title:
Automated Correction for Syntax Errors in Programming Assignments using
  Recurrent Neural Networks

abstract:
  We present a method for automatically generating repair feedback for syntax
errors for introductory programming problems. Syntax errors constitute one of
the largest classes of errors (34%) in our dataset of student submissions
obtained from a MOOC course on edX. The previous techniques for generating
automated feed- back on programming assignments have focused on functional
correctness and style considerations of student programs. These techniques
analyze the program AST of the program and then perform some dynamic and
symbolic analyses to compute repair feedback. Unfortunately, it is not possible
to generate ASTs for student pro- grams with syntax errors and therefore the
previous feedback techniques are not applicable in repairing syntax errors.
  We present a technique for providing feedback on syntax errors that uses
Recurrent neural networks (RNNs) to model syntactically valid token sequences.
Our approach is inspired from the recent work on learning language models from
Big Code (large code corpus). For a given programming assignment, we first
learn an RNN to model all valid token sequences using the set of syntactically
correct student submissions. Then, for a student submission with syntax errors,
we query the learnt RNN model with the prefix to- ken sequence to predict token
sequences that can fix the error by either replacing or inserting the predicted
token sequence at the error location. We evaluate our technique on over 14, 000
student submissions with syntax errors. Our technique can completely re- pair
31.69% (4501/14203) of submissions with syntax errors and in addition partially
correct 6.39% (908/14203) of the submissions.


title:
Reinterpretation of ATLAS 8 TeV searches for Natural SUSY with a
  $R$-Sneutrino LSP

abstract:
  The data obtained by the LHC collaborations clearly show that supersymmetric
models are not realized in nature in a vanilla form and that in particular
strongly interacting supersymmetric particles are most likely heavier than
expected. An exception are the partners of the third generation quarks, which
also play a dominant role in the breaking of the electroweak symmetry. We
consider here an extended class of so-called `natural supersymmetric models'
where we allow for a sneutrino as the lightest supersymmetric particle as it
appears for example in left-right symmetric models and/or models where
supersymmetry is explained via an inverse seesaw mechanism. We evaluate how
much existing ATLAS data constrain such scenarios and obtain roughly a bound of
300 GeV on the charginos if the sneutrinos are lighter than about 120 GeV. For
the stop we find that only masses up to 300 GeV are excluded independent of the
mixing angle. For larger values the exclusion depends on the detail of the
scenario and if the mass exceeds 800 GeV no bound is obtained.


title: Higgs naturalness and the scalar boson proliferation instability problem

abstract:
  Sensitivity to the square of the cutoff scale of quantum corrections of the
Higgs boson mass self-energy has led many authors to conclude that the Higgs
theory suffers from a naturalness or fine-tuning problem. However, speculative
new physics ideas to solve this problem have not manifested themselves yet at
high-energy colliders, such as the Large Hadron Collider at CERN. For this
reason, the role of naturalness as a guide to theory model-building is being
severely questioned. Most attacks suggest that one should not resort to
arguments involving gravity, which is a much less understood quantum field
theory. Another line of attack is against the assumption that there exists a
multitude of additional heavy states specifically charged under the Standard
Model gauge symmetries. Nevertheless, if we give ground on both of these
assaults on naturalness, what remains is a naturalness concern over the
prospect of numerous additional spin-zero scalar states in nature. The
proliferation of heavy scalars generically destabilizes the Higgs boson mass,
raising it to the highest and most remote scalar mass values in nature, thus
straining the legitimacy of the Standard Model. The copious use of extra
scalars in theory model building, from explaining flavor physics to providing
an inflationary potential and more, and the generic expectation of extra scalar
bosons in nature argues for the proliferation instability problem being the
central concern for naturalness of the Standard Model. Some approaches to
solving this problem are presented.


title: Linearizing nonlinear optics

abstract:
  In the framework of linear optics, light fields do not interact with each
other in a medium. Yet, when their field amplitude becomes comparable to the
electron binding energies of matter, the nonlinear motion of these electrons
emits new dipole radiation whose amplitude, frequency and phase differ from the
incoming fields. Such high fields are typically achieved with ultra-short,
femtosecond (1fs = 10-15 sec.) laser pulses containing very broad frequency
spectra. Here, the matter not only couples incoming and outgoing fields but
also causes different spectral components to interact and mix through a
convolution process. In this contribution, we describe how frequency domain
nonlinear optics overcomes the shortcomings arising from this convolution in
conventional time domain nonlinear optics1. We generate light fields with
previously inaccessible properties because the uncontrolled coupling of
amplitudes and phases is turned off. For example, arbitrary phase functions are
transferred linearly to the second harmonic frequency while maintaining the
exact shape of the input power spectrum squared.
  This nonlinear control over output amplitudes and phases opens up new avenues
for applications based on manipulation of coherent light fields. One could
investigate c.f. the effect of tailored nonlinear perturbations on the
evolution of discrete eigenmodes in Anderson localization2. Our approach might
also open a new chapter for controlling electronic and vibrational couplings in
2D-spectroscopy3 by the geometrical optical arrangement.


title: An Observation About Passphrases: Syntax vs Entropy

abstract:
  On the premise that we are using passwords composed of multiple English
words, we argue that using syntactically correct passphrases has no significant
impact on the security in comparison to randomly arranged collections of words.
We only analyze the contribution of the syntax itself. A comparison to the
other kinds of passwords is out of the scope.


title:
Sardinia Radio Telescope: General Description, Technical Commissioning
  and First Light

abstract:
  In the period 2012 June - 2013 October, the Sardinia Radio Telescope (SRT)
went through the technical commissioning phase. The characterization involved
three first-light receivers, ranging in frequency between 300MHz and 26GHz,
connected to a Total Power back-end. It also tested and employed the telescope
active surface installed in the main reflector of the antenna. The instrument
status and performance proved to be in good agreement with the expectations in
terms of surface panels alignment (at present 300 um rms to be improved with
microwave holography), gain (~0.6 K/Jy in the given frequency range), pointing
accuracy (5 arcsec at 22 GHz) and overall single-dish operational capabilities.
Unresolved issues include the commissioning of the receiver centered at 350
MHz, which was compromised by several radio frequency interferences, and a
lower-than-expected aperture efficiency for the 22-GHz receiver when pointing
at low elevations. Nevertheless, the SRT, at present completing its
Astronomical Validation phase, is positively approaching its opening to the
scientific community.


title:
Cauchy difference priors for edge-preserving Bayesian inversion with an
  application to X-ray tomography

abstract:
  We study Cauchy-distributed difference priors for edge-preserving Bayesian
statistical inverse problems. On the contrary to the well-known total variation
priors, one-dimensional Cauchy priors are non-Gaussian priors also in the
discretization limit. Cauchy priors have independent and identically
distributed increments. One-dimensional Cauchy and Gaussian random walks are
special cases of L\'evy $\alpha$-stable random walks with $\alpha=1$ and
$\alpha=2$, respectively. Both random walks can be written in closed-form, and
as priors, they provide smoothing and edge-preserving properties. We briefly
discuss also continuous and discrete L\'evy $\alpha$-stable random walks, and
generalize the methodology to two-dimensional priors.
  We apply the developed algorithm to one-dimensional deconvolution and
two-dimensional X-ray tomography problems. We compute conditional mean
estimates with single-component Metropolis-Hastings and maximum a posteriori
estimates with Gauss-Newton-type optimization method. We compare the proposed
tomography reconstruction method to filtered back-projection estimate and
conditional mean estimates with Gaussian and total variation priors.


title:
End-cretaceous cooling and mass extinction driven by a dark cloud
  encounter

abstract:
  We have identified iridium in an ~5 m-thick section of pelagic sediment cored
in the deep sea floor at Site 886C, in addition to a distinct spike in iridium
at the K-Pg boundary related to the Chicxulub asteroid impact. We distinguish
the contribution of the extraterrestrial matter in the sediments from those of
the terrestrial matter through a Co-Ir diagram, calling it the
"extraterrestrial index" fEX. This new index reveals a broad iridium anomaly
around the Chicxulub spike. Any mixtures of materials on the surface of the
Earth cannot explain the broad iridium component. On the other hand, we find
that an encounter of the solar system with a giant molecular cloud can aptly
explain the component, especially if the molecular cloud has a size of ~100 pc
and the central density of over 2000 protons/cm^3. Kataoka et al. (2013, 2014)
pointed that an encounter with a dark cloud would drive an environmental
catastrophe leading to mass extinction. Solid particles from the hypothesized
dark cloud would combine with the global environment of Earth, remaining in the
stratosphere for at least several months or years. With a sunshield effect
estimated to be as large as -9.3 W m^-2, the dark cloud would have caused
global climate cooling in the last 8 Myr of the Cretaceous period, consistent
with the variations of stable isotope ratios in oxygen (Barrera and Huber,
1990; Li and Keller, 1998; Barrera and Savin, 1999; Li and Keller, 1999) and
strontium (Barrera and Huber, 1990; Ingram, 1995; Sugarman et al., 1995). The
resulting growth of the continental ice sheet also resulted in a regression of
the sea level. The global cooling, which appears to be associated with a
decrease in the diversity of fossils, eventually led to the mass extinction at
the K-Pg boundary.


title:
Voyager observations in the distant heliosheath: An analogy with ISEE-3
  observations in the deep geomagnetic tail

abstract:
  We suggest an analogy between energetic particle and magnetic field
observations made by the Voyager 1 spacecraft in the distant heliosheath at 122
AU in August 2012, and those made in the distant geomagnetic tail by the ISEE 3
spacecraft in 1982-1983, despite large differences in the time and distance
scales. The analogy suggests that in August, 2012, Voyager 1 may not have moved
from the anomalous cosmic ray (ACR)-dominated heliosheath into the interstellar
medium but into a region equivalent to the "lobes" of the geomagnetic tail,
composed of heliospheric field lines which have reconnected with the
interstellar medium beyond the spacecraft and so are open to the entry of
cosmic rays, while heliospheric particles (e.g., ACRs) are free to escape, and
which maintain a ~Parker spiral configuration. The heliopause, analogous to the
magnetopause forming the outer boundary of the lobes, may then lie beyond this
so-called "heliocliff". Even if this analogy is incorrect, the remarkable
similarities between the energetic particle and magnetic field observations in
these very different regions are worth noting.


