title: A fast in-place interpreter for WebAssembly

abstract:
  WebAssembly (Wasm) is a compact, well-specified bytecode format that offers a
portable compilation target with near-native execution speed. The bytecode
format was specifically designed to be fast to parse, validate, and compile,
positioning itself as a portable alternative to native code. It was pointedly
not designed to be interpreted directly. Instead, design considerations at the
time focused on competing with native code, utilizing optimizing compilers as
the primary execution tier. Yet, in JIT scenarios, compilation time and memory
consumption critically impact application startup, leading many Wasm engines to
later deploy baseline (single-pass) compilers. Though faster, baseline
compilers still take time and waste code space for infrequently executed code.
A typical interpreter being infeasible, some engines resort to compiling Wasm
not to machine code, but to a more compact, but easy to interpret format. This
still takes time and wastes memory. Instead, we introduce in this article a
fast in-place interpreter for WebAssembly, where no rewrite and no separate
format is necessary. Our evaluation shows that in-place interpretation of Wasm
code is space-efficient and fast, achieving performance on-par with
interpreting a custom-designed internal format. This fills a hole in the
execution tier space for Wasm, allowing for even faster startup and lower
memory footprint than previous engine configurations.


title:
Performance Weighting for Robust Federated Learning Against Corrupted
  Sources

abstract:
  Federated Learning has emerged as a dominant computational paradigm for
distributed machine learning. Its unique data privacy properties allow us to
collaboratively train models while offering participating clients certain
privacy-preserving guarantees. However, in real-world applications, a federated
environment may consist of a mixture of benevolent and malicious clients, with
the latter aiming to corrupt and degrade federated model's performance.
Different corruption schemes may be applied such as model poisoning and data
corruption. Here, we focus on the latter, the susceptibility of federated
learning to various data corruption attacks. We show that the standard global
aggregation scheme of local weights is inefficient in the presence of corrupted
clients. To mitigate this problem, we propose a class of task-oriented
performance-based methods computed over a distributed validation dataset with
the goal to detect and mitigate corrupted clients. Specifically, we construct a
robust weight aggregation scheme based on geometric mean and demonstrate its
effectiveness under random label shuffling and targeted label flipping attacks.


title: Transition Phenomena for the Attractor of an Iterated Function System

abstract:
  Iterated function systems (IFSs) and their attractors have been central to
the theory of fractal geometry almost from its inception. And contractivity of
the functions in the IFS has been central to the theory of iterated functions
systems. If the functions in the IFS are contractions, then the IFS is
guaranteed to have a unique attractor. Recently, however, there has been an
interest in what occurs to the attractor at the boundary between contractvity
and expansion of the IFS. That is the subject of this paper. For a family $F_t$
of IFSs depending on a real parameter $t>0$, the existence and properties of
two types of transition attractors, called the lower transition attractor
$A_{\bullet}$ and the upper transition attractor $A^{\bullet}$, are
investigated. A main theorem states that, for a wide class of IFS families,
there is a threshold $t_0$ such that the IFS $F_t$ has a unique attractor $A_t$
for $t<t_0$ and no attractor for $t>t_0$. At the threshold $t_0$, there is an
$F_{t_0}$-invariant set $A^{\bullet}$ such that $A^{\bullet} =
\lim_{t\rightarrow t_0} A_t$.


title:
The pedagogical value of the four-dimensional picture I: Relativistic
  mechanics of point particles

abstract:
  We outline two subjects of relativistic mechanics: (i) the set of allowable
world lines, and (ii) the origin of the relativistic law of dynamics governing
point particles. We show that: (i) allowable world lines in the classical
theory of particles and fields are quite simple geometric objects as opposed to
their associated three-dimensional trajectories, and (ii) Newton's second law
requires neither modification nor generalization, it should be only smoothly
embedded in the four-dimensional geometry of Minkowski spacetime to yield the
dynamical law for relativistic particles.


title:
GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type
  Identification

abstract:
  The cellular composition of the tumor microenvironment can directly impact
cancer progression and the efficacy of therapeutics. Understanding immune cell
activity, the body's natural defense mechanism, in the vicinity of cancerous
cells is essential for developing beneficial treatments. Single cell RNA
sequencing (scRNA-seq) enables the examination of gene expression on an
individual cell basis, providing crucial information regarding both the
disturbances in cell functioning caused by cancer and cell-cell communication
in the tumor microenvironment. This novel technique generates large amounts of
data, which require proper processing. Various tools exist to facilitate this
processing but need to be organized to standardize the workflow from data
wrangling to visualization, cell type identification, and analysis of changes
in cellular activity, both from the standpoint of malignant cells and immune
stromal cells that eliminate them. We aimed to develop a standardized pipeline
(GeoTyper, https://github.com/celineyayifeng/GeoTyper) that integrates multiple
scRNA-seq tools for processing raw sequence data extracted from NCBI GEO,
visualization of results, statistical analysis, and cell type identification.
This pipeline leverages existing tools, such as Cellranger from 10X Genomics,
Alevin, and Seurat, to cluster cells and identify cell types based on gene
expression profiles. We successfully tested and validated the pipeline on
several publicly available scRNA-seq datasets, resulting in clusters
corresponding to distinct cell types. By determining the cell types and their
respective frequencies in the tumor microenvironment across multiple cancers,
this workflow will help quantify changes in gene expression related to
cell-cell communication and identify possible therapeutic targets.


title:
Predicting Time-to-conversion for Dementia of Alzheimer's Type using
  Multi-modal Deep Survival Analysis

abstract:
  Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by
numerous factors, but it is unclear how each factor contributes to disease
progression. An in-depth examination of these factors may yield an accurate
estimate of time-to-conversion to DAT for patients at various disease stages.
We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive
tests, Demographic, and CSF) data modalities in the Alzheimer's Disease
Neuroimaging Initiative (ADNI) database. We used a deep learning-based survival
analysis model that extends the classic Cox regression model to predict
time-to-conversion to DAT. Our findings showed that genetic features
contributed the least to survival analysis, while CDC features contributed the
most. Combining MRI and genetic features improved survival prediction over
using either modality alone, but adding CDC to any combination of features only
worked as well as using only CDC features. Consequently, our study demonstrated
that using the current clinical procedure, which includes gathering cognitive
test results, can outperform survival analysis results produced using costly
genetic or CSF data.


title: A Critique of Uribe's "P vs. NP"

abstract:
  In this critique, we examine the technical report by Daniel Uribe entitled "P
vs. NP." The paper claims to show an exponential lower bound on the runtime of
algorithms that decide CLIQUE. We show that the paper's proofs fail to
generalize to all possible algorithms and that, even on those algorithms to
which the proofs do apply, the proofs' arguments are flawed.


title:
Fluorescence Brightness, Photostability and Energy Transfer Enhancement
  of Immobilized Single Molecules in Zero-Mode Waveguides Nanoapertures

abstract:
  Zero-mode waveguide (ZMW) nanoapertures are widely used to monitor single
molecules beyond the range accessible to normal microscopes. However, several
aspects of the ZMW influence on the photophysics of fluorophores remain
inadequately documented and sometimes controversial. Here, we thoroughly
investigate the ZMW influence on the fluorescence of single immobilized Cy3B
and Alexa 647 molecules, detailing the interplays between brightness, lifetime,
photobleaching time, total number of emitted photons and F\"orster resonance
energy transfer (FRET). Despite the plasmonic-enhanced excitation intensity in
the ZMW, we find that the photostability is preserved with similar
photobleaching times as on the glass reference. Both the fluorescence
brightness and the total numbers of photons detected before photobleaching are
increased, with an impressive gain near five times found for Alexa 647 dyes.
Finally, the single-molecule data importantly allow a loophole-free
characterization of the ZMW influence on the FRET process. We show that the
FRET rate constant is enhanced by 50%, demonstrating that nanophotonics can
mediate the energy transfer. These results deepen our understanding of the
fluorescence enhancement in ZMWs and are of immediate relevance for
single-molecule biophysical applications.


