  We examine systematically the (in)consistency between cosmological
constraints as obtained from various current data sets of the expansion
history, Large Scale Structure (LSS), and Cosmic Microwave Background (CMB)
from Planck. We run (dis)concordance tests within each set and across the sets
using a recently introduced index of inconsistency (IOI) capable of dissecting
inconsistencies between two or more data sets. First, we compare the
constraints on $H_0$ from five different methods and find that the IOI drops
from 2.85 to 0.88 (on Jeffreys' scales) when the local $H_0$ measurements is
removed. This seems to indicate that the local measurement is an outlier, thus
favoring a systematics-based explanation. We find a moderate inconsistency
(IOI=2.61) between Planck temperature and polarization. We find that current
LSS data sets including WiggleZ, SDSS RSD, CFHTLenS, CMB lensing and SZ cluster
count, are consistent one with another and when all combined. However, we find
a persistent moderate inconsistency between Planck and individual or combined
LSS probes. For Planck TT+lowTEB versus individual LSS probes, the IOI spans
the range 2.92--3.72 and increases to 3.44--4.20 when the polarization data is
added in. The joint LSS versus the combined Planck temperature and polarization
has an IOI of 2.83 in the most conservative case. But if Planck lowTEB is added
to the joint LSS to constrain $\tau$ and break degeneracies, the inconsistency
between Planck and joint LSS data increases to the high-end of the moderate
range with IOI=4.81. Whether due to systematic effects in the data or to the
underlying model, these inconsistencies need to be resolved. Finally, we
perform forecast calculations using LSST and find that the discordance between
Planck and future LSS data, if it persists as present, can rise up to a high
IOI of 17, thus falling in the strong range of inconsistency. (Abridged).


  3D MHD simulation of accretion onto neutron stars have shown in the last few
years that the footprint (hotspot) of the accretion flow changes with time. Two
different kinds of accretion, namely the funnel flow and the equatorial
accretion produced by instabilities at the inner disk, produce different kinds
of motion of the hotspot. The funnel flow produces hotspots that move around
the magnetic pole, while instabilities produce other hotspots that appear
randomly and move along the equator or slightly above. The angular velocities
of the two hotspots are different, the equatorial one being higher and both
close to the Keplerian velocity in the inner region. Modeling of the
lightcurves of these hotspots with Monte Carlo simulations show that the
signatures produced in power specra by them, if observed, are QPOs plus low
frequency components. Their frequencies, general behavior and features describe
correctly most of the properties of kHz QPOs, if we assume the funnel flow
hotspots as the origin of the lower kHz QPO and instabilities as the origin of
the upper kHz QPO.


  With rapid progress in neural text-to-speech (TTS) models, personalized
speech generation is now in high demand for many applications. For practical
applicability, a TTS model should generate high-quality speech with only a few
audio samples from the given speaker, that are also short in length. However,
existing methods either require to fine-tune the model or achieve low
adaptation quality without fine-tuning. In this work, we propose StyleSpeech, a
new TTS model which not only synthesizes high-quality speech but also
effectively adapts to new speakers. Specifically, we propose Style-Adaptive
Layer Normalization (SALN) which aligns gain and bias of the text input
according to the style extracted from a reference speech audio. With SALN, our
model effectively synthesizes speech in the style of the target speaker even
from single speech audio. Furthermore, to enhance StyleSpeech's adaptation to
speech from new speakers, we extend it to Meta-StyleSpeech by introducing two
discriminators trained with style prototypes, and performing episodic training.
The experimental results show that our models generate high-quality speech
which accurately follows the speaker's voice with single short-duration (1-3
sec) speech audio, significantly outperforming baselines.


  The fusion of causal models with deep learning introducing increasingly
intricate data sets, such as the causal associations within images or between
textual components, has surfaced as a focal research area. Nonetheless, the
broadening of original causal concepts and theories to such complex,
non-statistical data has been met with serious challenges. In response, our
study proposes redefinitions of causal data into three distinct categories from
the standpoint of causal structure and representation: definite data,
semi-definite data, and indefinite data. Definite data chiefly pertains to
statistical data used in conventional causal scenarios, while semi-definite
data refers to a spectrum of data formats germane to deep learning, including
time-series, images, text, and others. Indefinite data is an emergent research
sphere inferred from the progression of data forms by us. To comprehensively
present these three data paradigms, we elaborate on their formal definitions,
differences manifested in datasets, resolution pathways, and development of
research. We summarize key tasks and achievements pertaining to definite and
semi-definite data from myriad research undertakings, present a roadmap for
indefinite data, beginning with its current research conundrums. Lastly, we
classify and scrutinize the key datasets presently utilized within these three
paradigms.


  The power-law increase of the conductivity with temperature in the nominally
insulating regime, recently reported for the dilute two-dimensional holes
[cond-mat/0603053], is found to systematically vary with the carrier density.
Based on the results from four different GaAs heterojunction-insulated-gate
field-effect-transistor samples, it is shown that the power law exponent
depends on a single dimensionless parameter, the ratio between the mean carrier
separation and the distance to the metallic gate that screens the Coulomb
interaction. This dependence suggests that the carriers form a correlated state
in which the interaction effects play a significant role in the transport
properties.


  We prove the Vollhardt and Wolfle hypothesis that the irreducible vertex
U_{kk'}(q) appearing in the Bethe--Salpeter equation contains a diffusion pole
(with the observable diffusion coefficient D(\omega,q)) in the limit k+k'\to 0.
The presence of a diffusion pole in U_{kk'}(q) makes it possible to represent
the quantum "collision operator" L as a sum of a singular operator L_{sing},
which has an infinite number of zero modes, and a regular operator L_{reg} of a
general form. Investigation of the response of the system to a change in
L_{reg} leads to a self-consistency equation, which replaces the rough
Vollhardt-Wolfle equation. Its solution shows that D(0,q) vanishes at the
transition point simultaneously for all q. The spatial dispersion of
D(\omega,q) at \omega \to 0 is found to be \sim 1 in relative units. It is
determined by the atomic scale, and it has no manifestations on the scale q
\sim \xi^{-1} associated with the correlation length \xi. The values obtained
for the critical exponent s of the conductivity and the critical exponent \nu
of the localization length in a d-dimensional space, s=1 (d>2) and \nu=1/(d-2)
(2<d<4), \nu=1/2 (d>4), agree with all reliably established results. With
respect to the character of the change in the symmetry, the Anderson transition
is found to be similar to the Curie point of an isotropic ferromagnet with an
infinite number of components. For such a magnet, the critical exponents are
known exactly and they agree with the exponents indicated above. This suggests
that the symmetry of the critical point has been established correctly and that
the exponents have been determined exactly.


  In this paper we find a unique normal form for the symplectic matrix
representation of the conjugacy class of a prime order element of the
mapping-class group. We find a set of generators for the fundamental group of a
surface with a conformal automorphism of prime order which reflects the action
the automorphism in an optimal way. This is called an {\sl adapted} homotopy
basis and there is a corresponding {\sl adapted presentation}. We also give a
necessary and sufficient condition for a prime order symplectic matrix to be
the image of a prime order element in the mapping-class group.


  Since the 1970s, the LIBOR has served as a fundamental measure for floating
term rates across multiple currencies and maturities. Loans and many derivative
securities, including swaps, caps and swaptions, still rely on LIBOR as the
reference forward-looking term rate. However, in 2017 the Financial Conduct
Authority announced the discontinuation of LIBOR from the end of 2021 and the
New York Fed declared the backward-looking SOFR as a candidate for a new
reference rate for interest rate swaps denominated in U.S. dollars. We first
outline the classical single-curve modelling framework before transitioning to
the multi-curve framework where we examine arbitrage-free pricing and hedging
of SOFR-linked swaps without and with collateral backing. As hedging
instruments, we take liquidly traded SOFR futures and either common or
idiosyncratic funding rates for the hedge and margin account. For concreteness,
a one-factor model based on Vasicek's equation is used to specify the joint
dynamics of several overnight interest rates, including the SOFR, EFFR, and
unsecured funding rate, although multi-factor term structure models could also
be employed.


  Using a filtration on the Grothendieck ring of triangulated categories, we
define the categorical dimension of a birational map between smooth projective
varieties. We show that birational automorphisms of bounded categorical
dimension form subgroups, which provide a nontrivial filtration of the Cremona
group. We discuss some geometrical aspect and some explicit example. In the
case of threefolds, we can moreover recover the genus of a birational
automorphism, and the filtration defined by Frumkin.


  We introduce VoiceFilter-Lite, a single-channel source separation model that
runs on the device to preserve only the speech signals from a target user, as
part of a streaming speech recognition system. Delivering such a model presents
numerous challenges: It should improve the performance when the input signal
consists of overlapped speech, and must not hurt the speech recognition
performance under all other acoustic conditions. Besides, this model must be
tiny, fast, and perform inference in a streaming fashion, in order to have
minimal impact on CPU, memory, battery and latency. We propose novel techniques
to meet these multi-faceted requirements, including using a new asymmetric
loss, and adopting adaptive runtime suppression strength. We also show that
such a model can be quantized as a 8-bit integer model and run in realtime.


  In this study, viscoelastic properties of polymeric composites were
investigated through a non-destructive test (NDT) method based on longitudinal
free vibration. First, three different polyester composites reinforced
separately with carbon, glass, and hemp fibers, as well as, one polyester
composite sample reinforced with poplar wood powder were manufactured via
pultrusion and hand lay-up methods, respectively. In the proposed resonant free
vibration non-destructive method, each rod-shaped test specimen was hit by a
hammer at one end of specimen and immediately the acoustic response of the
specimen was recorded by a microphone at the other end of the sample.
Subsequently, the recorded sounds were analysed using fast Fourier transform
technique. Then, frequency of the first mode of vibration for each composite
specimen was utilised to calculate modulus of elasticity. Further, decrement in
vibration energy as a function of time was examined to obtain loss parameter
(tan {\delta}) of the provided composites. Moreover, parameters (i.e., elastic
modulus and tan {\delta}) were also compared with those determined by dynamic
mechanical thermal analysis (DMTA). It is found that the results obtained from
the examined non-destructive test method are in consistent with those measured
by DMTA approach, providing reliable, accurate and quick responses.