title: High Dimensional Tests for Functional Networks of Brain Anatomic Regions

abstract:
  There has been increasing interests in learning resting-state brain
functional connectivity of autism disorders using functional magnetic resonance
imaging (fMRI) data. The data in a standard brain template consist of over
200,000 voxel specific time series for each single subject. Such an ultra-high
dimensionality of data makes the voxel-level functional connectivity analysis
(involving four billion voxel pairs) lack of power and extremely inefficient.
In this work, we introduce a new framework to identify functional brain network
at brain anatomic region-level for each individual. We propose two pairwise
tests to detect region dependence, and one multiple testing procedure to
identify global structures of the network. The limiting null distributions of
the test statistics are derived. It is also shown that the tests are rate
optimal when the alternative networks are sparse. The numerical studies show
the proposed tests are valid and powerful. We apply our method to a
resting-state fMRI study on autism and identify patient-unique and
control-unique hub regions. These findings are consistent with autism clinical
symptoms.


title: On the dynamics of laminar-turbulent patterns in plane Couette flow

abstract:
  Plane Couette flow presents a regular oblique turbulent-laminar pattern over
a wide range of Reynolds numbers R between the globally stable base flow
profile at low R<R_g and a uniformly turbulent regime at sufficiently large
R>R_t. The numerical simulations that we have performed on a pattern displaying
a wavelength modulation show a relaxation of that modulation in agreement with
what one would expect from a standard approach in terms of dissipative
structures in extended geometry though the structuration develops on a
turbulent background. Some consequences are discussed.


title:
Adaptive coherence estimator (ACE) for explosive hazard detection using
  wideband electromagnetic induction (WEMI)

abstract:
  The adaptive coherence estimator (ACE) estimates the squared cosine of the
angle between a known target vector and a sample vector in a whitened
coordinate space. The space is whitened according to an estimation of the
background statistics, which directly effects the performance of the statistic
as a target detector. In this paper, the ACE detection statistic is used to
detect buried explosive hazards with data from a Wideband Electromagnetic
Induction (WEMI) sensor. Target signatures are based on a dictionary defined
using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are
summarized as a receiver operator curve (ROC) and compared to other leading
methods.


title: Evolving Shepherding Behavior with Genetic Programming Algorithms

abstract:
  We apply genetic programming techniques to the `shepherding' problem, in
which a group of one type of animal (sheep dogs) attempts to control the
movements of a second group of animals (sheep) obeying flocking behavior. Our
genetic programming algorithm evolves an expression tree that governs the
movements of each dog. The operands of the tree are hand-selected features of
the simulation environment that may allow the dogs to herd the sheep
effectively. The algorithm uses tournament-style selection, crossover
reproduction, and a point mutation. We find that the evolved solutions
generalize well and outperform a (naive) human-designed algorithm.


title:
How to assess case-finding in chronic diseases: Comparison of different
  indices

abstract:
  Recently, we have proposed a new illness-death model that comprises a state
of undiagnosed chronic disease preceding the diagnosed disease. Based on this
model, the question arises how case-finding can be assessed in the presence of
mortality from all these states. We simulate two scenarios of different
performance of case-finding and apply several indices to assess case-finding in
both scenarios. One of the prevalence based indices leads to wrong conclusions.
Some indices are partly insensitive to distinguish the quality of case-finding.
The incidence based indices perform well. If possible, incidence based indices
should be preferred.


title:
Neurally-Guided Procedural Models: Amortized Inference for Procedural
  Graphics Programs using Neural Networks

abstract:
  Probabilistic inference algorithms such as Sequential Monte Carlo (SMC)
provide powerful tools for constraining procedural models in computer graphics,
but they require many samples to produce desirable results. In this paper, we
show how to create procedural models which learn how to satisfy constraints. We
augment procedural models with neural networks which control how the model
makes random choices based on the output it has generated thus far. We call
such models neurally-guided procedural models. As a pre-computation, we train
these models to maximize the likelihood of example outputs generated via SMC.
They are then used as efficient SMC importance samplers, generating
high-quality results with very few samples. We evaluate our method on
L-system-like models with image-based constraints. Given a desired quality
threshold, neurally-guided models can generate satisfactory results up to 10x
faster than unguided models.


title:
Star-disk interaction in classical T Tauri stars revealed using wavelet
  analysis

abstract:
  The extension of the corona of classical T Tauri stars (CTTS) is under
discussion. The standard model of magnetic configuration of CTTS predicts that
coronal magnetic flux tubes connect the stellar atmosphere to the inner region
of the disk. However, differential rotation may disrupt these long loops. The
results from Hydrodynamic modeling of X-ray flares observed in CTTS confirming
the star-disk connection hypothesis are still controversial. Some authors
suggest the presence of the accretion disk prevent the stellar corona to extent
beyond the co-rotation radius, while others simply are not confident with the
methods used to derive loop lengths. We use independent procedures to determine
the length of flaring loops in stars of the Orion Nebula Cluster previously
analyzed using Hydrodynamic models. Our aim is to disentangle between the two
scenarios proposed. We present a different approach to determine the length of
flaring loops based on the oscillatory nature of the loops after strong flares.
We use wavelet tools to reveal oscillations during several flares. The
subsequent analysis of such oscillations is settle on the Physics of coronal
seismology. Our results likely confirm the large extension of the corona of
CTTS and the hypothesis of star-disk magnetic interaction in at least three
CTTS of the Orion Nebula Cluster. Analyzing oscillations in flaring events is a
powerful tool to determine the physical characteristics of magnetic loops in
coronae in stars other than the Sun. The results presented in this work confirm
the star-disk magnetic connection in CTTS.


title:
Conditional Screening for Ultra-high Dimensional Covariates with
  Survival Outcomes

abstract:
  Identifying important biomarkers that are predictive for cancer patients'
prognosis is key in gaining better insights into the biological influences on
the disease and has become a critical component of precision medicine. The
emergence of large-scale biomedical survival studies, which typically involve
excessive number of biomarkers, has brought high demand in designing efficient
screening tools for selecting predictive biomarkers. The vast amount of
biomarkers defies any existing variable selection methods via regularization.
The recently developed variable screening methods, though powerful in many
practical setting, fail to incorporate prior information on the importance of
each biomarker and are less powerful in detecting marginally weak while jointly
important signals. We propose a new conditional screening method for survival
outcome data by computing the marginal contribution of each biomarker given
priorly known biological information. This is based on the premise that some
biomarkers are known to be associated with disease outcomes a priori. Our
method possesses sure screening properties and a vanishing false selection
rate. The utility of the proposal is further confirmed with extensive
simulation studies and analysis of a Diffuse large B-cell lymphoma (DLBCL)
dataset.


title: Numerical Relativity and High Energy Physics: Recent Developments

abstract:
  We review recent progress in the application of numerical relativity
techniques to astrophysics and high-energy physics. We focus on some
developments that took place within the "Numerical Relativity and High Energy
Physics" network, a Marie Curie IRSES action that we coordinated, namely: spin
evolution in black hole binaries, high-energy black hole collisions, compact
object solutions in scalar-tensor gravity, superradiant instabilities and hairy
black hole solutions in Einstein's gravity coupled to fundamental fields, and
the possibility to gain insight into these phenomena using analog gravity
models.


title:
A Character-Level Decoder without Explicit Segmentation for Neural
  Machine Translation

abstract:
  The existing machine translation systems, whether phrase-based or neural,
have relied almost exclusively on word-level modelling with explicit
segmentation. In this paper, we ask a fundamental question: can neural machine
translation generate a character sequence without any explicit segmentation? To
answer this question, we evaluate an attention-based encoder-decoder with a
subword-level encoder and a character-level decoder on four language
pairs--En-Cs, En-De, En-Ru and En-Fi-- using the parallel corpora from WMT'15.
Our experiments show that the models with a character-level decoder outperform
the ones with a subword-level decoder on all of the four language pairs.
Furthermore, the ensembles of neural models with a character-level decoder
outperform the state-of-the-art non-neural machine translation systems on
En-Cs, En-De and En-Fi and perform comparably on En-Ru.


title:
Scattering, bound and quasi-bound states of the generalized symmetric
  Woods-Saxon potential

abstract:
  The exact analytical solutions of the Schr\"odinger equation for the
generalized symmetrical Woods-Saxon potential are examined for the scattering,
bound and quasi-bound states in one dimension. The reflection and transmission
coefficients are analytically obtained. Then, the correlations between the
potential parameters and the reflection-transmission coefficients are
investigated, and a transmission resonance condition is derived. Occurrence of
the transmission resonance has been shown when incident energy of the particle
is equal to one of the resonance energies of the quasi-bound states.


title: Context Directed Reversals and the Ciliate Decryptome

abstract:
  Prior studies of the efficiency of the block interchange (swap) and the
reversal sorting operations on (signed) permutations identified specialized
versions of the these operations. These specialized operations are here called
context directed reversal, abbreviated cdr, and context directed swap,
abbreviated cds. Prior works have also characterized which (signed)
permutations are sortable by cdr or by cds.
  It is now known that when a permutation is cds sortable in n steps, then any
application of n consecutive applicable cds operations will sort it. Examples
show that this is not the case for cdr. This phenomenon is the focus of this
paper. It is proven that if a signed permutation is cdr sortable, then any cdr
fixed point of it is cds sortable (the cds Rescue Theorem). The cds Rescue
Theorem is discussed in the context of a mathematical model for ciliate
micronuclear decryption.
  It is also proven that though for a given permutation the number of cdr
operations leading to different cdr fixed points may be different from each
other, the parity of these two numbers is the same (the cdr Parity Theorem).
This result provides a solution to two previously formulated decision problems
regarding certain combinatorial games.


