  Microstrip antennas are extensively used in spacecraft systems and other
applications where they encounter a plasma environment. A detailed
computational investigation of change in antenna radiation properties in the
presence of plasma has been presented in this paper. The study shows antenna
properties such as the resonant frequency, return loss, radiation properties
and the different characteristics of the antenna changes when it is surrounded
by plasma. Particular focus of the work is to understand the causes behind
these changes by correlating the complex propagation constant in the plasma
medium, field distribution on the patch and effective dielectric of the antenna
substrate with antenna parameter variations. The study also provides important
insights to explore the possibilities of designing tunable microstrip antenna
where the substrate can be replaced with plasma and important antenna
characteristics can be controlled by varying the plasma density.


  Despite extensive research into adversarial attacks, we do not know how
adversarial attacks affect image pixels. Knowing how image pixels are affected
by adversarial attacks has the potential to lead us to better adversarial
defenses. Motivated by instances that we find where strong attacks do not
transfer, we delve into adversarial examples at pixel level to scrutinize how
adversarial attacks affect image pixel values. We consider several ImageNet
architectures, InceptionV3, VGG19 and ResNet50, as well as several strong
attacks. We find that attacks can have different effects at pixel level
depending on classifier architecture. In particular, input pre-processing plays
a previously overlooked role in the effect that attacks have on pixels. Based
on the insights of pixel-level examination, we find new ways to detect some of
the strongest current attacks.


  Graph filtering is the cornerstone operation in graph signal processing
(GSP). Thus, understanding it is key in developing potent GSP methods. Graph
filters are local and distributed linear operations, whose output depends only
on the local neighborhood of each node. Moreover, a graph filter's output can
be computed separately at each node by carrying out repeated exchanges with
immediate neighbors. Graph filters can be compactly written as polynomials of a
graph shift operator (typically, a sparse matrix description of the graph).
This has led to relating the properties of the filters with the spectral
properties of the corresponding matrix -- which encodes global structure of the
graph. In this work, we propose a framework that relies solely on the local
distribution of the neighborhoods of a graph. The crux of this approach is to
describe graphs and graph signals in terms of a measurable space of rooted
balls. Leveraging this, we are able to seamlessly compare graphs of different
sizes and coming from different models, yielding results on the convergence of
spectral densities, transferability of filters across arbitrary graphs, and
continuity of graph signal properties with respect to the distribution of local
substructures.


  The analysis of industrial processes, modelled as descriptor systems, is
often computationally hard due to the presence of both algebraic couplings and
difference equations of high order. In this paper, we introduce a control
refinement notion for these descriptor systems that enables analysis and
control design over related reduced-order systems. Utilising the behavioural
framework, we extend upon the standard hierarchical control refinement for
ordinary systems and allow for algebraic couplings inherent to descriptor
systems.


  This paper analyzes team collaboration in the field of Artificial
Intelligence (AI) from the perspective of geographic distance. We obtained
1,584,175 AI related publications during 1950-2019 from the Microsoft Academic
Graph. Three latitude-and-longitude-based indicators were employed to quantify
the geographic distance of collaborations in AI over time at domestic and
international levels. The results show team collaborations in AI has been more
popular in the field over time with around 42,000 (38.4%) multiple-affiliation
AI publications in 2019. The changes in geographic distances of team
collaborations indicate the increase of breadth and density for both domestic
and international collaborations in AI over time. In addition, the United
States produced the largest number of single-country and internationally
collaborated AI publications, and China has played an important role in
international collaborations in AI after 2010.


  In this work we apply field regularization techniques to formulate a number
of new phenomena related to momentum induced by electromagnetic zero-point
fluctuations. We discuss the zero-point momentum associated with
magneto-electric media, with moving media, and with magneto-chiral media.


  A variety of constitutive models have been developed for soft tissue
mechanics. However, there is no established criterion to select a suitable
model for a specific application. Although the model that best fits the
experimental data can be deemed the most suitable model, this practice often
can be insufficient given the inter-sample variability of experimental
observations. Herein, we present a Bayesian approach to calculate the relative
probabilities of constitutive models based on biaxial mechanical testing of
tissue samples. 46 samples of porcine aortic valve tissue were tested using a
biaxial stretching setup. For each sample, seven ratios of stresses along and
perpendicular to the fiber direction were applied. The probabilities of eight
invariant-based constitutive models were calculated based on the experimental
data using the proposed model selection framework. The calculated probabilities
showed that, out of the considered models and based on the information
available through the utilized experimental dataset, the May--Newman model was
the most probable model for the porcine aortic valve data. When the samples
were grouped into different cusp types, the May--Newman model remained the most
probable for the left- and right-coronary cusps, whereas for non-coronary cusps
two models were found to be equally probable: the Lee--Sacks model and the
May--Newman model. This difference between cusp types was found to be
associated with the first principal component analysis (PCA) mode, where this
mode's amplitudes of the non-coronary and right-coronary cusps were found to be
significantly different. Our results show that a PCA-based statistical model
can capture significant variations in the mechanical properties of soft
tissues. The presented framework is applicable to any tissue type, and has the
potential to provide a structured and rational way of making simulations
population-based.


  The fabrication of single atom transistors paved the way for electronics
based on single dopants. Recently the spectrum of a single dopant was measured
electrically by coupling two such devices. The next step towards promising
functionalities for future nanoelectronics consists in manipulating a single
electron over two dopants. Here we demonstrate electron pumping through two
phosphorus donors in series implanted in a silicon nanowire. While quantized
pumping is achieved in the low frequency adiabatic regime, we observe
remarkable features at higher frequency when the charge transfer is limited by
the different tunneling rates. The transitions between quantum states are
modeled involving a Landau-Zener transition, allowing to reproduce in detail
the characteristic signatures observed in the non-adiabatic regime.


  A method of formation of the tightly confined distortion-free fs pulses with
the step-like decreasing of intensity under the finite-length propagation in
free space is described. Such pulses are formed by the Fresnel source of a high
refraction-index waveguide. The source reproduces in free space a
propagation-invariant (distortion-free) pulse confined by the waveguide.
Converse to the case of material waveguides, when the pulse goes out from the
Fresnel (virtual) waveguide its shape is not changed, but the intensity
immediately drops down to the near-zero level.


  Wave-particle duality has long been considered a fundamental signature of the
non-classical behavior of quantum phenomena, specially in a delayed choice
experiment (DCE), where the experimental setup revealing either the particle or
wave nature of the system is decided after the system has entered the
apparatus. However, as counter-intuitive as it might seem, usual DCEs do have a
simple causal explanation. Here, we take a different route and under a natural
assumption about the dimensionality of the system under test, we present an
experimental proof of the non-classicality of a DCE based on the violation of a
dimension witness inequality. Our conclusion is reached in a device-independent
and loophole-free manner, that is, based solely on the observed data and
without the need on any assumptions about the measurement apparatus.


  Multipath TCP (MPTCP) is a transport layer protocol that allows network
devices to transfer data over multiple concurrent paths, and hence, utilizes
the network resources more effectively than does the traditional single-path
TCP. However, as a reliable protocol, MPTCP still needs to deliver data packets
(to the upper application) at the receiver in the same order they are
transmitted at the sender. The out-of-order packet problem becomes more severe
for MPTCP due to the heterogeneous nature of delay and bandwidth of each path.
In this paper, we propose the forward-delay-based packet scheduling (FDPS)
algorithm for MPTCP to address that problem. The main idea is that the sender
dispatches packets via concurrent paths according to their estimated forward
delay and throughput differences. Via simulations with various network
conditions, the results show that our algorithm significantly maintains
in-order arrival packets at the receiver compared with several previous
algorithms.


  Errors quoted on results are often given in asymmetric form. An account is
given of the two ways these can arise in an analysis, and the combination of
asymmetric errors is discussed. It is shown that the usual method has no basis
and is indeed wrong. For asymmetric systematic errors, a consistent method is
given, with detailed examples. For asymmetric statistical errors a general
approach is outlined.


  Future cellular systems will make use of millimeter wave (mmWave) frequency
bands. Many users in these bands are located indoors, i.e., inside buildings,
homes, and offices. Typical building material attenuations in these high
frequency ranges are of interest for link budget calculations. In this paper,
we report on a collaborative measurement campaign to find the attenuation of
several typical building materials in three potential mmWave bands (28, 73, 91
GHz). Using directional antennas, we took multiple measurements at multiple
locations using narrow-band and wide-band signals, and averaged out residual
small-scale fading effects. Materials include clear glass, drywall
(plasterboard), plywood, acoustic ceiling tile, and cinder blocks. Specific
attenuations range from approximately 0.5 dB/cm for ceiling tile at 28 GHz to
approximately 19 dB/cm for clear glass at 91 GHz.


  We have systematically studied the X-ray emission properties of globular
cluster millisecond pulsars in order to evaluate their spectral properties and
luminosities in a uniform way. Cross-correlating the radio timing positions of
the cluster pulsars with the high resolution Chandra images revealed 31 X-ray
counterparts identified in nine different globular cluster systems, including
those in 47 Tuc. Timing analysis has been performed for all sources
corresponding to the temporal resolution available in the archival Chandra
data. Making use of unpublished data on M28, M4 and NGC 6752 allowed us to
obtain further constraints for the millisecond pulsar counterparts located in
these clusters. Counting rate and energy flux upper limits were computed for
those 36 pulsars for which no X-ray counterparts could be detected. Comparing
the X-ray and radio pulse profiles of PSR J1821-2452 in M28 and the 47 Tuc
pulsars PSR J0024-7204D,O,R indicated some correspondence between both
wavebands. The X-ray efficiency of the globular cluster millisecond pulsars was
found to be in good agreement with the efficiency Lx ~ 10^-3 Edot observed in
Galactic field rotation-powered pulsars. Millisecond pulsars in the galactic
plane and in globular clusters appear to show no distinct differences in their
X-ray emission properties.


