  The problem of entanglement detection for arbitrary spin systems is analyzed.
We demonstrate how a single measurement of the squared total spin can
probabilistically discern separable from entangled many-particle states. For
achieving this goal, we construct a tripartite analogy between the degeneracy
of entanglement witness eigenstates, tensor products of $SO(3)$ representations
and classical lattice walks with special constraints. Within this framework,
degeneracies are naturally given by generalized Catalan numbers and determine
the fraction of states that are decidedly entangled and also known to be
somewhat protected against decoherence. In addition, we introduce the concept
of a "sterile entanglement witness", which for large enough systems detects
entanglement without affecting much the system's state. We discuss when our
proposed entanglement witness can be regarded as a sterile one.


  R. P. Boas showed that any single-index sequence $\left\{ \beta_i
\right\}_{i=0}^\infty$ of real numbers can be represented as $\beta_i
=\int_0^\infty x^i \, d\mu$ ($i=0,1,2,\ldots$), where $\mu$ is a signed
measure. As Boas said his observation seemed to be quite unexpected; however,
it is even possible to extend the result to any multi-index sequence of real
numbers. In addition, we can also prove that any multi-index finite sequence
admits a measure of a similar type.


  A Cherenkov imaging camera for the CANGAROO-III experiment has been developed
for observations of gamma-ray induced air-showers at energies from 10$^{11}$ to
10$^{14}$ eV. The camera consists of 427 pixels, arranged in a hexagonal shape
at 0.17$^\circ$ intervals, each of which is a 3/4-inch diameter photomultiplier
module with a Winston-cone--shaped light guide. The camera was designed to have
a large dynamic range of signal linearity, a wider field of view, and an
improvement in photon collection efficiency compared with the CANGAROO-II
camera. The camera, and a number of the calibration experiments made to test
its performance, are described in detail in this paper.


  We investigate hyperderivatives of Drinfeld modular forms and determine
formulas for these derivatives in terms of Goss polynomials for the kernel of
the Carlitz exponential. As a consequence we prove that v-adic modular forms in
the sense of Serre, as defined by Goss and Vincent, are preserved under
hyperdifferentiation. Moreover, upon multiplication by a Carlitz factorial,
hyperdifferentiation preserves v-integrality.


  We describe a graph reduction operation, generalizing three graph reduction
operations related to gene assembly in ciliates. The graph formalization of
gene assembly considers three reduction rules, called the positive rule, double
rule, and negative rule, each of which removes one or two vertices from a
graph. The graph reductions we define consist precisely of all compositions of
these rules. We study graph reductions in terms of the adjacency matrix of a
graph over the finite field with two elements, and show that they are path
invariant, in the sense that the result of a sequence of graph reductions
depends only on the vertices removed. The binary rank of a graph is the rank of
its adjacency matrix over the finite field with two elements. We show that the
binary rank of a graph determines how many times the negative rule is applied
in any sequence of positive, double, and negative rules reducing the graph to
the empty graph, resolving two open problems posed by Harju, Li, and Petre. We
also demonstrate the close relation between graph reductions and the matrix
pivot operation, both of which can be studied in terms of the poset of subsets
of vertices of a graph that can be removed by a graph reduction.


  The rising volume of datasets has made training machine learning (ML) models
a major computational cost in the enterprise. Given the iterative nature of
model and parameter tuning, many analysts use a small sample of their entire
data during their initial stage of analysis to make quick decisions (e.g., what
features or hyperparameters to use) and use the entire dataset only in later
stages (i.e., when they have converged to a specific model). This sampling,
however, is performed in an ad-hoc fashion. Most practitioners cannot precisely
capture the effect of sampling on the quality of their model, and eventually on
their decision-making process during the tuning phase. Moreover, without
systematic support for sampling operators, many optimizations and reuse
opportunities are lost.
  In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML
training. BlinkML allows users to make error-computation tradeoffs: instead of
training a model on their full data (i.e., full model), BlinkML can quickly
train an approximate model with quality guarantees using a sample. The quality
guarantees ensure that, with high probability, the approximate model makes the
same predictions as the full model. BlinkML currently supports any ML model
that relies on maximum likelihood estimation (MLE), which includes Generalized
Linear Models (e.g., linear regression, logistic regression, max entropy
classifier, Poisson regression) as well as PPCA (Probabilistic Principal
Component Analysis). Our experiments show that BlinkML can speed up the
training of large-scale ML tasks by 6.26x-629x while guaranteeing the same
predictions, with 95% probability, as the full model.


  An algebraic expression for the Preisach hysteron, which is a non-ideal
(delayed) relay operator, is formulated for a computationally efficient
real-time implementation. This allows representing the classical scalar
Preisach hysteresis model as a summation of a large number of weighted
hysterons which computation can be accomplished in parallel. The latter makes
possible an efficient FPGA or ASIC realization of the scalar Preisach
hysteresis model that can be useful for multiple applications. The signal flow
which specifies the model implementation is provided in form of the block
diagram. The proposed computation of Preisach hysterons, aggregated to the
entire Preisach hysteresis model, is evaluated numerically and on a real-time
hardware platform.


  Fog computing has emerged as a promising technology that can bring the cloud
applications closer to the physical IoT devices at the network edge. While it
is widely known what cloud computing is, and how data centers can build the
cloud infrastructure and how applications can make use of this infrastructure,
there is no common picture on what fog computing and a fog node, as its main
building block, really is. One of the first attempts to define a fog node was
made by Cisco, qualifying a fog computing system as a mini-cloud, located at
the edge of the network and implemented through a variety of edge devices,
interconnected by a variety, mostly wireless, communication technologies. Thus,
a fog node would be the infrastructure implementing the said mini-cloud. Other
proposals have their own definition of what a fog node is, usually in relation
to a specific edge device, a specific use case or an application. In this
paper, we first survey the state of the art in technologies for fog computing
nodes as building blocks of fog computing, paying special attention to the
contributions that analyze the role edge devices play in the fog node
definition. We summarize and compare the concepts, lessons learned from their
implementation, and show how a conceptual framework is emerging towards a
unifying fog node definition. We focus on core functionalities of a fog node as
well as in the accompanying opportunities and challenges towards their
practical realization in the near future.


  The possibility to use functionals of occupation numbers and natural orbitals
for interacting fermions is discussed as an alternative to multi-reference
energy density functional method. An illustration based on the two-level Lipkin
model is discussed.


  Many of the recent advances in speech separation are primarily aimed at
synthetic mixtures of short audio utterances with high degrees of overlap. Most
of these approaches need an additional stitching step to stitch the separated
speech chunks for long form audio. Since most of the approaches involve
Permutation Invariant training (PIT), the order of separated speech chunks is
nondeterministic and leads to difficulty in accurately stitching homogenous
speaker chunks for downstream tasks like Automatic Speech Recognition (ASR).
Also, most of these models are trained with synthetic mixtures and do not
generalize to real conversational data. In this paper, we propose a speaker
conditioned separator trained on speaker embeddings extracted directly from the
mixed signal using an over-clustering based approach. This model naturally
regulates the order of the separated chunks without the need for an additional
stitching step. We also introduce a data sampling strategy with real and
synthetic mixtures which generalizes well to real conversation speech. With
this model and data sampling technique, we show significant improvements in
speaker-attributed word error rate (SA-WER) on Hub5 data.


  Computational power in measurement-based quantum computing stems from the
symmetry-protected topological (SPT) order of entangled resource states.
However, resource states are prone to preparation errors. We introduce a
quantum error correction approach using redundant nonlocal symmetry of the
resource state. We demonstrate it within a teleportation protocol based on
extending the $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry of one-dimensional
cluster states to other graph states. Qubit ZZ-crosstalk errors, which are
prominent in quantum devices, degrade the teleportation fidelity of the usual
cluster state. However, as we demonstrate on quantum hardware, once we grow
graph states with redundant symmetry, perfect teleportation fidelity is
restored. We identify the underlying redundant-SPT order as error-protected
degeneracies in the entanglement spectrum.


  Unimodular gravity is an alternative theory of gravity to general relativity.
The gravitational field equations are given by the trace-free version of
Einstein's field equations. Due to the structure of the theory, unimodular
gravity admits a diffusion term that characterizes a possible non-conservation
of the canonical energy-momentum tensor locally. Employing this feature of
unimodular gravity, in the present work, we explicitly show how to construct an
inflationary phase that can be contrasted with current observations. In
particular, we focus on three different inflationary scenarios of physical
interest. An important element in these scenarios is that the accelerated
expansion is driven by the diffusion term exclusively, i.e. there is no
inflaton. Furthermore, the primordial spectrum during inflation is generated by
considering inhomogeneous perturbations associated to standard hydrodynamical
matter (modeled as a single ultra-relativistic fluid). For each of the
scenarios, we obtain the prediction for the primordial spectrum and contrast it
with recent observational bounds.


  This paper improves and demonstrates the usefulness of the first quantized
plane-wave algorithms for the quantum simulation of electronic structure,
developed by Babbush et al. and Su et al. We describe the first quantum
algorithm for first quantized simulation that accurately includes
pseudopotentials. We focus on the Goedecker-Tetter-Hutter (GTH)
pseudopotential, which is among the most accurate and widely used
norm-conserving pseudopotentials enabling the removal of core electrons from
the simulation. The resultant screened nuclear potential regularizes cusps in
the electronic wavefunction so that orders of magnitude fewer plane waves are
required for a chemically accurate basis. Despite the complicated form of the
GTH pseudopotential, we are able to block encode the associated operator
without significantly increasing the overall cost of quantum simulation. This
is surprising since simulating the nuclear potential is much simpler without
pseudopotentials, yet is still the bottleneck. We also generalize prior methods
to enable the simulation of materials with non-cubic unit cells, which requires
nontrivial modifications. Finally, we combine these techniques to estimate the
block-encoding costs for commercially relevant instances of heterogeneous
catalysis (e.g. carbon monoxide adsorption on transition metals) and compare to
the quantum resources needed to simulate materials in second quantization. We
conclude that for computational cells with many particles, first quantization
often requires meaningfully less spacetime volume.