  A code construction and repair scheme for optimal functional regeneration of
multiple node failures is presented, which is based on stitching together short
MDS codes on carefully chosen sets of points lying on a linearized polynomial.
The nodes are connected wirelessly, hence all transmissions by helper nodes
during a repair round are available to all the nodes being repaired. The scheme
is simple and practical because of low subpacketization, low I/O cost and low
computational cost. Achievability of the minimum-bandwidth regenerating (MBR)
point, as well as an interior point, on the optimal storage-repair bandwidth
tradeoff curve is shown. The subspace properties derived in the paper provide
insight into the general properties of functional regenerating codes.


  In the framework of Clifford analysis, a chain of harmonic and monogenic
potentials is constructed in the upper half of Euclidean space $\mR^{m+1}$,
including a higher dimensional generalization of the complex logarithmic
function. Their distributional limits at the boundary $\mR^{m}$ turn out to be
well-known distributions such as the Dirac distribution, the Hilbert kernel,
the fundamental solution of the Laplace and Dirac operators, the square root of
the negative Laplace operator, and the like. It is shown how each of those
potentials may be recovered from an adjacent kernel in the chain by an
appropriate convolution with such a distributional limit.


  By collecting and expanding several numerical recipes developed in previous
work, we implement an object-oriented Python code, based on the networkX
library, for the realization of the configuration model and Newman rewiring.
The software can be applied to any kind of network and "target" correlations,
but it is tested with focus on scale-free networks and assortative
correlations. For generating the degree sequence we use the method of "random
hubs", which gives networks with minimal fluctuations. For the assortative
rewiring we use the simple Vazquez-Weigt matrix as a test in the case of random
networks; since it appears to be not effective in the case of scale-free
networks we then turn to another recipe which generates matrices with
decreasing off-diagonal elements. The rewiring procedure is important also at
the theoretical level, in order to test which types of statistically acceptable
correlations can actually be realized in concrete networks. From the
applicative point of view, its main use is in the construction of correlated
networks for the solution of dynamical or diffusion processes by analyzing the
evolution of single nodes, i.e., beyond the Heterogeneous Mean Field
approximation. As an example, we report on an application to the Bass diffusion
model, with calculations of the time $t_{max}$ of the diffusion peak. The same
networks can additionally be exported in environments for agent-based
simulations like NetLogo.


  Data augmentation is one of the most effective techniques to improve the
generalization performance of deep neural networks. Yet, despite often facing
limited data availability in medical image analysis, it is frequently
underutilized. This appears to be due to a gap in our collective understanding
of the efficacy of different augmentation techniques across medical imaging
tasks and modalities. One domain where this is especially true is breast
ultrasound images. This work addresses this issue by analyzing the
effectiveness of different augmentation techniques for the classification of
breast lesions in ultrasound images. We assess the generalizability of our
findings across several datasets, demonstrate that certain augmentations are
far more effective than others, and show that their usage leads to significant
performance gains.


  Previous studies have typically assumed that large language models are unable
to accurately perform arithmetic operations, particularly multiplication of >8
digits, and operations involving decimals and fractions, without the use of
calculator tools. This paper aims to challenge this misconception. With
sufficient training data, a 2 billion-parameter language model can accurately
perform multi-digit arithmetic operations with almost 100% accuracy without
data leakage, significantly surpassing GPT-4 (whose multi-digit multiplication
accuracy is only 4.3%). We also demonstrate that our MathGLM, fine-tuned from
GLM-10B on a dataset with additional multi-step arithmetic operations and math
problems described in text, achieves similar performance to GPT-4 on a
5,000-samples Chinese math problem test set. Our code and data are public at
https://github.com/THUDM/MathGLM.


  Important workloads, such as machine learning and graph analytics
applications, heavily involve sparse linear algebra operations. These
operations use sparse matrix compression as an effective means to avoid storing
zeros and performing unnecessary computation on zero elements. However,
compression techniques like Compressed Sparse Row (CSR) that are widely used
today introduce significant instruction overhead and expensive pointer-chasing
operations to discover the positions of the non-zero elements. In this paper,
we identify the discovery of the positions (i.e., indexing) of non-zero
elements as a key bottleneck in sparse matrix-based workloads, which greatly
reduces the benefits of compression. We propose SMASH, a hardware-software
cooperative mechanism that enables highly-efficient indexing and storage of
sparse matrices. The key idea of SMASH is to explicitly enable the hardware to
recognize and exploit sparsity in data. To this end, we devise a novel software
encoding based on a hierarchy of bitmaps. This encoding can be used to
efficiently compress any sparse matrix, regardless of the extent and structure
of sparsity. At the same time, the bitmap encoding can be directly interpreted
by the hardware. We design a lightweight hardware unit, the Bitmap Management
Unit (BMU), that buffers and scans the bitmap hierarchy to perform
highly-efficient indexing of sparse matrices. SMASH exposes an expressive and
rich ISA to communicate with the BMU, which enables its use in accelerating any
sparse matrix computation. We demonstrate the benefits of SMASH on four use
cases that include sparse matrix kernels and graph analytics applications.


  The NGC 1407 Group stands out among nearby structures by its properties that
suggest it is massive and evolved. It shares properties with entities that have
been called fossil groups: the 1.4 magnitude differential between the dominant
elliptical galaxy and the second brightest galaxy comes close to satisfying the
definition that has been used to define the fossil class. There are few
intermediate luminosity galaxies, but a large number of dwarfs in the group. We
estimate there are 250 group members to the depth of our survey. The slope of
the faint end of the luminosity function (reaching M_R = -12) is alpha = -1.35.
Velocities for 35 galaxies demonstrate that this group with one dominant galaxy
has a mass of 7 X 10^13 M_sun and M/L_R = 340. Two galaxies in close proximity
to NGC 1407 have very large blueshifts. The most notable is the second
brightest galaxy, NGC 1400, with a velocity of -1072 km/s with respect to the
group mean. We report the detection of X-ray emission from this galaxy and from
the group.


  We report on "graphene-like" mechanical exfoliation of single-crystal Bi2Te3
films and thermoelectric characterization of the stacks of such films. Thermal
conductivity of the resulting "pseudo-superlattices" was measured by the "hot
disk" and "laser flash" techniques. The room-temperature in-plane (cross-plane)
thermal conductivity of the stacks decreases by a factor of ~2.4 (3.5) as
compared to bulk. The thermal conductivity reduction with preserved electrical
properties leads to strong increase in the thermoelectric figure of merit. It
is suggested that the film thinning to few-quintuples and tuning of the Fermi
level can help in achieving the topological-insulator surface transport regime
with an extraordinary thermoelectric efficiency.


  We consider a detailed-balance violating dynamics whose stationary state is a
prescribed Boltzmann distribution. Such dynamics have been shown to be faster
than any equilibrium counterpart. We quantify the gain in convergence speed for
a system whose energy landscape displays one, and then an infinite number of,
energy barriers. In the latter case, we work with the mean-field disordered
$p$-spin, and show that the convergence to equilibrium or to the nonergodic
phase is accelerated, both during the $\beta$ and $\alpha$-relaxation stages.
An interpretation in terms of trajectories in phase space and of an accidental
fluctuation-dissipation theorem is provided.


  Virtual Reality (VR) has an increasing impact on the market in many fields,
from education and medicine to engineering and entertainment, by creating
different applications that replicate or in the case of augmentation enhance
real-life scenarios. Intending to present realistic environments, VR
applications are including text that we are surrounded by every day. However,
text can only add value to the virtual environment if it is designed and
created in such a way that users can comfortably read it. With the aim to
explore what values for text parameters users find comfortable while reading in
virtual reality, a study was conducted allowing participants to manipulate text
parameters such as font size, distance, and contrast. Therefore two different
standalone virtual reality devices were used, Oculus Go and Quest, together
with three different text samples: Short (2 words), medium (21 words), and long
(51 words). Participants had the task of setting text parameters to the best
and worst possible value. Additionally, participants were asked to rate their
experience of reading in virtual reality. Results report mean values for
angular size (the combination of distance and font size) and color contrast
depending on the different device used as well as the varying text length, for
both tasks. Significant differences were found for values of angular size,
depending on the length of the displayed text. However, different device types
had no significant influence on text parameters but on the experiences reported
using the self-assessment manikin (SAM) scale.


  Vascular structure segmentation plays a crucial role in medical analysis and
clinical applications. The practical adoption of fully supervised segmentation
models is impeded by the intricacy and time-consuming nature of annotating
vessels in the 3D space. This has spurred the exploration of weakly-supervised
approaches that reduce reliance on expensive segmentation annotations. Despite
this, existing weakly supervised methods employed in organ segmentation, which
encompass points, bounding boxes, or graffiti, have exhibited suboptimal
performance when handling sparse vascular structure. To alleviate this issue,
we employ maximum intensity projection (MIP) to decrease the dimensionality of
3D volume to 2D image for efficient annotation, and the 2D labels are utilized
to provide guidance and oversight for training 3D vessel segmentation model.
Initially, we generate pseudo-labels for 3D blood vessels using the annotations
of 2D projections. Subsequently, taking into account the acquisition method of
the 2D labels, we introduce a weakly-supervised network that fuses 2D-3D deep
features via MIP to further improve segmentation performance. Furthermore, we
integrate confidence learning and uncertainty estimation to refine the
generated pseudo-labels, followed by fine-tuning the segmentation network. Our
method is validated on five datasets (including cerebral vessel, aorta and
coronary artery), demonstrating highly competitive performance in segmenting
vessels and the potential to significantly reduce the time and effort required
for vessel annotation. Our code is available at:
https://github.com/gzq17/Weakly-Supervised-by-MIP.


  Let $M,N$ be coprime square-free integers. Let $f$ be a holomorphic cusp form
of level $N$ and $g$ be either a holomorphic or a Maa{\ss} form with level $M$.
Using a large sieve inequality, we establish a bound of the form
$\sum_{g}\left|L^{(j)}\left(1/2+it,f \otimes g\right)\right|^2 \ll_t
M+M^{2/3-\beta}N^{4/3}$ where $\beta \approx 1/500$. As a consequence, we
obtain subconvexity bounds for $L^{(j)}\left(1/2+it,f \otimes g\right)\ll
(MN)^{1/2 - \alpha}$ for any $N<M$ satisfying the conditions above without
using amplification methods. Moreover, by the symmetry, we establish a level
aspect hybrid subconvexity bound for the full range when both forms are
holomorphic.


