  In this article, we study the problem of selecting a grasping pose on the
surface of an object to be manipulated by considering three post-grasp
objectives. These objectives include (i) kinematic manipulation capability,
(ii) torque effort \cite{mavrakis2016analysis} and (iii) impact force in case
of a collision during post-grasp manipulative actions. In these works, the main
assumption is that a manipulation task, i.e. trajectory of the centre of mass
(CoM) of an object is given. In addition, inertial properties of the object to
be manipulated is known. For example, a robot needs to pick an object located
at point A and place it at point B by moving it along a given path. Therefore,
the problem to be solved is to find an initial grasp pose that yields the
maximum kinematic manipulation capability, minimum joint effort and effective
mass along a given post-grasp trajectories. However, these objectives may
conflict in some cases making it impossible to obtain the best values for all
of them. We perform a series of experiments to show how different objectives
change as the grasping pose on an object alters. The experimental results
presented in this paper illustrate that these objectives are conflicting for
some desired post-grasp trajectories. This indicates that a detailed
multi-objective optimization is needed for properly addressing this problem in
a future work.


  We consider a random walk with catastrophes which was introduced to model
population biology. It is known that this Markov chain gets eventually absorbed
at $0$ for all parameter values. Recently, it has been shown that this chain
exhibits a metastable behavior in the sense that it can persist for a very long
time before getting absorbed. In this paper we study this metastable phase by
making the parameters converge to extreme values. We obtain four different
limits that we believe shed light on the metastable phase.


  We present here optical spectroscopy and BVRJHK(s) photometry of the recently
discovered ultra-compact group of galaxies CG J1720-67.8. This work represents
a considerable extension of the preliminary results we presented in a previous
paper. Despite the complicated morphology of the group, a quantitative
morphological classification of the three brightest members of the group is
attempted based on photometric analysis. We find that one galaxy is consistent
with a morphological type S0, while the other two are most probably late-type
spirals that are already losing their identity due tothe interaction process.
Information on the star formation activity and dust content derived from both
spectroscopic data and optical and near-infrared colors are complemented with a
reconstruction of far-infrared (FIR) maps from IRAS raw data. Enhanced star
formation activity is revealed in all the group's members, including the
early-type galaxy and the extended tidal tail, along which several tidal dwarf
galaxy candidates are identified. The metallicity of the gaseous component is
investigated and photoionization models are applied to the three main galaxies
of the group, while a detailed study of the tidal dwarf candidates will appear
in a companion paper. Subsolar metal abundances are found for all the three
galaxies, the highest values being shown by the early-type galaxy (Z ~ 0.5
Zsolar).


  The purpose of this paper is to give a counterexample of Theorem 10.4 in
[Ann. of Math. 102 (1975), 223-290]. In the Harvey-Lawson paper, a global
result is claimed, but only a local result is proven. This theorem has had a
big impact on CR geometry for almost a quarter of a century because one can use
the theory of isolated singularities to study the theory of CR manifolds and
vice versa.


  Assuming there is a blazar type continuum in every radio-loud quasar, we find
that the free-free heating due to the beamed infrared continuum can greatly
enhance collisionally excited lines, and thus explain the stronger CIV
$\lambda$1549 line emission observed in radio loud quasars. We further predict
that the CIV line should show variability {\it not} associated with observed
continuum or Ly$\alpha$ variability.


  The Message Passing Interface (MPI) is the prevalent programming model used
on today's supercomputers. Therefore, MPI library developers are looking for
the best possible performance (shortest run-time) of individual MPI functions
across many different supercomputer architectures. Several MPI benchmark suites
have been developed to assess the performance of MPI implementations.
Unfortunately, the outcome of these benchmarks is often neither reproducible
nor statistically sound. To overcome these issues, we show which experimental
factors have an impact on the run-time of blocking collective MPI operations
and how to control them. We address the problem of process and clock
synchronization in MPI benchmarks. Finally, we present a new experimental
method that allows us to obtain reproducible and statistically sound MPI
measurements.


  When some of the parties of a multipartite entangled pure state are lost, the
question arises whether the residual mixed state is also entangled, in which
case the initial entangled pure state is said to be robust against particle
loss. In this paper, we investigate this entanglement robustness for $N$-qubit
pure states. We identify exhaustively all entangled states that are fragile,
i.e., not robust, with respect to the loss of any single qubit of the system.
We also study the entanglement robustness properties of symmetric states and
put these properties in the perspective of the classification of states with
respect to stochastic local operations assisted with classic communication
(SLOCC classification).


  We use a generalized Master equation (GME) formalism to describe the
non-equilibrium time-dependent transport through a short quantum wire connected
to semi-infinite biased leads. The contact strength between the leads and the
wire are modulated by out-of-phase time-dependent functions which simulate a
turnstile device. One lead is fixed at one end of the sample whereas the other
lead has a variable placement. The system is described by a lattice model. We
find that the currents in both leads depend on the placement of the second
lead. In the rather small bias regime we obtain transient currents flowing
against the bias for short time intervals. The GME is solved numerically in
small time steps without resorting to the traditional Markov and rotating wave
approximations. The Coulomb interaction between the electrons in the sample is
included via the exact diagonalization method.


  We extend the De Giorgi-Nash-Moser theory to nonlocal, possibly degenerate
integro-differential operators.


  We study the geodesic X-ray transform $X$ on compact Riemannian surfaces with
conjugate points. Regardless of the type of the conjugate points, we show that
we cannot recover the singularities and therefore, this transform is always
unstable (ill-posed). We describe the microlocal kernel of $X$ and relate it to
the conjugate locus. We present numerical examples illustrating the
cancellation of singularities. We also show that the attenuated X-ray transform
is well posed if the attenuation is positive and there are no more than two
conjugate points along each geodesic; but still ill-posed, if there are three
or more conjugate points. Those results follow from our analysis of the
weighted X-ray transform.


  Consider a card guessing game with complete feedback in which a deck of $n$
cards ordered $1,\dots, n$ is riffle-shuffled once. With the goal to maximize
the number of correct guesses, a player guesses cards from the top of the deck
one at a time under the optimal strategy until no cards remain. We provide an
expression for the expected number of correct guesses with arbitrary number of
terms, an accuracy improvement over the results of Liu (2021). In addition,
using generating functions, we give a unified framework for systematically
calculating higher-order moments. Although the extension of the framework to
$k\geq2$ shuffles is not immediately straightforward, we are able to settle a
long-standing McGrath's conjectured optimal strategy described in Bayer and
Diaconis (1992) by showing that the optimal guessing strategy for $k=1$ riffle
shuffle does not necessarily apply to $k\geq2$ shuffles.


  We present a novel formulation for biochemical reaction networks in the
context of signal transduction. The model consists of input-output transfer
functions, which are derived from differential equations, using stable
equilibria. We select a set of 'source' species, which receive input signals.
Signals are transmitted to all other species in the system (the 'target'
species) with a specific delay and transmission strength. The delay is computed
as the maximal reaction time until a stable equilibrium for the target species
is reached, in the context of all other reactions in the system. The
transmission strength is the concentration change of the target species. The
computed input-output transfer functions can be stored in a matrix, fitted with
parameters, and recalled to build discrete dynamical models. By separating
reaction time and concentration we can greatly simplify the model,
circumventing typical problems of complex dynamical systems. The transfer
function transformation can be applied to mass-action kinetic models of signal
transduction. The paper shows that this approach yields significant insight,
while remaining an executable dynamical model for signal transduction. In
particular we can deconstruct the complex system into local transfer functions
between individual species. As an example, we examine modularity and signal
integration using a published model of striatal neural plasticity. The modules
that emerge correspond to a known biological distinction between
calcium-dependent and cAMP-dependent pathways. We also found that overall
interconnectedness depends on the magnitude of input, with high connectivity at
low input and less connectivity at moderate to high input. This general result,
which directly follows from the properties of individual transfer functions,
contradicts notions of ubiquitous complexity by showing input-dependent signal
transmission inactivation.


  We investigate the optical response of strongly disordered quantum Hall
states in two-dimensional Dirac materials and find qualitatively different
effects in the radiation properties of the bulk versus the edge. We show that
the far-field radiation from the edge is characterized by large multipole
moments (> 50) due to the efficient transfer of angular momentum from the
electrons into the scattered light. The maximum multipole transition moment is
a direct measure of the coherence length of the edge states. Accessing these
multipole transitions would provide new tools for optical spectroscopy and
control of quantum Hall edge states. On the other hand, the far-field radiation
from the bulk appears as random dipole emission with spectral properties that
vary with the local disorder potential. We determine the conditions under which
this bulk radiation can be used to image the disorder landscape. Such optical
measurements can probe sub-micron length scales over large areas and provide
complementary information to scanning probe techniques. Spatially resolving
this bulk radiation would serve as a novel probe of the percolation transition
near half-filling.


  Understanding and tuning the performance of extreme-scale parallel computing
systems demands a streaming approach due to the computational cost of applying
offline algorithms to vast amounts of performance log data. Analyzing large
streaming data is challenging because the rate of receiving data and limited
time to comprehend data make it difficult for the analysts to sufficiently
examine the data without missing important changes or patterns. To support
streaming data analysis, we introduce a visual analytic framework comprising of
three modules: data management, analysis, and interactive visualization. The
data management module collects various computing and communication performance
metrics from the monitored system using streaming data processing techniques
and feeds the data to the other two modules. The analysis module automatically
identifies important changes and patterns at the required latency. In
particular, we introduce a set of online and progressive analysis methods for
not only controlling the computational costs but also helping analysts better
follow the critical aspects of the analysis results. Finally, the interactive
visualization module provides the analysts with a coherent view of the changes
and patterns in the continuously captured performance data. Through a
multi-faceted case study on performance analysis of parallel discrete-event
simulation, we demonstrate the effectiveness of our framework for identifying
bottlenecks and locating outliers.