title: Taming graphs with no large creatures and skinny ladders

abstract:
  We confirm a conjecture of Gartland and Lokshtanov [arXiv:2007.08761]: if for
a hereditary graph class $\mathcal{G}$ there exists a constant $k$ such that no
member of $\mathcal{G}$ contains a $k$-creature as an induced subgraph or a
$k$-skinny-ladder as an induced minor, then there exists a polynomial $p$ such
that every $G \in \mathcal{G}$ contains at most $p(|V(G)|)$ minimal separators.
By a result of Fomin, Todinca, and Villanger [SIAM J. Comput. 2015] the latter
entails the existence of polynomial-time algorithms for Maximum Weight
Independent Set, Feedback Vertex Set and many other problems, when restricted
to an input graph from $\mathcal{G}$. Furthermore, as shown by Gartland and
Lokshtanov, our result implies a full dichotomy of hereditary graph classes
defined by a finite set of forbidden induced subgraphs into tame (admitting a
polynomial bound of the number of minimal separators) and feral (containing
infinitely many graphs with exponential number of minimal separators).


title: Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer

abstract:
  The quantum approximate optimization algorithm (QAOA) promises to solve
classically intractable computational problems in the area of combinatorial
optimization. A growing amount of evidence suggests that the originally
proposed form of the QAOA ansatz is not optimal, however. To address this
problem, we propose an alternative ansatz, which we call QAOA+, that augments
the traditional $p = 1$ QAOA ansatz with an additional multiparameter
problem-independent layer. The QAOA+ ansatz allows obtaining higher
approximation ratios than $p = 1$ QAOA while keeping the circuit depth below
that of $p = 2$ QAOA, as benchmarked on the MaxCut problem for random regular
graphs. We additionally show that the proposed QAOA+ ansatz, while using a
larger number of trainable classical parameters than with the standard QAOA, in
most cases outperforms alternative multiangle QAOA ans\"atze.


title:
Biological Homochirality and the Search for Extraterrestrial
  Biosignatures

abstract:
  Most amino acids and sugars molecules occur in mirror, or chiral, images of
each other, knowns as enantiomers. However, life on Earth is mostly homochiral:
proteins contain almost exclusively L-amino acids, while only D-sugars appear
in RNA and DNA. The mechanism behind this fundamental asymmetry of life remains
unknown, despite much progress in the theoretical and experimental
understanding of homochirality in the past decades. We review three potential
mechanisms for the emergence of biological homochirality on primal Earth and
explore their implications for astrobiology: the first, that biological
homochirality is a stochastic process driven by local environmental
fluctuations; the second, that it is driven by circularly-polarized ultraviolet
radiation in star-forming regions; and the third, that it is driven by parity
violation at the elementary particle level. We argue that each of these
mechanisms leads to different observational consequences for the existence of
enantiomeric excesses in our solar system and in exoplanets, pointing to the
possibility that the search for life elsewhere will help elucidate the origins
of homochirality on Earth.


title:
Time-resolved vibrational-pump visible-probe spectroscopy for thermal
  conductivity measurement of metal-halide perovskites

abstract:
  Understanding thermal transport at the micro- to nanoscale is crucially
important for a wide range of technologies ranging from device thermal
management and protection systems to thermal-energy regulation and harvesting.
In the past decades, non-contact optical methods such as time-domain and
frequency-domain thermoreflectance (TDTR and FDTR) have emerged as extremely
powerful and versatile thermal metrological techniques for the measurement of
material thermal conductivities. Here, we report the measurement of thermal
conductivity of thin films of CH3NH3PbI3 (MAPbI3), a prototypical metal-halide
perovskite, by developing a time-resolved optical technique called
vibrational-pump visible-probe (VPVP) spectroscopy. The VPVP technique relies
on the direct thermal excitation of MAPbI3 by femtosecond (fs) mid-infrared
(MIR) optical pump pulses that are wavelength-tuned to a vibrational mode of
the material, after which the time dependent optical transmittance across the
visible range is probed in the ns to the us time window using a broadband
pulsed laser. Using the VPVP method, we determine the thermal conductivities of
MAPbI3 thin films deposited on different substrates. The transducer-free VPVP
method reported here is expected to permit spectrally resolving and
spatiotemporally imaging the dynamic lattice temperature variations in organic,
polymeric, and hybrid organic-inorganic semiconductors.


title: Exotic heavy hadrons with a three-body nature

abstract:
  In this talk we present a summary of our latest results on the investigation
of three-body systems with explicit/hidden charm and with explicit/hidden
strangeness. To be more concrete, in case of explicit strangeness quantum
number, we pay attention on the $K D D $ and $K D\bar D^*$ systems, where, in
the former, a charm $+2$, isospin $1/2$ and strangeness $+1$ state is obtained
with a mass around 4140 MeV, while in the latter, a $K^*$ state, with hidden
charm, and a mass close to 4307 MeV is found. In case of hidden strangeness,
the $DK\bar K$ system is studied, while in the hidden charm and no strangeness
quantum numbers sector, the $ND\bar D^*$ system is investigated. In the former
a meson $D$ with mass around 2900 MeV is found to be generated, while in the
latter several $N^*$ and $\Delta^*$ states with hidden charm are obtained with
masses about $4600$ MeV. All these states constitute predictions of our model
and future experimental confirmation of them would be relevant to elucidate the
properties of the strong interaction in the presence of heavy quarks.


title:
Strong Stationarity Conditions for Optimal Control Problems Governed by
  a Rate-Independent Evolution Variational Inequality

abstract:
  We prove strong stationarity conditions for optimal control problems that are
governed by a prototypical rate-independent evolution variational inequality,
i.e., first-order necessary optimality conditions in the form of a primal-dual
multiplier system that are equivalent to the purely primal notion of Bouligand
stationarity. Our analysis relies on recent results on the Hadamard directional
differentiability of the scalar stop operator and a new concept of temporal
polyhedricity that generalizes classical ideas of Mignot. The established
strong stationarity system is compared with known optimality conditions for
optimal control problems governed by elliptic obstacle-type variational
inequalities and stationarity systems obtained by regularization.


title: Boosting Video Object Segmentation based on Scale Inconsistency

abstract:
  We present a refinement framework to boost the performance of pre-trained
semi-supervised video object segmentation (VOS) models. Our work is based on
scale inconsistency, which is motivated by the observation that existing VOS
models generate inconsistent predictions from input frames with different
sizes. We use the scale inconsistency as a clue to devise a pixel-level
attention module that aggregates the advantages of the predictions from
different-size inputs. The scale inconsistency is also used to regularize the
training based on a pixel-level variance measured by an uncertainty estimation.
We further present a self-supervised online adaptation, tailored for test-time
optimization, that bootstraps the predictions without ground-truth masks based
on the scale inconsistency. Experiments on DAVIS 16 and DAVIS 17 datasets show
that our framework can be generically applied to various VOS models and improve
their performance.


title:
NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack
  Detection Algorithms

abstract:
  Crack detection plays a key role in automated pavement inspection. Although a
large number of algorithms have been developed in recent years to further boost
performance, there are still remaining challenges in practice, due to the
complexity of pavement images. To further accelerate the development and
identify the remaining challenges, this paper conducts a comparison study to
evaluate the performance of the state of the art crack detection algorithms
quantitatively and objectively. A more comprehensive annotated pavement crack
dataset (NHA12D) that contains images with different viewpoints and pavements
types is proposed. In the comparison study, crack detection algorithms were
trained equally on the largest public crack dataset collected and evaluated on
the proposed dataset (NHA12D). Overall, the U-Net model with VGG-16 as backbone
has the best all-around performance, but models generally fail to distinguish
cracks from concrete joints, leading to a high false-positive rate. It also
found that detecting cracks from concrete pavement images still has huge room
for improvement. Dataset for concrete pavement images is also missing in the
literature. Future directions in this area include filling the gap for concrete
pavement images and using domain adaptation techniques to enhance the detection
results on unseen datasets.


title: On the maximum of a special random assignment process

abstract:
  We consider the asymptotic behavior of the expectation of the maximum for a
special assignment process with constant or i.i.d. coefficients. We show how it
depends on the coefficients' distribution.