  Educating the users on the essential of information security is very vital
and important to the mission of establishing a sustainable information security
in any organization and institute. At the University Technology Malaysia (UTM),
we have recognized the fact that, it is about time information security should
no longer be a lacking factor in productivity, both information security and
productivity must work together in closed proximity. We have recently
implemented a broad campus information security awareness program to educate
faculty member, staff, students and non-academic staff on this essential topic
of information security. The program consists of training based on web,
personal or individual training with a specific monthly topic, campus
campaigns, guest speakers and direct presentations to specialized groups. The
goal and the objective are to educate the users on the challenges that are
specific to information security and to create total awareness that will change
the perceptions of people thinking and ultimately their reactions when it comes
to information security. In this paper, we explain how we created and
implemented our information security awareness training (ISAT) program and
discuss the impediment we encountered along the process. We explore different
methods of deliveries such as target audiences, and probably the contents as we
believe might be vital to a successful information security program. Finally,
we discuss the importance and the flexibility of establishing a sustainable
information security training program that could be adopted to meet current and
future needs and demands while still relevant to our current users.


  This paper concerns the structure of the group of local unitary cocycles,
also called the gauge group, of an E_0-semigroup. The gauge group of a spatial
E_0-semigroup has a natural action on the set of units by operator
multiplication. Arveson has characterized completely the gauge group of
E_0-semigroups of type I, and as a consequence it is known that in this case
the gauge group action is transitive. In fact, if the semigroup has index k,
then the gauge group action is transitive on the set of k+1-tuples of
appropriately normalized independent units. An action of the gauge group having
this property is called k+1-fold transitive.
  We construct examples of E_0-semigroups of type II and index 1 which are not
2-fold transitive. These new examples also illustrate that an E_0-semigroup of
type II_k need not be a tensor product of an E_0-semigroup of type II_0 and
another of type I_k.


  We propose a new paradigm for universal information extraction (IE) that is
compatible with any schema format and applicable to a list of IE tasks, such as
named entity recognition, relation extraction, event extraction and sentiment
analysis. Our approach converts the text-based IE tasks as the token-pair
problem, which uniformly disassembles all extraction targets into joint span
detection, classification and association problems with a unified extractive
framework, namely UniEX. UniEX can synchronously encode schema-based prompt and
textual information, and collaboratively learn the generalized knowledge from
pre-defined information using the auto-encoder language models. We develop a
traffine attention mechanism to integrate heterogeneous factors including
tasks, labels and inside tokens, and obtain the extraction target via a scoring
matrix. Experiment results show that UniEX can outperform generative universal
IE models in terms of performance and inference-speed on $14$ benchmarks IE
datasets with the supervised setting. The state-of-the-art performance in
low-resource scenarios also verifies the transferability and effectiveness of
UniEX.


  We analyse the effect on adiabatic stellar oscillation frequencies of
replacing the near-surface layers in 1D stellar structure models with averaged
3D stellar surface convection simulations. The main difference is an expansion
of the atmosphere by 3D convection, expected to explain a major part of the
asteroseismic surface effect; a systematic overestimation of p-mode frequencies
due to inadequate surface physics.
  We employ pairs of 1D stellar envelope models and 3D simulations from a
previous calibration of the mixing-length parameter, alpha. That calibration
constitutes the hitherto most consistent matching of 1D models to 3D
simulations, ensuring that their differences are not spurious, but entirely due
to the 3D nature of convection. The resulting frequency shift is identified as
the structural part of the surface effect. The important, typically
non-adiabatic, modal components of the surface effect are not included in the
present analysis, but relegated to future papers.
  Evaluating the structural surface effect at the frequency of maximum mode
amplitude, $\nu_{\rm max}$, we find shifts from $\delta\nu$=-0.8 microHz for
giants at $\log g$=2.2 to -35 microHz for a ($T_{\rm eff}=6901$ K, $\log
g$=4.29) dwarf. The fractional effect $\delta\nu(\nu_{\rm max})/\nu_{\rm max}$,
ranges from -0.1% for a cool dwarf (4185 K, 4.74) to -6% for a warm giant (4962
K, 2.20).


  There are many classical problems in P whose time complexities have not been
improved over the past decades. Recent studies of "Hardness in P" have revealed
that, for several of such problems, the current fastest algorithm is the best
possible under some complexity assumptions. To bypass this difficulty, Fomin et
al. (SODA 2017) introduced the concept of fully polynomial FPT algorithms. For
a problem with the current best time complexity $O(n^c)$, the goal is to design
an algorithm running in $k^{O(1)}n^{c'}$ time for a parameter $k$ and a
constant $c'<c$. In this paper, we investigate the complexity of graph problems
in P parameterized by tree-depth, a graph parameter related to tree-width. We
show that a simple divide-and-conquer method can solve many graph problems,
including Weighted Matching, Negative Cycle Detection, Minimum Weight Cycle,
Replacement Paths, and 2-hop Cover, in $O(\mathrm{td}\cdot m)$ time or
$O(\mathrm{td}\cdot (m+n\log n))$ time, where $\mathrm{td}$ is the tree-depth
of the input graph. Because any graph of tree-width $\mathrm{tw}$ has
tree-depth at most $(\mathrm{tw}+1)\log_2 n$, our algorithms also run in
$O(\mathrm{tw}\cdot m\log n)$ time or $O(\mathrm{tw}\cdot (m+n\log n)\log n)$
time. These results match or improve the previous best algorithms parameterized
by tree-width. Especially, we solve an open problem of fully polynomial FPT
algorithm for Weighted Matching parameterized by tree-width posed by Fomin et
al.


  Diffusion-weighted imaging (DWI) in the female breast is a magnetic resonance
imaging (MRI) technique, which may potentially replace or effectively
complement both x-ray mammography and contrast-enhanced MR mammography. To
further improve specificity of DWI in the breast, stronger and faster diffusion
weighting is advantageous. With the target application in mind, the gradient
coil is designed on an irregularly shaped semi-open current-carrying surface.
Due to the coil former closely fitting the non-spherical target region,
non-linear encoding fields become particularly advantageous for achieving
locally exceptionally high gradient strengths. As the breast tissue has a
predominantly isotropic cellular microstructure, the direction of the
diffusion-weighting gradient may be allowed to vary within the target volume.
However, due to the quadratic dependency of the b-factor on the gradient
strength, variation of the gradient magnitude needs to be carefully controlled.
  To achieve the above design goals the corresponding multi-objective
optimization problem is reformulated as a constrained optimization, allowing
for flexible and precise control of the coil properties. A novel constraint is
proposed limiting the gradient magnitude variation within every slice while
allowing for variations both the direction of the gradient within the slice and
the magnitude across the slices. The above innovations enable the design of a
unilateral coil for diffusion weighting in the female breast with the local
gradient strengths exceeding 1~T/m with highly homogeneous diffusion weighting
for imaging in the coronal slice orientation.


  We consider the propagation of a flame front in a solid medium with a
periodic structure. The model is governed by a free boundary system for the
pair" temperature-front. "The front's normal velocity depends on the
temperature via a (degenerate) Arrhenius kinetic. It also depends on the
front's mean curvature. We show the existence of travelling wave solutions for
the full system and consider their homogenization as the period tends to zero.
We analyze the curvature effects on the homogenization and obtain a continuum
of limiting waves parametrized by the limiting ratio "curvature
coefficient/period." This analysis provides valuable information on the
heterogeneous propagation as well.


  The two major goals in fundamental physics are: 1) Unification of all forces
incorporating relativity and quantum theory, 2) Understanding the origin and
evolution of the Universe as well as explaining the smallness of the
cosmological constant.
  Several efforts have been made in the last few decades towards achieving
these goals with some successes and failures. The current best theory we have
for unification of all forces is Superstring/M Theory. However current evidence
suggests our Universe is flat and accelerating. A Universe with a positive
cosmological constant will have serious implications for string theory since
the S-Matrix cannot be well defined and Superstring/M Theory is only formulated
in flat Minkowski background. Holographic principle provides a way out as shown
by the AdS/CFT and dS/CFT correspondence, but it remains to be proved if it is
valid for our non-conformal, non-supersymmetric Universe. Aside from the issue
of defining M-Theory in a de Sitter background, why the cosmological constant
is so small remains puzzling and needs to be understood. The ``cosmological
constant problem'' has brought physics to a standstill towards any major
development and remains currently the most disturbing issue.
  Conventional big bang cosmology has not yet produced a satisfactory
explanation of the small value of the cosmological constant. An attempt by
SuperString/M Theory in this direction is given by the Ekpyrotic/Cyclic model.
  The aim of this review is not to introduce any new concepts not already
known, but give an overview of current state of affairs in high energy physics,
highlighting some successes and failures and making some few suggestions on
areas to focus to resolve some of these outstanding issues.


  With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.


  We present the results of a spectroscopic survey of the Ca II H & K core
strengths in a sample of 60 solar-type stars that are members of the solar-age
and solar-metallicity open cluster M67. We adopt the HK index, defined as the
summed H+K core strengths in 0.1 nm bandpasses centered on the H and K lines,
respectively, as a measure of the chromospheric activity that is present. We
compare the distribution of mean HK index values for the M67 solar-type stars
with the variation of this index as measured for the Sun during the
contemporary solar cycle. We find that the stellar distribution in our HK index
is broader than that for the solar cycle. Approximately 17% of the M67 sun-like
stars exhibit average HK indices that are less than solar minimum. About 7%-12%
are characterized by relatively high activity in excess of solar maximum values
while 72%-80% of the solar analogs exhibit Ca II H+K strengths within the range
of the modern solar cycle. The ranges given reflect uncertainties in the most
representative value of the maximum in the HK index to adopt for the solar
cycle variations observed during the period A.D. 1976--2004. Thus, ~ 20% - 30%
of our homogeneous sample of sun-like stars have mean chromospheric H+K
strengths that are outside the range of the contemporary solar cycle. Any
cycle-like variability that is present in the M67 solar-type stars appears to
be characterized by periods greater than ~ 6 years. Finally, we estimate a mean
chromospheric age for M67 in the range of 3.8--4.3 Gyr.


