  We present a linear spin and orbital wave theory to account for the spin and
orbital orderings observed experimentally in undoped manganite. It is found
that the anisotropy of the magnetic structure is closely related to the orbital
ordering, and the Jahn-Teller effect stabilizes the orbital ordering. The phase
diagram and the low energy excitation spectra for both spin and orbital
orderings are obtained. The calculated critical temperatures can be
quantitatively comparable to the experimental data.


  The spectral projectors method is a way to obtain a theoretically well posed
definition of the topological susceptibility on the lattice. Up to now this
method has been defined and applied only to Wilson fermions. The goal of this
work is to extend the method to staggered fermions, giving a definition for the
staggered topological susceptibility and testing it in the pure $SU(3)$ gauge
theory. Besides, we also generalize the method to higher-order cumulants of the
topological charge distribution.


  In mechanism design theory, a designer would like to implement a desired
social choice function which specifies her favorite outcome for each possible
profile of agents' types. To do so, the designer constructs a mechanism which
describes each agent's feasible strategy set and the outcome function.
Generally speaking, each agent's strategy in a mechanism has two possible
formats: an action, or a message. In this paper, we focus on the former format
and claim that the notion of Bayesian Nash equilibrium of a mechanism should be
based on a profit function instead of the conventional utility function when
strategies of agents are costly actions. Next, we derive the main result: Given
a social choice function which can be implemented by an indirect mechanism in
Bayesian Nash equilibrium, if all strategies of agents are costly actions, then
it cannot be inferred that there exists a direct mechanism that can truthfully
implement the social choice function in Bayesian Nash equilibrium.


  Quantum state tomography suffers from the measurement effort increasing
exponentially with the number of qubits. Here, we demonstrate permutationally
invariant tomography for which, contrary to conventional tomography, all
resources scale polynomially with the number of qubits both in terms of the
measurement effort as well as the computational power needed to process and
store the recorded data. We demonstrate the benefits of combining
permutationally invariant tomography with compressed sensing by studying the
influence of the pump power on the noise present in a six-qubit symmetric Dicke
state, a case where full tomography is possible only for very high pump powers.


  Low-energy results from measurements of leptonic dipole moments are used to
derive constraints on the CP-violating phases of the dimensionful parameters of
the minimal supersymmetric extension of the standard model (MSSM). We use these
(known) bounds to investigate the impact of these phases on CP-even cross
sections at high-energy e^+e^- and e^-e^- colliders. To that end we define two
measures of the significance with which the existence of non--vanishing phases
could be deduced from the measurements of these cross sections. We find that
highly significant evidence for deviations from the CP-conserving MSSM could be
obtained at the next e^+e^- collider even if the electric dipole moment of the
electron is very small or zero. We also analyze a CP-odd final state
polarization, which can be large when two different charginos or neutralinos
are produced. Finally, we study correlations between the phase--sensitive
observables.


  As the usage of deep learning becomes increasingly popular in mobile and
embedded solutions, it is necessary to convert the framework-specific network
representations into executable code for these embedded platforms. This paper
consists of two parts: The first section is made up of a survey and benchmark
of the available open source deep learning compiler toolchains, which focus on
the capabilities and performance of the individual solutions in regard to
targeting embedded devices and microcontrollers that are combined with a
dedicated accelerator in a heterogeneous fashion. The second part explores the
implementation and evaluation of a compilation flow for such a heterogeneous
device and reuses one of the existing toolchains to demonstrate the necessary
steps for hardware developers that plan to build a software flow for their own
hardware.


  A simultaneous analysis (in the model-independent approach) of the $\pi\pi$
scattering (from the threshold to 1.9 GeV) and of the $\pi\pi\to K\overline{K}$
process (from the threshold to $\sim$ 1.46 GeV) is carried out in channel with
$I^GJ^{PC}=0^+0^{++}$. The existence of the $f_0(665)$ state with properies of
the $\sigma$-meson is proved. An indication for the glueball nature of the
$f_0(1500)$ is obtained. A minimum scenario of the simultaneous description of
the processes $\pi\pi\to\pi\pi,K\overline{K}$ does without the ${f_0}(1370)$
resonance. A version including this state is also considered. In this case the
${f_0}(1370)$ resonance has the dominant $s{\bar s}$ component (the ratio of
its coupling constant with the $\pi\pi$ channel to the one with the
$K\overline{K}$ channel is 0.12). The coupling constants of the observed states
with the $\pi\pi$ and $K\overline{K}$ systems and the $\pi\pi$ and
$K\overline{K}$ scattering lengths are obtained. A parameterless description of
the $\pi\pi$ background is given by allowance for the left-hand branch-point in
the uniformizing variable. On the basis of this and of the fact that all the
adjusted parameters for the $\pi\pi$ scattering are only the positions of poles
describing resonances, it is concluded that our model-independent approach is a
valuable tool for studying the realization schemes of chiral symmetry. The
existence of the $f_0(665)$ state and the obtained $\pi\pi$-scattering length
($a_0^0\approx 0.27 m_{\pi^+}^{-1}$) seem to suggest the linear realization of
chiral symmetry.


  The expression of causality depends on an underlying choice of chronology.
Since a chronology is provided by any Lorentzian metric in relativistic
theories, there are as many expressions of causality as there are
non-conformally related metrics over spacetime. Although tempting, a definitive
choice of a preferred metric to which one may refer to is not satisfying. It
would indeed be in great conflict with the spirit of general covariance.
Moreover, a theory which appear to be non causal with respect to (hereafter,
w.r.t) this metric, may well be causal w.r.t another metric. In a theory
involving fields that propagate at different speeds (e.g. due to some
spontaneous breaking of Lorentz invariance), spacetime is endowed with such a
finite set of non-conformally related metrics. In that case one must look for a
new notion of causality, such that 1. no particular metric is favored and 2.
there is an unique answer to the question : ``is the theory causal?''. This new
causality is unique and defined w.r.t the metric drawing the wider cone in the
tangent space of a given point of the manifold. Moreover, which metric defines
the wider cone may depend on the location on spacetime. In that sense,
superluminal fields are generically causal, provided that some other basic
requirements are met.


  The authors report that anisotropic confining potentials in laterally-coupled
semiconductor quantum dots (QDs) have large impacts in optical transitions and
energies of inter-shell collective electronic excitations. The observed
anisotropies are revealed by inelastic light scattering as a function of the
in-plane direction of light polarization and can be finely controlled by
modifying the geometrical shape of the QDs. These experiments show that the
tuning of the QD confinement potential offers a powerful method to manipulate
electronic states and far-infrared inter-shell optical transitions in quantum
dots.


  One of the primitive aims of the two-dimensional BTW model had been to
explain the $1/f^{\alpha}$ noise which is widely seen in the natural systems.
In this paper we study some time signals, namely the activity inside an
avalanche ($x(t)$), the avalanches sizes ($s(T)$) and the rare events waiting
time (REWT) ($\tau(n)$ as a new type of noise). The latter is expected to be
important in predicting the period and also the vastness of the upcoming large
scale events in a sequence of self-organized natural events. Especially we
report some exponential anti-correlation behaviors for $s(T)$ and $\tau(n)$
which are finite-size effects. Two characteristic time scales $\delta T_{s}$
and $\delta T_{\tau}$ emerge in our analysis. It is proposed that the power
spectrum of $s(T)$ and $\tau(n)$ behave like $\left(
b_{s,\tau}(L)^2+\omega^2\right)^{-\frac{1}{2}}$, in which $b_{s}$ and
$b_{\tau}$ are some $L$-dependent parameters and $\omega$ is the angular
frequency. The $1/f$ noise is therefore obtained in the limit $\omega\gg
b_{s,\tau}$. $b_{s}$ and $b_{\tau}$ decrease also in a power-law fashion with
the system size $L$, which signals the fact that in the thermodynamic limit the
power spectrum tends to the Dirac delta function.


  The measured values of the Higgs and top quark mass indicate that the
electroweak vacuum is metastable if there is no new physics below the Planck
scale. This is at odds with a period of high scale inflation. A non-minimal
coupling between the Higgs field and the Ricci scalar can stabilize the vacuum
as it generates a large effective Higgs mass during inflation. We consider the
effect of this coupling during preheating, when Higgs modes can be produced
very efficiently due to the oscillating Ricci scalar. We compute their effect
on the effective potential and the energy density. The Higgs excitations are
defined with respect to the adiabatic vacuum. We study the adiabaticity
conditions and find that the dependence of our results on the choice of the
order of the adiabatic vacuum increases with time. For large enough coupling
particle production is so efficient that the Higgs decays to the true vacuum
before this is an issue. However, for smaller values of the Higgs-curvature
coupling no definite statements can be made as the vacuum dependence is large.


  We study the intersection of two copies of $\mathrm{Gr}(2,5)$ embedded in
$\mathbf{P}^9$, and the intersection of the two projectively dual Grassmannians
in the dual projective space. These intersections are deformation equivalent,
derived equivalent Calabi-Yau threefolds. We prove that generically they are
not birational. As a consequence, we obtain a counterexample to the birational
Torelli problem for Calabi-Yau threefolds. We also show that these threefolds
give a new pair of varieties whose classes in the Grothendieck ring of
varieties are not equal, but whose difference is annihilated by a power of the
class of the affine line. Our proof of non-birationality involves a detailed
study of the moduli stack of Calabi-Yau threefolds of the above type, which may
be of independent interest.


  Fish schooling is often modeled with self-propelled particles subject to
phenomenological behavioral rules. Although fish are known to sense and exploit
flow features, these models usually neglect hydrodynamics. Here, we propose a
novel model that couples behavioral rules with far-field hydrodynamic
interactions. We show that (1) a new "collective turning" phase emerges; (2) on
average individuals swim faster thanks to the fluid; (3) the flow enhances
behavioral noise. The results of this model suggest that hydrodynamic effects
should be considered to fully understand the collective dynamics of fish.


  The energy spectra of mesoscopic, i.e. few-body quantum systems are of great
interest in several areas of physics such as nuclear physics, cluster physics
or magnetism. One way to obtain an approximate spectrum is to diagonalize with
reduced but carefully chosen basis sets. In this Letter we propose for the case
of mesoscopic antiferromagnets to use the eigenstates of the rotational band
Hamiltonian for this purpose. These states are not only well adapted, they can
also be easily constructed.


