  The rare decay B --> Phi K_S is a well-known probe of physics beyond the
Standard Model because it arises only through loop effects yet has the same
time-dependent CP asymmetry as B --> Psi K_S. Motivated by recent data
suggesting new physics in B --> Phi K_S, we look to supersymmetry for possible
explanations, including contributions mediated by gluino loops and by Higgs
bosons. Chirality-preserving LL and RR gluino contributions are generically
small, unless gluinos and squarks masses are close to the current lower bounds.
Higgs contributions are also too small to explain a large asymmetry if we
impose the current upper limit on B(B_s --> mu mu). On the other hand,
chirality-flipping LR and RL gluino contributions can provide sizable effects
and while remaining consistent with related results in B --> Psi K_S, Delta
M_s, B --> X_s gamma and other processes. We discuss how the LR and RL
insertions can be distinguished using other observables, and we provide a
string-based model and other estimates to show that the needed sizes of mass
insertions are reasonable.


  I derive formulas for the electrostatic potential of a charge in or near a
membrane modeled as one or more dielectric slabs lying between two
semi-infinite dielectrics. One can use these formulas in Monte Carlo codes to
compute the distribution of ions near cell membranes more accurately than by
using Poisson-Boltzmann theory or its linearized version. Here I use them to
discuss the electric field of a uniformly charged membrane, the image charges
of an ion, the distribution of salt ions near a charged membrane, the energy of
a zwitterion near a lipid slab, and the effect of including the phosphate head
groups as thin layers of high electric permittivity.


  We consider the "all-for-one" decentralized learning problem for generalized
linear models. The features of each sample are partitioned among several
collaborating agents in a connected network, but only one agent observes the
response variables. To solve the regularized empirical risk minimization in
this distributed setting, we apply the Chambolle--Pock primal--dual algorithm
to an equivalent saddle-point formulation of the problem. The primal and dual
iterations are either in closed-form or reduce to coordinate-wise minimization
of scalar convex functions. We establish convergence rates for the empirical
risk minimization under two different assumptions on the loss function
(Lipschitz and square root Lipschitz), and show how they depend on the
characteristics of the design matrix and the Laplacian of the network.


  In ontology-based data access (OBDA), the classical database is enhanced with
an ontology in the form of logical assertions generating new intensional
knowledge. A powerful form of such logical assertions is the tuple-generating
dependencies (TGDs), also called existential rules, where Horn rules are
extended by allowing existential quantifiers to appear in the rule heads. In
this paper we introduce a new language called loop restricted (LR) TGDs
(existential rules), which are TGDs with certain restrictions on the loops
embedded in the underlying rule set. We study the complexity of this new
language. We show that the conjunctive query answering (CQA) under the LR TGDs
is decid- able. In particular, we prove that this language satisfies the
so-called bounded derivation-depth prop- erty (BDDP), which implies that the
CQA is first-order rewritable, and its data complexity is in AC0 . We also
prove that the combined complexity of the CQA is EXPTIME complete, while the
language membership is PSPACE complete. Then we extend the LR TGDs language to
the generalised loop restricted (GLR) TGDs language, and prove that this class
of TGDs still remains to be first-order rewritable and properly contains most
of other first-order rewritable TGDs classes discovered in the literature so
far.


  Advanced image editing techniques, particularly inpainting, are essential for
seamlessly removing unwanted elements while preserving visual integrity.
Traditional GAN-based methods have achieved notable success, but recent
advancements in diffusion models have produced superior results due to their
training on large-scale datasets, enabling the generation of remarkably
realistic inpainted images. Despite their strengths, diffusion models often
struggle with object removal tasks without explicit guidance, leading to
unintended hallucinations of the removed object. To address this issue, we
introduce CLIPAway, a novel approach leveraging CLIP embeddings to focus on
background regions while excluding foreground elements. CLIPAway enhances
inpainting accuracy and quality by identifying embeddings that prioritize the
background, thus achieving seamless object removal. Unlike other methods that
rely on specialized training datasets or costly manual annotations, CLIPAway
provides a flexible, plug-and-play solution compatible with various
diffusion-based inpainting techniques.


  Implicit discourse relation recognition is a challenging task that involves
identifying the sense or senses that hold between two adjacent spans of text,
in the absence of an explicit connective between them. In both PDTB-2 and
PDTB-3, discourse relational senses are organized into a three-level hierarchy
ranging from four broad top-level senses, to more specific senses below them.
Most previous work on implicit discourse relation recognition have used the
sense hierarchy simply to indicate what sense labels were available. Here we do
more -- incorporating the sense hierarchy into the recognition process itself
and using it to select the negative examples used in contrastive learning. With
no additional effort, the approach achieves state-of-the-art performance on the
task.


  The main purpose of this note is to point out the relevance of the
Mittag-Leffler probability distribution in the so-called thinning theory for a
renewal process with a queue of power law type. This theory, formerly
considered by Gnedenko and Kovalenko in 1968 without the explicit reference to
the Mittag-Leffler function, was used by the authors in the theory of
continuous random walk and consequently of fractional diffusion in a plenary
lecture by the late Prof Gorenflo at a Seminar on Anomalous Transport held in
Bad-Honnef in July 2006, published in a 2008 book. After recalling the basic
theory of renewal processes including the standard and the fractional Poisson
processes, here we have revised the original approach by Gnedenko and Kovalenko
for convenience of the experts of stochastic processes who are not aware of the
relevance of the Mittag-Leffler function


  We introduce a new proxy score for evaluating bitext mining based on
similarity in a multilingual embedding space: xSIM++. In comparison to xSIM,
this improved proxy leverages rule-based approaches to extend English sentences
in any evaluation set with synthetic, hard-to-distinguish examples which more
closely mirror the scenarios we encounter during large-scale mining. We
validate this proxy by running a significant number of bitext mining
experiments for a set of low-resource languages, and subsequently train NMT
systems on the mined data. In comparison to xSIM, we show that xSIM++ is better
correlated with the downstream BLEU scores of translation systems trained on
mined bitexts, providing a reliable proxy of bitext mining performance without
needing to run expensive bitext mining pipelines. xSIM++ also reports
performance for different error types, offering more fine-grained feedback for
model development.


  With suitably designed Monte Carlo simulations we have investigated the
properties of mobile, impenetrable, yet deformable particles that are immersed
into a porous matrix, the latter one realized via a frozen configuration of
spherical particles. By virtue of a model put forward by Batista and Miller
[Phys. Rev. Lett. {\bf 105}, 088305 (2010)] the fluid particles can change
under the impact of their surrounding (i.e., either other fluid particles or
the matrix) their shape within the class of ellipsoids of revolution; such a
change in shape is related to an energy change which is fed into suitably
defined selection rules in the deformation "moves" of the Monte Carlo
simulations. This concept represents a simple, yet powerful model of realistic,
deformable molecules with complex internal structures (such as dendrimers or
polymers). For the evaluation of the properties of the system we have used the
well-known quenched-annealed protocol (with its characteristic double average
prescription) and have analysed the simulation data in terms of static
properties (radial distribution function and aspect ratio distribution of the
ellipsoids) and dynamic features (notably the mean squared displacement). Our
data provide evidence that the degree of deformability of the fluid particles
has a distinct impact on the aforementioned properties of the system.


  Implementations of tracking systems have become prevalent issues in modern
technology due to its advantage of location detection of objects. Objects are
usually tracked using trackers based on GPS, GSM, RFID and Bluetooth signal
strength implementation. These mechanisms usually require line of sight
operations, limited coverage and low level programming language for accessing
Bluetooth signal strength. This paper presents an alternative technique for
tracking the movement of indoor objects based on Bluetooth communication
technology, principles of motion and least square statistical method.
Algorithms are designed and implemented using Java


  We develop a novel identification strategy as well as a new estimator for
context-dependent causal inference in non-parametric triangular models with
non-separable disturbances. Departing from the common practice, our analysis
does not rely on the strict monotonicity assumption. Our key contribution lies
in leveraging on diffusion models to formulate the structural equations as a
system evolving from noise accumulation to account for the influence of the
latent context (confounder) on the outcome. Our identifiability strategy
involves a system of Fredholm integral equations expressing the distributional
relationship between a latent context variable and a vector of observables.
These integral equations involve an unknown kernel and are governed by a set of
structural form functions, inducing a non-monotonic inverse problem. We prove
that if the kernel density can be represented as an infinite mixture of
Gaussians, then there exists a unique solution for the unknown function. This
is a significant result, as it shows that it is possible to solve a
non-monotonic inverse problem even when the kernel is unknown. On the
methodological front we leverage on a novel and enriched Contaminated
Generative Adversarial (Neural) Networks (CONGAN) which we provide as a
solution to the non-monotonic inverse problem.


  The decomposition of $Spin^{c}(4)$ gauge potential in terms of the Dirac 4%
-spinor is investigated, where an important characterizing equation $\Delta
A_{\mu}=-\lambda A_{\mu}$ has been discovered. Here $\lambda $ is the vacuum
expectation value of the spinor field, $\lambda =\Vert \Phi \Vert ^{2}$, and
$A_{\mu}$ the twisting U(1) potential. It is found that when $\lambda $ takes
constant values, the characterizing equation becomes an eigenvalue problem of
the Laplacian operator. It provides a revenue to determine the modulus of the
spinor field by using the Laplacian spectral theory. The above study could be
useful in determining the spinor field and twisting potential in the
Seiberg-Witten equations. Moreover, topological characteristic numbers of
instantons in the self-dual sub-space are also discussed.


  Existing dominant approaches for cross-modal video-text retrieval task are to
learn a joint embedding space to measure the cross-modal similarity. However,
these methods rarely explore long-range dependency inside video frames or
textual words leading to insufficient textual and visual details. In this
paper, we propose a stacked convolutional deep encoding network for video-text
retrieval task, which considers to simultaneously encode long-range and
short-range dependency in the videos and texts. Specifically, a multi-scale
dilated convolutional (MSDC) block within our approach is able to encode
short-range temporal cues between video frames or text words by adopting
different scales of kernel size and dilation size of convolutional layer. A
stacked structure is designed to expand the receptive fields by repeatedly
adopting the MSDC block, which further captures the long-range relations
between these cues. Moreover, to obtain more robust textual representations, we
fully utilize the powerful language model named Transformer in two stages:
pretraining phrase and fine-tuning phrase. Extensive experiments on two
different benchmark datasets (MSR-VTT, MSVD) show that our proposed method
outperforms other state-of-the-art approaches.