title:
Dirac electrons in the presence of matrix potential barrier: application
  to graphene and topological insulators

abstract:
  Scattering of a 2D Dirac electrons on a rectangular matrix potential barrier
is considered using the formalism of spinor transfer matrices. It is shown, in
particular, that in the absence of the mass term, the Klein tunneling is not
necessarily suppressed but occurs at oblique incidence. The formalism is
applied to studying waveguiding modes of the barrier, which are supported by
the edge and bulk states. The condition of existence of the uni-directionality
property is found. We show that the band of edge states is always finite with
massless excitations, while the spectrum of the bulk states, depending on
parameters of the barrier, may consist of the infinite or finite band with
both, massive and massless, low-energy excitations. The effect of the Zeeman
term is considered and the condition of appearance of two distinct energy
dependent directions corresponding to the Klein tunneling is found.


title:
A compressive sensing approach for enhancing breast cancer detection
  using a hybrid DBT / NRI configuration

abstract:
  This work presents a novel breast cancer imaging approach that uses
compressive sensing in a hybrid Digital Breast Tomosynthesis (DBT) / Nearfield
Radar Imaging (NRI) system configuration. The non-homogeneous tissue
distribution of the breast, described in terms of dielectric constant and
conductivity, is extracted from the DBT image, and it is used by a full-wave
Finite Difference in the Frequency Domain (FDFD) method to build a linearized
model of the non-linear NRI imaging problem. The inversion of the linear
problem is solved using compressive sensing imaging techniques, which lead to a
reduction on the required number of sensing antennas and operational bandwidth
without loss of performance.


title: Motivic equivalence under semismall flops

abstract:
  We prove that under semismall smooth flops, smooth projective varieties have
(non-canonically) isomorphic Chow motives with coefficients in any noetherian
local ring $\Lambda$ by comparing the pushforward of the constant intersection
complexes through flopping contractions.


title:
Rotational invariance conditions in elasticity, gradient elasticity and
  its connection to isotropy

abstract:
  For homogeneous higher gradient elasticity models we discuss
frame-indifference and isotropy requirements. To this end, we introduce the
notions of local versus global SO(3)-invariance and identify frame-indifference
(traditionally) with global left SO(3)-invariance and isotropy with global
right SO(3)-invariance. For specific restricted representations, the energy may
also be local left SO(3)-invariant as well as local right SO(3)-invariant. Then
we turn to linear models and consider a consequence of frame-indifference
together with isotropy in nonlinear elasticity and apply this joint invariance
condition to some specific linear models. The interesting point is the
appearance of finite rotations in transformations of a geometrically linear
model. It is shown that when starting with a linear model defined already in
the infinitesimal symmetric strain $\varepsilon = {\rm sym} \, {\rm Grad}[u]$,
the new invariance condition is equivalent to isotropy of the linear
formulation. Therefore, it may be used also in higher gradient elasticity
models for a simple check of isotropy and for extensions to anisotropy. In this
respect we consider in more detail variational formulations of the linear
indeterminate couple stress model, a new variant of it with symmetric force
stresses and general linear gradient elasticity.


title: Magnetic control of dipolaritons in quantum dots

abstract:
  Dipolaritons are quasiparticles that arise in coupled quantum wells embedded
in a microcavity, they are a superposition of a photon, a direct exciton and an
indirect exciton. We propose the existence of dipolaritons in a system of two
coupled quantum dots inside a microcavity in direct analogy with the quantum
well case and find that, despite some similarities, dipolaritons in quantum
dots have different properties and can lead to true dark polariton states. We
use a finite system theory to study the effects of the magnetic field on the
system, including the emission, and find that it can be used as a control
parameter of the properties of excitons and dipolaritons, and the overall
magnetic behaviour of the structure.


title: A Persona-Based Neural Conversation Model

abstract:
  We present persona-based models for handling the issue of speaker consistency
in neural response generation. A speaker model encodes personas in distributed
embeddings that capture individual characteristics such as background
information and speaking style. A dyadic speaker-addressee model captures
properties of interactions between two interlocutors. Our models yield
qualitative performance improvements in both perplexity and BLEU scores over
baseline sequence-to-sequence models, with similar gains in speaker consistency
as measured by human judges.


title:
Dynamics of spatial coherence and momentum distribution of polaritons in
  a semiconductor microcavity under conditions of Bose-Einstein condensation

abstract:
  The dynamics of spatial coherence and momentum distribution of polaritons in
the regime of Bose-Einstein condensation are investigated in a GaAs microcavity
with embedded quantum wells under nonresonant excitation with picosecond laser
pulses. It is shown that the onset of the condensate first order sparial
coherence is accompanied by narrowing of the polariton momentum distribution.
At the same time, at sufficiently high excitation densities, there is
significant qualitative discrepancy between the dynamic behavior of the width
of the polariton momentum distribution determined from direct measurements and
that calculated from the coherence spatial distribution. This discrepancy is
observed at the fast initial stage of the polariton system kinetics and,
apparently, results from the strong spatial nonuniformity of the phase of the
condensate wave function, which equilibrates on a much longer time scale.


title: Deformed Calogero-Sutherland model and fractional Quantum Hall effect

abstract:
  The deformed Calogero-Sutherland (CS) model is a quantum integrable system
with arbitrary numbers of two types of particles and reducing to the standard
CS model in special cases. We show that a known collective field description of
the CS model, which is based on conformal field theory (CFT), is actually a
collective field description of the deformed CS model. This provides a natural
application of the deformed CS model in Wen's effective field theory of the
fractional quantum Hall effect (FQHE), with the two kinds of particles
corresponding to electrons and quasi-hole excitations. In particular, we use
known mathematical results about super Jack polynomials to obtain simple
explicit formulas for the orthonormal CFT basis proposed by van Elburg and
Schoutens in the context of the FQHE.


title:
Radio-loud AGN-jet morphology and polarization: the role of ultra-high
  resolution radio surveys

abstract:
  A review of the current challenges for the understanding of the physics of
extragalactic radio jets from supermassive black holes is presented. Also, a
prospect is given about how both very long baseline interferometry (VLBI) and
polarimetric observations help us to understand their physics and the ones of
their magnetic fields. This paper focuses on the impact that previous
ultra-high resolution polarimetric surveys have had on our current knowledge on
radio loud AGN. It first concentrates on the expectations about the improvement
that the Square Kilometer Array, used as a super-sensitive VLBI station on
existing and future very long baseline interferometric arrays, will provide in
terms of access to new source classes, and in terms of a deeper portion of the
Universe than currently accessible. A series of key new radio loud AGN science
fields, to be opened by the Square Kilometer Array, are outlined together with
a collection of preliminary ideas about possible programs of interest to deep
in such fields. The complementary aspects of future super-sensitive VLBI
observations as compared to deep continuum surveys with the Square Kilometer
Array and its precursors are also outlined here.


title: Fast Incremental Method for Nonconvex Optimization

abstract:
  We analyze a fast incremental aggregated gradient method for optimizing
nonconvex problems of the form $\min_x \sum_i f_i(x)$. Specifically, we analyze
the SAGA algorithm within an Incremental First-order Oracle framework, and show
that it converges to a stationary point provably faster than both gradient
descent and stochastic gradient descent. We also discuss a Polyak's special
class of nonconvex problems for which SAGA converges at a linear rate to the
global optimum. Finally, we analyze the practically valuable regularized and
minibatch variants of SAGA. To our knowledge, this paper presents the first
analysis of fast convergence for an incremental aggregated gradient method for
nonconvex problems.


title: Stochastic Variance Reduction for Nonconvex Optimization

abstract:
  We study nonconvex finite-sum problems and analyze stochastic variance
reduced gradient (SVRG) methods for them. SVRG and related methods have
recently surged into prominence for convex optimization given their edge over
stochastic gradient descent (SGD); but their theoretical analysis almost
exclusively assumes convexity. In contrast, we prove non-asymptotic rates of
convergence (to stationary points) of SVRG for nonconvex optimization, and show
that it is provably faster than SGD and gradient descent. We also analyze a
subclass of nonconvex problems on which SVRG attains linear convergence to the
global optimum. We extend our analysis to mini-batch variants of SVRG, showing
(theoretical) linear speedup due to mini-batching in parallel settings.


title: On Kemnitz' Conjecture Concerning Lattice Points in the Plane

abstract:
  In 1961, P. Erd\H{o}s, A. Ginzburg, and A. Ziv proved a remarkable theorem
stating that each set of $2n-1$ integers contains a subset of size $n$, the sum
of whose elements is divisible by $n$. We will prove a similar result for pairs
of integers, i.e., planar lattice points, usually referred to as Kemnitz'
conjecture.


title:
Idempotent bonding relations are nontrivial if and only if they satisfy
  condition Gamma

abstract:
  A relation $f\subseteq X^2$ satisfies condition $\Gamma$ if there exist
distinct $x,y\in X$ with $\langle x,x\rangle ,\langle x,y\rangle ,\langle
y,y\rangle \in f$. The authors improve a previous result by characterizing
nontrivial idempotent bonding relations on compact Hausdorff spaces as those
satisfying condition $\Gamma$.


title: Collision Avoidance for Bi-Steerable Car Using Analytic Left Inversion

abstract:
  A case study is presented of a collision avoidance system that directly
integrates the kinematics of a bi-steerable car with a suitable path planning
algorithm. The first step is to identify a path using the method of rapidly
exploring random trees, and then a spline approximation is computed. The second
step is to solve the output tracking problem by explicitly computing the left
inverse of the kinematics of the system to render the Taylor series of the
desired input for each polynomial section of the spline approximation. The
method is demonstrated by numerical simulation.


title: The parity search problem

abstract:
  We prove that for any positive integers $n$ and $d$ there exists a collection
consisting of $f=d\log n+O(1)$ subsets $A_1, A_2, \ldots, A_f$ of $[n]$ such
that for any two distinct subsets $X$ and $Y$ of $[n]$ whose size is at most
$d$ there is an index $i\in [f]$ for which $| A_i\cap X|$ and $|A_i\cap Y|$
have different parity. Here we think of $d$ as fixed whereas $n$ is thought of
as tending to infinity, and the base of the logarithm is $2$.
  Translated into the language of combinatorial search theory, this tells us
that \[
  d \log n+O(1) \] queries suffice to identify up to $d$ marked items from a
totality of $n$ items if the answers one gets are just whether an even or an
odd number of marked elements has been queried, even if the search is performed
non-adaptively. Since the entropy method easily yields a matching lower bound
for the adaptive version of this problem, our result is asymptotically best
possible.
  This answers a question posed by D\'aniel Gerbner and Bal\'azs Patk\'os in
Gyula O.H. Katona's Search Theory Seminar at the R\'enyi institute.