  Scene graphs have been recently introduced into 3D spatial understanding as a
comprehensive representation of the scene. The alignment between 3D scene
graphs is the first step of many downstream tasks such as scene graph aided
point cloud registration, mosaicking, overlap checking, and robot navigation.
In this work, we treat 3D scene graph alignment as a partial graph-matching
problem and propose to solve it with a graph neural network. We reuse the
geometric features learned by a point cloud registration method and associate
the clustered point-level geometric features with the node-level semantic
feature via our designed feature fusion module. Partial matching is enabled by
using a learnable method to select the top-k similar node pairs. Subsequent
downstream tasks such as point cloud registration are achieved by running a
pre-trained registration network within the matched regions. We further propose
a point-matching rescoring method, that uses the node-wise alignment of the 3D
scene graph to reweight the matching candidates from a pre-trained point cloud
registration method. It reduces the false point correspondences estimated
especially in low-overlapping cases. Experiments show that our method improves
the alignment accuracy by 10~20% in low-overlap and random transformation
scenarios and outperforms the existing work in multiple downstream tasks.


  We show bounds on tail probabilities for quadratic forms in sub-gaussian
non-necessarily independent random variables. Our main tool will be estimates
of the Luxemburg norms of such forms. This will allow us to formulate the
above-mentioned bounds. As an example we give estimates of the excess loss in
fixed design linear regression in dependent observations.


  We conduct experimental simulations of many body quantum systems using a
\emph{hybrid} classical-quantum algorithm. In our setup, the wave function of
the transverse field quantum Ising model is represented by a restricted
Boltzmann machine. This neural network is then trained using variational Monte
Carlo assisted by a D-Wave quantum sampler to find the ground state energy. Our
results clearly demonstrate that already the first generation of quantum
computers can be harnessed to tackle non-trivial problems concerning physics of
many body quantum systems.


  The Dushnik-Miller dimension of a poset $\le$ is the minimal number $d$ of
linear extensions $\le_1, \ldots , \le_d$ of $\le$ such that $\le$ is the
intersection of $\le_1, \ldots , \le_d$. Supremum sections are simplicial
complexes introduced by Scarf and are linked to the Dushnik-Miller as follows:
the inclusion poset of a simplicial complex is of Dushnik-Miller dimension at
most $d$ if and only if it is included in a supremum section coming from a
representation of dimension $d$. Collapsibility is a topoligical property of
simplicial complexes which has been introduced by Whitehead and which resembles
to shellability. While Ossona de Mendez proved in that a particular type of
supremum sections are shellable, we show in this article that supremum sections
are in general collapsible thanks to the discrete Morse theory developped by
Forman.


  In the field of medical image analysis, deep learning models have
demonstrated remarkable success in enhancing diagnostic accuracy and
efficiency. However, the reliability of these models is heavily dependent on
the quality of training data, and the existence of label noise (errors in
dataset annotations) of medical image data presents a significant challenge.
This paper introduces a new sample selection method that enhances the
performance of neural networks when trained on noisy datasets. Our approach
features estimating the noise rate of a dataset by analyzing the distribution
of loss values using Linear Regression. Samples are then ranked according to
their loss values, and potentially noisy samples are excluded from the dataset.
Additionally, we employ sparse regularization to further enhance the noise
robustness of our model. Our proposed method is evaluated on five benchmark
datasets and a real-life noisy medical image dataset. Notably, two of these
datasets contain 3D medical images. The results of our experiments show that
our method outperforms existing noise-robust learning methods, particularly in
scenarios with high noise rates. Key words: noise-robust learning, medical
image analysis, noise rate estimation, sample selection, sparse regularization


  Deep learning models perform poorly on tasks that require commonsense
reasoning, which often necessitates some form of world-knowledge or reasoning
over information not immediately present in the input. We collect human
explanations for commonsense reasoning in the form of natural language
sequences and highlighted annotations in a new dataset called Common Sense
Explanations (CoS-E). We use CoS-E to train language models to automatically
generate explanations that can be used during training and inference in a novel
Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the
state-of-the-art by 10% on the challenging CommonsenseQA task. We further study
commonsense reasoning in DNNs using both human and auto-generated explanations
including transfer to out-of-domain tasks. Empirical results indicate that we
can effectively leverage language models for commonsense reasoning.


  The layered van der Waals ferromagnetic Fe$_3$GeTe$_2$ harbours an
unconventional interplay between topology and magnetism, leading to a large
anomalous Hall conductivity at low temperatures. Here, we investigate the
temperature dependence of its charge dynamics and reveal that upon entering the
ferromagnetic state at $T_C \sim 200$ K and further lowering the temperature
there is the onset of a gradual spectral weight reshuffling from the
mid-infrared range towards far- as well as near-infrared frequencies. This
two-fold spectral weight transfer indicates the important role of the Hund's
coupling as primary source for electronic correlations and signals an
incoherent-coherent crossover at low temperatures. Our findings also convey the
electronic environment, based on nodal-line topological states, favouring the
large anomalous Hall conductivity.


  The Double Chooz reactor neutrino experiment will be the next detector to
search for a non vanishing theta13 mixing angle with unprecedented sensitivity,
which might open the way to unveiling CP violation in the leptonic sector. The
measurement of this angle will be based in a precise comparison of the
antineutrino spectrum at two identical detectors located at different distances
from the Chooz nuclear reactor cores in France. Double Chooz is particularly
attractive because of its capability to measure sin2(2theta13) to 3 sigmas if
sin2(2theta13) > 0.05 or to exclude sin2(2theta13) down to 0.03 at 90% C.L. for
Dm2 = 2.5 x 10-3 eV2 in three years of data taking with both detectors. The
construction of the far detector starts in 2008 and the first neutrino results
are expected in 2009. The current status of the experiment, its physics
potential and design and expected performance of the detector are reviewed.


  This paper presents BattleAgent, an emulation system that combines the Large
Vision-Language Model and Multi-agent System. This novel system aims to
simulate complex dynamic interactions among multiple agents, as well as between
agents and their environments, over a period of time. It emulates both the
decision-making processes of leaders and the viewpoints of ordinary
participants, such as soldiers. The emulation showcases the current
capabilities of agents, featuring fine-grained multi-modal interactions between
agents and landscapes. It develops customizable agent structures to meet
specific situational requirements, for example, a variety of battle-related
activities like scouting and trench digging. These components collaborate to
recreate historical events in a lively and comprehensive manner while offering
insights into the thoughts and feelings of individuals from diverse viewpoints.
The technological foundations of BattleAgent establish detailed and immersive
settings for historical battles, enabling individual agents to partake in,
observe, and dynamically respond to evolving battle scenarios. This methodology
holds the potential to substantially deepen our understanding of historical
events, particularly through individual accounts. Such initiatives can also aid
historical research, as conventional historical narratives often lack
documentation and prioritize the perspectives of decision-makers, thereby
overlooking the experiences of ordinary individuals. BattelAgent illustrates
AI's potential to revitalize the human aspect in crucial social events, thereby
fostering a more nuanced collective understanding and driving the progressive
development of human society.


  Broadly, the goal when clustering data is to separate observations into
meaningful subgroups. The rich variety of methods for clustering reflects the
fact that the relevant notion of meaningful clusters varies across
applications. The classical Bayesian approach clusters observations by their
association with components of a mixture model; the choice in class of
components allows flexibility to capture a range of meaningful cluster notions.
However, in practice the range is somewhat limited as difficulties with
computation and cluster identifiability arise as components are made more
flexible. Instead of mixture component attribution, we consider clusterings
that are functions of the data and the density $f$, which allows us to separate
flexible density estimation from clustering. Within this framework, we develop
a method to cluster data into connected components of a level set of $f$. Under
mild conditions, we establish that our Bayesian level-set (BALLET) clustering
methodology yields consistent estimates, and we highlight its performance in a
variety of toy and simulated data examples. Finally, through an application to
astronomical data we show the method performs favorably relative to the popular
level-set clustering algorithm DBSCAN in terms of accuracy, insensitivity to
tuning parameters, and quantification of uncertainty.


  This paper proposes AB-SAGA, a first-order distributed stochastic
optimization method to minimize a finite-sum of smooth and strongly convex
functions distributed over an arbitrary directed graph. AB-SAGA removes the
uncertainty caused by the stochastic gradients using a node-level variance
reduction and subsequently employs network-level gradient tracking to address
the data dissimilarity across the nodes. Unlike existing methods that use the
nonlinear push-sum correction to cancel the imbalance caused by the directed
communication, the consensus updates in AB-SAGA are linear and uses both row
and column stochastic weights. We show that for a constant step-size, AB-SAGA
converges linearly to the global optimal. We quantify the directed nature of
the underlying graph using an explicit directivity constant and characterize
the regimes in which AB-SAGA achieves a linear speed-up over its centralized
counterpart. Numerical experiments illustrate the convergence of AB-SAGA for
strongly convex and nonconvex problems.


  Pseudocode in a scholarly paper provides a concise way to express the
algorithms implemented therein. Pseudocode can also be thought of as an
intermediary representation that helps bridge the gap between programming
languages and natural languages. Having access to a large collection of
pseudocode can provide various benefits ranging from enhancing algorithmic
understanding, facilitating further algorithmic design, to empowering NLP or
computer vision based models for tasks such as automated code generation and
optical character recognition (OCR). We have created a large pseudocode
collection by extracting nearly 320,000 pseudocode examples from arXiv papers.
This process involved scanning over $2.2$ million scholarly papers, with 1,000
of them being manually inspected and labeled. Our approach encompasses an
extraction mechanism tailored to optimize the coverage and a validation
mechanism based on random sampling to check its accuracy and reliability, given
the inherent heterogeneity of the collection. In addition, we offer insights
into common pseudocode structures, supported by clustering and statistical
analyses. Notably, these analyses indicate an exponential-like growth in the
usage of pseudocodes, highlighting their increasing significance.


  The report illustrates the state of the art of the most successful AAL
applications and functions based on audio and video data, namely (i)
lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii)
emotional state recognition, (iv) food intake monitoring, activity and
behaviour recognition, (v) activity and personal assistance, (vi) gesture
recognition, (vii) fall detection and prevention, (viii) mobility assessment
and frailty recognition, and (ix) cognitive and motor rehabilitation. For these
application scenarios, the report illustrates the state of play in terms of
scientific advances, available products and research project. The open
challenges are also highlighted.