  We study two models of correlated bond- and site-disorder on the kagome
lattice considering both translationally invariant and completely disordered
systems. The models are shown to exhibit a perfectly flat ground state band in
the presence of disorder for which we provide exact analytic solutions. Whereas
in one model the flat band remains gapped and touches the dispersive band, the
other model has a finite gap, demonstrating that the band touching is not
protected by topology alone. Our model also displays fully saturated
ferromagnetic groundstates in the presence of repulsive interactions, an
example of disordered flat band ferromagnetism.


  Attributed network representation learning aims at learning node embeddings
by integrating network structure and attribute information. It is a challenge
to fully capture the microscopic structure and the attribute semantics
simultaneously, where the microscopic structure includes the one-step, two-step
and multi-step relations, indicating the first-order, second-order and
high-order proximity of nodes, respectively. In this paper, we propose a deep
attributed network representation learning via attribute enhanced neighborhood
(DANRL-ANE) model to improve the robustness and effectiveness of node
representations. The DANRL-ANE model adopts the idea of the autoencoder, and
expands the decoder component to three branches to capture different order
proximity. We linearly combine the adjacency matrix with the attribute
similarity matrix as the input of our model, where the attribute similarity
matrix is calculated by the cosine similarity between the attributes based on
the social homophily. In this way, we preserve the second-order proximity to
enhance the robustness of DANRL-ANE model on sparse networks, and deal with the
topological and attribute information simultaneously. Moreover, the sigmoid
cross-entropy loss function is extended to capture the neighborhood character,
so that the first-order proximity is better preserved. We compare our model
with the state-of-the-art models on five real-world datasets and two network
analysis tasks, i.e., link prediction and node classification. The DANRL-ANE
model performs well on various networks, even on sparse networks or networks
with isolated nodes given the attribute information is sufficient.


  We find the conditions for the existence of fermionic zero modes of the
fundamental representation in the background of a Kaluza-Klein (KK) monopole.
We show that while there is no zero mode without a real mass, a normalizable
zero mode appears once the real mass is sufficiently large. This provides an
elegant explanation for the known decoupling of KK monopole effects in
supersymmetric theories when a large real mass term is added. We also present
an application where the correct counting of KK zero modes plays an essential
role in understanding the non-perturbative effects determining the low-energy
dynamics.


  Major obstacles to the successful treatment of gliolastoma multiforme are
mostly related to the acquired resistance to chemotherapy drugs and, after
surgery, to the cancer recurrence in correspondence of residual microscopic
foci. As innovative anticancer approach, low-intensity electric stimulation
represents a physical treatment able to reduce multidrug resistance of cancer
and to induce remarkable anti-proliferative effects by interfering with Ca2+
and K+ homeostasis and by affecting the organization of the mitotic spindles.
However, to preserve healthy cells, it is utterly important to direct the
electric stimuli only to malignant cells. In this work, we propose a
nanotechnological approach based on ultrasound-sensitive piezoelectric
nanoparticles to remotely deliver electric stimulations to glioblastoma cells.
Barium titanate nanoparticles (BTNPs) have been functionalized with an antibody
against the transferrin receptor (TfR) in order to obtain the dual targeting of
blood-brain barrier and of glioblastoma cells. The remote ultrasound-mediated
piezo-stimulation allowed to significantly reduce in vitro the proliferation of
glioblastoma cells and, when combined with a sub-toxic concentration of
temozolomide, induced an increased sensitivity to the chemotherapy treatment
and remarkable anti-proliferative and pro-apoptotic effects.


  We study a two-dimensional fermionic QFT used to model 1D strongly correlated
electrons in the presence of a time-dependent impurity that drives the system
out of equilibrium. In contrast to previous investigations, we consider a
dynamic barrier switched on at a finite time. We compute the total energy
density (TED) of the system and establish two well defined regimes in terms of
the relationship between the frequency of the time-dependent perturbation
$\Omega$ and the electron energy $\omega$. Finally, we derive a relaxation time
$t_{R}$ such that for times shorter than $t_{R}$ the finite-time switching
process is relevant.


  The task of multimedia geolocation is becoming an increasingly essential
component of the digital forensics toolkit to effectively combat human
trafficking, child sexual exploitation, and other illegal acts. Typically,
metadata-based geolocation information is stripped when multimedia content is
shared via instant messaging and social media. The intricacy of geolocating,
geotagging, or finding geographical clues in this content is often overly
burdensome for investigators. Recent research has shown that contemporary
advancements in artificial intelligence, specifically computer vision and deep
learning, show significant promise towards expediting the multimedia
geolocation task. This systematic literature review thoroughly examines the
state-of-the-art leveraging computer vision techniques for multimedia
geolocation and assesses their potential to expedite human trafficking
investigation. This includes a comprehensive overview of the application of
computer vision-based approaches to multimedia geolocation, identifies their
applicability in combating human trafficking, and highlights the potential
implications of enhanced multimedia geolocation for prosecuting human
trafficking. 123 articles inform this systematic literature review. The
findings suggest numerous potential paths for future impactful research on the
subject.


  The possible occurence of highly deformed configurations is investigated in
the $^{40}$Ca and $^{56}$Ni di-nuclear systems as formed in the $^{28}$Si +
$^{12}$C and $^{28}$Si + $^{28}$Si reactions, respectively, by using the
properties of emitted light charged particles. Inclusive as well as exclusive
data of the heavy fragments (A $\geq$ 6) and their associated light charged
particles (p, d, t, and $\alpha$-particles) have been collected at the IReS
Strasbourg VIVITRON Tandem facility with two bombarding energies
$E_{lab}(^{28}$Si) = 112 and 180 MeV by using the ICARE charged particle
multidetector array, which consists of nearly 40 telescopes. The measured
energy spectra, velocity distributions, in-plane and out-of-plane angular
correlations are analysed by Monte Carlo CASCADE statistical-model calculations
using a consistent set of parameters with spin-dependent level densities.
Although significant deformation effects at high spin are needed, the remaining
disagreement observed in the $^{28}$Si + $^{12}$C reaction for the S
evaporation residue suggests an unexpected large unstable $^{8}$Be cluster
emission of a binary nature.


  In this work, we investigate the practice of patch construction in the Linux
kernel development, focusing on the differences between three patching
processes: (1) patches crafted entirely manually to fix bugs, (2) those that
are derived from warnings of bug detection tools, and (3) those that are
automatically generated based on fix patterns. With this study, we provide to
the research community concrete insights on the practice of patching as well as
how the development community is currently embracing research and commercial
patching tools to improve productivity in repair. The result of our study shows
that tool-supported patches are increasingly adopted by the developer community
while manually-written patches are accepted more quickly. Patch application
tools enable developers to remain committed to contributing patches to the code
base. Our findings also include that, in actual development processes, patches
generally implement several change operations spread over the code, even for
patches fixing warnings by bug detection tools. Finally, this study has shown
that there is an opportunity to directly leverage the output of bug detection
tools to readily generate patches that are appropriate for fixing the problem,
and that are consistent with manually-written patches.


  Traditionally the improvement of static magnetic field homogeneity of the
magnet in Nuclear Magnetic Resonance (NMR) spectroscopy is performed manually,
which has many limitations. However, in recent years a number of automated
shimming techniques based on Fourier Imaging Technique have been proposed.
Existing 3D automated shimming methods require special, Pulsed Field Gradient
(PFG) hardware, which is not available on majority of high-resolution NMR
spectrometers. The modified technique, presented in this thesis uses the normal
NMR hardware provided with the majority of high-resolution NMR spectrometers.
The 3D shimming technique described was optimised for use with Varian UNITY
INOVA spectrometers and successfully tested with both protonated and deuterated
solvents. A method for calibrating linear transverse shim field gradients and
correcting any non-orthogonality and imbalance of strengths is proposed. The
effect of thermal convection on field mapping was observed and is reported here
for the first time.


  The major intrinsic protein (MIP) of the lens fiber cell membrane plays a
role in lens biogenesis and maintenance. Its polypeptide chains span the
membrane six times, and the protein is naturally divided into two halves. We
use modern sequence analysis to identify differences between halves for humans
(common to animals) and chickens (common to birds).


  Non-autonomous dynamical systems help us to understand the implications of
real systems which are in contact with their environment as it actually occurs
in nature. Here, we focus on systems where a parameter changes with time at
small but non-negligible rates before settling at a stable value, by using the
Lorenz system for illustration. This kind of systems commonly show a long-term
transient dynamics previous to a sudden transition to a steady state. This can
be explained by the crossing of a bifurcation in the associated frozen-in
system. We surprisingly uncover a scaling law relating the duration of the
transient to the rate of change of the parameter for a case where a chaotic
attractor is involved. Additionally, we analyze the viability of recovering the
transient dynamics by reversing the parameter to its original value, as an
alternative to the control theory for systems with parameter drifts. We obtain
the relationship between the paramater change rate and the number of
trajectories that tip back to the initial attractor corresponding to the
transient state.