title: Lattice Minors and Eulerian Posets

abstract:
  Deletion and contraction operations on lattices enriched with a generating
set were introduced by the author in [arXiv:2112.04600]; and from these a
notion of minors. When applied to the lattice of flats of a graph, these
correspond to the same operations applied to the graph when the vertices are
labeled and the edges unlabeled, followed by simplifying the resultant graph.
We introduce a partial ordering of the minors of a given generator enriched
lattice which we call the minor poset. We show that the minor poset of any
generator enriched lattice is isomorphic to the face poset of a regular CW
sphere, and in particular, is Eulerian. Furthermore, inequalities for the
coefficients of cd-indices of minor posets are established. We also give a
forbidden minor characterization of the generator enriched lattices whose minor
poset is itself a lattice. Minor posets admit a decomposition into a disjoint
union of Boolean algebras. We use this decomposition to derive formulas for the
rank generating function of minor posets associated to geometric lattices with
generating set consisting of the join irreducibles, and for minor posets
associated to a class generalizing distributive lattices.


title: An Interferometric View of H-MM1. I. Direct Observation of NH3 Depletion

abstract:
  Spectral lines of ammonia, NH$_3$, are useful probes of the physical
conditions in dense molecular cloud cores. In addition to advantages in
spectroscopy, ammonia has also been suggested to be resistant to freezing onto
grain surfaces, which should make it a superior tool for studying the interior
parts of cold, dense cores. Here we present high-resolution NH$_3$ observations
with the Very Large Array (VLA) and Green Bank Telescope (GBT) towards a
prestellar core. These observations show an outer region with a fractional
NH$_3$ abundance of X(NH$_3$) = (1.975$\pm$0.005)$\times 10^{-8}$ ($\pm 10\%$
systematic), but it also reveals that after all, the X(NH$_3$) starts to
decrease above a H$_2$ column density of $\approx 2.6 \times 10^{22}$
cm$^{-2}$. We derive a density model for the core and find that the break-point
in the fractional abundance occurs at the density n(H$_2$) $\sim 2\times10^5$
cm$^{-3}$, and beyond this point the fractional abundance decreases with
increasing density, following the power law $n^{-1.1}$. This power-law behavior
is well reproduced by chemical models where adsorption onto grains dominates
the removal of ammonia and related species from the gas at high densities. We
suggest that the break-point density changes from core to core depending on the
temperature and the grain properties, but that the depletion power law is
anyway likely to be close to $n^{-1}$ owing to the dominance of accretion in
the central parts of starless cores.


title:
POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping
  in Semi-Static Scenes

abstract:
  Maintaining an up-to-date map to reflect recent changes in the scene is very
important, particularly in situations involving repeated traversals by a robot
operating in an environment over an extended period. Undetected changes may
cause a deterioration in map quality, leading to poor localization, inefficient
operations, and lost robots. Volumetric methods, such as truncated signed
distance functions (TSDFs), have quickly gained traction due to their real-time
production of a dense and detailed map, though map updating in scenes that
change over time remains a challenge. We propose a framework that introduces a
novel probabilistic object state representation to track object pose changes in
semi-static scenes. The representation jointly models a stationarity score and
a TSDF change measure for each object. A Bayesian update rule that incorporates
both geometric and semantic information is derived to achieve consistent online
map maintenance. To extensively evaluate our approach alongside the
state-of-the-art, we release a novel real-world dataset in a warehouse
environment. We also evaluate on the public ToyCar dataset. Our method
outperforms state-of-the-art methods on the reconstruction quality of
semi-static environments.


title:
Mass-Metallicity and Star Formation Rate in Galaxies: a complex relation
  tuned to stellar age

abstract:
  In this work we study the stellar mass -- metallicity relation (MZR) of an
extended sample of star-forming galaxies in the local Universe and its possible
dependence with the star formation rate (SFR). A sample of $\sim$195000 Sloan
Digital Sky Survey (SDSS) star-forming galaxies has been selected up to z=0.22
with the aim of analysing the behaviour of the relation of MZR with respect to
SFR and taking into account the age of their stellar populations. For this
sample we have obtained, for the first time, aperture corrected oxygen and
nitrogen-to-oxygen abundances (O/H and N/O, respectively) and SFR using the
empirical prescriptions from the Calar Alto Legacy Integral Field Area (CALIFA)
survey. To perform this study we make use also of the stellar mass of the
galaxies and the parameter Dn(4000) as a proxy of the age of the stellar
population. We derive a robust MZR locus, which is found to be fully consistent
with the "anchoring" points of a selected set of well studied nearby galaxies
with a direct derivation of the chemical abundance. A complex relation between
MZR and SFR across the whole range of galaxy mass and metallicity has been
observed, where the slope changes seen in the O/H -- SFR plane present a
pattern which seems to be tuned to the galaxies' stellar age, and therefore,
stellar age has to be taken into account in the stellar mass -- metallicity --
SFR relation. In order to provide an answer to the question of whether or not
the MZR depends on the SFR it is essential to take into account the age of the
stellar populations of galaxies. A strong dependence between the MZR and SFR is
observed mainly for star-forming galaxies with strong SFR values and low
Dn(4000). The youngest galaxies of our SDSS sample show the highest SFR
measured for their stellar mass.


title:
Multi-Task Text Classification using Graph Convolutional Networks for
  Large-Scale Low Resource Language

abstract:
  Graph Convolutional Networks (GCN) have achieved state-of-art results on
single text classification tasks like sentiment analysis, emotion detection,
etc. However, the performance is achieved by testing and reporting on
resource-rich languages like English. Applying GCN for multi-task text
classification is an unexplored area. Moreover, training a GCN or adopting an
English GCN for Indian languages is often limited by data availability, rich
morphological variation, syntax, and semantic differences. In this paper, we
study the use of GCN for the Telugu language in single and multi-task settings
for four natural language processing (NLP) tasks, viz. sentiment analysis (SA),
emotion identification (EI), hate-speech (HS), and sarcasm detection (SAR). In
order to evaluate the performance of GCN with one of the Indian languages,
Telugu, we analyze the GCN based models with extensive experiments on four
downstream tasks. In addition, we created an annotated Telugu dataset, TEL-NLP,
for the four NLP tasks. Further, we propose a supervised graph reconstruction
method, Multi-Task Text GCN (MT-Text GCN) on the Telugu that leverages to
simultaneously (i) learn the low-dimensional word and sentence graph embeddings
from word-sentence graph reconstruction using graph autoencoder (GAE) and (ii)
perform multi-task text classification using these latent sentence graph
embeddings. We argue that our proposed MT-Text GCN achieves significant
improvements on TEL-NLP over existing Telugu pretrained word embeddings, and
multilingual pretrained Transformer models: mBERT, and XLM-R. On TEL-NLP, we
achieve a high F1-score for four NLP tasks: SA (0.84), EI (0.55), HS (0.83) and
SAR (0.66). Finally, we show our model's quantitative and qualitative analysis
on the four NLP tasks in Telugu.


title: Unifying thermodynamic geometries

abstract:
  Controlling thermodynamic cycles to minimize the dissipated heat is a
longstanding goal in thermodynamics, and more recently, a central challenge in
stochastic thermodynamics for nanoscale systems. Here, we introduce a
theoretical and computational framework for optimizing nonequilibrium control
protocols that can transform a system between two distributions in a minimally
dissipative fashion. These protocols optimally transport a system along a
Wasserstein geodesic, paths through the space of probability distributions that
minimize the dissipative cost of a transformation. Furthermore, we show that
the thermodynamic metric -- determined via a linear response approach -- can be
directly derived from a more general formulation based on optimal transport
distances, thus providing a unified perspective on thermodynamic geometries. We
investigate this unified geometric framework in two model systems and observe
that our procedure for optimizing control protocols is robust beyond linear
response.


title: A stable sampling method for inverse scattering from periodic media

abstract:
  This paper is concerned with the inverse problem of determining the shape of
penetrable periodic scatterers from scattered field data. We propose a sampling
method with a new imaging functional for solving this inverse problem. This
imaging functional is very simple to implement, robust against noise in the
data, and does not require regularization. The resolution and stability
analysis of the imaging functional is analyzed. Our numerical study shows that
the proposed sampling method is more stable than the factorization method and
more efficient than the orthogonality sampling method in reconstructing
periodic scattering media.