  Statistical methodology for the design and analysis of clinical Phase II dose
response studies, with related software implementation, are well developed for
the case of a normally distributed, homoscedastic response considered for a
single timepoint in parallel group study designs. In practice, however, binary,
count, or time-to-event endpoints are often used, typically measured repeatedly
over time and sometimes in more complex settings like crossover study designs.
In this paper we develop an overarching methodology to perform efficient
multiple comparisons and modeling for dose finding, under uncertainty about the
dose-response shape, using general parametric models. The framework described
here is quite general and covers dose finding using generalized non-linear
models, linear and non-linear mixed effects models, Cox proportional hazards
(PH) models, etc. In addition to the core framework, we also develop a general
purpose methodology to fit dose response data in a computationally and
statistically efficient way. Several examples, using a variety of different
statistical models, illustrate the breadth of applicability of the results. For
the analyses we developed the R add-on package DoseFinding, which provides a
convenient interface to the general approach adopted here.


  A quantum-mechanical stability analysis of metallic nanowires within the
free-electron model is presented. The stability is determined by an interplay
of electron-shell effects, the Rayleigh instability due to surface tension, and
the Peierls instability. Although the latter effect limits the maximum length
also for wires with "magic radii", it is found that nanowires in the micrometer
range can be stable at room temperature.


  Adoption and deployment of robotic and autonomous systems in industry are
currently hindered by the lack of transparency, required for safety and
accountability. Methods for providing explanations are needed that are agnostic
to the underlying autonomous system and easily updated. Furthermore, different
stakeholders with varying levels of expertise, will require different levels of
information. In this work, we use surrogate models to provide transparency as
to the underlying policies for behaviour activation. We show that these
surrogate models can effectively break down autonomous agents' behaviour into
explainable components for use in natural language explanations.


  An expression that relates thermal current fluctuations in two terminal
networks at quasi-equilibrium to their current voltage characteristics is
presented. It is based upon the observation that the available thermal noise
power at quasi-equilibrium is very close to the equilibrium value. As an
example, current noise in ideal p-n junctions is obtained from their
characteristics. Thermal current noise in field effect transistors is predicted
by this approach, as well as "shot noise suppression" due to barrier lowering
in solid-state diodes.


  Autonomous robots need to be able to handle uncertainties when deployed in
the real world. For the robot to be able to robustly work in such an
environment, it needs to be able to adapt both its architecture as well as its
task plan. Architecture adaptation and task plan adaptation are mutually
dependent, and therefore require the system to apply runtime architecture and
task plan co-adaptation. This work presents Metaplan, which makes use of models
of the robot and its environment, together with a PDDL planner to apply runtime
architecture and task plan co-adaptation. Metaplan is designed to be easily
reusable across different domains. Metaplan is shown to successfully perform
runtime architecture and task plan co-adaptation with a self-adaptive unmanned
underwater vehicle exemplar, and its reusability is demonstrated by applying it
to an unmanned ground vehicle.


  Two novel schemes are proposed for the dissipative preparation of large W
states, of the order of ten qubits, within the context of Cavity QED. By
utilizing properties of the irreducible representations of su(3), we are able
to construct protocols in which it is possible to restrict the open system
dynamics to a fully symmetric irreducible subspace of the total Hilbert space,
and hence obtain analytic solutions for effective ground state dynamics of
arbitrary sized ensembles of $\Lambda$ atoms within an optical cavity. In the
proposed schemes, the natural decay processes of spontaneous emission and
photon loss are no longer undesirable, but essential to the required dynamics.
All aspects of the proposed schemes relevant to implementation in currently
available optical cavities are explored, especially with respect to increasing
system size.


  We realize Derksen-Weyman-Zelevinsky's mutations of representations as
densely-defined regular maps on representation spaces, and study the generic
values of Caldero-Chapoton functions with coefficients, giving, for instance, a
sufficient combinatorial condition for their linear independence.
  For a quiver with potential $(Q,S)$, we show that if $k$ is a vertex not
incident to any oriented 2-cycle, then every generically $\tau$-reduced
irreducible component $Z$ of any affine variety of (decorated) representations
has a dense open subset $U$ on which Derksen-Weyman-Zelevinsky's mutation of
representations $\mu_k$ can be defined consistently as a regular map to an
affine variety of (decorated) representations of the Jacobian algebra of the
mutated QP $\mu_k(Q,S)$.
  Our techniques involve only basic linear algebra and elementary algebraic
geometry, and do not require to assume Jacobi-finiteness. Thus, the paper
yields a new and more general proof of the mutation invariance of generic
Caldero-Chapoton functions, generalizing and providing a new natural geometric
perspective on results of Derksen-Weyman-Zelevinsky and Plamondon. (For
Jacobi-finite non-degenerate quivers with potential, this invariance was shown
by Plamondon using the machinery of Ginzburg dg-algebras and Hom-finite
generalized cluster categories.)
  We apply our results, together with results of Mills, Muller and Qin, to
prove that for any choice of geometric coefficient systems, not necessarily of
full rank, the cluster algebra associated to a possibly punctured surface with
at least two marked points on the boundary has the generic Caldero-Chapoton
functions as a basis over the Laurent polynomial ring of coefficients.


  It has been conjectured that the internal energy density of the Potts model
on a semi-infinite strip with a width $L$ does not have any finite-size
corrections at the critical point $K=K_c$. By factorizing the transfer matrix
for the kagome lattice with larger widths, we have found that this conjecture
is not correct in that the internal energy density slightly varies with $L$ at
the critical point. From this size dependence of the internal energy density,
we obtain an upper bound as $K_c < 1.0565615$, which is close to a recent
estimate $K_c^{\rm JS} = 1.0565600(7)$ by Jacobsen and Scullard
[arXiv:1204.0622]. We also obtain a lower bound as $K_c > 1.0560$ by
calculating the correlation length along the strips.


  This review summarizes our current understanding of the outer heliosphere and
local interstellar medium (LISM) inferred from observations and modeling of
interplanetary Lyman-$\alpha$ emission. The emission is produced by solar
Lyman-alpha photons (121.567 nm) backscattered by interstellar H atoms
inflowing to the heliosphere from the LISM. Studies of Lyman-alpha radiation
determined the parameters of interstellar hydrogen within a few astronomical
units from the Sun. The interstellar hydrogen atoms appeared to be decelerated,
heated, and shifted compared to the helium atoms. The detected deceleration and
heating proved the existence of secondary hydrogen atoms created near the
heliopause. This finding supports the discovery of a Hydrogen Wall beyond the
heliosphere consisting of heated hydrogen observed in HST/GHRS Lyman-alpha
absorption spectra toward nearby stars. The shift of the interstellar hydrogen
bulk velocity was the first observational evidence of the global heliosphere
asymmetry confirmed later by Voyager in situ measurements. SOHO/SWAN all-sky
maps of the backscattered Lyman-alpha intensity identified variations of the
solar wind mass flux with heliolatitude and time. In particular, two maxima at
mid-latitudes were discovered during solar activity maximum, which Ulysses
missed due to its specific trajectory. Finally, Voyager/UVS and New
Horizons/Alice UV spectrographs discovered extra-heliospheric Lyman-alpha
emission. We review these scientific breakthroughs, outline open science
questions, and discuss potential future heliospheric Lyman-alpha experiments.


  We optically assess Fresnel zone plates (FZPs) that are designed to guide
cold atoms. Imaging of various ring patterns produced by the FZPs gives an
average RMS error in the brightest part of the ring of 3% with respect to trap
depth. This residue will be due to the imaging system, incident beam shape and
FZP manufacturing tolerances. Axial propagation of the potentials is presented
experimentally and through numerical simulations, illustrating prospects for
atom guiding without requiring light sheets.


  We test a nonlinear force-free field (NLFFF) optimization code in spherical
geometry using an analytical solution from Low and Lou. Several tests are run,
ranging from idealized cases where exact vector field data are provided on all
boundaries, to cases where noisy vector data are provided on only the lower
boundary (approximating the solar problem). Analytical tests also show that the
NLFFF code in the spherical geometry performs better than that in the Cartesian
one when the field of view of the bottom boundary is large, say, $20^\circ
\times 20^\circ$. Additionally, We apply the NLFFF model to an active region
observed by the Helioseismic and Magnetic Imager (HMI) on board the Solar
Dynamics Observatory (SDO) both before and after an M8.7 flare. For each
observation time, we initialize the models using potential field source surface
(PFSS) extrapolations based on either a synoptic chart or a flux-dispersal
model, and compare the resulting NLFFF models. The results show that NLFFF
extrapolations using the flux-dispersal model as the boundary condition have
slightly lower, therefore better, force-free and divergence-free metrics, and
contain larger free magnetic energy. By comparing the extrapolated magnetic
field lines with the extreme ultraviolet (EUV) observations by the Atmospheric
Imaging Assembly (AIA) on board SDO, we find that the NLFFF performs better
than the PFSS not only for the core field of the flare productive region, but
also for large EUV loops higher than 50 Mm.


  Weak measurements of a superconducting qubit produce noisy voltage signals
that are weakly correlated with the qubit state. To recover individual quantum
trajectories from these noisy signals, traditional methods require slow qubit
dynamics and substantial prior information in the form of calibration
experiments. Monitoring rapid qubit dynamics, e.g. during quantum gates,
requires more complicated methods with increased demand for prior information.
Here, we experimentally demonstrate an alternative method for accurately
tracking rapidly driven superconducting qubit trajectories that uses a
Long-Short Term Memory (LSTM) artificial neural network with minimal prior
information. Despite few training assumptions, the LSTM produces trajectories
that include qubit-readout resonator correlations due to a finite detection
bandwidth. In addition to revealing rotated measurement eigenstates and a
reduced measurement rate in agreement with theory for a fixed drive, the
trained LSTM also correctly reconstructs evolution for an unknown drive with
rapid modulation. Our work enables new applications of weak measurements with
faster or initially unknown qubit dynamics, such as the diagnosis of coherent
errors in quantum gates.