  The use of an electron beam to remove ultracold atoms from selected sites in
an optical lattice has opened up new opportunities to study transport in
quantum systems [R. Labouvie {\it et al.\ }, Phys.\ Rev.\ Lett.\ {\bf 115},
050601 (2015)]. Inspired by this experimental result, we examine the effects of
number difference, dephasing, and initial quantum statistics on the filling of
an initially depleted middle well in the three-well inline Bose-Hubbard model.
We find that the well-known phenomenon of macroscopic self-trapping is the main
contributor to oscillatory negative differential conductivity in our model,
with phase diffusion being a secondary effect. However, we find that phase
diffusion is required for the production of direct atomic current, with the
coherent process showing damped oscillatory currents. We also find that our
results are highly dependent on the initial quantum states of the atoms in the
system.


  Motivated by a class of near BPS Skyrme models introduced by Adam,
S\'anchez-Guill\'en and Wereszczy\'nski, the following variant of the harmonic
map problem is introduced: a map $\phi:(M,g)\rightarrow (N,h)$ between
Riemannian manifolds is restricted harmonic (RH) if it locally extremizes $E_2$
on its $SDiff(M)$ orbit, where $SDiff(M)$ denotes the group of volume
preserving diffeomorphisms of $(M,g)$, and $E_2$ denotes the Dirichlet energy.
It is conjectured that near BPS skyrmions tend to RH maps in the BPS limit. It
is shown that $\phi$ is RH if and only if $\phi^*h$ has exact divergence, and a
linear stability theory of RH maps is developed, whence it follows that all
weakly conformal maps, for example, are stable RH. Examples of RH maps in every
degree class $R^3\to SU(2)$ and $R^2\to S^2$ are constructed. It is shown that
the axially symmetric BPS skyrmions on which all previous analytic studies of
near BPS Skyrme models have been based, are not RH, so each such field can be
deformed along $SDiff(R^3)$ to yield BPS skyrmions with lower $E_2$, casting
doubt on the predictions of such studies. The problem of minimizing $E_2$ for
$\phi:R^k\to N$ over all linear volume preserving diffeomorphisms is solved
explicitly, and a deformed axially symmetric family of Skyrme fields
constructed which are candidates for approximate near BPS skyrmions at low
baryon number. The notion of restricted harmonicity is generalized to
restricted $F$-criticality where $F$ is any functional on maps $(M,g)\to (N,h)$
which is, in a precise sense, geometrically natural. The case where $F$ is a
linear combination of $E_2$ and $E_4$, the usual Skyrme term, is studied in
detail, and it is shown that inverse stereographic projection $R^3\to S^3\equiv
SU(2)$ is stable restricted $F$-critical for every such $F$.


  We show that the stellar masses implied by our predictions of the wind
properties of massive stars are in agreement with masses derived from evolution
theory and from direct measurements using spectroscopic binaries, contrary to
previous attempts to derive masses from wind theory.


  Thermal hysteresis is recognized as one of the main drawbacks for cyclical
applications of magnetocaloric and ferromagnetic shape memory materials with
first order transformations. As such, the challenge is to develop strategies
that improve the compatibility between the phases involved in the transitions
and study its influence on thermal hysteresis. With this purpose, we explore
the thermal, structural and magnetic properties of the Ni2Mn1-xCuxGa0.84Al0.16
Heusler alloys. The alloys present a thermal hysteresis reduction of ~60% when
the Cu content in the compound varies from x = 0.10 to x = 0.25, with a minimum
hysteresis width of 6 K being achieved. We applied the geometric non-linear
theory of martensite to address the phase compatibility, quantified by the
parameter lambda2, the middle eigenvalue of the transformation stretch tensor,
and found that the minimum of hysteresis is associated with a better
crystallographic compatibility (lambda2 closer to 1) between the austenite and
martensite phases. In addition, we show that the valley-like properties of
hysteresis found in the Ni2Mn1-xCuxGa0.84Al0.16 compounds is present in several
other alloys in the literature. These results provide new pathways to
understand as well as to masters the phase compatibility and ultimately achieve
a low thermal hysteresis in multifunctional Heusler alloys.


  This paper presents a complex permittivity measurement method for
low-dispersive materials as a function of frequency. The introduced method
relies only on transmitted power signals which are collected using a spectrum
analyzer/power meter, removing the need for phase measurements and a vector
network analyzer. This method provides a very good accuracy along with easy and
inexpensive permittivity measurements.


  We generalize and improve the original characterization given by Valadier
[20, Theorem 1] of the subdifferential of the pointwise supremum of convex
functions, involving the subdifferentials of the data functions at nearby
points. We remove the continuity assumption made in that work and obtain a
general formula for such a subdifferential. In particular, when the supremum is
continuous at some point of its domain, but not necessarily at the reference
point, we get a simpler version which gives rise to Valadier formula. Our
starting result is the characterization given in [10, Theorem 4], which uses
the epsilon-subdiferential at the reference point.


  We present a model for the evolution of networks of occupied sites on
undirected regular graphs. At every iteration step in a parallel update I
randomly chosen empty sites are occupied and occupied sites having degree
outside of a given interval (t_l,t_u) are set empty. Depending on the influx I
and the values of both lower threshold and upper threshold of the degree
different kinds of behaviour can be observed. In certain regimes stable
long-living patterns appear. We distinguish two types of pattern: static
patterns arising on graphs with low connectivity and dynamic patterns found on
high connectivity graphs. Increasing I patterns become unstable and transitions
between almost stable patterns, interrupted by disordered phases, occur. For
still larger I the lifetime of occupied sites becomes very small and network
structures are dominated by randomness. We develop methods to analyze nature
and dynamics of these network patterns, give a statistical description of
defects and fluctuations around them, and elucidate transitions between
different patterns. Results and methods presented can be applied to a variety
of problems in different fields and a broad class of graphs. Aiming chiefly at
the modeling of functional networks of interacting antibodies and B-cells of
the immune system (idiotypic networks) we focus on a class of graphs
constructed by bit-chains. The biological relevance of the patterns and
possible operational modes of idiotypic networks are discussed.


  The oscillation periods bounded by a simple pendulum and an oscillating rigid
rod are illustrated using a multiple pendulum. Oscillation periods between
these two limits are obtained. A theoretical approach using the Lagrangian
formalism and the set up of three simple experiments are presented in order to
probe our approach.


  This paper describes our submission to the WMT20 sentence filtering task. We
combine scores from (1) a custom LASER built for each source language, (2) a
classifier built to distinguish positive and negative pairs by semantic
alignment, and (3) the original scores included in the task devkit. For the
mBART finetuning setup, provided by the organizers, our method shows 7% and 5%
relative improvement over baseline, in sacreBLEU score on the test set for
Pashto and Khmer respectively.


  We study singular rational curves in projective space, deducing conditions on
their parametrizations from the value semigroups $\sss$ of their singularities.
In particular, we prove that a natural heuristic for the codimension of the
space of nondegenerate rational curves of arithmetic genus $g>0$ and degree $d$
in $\mb{P}^n$, viewed as a subspace of all degree-$d$ rational curves in
$\mb{P}^n$, holds whenever $g$ is small. On the other hand, we show that this
heuristic fails in general, by exhibiting an infinite family of examples of
Severi-type varieties of rational curves containing "excess" components of
dimension strictly larger than the space of $g$-nodal rational curves.


  Deep learning methods have proven extremely effective at performing a variety
of medical image analysis tasks. With their potential use in clinical routine,
their lack of transparency has however been one of their few weak points,
raising concerns regarding their behavior and failure modes. While most
research to infer model behavior has focused on indirect strategies that
estimate prediction uncertainties and visualize model support in the input
image space, the ability to explicitly query a prediction model regarding its
image content offers a more direct way to determine the behavior of trained
models. To this end, we present a novel Visual Question Answering approach that
allows an image to be queried by means of a written question. Experiments on a
variety of medical and natural image datasets show that by fusing image and
question features in a novel way, the proposed approach achieves an equal or
higher accuracy compared to current methods.


  The electrokinetic transport in a neutral system consists of an aqueous NaCl
solution confined in a nanochannel with two similar parallel phosphorene walls,
and is investigated for different black, blue, red, and green phosphorene
allotropes in the presence of an external electric field in the directions $x$
(parallel to the walls roughness axis) and $y$ (perpendicular to the walls
roughness axis). The results show that irrespective of the electric field
direction, the thickness of the Stern layer increases with the increase in the
magnitude of the negative electric surface charge density (ESCD) on the
nanochannel walls, and it also increases with the increase in the roughness
ratio for different allotropes. Moreover, three different regimes of
Debye--H\"{u}ckel (DH), intermediate, and flow reversal appear as the absolute
value of the negative ESCD on the walls grows. With the increase in the
absolute value of the negative ESCD, in the DH regime, the flow velocity grows,
then in the intermediate regime, it decreases, and finally, at sufficiently
high ESCD, the flow reversal occurs. When the external electric field is
applied in the $y$ direction, the dynamics of the system are slower than that
of the $x$ direction; therefore, the flow reversal occurs at the smaller
absolute values of the negative ESCD.


  In this paper, we provide theoretical and empirical estimates for the
likelihood of a negative participation paradox under instant runoff voting in
three-candidate elections. We determine the probability of the paradox and
related conditional probabilities based on the impartial anonymous culture and
impartial culture models for both complete and partial ballots. We compare
these results to the empirical likelihood of a negative participation paradox
occurring under instant runoff voting in a large database of voter profiles
which have been reduced to three candidates. Lastly, we analyze the relative
likelihood of this paradox in comparison to other well-known paradoxes.


  Thermally induced conductance jumps of metal nanowires are modeled using
stochastic Ginzburg-Landau field theories. Changes in radius are predicted to
occur via the nucleation of surface kinks at the wire ends, consistent with
recent electron microscopy studies. The activation rate displays nontrivial
dependence on nanowire length, and undergoes first- or second-order-like
transitions as a function of length. The activation barriers of the most stable
structures are predicted to be universal, i.e., independent of the radius of
the wire, and proportional to the square root of the surface tension. The
reduction of the activation barrier under strain is also determined.


  A convincing, uncontroversial observation of string breaking, when the static
potential is extracted from Wilson loops only, is still missing. This failure
can be understood if the overlap of the Wilson loop with the broken string is
exponentially small. In that case, the broken string ground state will only be
seen if the Wilson loop is long enough. Our preliminary results show string
breaking in the context of the 3d SU(2) adjoint static potential, using the
L\"uscher-Weisz exponential variance reduction approach. As a by-product, we
measure the fundamental SU(2) static potential with improved accuracy and see
clear deviations from Casimir scaling.