title:
Perturbation theory and thermal transport in mass-disordered alloys:
  Insights from Green's function methods

abstract:
  Lowest-order quantum perturbation theory (Fermi's golden rule) for
phonon-disorder scattering has been used to predict thermal conductivities in
several semiconducting alloys with surprising success given its underlying
hypothesis of weak and dilute disorder. In this paper, we explain how this is
possible by focusing on the case of maximally mass-disordered
Mg$_2$Si$_{1-x}$Sn$_x$. We use a Chebyshev polynomials Green's function method
that allows a full treatment of disorder on very large systems (tens of
millions of atoms) to probe individual phonon linewidths and frequency-resolved
thermal transport. We demonstrate that the success of perturbation theory
originates from the specific form of mass disorder terms in the phonon Green's
function and from the interplay between anharmonic and disorder scattering.


title: Decoherence and energy flow in the sunburst quantum Ising model

abstract:
  We study the post-quench unitary dynamics of a quantum sunburst spin model,
composed of a transverse-field quantum Ising ring which is suddenly coupled to
a set of independent external qubits along the longitudinal direction, in a way
to respect a residual translation invariance and the Ising $\mathbb{Z}_2$
symmetry. Starting from the different equilibrium quantum phases of the system,
we characterize the decoherence and the energy storage in the external qubits,
which may be interpreted as a probing apparatus for the inner Ising ring. Our
results show that, in proximity of the quantum transitions of the Ising ring,
either first-order or continuous, it is possible to put forward dynamic FSS
frameworks which unveil peculiar scaling regimes, depending on the way in which
the large-size limit is taken: either by fixing the number $n$ of probing
qubits, or their interspace distance $b$. In any case, the dependence of the
various observables on $n$ can be reabsorbed into a redefinition of the quench
parameter by a $\sqrt{n}$ prefactor. We also address the role of a
nearest-neighbor coupling between the external qubits.


title:
Machine Learning Interatomic Potential for Simulations of Carbon at
  Extreme Conditions

abstract:
  A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential
(MLIP) has been developed for simulations of carbon at extreme pressures (up to
5 TPa) and temperatures (up to 20,000 K). This was achieved using a large
database of experimentally relevant quantum molecular dynamics (QMD) data,
training the SNAP potential using a robust machine learning methodology, and
performing extensive validation against QMD and experimental data. The
resultant carbon MLIP demonstrates unprecedented accuracy and transferability
in predicting the carbon phase diagram, melting curves of crystalline phases,
and the shock Hugoniot, all within 3% of QMD. By achieving quantum accuracy and
efficient implementation on leadership class high performance computing
systems, SNAP advances frontiers of classical MD simulations by enabling
atomic-scale insights at experimental time and length scales.


title:
Applications of Deep Learning to the Design of Enhanced Wireless
  Communication Systems

abstract:
  Innovation in the physical layer of communication systems has traditionally
been achieved by breaking down the transceivers into sets of processing blocks,
each optimized independently based on mathematical models. Conversely, deep
learning (DL)-based systems are able to handle increasingly complex tasks for
which no tractable models are available. This thesis aims at comparing
different approaches to unlock the full potential of DL in the physical layer.
  First, we describe a neural network (NN)-based block strategy, where an NN is
optimized to replace a block in a communication system. We apply this strategy
to introduce a multi-user multiple-input multiple-output (MU-MIMO) detector
that builds on top of an existing DL-based architecture. Second, we detail an
end-to-end strategy, in which the transmitter and receiver are modeled as an
autoencoder. This approach is illustrated with the design of waveforms that
achieve high throughputs while satisfying peak-to-average power ratio (PAPR)
and adjacent channel leakage ratio (ACLR) constraints. Lastly, we propose a
hybrid strategy, where multiple DL components are inserted into a traditional
architecture but are trained to optimize the end-to-end performance. To
demonstrate its benefits, we propose a DL-enhanced MU-MIMO receiver that both
enable lower bit error rates (BERs) compared to a conventional receiver and
remains scalable to any number of users.
  Each approach has its own strengths and shortcomings. While the first one is
the easiest to implement, its individual block optimization does not ensure the
overall system optimality. On the other hand, systems designed with the second
approach are computationally complex but allow for new opportunities such as
pilotless transmissions. Finally, the combined flexibility and end-to-end
performance gains of the third approach motivate its use for short-term
practical implementations.


title:
X-ray pulse generation with ultra-fast flipping of its orbital angular
  momentum

abstract:
  A method to temporally tailor the properties of X-ray radiation carrying
Orbital Angular Momentum (OAM) is presented. In simulations, an electron beam
is prepared with a temporally modulated micro-bunching structure which, when
radiating at the second harmonic in a helical undulator, generates OAM light
with a corresponding temporally modulated intensity. This method is shown to
generate attosecond pulse trains of OAM light without the need for any
additional external optics, making the wavelength range tunable. In addition to
the OAM pulse train, the method can be adapted to generate radiation where the
handedness of the OAM mode may also be temporally modulated (flipped).


title: Streaming Inference for Infinite Non-Stationary Clustering

abstract:
  Learning from a continuous stream of non-stationary data in an unsupervised
manner is arguably one of the most common and most challenging settings facing
intelligent agents. Here, we attack learning under all three conditions
(unsupervised, streaming, non-stationary) in the context of clustering, also
known as mixture modeling. We introduce a novel clustering algorithm that
endows mixture models with the ability to create new clusters online, as
demanded by the data, in a probabilistic, time-varying, and principled manner.
To achieve this, we first define a novel stochastic process called the
Dynamical Chinese Restaurant Process (Dynamical CRP), which is a
non-exchangeable distribution over partitions of a set; next, we show that the
Dynamical CRP provides a non-stationary prior over cluster assignments and
yields an efficient streaming variational inference algorithm. We conclude with
experiments showing that the Dynamical CRP can be applied on diverse synthetic
and real data with Gaussian and non-Gaussian likelihoods.


title: Line-of-Sight MIMO via Reflection From a Smooth Surface

abstract:
  We provide a deterministic channel model for a scenario where wireless
connectivity is established through a reflection from a planar smooth surface
of an infinite extent. The developed model is rigorously built upon the physics
of wave propagation, and is as precise as tight are the unboundedness and
smoothness assumptions on the surface. This model allows establishing that
line-of-sight spatial multiplexing can take place via reflection off an
electrically large surface, a situation of high interest for mmWave and
terahertz frequencies.


title:
An improvement to a result about graph isomorphism networks using the
  prime factorization theorem

abstract:
  The unique prime factorization theorem is used to show the existence of a
function on a countable set $\mathcal{X}$ so that the sum aggregator function
is injective on all multisets of $\mathcal{X}$ of finite size.


title:
The state of stress and strain adjacent to notches in a new class of
  nonlinear elastic bodies

abstract:
  In this paper we study the deformation of a body with a notch subject to an
anti-plane state of stress within the context of a new class of elastic models.
These models stem as approximations of constitutive response functions for an
elastic body that is defined within the context of an implicit constitutive
relation between the stress and the deformation gradient. Gum metal and many
metallic alloys are described well by such constitutive relations. We consider
the state of anti-plane stress of a body with a smoothened V-notch within the
context of constitutive relations for the linearized strain in terms of a
power-law for the stretch. The problem is solved numerically and the
convergence and the stability of the solution is studied.


title:
Estimating beneficiaries of the child tax credit: past, present, and
  future

abstract:
  Government efforts to address child poverty commonly encompass economic
assistance programs that bolster household income. The Child Tax Credit (CTC)
is the most prominent example of this. Introduced by the United States Congress
in 1997, the program endeavors to help working parents via income
stabilization. Our work examines the extent to which the CTC has done so. Our
study, which documents clear, consistent, and compelling evidence of gender
inequity in benefits realization, yields four key findings. First, stringent
requisite income thresholds disproportionally disadvantage single mothers, a
reflection of the high concentration of this demographic in lower segments of
the income distribution. Second, married parents and, to a lesser extent,
single fathers, are the primary beneficiaries of the CTC program when benefits
are structured as credits rather than refunds. Third, making program benefits
more generous disproportionally reduces how many single mothers, relative to
married parents and single fathers, can claim this benefit. Fourth and finally,
increasing credit refundability can mitigate gender differences in relief
eligibility, although doing so imposes externalities of its own. Our findings
can inform public policy discourse surrounding the efficacy of programs like
the CTC and the effectiveness of programs aimed at alleviating child poverty.