  Procedural models are being widely used to synthesize scenes for graphics,
gaming, and to create (labeled) synthetic datasets for ML. In order to produce
realistic and diverse scenes, a number of parameters governing the procedural
models have to be carefully tuned by experts. These parameters control both the
structure of scenes being generated (e.g. how many cars in the scene), as well
as parameters which place objects in valid configurations. Meta-Sim aimed at
automatically tuning parameters given a target collection of real images in an
unsupervised way. In Meta-Sim2, we aim to learn the scene structure in addition
to parameters, which is a challenging problem due to its discrete nature.
Meta-Sim2 proceeds by learning to sequentially sample rule expansions from a
given probabilistic scene grammar. Due to the discrete nature of the problem,
we use Reinforcement Learning to train our model, and design a feature space
divergence between our synthesized and target images that is key to successful
training. Experiments on a real driving dataset show that, without any
supervision, we can successfully learn to generate data that captures discrete
structural statistics of objects, such as their frequency, in real images. We
also show that this leads to downstream improvement in the performance of an
object detector trained on our generated dataset as opposed to other baseline
simulation methods. Project page:
https://nv-tlabs.github.io/meta-sim-structure/.


  Space motions are given for 38 RR Lyrae (RRL) stars and 79 blue horizontal
branch (BHB) stars in a ~200 deg2 area around the North Galactic Pole (NGP)
using a homogeneous distance scale consistent with (m-M)0=18.52 for the LMC.
  The kinematics of the 26 RRL and 52 BHB stars in the 10.4 cubic kpc volume
that have Z<8 kpc are not homogeneous. Our BHB sample (like that of Sirko et
al. 2004b) has a zero galactic rotation (V_phi) and roughly isotropic velocity
dispersions. The RRL sample shows a definite retrograde rotation (V_phi =
-95+/-29 km/s) and non-isotropic velocity dispersions. The combined BHB and RRL
sample has a retrograde galactic rotation (V) that is similar to that found by
Majewski (1992) for his sample of subdwarfs in SA 57. The velocity dispersion
of the RRL stars that have a positive W motion is significantly smaller than
the dispersion of those "streaming down" with a negative W.
  One component of our sample (rich in RRL's) shows retrograde rotation and the
streaming motion that we associate with the accretion process. The other
(traced by the BHB stars) shows essentially no rotation and less evidence of
streaming. These two components have HB morphologies that suggest that they may
be the field star equivalents of the young and old halo globular clusters
respectively.


  Within the framework of mappings between affine spaces, the notion of $n$-th
polarization of a function will lead to an intrinsic characterization of
polynomial functions. We prove that the characteristic features of derivations,
such as linearity, iterability, Leibniz and chain rules, are shared -- at the
finite level -- by the polarization operators. We give these results by means
of explicit general formulae, which are valid at any order $n$, and are based
on combinatorial identities. The infinitesimal limits of the $n$-th
polarizations of a function will yield its $n$-th derivatives (without
resorting to the usual recursive definition), and the above mentioned
properties will be recovered directly in the limit. Polynomial functions will
allow us to produce a coordinate free version of Taylor's formula.


  Considering the complementarity of scene flow estimation in the spatial
domain's focusing capability and 3D object tracking in the temporal domain's
coherence, this study aims to address a comprehensive new task that can
simultaneously capture fine-grained and long-term 3D motion in an online
manner: long-term scene flow estimation (LSFE). We introduce SceneTracker, a
novel learning-based LSFE network that adopts an iterative approach to
approximate the optimal trajectory. Besides, it dynamically indexes and
constructs appearance and depth correlation features simultaneously and employs
the Transformer to explore and utilize long-range connections within and
between trajectories. With detailed experiments, SceneTracker shows superior
capabilities in handling 3D spatial occlusion and depth noise interference,
highly tailored to the LSFE task's needs. Finally, we build the first
real-world evaluation dataset, LSFDriving, further substantiating
SceneTracker's commendable generalization capacity. The code and data for
SceneTracker is available at https://github.com/wwsource/SceneTracker.


  Privacy is a concept found throughout human history and opinion polls suggest
that the public value this principle. However, while many individuals claim to
care about privacy, they are often perceived to express behaviour to the
contrary. This phenomenon is known as the Privacy Paradox and its existence has
been validated through numerous psychological, economic and computer science
studies. Several contributory factors have been suggested including user
interface design, risk salience, social norms and default configurations. We
posit that the further proliferation of the Internet-of-Things (IoT) will
aggravate many of these factors, posing even greater risks to individuals'
privacy. This paper explores the evolution of both the paradox and the IoT,
discusses how privacy risk might alter over the coming years, and suggests
further research required to address a reasonable balance. We believe both
technological and socio-technical measures are necessary to ensure privacy is
protected in a world of ubiquitous technology.


  The structure of the graph defined by the interactions in a Boolean network
can determine properties of the asymptotic dynamics. For instance, considering
the asynchronous dynamics, the absence of positive cycles guarantees the
existence of a unique attractor, and the absence of negative cycles ensures
that all attractors are fixed points. In presence of multiple attractors, one
might be interested in properties that ensure that attractors are sufficiently
"isolated", that is, they can be found in separate subspaces or even trap
spaces, subspaces that are closed with respect to the dynamics. Here we
introduce notions of separability for attractors and identify corresponding
necessary conditions on the interaction graph. In particular, we show that if
the interaction graph has at most one positive cycle, or at most one negative
cycle, or if no positive cycle intersects a negative cycle, then the attractors
can be separated by subspaces. If the interaction graph has no path from a
negative to a positive cycle, then the attractors can be separated by trap
spaces. Furthermore, we study networks with interaction graphs admitting two
vertices that intersect all cycles, and show that if their attractors cannot be
separated by subspaces, then their interaction graph must contain a copy of the
complete signed digraph on two vertices, deprived of a negative loop. We thus
establish a connection between a dynamical property and a complex network
motif. The topic is far from exhausted and we conclude by stating some open
questions.


  The symmetry properties of a proposal to go beyond relativistic quantum field
theory based on a modification of the commutation relations of fields are
identified. Poincar\'e invariance in an auxiliary spacetime is found in the
Lagrangian version of the path integral formulation. This invariance is
contrasted with the idea of Doubly (or Deformed) Special Relativity (DSR). This
analysis is then used to go from the free theory of a complex field to an
interacting field theory.


  We combine features extracted from pre-trained convolutional neural networks
(CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of
both: the high detection performance of CNNs, automatic feature engineering,
fast model learning from few training samples and efficient sliding-window
detection. The Adaptive Real-Time Object Detection System (ARTOS) has been
refactored broadly to be used in combination with Caffe for the experimental
studies reported in this work.


  With the rapid development of cloud computing and big data technologies,
storage systems have become a fundamental building block of datacenters,
incorporating hardware innovations such as flash solid state drives and
non-volatile memories, as well as software infrastructures such as RAID and
distributed file systems. Despite the growing popularity and interests in
storage, designing and implementing reliable storage systems remains
challenging, due to their performance instability and prevailing hardware
failures.
  Proactive prediction greatly strengthens the reliability of storage systems.
There are two dimensions of prediction: performance and failure. Ideally,
through detecting in advance the slow IO requests, and predicting device
failures before they really happen, we can build storage systems with
especially low tail latency and high availability. While its importance is well
recognized, such proactive prediction in storage systems, on the other hand, is
particularly difficult. To move towards predictability of storage systems,
various mechanisms and field studies have been proposed in the past few years.
In this report, we present a survey of these mechanisms and field studies,
focusing on machine learning based black-box approaches. Based on three
representative research works, we discuss where and how machine learning should
be applied in this field. The strengths and limitations of each research work
are also evaluated in detail.


  The transverse momentum spectra of different types of particles produced in
central and peripheral gold-gold (Au-Au) and (inelastic) proton-proton ($pp$)
collisions at the Relativistic Heavy Ion Collider (RHIC), as well as in central
and peripheral lead-lead (Pb-Pb) and $pp$ collisions at the Large Hadron
Collider (LHC) are analyzed by the standard distribution in terms of
multi-component. The obtained results from the standard distribution give an
approximate agreement with the measured experimental data by the STAR, PHENIX
and ALICE Collaborations. The methodical behavior of the effective (kinetic
freeze-out) temperature, transverse flow velocity and kinetic freeze-out volume
with the mass dependence for different particles is obtained, which observes
the early kinetic freeze-out of heavier particles as compared to the lighter
particles. The parameters for emissions of different particles are observed to
be different, which reveals a direct signature of the mass dependent
differential kinetic freeze-out. It is also observed that the peripheral
nucleus-nucleus ($AA$) and $pp$ collisions at the same center-of-mass energy
per nucleon pair are close in terms of the extracted parameters.


  We study the effect of permeabilizing electric fields applied to two
different types of giant unilamellar vesicles, the first formed from EggPC
lipids and the second formed from DOPC lipids. Experiments on vesicles of both
lipid types show a decrease in vesicle radius which is interpreted as being due
to lipid loss during the permeabilization process. We show that the decrease in
size can be qualitatively explained as a loss of lipid area which is
proportional to the area of the vesicle which is permeabilized. Three possible
mechanisms responsible for lipid loss were directly observed: pore formation,
vesicle formation and tubule formation.