  Knowledge Graph Construction (KGC) can be seen as an iterative process
starting from a high quality nucleus that is refined by knowledge extraction
approaches in a virtuous loop. Such a nucleus can be obtained from knowledge
existing in an open KG like Wikidata. However, due to the size of such generic
KGs, integrating them as a whole may entail irrelevant content and scalability
issues. We propose an analogy-based approach that starts from seed entities of
interest in a generic KG, and keeps or prunes their neighboring entities. We
evaluate our approach on Wikidata through two manually labeled datasets that
contain either domain-homogeneous or -heterogeneous seed entities. We
empirically show that our analogy-based approach outperforms LSTM, Random
Forest, SVM, and MLP, with a drastically lower number of parameters. We also
evaluate its generalization potential in a transfer learning setting. These
results advocate for the further integration of analogy-based inference in
tasks related to the KG lifecycle.


  We investigate the properties of composite polaron containing the effects of
electron-phonon coupling and interaction between impurity and electron-hole
pair. A model of a two-dimensional electron gas occupying the surface of
two-dimensional Dirac honeycomb lattice is constructed. We focus on the weak
coupling regime throughout the whole paper. Our results are meaningful to the
study of pairing mechanism as well as the phonon-mediated high-temperature
superconductivity.


  Using equivariant topology, we prove that it is always possible to find $n$
points in the $d$-dimensional faces of a $nd$-dimensional convex polytope $P$
so that their center of mass is a target point in $P$. Equivalently, the
$n$-fold Minkowski sum of a $nd$-polytope's $d$-skeleton is that polytope
scaled by $n$. This verifies a conjecture by Takeshi Tokuyama.


  Task-oriented conversational systems often use dialogue state tracking to
represent the user's intentions, which involves filling in values of
pre-defined slots. Many approaches have been proposed, often using
task-specific architectures with special-purpose classifiers. Recently, good
results have been obtained using more general architectures based on pretrained
language models. Here, we introduce a new variation of the language modeling
approach that uses schema-driven prompting to provide task-aware history
encoding that is used for both categorical and non-categorical slots. We
further improve performance by augmenting the prompting with schema
descriptions, a naturally occurring source of in-domain knowledge. Our purely
generative system achieves state-of-the-art performance on MultiWOZ 2.2 and
achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M.
The data and code will be available at
https://github.com/chiahsuan156/DST-as-Prompting.


  Virtual Trusted Platform Modules (vTPMs) have been widely used in commercial
cloud platforms (e.g. Google Cloud, VMware Cloud, and Microsoft Azure) to
provide virtual root-of-trust for virtual machines. Unfortunately, current
state-of-the-art vTPM implementations are suffering from confidential data
leakage and high performance overhead. In this paper, we present SvTPM, a
secure and efficient software-based vTPM implementation based on
hardware-rooted Trusted Execution Environment (TEE), providing a whole life
cycle protection of vTPMs in the cloud. SvTPM offers strong isolation
protection, so that cloud tenants or even cloud administrators cannot get
vTPM's private keys or any other sensitive data. In SvTPM, we identify and
solve a couple of critical security challenges for vTPM protection with SGX,
such as NVRAM replacement attack, rollback attacks, trust establishment, and a
fine-grained trusted clock. We implement a prototype of SvTPM on both QEMU and
KVM. Performance evaluation results show that SvTPM achieves orders of
magnitude of performance gains comparing to the vTPMs protected with physical
TPM. The launch time of SvTPM is 2600$\times$ faster than vTPMs built upon
hardware TPM. In the micro-benchmarks evaluation, we find that the command
execution latency of SvTPM is smaller than or equal to the existing schemes.


  We report the first experimental demonstration of a TE-polarization photonic
band gap (PBG) in a 2D isotropic hyperuniform disordered solid (HUDS) made of
dielectric media with a index contrast of 1.6:1, very low for PBG formation.
The solid is composed of a connected network of dielectric walls enclosing
air-filled cells. Direct comparison with photonic crystals and quasicrystals
permitted us to investigate band-gap properties as a function of increasing
rotational isotropy. We present results from numerical simulations proving that
the PBG observed experimentally for HUDS at low index contrast has zero density
of states. The PBG is associated with the energy difference between
complementary resonant modes above and below the gap, with the field
predominantly concentrated in the air or in the dielectric. The intrinsic
isotropy of HUDS may offer unprecedented flexibilities and freedom in
applications (i. e. defect architecture design) not limited by crystalline
symmetries.


  We introduce two new algorithms to minimise smooth difference of convex (DC)
functions that accelerate the convergence of the classical DC algorithm (DCA).
We prove that the point computed by DCA can be used to define a descent
direction for the objective function evaluated at this point. Our algorithms
are based on a combination of DCA together with a line search step that uses
this descent direction. Convergence of the algorithms is proved and the rate of
convergence is analysed under the Lojasiewicz property of the objective
function. We apply our algorithms to a class of smooth DC programs arising in
the study of biochemical reaction networks, where the objective function is
real analytic and thus satisfies the Lojasiewicz property. Numerical tests on
various biochemical models clearly show that our algorithms outperforms DCA,
being on average more than four times faster in both computational time and the
number of iterations. Numerical experiments show that the algorithms are
globally convergent to a non-equilibrium steady state of various biochemical
networks, with only chemically consistent restrictions on the network topology.


  In the search for the building blocks of life, nitrogen-bearing molecules are
of particular interest since nitrogen-containing bonds are essential for the
linking of amino acids and ultimately the formation of larger biological
structures. The elusive molecule methylamine (CH$_3$NH$_2$) is thought to be a
key pre-biotic species but has so far only been securely detected in the giant
molecular cloud Sgr B2. We identify CH$_3$NH$_2$ and other simple
nitrogen-bearing species towards three hot cores in NGC 6334I. Column density
ratios are derived in order to investigate the relevance of the individual
species as precursors of biotic molecules. Observations obtained with ALMA were
used to study transitions of CH$_3$NH$_2$, CH$_2$NH, NH$_2$CHO, and the
$^{13}$C- and $^{15}$N-methyl cyanide (CH$_3$CN) isotopologues. Column
densities are derived for each species assuming LTE and excitation temperatures
in the range 220-340 K for CH$_3$NH$_2$, 70-110 K for the CH$_3$CN
isotopologues, and 120-215 K for NH$_2$CHO and CH$_2$NH. We report the first
detections of CH$_3$NH$_2$ towards NGC 6334I with column density ratios with
respect to CH$_3$OH of 5.9$\times$10$^{-3}$, 1.5$\times$10$^{-3}$, and
5.4$\times$10$^{-4}$ for the three hot cores MM1, MM2, and MM3, respectively.
These values are slightly lower than the values derived for Sgr B2 but higher
by more than order of magnitude as compared with the values derived for the
low-mass protostar IRAS 16293-2422B. The detections of CH$_3$NH$_2$ in the hot
cores of NGC 6334I hint that CH$_3$NH$_2$ is generally common in the
interstellar medium, albeit high-sensitivity observations are essential for its
detection. The good agreement between model predictions of CH$_3$NH$_2$ ratios
and the observations towards NGC 6334I indicate a main formation pathway via
radical recombination on grain surfaces.


  We introduce dodecaDialogue: a set of 12 tasks that measures if a
conversational agent can communicate engagingly with personality and empathy,
ask questions, answer questions by utilizing knowledge resources, discuss
topics and situations, and perceive and converse about images. By multi-tasking
on such a broad large-scale set of data, we hope to both move towards and
measure progress in producing a single unified agent that can perceive, reason
and converse with humans in an open-domain setting. We show that such
multi-tasking improves over a BERT pre-trained baseline, largely due to
multi-tasking with very large dialogue datasets in a similar domain, and that
the multi-tasking in general provides gains to both text and image-based tasks
using several metrics in both the fine-tune and task transfer settings. We
obtain state-of-the-art results on many of the tasks, providing a strong
baseline for this challenge.


  Image classification is an enthusiastic research field where large amount of
image data is classified into various classes based on their visual contents.
Researchers have presented various low-level features-based techniques for
classifying images into different categories. However, efficient and effective
classification and retrieval is still a challenging problem due to complex
nature of visual contents. In addition, the traditional information retrieval
techniques are vulnerable to security risks, making it easy for attackers to
retrieve personal visual contents such as patients records and law enforcement
agencies databases. Therefore, we propose a novel ontology-based framework
using image steganography for secure image classification and information
retrieval. The proposed framework uses domain-specific ontology for mapping the
low-level image features to high-level concepts of ontologies which
consequently results in efficient classification. Furthermore, the proposed
method utilizes image steganography for hiding the image semantics as a secret
message inside them, making the information retrieval process secure from third
parties. The proposed framework minimizes the computational complexity of
traditional techniques, increasing its suitability for secure and real-time
visual contents retrieval from personalized image databases. Experimental
results confirm the efficiency, effectiveness, and security of the proposed
framework as compared with other state-of-the-art systems.


  A composite multiferroic chain with an interfacial linear magneto-electric
coupling is used to study the magnetic and electric responses to an external
magnetic or electric field. The simulation uses continuous spin dynamics
through the Landau-Lifshitz-Gilbert equations of the magnetic spin and the
electric pseudo-spin. The results demonstrate an accurate description of the
distribution of the magnetisation and polarisation are induced by applied
electric and magnetic field, respectively.


  We investigate the recently reported analogies between pinned vortices in
nano-structured superconductors or colloids in optical traps, and spin ice
materials. The frustration of colloids and vortices differs essentially from
spin ice. However, their effective energetics is made identical by the
contribution of an emergent field associated to the topological charge, thus
leading to a (quasi) ice manifold for lattices of even (odd) coordination. The
equivalence extends to the local low-energy dynamics of the ice manifold, where
the effect of geometric hard constraints can be subsumed into the spatial
modulation of the emergent field, which mediates an entropic interaction
between topological charges. There, as in spin ice materials, genuine ice
manifolds enter a Coulomb phase, whereas quasi-ice manifolds posses a well
defined screening length, provided by a plasma of embedded topological charges.
The equivalence between the two systems breaks down in lattices of mixed
coordination because of topological charge transfer between sub-latices. We
discuss extensions to social and economical networks.