  In this paper we study the computational complexity of the (extended) minimum
cost homomorphism problem (Min-Cost-Hom) as a function of a constraint
language, i.e. a set of constraint relations and cost functions that are
allowed to appear in instances. A wide range of natural combinatorial
optimisation problems can be expressed as Min-Cost-Homs and a classification of
their complexity would be highly desirable, both from a direct, applied point
of view as well as from a theoretical perspective.
  Min-Cost-Hom can be understood either as a flexible optimisation version of
the constraint satisfaction problem (CSP) or a restriction of the
(general-valued) valued constraint satisfaction problem (VCSP). Other
optimisation versions of CSPs such as the minimum solution problem (Min-Sol)
and the minimum ones problem (Min-Ones) are special cases of Min-Cost-Hom.
  The study of VCSPs has recently seen remarkable progress. A complete
classification for the complexity of finite-valued languages on arbitrary
finite domains has been obtained Thapper and Zivny [STOC'13]. However,
understanding the complexity of languages that are not finite-valued appears to
be more difficult. Min-Cost-Hom allows us to study problematic languages of
this type without having to deal with with the full generality of the VCSP. A
recent classification for the complexity of three-element Min-Sol, Uppman
[ICALP'13], takes a step in this direction. In this paper we extend this result
considerably by determining the complexity of three-element Min-Cost-Hom.


  We present the results of charged current quasielastic(CCQE) scattering cross
sections from free as well as bound nucleons like in $^{12}C$, $^{16}O$,
$^{40}Ar$ and $^{208}Pb$ nuclear targets in $E_\nu(_{\bar\nu})~\le~ $1 GeV
energy region.
  The results are obtained using local Fermi gas model with and without RPA
effect.
  The differences those may arise in the electron and muon production cross
sections due to the different lepton mass, uncertainties in the axial dipole
mass $M_A$ and pseudoscalar form factor, and due to the inclusion of second
class currents have been highlighted for neutrino/antineutrino induced
processes.


  Triangulum, M33, is a low mass, relatively undisturbed spiral galaxy that
offers a new regime in which to test models of dynamical heating. In spite of
its proximity, the dynamical heating history of M33 has not yet been well
constrained. In this work, we present the TREX Survey, the largest stellar
spectroscopic survey across the disk of M33. We present the stellar disk
kinematics as a function of age to study the past and ongoing dynamical heating
of M33. We measure line of sight velocities for ~4,500 disk stars. Using a
subset, we divide the stars into broad age bins using Hubble Space Telescope
and Canada-France-Hawaii-Telescope photometric catalogs: massive main sequence
stars and helium burning stars (~80 Myr), intermediate mass asymptotic branch
stars (~1 Gyr), and low mass red giant branch stars (~4 Gyr). We compare the
stellar disk dynamics to that of the gas using existing HI, CO, and Halpha
kinematics. We find that the disk of M33 has relatively low velocity dispersion
(~16 km/s), and unlike in the Milky Way and Andromeda galaxies, there is no
strong trend in velocity dispersion as a function of stellar age. The youngest
disk stars are as dynamically hot as the oldest disk stars and are dynamically
hotter than predicted by most M33 like low mass simulated analogs in Illustris.
The velocity dispersion of the young stars is highly structured, with the large
velocity dispersion fairly localized. The cause of this high velocity
dispersion is not evident from the observations and simulated analogs presented
here.


  Americans across demographic groups tend to have low financial literacy, with
low-income people and minorities at highest risk. This opens the door to the
exploitation of unbanked low-income families through high-interest alternative
financial services. This paper studies the causes and effects of financial
illiteracy and exclusion in the most at-risk demographic groups, and solutions
proven to bring them into the financial mainstream. This paper finds that
immigrants, ethnic minorities, and low-income families are most likely to be
unbanked. Furthermore, the causes for being unbanked include the high fees of
bank accounts, the inability of Americans to maintain bank accounts due to low
financial assets or time, banking needs being met by alternative financial
services, and being provided minimal help while transitioning from welfare to
the workforce. The most effective solutions to financial illiteracy and
exclusion include partnerships between nonprofits, banks, and businesses that
use existing alternative financial service platforms to transition the unbanked
into using products that meet their needs, educating the unbanked in the use of
mobile banking, and providing profitable consumer credit products targeting
unbanked families with features that support their needs in addition to
targeted and properly implemented financial literacy programs.


  Locally repairable codes (LRCs) have emerged as an important coding scheme in
distributed storage systems (DSSs) with relatively low repair cost by accessing
fewer non-failure nodes. Theoretical bounds and optimal constructions of LRCs
have been widely investigated. Optimal LRCs via cyclic and constacyclic codes
provide significant benefit of elegant algebraic structure and efficient
encoding procedure. In this paper, we continue to consider the constructions of
optimal LRCs via cyclic and constacyclic codes with long code length.
Specifically, we first obtain two classes of $q$-ary cyclic Singleton-optimal
$(n, k, d=6;r=2)$-LRCs with length $n=3(q+1)$ when $3 \mid (q-1)$ and $q$ is
even, and length $n=\frac{3}{2}(q+1)$ when $3 \mid (q-1)$ and $q \equiv
1(\bmod~4)$, respectively. To the best of our knowledge, this is the first
construction of $q$-ary cyclic Singleton-optimal LRCs with length $n>q+1$ and
minimum distance $d \geq 5$. On the other hand, an LRC acheiving the
Hamming-type bound is called a perfect LRC. By using cyclic and constacyclic
codes, we construct two new families of $q$-ary perfect LRCs with length
$n=\frac{q^m-1}{q-1}$, minimum distance $d=5$ and locality $r=2$.


  We derive an explicit formula for the fundamental solution
$K_{T_{q+1}}(x,x_{0};t)$ to the discrete-time diffusion equation on the
$(q+1)$-regular tree $T_{q+1}$ in terms of the discrete $I$-Bessel function. We
then use the formula to derive an explicit expression for the fundamental
solution $K_{X}(x,x_{0};t)$ to the discrete-time diffusion equation on any
$(q+1)$-regular graph $X$. Going further, we develop three applications. The
first one is to derive a general trace formula that relates the spectral data
on $X$ to its topological data. Though we emphasize the results in the case
when $X$ is finite, our method also applies when $X$ has a countably infinite
number of vertices. As a second application, we obtain a closed-form expression
for the return time probability distribution of the uniform random walk on any
$(q+1)$-regular graph. The expression is obtained by relating
$K_{X}(x,x_{0};t)$ to the uniform random walk on a $(q+1)$-regular graph. We
then show that if $\{X_{h}\}$ is a sequence of $(q+1)$-regular graphs whose
number of vertices goes to infinity and which satisfies a certain natural
geometric condition, then the limit of the return time probability
distributions from $\{X_{h}\}$ is equal to the return time probability
distribution on the tree $T_{q+1}$. As a third application, we derive formulas
which express the number of distinct closed irreducible walks without tails on
a finite graph $X$ in terms of moments of the spectrum of its adjacency matrix.


  Edge computing (EC) promises to deliver low-latency and ubiquitous
computation to numerous devices at the network edge. This paper aims to jointly
optimize edge node (EN) placement and resource allocation for an EC platform,
considering demand uncertainty. Diverging from existing approaches treating
uncertainties as exogenous, we propose a novel two-stage decision-dependent
distributionally robust optimization (DRO) framework to effectively capture the
interdependence between EN placement decisions and uncertain demands. The first
stage involves making EN placement decisions, while the second stage optimizes
resource allocation after uncertainty revelation. We present an exact
mixed-integer linear program reformulation for solving the underlying
``min-max-min" two-stage model. We further introduce a valid inequality method
to enhance computational efficiency, especially for large-scale networks.
Extensive numerical experiments demonstrate the benefits of considering
endogenous uncertainties and the advantages of the proposed model and approach.


  Large Language Models (LLMs) have achieved remarkable success in various
fields, but their training and finetuning require massive computation and
memory, necessitating parallelism which introduces heavy communication
overheads. Driven by advances in packaging, the chiplet architecture emerges as
a potential solution, as it can integrate computing power, as well as utilize
on-package links with better signal integrity, higher bandwidth, and lower
energy consumption. However, most existing chiplet-related works focus on DNN
inference. Directly porting them to LLM training introduces significantly large
quantities of DRAM access and network-on-package (NoP) overheads which make
state-of-the-art chiplet designs fail, highlighting a research gap.
  This work proposes Hecaton, a scalable and cost-effective chiplet system for
LLM training and finetuning. We first provide a chiplet architecture with
tailored scheduling that can largely reduce DRAM accesses. We further design an
efficient distributed training method that reduces NoP communication complexity
and relieves constraints on SRAM capacity and layout. Theoretical analysis
shows that the entire system achieves weak scaling: as the workload and
hardware resources grow proportionally, the computation-to-communication ratio
remains nearly constant. Experiments with various workloads and hardware
configurations verify the property, and Hecaton achieves $4.98\times$
performance improvement and $2.35\times$ energy reduction on Llama2-70B,
compared to the tensor parallelism in Megatron. To the best of our knowledge,
we propose the first chiplet architecture specifically used for LLM training or
finetuning, with guaranteed performance regardless of the problem scale.


  Sampling complex potential energies is one of the most pressing challenges of
contemporary computational science. Inspired by recent efforts that use quantum
effects and discretized Feynman's path integrals to overcome large barriers we
propose a replica exchange method. In each replica two copies of the same
system with halved potential strengths interact via inelastic springs. The
strength of the spring is varied in the different replicas so as to bridge the
gap between the infinitely strong spring, that corresponds to the Boltzmann
replica and the less tight ones. We enhance the spring length fluctuations
using Metadynamics. We test the method on simple yet challenging problems.


  Transcripts generated by automatic speech recognition (ASR) systems for
spoken documents lack structural annotations such as paragraphs, significantly
reducing their readability. Automatically predicting paragraph segmentation for
spoken documents may both improve readability and downstream NLP performance
such as summarization and machine reading comprehension. We propose a sequence
model with self-adaptive sliding window for accurate and efficient paragraph
segmentation. We also propose an approach to exploit phonetic information,
which significantly improves robustness of spoken document segmentation to ASR
errors. Evaluations are conducted on the English Wiki-727K document
segmentation benchmark, a Chinese Wikipedia-based document segmentation dataset
we created, and an in-house Chinese spoken document dataset. Our proposed model
outperforms the state-of-the-art (SOTA) model based on the same BERT-Base,
increasing segmentation F1 on the English benchmark by 4.2 points and on
Chinese datasets by 4.3-10.1 points, while reducing inference time to less than
1/6 of inference time of the current SOTA.