  The realization that selfish interests need to be accounted for in the design
of algorithms has produced many contributions in computer science under the
umbrella of algorithmic mechanism design. Novel algorithmic properties and
paradigms have been identified and studied. Our work stems from the observation
that selfishness is different from rationality; agents will attempt to
strategize whenever they perceive it to be convenient according to their
imperfect rationality. Recent work has focused on a particular notion of
imperfect rationality, namely absence of contingent reasoning skills, and
defined obvious strategyproofness (OSP) as a way to deal with the selfishness
of these agents. Essentially, this definition states that to care for the
incentives of these agents, we need not only pay attention about the
relationship between input and output, but also about the way the algorithm is
run. However, it is not clear what algorithmic approaches must be used for OSP.
In this paper, we show that, for binary allocation problems, OSP is fully
captured by a combination of two well-known algorithmic techniques: forward and
reverse greedy. We call two-way greedy this algorithmic design paradigm. Our
main technical contribution establishes the connection between OSP and two-way
greedy. We build upon the recently introduced cycle monotonicity technique for
OSP. By means of novel structural properties of cycles and queries of OSP
mechanisms, we fully characterize these mechanisms in terms of extremal
implementations. These are protocols that ask each agent to consistently
separate one extreme of their domain at the current history from the rest.
Through the connection with the greedy paradigm, we are able to import a host
of approximation bounds to OSP and strengthen the strategic properties of this
family of algorithms. Finally, we begin exploring the power of two-way greedy
for set systems.


  Piping and Instrumentation Diagrams (P&ID) are ubiquitous in several
manufacturing, oil and gas enterprises for representing engineering schematics
and equipment layout. There is an urgent need to extract and digitize
information from P&IDs without the cost of annotating a varying set of symbols
for each new use case. A robust one-shot learning approach for symbol
recognition i.e., localization followed by classification, would therefore go a
long way towards this goal. Our method works by sampling pixels sequentially
along the different contour boundaries in the image. These sampled points form
paths which are used in the prototypical line diagram to construct a graph that
captures the structure of the contours. Subsequently, the prototypical graphs
are fed into a Dynamic Graph Convolutional Neural Network (DGCNN) which is
trained to classify graphs into one of the given symbol classes. Further, we
append embeddings from a Resnet-34 network which is trained on symbol images
containing sampled points to make the classification network more robust.
Since, many symbols in P&ID are structurally very similar to each other, we
utilize Arcface loss during DGCNN training which helps in maximizing symbol
class separability by producing highly discriminative embeddings. The images
consist of components attached on the pipeline (straight line). The sampled
points segregated around the symbol regions are used for the classification
task. The proposed pipeline, named OSSR-PID, is fast and gives outstanding
performance for recognition of symbols on a synthetic dataset of 100 P&ID
diagrams. We also compare our method against prior-work on a real-world private
dataset of 12 P&ID sheets and obtain comparable/superior results. Remarkably,
it is able to achieve such excellent performance using only one prototypical
example per symbol.


  A new calibration method for H-fs-TALIF is proposed, and the ratio of
two-photon absorption cross-sections $\sigma^{(2)}$ for atomic hydrogen (H) and
krypton (Kr) is determined for the broadband emission of a femtosecond laser
system. The obtained estimate of the ratio of two-photon absorption
cross-sections $\sigma^{(2)}$ for H and Kr $\sigma^{(2)}$(Kr)/$\sigma^{(2)}$(H)
= $0.034\pm 0.006$, which is almost twenty times lower than the values
previously obtained using narrowband nanosecond lasers. This difference is
explained by the significantly different spectral width of the excitation line,
and demonstrates the need for independent calibration of the TALIF measurements
in the femtosecond range.


  Evolution of a suspension drop entrained by Poiseuille flow is studied
numerically at a low Reynolds number. A suspension drop is modelled by a cloud
of many non-touching particles, initially randomly distributed inside a
spherical volume of a viscous fluid which is identical to the host fluid
outside the drop. Evolution of particle positions and velocities is evaluated
by the accurate multipole method corrected for lubrication, implemented in the
{\sc hydromultipole} numerical code. Deformation of the drop is shown to be
smaller for a larger volume fraction. At high concentrations, hydrodynamic
interactions between close particles significantly decrease elongation of the
suspension drop along the flow in comparison to the corresponding elongation of
the pure-fluid drop. Owing to hydrodynamic interactions, the particles inside a
dense-suspension drop tend to stay for a long time together in the central part
of the drop; later on, small clusters occasionally separate out from the drop,
and are stabilized by quasi-periodic orbits of the constituent non-touching
particles. Both effects significantly reduces the drop spreading along the
flow. At large volume fractions, suspension drops destabilize by fragmentation,
and at low volume fractions, by dispersing into single particles.


  Connection between the concept of entanglement and origin of nonlinear
phenomena in optics is discussed.


  The union-closed sets conjecture, attributed to P\'eter Frankl from 1979,
states that for any non-empty finite union-closed family of finite sets not
consisting of only the empty set, there is an element that is in at least half
of the sets in the family. We prove a version of Frankl's conjecture for
families distributed according to any one of infinitely many distributions. As
a corollary, in the intersection-closed reformulation of Frankl's conjecture,
we obtain that it is true for families distributed according to any one of
infinitely many Maxwell--Boltzmann distributions with inverse temperatures
bounded below by a positive universal constant. Frankl's original conjecture
corresponds to zero inverse temperature.


  This paper proposes a novel channel estimation method and a cluster-based
opportunistic scheduling policy, for a wireless energy transfer (WET) system
consisting of multiple low-complex energy receivers (ERs) with limited
processing capabilities. Firstly, in the training stage, the energy transmitter
(ET) obtains a set of Received Signal Strength Indicator (RSSI) feedback values
from all ERs, and these values are used to estimate the channels between the ET
and all ERs. Next, based on the channel estimates, the ERs are grouped into
clusters, and the cluster that has its members closest to its centroid in phase
is selected for dedicated WET. The beamformer that maximizes the minimum
harvested energy among all ERs in the selected cluster is found by solving a
convex optimization problem. All ERs have the same chance of being selected
regardless of their distances from the ET, and hence, this scheduling policy
can be considered to be opportunistic as well as fair. It is shown that the
proposed method achieves significant performance gains over benchmark schemes.


  We have performed a detailed high-resolution electron spectroscopic
investigation of NiS$_2$ and related Se-substituted compounds
NiS$_{2-x}$Se$_x$, which are known to be gapped insulators in the bulk at all
temperatures. A large spectral weight at the Fermi energy of the room
temperature spectrum, in conjunction with the extreme surface sensitivity of
the experimental probe, however, suggests that the surface layer is metallic at
300 K. Interestingly, the evolution of the spectral function with decreasing
temperature is characterized by a continuous depletion of the single-particle
spectral weight at the Fermi energy and the development of a gap-like structure
below a characteristic temperature, providing evidence for a metal-insulator
crossover behavior at the surfaces of NiS$_2$ and of related compounds. These
results provide a consistent description of the unusual transport properties
observed in these systems.


  End-to-end automatic speech recognition systems represent the state of the
art, but they rely on thousands of hours of manually annotated speech for
training, as well as heavyweight computation for inference. Of course, this
impedes commercialization since most companies lack vast human and
computational resources. In this paper, we explore training and deploying an
ASR system in the label-scarce, compute-limited setting. To reduce human labor,
we use a third-party ASR system as a weak supervision source, supplemented with
labeling functions derived from implicit user feedback. To accelerate
inference, we propose to route production-time queries across a pool of CUDA
graphs of varying input lengths, the distribution of which best matches the
traffic's. Compared to our third-party ASR, we achieve a relative improvement
in word-error rate of 8% and a speedup of 600%. Our system, called SpeechNet,
currently serves 12 million queries per day on our voice-enabled smart
television. To our knowledge, this is the first time a large-scale,
Wav2vec-based deployment has been described in the academic literature.


  We study controllability issues for the Navier-Stokes Equation on a two
dimensional rectangle with so-called Lions boundary conditions. Rewriting the
Equation using a basis of harmonic functions we arrive to an
infinite-dimensional system of ODEs. Methods of Geometric/Lie Algebraic Control
Theory are used to prove controllability by means of low modes forcing of
finite-dimensional Galerkin approximations of that system. Proving the
continuity of the ``control $\mapsto$ solution'' map in the so-called
relaxation metric we use it to prove both solid controllability on observed
component and $\mathbf L^2$-approximate controllability of the Equation (full
system) by low modes forcing.


  This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic
interactions, where interlocutors perform competitive and collaborative tasks
with different behavior elicitation and cognitive workload. The dataset
consists of 90.5 hours of dyadic interactions among 147 participants
distributed in 188 sessions, recorded using multiple audiovisual and
physiological sensors. Currently, it includes sociodemographic, self- and
peer-reported personality, internal state, and relationship profiling from
participants. As an initial analysis on UDIVA, we propose a transformer-based
method for self-reported personality inference in dyadic scenarios, which uses
audiovisual data and different sources of context from both interlocutors to
regress a target person's personality traits. Preliminary results from an
incremental study show consistent improvements when using all available context
information.


  We review current state of neutron star cooling theory and discuss the
prospects to constrain the equation of state, neutrino emission and superfluid
properties of neutron star cores by comparing the cooling theory with
observations of thermal radiation from isolated neutron stars.


  These notes were prepared for the introductory lectures on Higher Spin
Holography presented at the Twelfth Modave Summer School in Mathematical
Physics 2016, aimed at Ph.D. students and researchers new to this topic.
Contents:
  1. Higher spin particles in AdS
  2. The AdS/CFT correspondence and higher spins
  3. The ambient space formalism
  4. Witten diagrams for higher spin fields / higher spin interactions from CFT


  This work demonstrates the potential and the limitations of
archaeo-geophysical techniques available at the Archaeological Geophysics
Laboratory of the Department of Physics at the University of Sofia with various
case studies in natural and artificial environment. Special attention is
focused on GPR which is the most powerful archaeogeophysical technique This
laboratory is the only one in Bulgaria, which develops new geophysical
techniques and equipment for survey of archaeological sites and their dating.


  We propose and experimentally evaluate a novel method that dynamically
changes the contention window of access points based on system load to improve
performance in a dense Wi-Fi deployment. A key feature is that no MAC protocol
changes, nor client side modifications are needed to deploy the solution. We
show that setting an optimal contention window can lead to throughput and
latency improvements up to 155%, and 50%, respectively. Furthermore, we devise
an online learning method that efficiently finds the optimal contention window
with minimal training data, and yields an average improvement in throughput of
53-55% during congested periods for a real traffic-volume workload replay in a
Wi-Fi test-bed.