  Grohe and Marx proved that if G does not contain H as a topological minor,
then there exist constants g=O(|V(H)|^4), D and t depending only on H such that
G is a clique sum of graphs that either contain at most t vertices of degree
greater than D or almost embed in some surface of genus at most g. We
strengthen this result, giving a more precise description of the latter kind of
basic graphs of the decomposition - we only allow graphs that (almost) embed in
ways that are impossible for H (similarly to the structure theorem for minors,
where only graphs almost embedded in surfaces in that H does not embed are
allowed). This enables us to give structural results for graphs avoiding a
fixed graph as an immersion and for graphs with bounded infinity-admissibility.


  Electric field-induced magnetization switching in multiferroics is intriguing
for both fundamental studies and potential technological applications. Here, we
review the recent developments on electric field-induced magnetization
switching in multiferroic heterostructures. Particularly, we study the dynamics
of magnetization switching between the two stable states in a shape-anisotropic
single-domain nanomagnet using stochastic Landau-Lifshitz-Gilbert (LLG)
equation in the presence of thermal fluctuations. For magnetostrictive
nanomagnets in strain-coupled multiferroic composites, such study of
magnetization dynamics, contrary to steady-state scenario, revealed intriguing
new phenomena on binary switching mechanism. While the traditional method of
binary switching requires to tilt the potential profile to the desired state of
switching, we show that no such tilting is necessary to switch successfully
since the magnetization's excursion out of magnet's plane can generate a
built-in asymmetry during switching. We also study the switching dynamics in
multiferroic heterostructures having magnetoelectric coupling at the interface
and magnetic exchange coupling that can facilitate to maintain the direction of
switching with the polarity of the applied electric field. We calculate the
performance metrics like switching delay and energy dissipation during
switching while simulating LLG dynamics. The performance metrics turn out to be
very encouraging for potential technological applications.


  The heavy to light form factors in $B$ decays are discussed. Critical
discussion of theoretical approaches is made with special emphasis on their
failure to describe the $B \to K^{(\ast )}\psi$ data.


  We reformulate MHV scattering amplitudes in 4D gauge theory and supergravity
as correlation functions of bilinear operators in a supersymmetric gaussian
matrix model. The model retains the symmetries of an S(4) of radius L and the
matrix variables are represented as linear operators acting on a
finite-dimensional Hilbert space. Bilinear fields of the model generate a
current algebra. In the large N double scaling limit where l_pl ~ L / sqrt(N)
is held fixed, there is an emergent flat 4D space-time with a built in short
distance cutoff.


  In this paper, we consider the problem of energy efficient uplink scheduling
with delay constraint for a multi-user wireless system. We address this problem
within the framework of constrained Markov decision processes (CMDPs) wherein
one seeks to minimize one cost (average power) subject to a hard constraint on
another (average delay). We do not assume the arrival and channel statistics to
be known. To handle state space explosion and informational constraints, we
split the problem into individual CMDPs for the users, coupled through their
Lagrange multipliers; and a user selection problem at the base station. To
address the issue of unknown channel and arrival statistics, we propose a
reinforcement learning algorithm. The users use this learning algorithm to
determine the rate at which they wish to transmit in a slot and communicate
this to the base station. The base station then schedules the user with the
highest rate in a slot. We analyze convergence, stability and optimality
properties of the algorithm. We also demonstrate the efficacy of the algorithm
through simulations within IEEE 802.16 system.


  The variations of Hodge structures of weight one associated to square-tiled
surfaces naturally generate interesting subgroups of integral symplectic
matrices called Kontsevich--Zorich monodromies. In this paper, we show that
arithmetic groups are frequent among the Kontsevich--Zorich monodromies of
square-tiled surfaces of low genera $g$.


  In this study, novel achiral mesogens containing 4,4'-biphenyl central core
connected on both sides through an ester function to
1-(4-(alkyloxy)phenyl)-1H-[1,2,3]-triazolyl group have been synthesized and
characterized. Different mesophases have been identified, with the appearance
of both a periodic striped pattern and propeller-patterned droplets close to
the transition between the nematic and the smectic-C phases. This periodic
texture has been found to be independent of the geometry of the sample cell.
These observations suggest a spontaneous symmetry breaking which can result
either from an intrinsic enantioselective self-sorting process with a chirality
synchronization mechanism, or from an extrinsic surface mechanism at the
interface between the two phases.


  Weyl Anomaly in the dilaton-scalar system in 2 dimensional gravity is
examined. We take the heat-kernel regularization for the ultraviolet
divergences. Generally the Weyl anomaly is determined by the 2nd order
differential (elliptic) operator of the system and the definition of the
measure. We have the freedom of the operator choice caused by the arbitrariness
of total divergences (surface terms) in the action. We examine the Weyl anomaly
in connection with such points and the hermiticity of the operator.


  We expand our group classification of quasilinear evolution equations (Acta
Appl.Math., v.69, 2001) to the case of general evolution equation in one
spatial variable. This enables obtaining several new classes of evolution
equations with nontrivial Lie symmetry. As a by-product, we derive a number of
nonlinear evolution equations admitting quasilocal symmetries.


  If $k$ is a sufficiently large positive integer, we show that the Diophantine
equation $$n (n+d) \cdots (n+ (k-1)d) = y^{\ell}$$ has at most finitely many
solutions in positive integers $n, d, y$ and $\ell$, with
$\operatorname{gcd}(n,d)=1$ and $\ell \geq 2$. Our proof relies upon
Frey-Hellegouarch curves and results on supersingular primes for elliptic
curves without complex multiplication, derived from upper bounds for short
character sums and sieves, analytic and combinatorial.


  This paper addresses the problem of real-time vision-based autonomous
obstacle avoidance in unstructured environments for quadrotor UAVs. We assume
that our UAV is equipped with a forward facing stereo camera as the only sensor
to perceive the world around it. Moreover, all the computations are performed
onboard. Feasible trajectory generation in this kind of problems requires rapid
collision checks along with efficient planning algorithms. We propose a
trajectory generation approach in the depth image space, which refers to the
environment information as depicted by the depth images. In order to predict
the collision in a look ahead robot trajectory, we create depth images from the
sequence of robot poses along the path. We compare these images with the depth
images of the actual world sensed through the forward facing stereo camera. We
aim at generating fuel optimal trajectories inside the depth image space. In
case of a predicted collision, a switching strategy is used to aggressively
deviate the quadrotor away from the obstacle. For this purpose we use two
closed loop motion primitives based on Linear Quadratic Regulator (LQR)
objective functions. The proposed approach is validated through simulation and
hardware experiments.


  A powerful new perspective in the analysis of absolute Galois groups has
recently emerged from the study of Galois modules related to classical
parameterizing spaces of certain Galois extensions. The recurring trend in
these decompositions is their stunning simplicity: almost all summands are free
over some quotient ring. The non-free summands which appear are exceptional not
only because they are different in form, but because they play the key role in
controlling arithmetic conditions that allow the remaining summands to be
easily described. In this way, these exceptional summands are the lynchpin for
a bevy of new properties of absolute Galois groups that have been gleaned from
these surprising decompositions.
  In one such recent decomposition, a remarkable new exceptional summand was
discovered which exhibited interesting properties that have not been seen
before. The exceptional summand is drawn from a particular finite family that
has not yet been investigated. The main goal of this paper is to introduce this
family of modules and verify their indecomposability. We believe this module
will be of interest to people working in Galois theory, representation theory,
combinatorics, and general algebra. The analysis of these modules includes some
interesting new tools, including analogs of $p$-adic expansions.


  We calculate the dominant three body Higgs decays, $H \to W^+W^-(Z^0,
\gamma)$ and $H \to t\overline{t}(Z^0,\gamma ,g)$, in the Standard Model. We
find that the branching ratios of these decays are of the order of few percent
for large Higgs masses. We comment on the behaviour of the partial decay width
$\Gamma (H \to t\overline{b}W^-)$ below the $t\overline{t}$ threshold.
Numerical results of the following three body top decays, $t \to W^+b(\gamma
,g,Z^0)$ and $t \to W^+bH$, are also given. We discuss the feasibility of
observing these Higgs and top decays at future high energy colliders.


  A Regge model of the Pomeron with intercept equal to one, leading to rising
cross-sections is considered. Analysis of the experimental data on hadron and
photon induced interactions is performed within the model. It is shown that the
available pure hadronic data as well as data on DIS are compatible with a high
energy logarithmic behaviour of cross-sections and do not require a Pomeron
intercept above one. The very important r\^ole of the preasymptotic
contributions is especially emphasized.


  We study the quasinormal modes (QNMs) of the Schwarzschild black hole
immersed in an electromagnetic (em) universe. The immersed Schwarzschild black
hole (ISBH) is originated from the metric of colliding em waves with double
polarization [Class. Quantum Grav. 12, 3013 (1995)]. The perturbation equations
of the scalar fields for the ISBH geometry are written in the form of separable
equations. We show that these equations can be transformed to the confluent
Heun's equations, for which we are able to use the known techniques to perform
the analytical quasinormal (QNM) analysis of the solutions. Furthermore, we
employ numerical methods [Mashhoon and $6^{th}$-order Wentzel-Kramers-Brillouin
(WKB)] to derive the QNMs. The results obtained are discussed and depicted with
the appropriate plots.


  In this paper, we report our multiwavelength observations of the C3.1
circular-ribbon flare SOL2015-10-16T10:20 in AR 12434. The flare consisted of a
circular flare ribbon (CFR), an inner flare ribbon (IFR) inside, and a pair of
short parallel flare ribbons (PFRs). During the impulsive phase of the flare,
"two-step" raster observations of \textit{IRIS} with a cadence of 6 s and an
exposure time of 2 s show plasma downflow at the CFR in the Si {\sc iv}
$\lambda$1402.77 line, suggesting chromospheric condensation. The downflow
speeds first increased rapidly from a few km s$^{-1}$ to the peak values of
45$-$52 km s$^{-1}$, before decreasing gradually to the initial levels. The
decay timescales of condensation were 3$-$4 minutes, indicating ongoing
magnetic reconnection. Interestingly, the downflow speeds are positively
correlated with logarithm of the Si {\sc iv} line intensity and time derivative
of the \textit{GOES} soft X-ray (SXR) flux in 1$-$8 {\AA}. The radio dynamic
spectra are characterized by a type \Rmnum{3} radio burst associated with the
flare, which implies that the chromospheric condensation was most probably
driven by nonthermal electrons. Using an analytical expression and the peak
Doppler velocity, we derived the lower limit of energy flux of the
precipitating electrons, i.e., 0.65$\times$10$^{10}$ erg cm$^{-2}$ s$^{-1}$.
The Si {\sc iv} line intensity and SXR derivative show quasi-periodic
pulsations with periods of 32$-$42 s.