title: Robust Mechanisms Under Common Valuation

abstract:
  We study robust mechanisms to sell a common-value good. We assume that the
mechanism designer knows the prior distribution of the buyers' common value but
is unsure of the buyers' information structure about the common value. We use
linear programming duality to derive mechanisms that guarantee a good revenue
among all information structures and all equilibria. Our mechanism maximizes
the revenue guarantee when there is one buyer. As the number of buyers tends to
infinity, the revenue guarantee of our mechanism converges to the full surplus.


title:
Reconstruction of CMB Temperature Anisotropies with Primordial CMB
  Induced Polarization in Galaxy Clusters

abstract:
  Scattering of cosmic microwave background (CMB) radiation in galaxy clusters
induces polarization signals determined by the quadrupole anisotropy in the
photon distribution at the location of clusters. This "remote quadrupole"
derived from the measurements of the induced polarization in galaxy clusters
provides an opportunity of reconstruction of local CMB temperature
anisotropies. In this {\em Letter} we develop an algorithm of the
reconstruction through the estimation of the underlying primordial
gravitational potential, which is the origin of the CMB temperature and
polarization fluctuations and CMB induced polarization in galaxy clusters. We
found a nice reconstruction for the quadrupole and octopole components of the
CMB temperature anisotropies with the assistance of the CMB induced
polarization signals. The reconstruction can be an important consistency test
on the puzzles of CMB anomaly, especially for the low quadrupole and axis of
evil problems reported in WMAP and Planck data.


title: $C^{1,\alpha}$ estimates for the parallel refractor

abstract:
  We consider the parallel refractor problem when the planar radiating source
lies in a medium having higher refractive index than the medium in which the
target is located. We prove local $C^{1,\alpha}$ estimates for parallel
refractors under suitable geometric assumptions on the source and target, and
under local regularity hypotheses on the target set. We also discuss existence
of refractors under energy conservation assumptions.


title: Topological String Partition Function on Generalised Conifolds

abstract:
  We show that the partition function on a generalised conifold $C_{m,n}$ with
${m+n \choose m}$ crepant resolutions can be equivalently computed on the
compound du Val singularity $A_{m+n-1}\times \mathbb C$ with a unique crepant
resolution.


title:
Towards Automatic Wild Animal Monitoring: Identification of Animal
  Species in Camera-trap Images using Very Deep Convolutional Neural Networks

abstract:
  Non intrusive monitoring of animals in the wild is possible using camera
trapping framework, which uses cameras triggered by sensors to take a burst of
images of animals in their habitat. However camera trapping framework produces
a high volume of data (in the order on thousands or millions of images), which
must be analyzed by a human expert. In this work, a method for animal species
identification in the wild using very deep convolutional neural networks is
presented. Multiple versions of the Snapshot Serengeti dataset were used in
order to probe the ability of the method to cope with different challenges that
camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and
98.1% in Top-5 in the evaluation set using a residual network topology. Also,
the results show that the proposed method outperforms previous approximations
and proves that recognition in camera-trap images can be automated.


title: Joint Stochastic Approximation learning of Helmholtz Machines

abstract:
  Though with progress, model learning and performing posterior inference still
remains a common challenge for using deep generative models, especially for
handling discrete hidden variables. This paper is mainly concerned with
algorithms for learning Helmholz machines, which is characterized by pairing
the generative model with an auxiliary inference model. A common drawback of
previous learning algorithms is that they indirectly optimize some bounds of
the targeted marginal log-likelihood. In contrast, we successfully develop a
new class of algorithms, based on stochastic approximation (SA) theory of the
Robbins-Monro type, to directly optimize the marginal log-likelihood and
simultaneously minimize the inclusive KL-divergence. The resulting learning
algorithm is thus called joint SA (JSA). Moreover, we construct an effective
MCMC operator for JSA. Our results on the MNIST datasets demonstrate that the
JSA's performance is consistently superior to that of competing algorithms like
RWS, for learning a range of difficult models.


title: Ideals on the Quantum Plane's jet space

abstract:
  The goal of this paper is to introduce some rings that play the role of the
jet spaces of the quantum plane and unlike the quantum plane itself possess
interesting nontrivial prime ideals. We will prove some results (theorems 1-4)
about the prime spectrum of these rings.


title:
Point splitting renormalization of Schwinger induced current in de
  Sitter spacetime

abstract:
  The covariant and gauge invariant calculation of the current expectation
value in the homogeneous electric field in 1+3 dimensional de Sitter spacetime
is shown. The result accords with previous work obtained by using adiabatic
subtraction scheme. We therefore conclude the counterintuitive behaviors of the
current in the infrared (IR) regime such as IR hyperconductivity and negative
current are not artifacts of the renormalization scheme, but are real IR
effects of the spacetime.


title: The Two-Modular Fourier Transform of Binary Functions

abstract:
  In this paper, we provide a solution to the open problem of computing the
Fourier transform of a binary function defined over $n$-bit vectors taking
$m$-bit vector values. In particular, we introduce the two-modular Fourier
transform (TMFT) of a binary function $f:G\rightarrow {\cal R}$, where $G =
(\mathbb{F}_2^n,+)$ is the group of $n$ bit vectors with bitwise modulo two
addition $+$, and ${\cal R}$ is a finite commutative ring of characteristic
$2$. Using the specific group structure of $G$ and a sequence of nested
subgroups of $G$, we define the fast TMFT and its inverse. Since the image
${\cal R}$ of the binary functions is a ring, we can define the convolution
between two functions $f:G\rightarrow {\cal R}$. We then provide the TMFT
properties, including the convolution theorem, which can be used to efficiently
compute convolutions. Finally, we derive the complexity of the fast TMFT and
the inverse fast TMFT.


title: Classification of graph algebras: A selective survey

abstract:
  This survey reports on current progress of programs to classify graph
C*-algebras and Leavitt path algebras up to Morita equivalence using K-theory.
Beginning with an overview and some history, we trace the development of the
classification of simple and nonsimple graph C*-algebras and state theorems
summarizing the current status of these efforts. We then discuss the much more
nascent efforts to classify Leavitt path algebras, and we describe the current
status of these efforts as well as outline current impediments that must be
solved for this classification program to progress. In particular, we give two
specific open problems that must be addressed in order to identify the correct
K-theoretic invariant for classification of simple Leavitt path algebras, and
we discuss the significance of various possible outcomes to these open
problems.


title: Dynamical capacity drop in a nonlinear stochastic traffic model

abstract:
  In this work, we show that the inverse-$\lambda$ shape in the fundamental
diagram of traffic flow can be produced dynamically by a simple nonlinear
mesoscopic model with stochastic noises. The proposed model is based on the
gas-kinetic theory of the traffic system. In our approach, the nonlinearity
leads to the coexistence of different traffic states. The scattering of the
data is thus attributed to the noise terms introduced in the stochastic
differential equations and the transition among the various traffic states.
Most importantly, the observed inverse-$\lambda$ shape and the associated
sudden jump of physical quantities arise due to the effect of stochastic noises
on the stability of the system. The model parameters are calibrated, and a
qualitative agreement is obtained between the data and the numerical
simulations.


title: On consecutive abundant numbers

abstract:
  A positive integer $n$ is called an abundant number if $\sigma (n)\ge 2n$,
where $\sigma (n)$ is the sum of all positive divisors of $n$. Let $E(x)$ be
the largest number of consecutive abundant numbers not exceeding $x$. In 1935,
P. Erd\H os proved that there are two positive constants $c_1$ and $c_2$ such
that $c_1\log\log\log x\le E(x)\le c_2\log\log\log x$. In this paper, we
resolve this old problem by proving that, $E(x)/\log \log\log x$ tends to a
limit as $x\to +\infty$, and the limit value has an explicit form which is
between $3$ and $4$.


title: Constraints and Conditions: the Lasso Oracle-inequalities

abstract:
  We study various constraints and conditions on the true coefficient vector
and on the design matrix to establish non-asymptotic oracle inequalities for
the prediction error, estimation accuracy and variable selection for the Lasso
estimator in high dimensional sparse regression models. We review results from
the literature and we provide simpler and detailed derivation for several
boundedness theorems. In addition, we complement the theory with illustrated
examples.


title: Lower bounds on the two-sided inhomogeneous approximation constant

abstract: We give lower bounds on the case of worst inhomogeneous approximation.


title: On the inhomogeneous spectrum of period two quadratics

abstract:
  We give a complete description of the inhomogeneous spectrum of period two
quadratics down to the first limit point.


title: Segmentation from Natural Language Expressions

abstract:
  In this paper we approach the novel problem of segmenting an image based on a
natural language expression. This is different from traditional semantic
segmentation over a predefined set of semantic classes, as e.g., the phrase
"two men sitting on the right bench" requires segmenting only the two people on
the right bench and no one standing or sitting on another bench. Previous
approaches suitable for this task were limited to a fixed set of categories
and/or rectangular regions. To produce pixelwise segmentation for the language
expression, we propose an end-to-end trainable recurrent and convolutional
network model that jointly learns to process visual and linguistic information.
In our model, a recurrent LSTM network is used to encode the referential
expression into a vector representation, and a fully convolutional network is
used to a extract a spatial feature map from the image and output a spatial
response map for the target object. We demonstrate on a benchmark dataset that
our model can produce quality segmentation output from the natural language
expression, and outperforms baseline methods by a large margin.


title:
Nontrivial Periodic Minimizer for Landau-Brazovskii Model with
  Constraint

abstract:
  Block copolymer, a synthesized polymer material, has found many applications
in industry. It is consisting of multiple sequences of monomer alternating in
series with different monomer blocks. The combination of different polymers
endows the polymer material with rich properties, which are the key to their
important applications. In this paper, we model the copolymers with
Landau-Brazovskii model with additional constraints reflecting physical
structures, which is in the form of a second order variational problem.
Critical points of the functional are interpreted as states of polymers. By
reducing to handy situations, we find a nontrivial periodic minimal solution.
Moreover, the proof is kept as simple and self-contained as possible in our
specific case.