  We prove that applying a projective functor to a holonomic simple module over
a semi-simple finite dimensional complex Lie algebra produces a module that has
an essential semi-simple submodule of finite length. This implies that
holonomic simple supermodules over certain Lie superalgebras are quotients of
modules that are induced from simple modules over the even part. We also
provide some further insight into the structure of Lie algebra modules that are
obtained by applying projective functors to simple modules.


  We review recent theoretical models and experiments dealing with the
diffraction of neutral atoms from a reflection grating, formed by a standing
evanescent wave. We analyze diffraction mechanisms proposed for normal and
grazing incidence, point out their scopes and confront the theory with
experiment.


  DP-coloring (also known as correspondence coloring) is a generalization of
list coloring developed recently by Dvo\v{r}\'{a}k and Postle. In this paper we
introduce and study the fractional DP-chromatic number $\chi_{DP}^\ast(G)$. We
characterize all connected graphs $G$ such that $\chi_{DP}^\ast(G) \leqslant
2$: they are precisely the graphs with no odd cycles and at most one even
cycle. By a theorem of Alon, Tuza, and Voigt, the fractional list-chromatic
number $\chi_\ell^\ast(G)$ of any graph $G$ equals its fractional chromatic
number $\chi^\ast(G)$. This equality does not extend to fractional
DP-colorings. Moreover, we show that the difference $\chi^\ast_{DP}(G) -
\chi^\ast(G)$ can be arbitrarily large, and, furthermore, $\chi^\ast_{DP}(G)
\geq d/(2 \ln d)$ for every graph $G$ of maximum average degree $d \geq 4$. On
the other hand, we show that this asymptotic lower bound is tight for a large
class of graphs that includes all bipartite graphs as well as many graphs of
high girth and high chromatic number.


  We establish, in general terms, the conditions to be satisfied by a
time-fractional approach formulation of the Poisson-Nernst-Planck model in
order to guarantee that the total current across the sample be solenoidal, as
required by the Maxwell equation. Only in this case the electric impedance of a
cell can be determined as the ratio between the applied difference of potential
and the current across the cell. We show that in the case of anomalous
diffusion, the model predicts for the electric impedance of the cell a constant
phase element behaviour in the low frequency region. In the parametric curve of
the reactance versus the resistance, the slope coincides with the order of the
fractional time derivative.


  We present a new family of exact vacuum solutions to Pfeifer and Wohlfarth's
field equation in Finsler gravity, consisting of Finsler metrics that are
Landsbergian but not Berwaldian, also known as unicorns due to their rarity.
Interestingly we find that these solutions have a physically viable light cone
structure, even though in some cases the signature is not Lorentzian but
positive definite. We furthermore find a promising analogy between our
solutions and classical FLRW cosmology. One of our solutions in particular has
cosmological symmetry, i.e. it is spatially homogeneous and isotropic, and it
is additionally conformally flat, with the conformal factor depending only on
the timelike coordinate. We show that this conformal factor can be interpreted
as the scale factor, we compute it as a function of cosmological time, and we
show that it corresponds to a linearly expanding (or contracting) Finsler
universe.


  We study theoretically the nonlinear optical response of disordered electrons
in the regime of weak (anti)localization. Our analytical and numerical
calculations reveal that, in orthogonal/symplectic class systems, two
consecutive, phase coherent optical pulses generates an electric current echo
that appears after the second pulse, and at a time equal to the pulse delay
time. The current echo reflects the quantum interference between a
self-intersecting electron path and its time reversal partner, and, therefore,
provide a time-domain interferometry of weak (anti)localization. Our results
can be potentially tested on disordered metal films by using terahertz
two-dimensional coherent spectroscopy or ultrafast transport measurements.


  This paper presents new measurements in a case study of the minimal SUGRA
model with m_0=100 GeV, mhalf=300 GeV, A_0=0, tan(beta)=2.1, and mu=+1 based on
four-body distributions from three-step decays and on minimum masses in such
decays. These measurements allow masses of supersymmetric particles to be
determined without relying on a model. The feasibility of testing slepton
universality at the ~0.1% level at high luminosity is discussed. In addition,
the effect of enlarging the parameter space of the minimal SUGRA model is
discussed. The direct production of left handed sleptons and the
non-observation of additional structure in the dilepton invariant mass
distributions is shown to provide additional constraints.


  The classical setting of optimal control theory assumes full knowledge of the
process dynamics and the costs associated with every control strategy. The
problem becomes much harder if the controller only knows a finite set of
possible running cost functions, but has no way of checking which of these
running costs is actually in place. In this paper we address this challenge for
a class of evasive path planning problems on a continuous domain, in which an
Evader needs to reach a target while minimizing his exposure to an enemy
Observer, who is in turn selecting from a finite set of known surveillance
plans. Our key assumption is that both the evader and the observer need to
commit to their (possibly probabilistic) strategies in advance and cannot
immediately change their actions based on any newly discovered information
about the opponent's current position. We consider two types of evader
behavior: in the first one, a completely risk-averse evader seeks a trajectory
minimizing his {\em worst-case} cumulative observability, and in the second,
the evader is concerned with minimizing the {\em average-case} cumulative
observability. The latter version is naturally interpreted as a semi-infinite
strategic game, and we provide an efficient method for approximating its Nash
equilibrium. The proposed approach draws on methods from game theory, convex
optimization, optimal control, and multiobjective dynamic programming. We
illustrate our algorithm using numerical examples and discuss the computational
complexity, including for the generalized version with multiple evaders.


  Generic Event Boundary Captioning (GEBC) aims to generate three sentences
describing the status change for a given time boundary. Previous methods only
process the information of a single boundary at a time, which lacks utilization
of video context information. To tackle this issue, we design a model that
directly takes the whole video as input and generates captions for all
boundaries parallelly. The model could learn the context information for each
time boundary by modeling the boundary-boundary interactions. Experiments
demonstrate the effectiveness of context information. The proposed method
achieved a 72.84 score on the test set, and we reached the $2^{nd}$ place in
this challenge. Our code is available at:
\url{https://github.com/zjr2000/Context-GEBC}


  Extremal rotating black holes in Einstein-Maxwell theory feature two
branches. On the branch emerging from the Myers-Perry solutions their angular
momentum is proportional to their horizon area, while on the branch emerging
from the Tangherlini solutions their angular momentum is proportional to their
horizon angular momentum. The transition between these branches occurs at a
critical value of the charge, which depends on the value of the angular
momentum. However, when a dilaton is included, the angular momentum is always
proportional to the horizon area.


  High-gain and low-noise signal amplification is a valuable tool in various
cryogenic microwave experiments. A microwave optomechanical device, in which a
vibrating capacitor modulates the frequency of a microwave cavity, is one
technique that is able to amplify microwave signals with high gain and large
dynamical range. Such optomechanical amplifiers typically rely on strong
backaction of microwave photons on the mechanical mode achieved in the
sideband-resolved limit of optomechanics. Here, we observe microwave
amplification in an optomechanical cavity in the extremely unresolved sideband
limit. A large gain is observed for any detuning of the single pump tone within
the cavity linewidth, a clear indication that the amplification is not induced
by dynamical backaction. By being able to amplify for any detuning of the pump
signal, the amplification center frequency can be tuned over the entire range
of the broad cavity linewidth. Additionally, by providing microwave
amplification without mechanical amplification, we predict that using this
scheme it is possible to achieve near-quantum-limited microwave amplification
despite a large thermal occupation of the mechanical mode.


  Higher-derivative analogues of multidimensional conformal particle and
many-body conformal mechanics are constructed. Their Newton-Hooke counterparts
are derived by applying appropriate coordinate transformations.


  A C*-algebra $A$ is C*-reflexive if any countably generated Hilbert C*-module
$M$ over $A$ is C*-reflexive, i.e. the second dual module $M''$ coincides with
$M$. We show that a commutative C*-algebra $A$ is C*-reflexive if and only if
for any sequence $I_k$ of disjoint non-zero C*-subalgebras, the canonical
inclusion $\oplus_k I_k\subset A$ doesn't extend to an inclusion of $\prod_k
I_k$.


  The digital revolution has brought most of the world on the world wide web.
The data available on WWW has increased many folds in the past decade. Social
networks, online clubs and organisations have come into existence. Information
is extracted from these venues about a real world entity like a person,
organisation, event, etc. However, this information may change over time, and
there is a need for the sources to be up-to-date. Therefore, it is desirable to
have a model to extract relevant data items from different sources and merge
them to build a complete profile of an entity (entity profiling). Further, this
model should be able to handle incorrect or obsolete data items. In this paper,
we propose a novel method for completing a profile. We have developed a two
phase method-1) The first phase (resolution phase) links records to the
queries. We have proposed and observed that the use of random forest for entity
resolution increases the performance of the system as this has resulted in more
records getting linked to the correct entity. Also, we used trustworthiness of
a source as a feature to the random forest. 2) The second phase selects the
appropriate values from records to complete a profile based on our proposed
selection criteria. We have used various metrics for measuring the performance
of the resolution phase as well as for the overall ReLiC framework. It is
established through our results that the use of biased sources has
significantly improved the performance of the ReLiC framework. Experimental
results show that our proposed system, ReLiC outperforms the state-of-the-art.


  We show that for a gradable finite dimensional algebra the perfect complexes
and bounded derived category cannot be distinguished by homotopy invariants.


  Despite the discovery of the Higgs boson decay in five separate channels many
parameters of the Higgs boson remain largely unconstrained. In this paper, we
present a new approach to constraining the Higgs total width by requiring the
Higgs to be resolved as a single high p$_T$ jet and measuring the inclusive
Higgs boson cross section. To measure the inclusive Higgs boson cross section,
we rely on new approaches from machine learning and a modified jet
reconstruction. This approach is found to be complementary to the existing
off-shell width measurement and, with the full HL-LHC luminosity, is capable of
yielding similar sensitivity to the off-shell projections. We outline the
theoretical and experimental limitations and present a path towards making this
approach a truly model-independent measurement of the Higgs boson total width.