  We study mechanism design for combinatorial cost sharing. Imagine that
multiple items or services are available to be shared among a set of interested
agents. The outcome of a mechanism in this setting consists of an assignment,
determining for each item the set of players who are granted service, together
with respective payments. Although there are several works studying specialized
versions of such problems, there has been almost no progress for general
combinatorial cost sharing domains until recently \cite{DobzinskiO17}. The main
goal of our work is to further understand this interplay in terms of budget
balance and social cost approximation. Towards this, we provide a refinement of
cross-monotonicity (trace-monotonicity) that is applicable to iterative
mechanisms. The trace here refers to the order in which players become
finalized. On top of this, we also provide two parameterizations of cost
functions which capture the behavior of their average cost-shares. Based on our
trace-monotonicity property, we design a scheme of ascending cost sharing
mechanisms which is applicable to the combinatorial cost sharing setting with
symmetric submodular valuations. Using our first cost function
parameterization, we identify conditions under which our mechanism is weakly
group-strategyproof, $O(1)$-budget-balanced and $O(H_n)$-approximate with
respect to the social cost. Finally, we consider general valuation functions
and exploit our second parameterization to derive a more fine-grained analysis
of the Sequential Mechanism introduced by Moulin. This mechanism is budget
balanced by construction, but in general only guarantees a poor social cost
approximation of $n$. We identify conditions under which the mechanism achieves
improved social cost approximation guarantees.


  In this Letter, we consider original Nash theory of gravity. In terms of the
scalar fields representation, Nash gravity is equivalent to the action of
bi-scalar tensor gravity with four derivative terms of metric tensor. We then
quantify the ghost in the theory. In order to study the gravitational wave
modes of the theory, we perform a small perturbation over a fixed Minkowski
background. We discover the standard wave equation and obtain the solutions as
those of the standard general relativity. In order to satisfy the GR in the
weak field limit, we further modify the original Nash theory by adding the
Einstein-Hibert term called modified Nash gravity. In this theory, we once
study the gravitational wave modes. We derive equations of motion and examine
the solutions. We discover the two-mode solutions: the massless graviton and
the massive one. Using a uniform prior probability on the graviton mass, we can
constrain mass parameter.


  Spectral clustering is one of the most popular algorithms to group high
dimensional data. It is easy to implement and computationally efficient.
Despite its popularity and successful applications, its theoretical properties
have not been fully understood. In this paper, we show that spectral clustering
is minimax optimal in the Gaussian Mixture Model with isotropic covariance
matrix, when the number of clusters is fixed and the signal-to-noise ratio is
large enough. Spectral gap conditions are widely assumed in the literature to
analyze spectral clustering. On the contrary, these conditions are not needed
to establish optimality of spectral clustering in this paper.


  Video object detection (VID) is challenging because of the high variation of
object appearance as well as the diverse deterioration in some frames. On the
positive side, the detection in a certain frame of a video, compared with that
in a still image, can draw support from other frames. Hence, how to aggregate
features across different frames is pivotal to VID problem. Most of existing
aggregation algorithms are customized for two-stage detectors. However, these
detectors are usually computationally expensive due to their two-stage nature.
This work proposes a simple yet effective strategy to address the above
concerns, which costs marginal overheads with significant gains in accuracy.
Concretely, different from traditional two-stage pipeline, we select important
regions after the one-stage detection to avoid processing massive low-quality
candidates. Besides, we evaluate the relationship between a target frame and
reference frames to guide the aggregation. We conduct extensive experiments and
ablation studies to verify the efficacy of our design, and reveal its
superiority over other state-of-the-art VID approaches in both effectiveness
and efficiency. Our YOLOX-based model can achieve promising performance
(\emph{e.g.}, 87.5\% AP50 at over 30 FPS on the ImageNet VID dataset on a
single 2080Ti GPU), making it attractive for large-scale or real-time
applications. The implementation is simple, we have made the demo codes and
models available at \url{https://github.com/YuHengsss/YOLOV}.


  Quantum optics dictates that amplification of a pure state by any linear
deterministic amplifier always introduces noise in the signal and results in a
mixed output state. However, it has recently been shown that noiseless
amplification becomes possible if the requirement of a deterministic operation
is relaxed. Here we propose and analyze a noiseless amplification scheme where
the energy required to amplify the signal originates from the stochastic
fluctuations in the field itself. In contrast to previous amplification setups,
our setup shows that a signal can be amplified even if no energy is added to
the signal from external sources. We investigate the relation between the
amplification and its success rate as well as the statistics of the output
states after successful and failed amplification processes. Furthermore, we
also optimize the setup to find the maximum success rates in terms of the
reflectivities of the beam splitters used in the setup and discuss the relation
of our setup with the previous setups.


  Recent investigations show insertion-deletion systems of small size that are
not complete and cannot generate all recursively enumerable languages. However,
if additional computational distribution mechanisms like P systems are added,
then the computational completeness is achieved in some cases. In this article
we take two insertion-deletion systems that are not computationally complete,
consider them in the framework of P systems and show that the computational
power is strictly increased by proving that any recursively enumerable language
can be generated. At the end some open problems are presented.


  In a canonical optomechanical system, mechanical vibrations are dynamically
encoded on an optical probe field which reciprocally exerts a backaction force.
Due to the weak single photon coupling strength achieved with macroscopic
oscillators, most of existing experiments were conducted with large photon
numbers to achieve sizeable effects, thereby causing a dilution of the original
optomechanical non-linearity. Here, we investigate the optomechanical
interaction of an ultrasensitive suspended nanowire inserted in a fiber-based
microcavity mode. This implementation allows to enter far into the hitherto
unexplored ultrastrong optomechanical coupling regime, where one single
intracavity photon can displace the oscillator by more than its zero point
fluctuations. To fully characterize our system, we implement nanowire-based
scanning probe measurements to map the vectorial optomechanical coupling
strength, but also to reveal the intracavity optomechanical force field
experienced by the nanowire. This work establishes that the single photon
cavity optomechanics regime is within experimental reach.


  Motivated by the large like-sign dimuon charge asymmetry observed recently,
whose explanation would require an enhanced decay rate of $B_s \to \tau^+
\tau^-$, we explore how large a branching ratio of this decay mode is allowed
by the present constraints. We use bounds from the lifetimes of $B_d$ and
$B_s$, constraints from the branching ratios of related $b \to s \tau^+ \tau^-$
modes, as well as measurements of the mass difference, width difference and
CP-violating phase in the $B_s$-$\bar{B}_s$ system. Using an effective field
theory approach, we show that a branching ratio as high as 15% may be allowed
while being consistent with the above constraints. The model with a scalar
leptoquark cannot increase the branching ratio to a per cent level. However, an
enhancement up to 5% is possible in the model with an extremely light $Z'$ with
flavor-dependent interactions, even after all the couplings are taken to be
perturbative. This however cannot account for the dimuon anomaly completely by
itself.


  We show that the number of low energy solutions of a double singularly
perturbed Schroedinger Maxwell system type on a smooth 3 dimensional manifold
(M,g) depends on the topological properties of the manifold. The result is
obtained via Lusternik Schnirelmann category theory.


  We develop the theory of pinchings for non-archimedean analytic spaces. In
particular, we show that although pinchings of affinoid spaces do not have to
be affinoid, pinchings of Hausdorff analytic spaces always exist in the
category of analytic spaces.


  This work is devoted to study the relation between regularity and decay of
solutions of some dissipative perturbations of the Korteweg-de Vries equation
in weighted and asymmetrically weighted Sobolev spaces.


  In the first part we study nearly Frobenius algebras. The concept of nearly
Frobenius algebras is a generalization of the concept of Frobenius algebras.
Nearly Frobenius algebras do not have traces, nor they are self-dual. We prove
that the known constructions: direct sums, tensor, quotient of nearly Frobenius
algebras admit natural nearly Frobenius structures.
  In the second part we study algebras associated to some families of quivers
and the nearly Frobenius structures that they admit. As a main theorem, we
prove that an indecomposable algebra associated to a bound quiver $(Q,I)$ with
no monomial relations admits a non trivial nearly Frobenius structure if and
only if the quiver is $\overrightarrow{\mb{A}_n}$ and I=0. We also present an
algorithm that determines the number of independent nearly Frobenius structures
for Gentle algebras without oriented cycles.


  With the recent advancements in edge artificial intelligence (AI), future
sixth-generation (6G) networks need to support new AI tasks such as
classification and clustering apart from data recovery. Motivated by the
success of deep learning, the semantic-aware and task-oriented communications
with deep joint source and channel coding (JSCC) have emerged as new paradigm
shifts in 6G from the conventional data-oriented communications with separate
source and channel coding (SSCC). However, most existing works focused on the
deep JSCC designs for one task of data recovery or AI task execution
independently, which cannot be transferred to other unintended tasks.
Differently, this paper investigates the JSCC semantic communications to
support multi-task services, by performing the image data recovery and
classification task execution simultaneously. First, we propose a new
end-to-end deep JSCC framework by unifying the coding rate reduction
maximization and the mean square error (MSE) minimization in the loss function.
Here, the coding rate reduction maximization facilitates the learning of
discriminative features for enabling to perform classification tasks directly
in the feature space, and the MSE minimization helps the learning of
informative features for high-quality image data recovery. Next, to further
improve the robustness against variational wireless channels, we propose a new
gated deep JSCC design, in which a gated net is incorporated for adaptively
pruning the output features to adjust their dimensions based on channel
conditions. Finally, we present extensive numerical experiments to validate the
performance of our proposed deep JSCC designs as compared to various benchmark
schemes.


  The scarcity of organs for transplant has led to large waiting lists of very
sick patients. In drug development, the time required for human trials greatly
increases the time to market. Drug companies are searching for alternative
environments where the in-vivo conditions can be closely replicated. Both these
problems could be addressed by manufacturing artificial human tissue. Recently,
researchers in tissue engineering have developed tissue generation methods
based on 3-D printing to fabricate artificial human tissue. Broadly, these
methods could be classified as laser-assisted and laser free. The former have
very fine spatial resolutions (10s of $\mu$m) but suffer from slow speed ( $<
10^2$ drops per second). The later have lower spatial resolutions (100s of
$\mu$ m) but are very fast (up to $5\times 10^3$ drops per second). In this
paper we review state-of-the-art methods in each of these classes and provide a
comparison based on reported resolution, printing speed, cell density and cell
viability.