title:
Modelling Temporal Information Using Discrete Fourier Transform for
  Video Classification

abstract:
  Recently, video classification attracts intensive research efforts. However,
most existing works are based on framelevel visual features, which might fail
to model the temporal information, e.g. characteristics accumulated along time.
In order to capture video temporal information, we propose to analyse features
in frequency domain transformed by discrete Fourier transform (DFT features).
Frame-level features are firstly extract by a pre-trained deep convolutional
neural network (CNN). Then, time domain features are transformed and
interpolated into DFT features. CNN and DFT features are further encoded by
using different pooling methods and fused for video classification. In this
way, static image features extracted from a pre-trained deep CNN and temporal
information represented by DFT features are jointly considered for video
classification. We test our method for video emotion classification and action
recognition. Experimental results demonstrate that combining DFT features can
effectively capture temporal information and therefore improve the performance
of both video emotion classification and action recognition. Our approach has
achieved a state-of-the-art performance on the largest video emotion dataset
(VideoEmotion-8 dataset) and competitive results on UCF-101.


title: Risk-Constrained Kelly Gambling

abstract:
  We consider the classic Kelly gambling problem with general distribution of
outcomes, and an additional risk constraint that limits the probability of a
drawdown of wealth to a given undesirable level. We develop a bound on the
drawdown probability; using this bound instead of the original risk constraint
yields a convex optimization problem that guarantees the drawdown risk
constraint holds. Numerical experiments show that our bound on drawdown
probability is reasonably close to the actual drawdown risk, as computed by
Monte Carlo simulation. Our method is parametrized by a single parameter that
has a natural interpretation as a risk-aversion parameter, allowing us to
systematically trade off asymptotic growth rate and drawdown risk. Simulations
show that this method yields bets that out perform fractional-Kelly bets for
the same drawdown risk level or growth rate. Finally, we show that a natural
quadratic approximation of our convex problem is closely connected to the
classical mean-variance Markowitz portfolio selection problem.


title:
An effective theory of GW and FJRW invariants of quintics Calabi-Yau
  manifolds

abstract:
  This is the second part of the project toward an effective algorithm to
evaluate all genus Gromov-Witten invariants of quintic Calabi-Yau threefolds.
In this paper, the localization formula is derived, and algorithms toward
evaluating these Gromov-Witten invariants are derived.


title:
Optimizing Data Freshness, Throughput, and Delay in Multi-Server
  Information-Update Systems

abstract:
  In this work, we investigate the design of information-update systems, where
incoming update packets are forwarded to a remote destination through multiple
servers (each server can be viewed as a wireless channel). One important
performance metric of these systems is the age-of-information or simply age,
which is defined as the time elapsed since the freshest packet at the
destination was generated. Recent studies on information-update systems have
shown that the age-of-information can be reduced by intelligently dropping
stale packets. However, packet dropping may not be appropriate in many
applications, such as news and social updates, where users are interested in
not just the latest updates, but also past news. Therefore, all packets may
need to be successfully delivered. In this paper, we study how to optimize
age-of-information without throughput loss. We consider a general scenario
where incoming update packets do not necessarily arrive in the order of their
generation times. We prove that a preemptive Last Generated First Served (LGFS)
policy simultaneous optimizes the age, throughput, and delay performance in
infinite buffer queueing systems. We also show age-optimality for the LGFS
policy for any finite queue size. These results hold for arbitrary, including
non-stationary, arrival processes. To the best of our knowledge, this paper
presents the first optimal result on minimizing the age-of-information in
communication networks with an external arrival process of information update
packets.


title: The Multiscale Laplacian Graph Kernel

abstract:
  Many real world graphs, such as the graphs of molecules, exhibit structure at
multiple different scales, but most existing kernels between graphs are either
purely local or purely global in character. In contrast, by building a
hierarchy of nested subgraphs, the Multiscale Laplacian Graph kernels (MLG
kernels) that we define in this paper can account for structure at a range of
different scales. At the heart of the MLG construction is another new graph
kernel, called the Feature Space Laplacian Graph kernel (FLG kernel), which has
the property that it can lift a base kernel defined on the vertices of two
graphs to a kernel between the graphs. The MLG kernel applies such FLG kernels
to subgraphs recursively. To make the MLG kernel computationally feasible, we
also introduce a randomized projection procedure, similar to the Nystr\"om
method, but for RKHS operators.


title:
Chebyshev-polynomial expansion of the localization length of Hermitian
  and non-Hermitian random chains

abstract:
  We carry Chebyshev-polynomial expansion of the inverse localization length of
Hermitian and non-Hermitian random chains as function of energy. For Hermitian
models, the expansion produces numerically this energy-dependent function in
one run of the algorithm. This is in strong contrast to the standard
transfer-matrix method, which produces the inverse localization length for a
fixed energy in each run. For non-Hermitian models, as in the transfer-matrix
method, our algorithm computes the inverse localization length for a fixed
(complex) energy. We also find a formula of the Chebyshev-polynomial expansion
of the density of states of non-Hermitian models. As explained in more detail
in the Introduction, our algorithm for non-Hermitian models may be the only
available efficient algorithm for finding the density of states of models with
interactions.


title: An angular momentum conserving Affine-Particle-In-Cell method

abstract:
  We present a new technique for transferring momentum and velocity between
particles and grid with Particle-In-Cell (PIC) calculations which we call
Affine-Particle-In-Cell (APIC). APIC represents particle velocities as locally
affine, rather than locally constant as in traditional PIC. We show that this
representation allows APIC to conserve linear and angular momentum across
transfers while also dramatically reducing numerical diffusion usually
associated with PIC. Notably, conservation is achieved with lumped mass, as
opposed to the more commonly used Fluid Implicit Particle (FLIP) transfers
which require a 'full' mass matrix for exact conservation. Furthermore, unlike
FLIP, APIC retains a filtering property of the original PIC and thus does not
accumulate velocity modes on particles as FLIP does. In particular, we
demonstrate that APIC does not experience velocity instabilities that are
characteristic of FLIP in a number of Material Point Method (MPM)
hyperelasticity calculations. Lastly, we demonstrate that when combined with
the midpoint rule for implicit update of grid momentum that linear and angular
momentum are exactly conserved.


title: Mutually Unbiased Unitaries Bases

abstract:
  We consider the notion of unitary transformations forming bases for subspaces
of $M(d,\mathbb{C})$ such that the square of Hilbert-Schmidt inner product of
matrices from the differing bases is a constant. Moving from the qubit case,
weconstruct the maximal number of such bases for the 4 and 2 dimensional
subspaces while proving the nonexistence of such a construction for the 3
dimensional case. Extending this to higher dimensions, we commit to such a
construct for the case of qutrits and provide evidence for the existence of
such unitaries for prime dimensional quantum systems. Focusing on the qubit
case, we show that the average fidelity for estimating any of such a
transformation is equal to the case for estimating a completely unknown unitary
from $\text{SU}(2)$. This is then followed by a quick application for such
unitaries in a quantum cryptographic setup.


title: Relative Frobenius Formula

abstract:
  For a finite group $G$, Frobenius found a formula for the values of the
function $\sum_{\mathrm{Irr} G} (\dim\, \pi)^{-s}$ for even integers $s$, where
$\mathrm{Irr} G$ is the set of irreducible representations of $G$. We
generalize this formula to the relative case: for a subgroup $H$, we find a
formula for the values of the function $\sum_{\mathrm{Irr} G} (\dim\, \pi)^{-s}
(\dim\, \pi ^H)^{-t}$. We apply our results to compute the E-polynomials of
Fock--Goncharov spaces and to relate the Gelfand property to the geometry of
generalized Fock--Goncharov spaces.


title:
Quadratic Exponential Semimartingales and Application to BSDEs with
  jumps

abstract:
  In this paper, we study a class of Quadratic Backward Stochastic Differential
Equations (QBSDE in short) with jumps and unbounded terminal condition. We
extend the class of quadratic semimartingales introduced by Barrieu and El
Karoui (2013) in the jump diffusion model. The properties of these class of
semimartingales lead us to prove existence result for the solution of a
quadratic BSDEs.


title: Graded PI-exponents of simple Lie superalgebras

abstract:
  We study $\mathbb{Z}_2$-graded identities of simple Lie superalgebras over a
field of characteristic zero. We prove the existence of the graded PI-exponent
for such algebras.


title: Hidden timescale in the response of harmonically driven chaotic systems

abstract:
  Linear response theory relates the response of a system to the power-spectrum
of its fluctuations. However, the response to external driving in realistic
models exhibits a pronounced non-linear blurring of the spectral line-shape.
Considering a driven Bose-Hubbard trimer model we figure out what is the hidden
time scale that controls this smearing effect. Contrary to conventional wisdom,
the Fermi-golden-rule picture fails miserably in predicting the non-linear
width of the transitions. Instead, if the system has a classical limit, the
determination of the hidden time scale requires taking into account the
underlying classical phase-space dynamics.


title:
A Monte Carlo estimate for the fraction of thermal Comptonized photons
  that impinge back on the soft source in neutron star LMXBs

abstract:
  In earlier works, it was shown that the energy dependent soft time lags
observed in kHz QPOs of neutron star low mass X-ray binaries (LMXBs) can be
explained as being due to Comptonization lags provided a significant fraction
($\eta \sim 0.2$ - $0.8$) of the Comptonized photons impinge back into the soft
photon source. Here we use a Monte Carlo scheme to verify if such a fraction is
viable or not. In particular we consider three different Comptonizing medium
geometries: (i) a spherical shell, (ii) a boundary layer like torus and (iii) a
corona on top of an accretion disk. Two set of spectral parameters
corresponding to the 'hot' and 'cold' seed photon models were explored. The
general result of the study is that for a wide range of sizes, the fraction
lies within $\eta \sim 0.3$ - $0.7$, and hence compatible with the range
required to explain the soft time lags. Since there is a large uncertainty in
the range, we cannot concretely rule out any of the geometries or spectral
models, but the analysis suggests that a boundary layer type geometry with a
'cold' seed spectral model is favoured over an accretion corona model. Better
quality data will allow one to constrain the geometry more rigorously. Our
results emphasise that there is significant heating of the soft photon source
by the Comptonized photons and hence this effect needs to be taken into account
for any detailed study of these sources.