title:
Insider Stories: Analyzing Internal Sustainability Efforts of Major US
  Companies from Online Reviews

abstract:
  It is hard to establish whether a company supports gender equality,
diversity, and general staff welfare, not least because of lack of
methodologies operationalizing these internal sustainability practices, and of
data honestly documenting such efforts. We developed and validated a
six-dimension framework reflecting Internal Sustainability Efforts (ISEs),
gathered more than 350K employee reviews of 104 major companies across the
whole US for the (2008-2020) years, and developed a deep-learning framework
scoring these reviews in terms of the six ISEs. Commitment to ISEs manifested
itself not only at micro-level (companies scoring high in ISEs enjoyed high
stock growth) but also at macro-level (states hosting these companies were
economically wealthy and equal, and attracted the so-called creative class).
This new conceptualization of ISEs offers both theoretical implications for the
literature in corporate sustainability and economic geography, and practical
implications for companies and policy makers.


title:
Detailed Study of Quark-Hadron Duality in Spin Structure Functions of
  the Proton and Neutron

abstract:
  In this paper, we present for the first time comprehensive and detailed
results on the correspondence between the extrapolated deep inelastic structure
function $g_1$ of both the proton and the neutron with the same quantity
measured in the nucleon resonance region. We use a QCD parameterization of the
world data on DIS spin structure functions, extrapolated into the nucleon
resonance region and averaged over various intervals in the scaling variable
$x$. We compare the results with the large data set collected in the
quark-hadron transition region by the CLAS collaboration, averaged over the
same intervals. We present this comparison as a function of the momentum
transfer $Q^2$. We find that, depending on the averaging interval and the
minimum momentum transfer chosen, a clear transition to quark-hadron duality
can be observed in both nucleon species. Furthermore, we show, for the first
time, the scaling behavior of $g_1$ measured in the resonance region at
sufficiently high momentum transfer. Our results can be used to quantify the
deviations from the applicability of pQCD for data taken at moderate energies,
and help with extraction of quark distribution functions from such data.


title:
A dual chirped-pulse electro-optical frequency comb method for
  simultaneous molecular spectroscopy and dynamics studies: Formic acid in the
  THz region

abstract:
  An electro-optic dual comb system based on chirped-pulse waveforms is used to
simultaneously acquire temporally magnified rapid passage signals and normal
spectral line shapes from the back-transformation to the time domain.
Multi-heterodyne THz wave generation and detection is performed with the
difference frequency mixing of two free-running lasers. The method is used to
obtain THz spectra of formic acid in the 10 cm-1 to 20 cm-1 (300 GHz to 600
GHz) region over a range of pressures. The method is widely applicable across
other spectral regions for investigations of the transient dynamics and
spectroscopy of molecular systems.


title:
Efficient Use of Quantum Linear System Algorithms in Interior Point
  Methods for Linear Optimization

abstract:
  Quantum computing has attracted significant interest in the optimization
community because it potentially can solve classes of optimization problems
faster than conventional supercomputers. Several researchers proposed quantum
computing methods, especially Quantum Interior Point Methods (QIPMs), to solve
convex optimization problems, such as Linear Optimization, Semidefinite
Optimization, and Second-order Cone Optimization problems. Most of them have
applied a Quantum Linear System Algorithm at each iteration to compute a Newton
step. However, using quantum linear solvers in QIPMs comes with many
challenges, such as having ill-conditioned systems and the considerable error
of quantum solvers. This paper investigates how one can efficiently use quantum
linear solvers in QIPMs. Accordingly, an Inexact Infeasible Quantum Interior
Point Method is developed to solve linear optimization problems. We also
discuss how can we get an exact solution by Iterative Refinement without
excessive time of quantum solvers. Finally, computational results with QISKIT
implementation of our QIPM using quantum simulators are analyzed.


title: Resolving 100 photons and quantum generation of unbiased random numbers

abstract:
  Macroscopic quantum phenomena, such as observed in superfluids and
superconductors, have led to promising technological advancements and some of
the most important tests of fundamental physics. At present, quantum detection
of light is mostly relegated to the microscale, where avalanche photodiodes are
very sensitive to distinguishing single-photon events from vacuum but cannot
differentiate between larger photon-number events. Beyond this, the ability to
perform measurements to resolve photon numbers is highly desirable for a
variety of quantum information applications including computation, sensing, and
cryptography. True photon-number resolving detectors do exist, but they are
currently limited to the ability to resolve on the order of 10 photons, which
is too small for certain proposals. In this work, we extend photon measurement
into the mesoscopic regime by implementing a detection scheme based on
multiplexing highly quantum-efficient transition-edge sensors to accurately
resolve photon numbers between zero and 100. We then demonstrate the use of our
system by implementing a quantum random number generator with no inherent bias.
This method is based on sampling a coherent state in the photon-number basis
and is robust against environmental noise, phase and amplitude fluctuations in
the laser, loss and detector inefficiency as well as eavesdropping. Beyond true
random number generation, our detection scheme serves as a means to implement
quantum measurement and engineering techniques valuable for photonic quantum
information processing.


title:
Leveraging Stochastic Predictions of Bayesian Neural Networks for Fluid
  Simulations

abstract:
  We investigate uncertainty estimation and multimodality via the
non-deterministic predictions of Bayesian neural networks (BNNs) in fluid
simulations. To this end, we deploy BNNs in three challenging experimental
test-cases of increasing complexity: We show that BNNs, when used as surrogate
models for steady-state fluid flow predictions, provide accurate physical
predictions together with sensible estimates of uncertainty. Further, we
experiment with perturbed temporal sequences from Navier-Stokes simulations and
evaluate the capabilities of BNNs to capture multimodal evolutions. While our
findings indicate that this is problematic for large perturbations, our results
show that the networks learn to correctly predict high uncertainties in such
situations. Finally, we study BNNs in the context of solver interactions with
turbulent plasma flows. We find that BNN-based corrector networks can stabilize
coarse-grained simulations and successfully create multimodal trajectories.


title: FINETUNA: Fine-tuning Accelerated Molecular Simulations

abstract:
  Machine learning approaches have the potential to approximate Density
Functional Theory (DFT) for atomistic simulations in a computationally
efficient manner, which could dramatically increase the impact of computational
simulations on real-world problems. However, they are limited by their accuracy
and the cost of generating labeled data. Here, we present an online active
learning framework for accelerating the simulation of atomic systems
efficiently and accurately by incorporating prior physical information learned
by large-scale pre-trained graph neural network models from the Open Catalyst
Project. Accelerating these simulations enables useful data to be generated
more cheaply, allowing better models to be trained and more atomistic systems
to be screened. We also present a method of comparing local optimization
techniques on the basis of both their speed and accuracy. Experiments on 30
benchmark adsorbate-catalyst systems show that our method of transfer learning
to incorporate prior information from pre-trained models accelerates
simulations by reducing the number of DFT calculations by 91%, while meeting an
accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a
technique for leveraging the interactive functionality built in to VASP to
efficiently compute single point calculations within our online active learning
framework without the significant startup costs. This allows VASP to work in
tandem with our framework while requiring 75% fewer self-consistent cycles than
conventional single point calculations. The online active learning
implementation, and examples using the VASP interactive code, are available in
the open source FINETUNA package on Github.


title:
COMET Flows: Towards Generative Modeling of Multivariate Extremes and
  Tail Dependence

abstract:
  Normalizing flows, a popular class of deep generative models, often fail to
represent extreme phenomena observed in real-world processes. In particular,
existing normalizing flow architectures struggle to model multivariate
extremes, characterized by heavy-tailed marginal distributions and asymmetric
tail dependence among variables. In light of this shortcoming, we propose COMET
(COpula Multivariate ExTreme) Flows, which decompose the process of modeling a
joint distribution into two parts: (i) modeling its marginal distributions, and
(ii) modeling its copula distribution. COMET Flows capture heavy-tailed
marginal distributions by combining a parametric tail belief at extreme
quantiles of the marginals with an empirical kernel density function at
mid-quantiles. In addition, COMET Flows capture asymmetric tail dependence
among multivariate extremes by viewing such dependence as inducing a
low-dimensional manifold structure in feature space. Experimental results on
both synthetic and real-world datasets demonstrate the effectiveness of COMET
Flows in capturing both heavy-tailed marginals and asymmetric tail dependence
compared to other state-of-the-art baseline architectures. All code is
available on GitHub at https://github.com/andrewmcdonald27/COMETFlows.