  We explore a way of universal quantum computation with particles which cannot
occupy the same position simultaneously and are symmetric under exchange of
particle labels. Therefore the associated creation and annihilation operators
are neither bosonic nor fermionic. In this work we first show universality of
our method and numerically address several examples. We demonstrate dynamics of
a Bloch electron system from a viewpoint of adiabatic quantum computation. In
addition we provide a novel Majorana fermion system and analyze phase
transitions with spin-coherent states and the time average of the OTOC
(out-of-time-order correlator). We report that a first-order phase transition
is avoided when it evolves in a non-stoquastic manner and the time average of
the OTOC diagnoses the phase transitions successfully.


  Environment, such as the accretion disk, could modify the signal of the
gravitational wave from the astrophysical black hole binaries. In this article,
we model the matter field around the intermediate-mass binary black holes by
means of an axion-like scalar field and investigate their joint evolution. In
details, we consider the equal mass binary black holes surrounded by a shell of
axion-like scalar field both in spherical symmetric and non-spherical symmetric
cases, and with different strength of the scalar field. Our result shows that
the environmental scalar field could essentially modify the dynamics. Firstly,
in the spherical symmetric case, with increasing of the scalar field strength,
the number of circular orbit of the binary black hole is reduced. It means that
the scalar field could significantly accelerate the merger process. Secondly,
once the scalar field strength exceeds certain critical value, the scalar field
could collapse into a third black hole with its mass being larger than the
binary. Consequently, the new black hole collapsed from the environmental
scalar field could accrete the binary promptly and the binary collides head-on
between each other. In this process, there is almost no any quadrupole signal
produced, namely the gravitational wave is greatly suppressed. Thirdly, when
the scalar field strength is relatively smaller than the critical value, the
black hole orbit could develop eccentricity through the accretion of the scalar
field. Fourthly, during the initial stage of the inspire, the gravitational
attractive force from the axion-like scalar field could induce a sudden turn in
the binary orbits, hence result in a transient wiggle in the gravitational
waveform. Finally, in the non-spherical case, the scalar field could
gravitationally attract the binary moving toward the mass center of the scalar
field and slow down the merger process.


  We study thermal effects on a decay process of a false vacuum in type IIA
string theory. At finite temperature, the potential of the theory is corrected
and also thermally excited modes enhance the decay rate. The false vacuum can
accommodate a string-like object. This cosmic string makes the bubble creation
rate much larger and causes an inhomogeneous vacuum decay. We investigate
thermal corrections to the DBI action for the bubble/string bound state and
discuss a thermally assisted tunneling process. We show that thermally excited
states enhance the tunneling rate of the decay process, which makes the
life-time of the false vacuum much shorter.


  Tuning parameters in supervised learning problems are often estimated by
cross-validation. The minimum value of the cross-validation error can be biased
downward as an estimate of the test error at that same value of the tuning
parameter. We propose a simple method for the estimation of this bias that uses
information from the cross-validation process. As a result, it requires
essentially no additional computation. We apply our bias estimate to a number
of popular classifiers in various settings, and examine its performance.


  We prove an integral version of the classical Albert-Brauer-Hasse-Noether
theorem regarding quaternion algebras over number fields. Let $\mathfrak A$ be
a quaternion algebra over a number field $K$ and assume that $\mathfrak A$
satisfies the Eichler condition; that is, there exists an archimedean prime of
$K$ which does not ramify in $\mathfrak A$. Let $\Omega$ be a commutative,
quadratic $\mathcal{O}_K$-order and let $\mathcal{R}\subset \mathfrak A$ be an
order of full rank. Assume that there exists an embedding of $\Omega$ into
$\mathcal R$. We describe a number of criteria which, if satisfied, imply that
every order in the genus of $\mathcal R$ admits an embedding of $\Omega$. In
the case that the relative discriminant ideal of $\Omega$ is coprime to the
level of $\mathcal R$ and the level of $\mathcal R$ is coprime to the
discriminant of $\mathfrak A$, we give necessary and sufficient conditions for
an order in the genus of $\mathcal R$ to admit an embedding of $\Omega$. We
explicitly parameterize the isomorphism classes of orders in the genus of
$\mathcal R$ which admit an embedding of $\Omega$. In particular, we show that
the proportion of the genus of $\mathcal{R}$ admitting an embedding of $\Omega$
is either 0, 1/2 or 1. Analogous statements are proven for optimal embeddings.


  Nuclear electric dipole moments of $^{3}He$ and $^{3}H$ are calculated using
Time Reversal Invariance Violating (TRIV) potentials based on the meson
exchange theory, as well as the ones derived by using pionless and pionful
effective field theories, with nuclear wave functions obtained by solving
Faddeev equations in configuration space for the complete Hamiltonians
comprising both TRIV and realistic strong interactions. The obtained results
are compared with the previous calculations of $^{3}He$ EDM and with time
reversal invariance violating effects in neutron-deuteron scattering.


  Multi-task neural network architectures provide a mechanism that jointly
integrates information from distinct sources. It is ideal in the context of
MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT)
scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic
multi-task network that estimates: 1) intrinsic uncertainty through a
heteroscedastic noise model for spatially-adaptive task loss weighting and 2)
parameter uncertainty through approximate Bayesian inference. This allows
sampling of multiple segmentations and synCTs that share their network
representation. We test our model on prostate cancer scans and show that it
produces more accurate and consistent synCTs with a better estimation in the
variance of the errors, state of the art results in OAR segmentation and a
methodology for quality assurance in radiotherapy treatment planning.


  In this paper, a method of prediction on continuous time series variables
from the production or flow -- an LSTM algorithm based on multivariate tuning
-- is proposed. The algorithm improves the traditional LSTM algorithm and
converts the time series data into supervised learning sequences regarding
industrial data's features. The main innovation of this paper consists in
introducing the concepts of periodic measurement and time window in the
industrial prediction problem, especially considering industrial data with time
series characteristics. Experiments using real-world datasets show that the
prediction accuracy is improved, 54.05% higher than that of traditional LSTM
algorithm.


  Path graphs are intersection graphs of paths in a tree.~In this paper we give
a "6\ good characterization" of path graphs, namely, we prove that path graph
membership is in $NP\cap CoNP$ without resorting to existing polynomial time
algorithms. The characterization is given in terms of the collection of the
\emph{attachedness graphs} of a graph, a novel device to deal with the
connected components of a graph after the removal of clique separators. On the
one hand, the characterization refines and simplifies the characterization of
path graphs due to Monma and Wei [C.L.~Monma,~and~V.K.~Wei, Intersection
{G}raphs of {P}aths in a {T}ree, J. Combin. Theory Ser. B, 41:2 (1986)
141--181], which we build on, by reducing a constrained vertex coloring problem
defined on the \emph{attachedness graphs} to a vertex 2-coloring problem on the
same graphs. On the other hand, the characterization allows us to exhibit two
exhaustive lists of obstructions to path graph membership in the form of
minimal forbidden induced/partial 2-edge colored subgraphs in each of the
\emph{attachedness graphs}.


  This paper presents an algorithm that relies on a series of dense and deep
neural networks for passive microwave retrieval of precipitation. The neural
networks learn from coincidences of brightness temperatures from the Global
Precipitation Measurement (GPM) Microwave Imager (GMI) with the active
precipitating retrievals from the Dual-frequency Precipitation Radar (DPR)
onboard GPM as well as those from the {CloudSat} Profiling Radar (CPR). The
algorithm first detects the precipitation occurrence and phase and then
estimates its rate, while conditioning the results to some key ancillary
information including parameters related to cloud microphysical properties. The
results indicate that we can reconstruct the DPR rainfall and CPR snowfall with
a detection probability of more than 0.95 while the probability of a false
alarm remains below 0.08 and 0.03, respectively. Conditioned to the occurrence
of precipitation, the unbiased root mean squared error in estimation of
rainfall (snowfall) rate using DPR (CPR) data is less than 0.8 (0.1) mm/hr over
oceans and land. Beyond methodological developments, comparing the results with
ERA5 reanalysis and official GPM products demonstrates that the uncertainty in
global satellite snowfall retrievals continues to be large while there is a
good agreement among rainfall products. Moreover, the results indicate that CPR
active snowfall data can improve passive microwave estimates of global snowfall
while the current CPR rainfall retrievals should only be used for detection and
not estimation of rates.


  Social media plays an increasing role in our communication with friends and
family, and our consumption of information and entertainment. Hence, to design
effective ranking functions for posts on social media, it would be useful to
predict the affective response to a post (e.g., whether the user is likely to
be humored, inspired, angered, informed). Similar to work on emotion
recognition (which focuses on the affect of the publisher of the post), the
traditional approach to recognizing affective response would involve an
expensive investment in human annotation of training data.
  We introduce CARE$_{db}$, a dataset of 230k social media posts annotated
according to 7 affective responses using the Common Affective Response
Expression (CARE) method. The CARE method is a means of leveraging the signal
that is present in comments that are posted in response to a post, providing
high-precision evidence about the affective response of the readers to the post
without human annotation. Unlike human annotation, the annotation process we
describe here can be iterated upon to expand the coverage of the method,
particularly for new affective responses. We present experiments that
demonstrate that the CARE annotations compare favorably with crowd-sourced
annotations. Finally, we use CARE$_{db}$ to train competitive BERT-based models
for predicting affective response as well as emotion detection, demonstrating
the utility of the dataset for related tasks.


  Active learning aims to select samples to be annotated that yield the largest
performance improvement for the learning algorithm. Many methods approach this
problem by measuring the informativeness of samples and do this based on the
certainty of the network predictions for samples. However, it is well-known
that neural networks are overly confident about their prediction and are
therefore an untrustworthy source to assess sample informativeness. In this
paper, we propose a new informativeness-based active learning method. Our
measure is derived from the learning dynamics of a neural network. More
precisely we track the label assignment of the unlabeled data pool during the
training of the algorithm. We capture the learning dynamics with a metric
called label-dispersion, which is low when the network consistently assigns the
same label to the sample during the training of the network and high when the
assigned label changes frequently. We show that label-dispersion is a promising
predictor of the uncertainty of the network, and show on two benchmark datasets
that an active learning algorithm based on label-dispersion obtains excellent
results.