  We provide three detailed case studies of vulnerabilities in smart contracts,
and show how property-based testing would have found them:
  1. the Dexter1 token exchange;
  2. the iToken;
  3. the ICO of Brave's BAT token.
  The last example is, in fact, new, and was missed in the auditing process. We
have implemented this testing in ConCert, a general executable
model/specification of smart contract execution in the Coq proof assistant.
ConCert contracts can be used to generate verified smart contracts in Tezos'
LIGO and Concordium's rust language. We thus show the effectiveness of
combining formal verification and property-based testing of smart contracts.


  We present the results of a study of the free energy of a monopole pair in
pure
  SU(2) theory at finite temperature, both below and above the deconfinement
tran sition. We find a Yukawa potential between monopoles in both phases. At
low temp erature, the screening mass is compatible with the lightest glueball
mass. At hi gh temperature, we observe an increased screening mass with no
apparent disconti nuity at the phase transition.


  In the present work, we develop a causal theory of relativistic non-ideal
fluids up to the second order in the Eckart frame using gradient expansion
scheme. Keeping the spirit of Mueller-Israel-Stewart formalism, the general
forms of bulk viscosity, shear viscosity tensor and the heat flow vector are
presented. Since each of the flux quantities explicitly carry curvature terms,
we show that our formalism finds application in astrophysics in particular in
the strong gravity regime. We elucidate two such applications namely in viscous
thick accretion disks also known as Polish doughnuts and in addressing
non-rotating equilibrium configuration, like neutron stars.


  We show that in a fibration the coformality of the base space implies the
coformality of the total space under reasonable conditions, and these
conditions can not be weakened. The result is partially dual to the classical
work of Lupton \cite{Lup} on the formality within a fibration. Our result has
two applications. First, we show that for certain cofibrations, the coformality
of the cofiber implies the coformality of the base. Secondly, we show that the
total spaces of certain spherical fibrations are Koszul in the sense of
Berglund \cite{Ber}.


  This review summarizes the research of Mercury's magnetosphere in the
Post-MESSENGER era and compares its dynamics to those in other planetary
magnetospheres, especially to those in Earth's magnetosphere. This review
starts by introducing the planet Mercury, including its interplanetary
environment, magnetosphere, exosphere, and conducting core. The frequent and
intense magnetic reconnection on the dayside magnetopause, which is represented
by the flux transfer event "shower", is reviewed on how they depend on
magnetosheath plasma beta and magnetic shear angle across the magnetopause,
following by how they contribute to the flux circulation and
magnetosphere-surface-exosphere coupling. In the next, the progress of
Mercury's magnetosphere under extreme solar events, including the core
induction and the reconnection erosion on the dayside magnetosphere, the
responses of the nightside magnetosphere, are reviewed. Then, the dawn-dusk
properties of the plasma sheet, including the features of the ions, the
structure of the current sheet, and the dynamics of magnetic reconnection, are
summarized. The last topic reviews the particle energization in Mercury's
magnetosphere, which includes the energization of the Kelvin-Helmholtz waves on
the magnetopause boundaries, reconnection-generated magnetic structures, and
the cross-tail electric field. In each chapter, the last section discusses the
open questions related with each topic, which can be considered by the
simulations and the future spacecraft mission. We close by summarizing the
future BepiColombo opportunities, which is a joint mission between ESA and
JAXA, and is en route to Mercury.


  Pattern recognition applications often suffer from skewed data distributions
between classes, which may vary during operations w.r.t. the design data.
Two-class classification systems designed using skewed data tend to recognize
the majority class better than the minority class of interest. Several
data-level techniques have been proposed to alleviate this issue by up-sampling
minority samples or under-sampling majority samples. However, some informative
samples may be neglected by random under-sampling and adding synthetic positive
samples through up-sampling adds to training complexity. In this paper, a new
ensemble learning algorithm called Progressive Boosting (PBoost) is proposed
that progressively inserts uncorrelated groups of samples into a Boosting
procedure to avoid loss of information while generating a diverse pool of
classifiers. Base classifiers in this ensemble are generated from one iteration
to the next, using subsets from a validation set that grows gradually in size
and imbalance. Consequently, PBoost is more robust to unknown and variable
levels of skew in operational data, and has lower computation complexity than
Boosting ensembles in literature. In PBoost, a new loss factor is proposed to
avoid bias of performance towards the negative class. Using this loss factor,
the weight update of samples and classifier contribution in final predictions
are set based on the ability to recognize both classes. Using the proposed loss
factor instead of standard accuracy can avoid biasing performance in any
Boosting ensemble. The proposed approach was validated and compared using
synthetic data, videos from the FIA dataset that emulates face
re-identification applications, and KEEL collection of datasets. Results show
that PBoost can outperform state of the art techniques in terms of both
accuracy and complexity over different levels of imbalance and overlap between
classes.


  We introduce stochastic driving in the Sznajd model of opinion spreading.
This stochastic effect is meant to mimic a social temperature, so that agents
can take random decisions with a varying probability. We show that a stochastic
driving has a tremendous impact on the system dynamics as a whole by inducing
an order-disorder nonequilibrium phase transition. Interestingly, under certain
conditions, this stochastic dynamics can spontaneously lead to agents in the
system who are analogous to Galam's contarians.


  We present the guarded lambda-calculus, an extension of the simply typed
lambda-calculus with guarded recursive and coinductive types. The use of
guarded recursive types ensures the productivity of well-typed programs.
Guarded recursive types may be transformed into coinductive types by a
type-former inspired by modal logic and Atkey-McBride clock quantification,
allowing the typing of acausal functions. We give a call-by-name operational
semantics for the calculus, and define adequate denotational semantics in the
topos of trees. The adequacy proof entails that the evaluation of a program
always terminates. We demonstrate the expressiveness of the calculus by showing
the definability of solutions to Rutten's behavioural differential equations.
We introduce a program logic with L\"{o}b induction for reasoning about the
contextual equivalence of programs.


  Driving an ion at a motional sideband transition induces the Jaynes--Cummings
(JC) interaction. This JC interaction creates an anharmonic ladder of JC
eigenstates, resulting in the suppression of phonon hopping due to energy
conservation. Here, we realize phonon blockade in the presence of multiple
local phonons in a trapped-ion chain. Our work establishes a key technological
component for quantum simulation with multiple bosonic particles, which can
simulate classically intractable problems.


  ^{31}P and ^{75}As NMR measurements were performed in superconducting
BaFe_2(As_{0.67}P_{0.33})_2 with T_c = 30 K. The nuclear-spin-lattice
relaxation rate T_1^{-1} and the Knight shift in the normal state indicate the
development of antiferromagnetic fluctuations, and T_1^{-1} in the
superconducting (SC) state decreases without a coherence peak just below T_c,
as observed in (Ba_{1-x}K_{x})Fe_2As_2. In contrast to other iron arsenide
superconductors, the T_1^{-1} \propto T behavior is observed below 4K,
indicating the presence of a residual density of states at zero energy. Our
results suggest that strikingly different SC gaps appear in
BaFe_2(As_{1-x}P_{x})_2 despite a comparable T_c value, an analogous phase
diagram, and similar Fermi surfaces to (Ba_{1-x}K_{x})Fe_2As_2.


  In this paper we prove the unique identifiability, module the natural
obstruction, for the Calder\'{o}n problem for quasilinear anisotropic
conductivities in two dimensions from the associated Dirichlet-to-Neumann map.
We reduce the problem to the isotropic case using isothermal coordinates. For
this case, we prove the result by using the method of higher order
linearization and Bukhgeim's CGO solutions.


  Speech Emotion Recognition (SER) in a single language has achieved remarkable
results through deep learning approaches in the last decade. However,
cross-lingual SER remains a challenge in real-world applications due to a great
difference between the source and target domain distributions. To address this
issue, we propose an unsupervised cross-lingual Neural Network with Pseudo
Multilabel (NNPM) that is trained to learn the emotion similarities between
source domain features inside an external memory adjusted to identify emotion
in cross-lingual databases. NNPM introduces a novel approach that leverages
external memory to store source domain features and generates pseudo multilabel
for each target domain data by computing the similarities between the external
memory and the target domain features. We evaluate our approach on multiple
different languages of speech emotion databases. Experimental results show our
proposed approach significantly improves the weighted accuracy (WA) across
multiple low-resource languages on Urdu, Skropus, ShEMO, and EMO-DB corpus. To
facilitate further research, code is available at
https://github.com/happyjin/NNPM


  The optimisation of scenarios and design of real-time-control in tokamaks,
especially for machines still in design phase, requires a comprehensive
exploration of solutions to the Grad-Shafranov (GS) equation over a
high-dimensional space of plasma and coil parameters. Emulators can bypass the
numerical issues in the GS equation, if a large enough library of equilibria is
available. We train an ensemble of neural networks to emulate the typical
shape-control targets (separatrix at midplane, X-points, divertor strike point,
flux expansion, poloidal beta) as a function of plasma parameters and active
coil currents for the range of plasma configurations relevant to spherical
tokamaks with a super-X divertor, with percent-level accuracy. This allows a
quick calculation of the classical-control shape matrices, potentially allowing
real-time calculation at any point in a shot with sub-ms latency. We devise a
hyperparameter sampler to select the optimal network architectures and quantify
uncertainties on the model predictions. To generate the relevant training set,
we devise a Markov-Chain Monte Carlo algorithm to produce large libraries of
forward Grad-Shafranov solutions without the need for user intervention. The
algorithm promotes equilibria with desirable properties, while avoiding
parameter combinations resulting in problematic profiles or numerical issues in
the integration of the GS equation.