  We propose a simple modification from a fixed margin triplet loss to an
adaptive margin triplet loss. While the original triplet loss is used widely in
classification problems such as face recognition, face re-identification and
fine-grained similarity, our proposed loss is well suited for rating datasets
in which the ratings are continuous values. In contrast to original triplet
loss where we have to sample data carefully, in out method, we can generate
triplets using the whole dataset, and the optimization can still converge
without frequently running into a model collapsing issue. The adaptive margins
only need to be computed once before the training, which is much less expensive
than generating triplets after every epoch as in the fixed margin case. Besides
substantially improved training stability (the proposed model never collapsed
in our experiments compared to a couple of times that the training collapsed on
existing triplet loss), we achieved slightly better performance than the
original triplet loss on various rating datasets and network architectures.


  The objective of this article is to report the parallel implementation of the
3D molecular dynamic simulation code for laser-cluster interactions. The
benchmarking of the code has been done by comparing the simulation results with
some of the experiments reported in the literature. Scaling laws for the
computational time is established by varying the number of processor cores and
number of macroparticles used. The capabilities of the code are highlighted by
implementing various diagnostic tools. To study the dynamics of the
laser-cluster interactions, the executable version of the code is available
from the author.


  We partially resolve conjectures of Deligne and Simpson concerning $\mathbb
Z$-local systems on quasi-projective varieties that underlie a polarized
variation of Hodge structure. For local systems of "compact type", we prove (1)
a relative form of Deligne's finiteness theorem, for any family of
quasi-projective varieties, and (2) algebraicity of the corresponding
non-abelian Hodge locus.


  The tree-level three-point correlation functions of local operators in the
general $(p,q)$ minimal models coupled to gravity are calculated in the
continuum approach. On one hand, the result agrees with the unitary series
($q=p+1$); and on the other hand, for $p=2, q=2k-1$, we find agreement with the
one-matrix model results.


  A formula for the sub\-differential of the sum of a series of convex
functions defined on a Banach space was provided by X. Y. Zheng in 1998. In
this paper, besides a slight extension to locally convex spaces of Zheng's
results, we provide a formula for the conjugate of a countable sum of convex
functions. Then we use these results for calculating the sub\-differentials and
the conjugates in two situations related to entropy minimization, and we study
a concrete example met in Statistical Physics.


  Ho proved in [A note on the total domination number, Util.Math. 77 (2008)
97--100] that the total domination number of the Cartesian product of any two
graphs with no isolated vertices is at least one half of the product of their
total domination numbers. We extend a result of Lu and Hou from [Total
domination in the Cartesian product of a graph and $K_2$ or $C_n$, Util. Math.
83 (2010) 313--322] by characterizing the pairs of graphs $G$ and $H$ for which
$\gamma_t(G\Box H)=\frac{1}{2}\gamma_t(G) \gamma_t(H)\,$, whenever
$\gamma_t(H)=2$. In addition, we present an infinite family of graphs $G_n$
with $\gamma_t(G_n)=2n$, which asymptotically approximate the equality in
$\gamma_t(G_n\Box G_n)\ge \frac{1}{2}\gamma_t(G_n)^2$.


  The purpose of this paper is to study the structure and the algebraic
varieties of BiHom-associative algebras. We provide a classication of
n-dimensional BiHom-associative and BiHom-bialgebras and BiHom Hopf algebras
for n $\le$ 3.


  Time-uniform log-Sobolev inequalities (LSI) satisfied by solutions of
semi-linear mean-field equations have recently appeared to be a key tool to
obtain time-uniform propagation of chaos estimates. This work addresses the
more general settings of time-inhomogeneous Fokker-Planck equations.
Time-uniform LSI are obtained in two cases, either with the bounded-Lipschitz
perturbation argument with respect to a reference measure, or with a coupling
approach at high temperature. These arguments are then applied to mean-field
equations, where, on the one hand, sharp marginal propagation of chaos
estimates are obtained in smooth cases and, on the other hand, time-uniform
global propagation of chaos is shown in the case of vortex interactions with
quadratic confinement potential on the whole space. In this second case, an
important point is to establish global gradient and Hessian estimates, which is
of independent interest. We prove these bounds in the more general situation of
non-attractive logarithmic and Riesz singular interactions.


  Performance of digitally beamformed phased arrays relies on accurate
calibration of the array by obtaining gains of each antenna in the array. The
stations of the Square Kilometer Array-Low (SKA-Low) are such digital arrays,
where the station calibration is currently performed using conventional
interferometric techniques. An alternative calibration technique similar to
holography of dish based telescopes has been suggested in the past. In this
paper, we develop a novel mathematical framework for holography employing
tensors, which are multi-way data structures. Self-holography using a reference
beam formed with the station under test itself and cross-holography using a
different station to obtain the reference beam are unified under the same
formalism. Besides, the relation between the two apparently distinct
holographic approaches in the literature for phased arrays is shown, and we
show that under certain conditions the two methods yield the same results. We
test the various holographic techniques on an SKA-Low prototype station
Aperture Array Verification System 2 (AAVS2) with the Sun as the calibrator. We
perform self-holography of AAVS2 and cross-holography with simultaneous
observations carried out with another station Engineering Development Array 2.
We find the results from the holographic techniques to be consistent among
themselves as well as with a more conventional calibration technique.


  The formation of circumstellar discs is a critical step in the formation of
stars and planets. Magnetic fields can strongly affect the evolution of angular
momentum during prestellar core collapse, potentially leading to the failure of
protostellar disc formation. This phenomenon, known as the magnetic braking
catastrophe, has been observed in ideal-MHD simulations. In this work, we
present results from ideal-MHD simulations of circumstellar disc formation from
realistic initial conditions of strongly magnetised, massive cores with masses
between $30 ~{\rm M}_\odot$ and $300 ~{\rm M}_\odot$ resolved by zooming into
Giant Molecular Clouds with masses $\sim 10^4 \ {\rm M}_\odot$ and initial
mass-to-flux ratios $0.6 \le \mu_0 \le 3$. Due to the large turbulence caused
by the non-axisymmetric gravitational collapse of the gas, the dominant
vertical support of discs is turbulent motion, while magnetic and turbulent
pressures contribute equally in the outer toroid. The magnetic field topology
is extremely turbulent and incoherent, reducing the effect of magnetic braking
by roughly one order of magnitude and leading to the formation of large
Keplerian discs even in magnetically critical or near-critical cores. Only
cores in GMCs with $\mu_0 < 1$ fail to form discs. Instead, they collapse into
a sheet-like structure and produce numerous low-mass stars. We also discuss a
universal $B-\rho$ relation valid over a large range of scales from the GMC to
massive cores, irrespective of the GMC magnetisation. This study differs from
the vast literature on this topic which typically focus on smaller mass discs
with idealised initial and boundary conditions, therefore providing insights
into the initial conditions of massive prestellar core collapse and disc
formation.


  Classical Cepheids are primary distance indicators and a crucial stepping
stone to determining the present-day Hubble constant Ho to the precision and
accuracy required to constrain apparent deviations from the LCDM Concordance
Cosmological Model. We have measured the iron and oxygen abundances of of 89
Cepheids in the LMC, one of the anchors of the local Distance Scale,
quadrupling the prior sample and including 68 of the 70 Cepheids used to
constrain Ho by the SH0ES program. The goal is to constrain the extent to which
the Cepheid luminosity is influenced by their chemical composition, an
important contributor to the uncertainty on the determination of the Ho itself
and a critical factor in the internal consistency of the distance ladder. We
have derived stellar parameters and abundances from a self-consistent
spectroscopic analysis based on Equivalent Width of absorption lines. The
[Fe/H] distribution of LMC Cepheids is a single Gaussian with a mean of
-0.4079+-0.003 dex (0.1 dex systematic uncertainty) and sigma 0.076+-0.003 dex.
The latter is fully compatible with the measurement error and supports the low
dispersion of 0.069 mag seen in the NIR HST LMC period-luminosity relation. The
uniformity of the abundance has the important consequence that the LMC Cepheids
alone cannot provide any meaningful constraint on the dependence of the Cepheid
Period-Luminosity relation on chemical composition at any wavelength. This
revises a prior claim based on a small sample of 22 LMC Cepheids that there was
little dependence (or uncertainty) between composition and NIR luminosity, a
conclusion which would produce a conflict between anchors of the distance
ladder with different mean abundance. The chemical homogeneity of the LMC
Cepheid population makes it an ideal environment to calibrate the metallicity
dependence between the more metal poor SMC and metal rich Milky Way and
NGC4258.


  This paper deals with a projection least squares estimator of the drift
function of a jump diffusion process $X$ computed from multiple independent
copies of $X$ observed on $[0,T]$. Risk bounds are established on this
estimator and on an associated adaptive estimator. Finally, some numerical
experiments are provided.


  We consider the limiting current from an emitting patch whose size is much
smaller than the anode-cathode spacing. The limiting current is formulated in
terms of an integral equation. It is solved iteratively, first to numerically
recover the classical one-dimensional Child-Langmuir law, including Jaffe's
extension to a constant, nonzero electron emission velocity. We extend to
2-dimensions in which electron emission is restricted to an infinitely long
stripe with infinitesimally narrow stripe width, so that the emitted electrons
form an electron sheet. We next extend to 3-dimensions in which electron
emission is restricted to a square tile (or a circular patch) with an
infinitesimally small tile size (or patch radius), so that the emitted
electrons form a needle-like line charge. Surprisingly, for the electron needle
problem, we only find the null solution for the total line charge current,
regardless of the assumed initial electron velocity. For the electron sheet
problem, we also find only the null solution for the total sheet current if the
electron emission velocity is assumed to be zero, and the total maximum sheet
current becomes a finite, nonzero value if the electron emission velocity is
assumed to be nonzero. These seemingly paradoxical results are shown to be
consistent with the earlier works of the Child-Langmuir law of higher
dimensions. They are also consistent with, or perhaps even anticipated by, the
more recent theories and simulations on thermionic cathodes that used realistic
work function distributions to account for patchy, nonuniform electron
emission. The mathematical subtleties are discussed.