  This paper provides an analysis of the effects of attrition and non-response
on employment and wages using the Canadian Survey of Labour and Income
Dynamics. We consider a structural model composed of three freely correlated
equations for nonattrition/response, employment and wages. The model is
estimated using microdata from 22,990 individuals who provided sufficient
information in the first wave of the 1996-2001 panel. The main findings of the
paper are that attrition is not random. Attritors and non-respondents likely
are less attached to employment and come from low-income population. The
correlation between non-attrition and employment is positive and statistically
significant, though small. Also, wage estimates are biased upwards. Observed
wages are on average higher than wages that would be observed if all the
individuals initially selected in the panel remained in the sample.


  It was proved by Valenti and Zaicev, in 2011, that, if $G$ is an abelian
group and $K$ is an algebraically closed field of characteristic zero, then any
$G$-grading on the algebra of upper block triangular matrices over $K$ is
isomorphic to a tensor product $M_n(K)\otimes UT(n_1,n_2,\ldots,n_d)$, where
$UT(n_1,n_2,\ldots,n_d)$ is endowed with an elementary grading and $M_n(K)$ is
provided with a division grading.
  In this paper, we prove the validity of the same result for a non necessarily
commutative group and over an adequate field (characteristic either zero or
large enough), not necessarily algebraically closed.


  Aims: To investigate solar polar fields during cycle 24, using measurements
of solar magnetic fields in the latitude range 55 - 90 degree and 78 - 90
degree, to report a prolonged and unusual hemispheric asymmetry in the polar
field reversal pattern in solar cycle 24. Methods: This study was carried out
using medium resolution line-of-sight synoptic magnetograms from the magnetic
database of the National Solar Observatory at Kitt Peak (NSO/KP), USA for the
period between February 1975 and October 2017, covering solar cycles 21-24 and
high-resolution line-of-sight synoptic magnetograms from the Michaelson Doppler
Imager instrument onboard the Solar Heliospheric Observatory. Synoptic
magnetograms using radial measurements from the Heliospheric Magnetic Imager
instrument onboard the Solar Dynamics Observatory, covering solar cycle 23 and
24, were also used. Results: We show that the Southern solar hemisphere
unambiguously reversed polarity in mid-2013 while the reversal in the field in
the Northern solar hemisphere started as early as June 2012, was followed by a
sustained period of near-zero field strength lasting until the end of 2014,
after which the field began to show a clear rise from its near-zero value.
While this study compliments a similar study carried out using microwave
brightness measurements (Gopalswamy et al. 2016) which claimed that the field
reversal process in cycle 24 was completed by the end of 2015, our results show
that the field reversal in cycle 24 was completed earlier i.e. in late 2014.
Signatures of this unusual field reversal pattern were also clearly
identifiable in the solar wind, using our observations of interplanetary
scintillation at 327 MHz which supported our magnetic field observations and
confirmed that the field reversal process was completed at the end of 2014.


  In Polaris, we introduced a cloud-native distributed query processor to
perform analytics at scale. In this paper, we extend the underlying Polaris
distributed computation framework, which can be thought of as a read-only
transaction engine, to execute general transactions (including updates,
deletes, inserts and bulk loads, in addition to queries) for Tier 1 warehousing
workloads in a highly performant and predictable manner. We take advantage of
the immutability of data files in log-structured data stores and build on SQL
Server transaction management to deliver full transactional support with
Snapshot Isolation semantics, including multi-table and multi-statement
transactions. With the enhancements described in this paper, Polaris supports
both query processing and transactions for T-SQL in Microsoft Fabric.


  We report results obtained during the characterization of a commercial
front-illuminated progressive scan interline transfer CCD camera. We
demonstrate that the unmodified camera operates successfully in temperature and
pressure conditions (-40C, 4mBar) representative of a high altitude balloon
mission. We further demonstrate that the centroid of a well-sampled star can be
determined to better than 2% of a pixel, even though the CCD is equipped with a
microlens array. This device has been selected for use in a closed-loop
star-guiding and tip-tilt correction system in the BIT-STABLE balloon mission.


  Large language models (LLMs), such as GPT-4, PaLM, and LLaMa, have been shown
to achieve remarkable performance across a variety of natural language tasks.
Recent advancements in instruction tuning bring LLMs with ability in following
user's instructions and producing human-like responses. However, the high costs
associated with training and implementing LLMs pose challenges to academic
research. Furthermore, the availability of pretrained LLMs and instruction-tune
datasets for Vietnamese language is limited. To tackle these concerns, we
leverage large-scale instruction-following datasets from open-source projects,
namely Alpaca, GPT4All, and Chat-Doctor, which cover general domain and
specific medical domain. To the best of our knowledge, these are the first
instructional dataset for Vietnamese. Subsequently, we utilize
parameter-efficient tuning through Low-Rank Adaptation (LoRA) on two open LLMs:
Bloomz (Multilingual) and GPTJ-6B (Vietnamese), resulting four models:
Bloomz-Chat, Bloomz-Doctor, GPTJ-Chat, GPTJ-Doctor.Finally, we assess the
effectiveness of our methodology on a per-sample basis, taking into
consideration the helpfulness, relevance, accuracy, level of detail in their
responses. This evaluation process entails the utilization of GPT-4 as an
automated scoring mechanism. Despite utilizing a low-cost setup, our method
demonstrates about 20-30\% improvement over the original models in our
evaluation tasks.


  One way of optically monitoring molecule concentrations is to utilise the
high sensitivity of the transmission and reflection rates of Fabry-Perot
cavities to changes of their optical properties. Up to now, intrinsic and
extrinsic Fabry-Perot cavity sensors have been considered with analytes either
being placed inside the resonator or coupled to evanescent fields on the
outside. Here we show that Fabry-Perot cavities can also be used to monitor
molecule concentrations non-invasively and remotely, since the reflection of
light from the target molecules back into the Fabry-Perot cavity adds upwards
peaks to the minima of its overall reflection rate. Detecting the amplitude of
these peaks reveals information about molecule concentrations. By using an
array of optical cavities, a wide range of frequencies can be probed at once
and a unique optical fingerprint can be obtained.


  Rayleigh-Taylor interfacial mixing has critical importance in a broad range
of processes in nature and technology. In most instances Rayleigh-Taylor
dynamics is induced by variable acceleration, whereas the bulk of existing
studies is focused on the cases of constant and impulsive accelerations
referred respectively as classical Rayleigh-Taylor and classical
Richtmyer-Meshkov dynamics. In this work we consider Rayleigh-Taylor mixing
induced by variable acceleration with power-law dependence on the spatial
coordinate in the acceleration direction. We apply group theory and momentum
model to find deterministic asymptotic solutions for self-similar RT mixing. We
further augment momentum model with a stochastic process to study numerically
the effect of fluctuations on statistical properties of self-similar mixing in
a broad parameter regime. We reveal that self-similar mixing can be
Rayleigh-Taylor-type and Richtmyer-Meshkov type depending on the acceleration
exponent. We further find the value of critical exponent separating
Rayleigh-Taylor-type mixing and Richtmyer-Meshkov-type mixing, and identify
invariant quantities characterizing Rayleigh-Taylor-type mixing and
Richtmyer-Meshkov-type mixing.


  We systematically and analytically construct a set of spinor wave functions
representing defects and textures that continuously penetrate interfaces
between coexisting, topologically distinct magnetic phases in a spin-2
Bose-Einstein condensate. These include singular and nonsingular vortices
carrying mass or spin circulation that connect across interfaces between
biaxial- and uniaxial nematic, cyclic and ferromagnetic phases, as well as
vortices terminating as monopoles on the interface ("boojums"). The
biaxial-nematic and cyclic phases exhibit discrete polytope symmetries
featuring non-Abelian vortices and we investigate a pair of non-commuting line
defects within the context of a topological interface. By numerical
simulations, we characterize the emergence of non-trivial defect core
structures, including the formation of composite defects. Our results
demonstrate the potential of spin-2 Bose-Einstein condensates as experimentally
accessible platforms for exploring interface physics, offering a wealth of
combinations of continuous and discrete symmetries.


  The rotation of the polarization vector for light propagating perpendicular
to an external constant external magnetic field $B$, is calculated in quantum
vacuum, where it leads to different photon eigenmodes of the magnetized photon
self-energy tensor for polarizations along and orthogonal to $B$ (Cotton-Mouton
effect in QED vacuum). Its analogies and differences with Faraday effect are
discussed and both phenomena are calculated for a relativistic electron gas at
low densities, by starting from the low energy limit of the photon self-energy
eigenvalues in presence of $B$. In the Cotton-Mouton case the polarization
vector describes an ellipse whose axes vary periodically from zero to a maximum
value. By assuming an effective electron density of order $10^3$ cm$^{-3}$ the
quantum relativistic eigenvalues lead to a rotation of the polarization plane
compatible with some of the limit values reported by PVLAS experiments. Other
consequences, which are interesting for astrophysics, are also discussed.


  In the context of group field theory condensate cosmology, we clarify the
extraction of cosmological variables from the microscopic quantum gravity
degrees of freedom. We show that an important implication of the second
quantized formalism is the dependence of cosmological variables and equations
on the quantum gravitational atomic number N (number of spin network
vertices/elementary simplices). We clarify the relation of the effective
cosmological equations with loop quantum cosmology, understood as an effective
(hydrodynamic-like) approximation of a more fundamental quantum gravity theory.
By doing so, we provide a fundamental basis to the idea of lattice refinement,
showing the dependence of the effective cosmological connection on N, and hence
indirectly on the scale factor. Our results open a new arena for exploring
effective cosmological dynamics, as this depends crucially on the new
observable N, which is entirely of quantum gravitational origin.


  ICE-T (International Concordia Explorer Telescope) is a double 60 cm f/1.1
photometric robotic telescope, on a parallactic mount, which will operate at
Dome C, in the long Antarctic night, aiming to investigate exoplanets and
activity of the hosting stars. Antarctic Plateau site is well known to be one
of the best in the world for observations because of sky transparency in all
wavelengths and low scintillation noise. Due to the extremely harsh
environmental conditions (the lowest average temperature is -80$^\circ$C) the
criteria adopted for an optimal design are really challenging. Here we present
the strategies we have adopted so far to fulfill the mechanical and optical
requirements.


  In this paper, we study the reflected backward stochastic differential
equations driven by G-Brownian motion with two reflecting obstacles, which
means that the solution lies between two prescribed processes. A new kind of
approximate Skorohod condition is proposed to derive the uniqueness and
existence of the solutions. The uniqueness can be proved by a priori estimates
and the existence is obtained via a penalization method.