  We study the multiplication operation of square matrices over lattices. If
the underlying lattice is distributive, then matrices form a semigroup; we
investigate idempotent and nilpotent elements and the maximal subgroups of this
matrix semigroup. We prove that matrix multiplication over nondistributive
lattices is antiassociative, and we determine the invertible matrices in the
case when the least or the greatest element of the lattice is irreducible.


  This article is devoted to the analysis of the convergence rates of several
nu- merical approximation schemes for linear and nonlinear Schr\"odinger
equations on the real line. Recently, the authors have introduced viscous and
two-grid numerical approximation schemes that mimic at the discrete level the
so-called Strichartz dispersive estimates of the continuous Schr\"odinger
equation. This allows to guarantee the convergence of numerical approximations
for initial data in L2(R), a fact that can not be proved in the nonlinear
setting for standard conservative schemes unless more regularity of the initial
data is assumed. In the present article we obtain explicit convergence rates
and prove that dispersive schemes fulfilling the Strichartz estimates are
better behaved for Hs(R) data if 0 < s < 1/2. Indeed, while dispersive schemes
ensure a polynomial convergence rate, non-dispersive ones only yield
logarithmic decay rates.


  One of the key tasks of sentiment analysis of product reviews is to extract
product aspects or features that users have expressed opinions on. In this
work, we focus on using supervised sequence labeling as the base approach to
performing the task. Although several extraction methods using sequence
labeling methods such as Conditional Random Fields (CRF) and Hidden Markov
Models (HMM) have been proposed, we show that this supervised approach can be
significantly improved by exploiting the idea of concept sharing across
multiple domains. For example, "screen" is an aspect in iPhone, but not only
iPhone has a screen, many electronic devices have screens too. When "screen"
appears in a review of a new domain (or product), it is likely to be an aspect
too. Knowing this information enables us to do much better extraction in the
new domain. This paper proposes a novel extraction method exploiting this idea
in the context of supervised sequence labeling. Experimental results show that
it produces markedly better results than without using the past information.


  We present a new catalogue of low-mass X-ray binaries (LMXBs) in the Galaxy.
The catalogue contains source names, coordinates, source types, fluxes,
distances, system parameters, and other characteristic properties of 349 LMXBs,
including systems that have been newly discovered or reclassified since the
most recently reported LMXB catalogues. The aim of this catalogue is to provide
a list of all currently known Galactic objects identified as LMXBs with some
basic information on each system (including X-ray and optical/IR properties
where possible). Literature published before May 2023 has been taken into
account where possible when compiling this information. References for all
reported properties as well as object-finding charts in several energy bands
are provided as part of the catalogue. We plan to update the catalogue
regularly, in particular to reflect new objects discovered in the ongoing
large-scale surveys such as Gaia and eROSITA.


  Given large-scale source code datasets available in open-source projects and
advanced large language models, recent code models have been proposed to
address a series of critical software engineering tasks, such as program repair
and code completion. The training data of the code models come from various
sources, not only the publicly available source code, e.g., open-source
projects on GitHub but also the private data such as the confidential source
code from companies, which may contain sensitive information (for example, SSH
keys and personal information). As a result, the use of these code models may
raise new privacy concerns.
  In this paper, we focus on a critical yet not well-explored question on using
code models: what is the risk of membership information leakage in code models?
Membership information leakage refers to the risk that an attacker can infer
whether a given data point is included in (i.e., a member of) the training
data. To answer this question, we propose Gotcha, a novel membership inference
attack method specifically for code models. We investigate the membership
leakage risk of code models. Our results reveal a worrying fact that the risk
of membership leakage is high: although the previous attack methods are close
to random guessing, Gotcha can predict the data membership with a high true
positive rate of 0.95 and a low false positive rate of 0.10. We also show that
the attacker's knowledge of the victim model (e.g., the model architecture and
the pre-training data) impacts the success rate of attacks. Further analysis
demonstrates that changing the decoding strategy can mitigate the risk of
membership leakage. This study calls for more attention to understanding the
privacy of code models and developing more effective countermeasures against
such attacks.


  The study of muon properties and decays played a crucial role in the early
years of particle physics and contributed over decades to build and consolidate
the Standard Model. At present, searches for muon decays beyond the Standard
Model are performed by exploiting intense beams of muons, and plans exist to
upgrade the present facilities or build new ones, which would open new
prospects for the quest of new physics in this sector. In this paper I review
the present status of the search for muon decays beyond the Standard Model,
with a special attention to the most conventional muon lepton flavor violation
experiments, but also considering more exotic scenarios and future outlooks.


  A regular truncated pyramid with rectangular bases;consists of two
rectangular bases whose centers are orthogonally aligned with respect to the
parallel planes containing their bases; and two pairs of congruent isosceles
trapezoids(the four lateral faces). Thrre are six lengths involved:the larger
base dimensions a and b; a>(or=)b. The smaller base dimensions c and d;
c>(or=d). The height H, and the common length t of the four lateral faces. When
a,b,c,d,H,t, and the volume V are all positive integers; we have an integral
regular truncated pyramid with rectangular bases(see Definition 1 in the
introduction). The two key geometric conditions that the above six lengths must
satisfy are, a/b=c/d(see Section 3) and the equation, 4t^2=
4H^2+(a-c)^2+(b-d)^2 (*), derived in Section4. When H,a,c,b,d,t; are all
positive integers. A modulo4 congruence shows that both the positive integers
a-c and b-d; must be even. Consequently, equation (*) reduces to the equation,
t^2= H^2+x^2+y^2 (**). All the positive integer solutions to (**) can be found,
parametrically described, in reference [1]. Using the general positive integer
solution to (**), we Proposition 1(Section7); which gives precise conditions
that describe the set of all integral regular truncated pyramids with
rectangular bases. In Proposition 2, we describe a 3-parameter family of such
pyramids. In Proposition 3, we describe the square case: a=b> c=d.


  We study stationary solutions in the differential kinetic equation, which was
introduced in for description of a local dual cascade wave turbulence. We give
a full classification of single-cascade states in which there is a finite flux
of only one conserved quantity. Analysis of the steady-state spectrum is based
on a phase-space analysis of orbits of the underlying dynamical system. The
orbits of the dynamical system demonstrate the blow-up behaviour which
corresponds to a "sharp front" where the spectrum vanishes at a finite wave
number. The roles of the KZ and thermodynamic scaling as intermediate
asymptotic, as well as of singular solutions, are discussed.


  We prove the rationality of the characteristic form for a degree one
character of the Galois group of an abelian extension of henselian discrete
valuation fields. We prove the integrality of the characteristic form for a
rank one sheaf on a regular excellent scheme. These properties are shown by
reducing to the corresponding properties of the refined Swan conductor proved
by Kato. We define the F-characteristic cycle of a rank one sheaf on an
arithmetic surface as a cycle on the FW-cotangent bundle using the
characteristic form on the basis of the computation of the characteristic cycle
in the equal characteristic case by Yatagawa. The rationality and the
integrality of the characteristic form are necessary for the definition of the
F-characteristic cycle. We prove the intersection of the F-characteristic cycle
with the 0-section computes the Swan conductor of cohomology of the generic
fiber.


  In the framework of the Floquet scattering-matrix theory we discuss how
electrical and heat currents accessible in mesoscopics are related to the state
of excitations injected by a single-electron source into an electron waveguide.
We put forward an interpretation of a single-particle heat current, which
differs essentially from that of an electrical current. We show that the
knowledge of both a time-dependent electrical current and a time-dependent heat
current allows the full reconstruction of a single-electron wave function. In
addition we compare electrical and heat shot noise caused by splitting of a
regular stream of single-electron excitations. If only one stream impinges on a
wave splitter, the heat shot noise is proportional to the well-known expression
of the charge shot noise, reflecting the partitioning of the incoming single
particles. The situation differs when two electronic streams collide at the
wave splitter. The shot noise suppression, due to the Pauli exclusion
principle, is governed by different overlap integrals in the case of charge and
of heat.


  Modern economies evolved from simpler human exchanges into very convoluted
systems. Today, a multitude of aspects can be regulated, tampered with, or left
to chance; these are economic {\em degrees of freedom} which together shape the
flow of wealth. Economic actors can exploit them, at a cost, and bend that flow
in their favor. If intervention becomes widespread, microeconomic strategies of
different actors can collide or resonate, building into macroeconomic effects.
How viable is a `rigged' economy, and how is this viability affected by growing
economic complexity and wealth? Here we capture essential elements of `rigged'
economies with a toy model. Nash equilibria of payoff matrices in simple cases
show how increased intervention turns economic degrees of freedom from minority
into majority games through a dynamical phase. These stages are reproduced by
agent-based simulations of our model, which allow us to explore scenarios out
of reach for payoff matrices. Increasing economic complexity is then revealed
as a mechanism that spontaneously defuses cartels or consensus situations. But
excessive complexity enters abruptly into a regime of large fluctuations that
threaten the system's viability. This regime results from non-competitive
efforts to intervene the economy coupled across degrees of freedom, becoming
unpredictable. Thus non-competitive actions can result in negative spillover
due to sheer economic complexity. Simulations suggest that wealth must grow
faster than linearly with economic complexity to avoid this regime and keep
economies viable in the long run. Our work provides testable conclusions and
phenomenological charts to guide policing of `rigged' economic systems.


  In this paper, we present PARTIME, a software library written in Python and
based on PyTorch, designed specifically to speed up neural networks whenever
data is continuously streamed over time, for both learning and inference.
  Existing libraries are designed to exploit data-level parallelism, assuming
that samples are batched, a condition that is not naturally met in applications
that are based on streamed data. Differently, PARTIME starts processing each
data sample at the time in which it becomes available from the stream. PARTIME
wraps the code that implements a feed-forward multi-layer network and it
distributes the layer-wise processing among multiple devices, such as Graphics
Processing Units (GPUs). Thanks to its pipeline-based computational scheme,
PARTIME allows the devices to perform computations in parallel. At inference
time this results in scaling capabilities that are theoretically linear with
respect to the number of devices. During the learning stage, PARTIME can
leverage the non-i.i.d. nature of the streamed data with samples that are
smoothly evolving over time for efficient gradient computations. Experiments
are performed in order to empirically compare PARTIME with classic non-parallel
neural computations in online learning, distributing operations on up to 8
NVIDIA GPUs, showing significant speedups that are almost linear in the number
of devices, mitigating the impact of the data transfer overhead.