  We revisit and extend previous calculations of glueball decay rates in the
Sakai-Sugimoto model, a holographic top-down approach for QCD with chiral
quarks based on D8 probe branes in Witten's holographic model of
nonsupersymmetric Yang-Mills theory. The rates for decays into two pions, two
vector mesons, four pions, and the strongly suppressed decay into four pi0 are
worked out quantitatively, using a range of the 't Hooft coupling which closely
reproduces the decay rate of rho and omega mesons and also leads to a gluon
condensate consistent with QCD sum rule calculations. The lowest holographic
glueball, which arises from a rather exotic polarization of gravitons in the
supergravity background, turns out to have a significantly lower mass and
larger width than the two widely discussed glueball candidates f0(1500) and
f0(1710). The lowest nonexotic and predominantly dilatonic scalar mode, which
has a mass of 1487 MeV in the Witten-Sakai-Sugimoto model, instead provides a
narrow glueball state, and we conjecture that only this nonexotic mode should
be identified with a scalar glueball component of f0(1500) or f0(1710).
Moreover the decay pattern of the tensor glueball is determined, which is found
to have a comparatively broad total width when its mass is adjusted to around
or above 2 GeV.


  Policy makers often make decisions based on parameters such as GDP,
unemployment rate, industrial output, etc. The primary methods to obtain or
even estimate such information are resource intensive and time consuming. In
order to make timely and well-informed decisions, it is imperative to be able
to come up with proxies for these parameters which can be sampled quickly and
efficiently, especially during disruptive events, like the COVID-19 pandemic.
Recently, there has been a lot of focus on using remote sensing data for this
purpose. The data has become cheaper to collect compared to surveys, and can be
available in real time. In this work, we present Regional GDP NightLight
(ReGNL), a neural network based model which is trained on a custom dataset of
historical nightlights and GDP data along with the geographical coordinates of
a place, and estimates the GDP of the place, given the other parameters. Taking
the case of 50 US states, we find that ReGNL is disruption-agnostic and is able
to predict the GDP for both normal years (2019) and for years with a disruptive
event (2020). ReGNL outperforms timeseries ARIMA methods for prediction, even
during the pandemic. Following from our findings, we make a case for building
infrastructures to collect and make available granular data, especially in
resource-poor geographies, so that these can be leveraged for policy making
during disruptive events.


  Developing automated and smart software vulnerability detection models has
been receiving great attention from both research and development communities.
One of the biggest challenges in this area is the lack of code samples for all
different programming languages. In this study, we address this issue by
proposing a transfer learning technique to leverage available datasets and
generate a model to detect common vulnerabilities in different programming
languages. We use C source code samples to train a Convolutional Neural Network
(CNN) model, then, we use Java source code samples to adopt and evaluate the
learned model. We use code samples from two benchmark datasets: NIST Software
Assurance Reference Dataset (SARD) and Draper VDISC dataset. The results show
that proposed model detects vulnerabilities in both C and Java codes with
average recall of 72\%. Additionally, we employ explainable AI to investigate
how much each feature contributes to the knowledge transfer mechanisms between
C and Java in the proposed model.


  We study the energy distributions of dipion transitions $\Upsilon(5S)$ to
$\Upsilon(1S,2S,3S)\pi^+\pi^-$ in the final state rescattering model. Since the
$\Upsilon(5S)$ is well above the open bottom thresholds, the dipion transitions
are expected to mainly proceed through the real processes $\Upsilon(5S)\to
B^{(*)}\bar{B}^{(*)}$ and $B^{(*)}\bar{B}^{(*)}\to
\Upsilon(1S,2S,3S)\pi^+\pi^-$. We find that the energy distributions of
$\Upsilon(1S,2S,3S)\pi^+\pi^-$ markedly differ from that of $\Upsilon(5S)\to
B^{(*)}\bar{B}^{(*)}$. In particular, the resonance peak will be pushed up by
about 7-20 MeV for these dipion transitions relative to the main hadronic decay
modes. These predictions can be used to test the final state rescattering
mechanism in hadronic transitions for heavy quarkonia above the open flavor
thresholds.


  We present a robust technique for calculating regional volume changes within
the lung from X-ray radiograph sequences captured during ventilation, without
the use of computed tomography (CT). This technique is based on the change in
transmitted X-ray intensity that occurs for each lung region as air displaces
the attenuating lung tissue. Lung air volumes calculated from X-ray intensity
changes showed a strong correlation ($R^2$=0.98) against the true volumes,
measured from high-resolution CT. This correlation enables us to accurately
convert projected intensity data into relative changes in lung air volume. We
have applied this technique to measure changes in regional lung volumes from
X-ray image sequences of mechanically ventilated, recently-deceased newborn
rabbits, without the use of CT. This method is suitable for biomedical research
studies and shows potential for clinical application.


  For 4H silicon carbide (4H-SiC), the values for the electron-hole pair
creation energy $\epsilon_{\text{i}}$ published in the literature vary
significantly. This work presents an experimental determination of
$\epsilon_{\text{i}}$ using $50$ $\mu$m 4H-SiC p-n diodes designed for particle
detection in high-energy physics. The detector response was measured for
$\alpha$ particles between 4.2 MeV and 5.6 MeV for 4H-SiC and a silicon
reference device. Different $\alpha$ energies were obtained by using multiple
nuclides and varying the effective air gap between the $\alpha$ source and the
detector. The energy deposited in the detectors was determined using a Monte
Carlo simulation, taking into account the device cross-sections. A linear fit
of the detector response to the deposited energy yields $\epsilon_{\text{i}} =
(7.83 \pm 0.02)\;\text{eV}$, which agrees well with the most recent literature.
For the 4H-SiC detectors, a linewidth of 28 keV FWHM was achieved,
corresponding to an energy resolution of 0.5\%.


  We show that for a Suslin ccc forcing notion $\mathbb Q$ adding a Hechler
real, ``$\text{ZF}+\text{DC}_{\omega_1}+$all sets of reals are $I_{\mathbb
Q,\aleph_0}$-measurable'' implies the existence of an inner model with a
measurable cardinal. We also introduce a wide class of Suslin ccc forcing
notions which add a Hechler real, so that the above result applies to them.


  Microengines have shown promise for a variety of applications in
nanotechnology, microfluidics and nanomedicine, including targeted drug
delivery, microscale pumping, and environmental remediation. However, achieving
precise control over their dynamics remains a significant challenge. In this
study, we introduce a microengine that exploits both optical and thermal
effects to achieve a high degree of controllability. We find that in the
presence of a strongly focused light beam, a gold-silica Janus particle becomes
confined at the stationary point where the optical and thermal forces balance.
By using circularly polarized light, we can transfer angular momentum to the
particle breaking the symmetry between the two forces and resulting in a
tangential force that drives directed orbital motion. We can simultaneously
control the velocity and direction of rotation of the particle changing the
ellipticity of the incoming light beam, while tuning the radius of the orbit
with laser power. Our experimental results are validated using a geometrical
optics phenomenological model that considers the optical force, the absorption
of optical power, and the resulting heating of the particle. The demonstrated
enhanced flexibility in the control of microengines opens up new possibilities
for their utilization in a wide range of applications, encompassing microscale
transport, sensing, and actuation.


  Magnetic Aharonov-Bohm effect (AB effect) was studied in hundreds of papers
starting with the seminal paper of Aharonov and Bohm [AB] published in 1959. We
give a new proof of the magnetic Aharonov-Bohm effect without using the
scattering theory and the theory of inverse boundary value problems. We
consider separately the cases of one and several obstacles. The electric AB
effect was studied much less. We give the first proof of the electric AB effect
in domains with moving boundaries. When the boundary does not move with the
time the electric AB effect is absent.


  The morphological structure of left ventricle segmented from cardiac magnetic
resonance images can be used to calculate key clinical parameters, and it is of
great significance to the accurate and efficient diagnosis of cardiovascular
diseases. Compared with traditional methods, the segmentation algorithms based
on fully convolutional neural network greatly improve the accuracy of semantic
segmentation. For the problem of left ventricular segmentation, a new fully
convolutional neural network structure named MS-FCN is proposed in this paper.
The MS-FCN network employs a multi-scale pooling module to ensure that the
network maximises the feature extraction ability and uses a dense connectivity
decoder to refine the boundaries of the object. Based on the Sunnybrook cine-MR
dataset provided by the MICCAI 2009 challenge, numerical experiments
demonstrate that our proposed model has obtained state-of-the-art segmentation
results: the Dice score of our method reaches 0.93 on the endocardium, and 0.96
on the epicardium.


  This talk presents Gopcaml-mode, the first structural editing plugin for
OCaml. We will give a tour of the main plugin features, discussing the plugin's
internal design and its integration with existing OCaml and GNU Emacs
toolchains.


  Representation learning has significantly been developed with the advance of
contrastive learning methods. Most of those methods have benefited from various
data augmentations that are carefully designated to maintain their identities
so that the images transformed from the same instance can still be retrieved.
However, those carefully designed transformations limited us to further explore
the novel patterns exposed by other transformations. Meanwhile, as found in our
experiments, the strong augmentations distorted the images' structures,
resulting in difficult retrieval. Thus, we propose a general framework called
Contrastive Learning with Stronger Augmentations~(CLSA) to complement current
contrastive learning approaches. Here, the distribution divergence between the
weakly and strongly augmented images over the representation bank is adopted to
supervise the retrieval of strongly augmented queries from a pool of instances.
Experiments on the ImageNet dataset and downstream datasets showed the
information from the strongly augmented images can significantly boost the
performance. For example, CLSA achieves top-1 accuracy of 76.2% on ImageNet
with a standard ResNet-50 architecture with a single-layer classifier
fine-tuned, which is almost the same level as 76.5% of supervised results. The
code and pre-trained models are available in
https://github.com/maple-research-lab/CLSA.