  The 21cm neutral hydrogen line is likely to be a key probe for studying the
epoch of reionization and comic dawn in the forthcoming decades. This prospect
stimulates the development of the theoretical basis for simulating the power
spectrum of this line. Because of the beam size of the upcoming radio
telescopes at high redshifts, most of the theoretical models are focused on the
inhomogeneities on scales above few comoving megaparsecs. Therefore, smaller
scales are often neglected and modeled with approximated sub-grid models. In
this study we explore whether the perturbations on small scales ($\lesssim
1h^{-1}\mathrm{Mpc}$) can affect the 21cm signal on larger scales. Two distinct
mechanism are discussed. First, we show that during the cosmic dawn small scale
perturbations regulate the formation time of the first Lyman-alpha Emitters
(LAE), and consequently the coupling timing of spin and kinetic temperatures.
Due to the low number density of LAE, the inhomogeneity of coupling includes
the shot noise and manifests itself in the observed 21cm power spectrum. Second
mechanism works during the reionization when the ionized bubbles actively grow
and overlap. Small scales perturbations affect the galactic properties and
merger histories, and consequently the number of ionizing photons produced by
each galaxy. The ionizing photons bring the perturbations from the galactic
scales to the scales of ionizing fronts, affecting the 21cm power spectrum. We
conclude that these two effects introduce stochasticity in the potentially
observed 21cm power spectrum and, moreover, might give another perspective into
the physics of the first galaxies.


  We study the contributions for $K\eta^{\prime}$ pair originating from the
scalar intermediate state $K_0^{*}(1430)$ in the three-body decays $B\to
K\eta^{\prime} h$ ($h=\pi, K$) within the perturbative QCD approach. The
contribution of $K^*_0(1430)\to K\eta^{\prime}$ is described by the Flatt${\rm
\acute{e}}$ formula with coupled channels $K\pi$, $K\eta$ and $K\eta^{\prime}$.
The strong coupling constants $g_{K^*_0K\eta^{(\prime)}}$ are extracted from
$g_{K^*_0 K\pi}$ within flavor SU$(3)$ symmetry. In spite of the strong
depression by phase space near the threshold of $K\eta^\prime$, the $CP$
averaged branching fractions for the $B\to K^*_0(1430) h \to K\eta^\prime h$
decays are predicted to be on the order of $10^{-8}$ to $10^{-5}$, which are
non-negligible for the corresponding three-body $B$ decays. Since the K$\eta$
system is almost decoupled from the even-spin strange mesons under flavor
SU$(3)$ symmetry, those quasi-two-body $B$ decays with subprocess $K^*_0(1430)
\to K \eta$ shall have quite small branching ratios and are not taken into
account in this work. We also estimate that the branching fraction for
$K_0^{*}(1430)\to K\eta^{\prime}$ is about one fifth of that for
$K_0^{*}(1430)\to K\pi$. The predictions for the relevant decays are expected
to be tested by the LHCb and Belle-II experiments in the future.


  This study introduces a novel approach to enhance communication networks in
the cislunar space by leveraging Reconfigurable Intelligent Surfaces (RIS).
Using the ability of RIS to dynamically control electromagnetic waves, this
paper tackles the challenges of signal attenuation, directivity, and divergence
in cislunar missions, primarily caused by immense distances and that
Earth-based station transmitters do not always face the Moon. A new
optimization problem is formulated, whose objective is to maximize the received
signal-to-noise ratio (SNR) for Earth-to-Moon communications. We derive a
closed-form solution to the problem of determining the optimal RIS phase shift
configuration based on the effective area of the RIS. Through extensive
simulations, this paper demonstrates how optimal adjustments in RIS phase
shifts can significantly enhance signal integrity, hinting at the substantial
potential of RIS technology to revolutionize long-distance cislunar
communication.


  We revisit the problem of Pauli shadow tomography: given copies of an unknown
$n$-qubit quantum state $\rho$, estimate $\text{tr}(P\rho)$ for some set of
Pauli operators $P$ to within additive error $\epsilon$. This has been a
popular testbed for exploring the advantage of protocols with quantum memory
over those without: with enough memory to measure two copies at a time, one can
use Bell sampling to estimate $|\text{tr}(P\rho)|$ for all $P$ using
$O(n/\epsilon^4)$ copies, but with $k\le n$ qubits of memory,
$\Omega(2^{(n-k)/3})$ copies are needed.
  These results leave open several natural questions. How does this picture
change in the physically relevant setting where one only needs to estimate a
certain subset of Paulis? What is the optimal dependence on $\epsilon$? What is
the optimal tradeoff between quantum memory and sample complexity?
  We answer all of these questions. For any subset $A$ of Paulis and any family
of measurement strategies, we completely characterize the optimal sample
complexity, up to $\log |A|$ factors. We show any protocol that makes
$\text{poly}(n)$-copy measurements must make $\Omega(1/\epsilon^4)$
measurements. For any protocol that makes $\text{poly}(n)$-copy measurements
and only has $k < n$ qubits of memory, we show that
$\widetilde{\Theta}(\min\{2^n/\epsilon^2, 2^{n-k}/\epsilon^4\})$ copies are
necessary and sufficient.
  The protocols we propose can also estimate the actual values
$\text{tr}(P\rho)$, rather than just their absolute values as in prior work.
Additionally, as a byproduct of our techniques, we establish tight bounds for
the task of purity testing and show that it exhibits an intriguing phase
transition not present in the memory-sample tradeoff for Pauli shadow
tomography.


  Language models have been shown to perform better with an increase in scale
on a wide variety of tasks via the in-context learning paradigm. In this paper,
we investigate the hypothesis that the ability of a large language model to
in-context learn-perform a task is not uniformly spread across all of its
underlying components. Using a 66 billion parameter language model (OPT-66B)
across a diverse set of 14 downstream tasks, we find this is indeed the case:
$\sim$70% of attention heads and $\sim$20% of feed forward networks can be
removed with minimal decline in task performance. We find substantial overlap
in the set of attention heads (un)important for in-context learning across
tasks and number of in-context examples. We also address our hypothesis through
a task-agnostic lens, finding that a small set of attention heads in OPT-66B
score highly on their ability to perform primitive induction operations
associated with in-context learning, namely, prefix matching and copying. These
induction heads overlap with task-specific important heads, reinforcing
arguments by Olsson et al. (arXiv:2209.11895) regarding induction head
generality to more sophisticated behaviors associated with in-context learning.
Overall, our study provides several insights that indicate large language
models may be under-trained for in-context learning and opens up questions on
how to pre-train language models to more effectively perform in-context
learning.


  We apply a friends-of-friends algorithm to an enhanced SDSS DR12
spectroscopic catalog including redshift from literature to construct a catalog
of $1588~N\ge3$ compact groups of galaxies containing 5179 member galaxies and
covering the redshift range $0.01 < z < 0.19$. This catalog contains 18 times
as many systems and reaches 3 times the depth of similar catalog of Barton et
al. (1996). We construct catalogs from both magnitude-limited and
volume-limited galaxy samples. Like Barton et al. (1996) we omit the frequently
applied isolation criterion in the compact group selection algorithm. Thus the
groups selected by fixed projected spatial and rest frame line-of-sight
velocity separation produce a catalog of groups with a redshift independent
median size. In contrast with previous catalogs, the enhanced SDSS DR12 catalog
(including galaxies with $r < 14.5$) includes many systems with $z\leq 0.05$.
The volume-limited samples are unique to this study. The compact group
candidates in these samples have a median stellar mass independent of redshift.
Groups with velocity dispersion $\leq 100$ km s$^{-1}$ show abundant evidence
for ongoing dynamical interactions among the members. The number density of the
volume-limited catalogs agrees with previous catalogs at the lowest redshifts
but decreases as the redshift increases. The SDSS fiber placement constraints
limit the catalog completeness. In spite of this issue the volume-limited
catalogs provide a promising basis for detailed spatially resolved probes of
the impact of galaxy-galaxy interactions within similar dense systems over a
broad redshift range.


  A compact polyhedron $X$ is said to have the Bounded Index Property for
Homotopy Equivalence (BIPHE) if there is a finite bound $\mathcal{B}$ such that
for any homotopy equivalence $f:X\rightarrow X$ and any fixed point class
$\mathbf{F}$ of $f$, the index $|\mathrm{ind}(f,\mathbf{F})|\leq \mathcal{B}$.
In this note, we consider the product of compact polyhedra, and give some
sufficient conditions for it to have BIPHE. Moreover, we show that the products
of closed Riemannian manifolds with negative sectional curvature, in particular
hyperbolic manifolds, have BIPHE, which gives an affirmative answer to a
special case of a question asked by Boju Jiang.


  A comparative study of high and zero temperature plasma for the case of
damping rate, drag and diffusion coefficients have been presented. In each of
these quantities, it is revealed how the magnetic interaction dominates over
the electric one at zero temperature unlike what happens at high temperature.


  Erbium (Er) doped GaN has been studied extensively for optoelectronic
applications, yet its defect physics is still not well understood. In this
work, we report a first-principles hybrid density functional study of the
structure, energetics, and thermodynamic transition levels of Er-related defect
complexes in GaN. We discover for the first time that Er$_{\rm Ga}$-C$_{\rm
N}$-$V_{\rm N}$, a defect complex of Er, a C impurity, and an N vacancy, and
Er$_{\rm Ga}$-O$_{\rm N}$-$V_{\rm N}$, a complex of Er, an O impurity, and an N
vacancy, form defect levels at 0.18 and 0.46 eV below the conduction band,
respectively. Together with Er$_{\rm Ga}$-$V_{\rm N}$, a defect complex of Er
and an N vacancy which has recently been found to produce a donor level at 0.61
eV, these defect complexes provide explanation for the Er-related defect levels
observed in experiments. The role of these defects in optical excitation of the
luminescent Er center is also discussed.


  We discuss the quenched tail estimates for the random walk in random scenery.
The random walk is the symmetric nearest neighbor walk and the random scenery
is assumed to be independent and identically distributed, non-negative, and has
a power law tail. We identify the long time aymptotics of the upper deviation
probability of the random walk in quenched random scenery, depending on the
tail of scenery distribution and the amount of the deviation. The result is in
turn applied to the tail estimates for a random walk in random conductance
which has a layered structure.