  Scalar theory of quantum electron beam optics, at the single-particle level,
derived from the Dirac equation using a Foldy-Wouthuysen-like transformation
technique is considered. Round magnetic electron lenses with Glaser and power
law models for the axial magnetic field $B(z)$ are studied. Paraxial quantum
propagator for the Glaser model lens is obtained in terms of the well known
fundamental solutions of its paraxial equation of motion. In the case of lenses
with the power law model for $B(z)$ the well known fundamental solutions of the
paraxial equations, obtained by solving the differential equation, are
constructed using the Peano-Baker series also. Quantum mechanics of aberrations
is discussed briefly. Role of quantum uncertainties in aberrations, and in the
nonlinear part of the equations of motion for a nonparaxial beam, is pointed
out. The main purpose of this article is to understand the quantum mechanics of
electron beam optics though the influence of quantum effects on the optics of
present-day electron beam devices might be negligible.


  The families of simplest cubic, simplest quartic and simplest sextic fields
and the related Thue equations are well known. The family of simplest cubic
Thue equations was already studied in the relative case, over imaginary
quadratic fields. In the present paper we give a similar extension of simplest
quartic and simplest sextic Thue equations over imaginary quadratic fields. We
explicitly give the solutions of these infinite parametric families of Thue
equations over arbitrary imaginary quadratic fields.


  We report the results of our search of planet candidates in Open Clusters and
Young Stellar Associations based on the TESS Objects of Interest Catalog. We
find one confirmed planet, one promising candidate, one brown dwarf, and three
unverified planet candidates in a sample of 1229 Open Clusters from the second
Gaia data release. We discuss individual planet-star systems based on their
basic parameters, membership probability, and the observation notes from the
ExoFOP-TESS website. We also find ten planet candidates (P $>$ 95%) in Young
Stellar Associations by using the BANYAN $\Sigma$ Bayesian Algorithm. Among the
ten candidates, five are known planet systems. We estimate the rotation periods
of the host stars using the TESS light curves and estimate their ages based on
gyrochronology. Two candidates with periodic variations are likely to be young
planets, but their exact memberships to Young Stellar Associations remain
unknown.


  We argue that a conformally invariant extension of general relativity coupled
to the Standard Model is the fundamental theory that needs to be quantized. We
show that it can be treated by loop quantum gravity techniques. Through a gauge
fixing and a modified Higgs mechanism particles acquire mass and one recovers
general relativity coupled to the Standard Model. The theory suggests new views
with respect to the definition of the Hamiltonian constraint in loop quantum
gravity, the semi-classical limit and the issue of finite renormalization in
quantum field theory in quantum space-time. It also gives hints about the
elimination of ambiguities that arise in quantum field theory in quantum
space-time in the calculation of back-reaction.


  Two nonparametric methods are presented for forecasting functional time
series (FTS). The FTS we observe is a curve at a discrete-time point. We
address both one-step-ahead forecasting and dynamic updating. Dynamic updating
is a forward prediction of the unobserved segment of the most recent curve.
Among the two proposed methods, the first one is a straightforward adaptation
to FTS of the $k$-nearest neighbors methods for univariate time series
forecasting. The second one is based on a selection of curves, termed \emph{the
curve envelope}, that aims to be representative in shape and magnitude of the
most recent functional observation, either a whole curve or the observed part
of a partially observed curve. In a similar fashion to $k$-nearest neighbors
and other projection methods successfully used for time series forecasting, we
``project'' the $k$-nearest neighbors and the curves in the envelope for
forecasting. In doing so, we keep track of the next period evolution of the
curves. The methods are applied to simulated data, daily electricity demand,
and NOx emissions and provide competitive results with and often superior to
several benchmark predictions. The approach offers a model-free alternative to
statistical methods based on FTS modeling to study the cyclic or seasonal
behavior of many FTS.


  We examine the problem of approximating a positive, semidefinite matrix
$\Sigma$ by a dyad $xx^T$, with a penalty on the cardinality of the vector $x$.
This problem arises in sparse principal component analysis, where a
decomposition of $\Sigma$ involving sparse factors is sought. We express this
hard, combinatorial problem as a maximum eigenvalue problem, in which we seek
to maximize, over a box, the largest eigenvalue of a symmetric matrix that is
linear in the variables. This representation allows to use the techniques of
robust optimization, to derive a bound based on semidefinite programming. The
quality of the bound is investigated using a technique inspired by Nemirovski
and Ben-Tal (2002).


  We propose a method for the blind separation of sounds of musical instruments
in audio signals. We describe the individual tones via a parametric model,
training a dictionary to capture the relative amplitudes of the harmonics. The
model parameters are predicted via a U-Net, which is a type of deep neural
network. The network is trained without ground truth information, based on the
difference between the model prediction and the individual time frames of the
short-time Fourier transform. Since some of the model parameters do not yield a
useful backpropagation gradient, we model them stochastically and employ the
policy gradient instead. To provide phase information and account for
inaccuracies in the dictionary-based representation, we also let the network
output a direct prediction, which we then use to resynthesize the audio signals
for the individual instruments. Due to the flexibility of the neural network,
inharmonicity can be incorporated seamlessly and no preprocessing of the input
spectra is required. Our algorithm yields high-quality separation results with
particularly low interference on a variety of different audio samples, both
acoustic and synthetic, provided that the sample contains enough data for the
training and that the spectral characteristics of the musical instruments are
sufficiently stable to be approximated by the dictionary.


  We study the properties of the Resonance-Spectrum Expansion near threshold in
I=0 S-wave pion-pion scattering. The real part of the amplitude, when
extrapolated from above threshold to below threshold, is found to vanish at a
positive non-zero value of the total invariant mass of the system, in agreement
with predictions from perturbative chiral models. In our exact analytic
expression, the total amplitude vanishes identically at zero invariant mass.


  We investigate the length of the period of validity of a classical
description for the cosmic axion field. To this end, we first show that we can
understand the oscillating axion solution as expectation value over an
underlying coherent quantum state. Once we include self-interaction of the
axion, the quantum state evolves so that the expectation value over it starts
to deviate from the classical solution. The time-scale of this process defines
the quantum break-time. For the hypothetical dark matter axion field in our
Universe, we show that quantum break-time exceeds the age of the Universe by
many orders of magnitude. This conclusion is independent of specific properties
of the axion model. Thus, experimental searches based on the classical
approximation of the oscillating cosmic axion field are fully justified.
Additionally, we point out that the distinction of classical nonlinearities and
true quantum effects is crucial for calculating the quantum break-time in any
system. Our analysis can also be applied to other types of dark matter that are
described as classical fluids in the mean field approximation.


  When faced with a data set too large to be processed all at once, an obvious
solution is to retain only part of it. In practice this takes a wide variety of
different forms, and among them "coresets" are especially appealing. A coreset
is a (small) weighted sample of the original data that comes with the following
guarantee: a cost function can be evaluated on the smaller set instead of the
larger one, with low relative error. For some classes of problems, and via a
careful choice of sampling distribution (based on the so-called "sensitivity"
metric), iid random sampling has turned to be one of the most successful
methods for building coresets efficiently. However, independent samples are
sometimes overly redundant, and one could hope that enforcing diversity would
lead to better performance. The difficulty lies in proving coreset properties
in non-iid samples. We show that the coreset property holds for samples formed
with determinantal point processes (DPP). DPPs are interesting because they are
a rare example of repulsive point processes with tractable theoretical
properties, enabling us to prove general coreset theorems. We apply our results
to both the k-means and the linear regression problems, and give extensive
empirical evidence that the small additional computational cost of DPP sampling
comes with superior performance over its iid counterpart. Of independent
interest, we also provide analytical formulas for the sensitivity in the linear
regression and 1-means cases.


  Conventional wisdom holds that any region of 3-space contains infinitely many
points, and the Planck length scale determines the uncertainty in every
measurement of distance between two separate points. Against such a backdrop,
this uncertainty may be interpreted as resulting from either foaminess or
discreteness of 3-space. But, as it is demonstrated in the present paper,
neither of those interpretations is consistent with the holographic principle.
In the paper it is shown that the statement ``The holographic principle holds
true'' and the statement ``Each region in 3-space contains only a finite number
of points'' are logically equivalent.


  Training multi-agent systems (MAS) to achieve realistic equilibria gives us a
useful tool to understand and model real-world systems. We consider a general
sum partially observable Markov game where agents of different types share a
single policy network, conditioned on agent-specific information. This paper
aims at i) formally understanding equilibria reached by such agents, and ii)
matching emergent phenomena of such equilibria to real-world targets. Parameter
sharing with decentralized execution has been introduced as an efficient way to
train multiple agents using a single policy network. However, the nature of
resulting equilibria reached by such agents has not been yet studied: we
introduce the novel concept of Shared equilibrium as a symmetric pure Nash
equilibrium of a certain Functional Form Game (FFG) and prove convergence to
the latter for a certain class of games using self-play. In addition, it is
important that such equilibria satisfy certain constraints so that MAS are
calibrated to real world data for practical use: we solve this problem by
introducing a novel dual-Reinforcement Learning based approach that fits
emergent behaviors of agents in a Shared equilibrium to externally-specified
targets, and apply our methods to a n-player market example. We do so by
calibrating parameters governing distributions of agent types rather than
individual agents, which allows both behavior differentiation among agents and
coherent scaling of the shared policy network to multiple agents.


  Complex, multivariable systems are often analyzed by grouping their
constituent units into components, sometimes referred to as latent features,
which afford physical or biological interpretation. However, a priori many
different types of latent features and data decompositions can be defined, and
one typically uses a trial and error approach to determine a decomposition that
is natural to the system and its data. It is highly desirable to develop
principled understanding of which decomposition is appropriate for given a data
set. In this work, we take a step in this direction and argue that
sample-sample correlations in the data carry important information to this
effect. For this we construct a generative random latent feature matrix model
of large data based on linear mixing of latent features. Key ingredient of our
model is that we allow for statistical dependence between the mixing
coefficients and argue that the model captures characteristic properties found
in many types of natural data. Latent dimensionality and correlation patterns
of the data are controlled by only two model parameters. The model's data
patterns include (overlapping) clusters, sparse mixing, and constrained
(non-negative) mixing. We describe the characteristic correlation and
eigenvalue distributions of each pattern. Finally, we fit the model on
correlation data from natural images and find a near perfect match with the
sparse mixing regime of our model. This finding is in line with the well-known
sparse coding structure in natural scene images and provides information about
the appropriate data decomposition, namely a sparse coding scheme. We believe
that our work will deliver similar insights for diverse data of biological
systems.