  We consider a multi-cell massive MIMO system where a time-division duplex
protocol is used to estimate the channel state information via uplink pilots.
When maximum ratio combining (MRC) is used at the BSs, the re-use of pilots
across cells causes the pilot contamination effect which yields interference
components that do not vanish as the number of base-station (BS) antennas $M
\rightarrow \infty$. When treating interference as noise (TIN), this phenomenon
limits the performance of multi-cell massive MIMO systems. In this paper, we
analyze more advanced schemes based on simultaneous unique decoding (SD) as
well as simultaneous non-unique decoding (SND) of the interference that can
provide unbounded rate as $M \rightarrow \infty$. We also establish a
worst-case uncorrelated noise technique for multiple-access channels to derive
achievable rate expressions for finite $M$. Furthermore, we study a much
simpler subset of SND (called S-SND) which provides a lower bound to SND and
achieves unbounded rate as $M \rightarrow \infty$, and also outperforms SD for
finite $M$. For the special cases of two-cell and three-cell systems, using a
maximum symmetric rate allocation policy we compare the performance of
different interference decoding schemes with that of TIN. Finally, we
numerically illustrate the improved performance of the proposed schemes.


  In this paper we investigate a new class of growth rate maximization problems
based on impulse control strategies such that the average number of trades per
time unit does not exceed a fixed level. Moreover, we include proportional
transaction costs to make the portfolio problem more realistic. We provide a
Verification Theorem to compute the optimal growth rate as well as an optimal
trading strategy. Furthermore, we prove the existence of a constant boundary
strategy which is optimal. At the end, we compare our approach to other
discrete-time growth rate maximization problems in numerical examples. It turns
out that constant boundary strategies with a small average number of trades per
unit perform nearly as good as the classical optimal solutions with infinite
activity.


  We study the dynamics of the two-point statistics of the Kraichnan ensemble
which describes the transport of a passive pollutant by a stochastic turbulent
flow characterized by scale invariant structure functions. The fundamental
equation of this problem consists in the Fokker-Planck equation for the
two-point correlation function of the density of particles performing spatially
correlated Brownian motions with scale invariant correlations. This problem is
equivalent to the stochastic motion of an effective particle driven by a
generic multiplicative noise. In this paper we propose an alternative and more
intuitive approach to the problem than the original one leading to the same
conclusions. The general features of this new approach make possible to fit it
to other more complex contexts.


  Neutral molecules with sufficiently large dipole moments can bind electrons
in diffuse nonvalence orbitals with most of their charge density far from the
nuclei, forming so-called dipole-bound anions. Because long-range correlation
effects play an important role in the binding of an excess electron and overall
binding energies are often only of the order of 10-100s of wave numbers,
predictively modeling dipole-bound anions remains a challenge. Here, we
demonstrate that quantum Monte Carlo methods can accurately characterize
molecular dipole-bound anions with near threshold dipole moments. We also show
that correlated sampling Auxiliary Field Quantum Monte Carlo is particularly
well-suited for resolving the fine energy differences between the neutral and
anionic species. These results shed light on the fundamental limitations of
quantum Monte Carlo methods and pave the way toward using them for the study of
weakly-bound species that are too large to model using traditional electron
structure methods.


  This article is devoted to the elliptic Stark conjecture formulated by
Darmon, Lauder and Rotger [DLR], which proposes a formula for the
transcendental part of a $p$-adic avatar of the leading term at $s=1$ of the
Hasse-Weil-Artin $L$-series $L(E,\varrho_1\otimes \varrho_2,s)$ of an elliptic
curve $E$ twisted by the tensor product $\varrho_1\otimes \varrho_2$ of two odd
$2$-dimensional Artin representations, when the order of vanishing is two. The
main ingredient of this formula is a $2\times 2$ $p$-adic regulator involving
the $p$-adic formal group logarithm of suitable Stark points on $E$. This
conjecture was proved in [DLR] in the setting where $\varrho_1$ and $\varrho_2$
are induced from characters of the same imaginary quadratic field $K$. In this
note we prove a refinement of this result, that was discovered experimentally
in Remark 3.4 of [DLR] in a few examples. Namely, we are able to determine the
algebraic constant up to which the main theorem of [DLR] holds in a particular
setting where the Hida-Rankin $p$-adic $L$-function associated to a pair of
Hida families can be exploited to provide an alternative proof of the same
result. This constant encodes local and global invariants of both $E$ and $K$.


  A binary mean operation m(x,y) is said to be compatible with a semigroup law
*, if * satisfies the Gauss' functional equation m(x,y) * m(x,y) = x * y for
all x, y. Thus the arithmetic mean is compatible with the group addition in the
set of real numbers, while the geometric mean is compatible with the group
multiplication in the set of all positive real numbers. Using one of Jacobi's
theta functions, Tanimoto has constructed a novel binary operation *
corresponding to the arithmetic-geometric mean agm(x,y) of Gauss. Tanimoto
shows that it is only a loop operation, but not associative. A natural question
is to ask if there exist a group law * compatible with arithmetic-geometric
mean. In this paper we prove that there is no semigroup law compatible with agm
and hence, in particular, no group law either. Among other things, this
explains why Tanimoto's novel operation * using theta functions must be
non-associative.


  Plasma frequency is the spectral boundary for low-loss propagation and
evanescent decay of surface plasmon polariton (SPP) waves, which corresponds to
a high cut-off phenomenon and is typically utilized for identifying SPPs. At
terahertz (THz) frequencies, a metal line with periodic metallic grooves can
mimic the conventional optical SPPs, which is referred to as designer SPPs.
Theoretically, the plasma frequency of THz SPPs decreases as the groove depth
increases. Here, by replacing the metallic grooves with graphene sheets,
dynamically sweeping SPP plasma frequency is demonstrated for the first time.
The metal-graphene hybrid structure comprises a metal line with periodic
graphene grooves, a thin-layer ion gel for gating graphene, and metallic tips
for uniforming gate field. As the chemical potential changes, the average
conductivity of graphene is modulated so that the effective depth of the
graphene grooves changes, which sweeps the plasma frequency of THz SPPs
consequently. Both simulated and experimental data demonstrate a red shift of
plasma frequency from 195 to 180 GHz at a low bias from -0.5 to 0.5 V. The
proposed structure reveals a novel approach to control the on/off status of SPP
propagation in the THz range.


  We present a new catalogue of 55,121 groups and clusters centred on Luminous
Red Galaxies from SDSS DR7 in the redshift range 0.15<z<0.4. We provide halo
mass estimates for each of these groups derived from a calibration between the
optical richness of bright galaxies (M_r<-20.5) within 1 Mpc, and X-ray-derived
mass for a small subset of 129 groups and clusters with X-ray measurements. We
derive the mean (stacked) surface number density profiles of galaxies as a
function of total halo mass in different mass bins. We find that derived
profiles can be well-described by a projected NFW profile with a concentration
parameter (<c>~2.6) that is approximately a factor of two lower than that of
the dark matter (as predicted by N-body cosmological simulations) and nearly
independent of halo mass. Interestingly, in spite of the difference in shape
between the galaxy and dark matter radial distributions, both exhibit a high
degree of self-similarity. A self-consistent comparison to several recent
semi-analytic models of galaxy formation indicates that: (1) beyond ~0.3 r_500
current models are able to reproduce both the shape and normalisation of the
satellite profiles; and (2) within ~0.3 r_500 the predicted profiles are
sensitive to the details of the satellite-BCG merger timescale calculation. The
former is a direct result of the models being tuned to match the global galaxy
luminosity function combined with the assumption that the satellite galaxies do
not suffer significant tidal stripping, even though their surrounding dark
matter haloes can be removed through this process. Combining our results with
measurements of the intracluster light should provide a way to inform
theoretical models on the efficacy of the tidal stripping and merging
processes.


  We perform the weak lensing mass mapping analysis to identify troughs, which
are defined as local minima in the mass map. Since weak lensing probes
projected matter along the line-of-sight, these troughs can be produced by
single voids or multiple voids projected along the line-of-sight. To scrutinise
the origins of the weak lensing troughs, we systematically investigate the
line-of-sight structure of troughs selected from the latest Subaru Hyper
Suprime-Cam (HSC) Year 3 weak lensing data covering $433.48 \, \mathrm{deg}^2$.
From a curved sky mass map constructed with the HSC data, we identify 15
troughs with the signal-to-noise ratio higher than $5.7$ and address their
line-of-sight density structure utilizing redshift distributions of two galaxy
samples, photometric luminous red galaxies observed by HSC and spectroscopic
galaxies detected by Baryon Oscillation Spectroscopic Survey. While most of
weak lensing signals due to the troughs are explained by multiple voids aligned
along the line-of-sight, we find that two of the 15 troughs potentially
originate from single voids at redshift $\sim 0.3$. The single void
interpretation appears to be consistent with our three-dimensional mass mapping
analysis. We argue that single voids can indeed reproduce observed weak lensing
signals at the troughs if these voids are not spherical but are highly
elongated along the line-of-sight direction.


  Webs are planar graphs with boundary that describe morphisms in a
diagrammatic representation category for $\mathfrak{sl}_k$. They are studied
extensively by knot theorists because braiding maps provide a categorical way
to express link diagrams in terms of webs, producing quantum invariants like
the well-known Jones polynomial. One important question in representation
theory is to identify the relationships between different bases; coefficients
in the change-of-basis matrix often describe combinatorial, algebraic, or
geometric quantities (like, e.g., Kazhdan-Lusztig polynomials). By "flattening"
the braiding maps, webs can also be viewed as the basis elements of a
symmetric-group representation.
  In this paper, we define two new combinatorial structures for webs: band
diagrams and their one-dimensional projections, shadows, that measure depths of
regions inside the web. As an application, we resolve an open conjecture that
the change-of-basis between the so-called Specht basis and web basis of this
symmetric-group representation is unitriangular for $\mathfrak{sl}_3$-webs. We
do this using band diagrams and shadows to construct a new partial order on
webs that is a refinement of the usual partial order. In fact, we prove that
for $\mathfrak{sl}_2$-webs, our new partial order coincides with the tableau
partial order on webs studied by the authors and others. We also prove that
though the new partial order for $\mathfrak{sl}_3$-webs is a refinement of the
previously-studied tableau order, the two partial orders do not agree for
$\mathfrak{sl}_3$.