title: Distinguishing CP-odd couplings of the Higgs boson to weak boson pairs

abstract:
  We consider the observable effects of CP-violating anomalous $ZZh$
interaction arising from gauge invariant dimension-6 operators at the Large
Hadron Collider (LHC), with the purpose of distinguishing them from not only
the standard model effects but also those of CP-even anomalous interactions of
similar nature. The postulation of a gauge invariant origin makes various
couplings of this kind interrelated. The updated constraints from the LHC as
well as limits from neutron and electron dipole moments are used in selecting
the benchmark interaction strengths. We use some asymmetry parameters that have
no contribution from standard or CP-even anomalous interactions. Parton
showering and detector level simulation is included to make our analysis as
realistic as possible. On the whole, we conclude that gauge invariant
interaction of strength $\geq 40/\rm TeV^{2} $ can be successfully isolated
using integrated luminosities in the 1.5-3.0 $\rm ab^{-1}$ range.


title:
Switching Economics for Physics and the Carbon Price Inflation: Problems
  in Integrated Assessment Models and their Implications

abstract:
  Integrated Assessment Models (IAMs) are mainstay tools for assessing the
long-term interactions between climate and the economy and for deriving optimal
policy responses in the form of carbon prices. IAMs have been criticized for
controversial discount rate assumptions, arbitrary climate damage functions,
and the inadequate handling of potentially catastrophic climate outcomes. We
review these external shortcomings for prominent IAMs before turning our focus
on an internal modeling fallacy: the widespread misapplication of the Constant
Elasticity of Substitution (CES) function for the technology transitions
modeled by IAMs. Applying CES, an economic modeling approach, on technical
factor inputs over long periods where an entire factor (the greenhouse gas
emitting fossil fuel inputs) must be substituted creates artifacts that fail to
match the S-curve patterns observed historically. A policy critical result, the
monotonically increasing cost of carbon, a universal feature of IAMs, is called
into question by showing that it is unrealistic as it is an artifact of the
modeling approach and not representative of the technical substitutability
potential nor of the expected cost of the technologies. We demonstrate this
first through a simple but representative example of CES application on the
energy system and with a sectoral discussion of the actual fossil substitution
costs. We propose a methodological modification using dynamically varying
elasticity of substitution as a plausible alternative to model the energy
transition in line with the historical observations and technical realities
within the existing modeling systems. Nevertheless, a fundamentally different
approach based on physical energy principles would be more appropriate.


title:
On base point free theorem and Mori dream spaces for log canonical
  threefolds over the algebraic closure of a finite field

abstract:
  The authors and D. Martinelli proved the base point free theorem for big line
bundles on a three-dimensional log canonical projective pair defined over the
algebraic closure of a finite field. In this paper, we drop the bigness
condition when the characteristic is larger than five. Additionally, we discuss
Mori dream spaces defined over the algebraic closure of a finite field.


title: A Selection Theorem for Banach Bundles and Applications

abstract:
  It is shown that certain lower semi-continuous maps from a paracompact space
to the family of closed subsets of the bundle space of a Banach bundle admit
continuous selections. This generalization of the theorem of Douady, dal
Soglio-Herault, and Hofmann on the fullness of Banach bundles has applications
to establishing conditions under which the induced maps between the spaces of
sections of Banach bundles are onto. Another application is to a generalization
of the theorem of Bartle and Graves for Banach bundle maps that are onto their
images. Other applications of the selection theorem are to the study begun by
Behrends and continued by Gierz of the M-ideals of the space of bounded
sections. A class of Banach bundles that generalizes the class of locally
trivial bundles is introduced and some properties of the Banach bundles in this
class are discussed.


title: Average position of quantum walks with an arbitrary initial state

abstract:
  We investigated discrete time quantum walks with an arbitrary initial state
$\mid\Psi_{0}(\theta,\phi,\varphi)\rangle=\cos\theta
e^{i\phi}\mid0L\rangle+\sin\theta e^{i\varphi}\mid0R\rangle$ with a U(2) coin
$U(\alpha,\beta,\gamma)$. We discover that the average position
$\bar{x}=\max(\bar{x})\cos(\alpha+\gamma+\phi-\varphi)$, with coin operator
$U(\alpha,\pi/4,\gamma)$ and initial state
$\mid\Phi_{0}(\pi/4,\phi,\varphi)\rangle=(e^{i\phi}\mid0L\rangle+e^{i\varphi}\mid0R\rangle)\sqrt{2}/2$.
If we set initial state and coin operator to
$\mid\Phi_{0}\rangle(\theta,\pi/2,0)=i\cos\theta\mid0L\rangle+\sin\theta\mid0R\rangle)$
and coin operator $U(0,\pi/4,0)$, for $\alpha+\gamma+\phi-\varphi=\pi/2$, we
discover that $\bar{x}=-\max(\bar{x})\cos(2\theta).$ Last we verify the result
above, and obtain the summarize properties of quantum walks with an arbitrary
state. We get that
$\bar{x}(\theta,\phi,\varphi,\alpha,\beta,\gamma,t)=\cos2\theta*\bar{x}_{|0L\rangle}(\beta,t)+\sin2\theta*\cos(\alpha+\gamma+\phi-\varphi)*\bar{x}_{(\mid0L\rangle+\mid0R\rangle)\sqrt{2}/2}(\alpha=\gamma=0,\beta,t)$.
If the average positions $\bar{x}$ with initial state $|0L\rangle$ and
$\mid\Psi_{0}\rangle=(\mid0L\rangle+\mid0R\rangle)\sqrt{2}/2$ and coin operator
$U(0,\beta,0)$ are known, we can get the average position result of quantum
walks with an arbitrary initial state and a U(2) coin operator.


title:
Assessing the Navigational Effects of Click Biases and Link Insertion on
  the Web

abstract:
  Websites have an inherent interest in steering user navigation in order to,
for example, increase sales of specific products or categories, or to guide
users towards specific information. In general, website administrators can use
the following two strategies to influence their visitors' navigation behavior.
First, they can introduce click biases to reinforce specific links on their
website by changing their visual appearance, for example, by locating them on
the top of the page. Second, they can utilize link insertion to generate new
paths for users to navigate over. In this paper, we present a novel approach
for measuring the potential effects of these two strategies on user navigation.
Our results suggest that, depending on the pages for which we want to increase
user visits, optimal link modification strategies vary. Moreover, simple
topological measures can be used as proxies for assessing the impact of the
intended changes on the navigation of users, even before these changes are
implemented.


title: A Survey on Object Detection in Optical Remote Sensing Images

abstract:
  Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.


title:
Extracting Predictive Information from Heterogeneous Data Streams using
  Gaussian Processes

abstract:
  Financial markets are notoriously complex environments, presenting vast
amounts of noisy, yet potentially informative data. We consider the problem of
forecasting financial time series from a wide range of information sources
using online Gaussian Processes with Automatic Relevance Determination (ARD)
kernels. We measure the performance gain, quantified in terms of Normalised
Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson
correlation, from fusing each of four separate data domains: time series
technicals, sentiment analysis, options market data and broker recommendations.
We show evidence that ARD kernels produce meaningful feature rankings that help
retain salient inputs and reduce input dimensionality, providing a framework
for sifting through financial complexity. We measure the performance gain from
fusing each domain's heterogeneous data streams into a single probabilistic
model. In particular our findings highlight the critical value of options data
in mapping out the curvature of price space and inspire an intuitive, novel
direction for research in financial prediction.


title: Top physics in ATLAS

abstract:
  These proceedings summarize the latest measurements on top production, top
properties and searches using the ATLAS detector at the LHC. The measurements
are performed on $pp$ collision data with a center of mass energy $\sqrt{s} =
7, 8$ and $13$ TeV.


title: Constructing Embeddings and Isomorphisms of Finite Abstract Semigroups

abstract:
  We present a search algorithm for constructing embeddings and deciding
isomorphisms of semigroups, working with their multiplication tables. The
algorithm is used for enumerating diagram semigroups up to isomorphism and for
finding minimal degree representations.


title:
An Ancient Diophantine Equation with applications to Numerical Curios
  and Geometric Series

abstract:
  In this paper we examine the diophantine equation $x^k-y^k=x-y$ where $k$ is
a positive integer $\geq 2$, and consider its applications. While the complete
solution of the equation $x^k-y^k=x-y$ in positive rational numbers is already
known when $k=2$ or $3$, till now only one numerical solution of the equation
in positive rational numbers has been published when $k=4$, and no nontrivial
solution is known when $k \geq 5$. We describe a method of generating
infinitely many positive rational solutions of the equation when $k=4$. We use
the positive rational solutions of the equation with $k=2,\, 3$ or 4 to produce
numerical curios involving square roots, cube roots and fourth roots, and as
another application of these solutions, we show how to construct examples of
geometric series with an interesting property.


title:
Conformally-related Einstein-Langevin equations for metric fluctuations
  in stochastic gravity

abstract:
  For a conformally-coupled scalar field we obtain the conformally-related
Einstein-Langevin equations, using appropriate transformations for all the
quantities in the equations between two conformally-related spacetimes. In
particular, we analyze the transformations of the influence action, the stress
energy tensor, the noise kernel and the dissipation kernel. In due course the
fluctuation-dissipation relation is also discussed. The analysis in this paper
thereby facilitates a general solution to the Einstein-Langevin equation once
the solution of the equation in a simpler, conformally-related spacetime is
known. For example, from the Minkowski solution of Martin and Verdaguer, those
of the Einstein-Langevin equations in conformally-flat spacetimes, especially
for spatially-flat Friedmann-Robertson-Walker models, can be readily obtained.


title: Borel summability of perturbative series in 4d N=2 and 5d N=1 theories

abstract:
  We study weak coupling perturbative series in 4d N=2 and 5d N=1
supersymmetric gauge theories with Lagrangians. We prove that the perturbative
series of these theories in zero instanton sector are Borel summable for
various observables. Our result for 4d $\mathcal{N}=2$ case supports an
expectation from a recent proposal on a semiclassical realization of infrared
renormalons in QCD-like theories, where the semiclassical solution does not
exist in N=2 theories and the perturbative series are unambiguous, namely Borel
summable. We also prove that the perturbative series in arbitrary number of
instanton sector are Borel summable for a wide class of theories. It turns out
that exact results can be obtained by summing over the Borel resummations in
each number of instanton sector.