title:
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign
  Classifiers in Autonomous Vehicles

abstract:
  Adversarial attacks can make deep neural network (DNN) models predict
incorrect output labels, such as misclassified traffic signs, for autonomous
vehicle (AV) perception modules. Resilience against adversarial attacks can
help AVs navigate safely on the road by avoiding misclassication of signs or
objects. This DNN-based study develops a resilient traffic sign classifier for
AVs that uses a hybrid defense method. We use transfer learning to retrain the
Inception-V3 and Resnet-152 models as traffic sign classifiers. This method
also utilizes a combination of three different strategies: random filtering,
ensembling, and local feature mapping. We use the random cropping and resizing
technique for random filtering, plurality voting as ensembling strategy and an
optical character recognition model as a local feature mapper. This DNN-based
hybrid defense method has been tested for the no attack scenario and against
well-known untargeted adversarial attacks (e.g., Projected Gradient Descent or
PGD, Fast Gradient Sign Method or FGSM, Momentum Iterative Method or MIM
attack, and Carlini and Wagner or C&W). We find that our hybrid defense method
achieves 99% average traffic sign classification accuracy for the no attack
scenario and 88% average traffic sign classification accuracy for all attack
scenarios. Moreover, the hybrid defense method, presented in this study,
improves the accuracy for traffic sign classification compared to the
traditional defense methods (i.e., JPEG filtering, feature squeezing, binary
filtering, and random filtering) up to 6%, 50%, and 55% for FGSM, MIM, and PGD
attacks, respectively.


title: Adversarial attacks on an optical neural network

abstract:
  Adversarial attacks have been extensively investigated for machine learning
systems including deep learning in the digital domain. However, the adversarial
attacks on optical neural networks (ONN) have been seldom considered
previously. In this work, we first construct an accurate image classifier with
an ONN using a mesh of interconnected Mach-Zehnder interferometers (MZI). Then
a corresponding adversarial attack scheme is proposed for the first time. The
attacked images are visually very similar to the original ones but the ONN
system becomes malfunctioned and generates wrong classification results in most
time. The results indicate that adversarial attack is also a significant issue
for optical machine learning systems.


title: Topological Band Systems and Finite Size Effects

abstract:
  The recent discoveries about topological insulators have been promoting
theoretical and experimental research. In this dissertation, the basic concepts
of topological insulators and the Quantum Hall Effect are reviewed focusing the
discussion on edge states and their band structure. Lattice models with pierced
magnetism are described and the Hofstadter model is presented for bounded
systems with and without an in-site disorder. An overview of the experimental
procedure based on cold atoms in optical lattices with synthetic dimensions is
given. In order to understand to what extent these small systems of cold atoms
mimic the behaviour of a topological insulator, an analysis of some finite size
effects is provided and a deduction of the gap opening in the band structure is
presented using perturbation theory.


title: Paragraph-based Transformer Pre-training for Multi-Sentence Inference

abstract:
  Inference tasks such as answer sentence selection (AS2) or fact verification
are typically solved by fine-tuning transformer-based models as individual
sentence-pair classifiers. Recent studies show that these tasks benefit from
modeling dependencies across multiple candidate sentences jointly. In this
paper, we first show that popular pre-trained transformers perform poorly when
used for fine-tuning on multi-candidate inference tasks. We then propose a new
pre-training objective that models the paragraph-level semantics across
multiple input sentences. Our evaluation on three AS2 and one fact verification
datasets demonstrates the superiority of our pre-training technique over the
traditional ones for transformers used as joint models for multi-candidate
inference tasks, as well as when used as cross-encoders for sentence-pair
formulations of these tasks.


title:
DCoflow: Deadline-Aware Scheduling Algorithm for Coflows in Datacenter
  Networks

abstract:
  Datacenter networks routinely support the data transfers of distributed
computing frameworks in the form of coflows, i.e., sets of concurrent flows
related to a common task. The vast majority of the literature has focused on
the problem of scheduling coflows for completion time minimization, i.e., to
maximize the average rate at which coflows are dispatched in the network
fabric. Modern applications, though, may generate coflows dedicated to online
services and mission-critical computing tasks which have to comply with
specific completion deadlines. In this paper, we introduce $\mathtt{DCoflow}$,
a lightweight deadline-aware scheduler for time-critical coflows in datacenter
networks. The algorithm combines an online joint admission control and
scheduling logic and returns a $\sigma$-order schedule which maximizes the
number of coflows that attain their deadlines. Extensive numerical results
demonstrate that the proposed solution outperforms existing ones.


title: Retrieval-Enhanced Machine Learning

abstract:
  Although information access systems have long supported people in
accomplishing a wide range of tasks, we propose broadening the scope of users
of information access systems to include task-driven machines, such as machine
learning models. In this way, the core principles of indexing, representation,
retrieval, and ranking can be applied and extended to substantially improve
model generalization, scalability, robustness, and interpretability. We
describe a generic retrieval-enhanced machine learning (REML) framework, which
includes a number of existing models as special cases. REML challenges
information retrieval conventions, presenting opportunities for novel advances
in core areas, including optimization. The REML research agenda lays a
foundation for a new style of information access research and paves a path
towards advancing machine learning and artificial intelligence.


title: ADDAI: Anomaly Detection using Distributed AI

abstract:
  When dealing with the Internet of Things (IoT), especially industrial IoT
(IIoT), two manifest challenges leap to mind. First is the massive amount of
data streaming to and from IoT devices, and second is the fast pace at which
these systems must operate. Distributed computing in the form of edge/cloud
structure is a popular technique to overcome these two challenges. In this
paper, we propose ADDAI (Anomaly Detection using Distributed AI) that can
easily span out geographically to cover a large number of IoT sources. Due to
its distributed nature, it guarantees critical IIoT requirements such as high
speed, robustness against a single point of failure, low communication
overhead, privacy, and scalability. Through empirical proof, we show the
communication cost is minimized, and the performance improves significantly
while maintaining the privacy of raw data at the local layer. ADDAI provides
predictions for new random samples with an average success rate of 98.4% while
reducing the communication overhead by half compared with the traditional
technique of offloading all the raw sensor data to the cloud.


title:
TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT
  Security

abstract:
  Despite AI's significant growth, its "black box" nature creates challenges in
generating adequate trust. Thus, it is seldom utilized as a standalone unit in
IoT high-risk applications, such as critical industrial infrastructures,
medical systems, and financial applications, etc. Explainable AI (XAI) has
emerged to help with this problem. However, designing appropriately fast and
accurate XAI is still challenging, especially in numerical applications. Here,
we propose a universal XAI model named Transparency Relying Upon Statistical
Theory (TRUST), which is model-agnostic, high-performing, and suitable for
numerical applications. Simply put, TRUST XAI models the statistical behavior
of the AI's outputs in an AI-based system. Factor analysis is used to transform
the input features into a new set of latent variables. We use mutual
information to rank these variables and pick only the most influential ones on
the AI's outputs and call them "representatives" of the classes. Then we use
multi-modal Gaussian distributions to determine the likelihood of any new
sample belonging to each class. We demonstrate the effectiveness of TRUST in a
case study on cybersecurity of the industrial Internet of things (IIoT) using
three different cybersecurity datasets. As IIoT is a prominent application that
deals with numerical data. The results show that TRUST XAI provides
explanations for new random samples with an average success rate of 98%.
Compared with LIME, a popular XAI model, TRUST is shown to be superior in the
context of performance, speed, and the method of explainability. In the end, we
also show how TRUST is explained to the user.


title:
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic
  Segmentation Model

abstract:
  Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to
identify objects in images based on a small number of images with pixel-level
labels, and many more images with only image-level labels. Most existing SWSSS
algorithms extract pixel-level pseudo-labels from an image classifier - a very
difficult task to do well, hence requiring complicated architectures and
extensive hyperparameter tuning on fully-supervised validation sets. We propose
a method called prediction filtering, which instead of extracting
pseudo-labels, just uses the classifier as a classifier: it ignores any
segmentation predictions from classes which the classifier is confident are not
present. Adding this simple post-processing method to baselines gives results
competitive with or better than prior SWSSS algorithms. Moreover, it is
compatible with pseudo-label methods: adding prediction filtering to existing
SWSSS algorithms further improves segmentation performance.