  The aim of this paper is to find an upper bound for the box-counting
dimension of uniform attractors for non-autonomous dynamical systems. Contrary
to the results in literature, we do not ask the symbol space to have finite
box-counting dimension. Instead, we ask a condition on the semi-continuity of
pullback attractors of the system as time goes to infinity. This
semi-continuity can be achieved if we suppose the existence of
finite-dimensional exponential uniform attractors for the limit symbols. After
showing these new results, we apply them to study the box-counting dimension of
the uniform attractor for a reaction-diffusion equation, and we find a specific
forcing term such that the symbol space has infinite box-counting dimension but
the uniform attractor has finite box-counting dimension anyway.


  We study the weak gravitational lensing effects caused by a stochastic
distribution of dark matter halos. We develop a simple approach to calculate
the magnification probability distribution function which allows us to easily
compute the magnitude bias and dispersion for an arbitrary data sample and a
given universe model. As an application we consider the effects of single-mass
large-scale cosmic inhomogeneities to the SNe magnitude-redshift relation, and
conclude that such structures could bias the PDF enough to affect the
extraction of cosmological parameters from the limited size of present-day SNe
data samples. We also release turboGL, a simple and very fast (<= 1s)
Mathematica code based on the method here presented.


  Clustered federated learning (FL) has been shown to produce promising results
by grouping clients into clusters. This is especially effective in scenarios
where separate groups of clients have significant differences in the
distributions of their local data. Existing clustered FL algorithms are
essentially trying to group together clients with similar distributions so that
clients in the same cluster can leverage each other's data to better perform
federated learning. However, prior clustered FL algorithms attempt to learn
these distribution similarities indirectly during training, which can be quite
time consuming as many rounds of federated learning may be required until the
formation of clusters is stabilized. In this paper, we propose a new approach
to federated learning that directly aims to efficiently identify distribution
similarities among clients by analyzing the principal angles between the client
data subspaces. Each client applies a truncated singular value decomposition
(SVD) step on its local data in a single-shot manner to derive a small set of
principal vectors, which provides a signature that succinctly captures the main
characteristics of the underlying distribution. This small set of principal
vectors is provided to the server so that the server can directly identify
distribution similarities among the clients to form clusters. This is achieved
by comparing the similarities of the principal angles between the client data
subspaces spanned by those principal vectors. The approach provides a simple,
yet effective clustered FL framework that addresses a broad range of data
heterogeneity issues beyond simpler forms of Non-IIDness like label skews. Our
clustered FL approach also enables convergence guarantees for non-convex
objectives. Our code is available at https://github.com/MMorafah/PACFL.


  Let C be the image of a canonical embedding C of the Atkin-Lehner quotient
X+0(N) associated to the Fricke involution wN. In this note we exhibit some
relations among the rational points of C. For each g = 3 (resp. the first g =
4) curve C we found that there are one or more lines (resp. planes) in
projectie space whose intersection with C consists entirely of rational Heegner
points or the cusp point, where N is prime. We also discuss an explanation of
the first non-hyperelliptic exceptional rational point.


  Recently, there has been a great interest in analysing dynamical flows, where
the stationary limit is the minimiser of a convex energy. Particular flows of
great interest have been continuous limits of Nesterov's algorithm and the Fast
Iterative Shrinkage-Thresholding Algorithm (FISTA), respectively.
  In this paper we approach the solutions of linear ill-posed problems by
dynamical flows. Because the squared norm of the residuum of a linear operator
equation is a convex functional, the theoretical results from convex analysis
for energy minimising flows are applicable. We prove that the proposed flows
for minimising the residuum of a linear operator equation are optimal
regularisation methods and that they provide optimal convergence rates for the
regularised solutions. In particular we show that in comparison to convex
analysis results the rates can be significantly higher, which is possible by
constraining the investigations to the particular convex energy functional,
which is the squared norm of the residuum.


  An optical source that produces single photon pulses on demand has potential
applications in linear optics quantum computation, provided that stringent
requirements on indistinguishability and collection efficiency of the generated
photons are met. We show that these are conflicting requirements for anharmonic
emitters that are incoherently pumped via reservoirs. As a model for a
coherently pumped single photon source, we consider cavity-assisted spin-flip
Raman transitions in a single charged quantum dot embedded in a microcavity. We
demonstrate that using such a source, arbitrarily high collection efficiency
and indistinguishability of the generated photons can be obtained
simultaneously with increased cavity coupling. We analyze the role of errors
that arise from distinguishability of the single photon pulses in linear optics
quantum gates by relating the gate fidelity to the strength of the two-photon
interference dip in photon cross-correlation measurements. We find that
performing controlled phase operations with error < 1% requires nano-cavities
with Purcell factors F_P >= 40 in the absence of dephasing, without
necessitating the strong coupling limit.


  Despite its age, quantum theory still suffers from serious conceptual
difficulties. To create clarity, mathematical physicists have been attempting
to formulate quantum theory geometrically and to find a rigorous method of
quantization, but this has not resolved the problem. In this article we argue
that a quantum theory recoursing to quantization algorithms is necessarily
incomplete. To provide an alternative approach, we argue that the Schroedinger
equation is a consequence of three partial differential equations governing the
time evolution of a given probability density. These equations, discovered by
E. Madelung, naturally ground the Schroedinger theory in Newtonian mechanics
and Kolmogorovian probability theory. A variety of far-reaching consequences
for the projection postulate, the correspondence principle, the measurement
problem, the uncertainty principle, and the modelling of particle creation and
annihilation are immediate. We also give a speculative interpretation of the
equations following Bohm, Vigier and Tsekov, by claiming that quantum
mechanical behavior is possibly caused by gravitational background noise.


  We present a new view for duality in classical electromagnetic theory, based
on the physical properties of a dual theory, eliminating the problems of the
usual treatment of the subject.


  One stylized feature of financial volatility impacting the modeling process
is long memory. This paper examines long memory for alternative risk measures,
observed absolute and squared returns for Daily REITs and compares the findings
for a non- REIT equity index. The paper utilizes a variety of tests for long
memory finding evidence that REIT volatility does display persistence, in
contrast to the actual return series. Trading volume is found to be strongly
associated with long memory. The results do however suggest differences in the
findings with regard to REITs in comparison to the broader equity sector which
may be due to relatively thin trading during the sample period.


  We discuss equations associated to Cournot-Nash Equilibria as put forward
recently by Blanchet and Carlier. These equations are related to an optimal
transport problem in which the source measure is known, but the target measure
is part of the problem. The resulting equation is a Monge-Amp\`ere type with
possible nonlocal terms. If the cost function is of a particular form, the
equation is vulnerable to standard optimal transportation PDE techniques, with
some modifications to deal with the new terms. We give some conditions on the
problem from which we can conclude that solutions are smooth.


  Novel implementations based on dense tensor storage are presented for the
singlet-reference perfect quadruples (PQ) [Parkhill, Lawler, and Head-Gordon,
J. Chem. Phys. 130, 084101 (2009)] and perfect hextuples (PH) [Parkhill and
Head-Gordon, J. Chem. Phys. 133, 024103 (2010)] models. The methods are
obtained as block decompositions of conventional coupled-cluster theory that
are exact for four electrons in four orbitals (PQ) and six electrons in six
orbitals (PH), but that can also be applied to much larger systems. PQ and PH
have storage requirements that scale as the square, and as the cube of the
number of active electrons, respectively, and exhibit quartic scaling of the
computational effort for large systems. Applications of the new implementations
are presented for full-valence calculations on linear polyenes (C n H n+2 ),
which highlight the excellent computational scaling of the present
implementations that can routinely handle active spaces of hundreds of
electrons. The accuracy of the models is studied in the {\pi} space of the
polyenes, in hydrogen chains (H 50 ), and in the {\pi} space of polyacene
molecules. In all cases, the results compare favorably to density matrix
renormalization group values. With the novel implementation of PQ, active
spaces of 140 electrons in 140 orbitals can be solved in a matter of minutes on
a single core workstation, and the relatively low polynomial scaling means that
very large systems are also accessible using parallel computing.


  In this work we used dissipative particle dynamics simulations to study the
copolymerization process in the presence of spatial heterogeneities caused by
incompatibility between polymerizing monomers. The polymer sequence details as
well as the resulting system spatial structure in the case if phase segregation
occurs during the chain growth can be predicted using the method. We performed
the model verification with the available literature data on styrene-acrylic
acid copolymerization in the bulk and a very good agreement between
experimental and simulated data for both chain average composition and triad
fractions was observed. Next, we studied the system properties for a model
symmetric reaction process with the reactivity ratios r1 = r2 = 0.5 at
different compositions and Flory-Huggins parameters \c{hi}. We found that the
system average copolymer "composition-feed" curve does not depend on the
\c{hi}-value, but there are significant changes in the copolymer sequences
caused by the density fluctuations. Finally, we investigated the formation of
gradient copolymers during living styrene-acrylic acid copolymerization at a
highly asymmetrical feed composition.


  The gamma ray burst apparent average isotropic power versus their red-shift
of all known GRB (Sept.2009) is reported. It calls for an unrealistic Gamma Ray
Burst Evolution around us or it just probe the need of a very thin gamma
precession-jet model. These precessing and spinning jet are originated by
Inverse Compton and-or Synchrotron Radiation at pulsars or micro-quasars
sources, by ultra-relativistic electrons. These Jets are most powerful at
Supernova birth, blazing, once on axis, to us and flashing GRB detector. The
trembling of the thin jet (spinning, precessing, bent by magnetic fields)
explains naturally the observed erratic multi-explosive structure of different
GRBs and its rare re-brightening. The jets are precessing (by binary companion
or inner disk asymmetry) and decaying by power law on time scales to a few
hours. GRB blazing occurs inside the observer cone of view only a seconds
duration times; because relativistic synchrotron (or IC) laws the jet angle is
thinner in gamma but wider in X band. Its apparent brightening is so well
correlated with its hardness (The Amati correlation). This explain the wider
and longer X GRB afterglow duration and the (not so much) rare presence of
X-ray precursors well before the apparent main GRB explosion. The jet lepton
maybe originated by an inner primary hadron core (as well as pions and muons
secondary Jets). The EGRET, AGILE and Fermi few hardest and late GeV gamma
might be PeV neutron beta decay in flight observed in-axis under a relativistic
shrinkage.