  In the context of a noncommutative differential calculus on the algebra of
real valued functions of an $n$-dimensional manifold $M$, a commutative and
associative product of 1-forms is naturally defined. Ordinary differential
calculus corresponds to this product being trivially zero. We consider the
minimal generalization in which the algebra of 1-forms with this product is
nilpotent of degree 3. Basic tensor analysis and differential geometry are
developped in this context and applied to the formulation of symplectic
geometry and Hamiltonian dynamics. It is shown that the corresponding Liouville
equation can take the form of a generalized Fokker-Planck equation, well known
in statistical mechanics of open systems, as well as in stochastic calculus.
Specifically it may be the same with that obtained via Ito stochastic calculus,
when solving a linear stochastic differential equation with a Wiener process as
the stochastic term. The close connection between noncommutative differential
structures of this kind, with semimartingale stochastic processes on manifolds
thus suggested, is further explored.


  Information-centric networking (ICN) is one of the promising solutions that
cater to the challenges of IP-based networking. ICN shifts the IP-based access
model to a data-centric model. Named Data Networking (NDN) is a flexible ICN
architecture, which is based on content distribution considering data as the
core entity rather than IP-based hosts. User-generated mobile contents for
real-time multimedia communication such as Internet telephony are very common
these days and are increasing both in quality and quantity. In NDN, producer
mobility is one of the challenging problems to support uninterrupted real-time
multimedia communication and needs to be resolved for the adoption of NDN as
future Internet architecture. We assert that mobile nodes' future location
prediction can aid in designing efficient anchor-less mobility management
techniques. In this article, we show how location prediction techniques can be
used to provide an anchor-less mobility management solution in order to ensure
seamless handover of the producer during real-time multimedia communication.
The results indicate that with a low level of location prediction accuracy, our
proposed methodology still profoundly reduces the total handover latency and
round trip time without creating network overhead.


  It is shown that the supersymmetry-preserving automorphisms of any non-linear
sigma-model on K3 generate a subgroup of the Conway group Co_1. This is the
stringy generalisation of the classical theorem, due to Mukai and Kondo,
showing that the symplectic automorphisms of any K3 manifold form a subgroup of
the Mathieu group M_{23}. The Conway group Co_1 contains the Mathieu group
M_{24} (and therefore in particular M_{23}) as a subgroup. We confirm the
predictions of the Theorem with three explicit CFT realisations of K3: the
T^4/Z_2 orbifold at the self-dual point, and the two Gepner models (2)^4 and
(1)^6. In each case we demonstrate that their symmetries do not form a subgroup
of M_{24}, but lie inside Co_1 as predicted by our Theorem.


  We study implementation of a social choice correspondence in the case of two
players who have von Neumann - Morgenstern utilities over a finite set of
social alternatives, and the mechanism is allowed to output lotteries. Our main
positive result shows that a close variant of the popular approval voting
mechanism succeeds in selecting only Pareto efficient alternatives as pure Nash
equilibria outcomes. Moreover, we provide an exact characterization of pure
Nash equilibria profiles and outcomes of the mechanism. The characterization
demonstrates a close connection between the approval voting mechanism and the
notion of average fixed point, which is a point that is equal to the average of
all points that it does not Pareto dominate.


  We examine the phenomenon of enhanced dissipation from the perspective of
H\"ormander's classical theory of second order hypoelliptic operators [31].
Consider a passive scalar in a shear flow, whose evolution is described by the
advection-diffusion equation \[ \partial_t f + b(y) \partial_x f - \nu \Delta f
= 0 \text{ on } \mathbb{T} \times (0,1) \times \mathbb{R}_+ \] with periodic,
Dirichlet, or Neumann conditions in $y$. We demonstrate that decay is enhanced
on the timescale $T \sim \nu^{-(N+1)/(N+3)}$, where $N-1$ is the maximal order
of vanishing of the derivative $b'(y)$ of the shear profile and $N=0$ for
monotone shear flows. In the periodic setting, we recover the known timescale
of Bedrossian and Coti Zelati [8]. Our results are new in the presence of
boundaries.


  I address the issue of dust and gas as seeds for metal-poor star formation by
reviewing what we know about star formation in nearby dwarf galaxies and its
relationship to the gas and dust. I (try to) speculate on the extent to which
processes in nearby galaxies mimic star formation in the early universe.


  With the high spatial and temporal resolution H$\alpha$ images from the New
Vacuum Solar Telescope, we focus on two groups of loops with a X-shaped
configuration in the dynamic chromosphere. We find that the anti-directed loops
approach each other and reconnect continually. The connectivity of the loops is
changed and new loops are formed and stack together. The stacked loops are
sharply bent, implying that they are greatly impacted by the magnetic tension
force. When another more reconnection process takes place, one new loop is
formed and stacks with the previously formed ones. Meanwhile, the stacked loops
retract suddenly and move toward the balance position, performing an overshoot
movement, which led to an oscillation with an average period of about 45 s. The
oscillation of newly formed loops after magnetic reconnection in the
chromosphere is observed for the first time. We suggest that the stability of
the stacked loops is destroyed due to the join of the last new loop and then
suddenly retract under the effect of magnetic tension. Because of the
retraction, another lower loop is pushed outward and performs an oscillation
with the period of about 25 s. The different oscillation periods may be due to
their difference in three parameters, i.e., loop length, plasma density, and
magnetic field strength.


  LXeGRIT is the first prototype of a novel concept of Compton telescope, based
on the complete 3-dimensional reconstruction of the sequence of interactions of
individual gamma rays in one position sensitive detector. This balloon-borne
telescope consists of an unshielded time projection chamber with an active
volume of 400 cm$^2 \times 7$ cm filled with high purity liquid xenon. Four VUV
PMTs detect the fast xenon scintillation light signal, providing the event
trigger. 124 wires and 4 anodes detect the ionization signals, providing the
event spatial coordinates and total energy. In the period 1999 -- 2001, LXeGRIT
has been extensively tested both in the laboratory and at balloon altitude, and
its response in the MeV region has been thoroughly characterized. Here we
summarize some of the results on pre-flight calibration, event reconstruction
techniques, and performance during a 27 hour balloon flight on October 4 -- 5.
We further present briefly the on-going efforts directed to improve the
performance of this prototype towards the requirements for a base module of a
next-generation Compton telescope.


  We obtain a series of estimates on the number of small integers and small
order Farey fractions which belong to a given coset of a subgroup of order $t$
of the group of units of the residue ring modulo a prime $p$, in the case when
$t$ is small compared to $p$. We give two applications of these results: to the
simultaneous distribution of two high degree monomials $x^{k_1}$ and $x^{k_2}$
modulo $p$ and to a question of J.Holden and P.Moree on fixed points of the
discrete logarithm.


  The information-theoretic framework promises to explain the predictive power
of neural networks. In particular, the information plane analysis, which
measures mutual information (MI) between input and representation as well as
representation and output, should give rich insights into the training process.
This approach, however, was shown to strongly depend on the choice of estimator
of the MI. The problem is amplified for deterministic networks if the MI
between input and representation is infinite. Thus, the estimated values are
defined by the different approaches for estimation, but do not adequately
represent the training process from an information-theoretic perspective. In
this work, we show that dropout with continuously distributed noise ensures
that MI is finite. We demonstrate in a range of experiments that this enables a
meaningful information plane analysis for a class of dropout neural networks
that is widely used in practice.


  Context. We have conducted a 3D MHD simulation of the solar corona above an
active region (AR) in full scale and high resolution, which shows coronal
loops, and plasma flows within them, similar to observations. Aims. We want to
find the connection between the photospheric energy input by field-line
braiding with the coronal energy conversion by Ohmic dissipation of induced
currents. Methods. To this end we compare the coronal energy input and
dissipation within our simulation domain above different fields of view, e.g.
for a small loops system in the AR core. We also choose an ensemble of field
lines to compare, e.g., the magnetic energy input to the heating per particle
along these field lines. Results. We find an enhanced Ohmic dissipation of
currents in the corona above areas that also have enhanced upwards-directed
Poynting flux. These regions coincide with the regions where hot coronal loops
within the AR core are observed. The coronal density plays a role in estimating
the coronal temperature due to the generated heat input. A minimum flux density
of about 200 Gauss is needed in the photosphere to heat a field line to coronal
temperatures of about 1 MK. Conclusions. This suggests that the field-line
braiding mechanism provides the coronal energy input and that the Ohmic
dissipation of induced currents dominates the coronal heating mechanism.


  Pretrained Language Models (PLMs) benefit from external knowledge stored in
graph structures for various downstream tasks. However, bridging the modality
gap between graph structures and text remains a significant challenge.
Traditional methods like linearizing graphs for PLMs lose vital graph
connectivity, whereas Graph Neural Networks (GNNs) require cumbersome processes
for integration into PLMs. In this work, we propose a novel graph-guided
self-attention mechanism, GraSAME. GraSAME seamlessly incorporates token-level
structural information into PLMs without necessitating additional alignment or
concatenation efforts. As an end-to-end, lightweight multimodal module, GraSAME
follows a multi-task learning strategy and effectively bridges the gap between
graph and textual modalities, facilitating dynamic interactions between GNNs
and PLMs. Our experiments on the graph-to-text generation task demonstrate that
GraSAME outperforms baseline models and achieves results comparable to
state-of-the-art (SOTA) models on WebNLG datasets. Furthermore, compared to
SOTA models, GraSAME eliminates the need for extra pre-training tasks to adjust
graph inputs and reduces the number of trainable parameters by over 100
million.