title: Scalable Tail Latency Estimation for Data Center Networks

abstract:
  In this paper, we consider how to provide fast estimates of flow-level tail
latency performance for very large scale data center networks. Network tail
latency is often a crucial metric for cloud application performance that can be
affected by a wide variety of factors, including network load, inter-rack
traffic skew, traffic burstiness, flow size distributions, oversubscription,
and topology asymmetry. Network simulators such as ns-3 and OMNeT++ can provide
accurate answers, but are very hard to parallelize, taking hours or days to
answer what if questions for a single configuration at even moderate scale.
Recent work with MimicNet has shown how to use machine learning to improve
simulation performance, but at a cost of including a long training step per
configuration, and with assumptions about workload and topology uniformity that
typically do not hold in practice.
  We address this gap by developing a set of techniques to provide fast
performance estimates for large scale networks with general traffic matrices
and topologies. A key step is to decompose the problem into a large number of
parallel independent single-link simulations; we carefully combine these
link-level simulations to produce accurate estimates of end-to-end flow level
performance distributions for the entire network. Like MimicNet, we exploit
symmetry where possible to gain additional speedups, but without relying on
machine learning, so there is no training delay. On large-scale networks where
ns-3 takes 11 to 27 hours to simulate five seconds of network behavior, our
techniques run in one to two minutes with 99th percentile accuracy within 9%
for flow completion times.


title: Triangular Dropout: Variable Network Width without Retraining

abstract:
  One of the most fundamental design choices in neural networks is layer width:
it affects the capacity of what a network can learn and determines the
complexity of the solution. This latter property is often exploited when
introducing information bottlenecks, forcing a network to learn compressed
representations. However, such an architecture decision is typically immutable
once training begins; switching to a more compressed architecture requires
retraining. In this paper we present a new layer design, called Triangular
Dropout, which does not have this limitation. After training, the layer can be
arbitrarily reduced in width to exchange performance for narrowness. We
demonstrate the construction and potential use cases of such a mechanism in
three areas. Firstly, we describe the formulation of Triangular Dropout in
autoencoders, creating models with selectable compression after training.
Secondly, we add Triangular Dropout to VGG19 on ImageNet, creating a powerful
network which, without retraining, can be significantly reduced in parameters.
Lastly, we explore the application of Triangular Dropout to reinforcement
learning (RL) policies on selected control problems.


title: Classical group matrix models and universal criticality

abstract:
  We study generalizations of the Gross--Witten--Wadia unitary matrix model for
the special orthogonal and symplectic groups. We show using a standard Coulomb
gas treatment -- employing a path integral formalism for the ungapped phase and
resolvent techniques for the gapped phase with one coupling constant -- that in
the large $N$ limit, the free energy normalized modulo the square of the gauge
group rank is twice the value for the unitary case. Using generalized Cauchy
identities for character polynomials, we then demonstrate the universality of
this phase transition for an arbitrary number of coupling constants by linking
this model to the random partition based on the Schur measure.

categories: hep-th cond-mat.stat-mech math-ph math.MP


title: Shape spaces: From geometry to biological plausibility

abstract:
  This paper reviews several Riemannian metrics and evolution equations in the
context of diffeomorphic shape analysis. After a short review of of various
approaches at building Riemannian spaces of shapes, with a special focus on the
foundations of the large deformation diffeomorphic metric mapping algorithm,
the attention is turned to elastic metrics, and to growth models that can be
derived from it. In the latter context, a new class of metrics, involving the
optimization of a growth tensor, is introduced and some of its properties are
studied.


title: Material Facts Obscured in Hansen's Modern Gauss-Markov Theorem

abstract:
  We show that the abstract and conclusion of Hansen's {\it Econometrica}
paper, \cite{Hansen22}, entitled a modern Gauss-Markov theorem (MGMT), obscures
a material fact, which in turn can confuse students. The MGMT places ordinary
least squares (OLS) back on a high pedestal by bringing in the Cramer-Rao
efficiency bound. We explain why linearity and unbiasedness are linked, making
most nonlinear estimators biased. Hence, MGMT extends the reach of the
century-old GMT by a near-empty set. It misleads students because it misdirects
attention back to the unbiased OLS from beneficial shrinkage and other tools,
which reduce the mean squared error (MSE) by injecting bias.


title:
A Performance-Consistent and Computation-Efficient CNN System for
  High-Quality Automated Brain Tumor Segmentation

abstract:
  The research on developing CNN-based fully-automated Brain-Tumor-Segmentation
systems has been progressed rapidly. For the systems to be applicable in
practice, a good The research on developing CNN-based fully-automated
Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems
to be applicable in practice, a good processing quality and reliability are the
must. Moreover, for wide applications of such systems, a minimization of
computation complexity is desirable, which can also result in a minimization of
randomness in computation and, consequently, a better performance consistency.
To this end, the CNN in the proposed system has a unique structure with 2
distinguished characters. Firstly, the three paths of its feature extraction
block are designed to extract, from the multi-modality input, comprehensive
feature information of mono-modality, paired-modality and cross-modality data,
respectively. Also, it has a particular three-branch classification block to
identify the pixels of 4 classes. Each branch is trained separately so that the
parameters are updated specifically with the corresponding ground truth data of
a target tumor areas. The convolution layers of the system are custom-designed
with specific purposes, resulting in a very simple config of 61,843 parameters
in total. The proposed system is tested extensively with BraTS2018 and
BraTS2019 datasets. The mean Dice scores, obtained from the ten experiments on
BraTS2018 validation samples, are 0.787+0.003, 0.886+0.002, 0.801+0.007, for
enhancing tumor, whole tumor and tumor core, respectively, and 0.751+0.007,
0.885+0.002, 0.776+0.004 on BraTS2019. The test results demonstrate that the
proposed system is able to perform high-quality segmentation in a consistent
manner. Furthermore, its extremely low computation complexity will facilitate
its implementation/application in various environments.


title:
Using Constraint Programming and Graph Representation Learning for
  Generating Interpretable Cloud Security Policies

abstract:
  Modern software systems rely on mining insights from business sensitive data
stored in public clouds. A data breach usually incurs significant (monetary)
loss for a commercial organization. Conceptually, cloud security heavily relies
on Identity Access Management (IAM) policies that IT admins need to properly
configure and periodically update. Security negligence and human errors often
lead to misconfiguring IAM policies which may open a backdoor for attackers. To
address these challenges, first, we develop a novel framework that encodes
generating optimal IAM policies using constraint programming (CP). We identify
reducing dark permissions of cloud users as an optimality criterion, which
intuitively implies minimizing unnecessary datastore access permissions.
Second, to make IAM policies interpretable, we use graph representation
learning applied to historical access patterns of users to augment our CP model
with similarity constraints: similar users should be grouped together and share
common IAM policies. Third, we describe multiple attack models and show that
our optimized IAM policies significantly reduce the impact of security attacks
using real data from 8 commercial organizations, and synthetic instances.


title: Propositional Equality for Gradual Dependently Typed Programming

abstract:
  Gradual dependent types can help with the incremental adoption of dependently
typed code by providing a principled semantics for imprecise types and proofs,
where some parts have been omitted. Current theories of gradual dependent
types, though, lack a central feature of type theory: propositional equality.
Lennon-Bertrand et al. show that, when the reflexive proof $\mathit{refl}$ is
the only closed value of an equality type, a gradual extension of CIC with
propositional equality violates static observational equivalences.
Extensionally-equal functions should be indistinguishable at run time, but the
combination of equality and type imprecision allows for contexts that
distinguish extensionally-equal but syntactically-different functions.
  This work presents a gradually typed language that supports propositional
equality. We avoid the above issues by devising an equality type where
$\mathit{refl}$ is not the only closed inhabitant. Instead, each equality proof
carries a term that is at least as precise as the equated terms, acting as a
witness of their plausible equality. These witnesses track partial type
information as a program runs, raising errors when that information shows that
two equated terms are undeniably inconsistent. Composition of type information
is internalized as a construct of the language, and is deferred for function
bodies whose evaluation is blocked by variables. By deferring, we ensure that
extensionally equal functions compose without error, thereby preventing
contexts from distinguishing them. We describe the challenges of designing
consistency and precision relations for this system, along with solutions to
these challenges. Finally, we prove important metatheory: type-safety,
conservative embedding of CIC, canonicity, and the gradual guarantees of Siek
et al.