  We investigate ultracold magnetic-field-assisted collisions in the so far
unexplored ErYb system. The nonsphericity of the Er atom leads to weakly
anisotropic interactions that provide the mechanism for Feshbach resonances to
emerge. The resonances are moderately sparsely distributed with a density of
$0.1\,{\rm G}^{-1}-0.3\,{\rm G}^{-1}$ and exhibit chaotic statistics
characterized by a Brody parameter $\eta \approx 0.5-0.7$. The chaotic
behaviour of Feshbach resonances is accompanied by strong mixing of magnetic
and rotational quantum numbers in near-threshold bound states. We predict the
existence of broad resonances at fields $<300\,{\rm G}$ that may be useful for
the precise control of scattering properties and magnetoassociation of ErYb
molecules. The high number of bosonic Er-Yb isotopic combinations gives many
opportunities for mass scaling of interactions. Uniquely, two isotopic
combinations have nearly identical reduced masses (differing by less than
$10^{-5}$ relative) that we expect to have strikingly similar Feshbach
resonance spectra, which would make it possible to experimentally measure their
sensitivity to hypothetical variations of proton-to-electron mass ratio.


  We give an alternative proof of Fedorchuk's recent result that dim X <= Dg X
for compact Hausdorff spaces X. We use the L\"{o}wenheim-Skolem theorem to
reduce the problem to the metric case.


  As financial institutions and professionals increasingly incorporate Large
Language Models (LLMs) into their workflows, substantial barriers, including
proprietary data and specialized knowledge, persist between the finance sector
and the AI community. These challenges impede the AI community's ability to
enhance financial tasks effectively. Acknowledging financial analysis's
critical role, we aim to devise financial-specialized LLM-based toolchains and
democratize access to them through open-source initiatives, promoting wider AI
adoption in financial decision-making. In this paper, we introduce FinRobot, a
novel open-source AI agent platform supporting multiple financially specialized
AI agents, each powered by LLM. Specifically, the platform consists of four
major layers: 1) the Financial AI Agents layer that formulates Financial
Chain-of-Thought (CoT) by breaking sophisticated financial problems down into
logical sequences; 2) the Financial LLM Algorithms layer dynamically configures
appropriate model application strategies for specific tasks; 3) the LLMOps and
DataOps layer produces accurate models by applying training/fine-tuning
techniques and using task-relevant data; 4) the Multi-source LLM Foundation
Models layer that integrates various LLMs and enables the above layers to
access them directly. Finally, FinRobot provides hands-on for both
professional-grade analysts and laypersons to utilize powerful AI techniques
for advanced financial analysis. We open-source FinRobot at
\url{https://github.com/AI4Finance-Foundation/FinRobot}.


  Within the year 2007, MAGIC will be upgraded to a two telescope system at La
Palma. Its main goal is to improve the sensitivity in the
stereoscopic/coincident operational mode. At the same time it will lower the
analysis threshold of the currently running single MAGIC telescope. Results
from the Monte Carlo simulations of this system will be discussed. A comparison
of the two telescope system with the performance of one single telescope will
be shown in terms of sensitivity, angular resolution and energy resolution.


  Oscillons are long-lived, spherically-symmetric, attractor scalar field
configurations that emerge as certain field configurations evolve in time. It
has been known for many years that there is a direct correlation between the
initial configuration's shape and the resulting oscillon lifetime: a shape
memory. In this paper we use an information entropic measure known as
differential configurational entropy (DCE) to obtain estimates of oscillon
lifetimes in scalar field theories with symmetric and asymmetric double-well
potentials. The time-dependent DCE is built form the Fourier-transform of the
two-point correlation function of the energy density of the scalar field. We
obtain a scaling law between oscillon lifetimes and measures obtained form the
time varying DCE. We also show that DCE acts as a pattern discriminator, able
to distinguish initial configurations that evolve into long-lived oscillons
from other non-perturbative short-lived fluctuations.


  The axioms of ZFC provide a foundation for mathematics, however, there are
statements independent of ZFC, such as the Continuum Hypothesis (CH). We
discuss Martin's axiom, which is an alternative to CH that roughly states that
if there is a cardinal strictly between $\omega$ and $2^{\omega}$ it "behaves"
like $\omega$.


  In many parameter estimation problems, the exact model is unknown and is
assumed to belong to a set of candidate models. In such cases, a predetermined
data-based selection rule selects a parametric model from a set of candidates
before the parameter estimation. The existing framework for estimation under
model misspecification does not account for the selection process that led to
the misspecified model. Moreover, in post-model-selection estimation, there are
multiple candidate models chosen based on the observations, making the
interpretation of the assumed model in the misspecified setting non-trivial. In
this work, we present three interpretations to address the problem of
non-Bayesian post-model-selection estimation as an estimation under model
misspecification problem: the naive interpretation, the normalized
interpretation, and the selective inference interpretation, and discuss their
properties. For each of these interpretations, we developed the corresponding
misspecified maximum likelihood estimator and the misspecified
Cram$\acute{\text{e}}$r-Rao-type lower bound. The relations between the
estimators and the performance bounds, as well as their properties, are
discussed. Finally, we demonstrate the performance of the proposed estimators
and bounds via simulations of estimation after channel selection. We show that
the proposed performance bounds are more informative than the oracle
Cram$\acute{\text{e}}$r-Rao Bound (CRB), where the third interpretation
(selective inference) results in the lowest mean-squared-error (MSE) among the
estimators.


  The letter presents 25 discovered supernova candidates from SDSS-DR7 with our
dedicated method, called Sample Decrease, and 10 of them were confirmed by
other research groups, and listed in this letter. Another 15 are first
discovered including 14 type Ia and one type II based on Supernova
Identification (SNID) analysis. The results proved that our method is reliable,
and the description of the method and some detailed spectra analysis procedures
were also presented in this letter.


  By analyzing a paradigmatic example of the theory of dissipative systems --
the classical and quantum dissipative standard map -- we are able to explain
the main features of the decay to the quantum equilibrium state. The classical
isoperiodic stable structures typically present in the parameter space of these
kind of systems play a fundamental role. In fact, we have found that the period
of stable structures that are near in this space determines the phase of the
leading eigenstates of the corresponding quantum superoperator. Moreover, the
eigenvectors show a strong localization on the corresponding periodic orbits
(limit cycles). We show that this sort of scarring phenomenon (an established
property of Hamiltonian and projectively open systems) is present in the
dissipative case and it is of extreme simplicity.


  Magnetic field energy harvesting (MFEH) is a method by which a system can
harness an ambient, alternating magnetic field in order to scavenge energy.
Presented in this article is a novel application of the concept aimed at the
magnetic fields surrounding the rail current in electrified railway. Due to its
non-invasive nature, the approach has the potential to be widely deployed as
part of low-cost trackside condition monitoring systems in order to increase
lifetime and reduce maintenance requirements. In this work, the viability of
MFEH in railway is substantiated experimentally -- two different configurations
are assessed both in a controlled laboratory environment, as well as in situ
along Norwegian railway. When placed near an emulated section of railway
carrying 200 A in the laboratory, the power output of the system is up to 40.5
mW at 50 Hz and 4.15 mW at 16 2/3 Hz. In the field, the prototype system
harvests 109 mJ from a single freight train passing by, rendering an estimated
daily energy output of 1.14 J in a moderately-trafficked location. It is argued
that the approach could indeed eliminate the need for battery replacements, and
potentially increase the lifetime of an energy-efficient, battery-powered
condition monitoring system indefinitely.


  Cavity ring-down spectroscopy is a ubiquitous optical method used to study
light-matter interactions with high resolution, sensitivity and accuracy.
However, it has never been performed with the multiplexing advantages of direct
frequency comb spectroscopy without sacrificing orders of magnitude of
resolution. We present dual-comb cavity ring-down spectroscopy (DC-CRDS) based
on the parallel heterodyne detection of ring-down signals with a local
oscillator comb to yield absorption and dispersion spectra. These spectra are
obtained from widths and positions of cavity modes. We present two approaches
which leverage the dynamic cavity response to coherently or randomly driven
changes in the amplitude or frequency of the probe field. Both techniques yield
accurate spectra of methane - an important greenhouse gas and breath biomarker.
The high sensitivity and accuracy of broadband DC-CRDS, shows promise for
applications like studies of the structure and dynamics of large molecules,
multispecies trace gas detection and isotopic composition.


  We review the properties of classical p-brane solutions to supergravity
theories, i.e. solutions that may be interpreted as Poincare-invariant
hyperplanes in spacetime. Topics covered include the distinction between
elementary/electric and solitonic/magnetic solutions, examples of singularity
and global structure, relations between mass densities, charge densities and
the preservation of unbroken supersymmetry, diagonal and vertical Kaluza-Klein
reduction families, Scherk-Schwarz reduction and domain walls, and the
classification of multiplicities using duality symmetries.


  Subgraph isomorphism counting is known as #P-complete and requires
exponential time to find the accurate solution. Utilizing representation
learning has been shown as a promising direction to represent substructures and
approximate the solution. Graph kernels that implicitly capture the
correlations among substructures in diverse graphs have exhibited great
discriminative power in graph classification, so we pioneeringly investigate
their potential in counting subgraph isomorphisms and further explore the
augmentation of kernel capability through various variants, including
polynomial and Gaussian kernels. Through comprehensive analysis, we enhance the
graph kernels by incorporating neighborhood information. Finally, we present
the results of extensive experiments to demonstrate the effectiveness of the
enhanced graph kernels and discuss promising directions for future research.