  The likelihood function is a fundamental component in Bayesian statistics.
However, evaluating the likelihood of an observation is computationally
intractable in many applications. In this paper, we propose a non-parametric
approximation of the likelihood that identifies a probability measure which
lies in the neighborhood of the nominal measure and that maximizes the
probability of observing the given sample point. We show that when the
neighborhood is constructed by the Kullback-Leibler divergence, by moment
conditions or by the Wasserstein distance, then our \textit{optimistic
likelihood} can be determined through the solution of a convex optimization
problem, and it admits an analytical expression in particular cases. We also
show that the posterior inference problem with our optimistic likelihood
approximation enjoys strong theoretical performance guarantees, and it performs
competitively in a probabilistic classification task.


  Alloyed MCrAlY bond coats, where M is usually cobalt and/or nickel, are
essential parts of modern turbine blades, imparting environmental resistance
while mediating thermal expansivity differences. Nanoindentation allows the
determination of their properties without the complexities of traditional
mechanical tests, but was not previously possible near turbine operating
temperatures.
  Here, we determine the hardness and modulus of CMSX-4 and an Amdry-386 bond
coat by nanoindentation up to 1000$^{\circ}$C. Both materials exhibit a
constant hardness until 400$^{\circ}$C followed by considerable softening,
which in CMSX-4 is attributed to the multiple slip systems operating underneath
a Berkovich indenter.
  The creep behaviour has been investigated via the nanoindentation hold
segments. Above 700$^{\circ}$C, the observed creep exponents match the
temperature-dependence of literature values in CMSX-4. In Amdry-386,
nanoindentation produces creep exponents very close to literature data,
implying high-temperature nanoindentation may be powerful in characterising
these coatings and providing inputs for material, model and process
optimisations.


  Following Darji, we say that a Borel subset $B$ of an abelian Polish group
$G$ is Haar meager if there is a compact metric space $K$ and a continuous
function $f : K \to G$ such that the preimage of the translate, $f^{-1}(B+g)$
is meager in $K$ for every $g \in G$. The set $B$ is called strongly Haar
meager if there is a compact set $C \subseteq G$ such that $(B+g) \cap C$ is
meager in $C$ for every $g \in G$. The main open problem in this area is
Darji's question asking whether these two notions are the same. Even though
there have been several partial results suggesting a positive answer, in this
paper we construct a counterexample. More specifically, we construct a
$G_\delta$ set in $\mathbb{Z}^\omega$ that is Haar meager but not strongly Haar
meager. We also show that no $F_\sigma$ counterexample exists, hence our result
is optimal.


  A non-perturbative s-wave renormalization of the pion in a hot and baryon
rich medium is presented. This approach proceeds via a mapping of the canonical
pion into the axial Noether's charge. The mapping was made dynamical in the
Hartree-Fock-Bogoliubov random phase approximation (HFB-RPA). It is shown that
this approach, while order mixing, is still symmetry conserving both in the
baryon free and baryon rich sectors, at zero as well as finite temperature. The
systematic character of this approach is emphasized and it is particularly
argued that it may constitute an interesting alternative for the
non-perturbative assessment of the nuclear matter saturation properties.


  The direct-current (DC) resistivity method is a commonly used geophysical
technique for surveying adverse geological conditions. Inversion can
reconstruct the resistivity model from data, which is an important step in the
geophysical survey. However, the inverse problem is a serious ill-posed problem
that makes it easy to obtain incorrect inversion results. Deep learning (DL)
provides new avenues for solving inverse problems, and has been widely studied.
Currently, most DL inversion methods for resistivity are purely data-driven and
depend heavily on labels (real resistivity models). However, real resistivity
models are difficult to obtain through field surveys. An inversion network may
not be effectively trained without labels. In this study, we built an
unsupervised learning resistivity inversion scheme based on the physical law of
electric field propagation. First, a forward modeling process was embedded into
the network training, which converted the predicted model to predicted data and
formed a data misfit to the observation data. Unsupervised training independent
of the real model was realized using the data misfit as a loss function.
Moreover, a dynamic smoothing constraint was imposed on the loss function to
alleviate the ill-posed inverse problem. Finally, a transfer learning scheme
was applied to adapt the trained network with simulated data to field data.
Numerical simulations and field tests showed that the proposed method can
accurately locate and depict geological targets.


  The SDHCAL technological prototype that has been completed in 2012 was
exposed to beams of pions and electrons of different energies at the CERN SPS
for a total time period of 5 weeks. The data has been analyzed within the
CALICE collaboration. Preliminary results indicate that a highly granular
hadronic calorimeter conceived for PFA application is also a powerful tool to
separate pions from electrons. The SDHCAL provides also a very good resolution
of hadronic showers energy measurement. The use of multi-threshold readout mode
shows a clear improvement of the resolution at energies exceeding 30 GeV with
respect to the binary readout mode. Simulations of the pion interactions in the
SDHCAL are presented and new ideas to improve on the energy resolution using
the topology of hadronic showers are mentioned.


  The Kerr solution to axidilaton gravity is analyzed in the
Debney--Kerr--Schild formalism. It is shown that the Kerr principal null
congruence retains its property to be geodesic and shear free, however, the
axidilatonic Kerr solution is not algebraically special. A limiting form of
this solution is considered near the ring-like Kerr singularity. This limiting
solution coincides with the field around a fundamental heterotic string
obtained by Sen.


  A semiconductor quantum dot (QD) is an attractive resource to generate
polarization-entangled photon pairs. We study the excitonic spin precession
(flip-flop) in a family of QDs with different excitonic fine-structure
splitting (FSS) and its impact on the entanglement of photons generated from
the excitonic-biexcitonic radiative cascade. Our results reveal that coherent
processes leave the time post-selected entanglement of QDs with finite FSS
unaffected while changing the eigenstates of the system. The flip-flop's
precession is observed via quantum tomography through anomalous oscillations of
the coincidences in the rectilinear basis. A theoretical model is constructed
with the inclusion of an excitonic flip-flop rate and is compared with a
two-photon quantum tomography measurement on a QD exhibiting the spin flip-flop
mechanism. A generalization of the theoretical model allows estimating the
degree of entanglement as a function of the FSS and the spin-flip rate. For a
finite temporal resolution, the negativity is found to be oscillating with
respect to both the FSS and the spin-flip rate. This oscillatory behavior
disappears for perfect temporal resolution and maximal entanglement is
retrieved despite the flip-flop process.


  We prove a functional law of iterated logarithm for the following kind of
anticipating stochastic differential equations
$$\xi^u_t=X_0^u+\frac{1}{\sqrt{\log\log u}}\sum_{j=1}^k \int_0^{t}
A_j^u(\xi^u_s)\circ dW_{s}^j+ \int_0^{t} A_0^u(\xi^u_s)ds,$$ where $u>e$,
$W=\{(W_t^1,...,W_t^k), 0\le t\le 1\}$ is a standard $k$-dimensional Wiener
process, $A_0^u,A_1^u,..., A_k^u:\mathbb{R}^d\longrightarrow \mathbb{R}^d$ are
functions of class $\mathcal{C}^2$ with bounded partial derivatives up to order
2, $X_0^u$ is a random vector not necessarily adapted and the first integral is
a generalized Stratonovich integral .


  We study AdS-waves in the three-dimensional new theory of massive gravity
recently proposed by Bergshoeff, Hohm, and Townsend. The general configuration
of this type is derived and shown to exhibit different branches, with different
asymptotic behaviors. In particular, for the special fine tuning
$m^2=\pm1/(2l^2)$, solutions with logarithmic fall-off arise, while in the
range $m^2>-1/(2l^2)$, spacetimes with Schrodinger isometry group are admitted
as solutions. Solutions that are asymptotically AdS$_3$, both for
Brown-Henneaux and for the weakened boundary conditions, are also identified.
The metric function that characterizes the profile of the AdS-wave behaves as a
massive excitation on the spacetime, with an effective mass given by
$m_{eff}^2=m^2-1/(2l^2)$. For the critical value $m^2=-1/(2l^2)$, the value of
the effective mass precisely saturates the Breitenlohner-Freedman bound for the
AdS$_3$ space where the wave is propagating on. The analogies with the AdS-wave
solutions of topologically massive gravity are also discussed. Besides, we
consider the coupling of both massive deformations to Einstein gravity and find
the exact configurations for the complete theory, discussing all the different
branches exhaustively. One of the effects of introducing the Chern-Simons
gravitational term is that of breaking the degeneracy in the effective mass of
the generic modes of pure New Massive Gravity, producing a fine structure due
to parity violation. Another effect is that the zoo of exact logarithmic
specimens becomes considerably enlarged.


  This report deals with the quantum field theory of particle oscillations in
vacuum. We first review the various controversies regarding quantum-mechanical
derivations of the oscillation formula, as well as the different
field-theoretical approaches proposed to settle them. We then clear up the
contradictions between the existing field-theoretical treatments by a thorough
study of the external wave packet model. In particular, we show that the latter
includes stationary models as a subcase. In addition, we explicitly compute
decoherence terms, which destroy interferences, in order to prove that the
coherence length can be increased without bound by more accurate energy
measurements. We show that decoherence originates not only in the width and in
the separation of wave packets, but also in their spreading through space-time.
In this review, we neither assume the relativistic limit nor the stability of
oscillating particles, so that the oscillation formula derived with
field-theoretical methods can be applied not only to neutrinos but also to
neutral K and B mesons. Finally, we discuss oscillations of correlated
particles in the same framework.


  We study the isospectrality problem for a free quantum particle confined in a
ring with a junction, analyzing all the self-adjoint realizations of the
corresponding Hamiltonian in terms of a boundary condition at the junction. In
particular, by characterizing the energy spectrum in terms of a spectral
function, we classify the self-adjoint realizations in two classes, identifying
all the families of isospectral Hamiltonians. These two classes turn out to be
discerned by the action of parity (i.e. space reflection), which plays a
central role in our discussion.


  Economic theory is a mathematically rich field in which there are
opportunities for the formal analysis of singularities and catastrophes. This
article looks at the historical context of singularities through the work of
two eminent Frenchmen around the late 1960s and 1970s. Ren\'e Thom (1923-2002)
was an acclaimed mathematician having received the Fields Medal in 1958,
whereas G\'erard Debreu (1921-2004) would receive the Nobel Prize in economics
in 1983. Both were highly influential within their fields and given the
fundamental nature of their work, the potential for cross-fertilisation would
seem to be quite promising. This was not to be the case: Debreu knew of Thom's
work and cited it in the analysis of his own work, but despite this and other
applied mathematicians taking catastrophe theory to economics, the theory never
achieved a lasting following and relatively few results were published. This
article reviews Debreu's analysis of the so called ${\it regular}$ and ${\it
crtitical}$ economies in order to draw some insights into the economic
perspective of singularities before moving to how singularities arise naturally
in the Nash equilibria of game theory. Finally a modern treatment of stochastic
game theory is covered through recent work on the quantal response equilibrium.
In this view the Nash equilibrium is to the quantal response equilibrium what
deterministic catastrophe theory is to stochastic catastrophe theory, with some
caveats regarding when this analogy breaks down discussed at the end.