  Biclustering, also called co-clustering, block clustering, or two-way
clustering, involves the simultaneous clustering of both the rows and columns
of a data matrix into distinct groups, such that the rows and columns within a
group display similar patterns. As a model problem for biclustering, we
consider the $k$-densest-disjoint biclique problem, whose goal is to identify
$k$ disjoint complete bipartite subgraphs (called bicliques) of a given
weighted complete bipartite graph such that the sum of their densities is
maximized. To address this problem, we present a tailored branch-and-cut
algorithm. For the upper bound routine, we consider a semidefinite programming
relaxation and propose valid inequalities to strengthen the bound. We solve
this relaxation in a cutting-plane fashion using a first-order method. For the
lower bound, we design a maximum weight matching rounding procedure that
exploits the solution of the relaxation solved at each node. Computational
results on both synthetic and real-world instances show that the proposed
algorithm can solve instances approximately 20 times larger than those handled
by general-purpose solvers.


  We introduce generalizations of Aldous' Brownian Continuous Random Tree as
scaling limits for multicritical models of discrete trees. These discrete
models involve trees with fine-tuned vertex-dependent weights ensuring a k-th
root singularity in their generating function. The scaling limit involves
continuous trees with branching points of order up to k+1. We derive explicit
integral representations for the average profile of this k-th order
multicritical continuous random tree, as well as for its history distributions
measuring multi-point correlations. The latter distributions involve
non-positive universal weights at the branching points together with fractional
derivative couplings. We prove universality by rederiving the same results
within a purely continuous axiomatic approach based on the resolution of a set
of consistency relations for the multi-point correlations. The average profile
is shown to obey a fractional differential equation whose solution involves
hypergeometric functions and matches the integral formula of the discrete
approach.


  Cloud platforms run many software agents on each server node. These agents
manage all aspects of node operation, and in some cases frequently collect data
and make decisions. Unfortunately, their behavior is typically based on
pre-defined static heuristics or offline analysis; they do not leverage on-node
machine learning (ML). In this paper, we first characterize the spectrum of
node agents in Azure, and identify the classes of agents that are most likely
to benefit from on-node ML. We then propose SOL, an extensible framework for
designing ML-based agents that are safe and robust to the range of failure
conditions that occur in production. SOL provides a simple API to agent
developers and manages the scheduling and running of the agent-specific
functions they write. We illustrate the use of SOL by implementing three
ML-based agents that manage CPU cores, node power, and memory placement. Our
experiments show that (1) ML substantially improves our agents, and (2) SOL
ensures that agents operate safely under a variety of failure conditions. We
conclude that ML-based agents show significant potential and that SOL can help
build them.


  This work develops compressive sampling strategies for computing the dynamic
mode decomposition (DMD) from heavily subsampled or output-projected data. The
resulting DMD eigenvalues are equal to DMD eigenvalues from the full-state
data. It is then possible to reconstruct full-state DMD eigenvectors using
$\ell_1$-minimization or greedy algorithms. If full-state snapshots are
available, it may be computationally beneficial to compress the data, compute a
compressed DMD, and then reconstruct full-state modes by applying the projected
DMD transforms to full-state snapshots.
  These results rely on a number of theoretical advances. First, we establish
connections between the full-state and projected DMD. Next, we demonstrate the
invariance of the DMD algorithm to left and right unitary transformations. When
data and modes are sparse in some transform basis, we show a similar invariance
of DMD to measurement matrices that satisfy the so-called restricted isometry
principle from compressive sampling. We demonstrate the success of this
architecture on two model systems. In the first example, we construct a spatial
signal from a sparse vector of Fourier coefficients with a linear dynamical
system driving the coefficients. In the second example, we consider the double
gyre flow field, which is a model for chaotic mixing in the ocean.


  The poster presents an analytic formalism describing metric properties of
undirected random graphs with arbitrary degree distributions and statistically
uncorrelated (i.e. randomly connected) vertices. The formalism allows to
calculate the main network characteristics like: the position of the phase
transition at which a giant component first forms, the mean component size
below the phase transition, the size of the giant component and the average
path length above the phase transition. Although most of the enumerated
properties were previously calculated by means of generating functions, we
think that our derivations are conceptually much simpler.


  Due to their capacity to encode rich structural information, labeled graphs
are often used for modeling various kinds of objects such as images, molecules,
and chemical compounds. If pattern recognition problems such as clustering and
classification are to be solved on these domains, a (dis-)similarity measure
for labeled graphs has to be defined. A widely used measure is the graph edit
distance (GED), which, intuitively, is defined as the minimum amount of
distortion that has to be applied to a source graph in order to transform it
into a target graph. The main advantage of GED is its flexibility and
sensitivity to small differences between the input graphs. Its main drawback is
that it is hard to compute.
  In this thesis, new results and techniques for several aspects of computing
GED are presented. Firstly, theoretical aspects are discussed: competing
definitions of GED are harmonized, the problem of computing GED is
characterized in terms of complexity, and several reductions from GED to the
quadratic assignment problem (QAP) are presented. Secondly, solvers for the
linear sum assignment problem with error-correction (LSAPE) are discussed.
LSAPE is a generalization of the well-known linear sum assignment problem
(LSAP), and has to be solved as a subproblem by many GED algorithms. In
particular, a new solver is presented that efficiently reduces LSAPE to LSAP.
Thirdly, exact algorithms for computing GED are presented in a systematic way,
and improvements of existing algorithms as well as a new mixed integer
programming (MIP) based approach are introduced. Fourthly, a detailed overview
of heuristic algorithms that approximate GED via upper and lower bounds is
provided, and eight new heuristics are described. Finally, a new easily
extensible C++ library for exactly or approximately computing GED is presented.


  We carry out a $U$-spin symmetry analysis for CP violation in $B^- $ decays
into three light $\pi^-\pi^-\pi^+$, $\pi^- K^-K^+$, $K^-K^-K^+$ and $K^-
\pi^-\pi^+$ mesons. We clarify some subtle points in constructing decay
amplitudes with $U=0$ formed by the two negatively charged light mesons in the
final states. $U$-spin conserving momentum independent and momentum dependent
decay amplitudes, and $U$-spin violating decay amplitudes due to quark mass
difference are constructed.


  Given a dataset of individuals each described by a covariate vector, a
treatment, and an observed outcome on the treatment, the goal of the individual
treatment effect (ITE) estimation task is to predict outcome changes resulting
from a change in treatment. A fundamental challenge is that in the
observational data, a covariate's outcome is observed only under one treatment,
whereas we need to infer the difference in outcomes under two different
treatments. Several existing approaches address this issue through training
with inferred pseudo-outcomes, but their success relies on the quality of these
pseudo-outcomes. We propose PairNet, a novel ITE estimation training strategy
that minimizes losses over pairs of examples based on their factual observed
outcomes. Theoretical analysis for binary treatments reveals that PairNet is a
consistent estimator of ITE risk, and achieves smaller generalization error
than baseline models. Empirical comparison with thirteen existing methods
across eight benchmarks, covering both discrete and continuous treatments,
shows that PairNet achieves significantly lower ITE error compared to the
baselines. Also, it is model-agnostic and easy to implement.


  Based on first-principles calculations, we have found a family of 2D
transition-metal (TM) chalcogenides MX5 (M = Zr, Hf and X = S, Se and Te) can
host quantum spin Hall (QSH) effect. The molecular dynamics simulation indicate
that they are all thermal-dynamically stable at room temperature, the largest
band gap is 0.19 eV. We have investigated MX5's electronic properties and found
their properties are very similar. The single-layer ZrX5 are all gapless
semimetals without consideration of spin-orbit coupling (SOC). The
consideration of SOC will result in insulating phases with band gaps of 0.05 eV
(direct), 0.18 eV (direct) and 0.13 eV (indirect) for ZrS5, ZrSe5 to ZrTe5,
respectively. The evolution of Wannier charge centers and edge states confirm
they are all QSH insulators. The mechanisms for QSH effect in ZrX5 originate
from the special nonsymmorphic space group features. In addition, the QSH state
of ZrS5 survives at a large range of strain as long as the interchain coupling
is not strong enough to reverse the band ordering. The single-layer ZrS5 will
occur a topological insulator (TI)-to-semimetal (metal) or metal-to-semimetal
transition under certain strain. Monolayer MX5 expand the TI materials based on
TM chalcogenides and may open up a new way to fabricate novel low power
spintronic devices at room temperature.


  Narrow-line Seyfert 1 galaxies (NLS1s) are a subclass of active galactic
nuclei (AGN). It is often believed that these AGN have small black hole mass,
which is responsible for the narrowness of the permitted lines. They are also
characterised by a high accretion rate, typically closer to the Eddington
limit. Nevertheless, narrow permitted lines might also be caused by a disk-like
broad-line region (BLR) viewed pole-on. This class of objects presents strong
X-ray emission, which is characterised by a very steep spectral index described
by a single power law. In particular, some of them exhibit particular features
around the iron K-shell energy at 6-8 keV. Recently, this different spectral
behaviour was attributed to inclination. In this work we are going to analyse
optical spectra to measure in different ways the width of H$\beta$, which is
another potential inclination indicator. Our aim is to search for a correlation
between the high-energy spectral complexity and FWHM of H$\beta$, in order to
verify whether or not the broad-line region could be flattened.


  We discuss the modeling of the electrical manipulation of spin qubits in the
linear-response regime where the Rabi frequency is proportional to the magnetic
field and to the radio-frequency electric field excitation. We show that the
Rabi frequency can be obtained from a generalized g-tensor magnetic resonance
formula featuring a g-matrix and its derivative g' with respect to the electric
field (or gate voltage) as inputs. These matrices can be easily calculated from
the wave functions of the qubit at zero magnetic field. The g-matrix formalism
therefore provides the complete dependence of the Larmor and Rabi frequencies
on the orientation of the magnetic field at very low computational cost. It
also provides a compact model for the control of the qubit, and a simple
framework for the analysis of the effects of symmetries on the anisotropy of
the Larmor and Rabi frequencies. The g-matrix formalism applies to a wide
variety of electron and hole qubits, and we focus on a hole qubit in a
silicon-on-insulator nanowire as an illustration. We show that the Rabi
frequency of this qubit shows a complex dependence on the orientation of the
magnetic field, and on the gate voltages that control the symmetry of the hole
wave functions. We point out that the qubit may be advantageously switched
between two bias points, one where it can be manipulated efficiently, and one
where it is largely decoupled from the gate field but presumably longer lived.
We also discuss the role of residual strains in such devices in relation to
recent experiments.