  Currently, one-stage frameworks have been widely applied for temporal action
detection, but they still suffer from the challenge that the action instances
span a wide range of time. The reason is that these one-stage detectors, e.g.,
Single Shot Multi-Box Detector (SSD), extract temporal features only applying a
single-level layer for each head, which is not discriminative enough to perform
classification and regression. In this paper, we propose a Multi-Level Temporal
Pyramid Network (MLTPN) to improve the discrimination of the features.
Specially, we first fuse the features from multiple layers with different
temporal resolutions, to encode multi-layer temporal information. We then apply
a multi-level feature pyramid architecture on the features to enhance their
discriminative abilities. Finally, we design a simple yet effective feature
fusion module to fuse the multi-level multi-scale features. By this means, the
proposed MLTPN can learn rich and discriminative features for different action
instances with different durations. We evaluate MLTPN on two challenging
datasets: THUMOS'14 and Activitynet v1.3, and the experimental results show
that MLTPN obtains competitive performance on Activitynet v1.3 and outperforms
the state-of-the-art approaches on THUMOS'14 significantly.


  The experiments covered by Machine Learning (ML) must consider two important
aspects to assess the performance of a model: datasets and algorithms. Robust
benchmarks are needed to evaluate the best classifiers. For this, one can adopt
gold standard benchmarks available in public repositories. However, it is
common not to consider the complexity of the dataset when evaluating. This work
proposes a new assessment methodology based on the combination of Item Response
Theory (IRT) and Glicko-2, a rating system mechanism generally adopted to
assess the strength of players (e.g., chess). For each dataset in a benchmark,
the IRT is used to estimate the ability of classifiers, where good classifiers
have good predictions for the most difficult test instances. Tournaments are
then run for each pair of classifiers so that Glicko-2 updates performance
information such as rating value, rating deviation and volatility for each
classifier. A case study was conducted hereby which adopted the OpenML-CC18
benchmark as the collection of datasets and pool of various classification
algorithms for evaluation. Not all datasets were observed to be really useful
for evaluating algorithms, where only 10% were considered really difficult.
Furthermore, the existence of a subset containing only 50% of the original
amount of OpenML-CC18 was verified, which is equally useful for algorithm
evaluation. Regarding the algorithms, the methodology proposed herein
identified the Random Forest as the algorithm with the best innate ability.


  We consider the relativistic Vlasov-Maxwell and Vlasov-Nordstr\"om systems
which describe large particle ensembles interacting by either electromagnetic
fields or a relativistic scalar gravity model. For both systems we derive a
radiation formula analogous to the Einstein quadrupole formula in general
relativity.


  This paper is concerned with non-cooperative parabolic reaction--diffusion
systems which share structural similarities with the scalar Fisher--KPP
equation. These similarities make it possible to prove, among other results, an
extinction and persistence dichotomy and, when persistence occurs, the
existence of a positive steady state, the existence of traveling waves with a
half-line of possible speeds and a positive minimal speed and the equality
between this minimal speed and the spreading speed for the Cauchy problem.
Non-cooperative KPP systems can model various phenomena where the following
three mechanisms occur: local diffusion in space, linear cooperation and
su-perlinear competition.


  Approximate Bayesian computation methods are useful for generative models
with intractable likelihoods. These methods are however sensitive to the
dimension of the parameter space, requiring exponentially increasing resources
as this dimension grows. To tackle this difficulty, we explore a Gibbs version
of the ABC approach that runs component-wise approximate Bayesian computation
steps aimed at the corresponding conditional posterior distributions, and based
on summary statistics of reduced dimensions. While lacking the standard
justifications for the Gibbs sampler, the resulting Markov chain is shown to
converge in distribution under some partial independence conditions. The
associated stationary distribution can further be shown to be close to the true
posterior distribution and some hierarchical versions of the proposed mechanism
enjoy a closed form limiting distribution. Experiments also demonstrate the
gain in efficiency brought by the Gibbs version over the standard solution.


  Measuring the average information that is necessary to describe the behaviour
of a dynamical system leads to a generalization of the Kolmogorov-Sinai
entropy. This is particularly interesting when the system has null entropy and
the information increases less than linearly with respect to time. We consider
two classes of maps of the interval with an indifferent fixed point at the
origin and an infinite natural invariant measure. We calculate that the average
information that is necessary to describe the behaviour of its orbits increases
with time $n$ approximately as $n^\alpha $, where $\alpha<1$ depends only on
the asymptotic behaviour of the map near the origin.


  The blasting of brittle materials with sharp particles is an important
fabrication technology in many industrial processes. In particular, for
micro-systems, it allows the production of devices with feature sizes down to
few tens of microns. An important parameter of this process is the surface
roughness of post-blasted surfaces. In this work the scaling properties of
Pyrex glass and silicon surfaces after bombardment with alumina particles is
investigated. The targets were bombarded at normal incidence using alumina
particles with two different average sizes, $29\mu m$ and $9\mu m$,
respectively. This investigation indicates that the resulting surfaces have
multifractal properties. Applying multifractal detrended fluctuation analysis
(MFDFA) allowed us to determine the singularity spectrum of the surfaces. This
spectrum did not depend on the target material or on the size of the particles.
Several parameters quantifying relevant quantities were determined. We argue
that for scales below $5 \mu m$, fracture processes are dominant while at large
scales long range correlations are responsible for the multifractal behaviour.


  The multiplicative Hamiltonian flow on the phase space for a system with 1
degree of freedom was constituted from infinite hierarchy Hamiltonian flows. A
new type of canonical transformation associated with the multiplicative
Hamiltonian was found and called the {\lambda}- extended class of the standard
canonical transformations.


  We study axisymmetric perturbations of neutron star endowed with a strong
magnetic field (magnetars), considering the coupled oscillations of the fluid
core with the solid crust. We recover discrete oscillations based mainly in the
crust and a continuum in the core. We also confirm the presence of "discrete
Alfv\'en modes" in the gap between two contiguous continua (see Van Hooven&
Levin (2011)) and, in addition, we can resolve some of them also inside the
continua. Our results can explain both the lower and the higher observed quasi
periodical oscillations (QPOs) in SGR 1806-20 and SGR 1900+14 and put
constrains on the mass, radius and crust thickness of the two magnetars.


  We describe a hybrid evolutionary algorithm that can simultaneously search
for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The
algorithm mixes evolutionary computation, Metropolis-Hastings methods and
Nested Sampling. The inspiral of SMBHBs presents an interesting problem for
gravitational wave data analysis since, due to the LISA response function, the
sources have a bi-modal sky solution. We show here that it is possible not only
to detect multiple SMBHBs in the data stream, but also to investigate
simultaneously all the various modes of the global solution. In all cases, the
algorithm returns parameter determinations within $5\sigma$ (as estimated from
the Fisher Matrix) of the true answer, for both the actual and antipodal sky
solutions.


  We investigate the simplest models where baryon and lepton numbers are
defined as local symmetries spontaneously broken at the low scale and discuss
the implications for cosmology. We define the simplest anomaly-free theory for
spontaneous baryon and lepton number violation which predicts the existence of
lepto-baryons. In this context we study the new sphaleron condition on the
chemical potentials and show the relation between the present baryon asymmetry
and the B-L asymmetry generated in the early universe. The properties of the
cold dark matter candidate for which stability is a natural consequence from
symmetry breaking are briefly discussed.


  We prove that the length of the projection of the vector joining the centers
of mass of a convex body on the plane and of its boundary to an arbitrary
direction does not exceed $\frac{1}{6}$ of the body width in this direction. It
follows that the distance between these centers of mass does not exceed
$\frac16$ of the diameter of the body and $\frac{1}{12}$ of its boundary
length. None of those constants can be improved.


  In this paper we prove trace formulae for the Reidemeister number of a group
endomorphism. This result implies the rationality of the Reidemeister zeta
function in the following cases: the group is a direct product of a finite
group and a finitely generated Abelian group; the group is finitely generated,
nilpotent and torsion free. We continue to study analytical properties of the
Nielsen zeta function. We connect the Reidemeister zeta function of an
endomorphism of a direct product of a finite group and a finitely generated
free Abelian group with the Lefschetz zeta function of the induced map on the
unitary dual of the group. As a consequence we obtain a relation between a
special value of the Reidemeister zeta function and a certain Reidemeister
torsion. We also prove congruences for Reidemeister numbers of iterates of an
endomorphism of a direct product of a finite group and a finitely generated
free Abelian group which are the same as those found by Dold for Lefschetz
numbers.


  We study the ramification divisors of projections of a smooth projective
variety onto a linear subspace of the same dimension. We prove that the
ramification divisors vary in a maximal dimensional family for a large class of
varieties. Going further, we study the map that associates to a linear
projection its ramification divisor. We show that this map is dominant for most
(but not all!) varieties of minimal degree, using (linked) limit linear series
of higher rank. We find the degree of this map in some cases, extending the
classical appearance of Catalan numbers in the geometry of rational normal
curves, and give a geometric explanation of its fibers in terms of torsion
points of naturally occurring elliptic curves in the case of the Veronese
surface and the quartic rational surface scroll.


  Evolution Strategies are inspired in biology and part of a larger research
field known as Evolutionary Algorithms. Those strategies perform a random
search in the space of admissible functions, aiming to optimize some given
objective function. We show that simple evolution strategies are a useful tool
in optimal control, permitting to obtain, in an efficient way, good
approximations to the solutions of some recent and challenging optimal control
problems.


  We discuss a new torsion pendulum design for ground testing of prototype LISA
(Laser Interferometer Space Antenna) displacement sensors. This new design is
directly sensitive to net forces and therefore provides a more representative
test of the noisy forces and parasitic stiffnesses acting on the test mass as
compared to previous ground-based experiments. We also discuss a specific
application to the measurement of thermal gradient effects.