title:
RotationNet: Joint Object Categorization and Pose Estimation Using
  Multiviews from Unsupervised Viewpoints

abstract:
  We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which are learned in an unsupervised manner during the training
using an unaligned object dataset. RotationNet is designed to use only a
partial set of multi-view images for inference, and this property makes it
useful in practical scenarios where only partial views are available. Moreover,
our pose alignment strategy enables one to obtain view-specific feature
representations shared across classes, which is important to maintain high
accuracy in both object categorization and pose estimation. Effectiveness of
RotationNet is demonstrated by its superior performance to the state-of-the-art
methods of 3D object classification on 10- and 40-class ModelNet datasets. We
also show that RotationNet, even trained without known poses, achieves the
state-of-the-art performance on an object pose estimation dataset. The code is
available on https://github.com/kanezaki/rotationnet


title: Broken ergodicity of right triangular billiard systems

abstract:
  A right triangular billiard system is equivalent to the system of two
colliding particles confined in a one-dimensional box. In spite of their
seeming simplicity, no definite conclusion has been drawn so far concerning
their ergodic properties. To answer this question, we transform the dynamics of
the right triangular billiard system to a piecewise map and analytically prove
the broken ergodicity. The mechanism leading to the broken ergodicity is
discussed, and some numerical evidence corroborating our conclusion is
provided.

categories: cond-mat.stat-mech math-ph math.MP nlin.CD


title: Approximation sequences on Banach spaces: a rich approach

abstract:
  Criteria for the stability of finite sections of a large class of convolution
type operators on $L^p(\mathbb{R})$ are obtained. In this class almost all
classical symbols are permitted, namely operators of multiplication with
functions in $[\textrm{PC} ,\textrm{SO}, L^\infty_0]$ and convolution operators
(as well as Wiener-Hopf and Hankel operators) with symbols in
$[\textrm{PC},\textrm{SO},\textrm{AP},\textrm{BUC}]_p$. We use a simpler and
more powerful algebraic technique than all previous works: the application of
$\mathcal{P}$-theory together with the rich sequences concept and localization.
Beyond stability we study Fredholm theory in sequence algebras. In particular,
formulas for the asymptotic behavior of approximation numbers and Fredholm
indices are given.


title: Tangent Lie groups are Riemannian naturally reductive spaces

abstract:
  Given a compact Lie group $G$ with Lie algebra $\mathfrak{g}$, we consider
its tangent Lie group $TG\cong G\ltimes_{\mathrm{Ad}} \mathfrak{g}$. In this
short note, we prove that $TG$ admits a left-invariant naturally reductive
Riemannian metric $g$ and a metric connection with skew torsion $\nabla$ such
that $(TG,g,\nabla)$ is naturally reductive. An alternative spinorial
description of the same connection on the direct product $G\times \mathfrak{g}$
generalizes in a rather subtle way to $TS^7$, which is in many senses almost a
tangent Lie group.


title:
Evaluation of a Tree-based Pipeline Optimization Tool for Automating
  Data Science

abstract:
  As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement an open source Tree-based Pipeline
Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a
series of simulated and real-world benchmark data sets. In particular, we show
that TPOT can design machine learning pipelines that provide a significant
improvement over a basic machine learning analysis while requiring little to no
input nor prior knowledge from the user. We also address the tendency for TPOT
to design overly complex pipelines by integrating Pareto optimization, which
produces compact pipelines without sacrificing classification accuracy. As
such, this work represents an important step toward fully automating machine
learning pipeline design.


title:
Note on the numerical solution of the scalar Helmholtz equation in a
  nanotorus with uniform Dirichlet boundary conditions

abstract:
  This note describes the solution of the Helmholtz equation inside a nanotorus
with uniform Dirichlet boundary conditions. The eigenfunction symmetry is
discussed and the lower-order eigenvalues and eigenfunctions are shown. The
similarity with the case of a long cylinder and with that of the vibrations of
a circular elastic membrane is discussed. This similarity is used to propose a
classification scheme of the eigenfunctions based on three indices.


title:
Three-dimensional resistivity switching between correlated electronic
  states in 1T-TaS2

abstract:
  Recent demonstrations of controlled switching between different ordered
macroscopic states by impulsive electromagnetic perturbations in complex
materials have opened some fundamental questions on the mechanisms responsible
for such remarkable behavior. Here we experimentally address the question of
whether two-dimensional (2D) Mott physics can be responsible for unusual
switching between states of different electronic order in the layered
dichalcogenide 1T-TaS2, or it is a result of subtle inter-layer orbitronic
re-ordering of its helical stacking structure. We report on the switching
properties both in-plane and perpendicular to the layers by current-pulse
injection, the anisotropy of electronic transport in the commensurate ground
state, and relaxation properties of the switched metastable state. Contrary to
recent theoretical calculations, which predict a uni-directional metal
perpendicular to the layers, we observe a large resistivity in this direction,
with a temperature-dependent anisotropy. Remarkably, large resistance ratios
are observed in the memristive switching both in-plane (IP) and out-of-plane
(OP). The relaxation dynamics of the metastable state for both IP and OP
electron transport are seemingly governed by the same mesoscopic quantum
re-ordering process. We conclude that 1T-TaS2 shows resistance switching
arising from an interplay of both IP and OP correlations.


title: Which series are Hilbert series of graded modules over polynomial rings?

abstract:
  Let $S$ be a multigraded polynomial ring such that the degree of each
variable is a unit vector; so $S$ is the homogeneous coordinate ring of a
product of projective spaces. In this setting, we characterize the formal
Laurent series which arise as Hilbert series of finitely generated $S$-modules.
  Also we provide necessary conditions for a formal Laurent series to be the
Hilbert series of a finitely generated module with a given depth. In the
bigraded case (corresponding to the product of two projective spaces), we
completely classify the Hilbert series of finitely generated modules of
positive depth.


title: Skew-t inference with improved covariance matrix approximation

abstract:
  Filtering and smoothing algorithms for linear discrete-time state-space
models with skew-t distributed measurement noise are presented. The proposed
algorithms improve upon our earlier proposed filter and smoother using the mean
field variational Bayes approximation of the posterior distribution to a skew-t
likelihood and normal prior. Our simulations show that the proposed variational
Bayes approximation gives a more accurate approximation of the posterior
covariance matrix than our earlier proposed method. Furthermore, the novel
filter and smoother outperform our earlier proposed methods and conventional
low complexity alternatives in accuracy and speed.


title: An Approximation Approach for Solving the Subpath Planning Problem

abstract:
  The subpath planning problem is a branch of the path planning problem, which
has widespread applications in automated manufacturing process as well as
vehicle and robot navigation. This problem is to find the shortest path or tour
subject for travelling a set of given subpaths. The current approaches for
dealing with the subpath planning problem are all based on meta-heuristic
approaches. It is well-known that meta-heuristic based approaches have several
deficiencies. To address them, we propose a novel approximation algorithm in
the O(n^3) time complexity class, which guarantees to solve any subpath
planning problem instance with the fixed ratio bound of 2. Also, the formal
proofs of the claims, our empirical evaluation shows that our approximation
method acts much better than a state-of-the-art method, both in result and
execution time.


title:
Solving the ${\rm U(2)}_L \times {\rm U(2)}_R$ symmetric
  Nambu--Jona-Lasinio model in 1+1 dimensions

abstract:
  A less well known variant of the Nambu--Jona-Lasinio model with Nc colors and
U(2)L X U(2)R chiral symmetry is studied in 1+1 dimensions. Using
semi-classical methods appropriate for the large Nc limit, we determine the
vacuum manifold, the meson spectrum, massless and massive multi-fermion bound
states and the phase diagram as a function of temperature, chemical potential
and isospin chemical potential. An important tool to understand soliton
dynamics is the generalization of the time-dependent Hartree-Fock approach to
two flavors along the lines recently developed by Takahashi in the context of
unconventional fermionic superfluids and superconductors.


title:
Oxygen Channels and Fractal Wave-Particle Duality in the Evolution of
  Myoglobin and Neuroglobin

abstract:
  The evolution of terrestrial and aquatic wild type (WT) globins is dominated
by changes in two proximate - distal Histidine ligand exit channels, here
monitored quantitatively by hydropathic waves. These waves reveal allometric
functional features inaccessible to single amino acid stereochemical contact
models, and even very large all-atom Newtonian simulations. The evolutionary
differences between these features between myoglobin and neuroglobin are
related to the two oxidation channels through hydropathic wave analysis, which
identifies subtle interspecies functional differences inaccessible to
traditional size and metabolic scaling studies. Our analysis involves dynamic
synchronization of allometric interactions across entire globins.


title: Flow of Information in Feed-Forward Deep Neural Networks

abstract:
  Feed-forward deep neural networks have been used extensively in various
machine learning applications. Developing a precise understanding of the
underling behavior of neural networks is crucial for their efficient
deployment. In this paper, we use an information theoretic approach to study
the flow of information in a neural network and to determine how entropy of
information changes between consecutive layers. Moreover, using the Information
Bottleneck principle, we develop a constrained optimization problem that can be
used in the training process of a deep neural network. Furthermore, we
determine a lower bound for the level of data representation that can be
achieved in a deep neural network with an acceptable level of distortion.


title:
Measurements of t t-bar charge asymmetry using dilepton final states in
  pp collisions at sqrt(s) = 8 TeV

abstract:
  The charge asymmetry in t t-bar events is measured using dilepton final
states produced in pp collisions at the LHC at sqrt(s) = 8 TeV. The data
sample, collected with the CMS detector, corresponds to an integrated
luminosity of 19.5 inverse femtobarns. The measurements are performed using
events with two oppositely charged leptons (electrons or muons) and two or more
jets, where at least one of the jets is identified as originating from a bottom
quark. The charge asymmetry is measured from differences in kinematic
distributions, unfolded to the parton level, of positively and negatively
charged top quarks and leptons. The t t-bar and leptonic charge asymmetries are
found to be 0.011 +/- 0.011 (stat) +/- 0.007 (syst) and 0.003 +/- 0.006 (stat)
+/- 0.003 (syst), respectively. These results, as well as charge asymmetry
measurements made as a function of the invariant mass, rapidity, and transverse
momentum of the t t-bar system, are in agreement with predictions of the
standard model.