  The broad iron K$\alpha$ emission line, commonly seen in the X-ray spectrum
of Seyfert nuclei, is thought to originate when the inner accretion disk is
illuminated by an active disk-corona. We show that relative motion between the
disk and the X-ray emitting material can have an important influence on the
observed equivalent width (EW) of this line via special relativistic aberration
and Doppler effects. We suggest this may be relevant to understanding why the
observed EW often exceeds the prediction of the standard X-ray reflection
model. Several observational tests are suggested that could disentangle these
special relativistic effects from iron abundance effects.


  Many decision-making scenarios, e.g., public policy, healthcare, business,
and disaster response, require accommodating the preferences of multiple
stakeholders. We offer the first formal treatment of reasoning with
multi-stakeholder qualitative preferences in a setting where stakeholders
express their preferences in a qualitative preference language, e.g., CP-net,
CI-net, TCP-net, CP-Theory. We introduce a query language for expressing
queries against such preferences over sets of outcomes that satisfy specified
criteria, e.g., $\mlangpref{\psi_1}{\psi_2}{A}$ (read loosely as the set of
outcomes satisfying $\psi_1$ that are preferred over outcomes satisfying
$\psi_2$ by a set of stakeholders $A$). Motivated by practical application
scenarios, we introduce and analyze several alternative semantics for such
queries, and examine their interrelationships. We provide a provably correct
algorithm for answering multi-stakeholder qualitative preference queries using
model checking in alternation-free $\mu$-calculus. We present experimental
results that demonstrate the feasibility of our approach.


  As digitalization and technological advancements continue to shape the
infrastructure landscape, the emergence of blockchain-based decentralized
physical infrastructure networks (DePINs) has gained prominence. However, a
systematic categorization of DePIN components and their interrelationships is
still missing. To address this gap, we conduct a literature review and analysis
of existing frameworks and derived a taxonomy of DePIN systems from a
conceptual architecture. Our taxonomy encompasses three key dimensions:
distributed ledger technology, cryptoeconomic design and physicial
infrastructure network. Within each dimension, we identify and define relevant
components and attributes, establishing a clear hierarchical structure.
Moreover, we illustrate the relationships and dependencies among the identified
components, highlighting the interplay between governance models, hardware
architectures, networking protocols, token mechanisms, and distributed ledger
technologies. This taxonomy provides a foundation for understanding and
classifying diverse DePIN networks, serving as a basis for future research and
facilitating knowledge exchange, fostering collaboration and standardization
within the emerging field of decentralized physical infrastructure networks.


  Based on a novel shower reconstruction algorithm, the study of two particle
separation with tile hadron calorimeter (HCAL) is performed. The separability
of two close-by particles is found to be strongly dependent on transverse and
longitudinal segmentation of tile HCAL.


  With the widespread use of deep neural networks (DNNs) in high-stake
applications, the security problem of the DNN models has received extensive
attention. In this paper, we investigate a specific security problem called
trojan attack, which aims to attack deployed DNN systems relying on the hidden
trigger patterns inserted by malicious hackers. We propose a training-free
attack approach which is different from previous work, in which trojaned
behaviors are injected by retraining model on a poisoned dataset. Specifically,
we do not change parameters in the original model but insert a tiny trojan
module (TrojanNet) into the target model. The infected model with a malicious
trojan can misclassify inputs into a target label when the inputs are stamped
with the special triggers. The proposed TrojanNet has several nice properties
including (1) it activates by tiny trigger patterns and keeps silent for other
signals, (2) it is model-agnostic and could be injected into most DNNs,
dramatically expanding its attack scenarios, and (3) the training-free
mechanism saves massive training efforts comparing to conventional trojan
attack methods. The experimental results show that TrojanNet can inject the
trojan into all labels simultaneously (all-label trojan attack) and achieves
100% attack success rate without affecting model accuracy on original tasks.
Experimental analysis further demonstrates that state-of-the-art trojan
detection algorithms fail to detect TrojanNet attack. The code is available at
https://github.com/trx14/TrojanNet.


  We address the problem of merging traffic from two roadways consisting of
both Connected Autonomous Vehicles (CAVs) and Human Driven Vehicles (HDVs).
Guaranteeing safe merging in such mixed traffic settings is challenging due to
the unpredictability of possibly uncooperative HDVs. We develop a hierarchical
controller where at each discrete time step first a coordinator determines the
best possible Safe Sequence (SS) which can be realized without any knowledge of
human driving behavior. Then, a lower-level decentralized motion controller for
each CAV jointly minimizes travel time and energy over a prediction horizon,
subject to hard safety constraints dependent on the given safe sequence. This
is accomplished using a Model Predictive Controller (MPC) subject to
constraints based on Control Barrier Functions (CBFs) which render it
computationally efficient. Extensive simulation results are included showing
that this hierarchical controller outperforms the commonly adopted Shortest
Distance First (SDF) passing sequence over the full range of CAV penetration
rates, while also providing safe merging guarantees.


  We calculate the magnetization of a two-dimensional superconductor in a
perpendicular magnetic field near its Kosterlitz-Thouless transition and at
lower temperatures. We find that the critical behavior is more complex than
assumed in the literature and that, in particular, the critical magnetization
is {\it not} field independent as naive scaling predicts. In the low
temperature phase we find a substantial fluctuation renormalization of the
mean-field result. We compare our analysis with the data on the cuprates.


  The study of Complex Systems is considered by many to be a new scientific
field, and is distinguished by being a discipline that has applications within
many separate areas of scientific study. The study of Neural Networks, Traffic
Patterns, Artificial Intelligence, Social Systems, and many other scientific
areas can all be considered to fall within the realm of Complex Systems, and
can be studied from this new perspective. The advent of more capable computer
systems has allowed these systems to be simulated and modeled with far greater
ease, and new understanding of computer modeling approaches has allowed the
fledgling science to be studied as never before.
  The preliminary focus of this paper will be to provide a general overview of
the science of Complex Systems, including terminology, definitions, history,
and examples. I will attempt to look at some of the most important trends in
different areas of research, and give a general overview of research methods
that have been used in parallel with computer modeling. Also, I will further
define the areas of the science that concern themselves with computer modeling
and simulation, and I will attempt to make it clear why the science only came
into its own when the proper modeling and simulation tools were finally
available. In addition, although there seems to be general agreement between
different authors and institutes regarding the generalities of the study, there
are some differences in terminology and methodology. I have attempted in this
paper to bring as many elements together as possible, as far as the scope of
the subject is concerned, without losing focus by studying Complex System
techniques that are bound to one particular area of scientific study, unless
that area is that of computer modeling.


  An important goal of environmental health research is to assess the risk
posed by mixtures of environmental exposures. Two popular classes of models for
mixtures analyses are response-surface methods and exposure-index methods.
Response-surface methods estimate high-dimensional surfaces and are thus highly
flexible but difficult to interpret. In contrast, exposure-index methods
decompose coefficients from a linear model into an overall mixture effect and
individual index weights; these models yield easily interpretable effect
estimates and efficient inferences when model assumptions hold, but, like most
parsimonious models, incur bias when these assumptions do not hold. In this
paper we propose a Bayesian multiple index model framework that combines the
strengths of each, allowing for non-linear and non-additive relationships
between exposure indices and a health outcome, while reducing the
dimensionality of the exposure vector and estimating index weights with
variable selection. This framework contains response-surface and exposure-index
models as special cases, thereby unifying the two analysis strategies. This
unification increases the range of models possible for analyzing environmental
mixtures and health, allowing one to select an appropriate analysis from a
spectrum of models varying in flexibility and interpretability. In an analysis
of the association between telomere length and 18 organic pollutants in the
National Health and Nutrition Examination Survey (NHANES), the proposed
approach fits the data as well as more complex response-surface methods and
yields more interpretable results.


  We develop a theory of complexity for numerical computations that takes into
account the condition of the input data and allows for roundoff in the
computations. We follow the lines of the theory developed by Blum, Shub, and
Smale for computations over R (which in turn followed those of the classical,
discrete, complexity theory as laid down by Cook, Karp, and Levin among
others). In particular, we focus on complexity classes of decision problems and
paramount among them, on appropriate versions of the classes P, NP and EXP of
polynomial, nondeterministic polynomial, and exponential time, respectively. We
prove some basic relationships between these complexity classes and exhibit
natural NP-complete problems.


  The kinetic inductance (KI) of superconducting devices can be exploited for
reducing the footprint of linear elements as well as for introducing
nonlinearity to the circuit. We characterize the linear and nonlinear
properties of a multimode resonator fabricated from amorphous tungsten silicide
(WSi) with a fundamental frequency of \(f_1 = 172\) MHz. We show how the
multimode structure of the device can be used to extract the different quality
factors and to aid the nonlinear characterization. In the linear regime the
footprint is reduced by a factor of \(\sim 2.9\) with standard lateral
dimensions with no significant degradation of the internal quality factor
compared to a similar Al device . In the nonlinear regime we observe self
positive frequency shifts at low powers which can be attributed to saturation
of tunneling two-level systems. The cross mode nonlinearities are described
well by a Kerr model with a self-Kerr coefficient in the order of
\(|K_{11}|/2\pi \approx 1.5\times10^{-7}\) Hz/photon. These properties together
with a reproducible fabrication process make WSi a promising candidate for
creating linear and nonlinear circuit QED elements.