  Finite automata are used to encode geometric figures, functions and can be
used for image compression and processing. The original approach is to
represent each point of a figure in $\mathbb{R}^n$ as a convolution of its $n$
coordinates written in some base. Then a figure is said to be encoded as a
finite automaton if the set of convolutions corresponding to the points in this
figure is accepted by a finite automaton. The only differentiable functions
which can be encoded as a finite automaton in this way are linear. In this
paper we propose a representation which enables to encode piecewise polynomial
functions with arbitrary degrees of smoothness that substantially extends a
family of functions which can be encoded as finite automata. Such
representation naturally comes from the framework of hierarchical tensor
product B-splines, which are piecewise polynomials widely utilized in numerical
computational geometry. We show that finite automata provide a suitable tool
for solving computational problems arising in this framework when the support
of a function is unbounded.


  The work is inspired by thermo-and photoacoustic imaging, where recent
efforts are devoted to take into account attenuation and varying wave speed
parameters. In this paper we derive and analyze causal equations describing
propagation of attenuated pressure waves. We also review standard models, like
frequency power laws, and the thermo-viscous equation and show that they lack
causality in the parameter range relevant for biological photoacoustic imaging.
To discuss causality in mathematical rigor we use the results and concepts of
linear system theory. We present some numerical experiments, which show the
physically unmeaningful behavior of standard attenuation models, and the
realistic behavior of the novel models.


  Cyber security attacks have become increasingly complex over time, with
various phases of their kill chain, involving binaries, scripts, documents,
executed commands, vulnerabilities, or network traffic.
  We propose a tool, GView, that is designed to investigate possible attacks by
providing guided analysis for various file types using automatic artifact
identification, extraction, coherent correlation &,inference, and meaningful &
intuitive views at different levels of granularity w.r.t. revealed information.
The concept behind GView simplifies navigation through all payloads in a
complex attack, streamlining the process for security researchers, and
Increasing the quality of analysis. GView is generic in the sense it supports a
variety of file types and has multiple visualization modes that can be
automatically adjusted for each file type alone. Our evaluation shows that
GView significantly improves the analysis time of an attack compared to
conventional tools used in forensics.


  Big data is a term used for a very large data sets that have many
difficulties in storing and processing the data. Analysis this much amount of
data will lead to information loss. The main goal of this paper is to share
data in a way that privacy is preserved while information loss is kept at
least. Data that include Government agencies, University details and Medical
history etc., are very necessary for an organization to do analysis and predict
trends and patterns, but it may prevent the data owner from sharing the data
because of privacy regulations [1]. By doing an analysis of several algorithms
of Anonymization such as k-anonymity, l-diversity and tcloseness, one can
achieve privacy at minimum loss. Admitting these techniques has some
limitations. We need to maintain trade-off between privacy and information
loss. We introduce a novel approach called Differential Privacy.


  We present ac susceptibility and specific heat measurements taken on samples
of LiHo$_x$Y$_{1-x}$F$_4$ in the dilute limit: x = 0.018, 0.045, 0.080 and
0.12. Susceptibility measurements show glassy behavior including wide
absorption spectra that continually broaden with decreasing temperature.
Dynamical scaling analyses show evidence of finite-temperature spin-glass
transitions, the temperatures of which match those of recent theoretical work.
A surprisingly long intrinsic time constant is observed in these samples and is
found to be inversely correlated with the concentration of magnetic moments, x.
Our results support the picture that this behavior is largely a single-ion
effect, related to the random transverse fields generated by the off-diagonal
component of the dipolar interaction and significantly slowed by the important
nuclear hyperfine interaction. Specific heat measurements show broad features
due to the electronic spins on top of a large Schottky-like nuclear
contribution. Unusually, the peak position of the electronic component is found
to be largely concentration independent, unlike the glass transition
temperature.


  Bugs that surface in mobile applications can be difficult to reproduce and
fix due to several confounding factors including the highly GUI-driven nature
of mobile apps, varying contextual states, differing platform versions and
device fragmentation. It is clear that developers need support in the form of
automated tools that allow for more precise reporting of application defects in
order to facilitate more efficient and effective bug fixes. In this paper, we
present a tool aimed at supporting application testers and developers in the
process of On-Device Bug Reporting. Our tool, called ODBR, leverages the
uiautomator framework and low-level event stream capture to offer support for
recording and replaying a series of input gesture and sensor events that
describe a bug in an Android application.


  Accurately and efficiently crowdsourcing complex, open-ended tasks can be
difficult, as crowd participants tend to favor short, repetitive "microtasks".
We study the crowdsourcing of large networks where the crowd provides the
network topology via microtasks. Crowds can explore many types of social and
information networks, but we focus on the network of causal attributions, an
important network that signifies cause-and-effect relationships. We conduct
experiments on Amazon Mechanical Turk (AMT) testing how workers propose and
validate individual causal relationships and introduce a method for independent
crowd workers to explore large networks. The core of the method, Iterative
Pathway Refinement, is a theoretically-principled mechanism for efficient
exploration via microtasks. We evaluate the method using synthetic networks and
apply it on AMT to extract a large-scale causal attribution network, then
investigate the structure of this network as well as the activity patterns and
efficiency of the workers who constructed this network. Worker interactions
reveal important characteristics of causal perception and the network data they
generate can improve our understanding of causality and causal inference.


  A self-regulatory genetic circuit, where a protein acts as a positive
regulator of its own production, is known to be a simplest form of biological
network with a positive feedback loop. Although at least three components, DNA,
RNA, and the protein, are required to form such a circuit, the stability
analysis of fixed points of the self-regulatory circuit has been performed only
after reducing the system into to a two-component system consisting of RNA and
protein only, assuming a fast equilibration of the DNA component. Here, the
stability of fixed points of the three-component positive feedback loop is
analyzed by obtaining eigenvalues of full three dimensional Hessian matrix. In
addition to rigorously identifying the stable fixed points and the saddle
points, detailed information can be obtained, such as the number of positive
eigenvalues near a saddle point. In particular, complex eigenvalues is shown to
exist for sufficiently slow binding and unbinding of the auto-regulatory
transcription factor to DNA, leading to oscillatory convergence to the steady
states, a novel feature unseen in the two-dimensional analysis.


  This thesis presents analysis of the properties and run-time of the
Rapidly-exploring Random Tree (RRT) algorithm. It is shown that the time for
the RRT with stepsize $\epsilon$ to grow close to every point in the
$d$-dimensional unit cube is $\Theta\left(\frac1{\epsilon^d} \log
\left(\frac1\epsilon\right)\right)$. Also, the time it takes for the tree to
reach a region of positive probability is $O\left(\epsilon^{-\frac32}\right)$.
Finally, a relationship is shown to the Nearest Neighbour Tree (NNT). This
relationship shows that the total Euclidean path length after $n$ steps is
$O(\sqrt n)$ and the expected height of the tree is bounded above by $(e +
o(1)) \log n$.


  Quasars emit more energy than any other objects in the universe, yet are not
much bigger than the solar system. We are almost certain that quasars are
powered by giant black holes of up to $10^{10}$ times the mass of the Sun, and
that black holes of between $10^6$ and $10^{10}$ solar masses---dead
quasars---are present at the centers of most galaxies. Our own galaxy contains
a black hole of $4.3\times10^6$ solar masses. The mass of the central black
hole appears to be closely related to other properties of its host galaxy, such
as the total mass in stars, but the origin of this relation and the role that
black holes play in the formation of galaxies are still mysteries.


  In this preprint we analyzed the exclusive photoproduction of
$\eta_{c}\gamma$ pairs in the collinear factorization framework and evaluated
its cross-section in leading order in the strong coupling $\alpha_{s}$. We
found that this process is mostly sensitive to the behavior of the unpolarized
gluon GPD $H_{g}$ in the Efremov-Radyushkin-Brodsky-Lepage kinematics. We
estimated numerically the cross-section and the expected counting rates in the
kinematics of middle-energy photoproduction experiments which can be realized
at the future Electron-Ion Collider and in the kinematics of ultraperipheral
collisions at LHC. We found that the cross-section is sufficiently large for
dedicated experimental study.


  SN\,2020zbf is a hydrogen-poor superluminous supernova (SLSN) at $z = 0.1947$
that shows conspicuous \ion{C}{II} features at early times, in contrast to the
majority of H-poor SLSNe. Its peak magnitude is $M_{\rm g}$ = $-21.2$~mag and
its rise time ($\lesssim 26.4$ days from first light) places SN\,2020zbf among
the fastest rising type I SLSNe. We used spectra taken from ultraviolet (UV) to
near-infrared wavelengths to identify spectral features. We paid particular
attention to the \ion{C}{II} lines as they present distinctive characteristics
when compared to other events. We also analyzed UV and optical photometric data
and modeled the light curves considering three different powering mechanisms:
radioactive decay of $^{56}$Ni, magnetar spin-down, and circumstellar medium
(CSM) interaction. The spectra of SN\,2020zbf match the model spectra of a
C-rich low-mass magnetar-powered supernova model well. This is consistent with
our light curve modeling, which supports a magnetar-powered event with an
ejecta mass $M_{\rm ej}$ = 1.5~$\rm M_\odot$. However, we cannot discard the
CSM-interaction model as it may also reproduce the observed features. The
interaction with H-poor, carbon-oxygen CSM near peak light could explain the
presence of \ion{C}{II} emission lines. A short plateau in the light curve
around 35 -- 45 days after peak, in combination with the presence of an
emission line at 6580~\AA,\ can also be interpreted as being due to a late
interaction with an extended H-rich CSM. Both the magnetar and CSM-interaction
models of SN\,2020zbf indicate that the progenitor mass at the time of
explosion is between 2 and 5~$\rm M_\odot$. Modeling the spectral energy
distribution of the host galaxy reveals a host mass of 10$^{8.7}$~$\rm
M_\odot$, a star formation rate of 0.24$^{+0.41}_{-0.12}$~$\rm
M_\odot$~yr$^{-1}$, and a metallicity of $\sim$ 0.4~$\rm Z_\odot$.