title: Quantum machine learning over infinite dimensions

abstract:
  Machine learning is a fascinating and exciting field within computer science.
Recently, this excitement has been transferred to the quantum information
realm. Currently, all proposals for the quantum version of machine learning
utilize the finite-dimensional substrate of discrete variables. Here we
generalize quantum machine learning to the more complex, but still remarkably
practical, infinite-dimensional systems. We present the critical subroutines of
quantum machine learning algorithms for an all-photonic continuous-variable
quantum computer that achieve an exponential speedup compared to their
equivalent classical counterparts. Finally, we also map out an experimental
implementation which can be used as a blueprint for future photonic
demonstrations.


title:
Beyond Binary Computers: How To Implement Multi-Switch Computer Hardware
  and Software and; The Advantage of a Multi-Switched Computer

abstract:
  This paper explores the possibilities of using a computing methodology
--hardware and software-- that employs technology other than binary. I refer to
this as "supra - binary" computing. Software constructs that use more than
binary techniques are discussed. The gains in supra - binary software are
demonstrated, which includes supra - binary code being RISC. Possible hardware
implementations of a computer with other than binary based architecture are
demonstrated and considered. The advantages and possible disadvantages of these
hardware implementations are discussed. The gain in computing speed is
evaluated and demonstrated. Supra - binary processing would streamline parallel
processing and make its implementation a built - in feature of the software and
hardware. Also, supra - binary would bring an advancement to neural networking.
This is discussed and demonstrated. In addition, possible applications of supra
- binary computing to database and neural networking are discussed. Also, the
possible implementations could be applied to telecommunications with dramatic
results.


title: Interpreting the Densities of the Kuiper Belt's Dwarf Planets

abstract:
  Kuiper Belt objects with absolute magnitude less than 3 (radius $\gtrsim$500
km), the dwarf planets, have a range of different ice/rock ratios, and are more
rock-rich than their smaller counterparts. Many of these objects have moons,
which suggests that collisions may have played a role in modifying their
compositions. We show that the dwarf planets fall into two categories when
analysed by their mean densities and satellite-to-primary size ratio. Systems
with large moons, such as Pluto/Charon and Orcus/Vanth, can form in
low-velocity grazing collisions in which both bodies retain their compositions.
We propose that these systems retain a primordial composition, with a density
of about 1.8 g/cm$^3$. Triton, thought to be a captured KBO, could have lost
enough ice during its early orbital evolution to explain its rock-enrichment
relative to the primordial material. Systems with small moons, Eris, Haumea,
and Quaoar, formed from a different type of collision in which icy material,
perhaps a few tens of percent of the total colliding mass, is lost. The
fragments would not remain in physical or dynamical proximity to the parent
body. The ice loss process has not yet been demonstrated numerically, which
could be due to the paucity of KBO origin simulations, or missing physical
processes in the impact models. If our hypothesis is correct, we predict that
large KBOs with small moons should be denser than the primordial material, and
that the mean density of Orcus should be close to the primordial value.


title:
A Hierarchy of Multi-Lane "Entropy Machines" with Unfair Resource
  Availability

abstract:
  We present a model system for objects, climbers, which have the ability to
move as ASEP particles with non-uniform forward and backward jumping rate
dynamics. These climbers are implemented as Mandal-Quan-Jarzynski [1] machines.
Climbers on a number of neighboring columns are considered. The low entropy
resource is provided abundantly to the first column, the unused part of this
resource is consecutively passed over to objects in neighboring columns. This
results in a hierarchy among the lanes. The system displays interesting
steady-states. Both open and periodic boundary conditions are considered and
results from Monte Carlo simulations of the system will be reported.
  [1] D. Mandal, H. T. Quan, and C. Jarzynski, Phys. Rev. Lett. 111, 030602
(2013)


title: On the complexity of the identifiable subgraph problem, revisited

abstract:
  A bipartite graph $G=(L,R;E)$ with at least one edge is said to be
identifiable if for every vertex $v\in L$, the subgraph induced by its
non-neighbors has a matching of cardinality $|L|-1$. An $\ell$-subgraph of $G$
is an induced subgraph of $G$ obtained by deleting from it some vertices in $L$
together with all their neighbors. The Identifiable Subgraph problem is the
problem of determining whether a given bipartite graph contains an identifiable
$\ell$-subgraph.
  We show that the Identifiable Subgraph problem is polynomially solvable,
along with the version of the problem in which the task is to delete as few
vertices from $L$ as possible together with all their neighbors so that the
resulting $\ell$-subgraph is identifiable. We also complement a known
APX-hardness result for the complementary problem in which the task is to
minimize the number of remaining vertices in $L$, by showing that two
parameterized variants of the problem are W[1]-hard.


title:
Cache Bypassing and Checkpointing to Circumvent Data Security Attacks on
  STTRAM

abstract:
  Spin-Transfer Torque RAM (STTRAM) is promising for cache applications.
However, it brings new data security issues that were absent in volatile memory
counterparts such as Static RAM (SRAM) and embedded Dynamic RAM (eDRAM). This
is primarily due to the fundamental dependency of this memory technology on
ambient parameters such as magnetic field and temperature that can be exploited
to tamper with the stored data. In this paper we propose three techniques to
enable error free computation without stalling the system, (a) stalling where
the system is halted during attack; (b) cache bypass during gradually ramping
attack where the last level cache (LLC) is bypassed and the upper level caches
interact directly with the main memory; and, (c) checkpointing along with
bypass during sudden attack where the processor states are saved periodically
and the LLC is written back at regular intervals. During attack the system goes
back to the last checkpoint and the computation continues with bypassed cache.
We performed simulation for different duration and frequency of attack on
SPLASH benchmark suite and the results show an average of 8% degradation in IPC
for a one-time attack lasting for 50% of the execution time. The energy
overhead is 2% for an attack lasting for the entire duration of execution.


title: Characteristic and hyperinvariant subspaces over the field GF(2)

abstract:
  Let $f$ be an endomorphism of a vector space $V$ over a field $K$. An
$f$-invariant subspace $X \subseteq V$ is called hyperinvariant (respectively
characteristic) if $X$ is invariant under all endomorphisms (respectively
automorphisms) that commute with $f$. If $|K| > 2$ then all characteristic
subspaces are hyperinvariant. If $|K| = 2$ then there are endomorphisms $f$
with invariant subspaces that are characteristic but not hyperinvariant. In
this paper we give a new proof of a theorem of Shoda, which provides a
necessary and sufficient condition for the existence of characteristic
non-hyperinvariant subspaces.


title: On semibounded Toeplitz operators

abstract:
  We show that a semibounded Toeplitz quadratic form is closable in the space
$\ell^2({\Bbb Z}_{+})$ if and only if its matrix elemens are Fourier
coefficients of an absolutely continuous measure. We also describe the domain
of the corresponding closed form. This allows us to define semibounded Toeplitz
operators under minimal assumptions on their matrix elements.


title: Bayesian methods for event analysis of intracellular currents

abstract:
  Investigation of neural circuit functioning often requires statistical
interpretation of events in subthreshold electrophysiological recordings. This
problem is non-trivial because recordings may have moderate levels of
structured noise and events may have distinct kinetics. In addition, novel
experimental designs that combine optical and electrophysiological methods will
depend upon statistical tools that combine multimodal data. We present a
Bayesian approach for inferring the timing, strength, and kinetics of
postsynaptic currents (PSCs) from voltage-clamp recordings on a per event
basis. The simple generative model for a single voltage-clamp recording
flexibly extends to include network-level structure to enable experiments
designed to probe synaptic connectivity. We validate the approach on simulated
and real data. We also demonstrate that extensions of the basic PSC detection
algorithm can handle recordings contaminated with optically evoked currents,
and we simulate a scenario in which calcium imaging observations, available for
a subset of neurons, can be fused with electrophysiological data to achieve
higher temporal resolution. We apply this approach to simulated and real ground
truth data to demonstrate its higher sensitivity in detecting small
signal-to-noise events and its increased robustness to noise compared to
standard methods for detecting PSCs. The new Bayesian event analysis approach
for electrophysiological recordings should allow for better estimation of
physiological parameters under more variable conditions and help support new
experimental designs for circuit mapping.


title: Gravity-Assisted Emergent Higgs Mechanism in the Post-Inflationary Epoch

abstract:
  We consider a non-standard model of gravity coupled to a neutral scalar
"inflaton" as well as to SU(2)xU(1) iso-doublet scalar with positive mass
squared and without self-interaction, and to SU(2)xU(1) gauge fields. The
principal new ingredient is employing two alternative non-Riemannian space-time
volume-forms (covariant integration measure densitities) independent of the
metric. The latter have a remarkable impact - although not introducing any
additional propagating degrees of freedom, their dynamics triggers a series of
important features: appearance of infinitely large flat regions of the
effective "inflaton" potential as well as dynamical generation of Higgs-like
spontaneous symmetry breaking effective potential for the SU(2)xU(1)
iso-doublet scalar.


title:
Remarks on Tsfasman-Boguslavsky Conjecture and Higher Weights of
  Projective Reed-Muller Codes

abstract:
  Tsfasman-Boguslavsky Conjecture predicts the maximum number of zeros that a
system of linearly independent homogeneous polynomials of the same positive
degree with coefficients in a finite field can have in the corresponding
projective space. We give a self-contained proof to show that this conjecture
holds in the affirmative in the case of systems of three homogeneous
polynomials, and also to show that the conjecture is false in the case of five
quadrics in the 3-dimensional projective space over a finite field. Connections
between the Tsfasman-Boguslavsky Conjecture and the determination of
generalized Hamming weights of projective Reed-Muller codes are outlined and
these are also exploited to show that this conjecture holds in the affirmative
in the case of systems of a "large" number of three homogeneous polynomials,
and to deduce the counterexample of 5 quadrics. An application to the
nonexistence of lines in certain Veronese varieties over finite fields is also
included.