  We explore various extensions of computational packages such as Feynarts and
FormCalc in application to calculation of the electron-proton scattering
asymmetries relevant to the G0 and QWeak experiments. Our calculations where
completed up to the Next-to-the-Leading-Order (one-loop) using the dipole form
factor in hadronic vertices. We saw no significant difference between monopole
and dipole type of asymmetry over the range of Q^2 = 0.1 - 1.0 GeV^2. Our
results are in the good agreement with theoretical predictions by the G0
collaboration, but show discrepancy from the experiment. This discrepancy from
the experimental results is due to the strange content of the proton and
possibly new physics as well.


  This paper investigates the automatic monitoring of tool usage during a
surgery, with potential applications in report generation, surgical training
and real-time decision support. Two surgeries are considered: cataract surgery,
the most common surgical procedure, and cholecystectomy, one of the most common
digestive surgeries. Tool usage is monitored in videos recorded either through
a microscope (cataract surgery) or an endoscope (cholecystectomy). Following
state-of-the-art video analysis solutions, each frame of the video is analyzed
by convolutional neural networks (CNNs) whose outputs are fed to recurrent
neural networks (RNNs) in order to take temporal relationships between events
into account. Novelty lies in the way those CNNs and RNNs are trained.
Computational complexity prevents the end-to-end training of "CNN+RNN" systems.
Therefore, CNNs are usually trained first, independently from the RNNs. This
approach is clearly suboptimal for surgical tool analysis: many tools are very
similar to one another, but they can generally be differentiated based on past
events. CNNs should be trained to extract the most useful visual features in
combination with the temporal context. A novel boosting strategy is proposed to
achieve this goal: the CNN and RNN parts of the system are simultaneously
enriched by progressively adding weak classifiers (either CNNs or RNNs) trained
to improve the overall classification accuracy. Experiments were performed in a
dataset of 50 cataract surgery videos and a dataset of 80 cholecystectomy
videos. Very good classification performance are achieved in both datasets:
tool usage could be labeled with an average area under the ROC curve of $A_z =
0.9961$ and $A_z = 0.9939$, respectively, in offline mode (using past, present
and future information), and $A_z = 0.9957$ and $A_z = 0.9936$, respectively,
in online mode (using past and present information only).


  We consider the numerical solution of scalar wave equations in domains which
are the union of a bounded domain and a finite number of infinite cylindrical
waveguides. The aim of this paper is to provide a new convergence analysis of
both the Perfectly Matched Layer (PML) method and the Hardy space infinite
element method in a unified framework. We treat both diffraction and resonance
problems. The theoretical error bounds are compared with errors in numerical
experiments.


  In this paper, by utilizing the idea of stabilizer codes, we give some
relationships between one local unitary representation of braid group in
N-qubit tensor space and the corresponding entanglement properties of the
N-qubit pure state $|\Psi\rangle$, where the N-qubit state $|\Psi\rangle$ is
obtained by applying the braiding operation on the natural basis. Specifically,
we show that the separability of $|\Psi\rangle=\mathcal{B}|0\rangle^{\otimes
N}$ is closely related to the diagrammatic version of the braid operator
$\mathcal{B}$. This may provide us more insights about the topological
entanglement and quantum entanglement.


  In this paper we address the problem of uncertainty management for robust
design, and verification of large dynamic networks whose performance is
affected by an equally large number of uncertain parameters. Many such networks
(e.g. power, thermal and communication networks) are often composed of weakly
interacting subnetworks. We propose intrusive and non-intrusive iterative
schemes that exploit such weak interconnections to overcome dimensionality
curse associated with traditional uncertainty quantification methods (e.g.
generalized Polynomial Chaos, Probabilistic Collocation) and accelerate
uncertainty propagation in systems with large number of uncertain parameters.
This approach relies on integrating graph theoretic methods and waveform
relaxation with generalized Polynomial Chaos, and Probabilistic Collocation,
rendering these techniques scalable. We analyze convergence properties of this
scheme and illustrate it on several examples.


  We introduce a novel image decomposition package, GALPHAT, that provides
robust estimates of galaxy surface brightness profiles using Bayesian Markov
Chain Monte Carlo. The GALPHAT-determined posterior distribution of parameters
enables us to assign rigorous statistical confidence intervals to maximum a
posteriori estimates and to test complex galaxy formation and evolution
hypotheses. We describe the GALPHAT algorithm, assess its performance using
test image data, and demonstrate that it has sufficient speed for production
analysis of a large galaxy sample. Finally we briefly introduce our ongoing
science program to study the distribution of galaxy structural properties in
the local universe using GALPHAT.


  Prompted by a new catalogue of M31 globular clusters, we have collected
together individual metallicity values for globular clusters in the Local
Group. Although we briefly describe the globular cluster systems of the
individual Local Group galaxies, the main thrust of our paper is to examine the
collective properties. In this way we are simulating the dissipationless merger
of the Local Group, into presumably an elliptical galaxy. Such a merger is
dominated by the Milky Way and M31, which appear to be fairly typical examples
of globular cluster systems of spiral galaxies.
  The Local Group `Elliptical' has about 700 +/- 125 globular clusters, with a
luminosity function resembling the `universal' one. The metallicity
distribution has peaks at [Fe/H] ~ -1.55 and -0.64 with a metal-poor to
metal-rich ratio of 2.5:1. The specific frequency of the Local Group Elliptical
is initially about 1 but rises to about 3, when the young stellar populations
fade and the galaxy resembles an old elliptical. The metallicity distribution
and stellar population corrected specific frequency are similar to that of some
known early type galaxies. Based on our results, we briefly speculate on the
origin of globular cluster systems in galaxies.


  We study the problem of creating a character model that can be controlled in
real time from a single image of an anime character. A solution to this problem
would greatly reduce the cost of creating avatars, computer games, and other
interactive applications.
  Talking Head Anime 3 (THA3) is an open source project that attempts to
directly address the problem. It takes as input (1) an image of an anime
character's upper body and (2) a 45-dimensional pose vector and outputs a new
image of the same character taking the specified pose. The range of possible
movements is expressive enough for personal avatars and certain types of game
characters. However, the system is too slow to generate animations in real time
on common PCs, and its image quality can be improved.
  In this paper, we improve THA3 in two ways. First, we propose new
architectures for constituent networks that rotate the character's head and
body based on U-Nets with attention that are widely used in modern generative
models. The new architectures consistently yield better image quality than the
THA3 baseline. Nevertheless, they also make the whole system much slower: it
takes up to 150 milliseconds to generate a frame. Second, we propose a
technique to distill the system into a small network (less than 2 MB) that can
generate 512x512 animation frames in real time (under 30 FPS) using consumer
gaming GPUs while keeping the image quality close to that of the full system.
This improvement makes the whole system practical for real-time applications.


  We propose a self-supervised shared encoder model that achieves strong
results on several visual, language and multimodal benchmarks while being data,
memory and run-time efficient. We make three key contributions. First, in
contrast to most existing works, we use a single transformer with all the
encoder layers processing both the text and the image modalities. Second, we
propose a stage-wise training strategy where the model is first trained on
images, then jointly with unimodal text and image datasets and finally jointly
with text and text-image datasets. Third, to preserve information across both
the modalities, we propose a training pipeline that learns simultaneously from
gradient updates of different modalities at each training update step. The
results on downstream text-only, image-only and multimodal tasks show that our
model is competitive with several strong models while using fewer parameters
and lesser pre-training data. For example, MoMo performs competitively with
FLAVA on multimodal (+3.1), image-only (+1.1) and text-only (-0.1) tasks
despite having 2/5th the number of parameters and using 1/3rd the image-text
training pairs. Finally, we ablate various design choices and further show that
increasing model size produces significant performance gains indicating
potential for substantial improvements with larger models using our approach.


  We present spectra of 59 nearby stars candidates, M dwarfs and white dwarfs,
previously identified using high proper motion catalogues and the DENIS
database. We review the existing spectral classification schemes and
spectroscopic parallax calibrations in the near-infrared $J$-band and derive
spectral types and distances of the nearby candidates. 42 stars have
spectroscopic distances smaller than 25 pc, three of them being white dwarfs.
Two targets lie within 10 pc, one M8 star at 10.0 pc (APMPM J0103-3738), and
one M4 star at 8.3 pc (LP 225-57). One star, LHS 73, is found to be among the
few subdwarfs lying within 20 pc. Furthermore, together with LHS 72, it
probably belongs to the closest pair of subdwarfs we know.


  We give an explicit upper bound for the number of equivalence classes of
binary forms with rational integral coefficients of given degree and given
discriminant, and with given splitting field. Further, we give an explicit
upper bound for the number of irreducible binary forms with rational integral
coefficients with given invariant order. Our bounds depend on as few parameters
as possible. For instance, we show that the number of equivalence classes of
irreducible binary forms with rational integral coefficients of degree r with
given invariant order has an upper bound depending only on r. We have proved
more general results for binary forms with coefficients in the ring of
S-integers of a number field.


  We propose a new nonparametric estimator for first-price auctions with
independent private values that imposes the monotonicity constraint on the
estimated inverse bidding strategy. We show that our estimator has a smaller
asymptotic variance than that of Guerre, Perrigne and Vuong's (2000) estimator.
In addition to establishing pointwise asymptotic normality of our estimator, we
provide a bootstrap-based approach to constructing uniform confidence bands for
the density function of latent valuations.


  We develop a systematic method for computing the angle combinations at all
vertices in an edge-to-edge tiling of the sphere by pentagons with the same
five angles. The method is a useful and necessary step in many tiling problems
about pentagonal tilings of the sphere. As an application, we find all
edge-to-edge tilings of the sphere by angle congruent pentagons, that allow
free and continuous choice of two angle values.