  3D Hand pose estimation from a single depth image is an essential topic in
computer vision and human-computer interaction. Although the rising of deep
learning method boosts the accuracy a lot, the problem is still hard to solve
due to the complex structure of the human hand. Existing methods with deep
learning either lose spatial information of hand structure or lack a direct
supervision of joint coordinates. In this paper, we propose a novel Pixel-wise
Regression method, which use spatial-form representation (SFR) and
differentiable decoder (DD) to solve the two problems. To use our method, we
build a model, in which we design a particular SFR and its correlative DD which
divided the 3D joint coordinates into two parts, plane coordinates and depth
coordinates and use two modules named Plane Regression (PR) and Depth
Regression (DR) to deal with them respectively. We conduct an ablation
experiment to show the method we proposed achieve better results than the
former methods. We also make an exploration on how different training
strategies influence the learned SFRs and results. The experiment on three
public datasets demonstrates that our model is comparable with the existing
state-of-the-art models and in one of them our model can reduce mean 3D joint
error by 25%.


  In this paper we consider the setting where machine learning models are
retrained on updated datasets in order to incorporate the most up-to-date
information or reflect distribution shifts. We investigate whether one can
infer information about these updates in the training data (e.g., changes to
attribute values of records). Here, the adversary has access to snapshots of
the machine learning model before and after the change in the dataset occurs.
Contrary to the existing literature, we assume that an attribute of a single or
multiple training data points are changed rather than entire data records are
removed or added. We propose attacks based on the difference in the prediction
confidence of the original model and the updated model. We evaluate our attack
methods on two public datasets along with multi-layer perceptron and logistic
regression models. We validate that two snapshots of the model can result in
higher information leakage in comparison to having access to only the updated
model. Moreover, we observe that data records with rare values are more
vulnerable to attacks, which points to the disparate vulnerability of privacy
attacks in the update setting. When multiple records with the same original
attribute value are updated to the same new value (i.e., repeated changes), the
attacker is more likely to correctly guess the updated values since repeated
changes leave a larger footprint on the trained model. These observations point
to vulnerability of machine learning models to attribute inference attacks in
the update setting.


  As a follow-up on our earlier paper we calculate the $\chi^{2}$ of various
$N\!N$ potential models with respect to the $np$ scattering data. We find that
only the most recent potential models give a reasonably good description of
these data. Almost none of the potentials is truly an $N\!N$ potential in the
sense that it gives a good description of both $pp$ and $np$ scattering
simultaneously.


  In general, we can propose the hybrid supersymmetry breakings and hybrid
mediations in the Supersymmetric Standard Models (SSMs). In this paper, we
study the hybrid mediation for supersymmetry (SUSY) breaking. In particular, we
study how to keep the good properties of gravity mediation, gauge mediation,
and anomaly mediation, while solve their problems simultaneously. As an
example, we consider the anomaly-gravity mediation, where all the
supersymmetric particles (sparticles) obtain the SUSY breaking soft terms from
the traditional gravity mediation while gauge mediation gives dominant
contributions to the soft terms in the colored sector due to the splitted
messengers. Thus, we can realize the electroweak supersymmetry naturally where
the sleptons, sneutrinos, and electroweakinos are light within one TeV while
the squarks and gluino are heavy around a few TeVs. And then we can explain 125
GeV Higgs mass, satisfy the LHC SUSY search bounds, and explain the anomalous
magnetic momement of muon, etc. Moreover, the gluino and squarks are well
beyond the current LHC Run II searches.


  The measured forward-backward asymmetry in the t tbar production at the
Tevatron might be explained by the additional exchange of a colored weak
singlet scalar. Such state appears in some of the grand unified theories and
its interactions with the up-quarks are purely antisymmetric in flavor space.
We systematically investigate the resulting impact on charm and top quark
physics. The constraints on the relevant Yukawa couplings come from the
experimentally measured observables related to D0--D0bar oscillations, as well
as di-jet and single top production measurements at the Tevatron. After fully
constraining the relevant Yukawa couplings, we predict possible signatures of
this model in rare top quark decays. In a class of grand unified models we
demonstrate how the obtained information enables to constrain the Yukawa
couplings of the up-quarks at very high energy scale.


  Using variational minimizing methods,we prove the existence of an odd
symmetric parabolic orbit for the 2-fixed center problems with weak force type
homogeneous potentials.


  Transformers have become a predominant machine learning workload, they are
not only the de-facto standard for natural language processing tasks, but they
are also being deployed in other domains such as vision and speech recognition.
Many of the transformer-based applications are real-time systems such as
machine translation and web search. These real time systems often come with
strict end-to-end inference latency requirements. Unfortunately, while the
majority of the transformer computation comes from matrix multiplications,
transformers also include several non-linear components that tend to become the
bottleneck during an inference. In this work, we accelerate the inference of
BERT models on the tensor streaming processor. By carefully fusing all the
nonlinear components with the matrix multiplication components, we are able to
efficiently utilize the on-chip matrix multiplication units resulting in a
deterministic tail latency of 130 $\mu$s for a batch-1 inference through
BERT-base, which is 6X faster than the current state-of-the-art.


  We write explicit and self-contained asymptotic expressions for the tensorial
B, C and D Passarino-Veltman functions. These include quadratic and linear
logarithmic terms, as well as subleading constant terms. Only mass-suppressed
O(m^2/s) contributions are neglected. We discuss the usefulness of such
expressions, particularly for studying one-loop effects in 2-to-2 body
processes at high energy.


  We present measurements of the Joule heating of a 2D hole gas (2DHG) formed
in a 30nm GaAs quantum well. The hole density is in the range
(4.6-18.9)*10^9cm^-2 and exhibits an apparent metal-to-insulator transition
(MIT) with a critical density 6*10^9 cm^-2. In the limit of zero heating power
density P, the GaAs lattice is within 2 mK of the 6 mK base temperature of our
dilution refrigerator determined by He-3 melting curve thermometry. Throughout
the range of heating power densities used (1 to 10^6 fW/cm^2), the temperature
rise of the lattice is estimated to be negligible compared to the temperature
rise of the hole gas. We argue that the hole scattering rate is only a function
of the hole temperature, with little dependence on the lattice or impurity
temperatures in the relevant temperature range below 150 mK. We have therefore
made measurements of the hole resistivity at negligible heating power density
(P<5fW/cm^2) as a function of measured lattice temperature in the range 6 to
150 mK. We then use the hole resistivity measured in a cold lattice to estimate
the temperature of the 2D hole gas as a function of P. In the low hole density
insulating phase, the heating power density P(T) that heats the hole gas to a
temperature T exhibits a dependence P ~ T^2 for T<30mK, gradually changing to P
~ T^4 for higher temperatures. On the metallic side of the MIT, P is
proportional to T^5 or T^6 with a magnitude roughly 4 times less than the power
density required to heat the insulating phase to the same temperature. Our
measurements are within a factor of two of the available quantitative
theoretical predictions for the hole energy loss rate as a function of
temperature.


  This paper proposes joint attention estimation in a single image. Different
from related work in which only the gaze-related attributes of people are
independently employed, (I) their locations and actions are also employed as
contextual cues for weighting their attributes, and (ii) interactions among all
of these attributes are explicitly modeled in our method. For the interaction
modeling, we propose a novel Transformer-based attention network to encode
joint attention as low-dimensional features. We introduce a specialized MLP
head with positional embedding to the Transformer so that it predicts pixelwise
confidence of joint attention for generating the confidence heatmap. This
pixelwise prediction improves the heatmap accuracy by avoiding the ill-posed
problem in which the high-dimensional heatmap is predicted from the
low-dimensional features. The estimated joint attention is further improved by
being integrated with general image-based attention estimation. Our method
outperforms SOTA methods quantitatively in comparative experiments. Code:
https://anonymous.4open.science/r/anonymized_codes-ECA4.


  Recently, deep Convolutional Neural Networks (CNNs) have proven to be
successful when employed in areas such as reduced order modeling of
parametrized PDEs. Despite their accuracy and efficiency, the approaches
available in the literature still lack a rigorous justification on their
mathematical foundations. Motivated by this fact, in this paper we derive
rigorous error bounds for the approximation of nonlinear operators by means of
CNN models. More precisely, we address the case in which an operator maps a
finite dimensional input $\boldsymbol{\mu}\in\mathbb{R}^{p}$ onto a functional
output $u_{\boldsymbol{\mu}}:[0,1]^{d}\to\mathbb{R}$, and a neural network
model is used to approximate a discretized version of the input-to-output map.
The resulting error estimates provide a clear interpretation of the
hyperparameters defining the neural network architecture. All the proofs are
constructive, and they ultimately reveal a deep connection between CNNs and the
Fourier transform. Finally, we complement the derived error bounds by numerical
experiments that illustrate their application.


  Practical dialogue systems require robust methods of detecting out-of-scope
(OOS) utterances to avoid conversational breakdowns and related failure modes.
Directly training a model with labeled OOS examples yields reasonable
performance, but obtaining such data is a resource-intensive process. To tackle
this limited-data problem, previous methods focus on better modeling the
distribution of in-scope (INS) examples. We introduce GOLD as an orthogonal
technique that augments existing data to train better OOS detectors operating
in low-data regimes. GOLD generates pseudo-labeled candidates using samples
from an auxiliary dataset and keeps only the most beneficial candidates for
training through a novel filtering mechanism. In experiments across three
target benchmarks, the top GOLD model outperforms all existing methods on all
key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median
baseline performance. We also analyze the unique properties of OOS data to
identify key factors for optimally applying our proposed method.