  Distributed machine learning is becoming increasingly popular for
geo-distributed data analytics, facilitating the collaborative analysis of data
scattered across data centers in different regions. This paradigm eliminates
the need for centralizing sensitive raw data in one location but faces the
significant challenge of high parameter synchronization delays, which stems
from the constraints of bandwidth-limited, heterogeneous, and fluctuating
wide-area networks. Prior research has focused on optimizing the
synchronization topology, evolving from starlike to tree-based structures.
However, these solutions typically depend on regular tree structures and lack
an adequate topology metric, resulting in limited improvements. This paper
proposes NetStorm, an adaptive and highly efficient communication scheduler
designed to speed up parameter synchronization across geo-distributed data
centers. First, it establishes an effective metric for optimizing a multi-root
FAPT synchronization topology. Second, a network awareness module is developed
to acquire network knowledge, aiding in topology decisions. Third, a multipath
auxiliary transmission mechanism is introduced to enhance network awareness and
facilitate multipath transmissions. Lastly, we design policy consistency
protocols to guarantee seamless updates of transmission policies. Empirical
results demonstrate that NetStorm significantly outperforms distributed
training systems like MXNET, MLNET, and TSEngine, with a speedup of 6.5~9.2
times over MXNET.


  We present the first high-dispersion spectroscopy of He I 10830 from the
hotspots in the ring around SN1987A, obtained at Gemini South, spatially
resolving the near and far sides of the ring. We compare these line profiles to
similar echelle spectra of H$\alpha$ and [N II] 6583 obtained at the Magellan
Observatory. We find that the He I profiles are much broader than H-alpha or [N
II], but the He I profiles also have different shapes -- they have enhanced
emission at high speeds, with extra blueshifted emission on the north side of
the ring, and extra redshifted emission on the south side. To explain this, we
invoke a simple geometric picture where the extra He I emission traces hotter
gas from faster shocks that strike the apex of the hotspots directly, while the
H-alpha preferentially traces cooler lower-ionization gas from slower
transverse shocks that penetrate into the sides of the ring.


  We consider a nonlinear Schr\"odinger equation in $\R^3$ with a bounded local
potential. The linear Hamiltonian is assumed to have two bound states with the
eigenvalues satisfying some resonance condition. Suppose that the initial data
is small and is near some nonlinear {\it excited} state. We give a sufficient
condition on the initial data so that the solution to the nonlinear
Schr\"odinger equation approaches to certain nonlinear {\it ground} state as
the time tends to infinity.


  Regularization for optimization is a crucial technique to avoid overfitting
in machine learning. In order to obtain the best performance, we usually train
a model by tuning the regularization parameters. It becomes costly, however,
when a single round of training takes significant amount of time. Very
recently, Neu and Rosasco show that if we run stochastic gradient descent (SGD)
on linear regression problems, then by averaging the SGD iterates properly, we
obtain a regularized solution. It left open whether the same phenomenon can be
achieved for other optimization problems and algorithms. In this paper, we
establish an averaging scheme that provably converts the iterates of SGD on an
arbitrary strongly convex and smooth objective function to its regularized
counterpart with an adjustable regularization parameter. Our approaches can be
used for accelerated and preconditioned optimization methods as well. We
further show that the same methods work empirically on more general
optimization objectives including neural networks. In sum, we obtain adjustable
regularization for free for a large class of optimization problems and resolve
an open question raised by Neu and Rosasco.


  This paper shows that accurate underwater 3D shape reconstruction is possible
using a single camera, observing a target through a refractive interface. We
provide unified reconstruction techniques for a variety of scenarios such as
single static camera and moving refractive interface, single moving camera and
static refractive interface, and single moving camera and moving refractive
interface. In our basic setup, we assume that the refractive interface is
planar, and simultaneously estimate the unknown transformations of the planar
interface and the camera, and the unknown target shape using bundle adjustment.
We also extend it to relax the planarity assumption, which enables us to use
waves of the refractive interface for the reconstruction task. Experiments with
real data show the superiority of our method to existing methods.


  We compute the Casimir energy for a free scalar field on the spaces
$\,R^{m+1}\,\times\,\tilde S^2\,$ where $,\tilde S^2\,$ is two-dimensional
deformed two-sphere.


  We use null spherical (observational) coordinates to describe a class of
inhomogeneous cosmological models. The proposed cosmological construction is
based on the observer past null cone. A known difficulty in using inhomogeneous
models is that the null geodesic equation is not integrable in general. Our
choice of null coordinates solves the radial ingoing null geodesic by
construction. Furthermore, we use an approach where the velocity field is
uniquely calculated from the metric rather than put in by hand. Conveniently,
this allows us to explore models in a non-comoving frame of reference. In this
frame, we find that the velocity field has shear, acceleration and expansion
rate in general. We show that a comoving frame is not compatible with expanding
perfect fluid models in the coordinates proposed and dust models are simply not
possible. We describe the models in a non-comoving frame. We use the dust
models in a non-comoving frame to outline a fitting procedure.


  We prove that for every $\epsilon>0$ and predicate $P:\{0,1\}^k\rightarrow
\{0,1\}$ that supports a pairwise independent distribution, there exists an
instance $\mathcal{I}$ of the $\mathsf{Max}P$ constraint satisfaction problem
on $n$ variables such that no assignment can satisfy more than a
$\tfrac{|P^{-1}(1)|}{2^k}+\epsilon$ fraction of $\mathcal{I}$'s constraints but
the degree $\Omega(n)$ Sum of Squares semidefinite programming hierarchy cannot
certify that $\mathcal{I}$ is unsatisfiable. Similar results were previously
only known for weaker hierarchies.


  We calculate the degree of horizon smoothness of multi- $M2$-brane solution
with branes along a common axis. We find that the metric is generically only
thrice continuously differentiable at any of the horizons. The four-form field
strength is found to be only twice continuously differentiable. We work with
Gaussian null-like co-ordinates which are obtained by solving geodesic
equations for multi-$M2$ brane geometry. We also find different, exact
co-ordinate transformations which take the metric from isotropic co-ordinates
to co-ordinates in which metric is thrice differentiable at the horizon. Both
methods give the same result that the multi-$M2$ brane metric is only thrice
continuously differentiable at the horizon.


  It has been found that stochastic algorithms often find good solutions much
more rapidly than inherently-batch approaches. Indeed, a very useful rule of
thumb is that often, when solving a machine learning problem, an iterative
technique which relies on performing a very large number of
relatively-inexpensive updates will often outperform one which performs a
smaller number of much "smarter" but computationally-expensive updates.
  In this thesis, we will consider the application of stochastic algorithms to
two of the most important machine learning problems. Part i is concerned with
the supervised problem of binary classification using kernelized linear
classifiers, for which the data have labels belonging to exactly two classes
(e.g. "has cancer" or "doesn't have cancer"), and the learning problem is to
find a linear classifier which is best at predicting the label. In Part ii, we
will consider the unsupervised problem of Principal Component Analysis, for
which the learning task is to find the directions which contain most of the
variance of the data distribution.
  Our goal is to present stochastic algorithms for both problems which are,
above all, practical--they work well on real-world data, in some cases better
than all known competing algorithms. A secondary, but still very important,
goal is to derive theoretical bounds on the performance of these algorithms
which are at least competitive with, and often better than, those known for
other approaches.


  Despite the progress in medical data collection the actual burden of
SARS-CoV-2 remains unknown due to under-ascertainment of cases. This was
apparent in the acute phase of the pandemic and the use of reported deaths has
been pointed out as a more reliable source of information, likely less prone to
under-reporting. Since daily deaths occur from past infections weighted by
their probability of death, one may infer the total number of infections
accounting for their age distribution, using the data on reported deaths. We
adopt this framework and assume that the dynamics generating the total number
of infections can be described by a continuous time transmission model
expressed through a system of non-linear ordinary differential equations where
the transmission rate is modelled as a diffusion process allowing to reveal
both the effect of control strategies and the changes in individuals behavior.
We develop this flexible Bayesian tool in Stan and study 3 pairs of European
countries, estimating the time-varying reproduction number($R_t$) as well as
the true cumulative number of infected individuals. As we estimate the true
number of infections we offer a more accurate estimate of $R_t$. We also
provide an estimate of the daily reporting ratio and discuss the effects of
changes in mobility and testing on the inferred quantities.


  We investigate the effect of strong electron-electron repulsion on the
electron-phonon interaction from a Fermi-liquid point of view: the strong
interaction is responsible for vertex corrections, which are strongly dependent
on the $v_Fq/\omega$ ratio. These corrections generically lead to a strong
suppression of the effective coupling between quasiparticles mediated by a
single phonon exchange in the $v_Fq/\omega \gg 1$ limit. However, such effect
is not present when $v_Fq/\omega \ll 1$. Analyzing the Landau stability
criterion, we show that a sizable electron-phonon interaction can push the
system towards a phase-separation instability. A detailed analysis is then
carried out using a slave-boson approach for the infinite-U three-band Hubbard
model. In the presence of a coupling between the local hole density and a
dispersionless optical phonon, we explicitly confirm the strong dependence of
the hole-phonon coupling on the transferred momentum versus frequency ratio. We
also find that the exchange of phonons leads to an unstable phase with negative
compressibility already at small values of the bare hole-phonon coupling. Close
to the unstable region, we detect Cooper instabilities both in s- and d-wave
channels supporting a possible connection between phase separation and
superconductivity in strongly correlated systems.


  We prove that an HNN extension of a torsion-free nilpotent group is
left-orderable. We also construct examples of non-left-orderable HNN extensions
of left-orderable groups


  We consider the one-dimensional target search process that involves an
immobile target located at the origin and $N$ searchers performing independent
Brownian motions starting at the initial positions $\vec x = (x_1,x_2,...,
x_N)$ all on the positive half space. The process stops when the target is
first found by one of the searchers. We compute the probability distribution of
the maximum distance $m$ visited by the searchers till the stopping time and
show that it has a power law tail: $P_N(m|\vec x)\sim B_N (x_1x_2...
x_N)/m^{N+1}$ for large $m$. Thus all moments of $m$ up to the order $(N-1)$
are finite, while the higher moments diverge. The prefactor $B_N$ increases
with $N$ faster than exponentially. Our solution gives the exit probability of
a set of $N$ particles from a box $[0,L]$ through the left boundary.
Incidentally, it also provides an exact solution of the Laplace's equation in
an $N$-dimensional hypercube with some prescribed boundary conditions. The
analytical results are in excellent agreement with Monte Carlo simulations.