  The metallurgy and materials communities have long known and exploited
fundamental links between chemical and structural ordering in metallic solids
and their mechanical properties. The highest reported strength achievable
through the combination of multiple metals (alloying) has rapidly climbed and
given rise to new classifications of materials with extraordinary properties.
Metallic glasses and high-entropy alloys are two limiting examples of how
tailored order can be used to manipulate mechanical behavior. Here, we show
that the complex electronic-structure mechanisms governing the peak strength of
alloys and pure metals can be reduced to a few physically-meaningful parameters
based on their atomic arrangements and used (with no fitting parameters) to
predict the maximum strength of any metallic solid, regardless of degree of
structural or chemical ordering. Predictions of maximum strength based on the
activation energy for a stress-driven phase transition to an amorphous state is
shown to accurately describe the breakdown in Hall-Petch behavior at the
smallest crystallite sizes for pure metals, intermetallic compounds, metallic
glasses, and high-entropy alloys. This activation energy is also shown to be
directly proportional to interstitial (electronic) charge density, which is a
good predictor of ductility, stiffness (moduli), and phase stability in
high-entropy alloys, and in solid metals generally. The proposed framework
suggests the possibility of coupling ordering and intrinsic strength to
mechanisms like dislocation nucleation, hydrogen embrittlement, and transport
properties. It additionally opens the prospect for greatly accelerated
structural materials design and development to address materials challenges
limiting more sustainable and efficient use of energy.


  Molecular lines observed towards protoplanetary disks carry information about
physical and chemical processes associated with planet formation. We present
ALMA Band 6 observations of C2H, HCN, and C18O in a sample of 14 disks spanning
a range of ages, stellar luminosities, and stellar masses. Using C2H and HCN
hyperfine structure fitting and HCN/H13CN isotopologue analysis, we extract
optical depth, excitation temperature, and column density radial profiles for a
subset of disks. C2H is marginally optically thick (\tau ~1-5) and HCN is quite
optically thick (\tau ~ 5-10) in the inner 200 AU. The extracted temperatures
of both molecules are low (10-30K), indicative of either sub-thermal emission
from the warm disk atmosphere or substantial beam dilution due to chemical
substructure. We explore the origins of C2H morphological diversity in our
sample using a series of toy disk models, and find that disk-dependent overlap
between regions with high UV fluxes and high atomic carbon abundances can
explain a wide range of C2H emission features (e.g. compact vs. extended and
ringed vs. ringless emission). We explore the chemical relationship between
C2H, HCN, and C18O and find a positive correlation between C2H and HCN fluxes,
but no relationship between C2H or HCN with C18O fluxes. We also see no
evidence that C2H and HCN are enhanced with disk age. C2H and HCN seem to share
a common driver, however more work remains to elucidate the chemical
relationship between these molecules and the underlying evolution of C, N, and
O chemistries in disks.


  We review the 5 sigma discovery contours for the charged MSSM Higgs boson at
the CMS experiment with 30/fb for the two cases M_H+ < m_t and M_H+ > m_t. In
order to analyze the search reach we combine the latest results for the CMS
experimental sensitivities based on full simulation studies with
state-of-the-art theoretical predictions of MSSM Higgs-boson production and
decay properties. Special emphasis is put on the SUSY parameter dependence of
the 5 sigma contours. The variation of $\mu$ can shift the prospective
discovery reach in tan_beta by up to Delta tan_beta = 40.


  We show that for nonarchimedean local fields $F$, the pairing from the
algebraic part of the Brauer group of a reductive group $G$ characterizes all
continuous homomorphisms from $G(F)$ into $\mathbb{Q}/\mathbb{Z}$. This
generalizes results of Loughran and Loughran-Tanimoto-Takloo-Bighash.


  The properties of the hydrogen bonds in ethylammonium nitrate are analyzed by
using molecular dynamics simulations and infrared as well as nuclear magnetic
resonance experiments. Ethylammonium nitrate features a flexible
three-dimensional network of hydrogen bonds with moderate strengths, which
makes it distinct from related triethylammonium-based ionic liquids. First, the
network's flexibility is manifested in a not very pronounced correlation of the
hydrogen bond geometries, which is caused by rapid interchanges of bonding
partners. The large flexibility of the network leads to a substantial
broadening of the mid-IR absorption band, with the contributions due to N-H
stretching motions ranging from 2800 to 3250~\cm. Finally, the different
dynamics are also seen in the rotational correlation of the N-H bond vector,
where a correlation time as short as 16.1~ps is observed.


  A {\em conflict-free coloring} of a graph {\em with respect to open} (resp.,
{\em closed}) {\em neighborhood} is a coloring of vertices such that for every
vertex there is a color appearing exactly once in its open (resp., closed)
neighborhood. Similarly, a {\em unique-maximum coloring} of a graph {\em with
respect to open} (resp., {\em closed}) {\em neighborhood} is a coloring of
vertices such that for every vertex the maximum color appearing in its open
(resp., closed) neighborhood appears exactly once. There is a vast amount of
literature on both notions where the colorings need not be proper, i.e.,
adjacent vertices are allowed to have the same color.
  In this paper, we initiate a study of both colorings in the proper settings
with the focus given mainly to planar graphs. We establish upper bounds for the
number of colors in the class of planar graphs for all considered colorings and
provide constructions of planar graphs attaining relatively high values of the
corresponding chromatic numbers. As a main result, we prove that every planar
graph admits a proper unique-maximum coloring with respect to open neighborhood
with at most 10 colors, and give examples of planar graphs needing at least $6$
colors for such a coloring. We also establish tight upper bounds for
outerplanar graphs. Finally, we provide several new bounds also for the
improper setting of considered colorings.


  The Dual-Axis Radiographic Hydrotest (DARHT) facility uses bremsstrahlung
radiation source spots produced by the focused electron beams from two linear
induction accelerators (LIAs) to radiograph large hydrodynamic experiments
driven by high explosives. Radiographic resolution is determined by the size of
the source spot, and beam emittance is the ultimate limitation to spot size. On
the DARHT Axis-II LIA we measure an emittance higher than predicted by
theoretical simulations, and even though this axis produces sub-millimeter
source spots, we are exploring ways to improve the emittance. Some of the
possible causes for the discrepancy have been investigated using
particle-in-cell (PIC) codes, although most of these are discounted based on
beam measurements. The most likely source of emittance growth is a mismatch of
the beam to the magnetic transport, which can cause beam halo.


  The two-dimensional frustrated next nearest neighbor Heisenberg model on the
square lattice is a prime example for a spin system where quantum fluctuations
can either destroy or stabilize magnetic order. The phase boundaries and
staggered moment dependence on the frustration ratio J_2/J_1 of the exchange
constants are fairly well understood both from approximate analytical and
numerical methods. In this work we use exact diagonalization for finite
clusters for an extensive investigation of the more general J_1a,b-J_2 model
which includes a spatial exchange anisotropy between next-neighbor spins. We
introduce a systematic way of tiling the square lattice and, for this low
symmetry model, define a controlled procedure for the finite size scaling that
is compatible with the possible magnetic phases. We obtain ground state
energies, structure factors and ordered moments and compare with the results of
spin wave calculations. We conclude that J_1a,b exchange anisotropy strongly
stabilizes the columnar antiferromagnetic phase for all frustration parameters,
in particular in the region of the spin nematic phase of the isotropic model.


  We consider domain walls in nematic quantum Hall ferromagnets predicted to
form in multivalley semiconductors, recently probed by scanning tunnelling
microscopy experiments on Bi(111) surfaces. We show that the domain wall
properties depend sensitively on the filling factor $\nu$ of the underlying
(integer) quantum Hall states. For $\nu=1$ and in the absence of impurity
scattering we argue that the wall hosts a single-channel Luttinger liquid whose
gaplessness is a consequence of valley and charge conservation. For $\nu=2$, it
supports a two-channel Luttinger liquid, which for sufficiently strong
interactions enters a symmetry-preserving thermal metal phase with a charge gap
coexisting with gapless neutral intervalley modes. The domain wall physics in
this state is identical to that of a bosonic topological insulator protected by
$U(1)\times U(1)$ symmetry, and we provide a formal mapping between these
problems. We discuss other unusual properties and experimental signatures of
these `anomalous' one-dimensional systems.


  We initiate the study of sub-linear sketching and streaming techniques for
estimating the output size of common dictionary compressors such as Lempel-Ziv
'77, the run-length Burrows-Wheeler transform, and grammar compression. To this
end, we focus on a measure that has recently gained much attention in the
information-theoretic community and which approximates up to a polylogarithmic
multiplicative factor the output sizes of those compressors: the normalized
substring complexity function $\delta$.
  We present a data sketch of $O(\epsilon^{-3}\log n + \epsilon^{-1}\log^2 n)$
words that allows computing a multiplicative $(1\pm \epsilon)$-approximation of
$\delta$ with high probability, where $n$ is the string length. The sketches of
two strings $S_1,S_2$ can be merged in $O(\epsilon^{-1}\log^2 n)$ time to yield
the sketch of $\{S_1,S_2\}$, speeding up by orders of magnitude tasks such as
the computation of all-pairs \emph{Normalized Compression Distances} (NCD). If
random access is available on the input, our sketch can be updated in
$O(\epsilon^{-1}\log^2 n)$ time for each character right-extension of the
string. This yields a polylogarithmic-space algorithm for approximating
$\delta$, improving exponentially over the working space of the
state-of-the-art algorithms running in nearly-linear time. Motivated by the
fact that random access is not always available on the input data, we then
present a streaming algorithm computing our sketch in $O(\sqrt n \cdot \log n)$
working space and $O(\epsilon^{-1}\log^2 n)$ worst-case delay per character. We
show that an implementation of our streaming algorithm can estimate {\delta} on
a dataset of 189GB with a throughput of 203MB per minute while using only 5MB
of RAM, and that our sketch speeds up the computation of all-pairs NCD
distances by one order of magnitude, with applications to phylogenetic tree
reconstruction.