  We study the problem of extracting biometric information of individuals by
looking at shadows of objects cast on diffuse surfaces. We show that the
biometric information leakage from shadows can be sufficient for reliable
identity inference under representative scenarios via a maximum likelihood
analysis. We then develop a learning-based method that demonstrates this
phenomenon in real settings, exploiting the subtle cues in the shadows that are
the source of the leakage without requiring any labeled real data. In
particular, our approach relies on building synthetic scenes composed of 3D
face models obtained from a single photograph of each identity. We transfer
what we learn from the synthetic data to the real data using domain adaptation
in a completely unsupervised way. Our model is able to generalize well to the
real domain and is robust to several variations in the scenes. We report high
classification accuracies in an identity classification task that takes place
in a scene with unknown geometry and occluding objects.


  We consider the problem of finding an approximate solution to $\ell_1$
regression while only observing a small number of labels. Given an $n \times d$
unlabeled data matrix $X$, we must choose a small set of $m \ll n$ rows to
observe the labels of, then output an estimate $\widehat{\beta}$ whose error on
the original problem is within a $1 + \varepsilon$ factor of optimal. We show
that sampling from $X$ according to its Lewis weights and outputting the
empirical minimizer succeeds with probability $1-\delta$ for $m >
O(\frac{1}{\varepsilon^2} d \log \frac{d}{\varepsilon \delta})$. This is
analogous to the performance of sampling according to leverage scores for
$\ell_2$ regression, but with exponentially better dependence on $\delta$. We
also give a corresponding lower bound of $\Omega(\frac{d}{\varepsilon^2} + (d +
\frac{1}{\varepsilon^2}) \log\frac{1}{\delta})$.


  Balder's well-known existence theorem (1983) for infinite-horizon optimal
control problems is extended to the case when the integral functional is
understood as an improper integral. Simultaneously, the condition of strong
uniform integrability (over all admissible controls and trajectories) of the
positive part $\max\{f_0,0\}$ of the utility function (integrand) $f_0$ is
relaxed to the requirement that the integrals of $f_0$ over intervals $[T,T']$
be uniformly bounded from above by a function $\omega(T,T')$ such that
$\omega(T,T')\to 0$ as $T,T'\to\infty$. This requirement was proposed by A.V.
Dmitruk and N.V. Kuz'kina (2005); however, the proof in the present paper does
not follow their scheme but is instead derived in a rather simple way from the
auxiliary results of Balder himself. An illustrative example is also given.


  In the last few years, there has been increasing interest in quantum
processes with indefinite causal order. Process matrices are a convenient
framework to study such processes. Ref. [1] defines higher order
transformations from process matrices to process matrices and shows that no
continuous and reversible transformation can change the causal order of a
process matrix. Ref. [2] argues, based on a set of examples, that there are
situations where a process can change its causal order over time. Ref. [2]
claims that the formalism of higher order transformations is not general enough
to capture its examples. Here we show that this claim is incorrect. Moreover, a
crucial example of Ref. [2] has already been explicitly considered in Ref. [1]
and shown to be compatible with its results.


  In this paper, we consider the problem of scheduling real-time traffic in
wireless networks under a conflict-graph interference model and single-hop
traffic. The objective is to guarantee that at least a certain fraction of
packets of each link are delivered within their deadlines, which is referred to
as delivery ratio. This problem has been studied before under restrictive
frame-based traffic models, or greedy maximal scheduling schemes like LDF
(Largest-Deficit First) that provide poor delivery ratio for general traffic
patterns. In this paper, we pursue a different approach through randomization
over the choice of maximal links that can transmit at each time. We design
randomized policies in collocated networks, multi-partite networks, and general
networks, that can achieve delivery ratios much higher than what is achievable
by LDF. Further, our results apply to traffic (arrival and deadline) processes
that evolve as positive recurrent Markov Chains. Hence, this work is an
improvement with respect to both efficiency and traffic assumptions compared to
the past work. We further present extensive simulation results over various
traffic patterns and interference graphs to illustrate the gains of our
randomized policies over LDF variants.


  Convolutional Neural Networks (CNNs) are one of the most successful deep
machine learning technologies for processing image, voice and video data. CNNs
require large amounts of processing capacity and memory, which can exceed the
resources of low power mobile and embedded systems. Several designs for
hardware accelerators have been proposed for CNNs which typically contain large
numbers of Multiply Accumulate (MAC) units. One approach to reducing data sizes
and memory traffic in CNN accelerators is "weight sharing", where the full
range of values in a trained CNN are put in bins and the bin index is stored
instead of the original weight value. In this paper we propose a novel MAC
circuit that exploits binning in weight-sharing CNNs. Rather than computing the
MAC directly we instead count the frequency of each weight and place it in a
bin. We then compute the accumulated value in a subsequent multiply phase. This
allows hardware multipliers in the MAC circuit to be replaced with adders and
selection logic. Experiments show that for the same clock speed our approach
results in fewer gates, smaller logic, and reduced power.


  In this work, we propose and analyze a generalized construction of
distributed network codes for a network consisting of M users sending different
information to a common base station through independent block fading channels.
The aim is to increase the diversity order of the system without reducing its
code rate. The proposed scheme, called generalized dynamic network codes
(GDNC), is a generalization of the dynamic network codes (DNC) recently
proposed by Xiao and Skoglund. The design of the network codes that maximizes
the diversity order is recognized as equivalent to the design of linear block
codes over a nonbinary finite field under the Hamming metric. The proposed
scheme offers a much better tradeoff between rate and diversity order. An
outage probability analysis showing the improved performance is carried out,
and computer simulations results are shown to agree with the analytical
results.


  This study was motivated by the problem of identifying fake documents on the
Internet. To explore possible solutions to this problem we introduce a model of
a network community in which members submit documents with verifiable content.
Documents are evaluated by three individuals, randomly selected from all the
members. The members whose documents are evaluated as authentic gain
reputation. Otherwise, they lose it. Members with higher reputation have a
greater chance to be selected to serve as evaluators. Analytical and
computational results suggest this evaluation mechanism is effective in a wide
range of parameters including the presence of members who consistently submit
fake content and do not evaluate documents of others objectively. The proposed
mechanism can be used in a variety of situations including evaluating photo and
video files with geolocation and time data and creating collections of digital
evidence of events clear from fake materials.


  Erd\"{o}s and Tur\'{a}n once conjectured that any set $A\subset\mathbb{N}$
with $\sum_{a\in A}{1}/{a}=\infty$ should contain infinitely many progressions
of arbitrary length $k\geq3$. For the two-dimensional case Graham conjectured
that if $B\subset \mathbb{N}\times\mathbb{N}$ satisfies $$\sum\limits_{(x,y)\in
B}\frac{1}{x^2+y^2}=\infty,$$ then for any $s\geq2$, $B$ contains an $s\times
s$ axes-parallel grid. In this paper it is shown that if the Graham conjecture
is true for some $s\geq2$, then the Erd\"{o}s-Tur\'{a}n conjecture is true for
$k=2s-1$.


  For the higher order derivative(with respect to the first variable) of
Hurwitz zeta function,we discuss as a function of the second variable,the
location and the nature of its singularities and obtain the formulae for its
derivative and primitive and also discuss its Riemann integrability on the unit
interval and on the interval[1,infinity).We also evaluate certain integrals on
the unit interval involving these functions.


  Indistinguishable particles in two dimensions can be characterized by anyonic
quantum statistics more general than those of bosons or fermions. Such anyons
emerge as quasiparticles in fractional quantum Hall states and certain
frustrated quantum magnets. Quantum liquids of anyons exhibit degenerate ground
states where the degeneracy depends on the topology of the underlying surface.
Here we present a novel type of continuous quantum phase transition in such
anyonic quantum liquids that is driven by quantum fluctuations of topology. The
critical state connecting two anyonic liquids on surfaces with different
topologies is reminiscent of the notion of a `quantum foam' with fluctuations
on all length scales. This exotic quantum phase transition arises in a
microscopic model of interacting anyons for which we present an exact solution
in a linear geometry. We introduce an intuitive physical picture of this model
that unifies string nets and loop gases, and provide a simple description of
topological quantum phases and their phase transitions.


  The traditional algorithms do not meet the latest multiple requirements
simultaneously for objects. Density-based method is one of the methodologies,
which can detect arbitrary shaped clusters where clusters are defined as dense
regions separated by low density regions. In this paper, we present a new
clustering algorithm to enhance the density-based algorithm DBSCAN. This
enables an automatic parameter generation strategy to create clusters with
different densities and enables noises recognition, and generates arbitrary
shaped clusters. The kdtree is used for increasing the memory efficiency.
Experimental result shows that proposed algorithm is capable of handling
complex objects with good memory efficiency and accuracy.


  The conventional unpolarized current injected into a {\em quantum-coherent}
semiconductor ring attached to two external leads can be modulated from perfect
conductor to perfect insulator limit via Rashba spin-orbit (SO) coupling. This
requires that ballistic propagation of electrons, whose spin precession is
induced by the Aharonov-Casher phase, takes place through a single conducting
channel ensuring that electronic quantum state remains a pure separable one in
the course of transport. We study the fate of such spin interference effects as
more than one orbital conducting channel becomes available for quantum
transport. Although the conductance of multichannel rings, in general, does not
go all the way to zero at any value of the SO coupling, some degree of current
modulation survives. We analyze possible scenarios that can lead to reduced
visibility of spin interference effects that are responsible for the zero
conductance at particular values of the Rashba interaction: (i) the transmitted
spin states remain fully coherent, but conditions for destructive interference
are different in different channels; (ii) the transmitted spins end up in
partially coherent quantum state arising from entanglement to the environment
composed of orbital degrees of freedom of the same particle to which the spin
is attached.