  Let $g$ be a entire function of exponential type on the complex plane
$\mathbb C$, $Z=\{ z_k\}_{k=1,2,\dots}$ be a sequence of points in $\mathbb C$.
We give a criterion for the existence of an entire function $f\neq 0$ of
exponential type that vanishes on $ Z$ and satisfies the constraint $\ln
|f(iy)|\leq \ln |g(iy)|+o(|y|)$, $y\to \pm\infty$. Our results generalize and
develop a joint results of P. Malliavin and L. A. Rubel. Applications to
multipliers for entire functions of exponential type, to analytic functionals
and their convolutions on the complex plane, as well as to the completeness of
exponential systems in the spaces of locally analytic functions on compacts in
terms of the width of these compacts are given.


  We investigate the radial dependence of the spectral break separating the
inertial from the dissipation range in power density spectra of interplanetary
magnetic field fluctuations, between $0.42$ and $5.3$ AU, during radial
alignments between MESSENGER and WIND for the inner heliosphere and between
WIND and ULYSSES for the outer heliosphere. We found that the spectral break
moves to higher and higher frequencies as the heliocentric distance decreases.
The radial dependence of the corresponding wavenumber is of the kind
$\kappa_b\sim R^{-1.08}$ in good agreement with that of the wavenumber derived
from the linear resonance condition for proton cyclotron damping. These results
support conclusions from previous studies which suggest that a
cyclotron-resonant dissipation mechanism must participate into the spectral
cascade together with other possible kinetic noncyclotron-resonant mechanisms.


  We propose an estimator of the Hilbert-Schmidt Independence Criterion
obtained from an appropriate modification of the usual estimator. We then get
asymptotic normality of this estimator both under independence hypothesis and
under the alternative hypothesis. A new test for independence of random
variables valued into metric spaces is then introduced, and a simulation study
that allows to compare the proposed test to an existing one is provided


  Alfv\'en eigenmodes (AEs) and energetic particle modes (EPMs) are often
excited by energetic particles (EPs) in tokamak plasmas. One of the main open
questions concerning EP driven instabilities is the non-linear evolution of the
mode structure. The aim of the present paper is to investigate the properties
of beta-induced AEs (BAEs) and EP driven geodesic acoustic modes (EGAMs)
observed in the ramp-up phase of off-axis NBI heated ASDEX Upgrade (AUG)
discharges. This paper focuses on the changes in the mode structure of
BAEs/EGAMs during the non-linear chirping phase. Our investigation has shown
that in case of the observed down-chirping BAEs the changes in the radial
structure are smaller than the uncertainty of our measurement. This behaviour
is most probably the consequence of that BAEs are normal modes, thus their
radial structure strongly depends on the background plasma parameters rather
than on the EP distribution. In the case of rapidly upward chirping EGAMs the
analysis consistently shows shrinkage of the mode structure. The proposed
explanation is that the resonance in the velocity space moves towards more
passing particles which have narrower orbit widths.


  Results from a six year systematic observational survey on candidate
eclipsing binaries with a $\delta$ delta Sct component are presented. More than
a hundred systems with component(s) of A-F spectral types were observed in the
frame of this survey in order to be checked for possible pulsational behaviour.
The 14% (13 cases) of the currently known such systems were discovered during
this survey. Using all the available information from the literature, an
updated list with all the currently known systems of this type is presented,
while possible correlations between their pulsational and binarity properties
are discussed.


  We propose an alternative evaluation of quantum entanglement by measuring the
maximum violation of the Bell's inequality without performing a partial trace
operation. This proposal is demonstrated by bridging the maximum violation of
the Bell's inequality and the concurrence of a pure state in an $n$-qubit
system, in which one subsystem only contains one qubit and the state is a
linear combination of two product states. We apply this relation to the ground
states of four qubits in the Wen-Plaquette model and show that they are
maximally entangled. A topological entanglement entropy of the Wen-Plaquette
model could be obtained by relating the upper bound of the maximum violation of
the Bell's inequality to the concurrences of a pure state with respect to
different bipartitions.


  We numerically and theoretically investigate the Boussinesq Eady model, where
a rapidly rotating density-stratified layer of fluid is subject to a meridional
temperature gradient in thermal wind balance with a uniform vertically sheared
zonal flow. Through a suite of numerical simulations, we show that the
transport properties of the resulting turbulent flow are governed by
quasi-geostrophic (QG) dynamics in the rapidly rotating strongly stratified
regime. The 'vortex gas' scaling predictions put forward in the context of the
two-layer QG model carry over to this fully 3D system: the functional
dependence of the meridional flux on the control parameters is the same, the
two ajustable parameters entering the theory taking slightly different values.
In line with the QG prediction, the meridional buoyancy flux is
depth-independent. The vertical buoyancy flux is such that turbulence
transports buoyancy along isopycnals, except in narrow layers near the the top
and bottom boundaries, the thickness of which decreases as the diffusivities go
to zero. The emergent (re)stratification is set by a simple balance between the
vertical buoyancy flux and diffusion along the vertical direction. Overall,
this study demonstrates how the vortex-gas scaling theory can be adapted to
quantitatively predict the magnitude and vertical structure of the meridional
and vertical buoyancy fluxes, and of the emergent stratification, without
additional fitting parameters.


  The Muon $(g-2)$ experiment, E821, at the Brookhaven AGS has the goal to
measure the muon anomalous magnetic moment to a relative accuracy of $\pm 3.5
\times 10^{-7}$. A superferric 14 m diameter storage ring has been constructed
and an averaged magnetic field uniformity over the 90 mm diameter muon storage
region of $\pm$ 1 part per million (ppm) has been achieved. A truncated
double-cosine superconducting septum magnet (the inflector) was constructed
along with a fast non-ferric kicker. The performance of the storage ring, along
with the physics results are reviewed.


  We discuss the effects of the gravitino on the big-bang nucleosynthesis
(BBN), paying particular attention to the hadronic decay mode of the gravitino.
We will see that the hadronic decay of the gravitino significantly affect the
BBN and, for the case where the hadronic branching ratio is sizable, very
stringent upper bound on the reheating temperature after inflation is obtained.


  Using the two-dimensional ionic Hubbard model as a simple basis for
describing the electronic structure of silicene in the presence of an electric
field induced by the substrate, we use the coherent-potential approximation to
calculate the zero-temperature phase diagram and associated spectral function
at half filling. We find that any degree of symmetry-breaking induced by the
electric field causes the silicene structure to lose its Dirac fermion
characteristics, thus providing a simple mechanism for the disappearance of the
Dirac cone.


  We consider focus point gauge mediation within the framework of the
next-to-minimal supersymmetric standard model, which substantially reduces the
degree of fine-tuning for the electroweak symmetry breaking. The milder
fine-tuning is realized by a messenger field in the adjoint representation of
$SU(5)$ gauge group with $SU(3)_c$ octet being heavy. Our model has a simple
ultraviolet completion. The fine-tuning measure $\Delta$ can be as small as
40-50 without any contradiction with LHC constraints.


  We introduce the Conditional Mutual Information (CMI) for the estimation of
the Markov chain order. For a Markov chain of $K$ symbols, we define CMI of
order $m$, $I_c(m)$, as the mutual information of two variables in the chain
being $m$ time steps apart, conditioning on the intermediate variables of the
chain. We find approximate analytic significance limits based on the estimation
bias of CMI and develop a randomization significance test of $I_c(m)$, where
the randomized symbol sequences are formed by random permutation of the
components of the original symbol sequence. The significance test is applied
for increasing $m$ and the Markov chain order is estimated by the last order
for which the null hypothesis is rejected. We present the appropriateness of
CMI-testing on Monte Carlo simulations and compare it to the Akaike and
Bayesian information criteria, the maximal fluctuation method (Peres-Shields
estimator) and a likelihood ratio test for increasing orders using
$\phi$-divergence. The order criterion of CMI-testing turns out to be superior
for orders larger than one, but its effectiveness for large orders depends on
data availability. In view of the results from the simulations, we interpret
the estimated orders by the CMI-testing and the other criteria on genes and
intergenic regions of DNA chains.


  In this paper we focus on the stochastic Euler-Poincar\'{e} equations with
pseudo-differential/multiplicative noise. We first establish two new
cancellation properties on pseudo-differential operators, which play a key role
in energy estimate. Then, we obtain results on local solution, blow-up
criterion and global existence. The interplay between stability on exiting
times and continuous dependence of solution on initial data are also studied
for the multiplicative noise case.


  Models for the steady state collisional evolution of low eccentricity
planetesimal belts identify debris disks with hot dust at 1AU, like eta Corvi
and HD69830, as anomalous since collisional processing should have removed most
of the planetesimal mass over their >1 Gyr lifetimes. This paper looks at the
effect of large planetesimal eccentricities (e>>0.3) on their collisional
lifetime and the amount of mass that can remain at late times M_{late}. For an
axisymmetric planetesimal disk with common pericentres and eccentricities e, we
find that M_{late} \propto e^{-5/3}(1+e)^{4/3}(1-e)^{-3}. For a scattered
disk-like population (i.e., common pericentres), in the absence of dynamical
evolution, the mass evolution at late times would be as if only planetesimals
with the largest eccentricity were present. Despite the increased remaining
mass, higher eccentricities do not increase the hot emission from the
collisional cascade until e>0.99, partly because most collisions occur near
pericentre thus increasing the dust blow-out diameter. However, at high
eccentricities (e>0.97) the blow-out population extending out from pericentre
may be detectable above the collisional cascade; higher eccentricities also
increase the probability of witnessing a recent collision. All of the imaging
and spectroscopic constraints for eta Corvi can be explained with a single
planetesimal population with pericentre at 0.75AU, apocentre at 150AU, and mass
5M_\oplus; however, the origin of such a high eccentricity population remains
challenging. The mid-IR excess to HD69830 can be explained by the ongoing
destruction of a debris belt produced in a recent collision in an eccentric
planetesimal belt, but the lack of far-IR emission requires small bound grains
to be absent from the parent planetesimal belt, possibly due to sublimation.