  This paper considers the problem of document ranking in information retrieval
systems by Learning to Rank. We propose ConvRankNet combining a Siamese
Convolutional Neural Network encoder and the RankNet ranking model which could
be trained in an end-to-end fashion. We prove a general result justifying the
linear test-time complexity of pairwise Learning to Rank approach. Experiments
on the OHSUMED dataset show that ConvRankNet outperforms systematically
existing feature-based models.


  While firms and factories often adopt technologies like robotics and advanced
manufacturing techniques at a fast rate, government agencies are often seen as
lagging in their adoption of such tools. We offer evidence about the adoption
of robotics from the case of American crime laboratories.


  A $r$-role assignment of a simple graph $G$ is an assignment of $r$ distinct
roles to the vertices of $G$, such that two vertices with the same role have
the same set of roles assigned to related vertices. Furthermore, a specific
$r$-role assignment defines a role graph, in which the vertices are the
distinct $r$ roles, and there is an edge between two roles whenever there are
two related vertices in the graph $G$ that correspond to these roles. We
consider complementary prisms, which are graphs formed from the disjoint union
of the graph with its respective complement, adding the edges of a perfect
matching between their corresponding vertices. In this work, we characterize
the complementary prisms that do not admit a $3$-role assignment. We highlight
that all of them are complementary prisms of disconnected bipartite graphs.
Moreover, using our findings, we show that the problem of deciding whether a
complementary prism has a $3$-role assignment can be solved in polynomial time.


  Let $F$ be a non-Archimedean locally compact field of residue characteristic
$p$, $G$ be an inner form of $GL_n(F)$, $n\ge1$, and $\ell$ be a prime number
different from $p$. We give a numerical criterion for an integral $\ell$-adic
irreducible cuspidal representation $\tilde\rho$ of $G$ to have a
super\-cuspidal irreducible reduction mod $\ell$, by counting inertial classes
of cuspidal representations that are congruent to the inertial class of
$\tilde\rho$, generalizing results by Vign{\'e}ras and Dat.
  In the case the reduction mod $\ell$ of $\tilde\rho$ is not super\-cuspidal
irreducible, we show that this counting argument allows us to compute its
length and the size of the supercuspidal support of its irreducible components.
We define an invariant $w(\tilde\rho)\ge1$ | the product of this length by this
size | which is expected to behave nicely through the local Jacquet-Langlands
correspondence.
  Given an $\ell$-modular irreducible cuspidal representation $\rho$ of $G$ and
a positive integer $a$, we give a criterion for the existence of an integral
$\ell$-adic irreducible cuspidal representation $\tilde\rho$ of $G$ such that
its reduction mod $\ell$ contains $\rho$ and has length $a$. This allows us to
obtain a formula for the cardinality of the set of reductions mod $\ell$ of
inertial classes of $\ell$-adic irreducible cuspidal representations
$\tilde\rho$ with given depth and invariant $w$.
  These results are expected to be useful to prove that the local
Jacquet-Langlands correspondence preserves congruences mod $\ell$.


  For the multiple zeta function zeta2(s1,s2) of two variables,we obtain its
integral representation(involving product of Hurwitz zeta functions) over the
interval [1,infinity),with respect to second variable of Hurwitz zeta function
and also obtain a good approximate to it as a function of s1 and s2,for s1>=1
and s2>1.In particular this approximate is explicitly computable,when s1,s2
differ by an even integer and is good,when s2 is large.We treat zeta3(s1,s2,s3)
likewise.


  We study the complexity of a particular class of board games, which we call
`slide and merge' games. Namely, we consider 2048 and Threes, which are among
the most popular games of their type. In both games, the player is required to
slide all rows or columns of the board in one direction to create a high value
tile by merging pairs of equal tiles into one with the sum of their values.
This combines features from both block pushing and tile matching puzzles, like
Push and Bejeweled, respectively. We define a number of natural decision
problems on a suitable generalization of these games and prove NP-hardness for
2048 by reducing from 3SAT. Finally, we discuss the adaptation of our reduction
to Threes and conjecture a similar result.


  Conditional image-to-video (cI2V) generation aims to synthesize a new
plausible video starting from an image (e.g., a person's face) and a condition
(e.g., an action class label like smile). The key challenge of the cI2V task
lies in the simultaneous generation of realistic spatial appearance and
temporal dynamics corresponding to the given image and condition. In this
paper, we propose an approach for cI2V using novel latent flow diffusion models
(LFDM) that synthesize an optical flow sequence in the latent space based on
the given condition to warp the given image. Compared to previous
direct-synthesis-based works, our proposed LFDM can better synthesize spatial
details and temporal motion by fully utilizing the spatial content of the given
image and warping it in the latent space according to the generated
temporally-coherent flow. The training of LFDM consists of two separate stages:
(1) an unsupervised learning stage to train a latent flow auto-encoder for
spatial content generation, including a flow predictor to estimate latent flow
between pairs of video frames, and (2) a conditional learning stage to train a
3D-UNet-based diffusion model (DM) for temporal latent flow generation. Unlike
previous DMs operating in pixel space or latent feature space that couples
spatial and temporal information, the DM in our LFDM only needs to learn a
low-dimensional latent flow space for motion generation, thus being more
computationally efficient. We conduct comprehensive experiments on multiple
datasets, where LFDM consistently outperforms prior arts. Furthermore, we show
that LFDM can be easily adapted to new domains by simply finetuning the image
decoder. Our code is available at https://github.com/nihaomiao/CVPR23_LFDM.


  In this paper, we give a conditional lower bound of $n^{\Omega(k)}$ on
running time for the classic k-median and k-means clustering objectives (where
n is the size of the input), even in low-dimensional Euclidean space of
dimension four, assuming the Exponential Time Hypothesis (ETH). We also
consider k-median (and k-means) with penalties where each point need not be
assigned to a center, in which case it must pay a penalty, and extend our lower
bound to at least three-dimensional Euclidean space.
  This stands in stark contrast to many other geometric problems such as the
traveling salesman problem, or computing an independent set of unit spheres.
While these problems benefit from the so-called (limited) blessing of
dimensionality, as they can be solved in time $n^{O(k^{1-1/d})}$ or
$2^{n^{1-1/d}}$ in d dimensions, our work shows that widely-used clustering
objectives have a lower bound of $n^{\Omega(k)}$, even in dimension four.
  We complete the picture by considering the two-dimensional case: we show that
there is no algorithm that solves the penalized version in time less than
$n^{o(\sqrt{k})}$, and provide a matching upper bound of $n^{O(\sqrt{k})}$.
  The main tool we use to establish these lower bounds is the placement of
points on the moment curve, which takes its inspiration from constructions of
point sets yielding Delaunay complexes of high complexity.


  In the emerging field of materials informatics, a fundamental task is to
identify physicochemically meaningful descriptors, or materials genes, which
are engineered from primary features and a set of elementary algebraic
operators through compositions. Standard practice directly analyzes the
high-dimensional candidate predictor space in a linear model; statistical
analyses are then substantially hampered by the daunting challenge posed by the
astronomically large number of correlated predictors with limited sample size.
We formulate this problem as variable selection with operator-induced structure
(OIS) and propose a new method to achieve unconventional dimension reduction by
utilizing the geometry embedded in OIS. Although the model remains linear, we
iterate nonparametric variable selection for effective dimension reduction.
This enables variable selection based on ab initio primary features, leading to
a method that is orders of magnitude faster than existing methods, with
improved accuracy. To select the nonparametric module, we discuss a desired
performance criterion that is uniquely induced by variable selection with OIS;
in particular, we propose to employ a Bayesian Additive Regression Trees
(BART)-based variable selection method. Numerical studies show superiority of
the proposed method, which continues to exhibit robust performance when the
input dimension is out of reach of existing methods. Our analysis of
single-atom catalysis identifies physical descriptors that explain the binding
energy of metal-support pairs with high explanatory power, leading to
interpretable insights to guide the prevention of a notorious problem called
sintering and aid catalysis design.


  BeppoSAX observations of the high redshift (z=4.72) blazar GB 1428+4217
confirm the presence of a complex soft X-ray spectrum first seen with the ROSAT
PSPC. Flattening below a rest frame energy of 5 keV can be accounted for by
absorption from an equivalent column density of (cold) gas with N_H ~ 8 x 10^22
cm^-2. Below 2 keV a (variable) excess of a factor ~ 20 above the extrapolated
absorbed spectrum is also detected. These findings are consistent with and
extend to higher redshifts the correlation between increasing soft X-ray
flattening and increasing z, previously pointed out for large samples of
radio-loud quasars. We propose that such features, including X-ray absorption,
soft excess emission as well as absorption in the optical spectra, can be
satisfactorily accounted for by the presence of a highly-ionized nuclear
absorber with column N_H ~ 10^23 cm^-2, with properties possibly related to the
conditions in the nuclear regions of the host galaxy. High energy X-ray
emission consistent with the extrapolation of the medium energy spectrum is
detected up to ~ 300 keV (rest frame).


  ChatGPT is a powerful language model from OpenAI that is arguably able to
comprehend and generate text. ChatGPT is expected to have a large impact on
society, research, and education. An essential step to understand ChatGPT's
expected impact is to study its domain-specific answering capabilities. Here,
we perform a systematic empirical assessment of its abilities to answer
questions across the natural science and engineering domains. We collected 594
questions from 198 faculty members across 5 faculties at Delft University of
Technology. After collecting the answers from ChatGPT, the participants
assessed the quality of the answers using a systematic scheme. Our results show
that the answers from ChatGPT are on average perceived as ``mostly correct''.
Two major trends are that the rating of the ChatGPT answers significantly
decreases (i) as the complexity level of the question increases and (ii) as we
evaluate skills beyond scientific knowledge, e.g., critical attitude.


  Projection-cost preservation is a low-rank approximation guarantee which
ensures that the cost of any rank-$k$ projection can be preserved using a
smaller sketch of the original data matrix. We present a general structural
result outlining four sufficient conditions to achieve projection-cost
preservation. These conditions can be satisfied using tools from the Randomized
Linear Algebra literature.