  Temporal irreversibility, often referred to as the arrow of time, is a
fundamental concept in statistical mechanics. Markers of irreversibility also
provide a powerful characterisation of information processing in biological
systems. However, current approaches tend to describe temporal irreversibility
in terms of a single scalar quantity, without disentangling the underlying
dynamics that contribute to irreversibility. Here we propose a broadly
applicable information-theoretic framework to characterise the arrow of time in
multivariate time series, which yields qualitatively different types of
irreversible information dynamics. This multidimensional characterisation
reveals previously unreported high-order modes of irreversibility, and
establishes a formal connection between recent heuristic markers of temporal
irreversibility and metrics of information processing. We demonstrate the
prevalence of high-order irreversibility in the hyperactive regime of a
biophysical model of brain dynamics, showing that our framework is both
theoretically principled and empirically useful. This work challenges the view
of the arrow of time as a monolithic entity, enhancing both our theoretical
understanding of irreversibility and our ability to detect it in practical
applications.


  We study the problem of matrix completion in this paper. A spectral scaled
Student prior is exploited to favour the underlying low-rank structure of the
data matrix. We provide a thorough theoretical investigation for our approach
through PAC-Bayesian bounds. More precisely, our PAC-Bayesian approach enjoys a
minimax-optimal oracle inequality which guarantees that our method works well
under model misspecification and under general sampling distribution.
Interestingly, we also provide efficient gradient-based sampling
implementations for our approach by using Langevin Monte Carlo. More
specifically, we show that our algorithms are significantly faster than Gibbs
sampler in this problem. To illustrate the attractive features of our inference
strategy, some numerical simulations are conducted and an application to image
inpainting is demonstrated.


  The concept of a laser-guided $e^+e^-$ collider in the high-energy regime is
presented and its feasibility discussed. Ultra-intense laser pulses and strong
static magnetic fields are employed to unite in one stage the electron and
positron acceleration and their head-on-head collision. We show that the
resulting coherent collisions in the GeV regime yield an enormous enhancement
of the luminosity with regard to conventional incoherent colliders.


  Recently, Convolutional Neural Networks (CNNs) have been successfully adopted
to solve the ill-posed single image super-resolution (SISR) problem. A commonly
used strategy to boost the performance of CNN-based SISR models is deploying
very deep networks, which inevitably incurs many obvious drawbacks (e.g., a
large number of network parameters, heavy computational loads, and difficult
model training). In this paper, we aim to build more accurate and faster SISR
models via developing better-performing feature extraction and fusion
techniques. Firstly, we proposed a novel Orientation-Aware feature extraction
and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional
kernels (i.e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware
features. Secondly, we adopt the channel attention mechanism as an effective
technique to adaptively fuse features extracted in different directions and in
hierarchically stacked convolutional stages. Based on these two important
improvements, we present a compact but powerful CNN-based model for
high-quality SISR via Channel Attention-based fusion of Orientation-Aware
features (SISR-CA-OA). Extensive experimental results verify the superiority of
the proposed SISR-CA-OA model, performing favorably against the
state-of-the-art SISR models in terms of both restoration accuracy and
computational efficiency. The source codes will be made publicly available.


  In this paper, we consider natural Hilbert-space representations $\left\{
\left(\mathbb{C}^{2},\pi_{t}\right)\right\} _{t\in\mathbb{R}}$ of the
hypercomplex system $\left\{ \mathbb{H}_{t}\right\} _{t\in\mathbb{R}}$, and
study the realizations $\pi_{t}\left(h\right)$ of hypercomplex numbers
$h\in\mathbb{H}_{t}$, as $\left(2\times2\right)$-matrices acting on
$\mathbb{C}^{2}$, for an arbitrarily fixed scale $t\in\mathbb{R}$. Algebraic,
operator-theoretic, spectral-analytic, and free-probabilistic properties of
them are considered.


  The discovery of reusable sub-routines simplifies decision-making and
planning in complex reinforcement learning problems. Previous approaches
propose to learn such temporal abstractions in a purely unsupervised fashion
through observing state-action trajectories gathered from executing a policy.
However, a current limitation is that they process each trajectory in an
entirely sequential manner, which prevents them from revising earlier decisions
about sub-routine boundary points in light of new incoming information. In this
work we propose SloTTAr, a fully parallel approach that integrates sequence
processing Transformers with a Slot Attention module and adaptive computation
for learning about the number of such sub-routines in an unsupervised fashion.
We demonstrate how SloTTAr is capable of outperforming strong baselines in
terms of boundary point discovery, even for sequences containing variable
amounts of sub-routines, while being up to 7x faster to train on existing
benchmarks.


  Machine learning algorithms for prediction are increasingly being used in
critical decisions affecting human lives. Various fairness formalizations, with
no firm consensus yet, are employed to prevent such algorithms from
systematically discriminating against people based on certain attributes
protected by law. The aim of this article is to survey how fairness is
formalized in the machine learning literature for the task of prediction and
present these formalizations with their corresponding notions of distributive
justice from the social sciences literature. We provide theoretical as well as
empirical critiques of these notions from the social sciences literature and
explain how these critiques limit the suitability of the corresponding fairness
formalizations to certain domains. We also suggest two notions of distributive
justice which address some of these critiques and discuss avenues for
prospective fairness formalizations.


  Terahertz radiation of the stack of intrinsic Josephson junctions in the mesa
structure of the layered high-Tc superconductors is analyzed and presented in
this work. The dependency of the radiated power to the geometrical parameters,
cavity-waveguide boundaries, and magnetic and electric bias has been
investigated. This has been done by numerical calculation of the previously
proposed coupled sine-Gordon equations, which characterize the electromagnetic
dynamics of the stack of the intrinsic Josephson junctions. Using the obtained
numerical results from these coupled equations, the effect of the design
parameters such as dimensions of the mesa structure and the magnitude of the
applied magnetic field and the dc current on the enhancement of the radiated
power is studied. Thus, the radiated power is optimized with respect to these
considered parameters. By variation of the number of layers, we also
investigate the effect of the number of intrinsic Josephson junctions on the
total radiated power. The results from this part are also compared with the
previous analytical models.


  An approach for engineering student exercises using the Internet is
described. In this approach, for a given exercise, each student receives the
same problem, but with different data. The exercise content can be static or
dynamic, and the dynamic form can be timeless or real-time. The implementation
provides immediate feedback to the students, letting them know if their
submitted answers are correct. Student results for each exercise are recorded
in log files which are available to the instructor. Example exercises from
engineering computer security and cryptography courses are presented.


  We present a theoretical framework to model the electric response of cell
aggregates. We establish a coarse representation for each cell as a combination
of membrane and cytoplasm dipole moments. Then we compute the effective
conductivity of the resulting system, and thereafter derive a Fokker-Planck
partial differential equation that captures the time-dependent evolution of the
distribution of induced cellular polarizations in an ensemble of cells. Our
model predicts that the polarization density parallel to an applied pulse
follows a skewed t-distribution, while the transverse polarization density
follows a symmetric t-distribution, which are in accordance with our direct
numerical simulations. Furthermore, we report a reduced order model described
by a coupled pair of ordinary differential equations that reproduces the
average and the variance of induced dipole moments in the aggregate. We extend
our proposed formulation by considering fractional order time derivatives that
we find necessary to explain anomalous relaxation phenomena observed in
experiments as well as our direct numerical simulations. Owing to its
time-domain formulation, our framework can be easily used to consider nonlinear
membrane effects or intercellular couplings that arise in several scientific,
medical and technological applications.


  Mostly forsaken, but revived after the emergence of all-dielectric
nanophotonics, the Kerker effect can be observed in a variety of nanostructures
from high-index constituents with strong electric and magnetic Mie resonances.
Necessary requirement for the existence of a magnetic response limits the use
of generally non-magnetic conventional plasmonic nanostructures for the Kerker
effect. In spite of this, we demonstrate here for the first time the emergence
of the lattice Kerker effect in regular plasmonic Al nanostructures. Collective
lattice oscillations emerging from delicate interplay between Rayleigh
anomalies and localized surface plasmon resonances both of electric and
magnetic dipoles, and electric and magnetic quadrupoles result in suppression
of the backscattering in a broad spectral range. Variation of geometrical
parameters of Al arrays allows for tailoring lattice Kerker effect throughout
UV and visible wavelength ranges, which is close to impossible to achieve using
other plasmonic or all-dielectric materials. It is argued that our results set
the ground for wide ramifications in the plasmonics and further application of
the Kerker effect.


  In this paper we revise and simplify the notion of observationally induced
algebra introduced by Simpson and Schroeder for the purpose of modelling
computational effects in the particular case where the ambient category is
given by classical domain theory. As examples of the general framework we
consider the various powerdomains. For the particular case of the Plotkin
powerdomain the general recipe leads to a somewhat unexpected result which,
however, makes sense from a Computer Science perspective. We analyze this
"deviation" and show how to reobtain the original Plotkin powerdomain by
imposing further conditions previously considered by R.~Heckmann and
J.~Goubault-Larrecq.


  We prove that integral projective curves with Cohen-Macaulay singularities
over an algebraically closed field of characteristic zero are determined by
their triangulated category of perfect complexes.This partially extends a
theorem of Bondal and Orlov to the case of singular projective varieties, and
in particular shows that all integral complex projective curves are determined
by their category of perfect complexes


  Inertial measurement units are widely used in different fields to estimate
the attitude. Many algorithms have been proposed to improve estimation
performance. However, most of them still suffer from 1) inaccurate initial
estimation, 2) inaccurate initial filter gain, and 3) non-Gaussian process
and/or measurement noise. In this paper, we leverage reinforcement learning to
compensate for the classical extended Kalman filter estimation, i.e., to learn
the filter gain from the sensor measurements. We also analyse the convergence
of the estimate error. The effectiveness of the proposed algorithm is validated
on both simulated data and real data.


  State-of-the-art numerical simulations of laser plasma by means of the
Particle-in-Cell method are often extremely computationally intensive.
Therefore there is a growing need for development of approaches for efficient
utilization of resources of modern supercomputers. In this paper, we address
the problem of a substantially non-uniform and dynamically varying distribution
of macroparticles in a computational area in simulating quantum electrodynamic
(QED) cascades. We propose and evaluate a load balancing scheme for shared
memory systems, which allows subdividing individual cells of the computational
domain into work portions with subsequent dynamic distribution of these
portions between OpenMP threads. Computational experiments on 1D, 2D, and 3D
QED simulations show that the proposed scheme outperforms the previously
developed standard and custom schemes in the PICADOR code by 2.1 to 10 times
when employing several Intel Cascade Lake CPUs.