  The problem of combinatorially determining the rank of the 3-dimensional
bar-joint {\em rigidity matroid} of a graph is an important open problem in
combinatorial rigidity theory. Maxwell's condition states that the edges of a
graph $G=(V, E)$ are {\em independent} in its $d$-dimensional generic rigidity
matroid only if $(a)$ the number of edges $|E|$ $\le$ $d|V| - {d+1\choose 2}$,
and $(b)$ this holds for every induced subgraph with at least $d$ vertices. We
call such graphs {\em Maxwell-independent} in $d$ dimensions. Laman's theorem
shows that the converse holds for $d=2$ and thus every maximal
Maxwell-independent set of $G$ has size equal to the rank of the 2-dimensional
generic rigidity matroid. While this is false for $d=3$, we show that every
maximal, Maxwell-independent set of a graph $G$ has size at least the rank of
the 3-dimensional generic rigidity matroid of $G$. This answers a question
posed by Tib\'or Jord\'an at the 2008 rigidity workshop at BIRS
\cite{bib:birs}.
  Along the way, we construct subgraphs (1) that yield alternative formulae for
a rank upper bound for Maxwell-independent graphs and (2) that contain a
maximal (true) independent set. We extend this bound to special classes of
non-Maxwell-independent graphs. One further consequence is a simpler proof of
correctness for existing algorithms that give rank bounds.


  Recent progress has shown that few-shot learning can be improved with access
to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We
introduce an SS-FSL approach, dubbed as Prototypical Random Walk
Networks(PRWN), built on top of Prototypical Networks (PN). We develop a random
walk semi-supervised loss that enables the network to learn representations
that are compact and well-separated. Our work is related to the very recent
development of graph-based approaches for few-shot learning. However, we show
that compact and well-separated class representations can be achieved by
modeling our prototypical random walk notion without needing additional
graph-NN parameters or requiring a transductive setting where a collective test
set is provided. Our model outperforms baselines in most benchmarks with
significant improvements in some cases. Our model, trained with 40$\%$ of the
data as labeled, compares competitively against fully supervised prototypical
networks, trained on 100$\%$ of the labels, even outperforming it in the 1-shot
mini-Imagenet case with 50.89$\%$ to 49.4$\%$ accuracy. We also show that our
loss is resistant to distractors, unlabeled data that does not belong to any of
the training classes, and hence reflecting robustness to labeled/unlabeled
class distribution mismatch. Associated GitHub page can be found at
https://prototypical-random-walk.github.io.


  Single electron transistors (SETs) made from single wall carbon nanotubes
(SWCNTs) are promising for quantum electronic devices operating with ultra-low
power consumption and allow fundamental studies of electron transport. We
report on SETs made by registered in-plane growth utilizing tailored nanoscale
catalyst patterns and chemical vapor deposition. Metallic SWCNTs have been
removed by an electrical burn-in technique and the common gate hysteresis was
removed using PMMA and baking, leading to field effect transistors with large
on/off ratios up to 10^5. Further segmentation into 200 nm short semiconducting
SWCNT devices created quantum dots which display conductance oscillations in
the Coulomb blockade regime. The demonstrated utilization of registered
in-plane growth opens possibilities to create novel SET device geometries which
are more complex, i.e. laterally ordered and scalable, as required for advanced
quantum electronic devices.


  We demonstrate a signal processing concept for imaging the sky at millisecond
rates with radio interferometers. The "Pocket Correlator" (PoCo) correlates the
signals from multiple elements of a radio interferometer fast enough to image
brief, dispersed pulses. By the nature of interferometry, a millisecond
correlator functions like a large, single-dish telescope, but with improved
survey speed, spatial localization, calibration, and interference rejection. To
test the concept, we installed PoCo at the Allen Telescope Array (ATA) to
search for dispersed pulses from the Crab pulsar, B0329+54, and M31 using
total-power, visibility-based, and image-plane techniques. In 1.7 hours of
observing, PoCo detected 191 giant pulses from the Crab pulsar brighter than a
typical 5 sigma sensitivity limit of 60 Jy over pulse widths of 3 milliseconds.
Roughly 40% of pulses from pulsar B0329+54 were detected by using novel
visibility-based techniques. Observations of M31 constrain the rate of pulses
brighter than 190 Jy in a three degree region surrounding the galaxy to
<4.3/hr. We calculate the computational demand of various visibility-based
pulse search algorithms and demonstrate how compute clusters can help meet this
demand. Larger implementations of the fast imaging concept will conduct blind
searches for millisecond pulses in our Galaxy and beyond, providing a valuable
probe of the interstellar/intergalactic media, discovering new kinds of radio
transients, and localizing them to constrain models of their origin.


  We analyze the performance of alternating minimization for loss functions
optimized over two variables, where each variable may be restricted to lie in
some potentially nonconvex constraint set. This type of setting arises
naturally in high-dimensional statistics and signal processing, where the
variables often reflect different structures or components within the signals
being considered. Our analysis relies on the notion of local concavity
coefficients, which has been proposed in Barber and Ha to measure and quantify
the concavity of a general nonconvex set. Our results further reveal important
distinctions between alternating and non-alternating methods. Since computing
the alternating minimization steps may not be tractable for some problems, we
also consider an inexact version of the algorithm and provide a set of
sufficient conditions to ensure fast convergence of the inexact algorithms. We
demonstrate our framework on several examples, including low rank + sparse
decomposition and multitask regression, and provide numerical experiments to
validate our theoretical results.


  In this paper, we introduce and analyze a normalization method for solving a
system of linear equations over tropical semirings. We use a normalization
method to construct an associated normalized matrix, which gives a technique
for solving the system. If solutions exist, the method can also determine the
degrees of freedom of the system. Moreover, we present a procedure to determine
the column rank and the row rank of a matrix. Flowcharts for this normalization
method and its applications are included as well.


  We apply symmetric function theory to study random processes formed by
singular values of products of truncations of Haar distributed symplectic and
orthogonal matrices. These product matrix processes are degenerations of
Macdonald processes introduced by Borodin and Corwin. Through this connection,
we obtain explicit formulae for the distribution of singular values of a
deterministic matrix multiplied by a truncated Haar orthogonal or symplectic
matrix under conditions where the latter factor acts as a rank $1$
perturbation. Consequently, we generalize the recent Kieburg-Kuijlaars-Stivigny
formula for the joint singular value density of a product of truncated unitary
matrices to symplectic and orthogonal symmetry classes. Specializing to
products of two symplectic matrices with a rank $1$ perturbative factor, we
show that the squared singular values form a Pfaffian point process.


  The lanthanide elements have well-documented similarities in their chemical
behavior, which makes the valuable trivalent lanthanide cations (Ln(III))
particularly difficult to separate from each other in water. In this work, we
apply ab initio molecular dynamics simulations to compare the free energies
(Delta G(ads)) associated with the adsorption of lanthanide cations to silica
surfaces at a pH condition where SiO- groups are present. The predicted Delta
G(ads) for lutetium (Lu(III)) and europium (Eu(III)) are similar within
statistical uncertainties; this is in qualitative agreement with our batch
adsorption measurements on silica. This finding is remarkable because the two
cations exhibit hydration free energies (Delta G(hyd}) that differ by >2 eV,
different hydration numbers, and different hydrolysis behavior far from silica
surfaces. We observe that the similarity in Lu(III) and Eu(III) Delta G(ads) is
the result of a delicate cancellation between the difference in Eu(III) and
Lu(III) hydration (Delta G(hyd})), and their difference in binding energies to
silica. We propose that disrupting this cancellation at the two end points,
either for adsorbed or completely desorbed lanthanides (e.g., via
nanoconfinment or mixed solvents), will lead to effective Ln separation.


  The boundary chiral ring of a 2d gauged linear sigma model on a K\"ahler
manifold $X$ classifies the topological D-brane sectors and the massless open
strings between them. While it is determined at small volume by simple group
theory, its continuation to generic volume provides highly non-trivial
information about the $D$-branes on $X$, related to the derived category
$D^\flat(X)$. We use this correspondence to elaborate on an extended notion of
McKay correspondence that captures more general than orbifold singularities. As
an illustration, we work out this new notion of McKay correspondence for a
class of non-compact Calabi-Yau singularities related to Grassmannians.


  Cloth manipulation is common in domestic and service tasks, and most studies
use fixed-base manipulators to manipulate objects whose sizes are relatively
small with respect to the manipulators' workspace, such as towels, shirts, and
rags. In contrast, manipulation of large-scale cloth, such as bed making and
tablecloth spreading, poses additional challenges of reachability and
manipulation control. To address them, this paper presents a novel framework to
spread large-scale cloth, with a single-arm mobile manipulator that can solve
the reachability issue, for an initial feasibility study. On the manipulation
control side, without modeling highly deformable cloth, a vision-based
manipulation control scheme is applied and based on an online-update Jacobian
matrix mapping from selected feature points to the end-effector motion. To
coordinate the control of the manipulator and mobile platform, Behavior Trees
(BTs) are used because of their modularity. Finally, experiments are conducted,
including validation of the model-free manipulation control for cloth spreading
in different conditions and the large-scale cloth spreading framework. The
experimental results demonstrate the large-scale cloth spreading task
feasibility with a single-arm mobile manipulator and the model-free deformation
controller.


  We compute the vacuum energy of three-dimensional asymptotically flat space
based on a Chern-Simons formulation for the Poincare group. The equivalent
action is nothing but the Einstein-Hilbert term in the bulk plus half of the
Gibbons-Hawking term at the boundary. The derivation is based on the evaluation
of the Noether charges in the vacuum. We obtain that the vacuum energy of this
space has the same value as the one of the asymptotically flat limit of
three-dimensional anti-de Sitter space.


  The Camassa-Holm equation with linear dispersion was originally derived as an
asymptotic equation in shallow water wave theory. Among its many interesting
mathematical properties, which include complete integrability, perhaps the most
striking is the fact that in the case where linear dispersion is absent it
admits weak multi-soliton solutions - "peakons" - with a peaked shape
corresponding to a discontinuous first derivative. There is a one-parameter
family of generalized Camassa-Holm equations, most of which are not integrable,
but which all admit peakon solutions. Numerical studies reported by Holm and
Staley indicate changes in the stability of these and other solutions as the
parameter varies through the family.
  In this article, we describe analytical results on one of these bifurcation
phenomena, showing that in a suitable parameter range there are stationary
solutions - "leftons" - which are orbitally stable.


