, many methods require a
repeated eigenvalue decomposition process, which is expensive and numerically
unstable. Therefore, how to learn an appropriate distance metric from weakly
supervised data remains an open but challenging problem. To address this issue,
in this paper, we propose a novel weakly supervised metric learning algorithm,
named MultimoDal Aware weakly supervised Metric Learning (MDaML). MDaML
partitions the data space into several clusters and allocates the local cluster
centers and weight for each sample. Then, combining it with the weighted
triplet loss can further enhance the local separability, which encourages the
local dissimilar samples to keep a large distance from the local similar
samples. Meanwhile, MDaML casts the metric learning problem into an
unconstrained optimization on the SPD manifold, which can be efficiently solved
by Riemannian Conjugate Gradient Descent (RCGD). Extensive experiments
conducted on 13 datasets validate the superiority of the proposed MDaML.


Abstract:
  The unprecedented growth in the easy availability of photo-editing tools has
endangered the power of digital images.An image was supposed to be worth more
than a thousand words,but now this can be said only if it can be authenticated
orthe integrity of the image can be proved to be intact. In thispaper, we
propose a digital image forensic technique for JPEG images. It can detect any
forgery in the image if the forged portion called a ghost image is having a
compression quality different from that of the cover image. It is based on
resaving the JPEG image at different JPEG qualities, and the detection of the
forged portion is maximum when it is saved at the same JPEG quality as the
cover image. Also, we can precisely predictthe JPEG quality of the cover image
by analyzing the similarity using Structural Similarity Index Measure (SSIM) or
the energyof the images. The first maxima in SSIM or the first minima inenergy
correspond to the cover image JPEG quality. We created adataset for varying
JPEG compression qualities of the ghost and the cover images and validated the
scalability of the experimental results.We also, experimented with varied
attack scenarios, e.g. high-quality ghost image embedded in low quality of
cover image,low-quality ghost image embedded in high-quality of cover image,and
ghost image and cover image both at the same quality.The proposed method is
able to localize the tampered portions accurately even for forgeries as small
as 10x10 sized pixel blocks.Our technique is also robust against other attack
scenarios like copy-move forgery, inserting text into image, rescaling
(zoom-out/zoom-in) ghost image and then pasting on cover image.


Abstract:
  Dr. Ron Knott constructed a graph of all Primitive Pythagorean Triples (PPTs)
with legs up to length 10,000, using Mathematica. The patterns are very
interesting, suggesting conic sections. We show that they indeed are parabolic
curves which follow in a natural way from the mathematics of the subject
matter.


Abstract:
  With recent advances in sequencing technology it has become affordable and
practical to sequence genomes to very high depth-of-coverage, allowing
researchers to discover low-frequency variants in the genome. However, due to
the errors in sequencing it is an active area of research to develop algorithms
that can separate noise from the true variants. LoFreq is a state of the art
algorithm for low-frequency variant detection but has a relatively long runtime
compared to other tools. In addition to this, the interface for running in
parallel could be simplified, allowing for multithreading as well as
distributing jobs to a cluster. In this work we describe some specific
contributions to LoFreq that remedy these issues.


Abstract:
  Question answering (QA) in English has been widely explored, but multilingual
datasets are relatively new, with several methods attempting to bridge the gap
between high- and low-resourced languages using data augmentation through
translation and cross-lingual transfer. In this project, we take a step back
and study which approaches allow us to take the most advantage of existing
resources in order to produce QA systems in many languages. Specifically, we
perform extensive analysis to measure the efficacy of few-shot approaches
augmented with automatic translations and permutations of
context-question-answer pairs. In addition, we make suggestions for future
dataset development efforts that make better use of a fixed annotation budget,
with a goal of increasing the language coverage of QA datasets and systems.
Code and data for reproducing our experiments are available here:
https://github.com/NavidRajabi/EMQA.


Abstract:
  We present a quantum synthesis algorithm designed to produce short circuits
and to scale well in practice. The main contribution is a novel representation
of circuits able to encode placement and topology using generic "gates", which
allows the QFAST algorithm to replace expensive searches over circuit
structures with few steps of numerical optimization. When compared against
optimal depth, search based state-of-the-art techniques, QFAST produces
comparable results: 1.19x longer circuits up to four qubits, with an increase
in compilation speed of 3.6x. In addition, QFAST scales up to seven qubits.
When compared with the state-of-the-art "rule" based decomposition techniques
in Qiskit, QFAST produces circuits shorter by up to two orders of magnitude
(331x), albeit 5.6x slower. We also demonstrate the composability with other
techniques and the tunability of our formulation in terms of circuit depth and
running time.


Abstract:
  We study non-Hermitian Aubry-Andr\'{e}-Harper models with p-wave pairing,
where the non-Hermiticity is introduced by on-site complex quasiperiodic
potentials. By analysing the $\mathcal{PT}$ symmetry breaking, winding numbers
of energy spectra, localization and fractal dimensions of states, and fate of
Majorana fermions, a complete phase diagram on Anderson localization and
topological phase transitions is obtained. In particular, the non-Hermitian
topological nature of Anderson localization phase transitions from extended to
critical and then to localized phases is identified, using both analytical and
numerical methods. In the critical phase the complex spectrum is topological
nontrivial with a fractional winding number. In the localized phase the
analytical localization length of states can apply to the Hermitian case, which
is absent so far. Both the non-Hermiticity and disorder are detrimental to
Majorana fermions.