  Let $X$ be a compact complex manifold with trivial canonical bundle and
satisfying the $\partial\bar{\partial}$-Lemma. We show that the Kuranishi space
of $X$ is a smooth universal deformation and that small deformations enjoy the
same properties as $X$. If, in addition, $X$ admits a complex symplectic form,
then the local Torelli theorem holds and we obtain some information about the
period map.
  We clarify the structure of such manifolds a little by showing that the
Albanese map is a surjective submersion.


  The recently observed two high-spin rotational bands in the proton emitter
$^{113}$Cs are investigated using the cranked shell model with pairing
correlations treated by a particle-number conserving method, in which the Pauli
blocking effects are taken into account exactly. By using the configuration
assignments of band 1 ($\pi 3/2^+[422], \alpha = -1/2$) and band 2 ($\pi
1/2^+[420], \alpha = 1/2$), the experimental moments of inertia and
quasiparticle alignments can be well reproduced by the present calculations,
which in turn strongly support these configuration assignments. Furthermore, by
analyzing the occupation probability $n_\mu$ of each cranked Nilsson level near
the Fermi surface and the contribution of each orbital to the angular momentum
alignments, the backbending mechanism of these two bands is also investigated.


  We introduce a new formulation of local clustering coefficient for weighted
correlation networks. This new formulation is based upon a definition
introduced previously in the neuroscience context and aimed at compensating for
spurious correlations caused by indirect interactions. We modify this
definition further by replacing Pearson's pairwise correlation coefficients and
three-way partial correlation coefficients by the respective Kendall's rank
correlations. This reduces statistical sample size requirements to compute the
correlations, which translates into the possibility of using shorter time
windows and hence into a shorter response time of the real-time climate network
analysis. We construct evolving climate networks of mean sea level pressure
fluctuations and analyze anomalies of local clustering coefficient in these
networks. We develop a broadly applicable statistical methodology to study
association between spatially inhomogeneous georeferenced multivariate time
series and binary-valued spatiotemporal data (or other data reducible to this
representation) and use it to compare the newly proposed formulation of local
clustering coefficient (for weighted correlation networks) to the conventional
one (for unweighted graphs) in terms of the association of these measures in
climate networks to tropical cyclones. Thus we substantiate the previously made
observation that tropical cyclones are associated with anomalously high values
of local clustering coefficient, and confirm that the new formulation shows a
stronger association.


  Any strategy and prior probability together are a coherent conditional
probability that can be extended, generally not in a unique way, to a full
conditional probability. The corresponding class of extensions is studied and a
closed form expression for its envelopes is provided. Then a topological
characterization of the subclasses of extensions satisfying the further
properties of full disintegrability and full strong conglomerability is given
and their envelopes are studied.


  Current wires are considered for compensation of long-range beam-beam
interactions for the High Luminosity upgrade (HL-LHC) of the Large Hadron
Collider at CERN. In this note, we demonstrate the advantage of using Electron
Lens for this purpose instead of a conventional current-bearing wire.


  We propose a definition of directional multivariate subexponential and
convolution equivalent densities and find a useful characterization of these
notions for a class of integrable and almost radial decreasing functions. We
apply this result to show that the density of the absolutely continuous part of
the compound Poisson measure built on a given density $f$ is directionally
convolution equivalent and inherits its asymptotic behaviour from $f$ if and
only if $ f$ is directionally convolution equivalent. We also extend this
characterization to the densities of more general infinitely divisible
distributions on $\mathbb{R}^d$, $d \geq 1$, which are not pure compound
Poisson.


  A free-space-to-chip pipeline is proposed to efficiently transport single
atoms from a magneto-optical trap to an on-chip evanescent field trap. Due to
the reflection of the dipole laser on the chip surface, the conventional
conveyor belt approach can only transport atoms close to the chip surface but
with a distance of about one wavelength, which prevents efficient interaction
between the atom and the on-chip waveguide devices. Here, based on a two-layer
photonic chip architecture, a diffraction beam of the integrated grating with
an incident angle of the Brewster angle is utilized to realize
free-space-to-chip atom pipeline. Numerical simulation verified that the
reflection of the dipole laser is suppressed and that the atoms can be brought
to the chip surface with a distance of only 100nm. Therefore, the pipeline
allows a smooth transport of atoms from free space to the evanescent field trap
of waveguides and promises a reliable atom source for a hybrid photonic-atom
chip.


  Several experimental studies claim to be able to predict the outcome of
simple decisions from brain signals measured before subjects are aware of their
decision. Often, these studies use multivariate pattern recognition methods
with the underlying assumption that the ability to classify the brain signal is
equivalent to predict the decision itself. Here we show instead that it is
possible to correctly classify a signal even if it does not contain any
predictive information about the decision. We first define a simple stochastic
model that mimics the random decision process between two equivalent
alternatives, and generate a large number of independent trials that contain no
choice-predictive information. The trials are first time-locked to the time
point of the final event and then classified using standard machine-learning
techniques. The resulting classification accuracy is above chance level long
before the time point of time-locking. We then analyze the same trials using
information theory. We demonstrate that the high classification accuracy is a
consequence of time-locking and that its time behavior is simply related to the
large relaxation time of the process. We conclude that when time-locking is a
crucial step in the analysis of neural activity patterns, both the emergence
and the timing of the classification accuracy are affected by structural
properties of the network that generates the signal.


  Reinforcement learning is able to solve complex sequential decision-making
tasks but is currently limited by sample efficiency and required computation.
To improve sample efficiency, recent work focuses on model-based RL which
interleaves model learning with planning. Recent methods further utilize policy
learning, value estimation, and, self-supervised learning as auxiliary
objectives. In this paper we show that, surprisingly, a simple representation
learning approach relying only on a latent dynamics model trained by latent
temporal consistency is sufficient for high-performance RL. This applies when
using pure planning with a dynamics model conditioned on the representation,
but, also when utilizing the representation as policy and value function
features in model-free RL. In experiments, our approach learns an accurate
dynamics model to solve challenging high-dimensional locomotion tasks with
online planners while being 4.1 times faster to train compared to
ensemble-based methods. With model-free RL without planning, especially on
high-dimensional tasks, such as the DeepMind Control Suite Humanoid and Dog
tasks, our approach outperforms model-free methods by a large margin and
matches model-based methods' sample efficiency while training 2.4 times faster.


  Building generalized models that can solve many computer vision tasks
simultaneously is an intriguing direction. Recent works have shown image itself
can be used as a natural interface for general-purpose visual perception and
demonstrated inspiring results. In this paper, we explore diffusion-based
vision generalists, where we unify different types of dense prediction tasks as
conditional image generation and re-purpose pre-trained diffusion models for
it. However, directly applying off-the-shelf latent diffusion models leads to a
quantization issue. Thus, we propose to perform diffusion in pixel space and
provide a recipe for finetuning pre-trained text-to-image diffusion models for
dense vision tasks. In experiments, we evaluate our method on four different
types of tasks and show competitive performance to the other vision
generalists.


  Algorithmic stability is a concept from learning theory that expresses the
degree to which changes to the input data (e.g., removal of a single data
point) may affect the outputs of a regression algorithm. Knowing an algorithm's
stability properties is often useful for many downstream applications -- for
example, stability is known to lead to desirable generalization properties and
predictive inference guarantees. However, many modern algorithms currently used
in practice are too complex for a theoretical analysis of their stability
properties, and thus we can only attempt to establish these properties through
an empirical exploration of the algorithm's behavior on various data sets. In
this work, we lay out a formal statistical framework for this kind of
"black-box testing" without any assumptions on the algorithm or the data
distribution and establish fundamental bounds on the ability of any black-box
test to identify algorithmic stability.


  The origin of superconductivity in FeSe monolayer on SrTiO$_3$ belongs to one
of the unresolved mysteries in condensed-matter physics. Here by investigation
of the temperature evolution of the dynamic charge response of FeSe/SrTiO$_3$
we demonstrate that the response of the monolayer itself is nearly temperature
independent. This indicates a constant Fermi surface over a wide range of
temperature, in stark contrast to that of the bulk FeSe and other Fe-based
superconductors. Our results, which manifest the peculiarity of the electronic
structure of the FeSe monolayer, may help for a microscopic understanding of
the superconductivity in Fe-chalcogenide monolayers on oxide surfaces in
general.


  Machine Reading Comprehension (MRC) for question answering (QA), which aims
to answer a question given the relevant context passages, is an important way
to test the ability of intelligence systems to understand human language.
Multiple-Choice QA (MCQA) is one of the most difficult tasks in MRC because it
often requires more advanced reading comprehension skills such as logical
reasoning, summarization, and arithmetic operations, compared to the extractive
counterpart where answers are usually spans of text within given passages.
Moreover, most existing MCQA datasets are small in size, making the learning
task even harder. We introduce MMM, a Multi-stage Multi-task learning framework
for Multi-choice reading comprehension. Our method involves two sequential
stages: coarse-tuning stage using out-of-domain datasets and multi-task
learning stage using a larger in-domain dataset to help model generalize better
with limited data. Furthermore, we propose a novel multi-step attention network
(MAN) as the top-level classifier for this task. We demonstrate MMM
significantly advances the state-of-the-art on four representative MCQA
datasets.


  Prior work on multilingual question answering has mostly focused on using
large multilingual pre-trained language models (LM) to perform zero-shot
language-wise learning: train a QA model on English and test on other
languages. In this work, we explore strategies that improve cross-lingual
transfer by bringing the multilingual embeddings closer in the semantic space.
Our first strategy augments the original English training data with machine
translation-generated data. This results in a corpus of multilingual
silver-labeled QA pairs that is 14 times larger than the original training set.
In addition, we propose two novel strategies, language adversarial training and
language arbitration framework, which significantly improve the (zero-resource)
cross-lingual transfer performance and result in LM embeddings that are less
language-variant. Empirically, we show that the proposed models outperform the
previous zero-shot baseline on the recently introduced multilingual MLQA and
TyDiQA datasets.