  Biomimetic nanoengineered metamaterials called chiral sculptured thin films
(CSTFs) are attractive platforms for optical sensing because their porosity,
morphology and optical properties can be tailored to order. Furthermore, their
ability to support more than one surface-plasmon-polariton (SPP) wave at a
planar interface with a metal offers functionality beyond that associated with
conventional SPP--based sensors. An empirical model was constructed to describe
SPP-wave propagation guided by the planar interface of a CSTF--infiltrated with
a fluid which supposedly contains analytes to be detected--and a metal. The
inverse Bruggeman homogenization formalism was first used to determine the
nanoscale model parameters of the CSTF. These parameters then served as inputs
to the forward Bruggeman homogenization formalism to determine the reference
relative permittivity dyadic of the infiltrated CSTF. By solving the
coresponding boundary-value problem for a modified Kretschmann configuration,
the characteristics of the multiple SPP modes at the planar interface were
investigated as functions of the refractive index of the fluid infiltrating the
CSTF and the rise angle of the CSTF. The SPP sensitivities thereby revealed
bode well for the implementation of fluid-infiltrated CSTFs as SPP-based
optical sensors.


  We study the longitudinal magnetotransport in three-dimensional multi-Weyl
semimetals, constituted by a pair of (anti)-monopole of arbitrary integer
charge ($n$), with $n=1,2$ and $3$ in a crystalline environment. For any $n>1$,
even though the distribution of the underlying Berry curvature is anisotropic,
the corresponding intrinsic component of the longitudinal magnetoconductivity
(LMC), bearing the signature of the chiral anomaly, is insensitive to the
direction of the external magnetic field ($B$) and increases as $B^2$, at least
when it is sufficiently weak (the semi-classical regime). In addition, the LMC
scales as $n^3$ with the monopole charge. We demonstrate these outcomes for two
distinct scenarios, namely when inter-particle collisions in the Weyl medium
are effectively described by (a) a single and (b) two (corresponding to inter-
and intra-valley) scattering times. While in the former situation the
contribution to LMC from chiral anomaly is inseparable from the non-anomalous
ones, these two contributions are characterized by different time scales in the
later construction. Specifically for sufficiently large inter-valley scattering
time the LMC is dominated by the anomalous contribution, arising from the
chiral anomaly. The predicted scaling of LMC and the signature of chiral
anomaly can be observed in recently proposed candidate materials, accommodating
multi-Weyl semimetals in various solid state compounds.


  We develop a general method of calculating Stimulated Brillouin Scattering
(SBS) gain coefficient in axially periodic waveguides. Applying this method to
a silicon periodic waveguide suspended in air, we demonstrate that SBS
nonlinearity can be dramatically enhanced at the brillouin zone boundary where
the decreased group velocity of light magnifies photon-phonon interaction. In
addition, we show that the symmetry plane perpendicular to the propagation axis
plays an important role in both forward and backward SBS processes. In forward
SBS, only elastic modes which are even about this plane are excitable. In
backward SBS, the SBS gain coefficients of elastic modes approach to either
infinity or constants, depending on their symmetry about this plane at $q=0$.


  The efficient numerical integration of large-scale matrix differential
equations is a topical problem in numerical analysis and of great importance in
many applications. Standard numerical methods applied to such problems require
an unduly amount of computing time and memory, in general. Based on a dynamical
low-rank approximation of the solution, a new splitting integrator is proposed
for a quite general class of stiff matrix differential equations. This class
comprises differential Lyapunov and differential Riccati equations that arise
from spatial discretizations of partial differential equations. The proposed
integrator handles stiffness in an efficient way, and it preserves the symmetry
and positive semidefiniteness of solutions of differential Lyapunov equations.
Numerical examples that illustrate the benefits of this new method are given.
In particular, numerical results for the efficient simulation of the weather
phenomenon El Ni\~no are presented.


  Aims. In this paper, we aim to study the time dependence of sunspot group
areas in a large sample composed of various databases spanning over 130 years,
used state-of-the-art statistical methods.
  Methods. For a carefully selected but unbiased sample, we use Bayesian
modelling to fit the temporal evolution of the combined umbral and penumbral
area of spot groups with a skew-normal function to determine the existence of
any asymmetry in spot growth or decay. Our primary selection criteria
guaranteed that only spot groups with a well-defined maximum area were taken
into account. We also analysed the covariance of the resulting model parameters
and their correlations with the physical parameters of the sunspots and the
ongoing solar cycle.
  Results. Our results show that the temporal evolution of well-observed
sunspot groups that reach at least 50 millionths of a solar hemisphere (MSH) at
their maximum can be fitted surprisingly well with our model. Furthermore, we
show significant asymmetry - described by a skew parameter of fitted curves -
between the growing and decaying phases of analysed sunspot groups. In
addition, we found a weak correlation between the values of skew parameters and
the maximum area of sunspot groups and their hemispherical latitude.


  Alignment-based optically pumped magnetometers (OPMs) are capable of
measuring oscillating magnetic fields with high sensitivity in the fT/sqrt(Hz)
range. Until now, alignment-based magnetometers have only used paraffin-coated
vapour cells to extend the spin relaxation lifetimes of the alkali vapour. The
drawback of these cells is that they are hand-blown and are therefore
time-intensive, and somewhat unreliable, to produce. Buffer gas cells, on the
other hand, can be manufactured on a mass scale using microfabrication
techniques. We present the first demonstration of an alignment-based
magnetometer using a buffer gas vapour cell containing caesium (Cs) alkali
vapour and nitrogen (N2) buffer gas. The OPM is operated at 55 degrees C and we
achieve a 325 fT/sqrt(Hz) sensitivity to 10 kHz oscillating magnetic fields
with an 800 Hz bandwidth. The alignment-based magnetometer uses a single laser
beam for optical pumping and probing and could potentially allow for more rapid
commercialisation of radio-frequency OPMs, due to the robustness of the
one-beam geometry and the potential for mass-scale microfabrication of buffer
gas cells.


  The distribution of metals in groups of galaxies holds important information
about the chemical enrichment history of the Universe. Here we present radial
profiles of temperature and the abundance of iron and silicon of the hot
intragroup medium for a sample of 15 nearby groups of galaxies observed by
Chandra, selected for their regular X-ray morphology. All but one group display
a cool core, the size of which is found to correlate with the mean temperature
of the group derived outside this core. When scaled to this mean temperature,
the temperature profiles are remarkably similar, being analogous to those of
more massive clusters at large radii but significantly flatter inwards of the
temperature peak. The Fe abundance generally shows a central excess followed by
a radial decline, reaching a typical value of 0.1 solar within r_500, a factor
of two lower than corresponding results for clusters. Si shows less systematic
radial variation, on average displaying a less pronounced decline than Fe and
showing evidence for a flattening at large radii. Off-centre abundance peaks
are seen both for Fe and Si in a number of groups with well-resolved cores.
Derived abundance ratios indicate that supernovae type Ia are responsible for
80 per cent of the Fe in the group core, but the type II contribution increases
with radius and completely dominates at r_500. We present fitting formulae for
the radial dependence of temperature and abundances, to facilitate comparison
to results of numerical simulations of group formation and evolution. In a
companion paper, we discuss the implications of these results for feedback and
enrichment in galaxy groups.


  Simultaneous segmentation and detection of liver tumors (hemangioma and
hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic
resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it
is still a challenging task due to: (1) the HCC information on NCMRI is
invisible or insufficient makes extraction of liver tumors feature difficult;
(2) diverse imaging characteristics in multi-modality NCMRI causes feature
fusion and selection difficult; (3) no specific information between hemangioma
and HCC on NCMRI cause liver tumors detection difficult. In this study, we
propose a united adversarial learning framework (UAL) for simultaneous liver
tumors segmentation and detection using multi-modality NCMRI. The UAL first
utilizes a multi-view aware encoder to extract multi-modality NCMRI information
for liver tumor segmentation and detection. In this encoder, a novel edge
dissimilarity feature pyramid module is designed to facilitate the
complementary multi-modality feature extraction. Second, the newly designed
fusion and selection channel is used to fuse the multi-modality feature and
make the decision of the feature selection. Then, the proposed mechanism of
coordinate sharing with padding integrates the multi-task of segmentation and
detection so that it enables multi-task to perform united adversarial learning
in one discriminator. Lastly, an innovative multi-phase radiomics guided
discriminator exploits the clear and specific tumor information to improve the
multi-task performance via the adversarial learning strategy. The UAL is
validated in corresponding multi-modality NCMRI (i.e. T1FS pre-contrast MRI,
T2FS MRI, and DWI) and three phases contrast-enhanced MRI of 255 clinical
subjects. The experiments show that UAL has great potential in the clinical
diagnosis of liver tumors.


  E-business, information serving, and ubiquitous computing will create heavy
request traffic from strangers or even incognitos. Such requests must be
managed automatically. Two ways of doing this are well known: giving every
incognito consumer the same treatment, and rendering service in return for
money. However, different behavior will be often wanted, e.g., for a university
library with different access policies for undergraduates, graduate students,
faculty, alumni, citizens of the same state, and everyone else.
  For a data or process server contacted by client machines on behalf of users
not previously known, we show how to provide reliable automatic access
administration conforming to service agreements. Implementations scale well
from very small collections of consumers and producers to immense client/server
networks. Servers can deliver information, effect state changes, and control
external equipment.
  Consumer privacy is easily addressed by the same protocol. We support
consumer privacy, but allow servers to deny their resources to incognitos. A
protocol variant even protects against statistical attacks by consortia of
service organizations.
  One e-commerce application would put the consumer's tokens on a smart card
whose readers are in vending kiosks. In e-business we can simplify supply chain
administration. Our method can also be used in sensitive networks without
introducing new security loopholes.


  In three dimensions, or more generally, below the upper critical dimension,
scaling laws for critical phenomena seem well understood, for both infinite and
for finite systems. Above the upper critical dimension of four, finite-size
scaling is more difficult.
  Chen and Dohm predicted deviation in the universality of the Binder cumulants
for three dimensions and more for the Ising model. This deviation occurs if the
critical point T = Tc is approached along lines of constant A = L*L*(T-Tc)/Tc,
then different exponents a function of system size L are found depending on
whether this constant A is taken as positive, zero, or negative. This effect
was confirmed by Monte Carlo simulations with Glauber and Creutz kinetics.
Because of the importance of this effect and the unclear situation in the
analogous percolation problem, we here reexamine the five-dimensional Glauber
kinetics.