  The purpose of the paper is the development of the formalism for the
treatment of rare events especially, when one applies active correlation method
to suppress background products in the heavy ion induced complete fusion
nuclear reactions. This formalism in fact is an extension of classical
background signal combinations formalism for the case of time multi intervals.


  Attack trees are considered a useful tool for security modelling because they
support qualitative as well as quantitative analysis. The quantitative approach
is based on values associated to each node in the tree, expressing, for
instance, the minimal cost or probability of an attack. Current quantitative
methods for attack trees allow the analyst to, based on an initial assignment
of values to the leaf nodes, derive the values of the higher nodes in the tree.
In practice, however, it shows to be very difficult to obtain reliable values
for all leaf nodes. The main reasons are that data is only available for some
of the nodes, that data is available for intermediate nodes rather than for the
leaf nodes, or even that the available data is inconsistent. We address these
problems by developing a generalisation of the standard bottom-up calculation
method in three ways. First, we allow initial attributions of non-leaf nodes.
Second, we admit additional relations between attack steps beyond those
provided by the underlying attack tree semantics. Third, we support the
calculation of an approximative solution in case of inconsistencies. We
illustrate our method, which is based on constraint programming, by a
comprehensive case study.


  Since Br adsorption on Au(100) displays an incommensurate ordered phase, a
lattice-gas treatment of the adlayer configurations is not reliable. We
therefore use density functional theory slab calculations to determine the
parameters necessary for the construction of an off-lattice model. We compute
and analyze the total energy and electron density as the lateral Br position
and coverage are varied. This allows the calculation of the corrugation
potential, the short-range lateral interactions, the dipole moment (long-range
interactions), and the residence charge. From these parameters, we construct an
off-lattice model with no freely adjustable parameters. The simulation results
compare remarkably well with experimental results.


  Consider a hiring process with candidates coming from different universities.
It is easy to order candidates with the same background, yet it can be
challenging to compare them otherwise. The latter case requires additional
costly assessments, leading to a potentially high total cost for the hiring
organization. Given an assigned budget, what would be an optimal strategy to
select the most qualified candidate? We model the above problem as a multicolor
secretary problem, allowing comparisons between candidates from distinct groups
at a fixed cost. Our study explores how the allocated budget enhances the
success probability of online selection algorithms.


  Modeling interactions between features improves the performance of machine
learning solutions in many domains (e.g. recommender systems or sentiment
analysis). In this paper, we introduce Exponential Machines (ExM), a predictor
that models all interactions of every order. The key idea is to represent an
exponentially large tensor of parameters in a factorized format called Tensor
Train (TT). The Tensor Train format regularizes the model and lets you control
the number of underlying parameters. To train the model, we develop a
stochastic Riemannian optimization procedure, which allows us to fit tensors
with 2^160 entries. We show that the model achieves state-of-the-art
performance on synthetic data with high-order interactions and that it works on
par with high-order factorization machines on a recommender system dataset
MovieLens 100K.


  Image analysis tasks in computational pathology are commonly solved using
convolutional neural networks (CNNs). The selection of a suitable CNN
architecture and hyperparameters is usually done through exploratory iterative
optimization, which is computationally expensive and requires substantial
manual work. The goal of this article is to evaluate how generic tools for
neural network architecture search and hyperparameter optimization perform for
common use cases in computational pathology. For this purpose, we evaluated one
on-premises and one cloud-based tool for three different classification tasks
for histological images: tissue classification, mutation prediction, and
grading.
  We found that the default CNN architectures and parameterizations of the
evaluated AutoML tools already yielded classification performance on par with
the original publications. Hyperparameter optimization for these tasks did not
substantially improve performance, despite the additional computational effort.
However, performance varied substantially between classifiers obtained from
individual AutoML runs due to non-deterministic effects.
  Generic CNN architectures and AutoML tools could thus be a viable alternative
to manually optimizing CNN architectures and parametrizations. This would allow
developers of software solutions for computational pathology to focus efforts
on harder-to-automate tasks such as data curation.


  Streamers often constitute the first stage of dielectric breakdown in strong
electric fields: a nonlinear ionization wave transforms a non-ionized medium
into a weakly ionized nonequilibrium plasma. New understanding of this old
phenomenon can be gained through modern concepts of (interfacial) pattern
formation. As a first step towards an effective interface description, we
determine the front width, solve the selection problem for planar fronts and
calculate their properties. Our results are in good agreement with many
features of recent three-dimensional numerical simulations.
  In the present long paper, you find the physics of the model and the
interfacial approach further explained. As a first ingredient of this approach,
we here analyze planar fronts, their profile and velocity. We encounter a
selection problem, recall some knowledge about such problems and apply it to
planar streamer fronts. We make analytical predictions on the selected front
profile and velocity and confirm them numerically.
  (abbreviated abstract)


  The Collider Detector at Fermilab (CDF) pursues a broad physics program at
Fermilab's Tevatron collider. Between Run II commissioning in early 2001 and
the end of operations in September 2011, the Tevatron delivered 12 fb-1 of
integrated luminosity of p-pbar collisions at sqrt(s)=1.96 TeV. Many physics
analyses undertaken by CDF require heavy flavor tagging with large charged
particle tracking acceptance. To realize these goals, in 2001 CDF installed
eight layers of silicon microstrip detectors around its interaction region.
These detectors were designed for 2--5 years of operation, radiation doses up
to 2 Mrad (0.02 Gy), and were expected to be replaced in 2004. The sensors were
not replaced, and the Tevatron run was extended for several years beyond its
design, exposing the sensors and electronics to much higher radiation doses
than anticipated. In this paper we describe the operational challenges
encountered over the past 10 years of running the CDF silicon detectors, the
preventive measures undertaken, and the improvements made along the way to
ensure their optimal performance for collecting high quality physics data. In
addition, we describe the quantities and methods used to monitor radiation
damage in the sensors for optimal performance and summarize the detector
performance quantities important to CDF's physics program, including vertex
resolution, heavy flavor tagging, and silicon vertex trigger performance.


  Planetary ephemerides are a good tool for studying general relativity at the
scale of our solar system. We present here new evaluations of advances of
perihelia for Mercury and Saturn.


  The phase transition of the even offspringed branching and annihilating
random walk is studied by N-cluster mean-field approximations on
one-dimensional lattices. By allowing to reach zero branching rate a phase
transition can be seen for any N <= 12.The coherent anomaly extrapolations
applied for the series of approximations results in $\nu_{\perp}=1.85(3)$ and
$\beta=0.96(2)$.


  We count unlabeled k-trees by properly coloring them in k+1 colors and then
counting orbits of these colorings under the action of the symmetric group on
the colors.


  This paper, a technical summary of our preceding publication, introduces a
robust machine learning framework for the detection of vocal activities of
Coppery titi monkeys. Utilizing a combination of MFCC features and a
bidirectional LSTM-based classifier, we effectively address the challenges
posed by the small amount of expert-annotated vocal data available. Our
approach significantly reduces false positives and improves the accuracy of
call detection in bioacoustic research. Initial results demonstrate an accuracy
of 95\% on instance predictions, highlighting the effectiveness of our model in
identifying and classifying complex vocal patterns in environmental audio
recordings. Moreover, we show how call classification can be done downstream,
paving the way for real-world monitoring.


  The dependence of the energy of interwall interaction in double-walled carbon
nanotubes (DWNT) on the relative position of walls has been calculated using
the density functional method. This dependence is used to evaluate forces that
are necessary for the relative telescopic motion of walls and to calculate the
shear strength of DWNT for the relative sliding of walls along the nanotube
axis and for their relative rotation about this axis. The possibility of
experimental verification of the obtained results is discussed.


  We show that the l-adic realizations of certain Picard 1-motives associated
to a G-Galois cover of smooth, projective curves defined over an algebraically
closed field are G-cohomologically trivial, for all primes l. In the process,
we generalize a well-known theorem of Nakajima on the Galois module structure
of certain spaces of Kahler differentials associated to the top curve of the
cover, assuming that the field of definition is of characteristic p. If the
cover and the Picard 1-motive are defined over a finite field and if G is
abelian, we compute the first Fitting ideal of each l-adic realization over a
certain profinite l-adic group algebra in terms of an equivariant Artin
L-function. This is a refinement of earlier work of Deligne and Tate on Picard
1-motives associated to global function fields. As a consequence, we prove
refined versions of the Brumer-Stark and the (\'etale) Coates-Sinnott
Conjectures for global function fields. In upcoming work, we use these results
to prove several other conjectures on special values of global L-functions in
characteristic p. Also, we prove analogous results in characteristic 0, where
the l-adic realizations of Picard 1-motives will be replaced by a new class of
Iwasawa modules.


  Microlensing searches toward the inner galaxy $(|l|,|b|\leq 22.\hskip-2pt'5)$
have several major advantages. First, the event rate is strongly dominated by
bulge-bulge lensing events where both the source and lens lie in the bulge.
Second, these bulge-bulge events have very short time scales $t_e\sim 2\,$days
and are therefore easily distinguished from the less frequent and much longer
bulge-disk and disk-disk events. Third, since the optical depth is similar to
that at higher impact parameters, while the events are shorter, the event rate
is high $\Gamma\sim 3\times 10^{-7}\rm day^{-1}$. Fourth, because the Einstein
rings are small, $r_e\sim 5\times 10^{12}\,$cm, and the source stars are large
$r_s\gsim 10^{12}\,$cm, the lens will transit the face of the source for a
significant fraction $(\sim 20\%)$ of events. For these transit events it will
often be possible to measure a second lens parameter, the angular Einstein
radius (or proper motion). In addition to the bulge-bulge events, the optical
depth of the disk is $\sim 7$ times larger toward the inner Galaxy than toward
Baade's Window. A microlensing search toward the inner Galaxy can be carried
out by making frequent $(\sim4\,\rm day^{-1})$ K band images of a large area
$\sim 0.5\,\rm deg^2$ to a depth of $K\sim 16$, and hence requires either a
$1024^2$ infrared array on a dedicated 2m telescope or four such arrays on a 1m
telescope.