title:
Physics-Based Inverse Rendering using Combined Implicit and Explicit
  Geometries

abstract:
  Mathematically representing the shape of an object is a key ingredient for
solving inverse rendering problems. Explicit representations like meshes are
efficient to render in a differentiable fashion but have difficulties handling
topology changes. Implicit representations like signed-distance functions, on
the other hand, offer better support of topology changes but are much more
difficult to use for physics-based differentiable rendering. We introduce a new
physics-based inverse rendering pipeline that uses both implicit and explicit
representations. Our technique enjoys the benefit of both representations by
supporting both topology changes and differentiable rendering of complex
effects such as environmental illumination, soft shadows, and interreflection.
We demonstrate the effectiveness of our technique using several synthetic and
real examples.


title:
Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced
  Misinformation Warnings on Twitter

abstract:
  Warning users about misinformation on social media is not a simple usability
task. Soft moderation has to balance between debunking falsehoods and avoiding
moderation bias while preserving the social media consumption flow. Platforms
thus employ minimally distinguishable warning tags with generic text under a
suspected misinformation content. This approach resulted in an unfavorable
outcome where the warnings "backfired" and users believed the misinformation
more, not less. In response, we developed enhancements to the misinformation
warnings where users are advised on the context of the information hazard and
exposed to standard warning iconography. We ran an A/B evaluation with the
Twitter's original warning tags in a 337 participant usability study. The
majority of the participants preferred the enhancements as a nudge toward
recognizing and avoiding misinformation. The enhanced warning tags were most
favored by the politically left-leaning and to a lesser degree moderate
participants, but they also appealed to roughly a third of the right-leaning
participants. The education level was the only demographic factor shaping
participants' preferences. We use our findings to propose user-tailored
improvements in the soft moderation of misinformation on social media.


title: Structural transition of skyrmion quasiparticles under compression

abstract:
  The competition between external drives and interparticle interactions are
important for the static and dynamic properties of topological quasiparticles
in confined geometries. Here, we study the dynamics of particlelike skyrmions
under a current-induced compression, where skyrmions are stabilized in a
confined space and form a skyrmion solid. We find that a lattice structural
transition of nanoscale skyrmions could be induced by the compression, which
depends on the initial lattice configuration and the compression level. The
compression effect in the skyrmion solid with strong skyrmion-skyrmion
repulsion could produce a wave front showing an abrupt change of the skyrmion
density, which may propagate through the skyrmion solid depending on the
compression level. Besides, a chain of skyrmions on the surface of the
compressed skyrmion solid could show a fast and stable motion along the surface
as long as it remains within the driving region. Some skyrmions may collapse
during the compression due to the destructive skyrmion-skyrmion collision,
which mainly happens in the area around the skyrmion injection boundary between
the driving and compressive regions. Our results are useful for understanding
the compression physics of quasiparticles carrying nontrivial topology.


title:
Non-binding of three two-component particles with contact interactions:
  condition on the heavy-light mass ratio

abstract:
  Binding of two heavy fermions interacting with a light particle via the
contact interaction is possible only for sufficiently large mass ratio. In this
work, the two-variable inequality is derived determining a specific value
$\mu^*$, which provides that the three-body bound states do not exist for any
mass ratio less than $\mu^*$. Its value is obtained by analyzing the inequality
for a total angular momentum and parity $L^P = 1^-$. Other $L^P$ sectors of
this problem are considered in a similar way and corresponding specific values
of mass ratio are determined. For generality, the method is extended to
non-binding of the system consisting of two identical bosons and a distinct
particle for different $L^P$ ($L > 0$) sectors.


title:
Heterogeneous Treatment Effects for Networks, Panels, and other Outcome
  Matrices

abstract:
  We are interested in the distribution of treatment effects for an experiment
where units are randomized to treatment but outcomes are measured for pairs of
units. For example, we might measure risk sharing links between households
enrolled in a microfinance program, employment relationships between workers
and firms exposed to a trade shock, or bids from bidders to items assigned to
an auction format. Such a double randomized experimental design may be
appropriate when there are social interactions, market externalities, or other
spillovers across units assigned to the same treatment. Or it may describe a
natural or quasi experiment given to the researcher. In this paper, we propose
a new empirical strategy based on comparing the eigenvalues of the outcome
matrices associated with each treatment. Our proposal is based on a new matrix
analog of the Fr\'echet-Hoeffding bounds that play a key role in the standard
theory. We first use this result to bound the distribution of treatment
effects. We then propose a new matrix analog of quantile treatment effects
based on the difference in the eigenvalues. We call this analog spectral
treatment effects.


title: Scheduling with Speed Predictions

abstract:
  Algorithms with predictions is a recent framework that has been used to
overcome pessimistic worst-case bounds in incomplete information settings. In
the context of scheduling, very recent work has leveraged machine-learned
predictions to design algorithms that achieve improved approximation ratios in
settings where the processing times of the jobs are initially unknown. In this
paper, we study the speed-robust scheduling problem where the speeds of the
machines, instead of the processing times of the jobs, are unknown and augment
this problem with predictions.
  Our main result is an algorithm that achieves a
$\min\{\eta^2(1+\epsilon)^2(1+\alpha), (1+\epsilon)(2 + 2/\alpha)\}$
approximation, for any constants $\alpha, \epsilon \in (0,1)$, where $\eta \geq
1$ is the prediction error. When the predictions are accurate, this
approximation improves over the previously best known approximation of $2-1/m$
for speed-robust scheduling, where $m$ is the number of machines, while
simultaneously maintaining a worst-case approximation of $(1+\epsilon)(2 +
2/\alpha)$ even when the predictions are wrong. In addition, we obtain improved
approximations for the special cases of equal and infinitesimal job sizes, and
we complement our algorithmic results with lower bounds. Finally, we
empirically evaluate our algorithm against existing algorithms for speed-robust
scheduling.


title:
Short time existence and smoothness of the nonlocal mean curvature flow
  of graphs

abstract:
  We consider the geometric evolution problem of entire graphs moving by
fractional mean curvature. For this, we study the associated nonlocal
quasilinear evolution equation satisfied by the family of graph functions. We
establish, using an analytic semigroup approach, short time existence,
uniqueness and optimal H\"older regularity in time and space of classical
solutions of the nonlocal equation, depending on the regularity of the initial
graph. The method also yields $C^\infty-$smoothness estimates of the evolving
graphs for positive times.


title: The Transverse Structure of the Deuteron with Drell-Yan

abstract:
  We propose to measure neutron and deuteron transversity TMDs. The quark
transversity distributions of the nucleon are decoupled from the deuteron gluon
transversity in the $Q^2$ evolution due to the chiral-odd property in the
transversely-polarized target. The gluon transversity TMD only exists for
targets of spin greater or equal to 1 and does not mix with quark distributions
at leading twist, thereby providing a particularly clean probe of gluonic
degrees of freedom. This experiment would be the first of its kind and would
probe the gluonic structure of the deuteron, investigating exotic glue
contributions in the nucleus not associated with individual nucleons. This
experiment can be performed with the SpinQuest polarized target recently
assembled for experiment E1039 and the spectrometer already in place in NM4.
This new experimental setup would require very minimal modification to the
target system and no modification to the detector package. An additional
RF-circuit and target coil are necessary to RF-modulate across the domain of
the Larmor frequency to manipulate the solid-state target spin population
densities. Dedicated beam-time with this novel target system is required to
achieve our physics goals.


title:
First Principle Study for Optical Properties of TMDCs/Graphene
  Heterostructures

abstract:
  The transition-metal dichalcogenide (TMDC) in the family of $\mathrm{MX}_2$
($\mathrm{M}=\mathrm{Mo},\mathrm{W}$; $\mathrm{X}=\mathrm{S},\mathrm{Se}$) and
the graphene monolayer are atomically thin semiconductors and semimetal,
respectively. The monolayer $\mathrm{MX}_2$ have been discovered as a new class
of semiconductors for electronics and optoelectronics applications. Because of
the hexagonal lattice structure of both the materials, $\mathrm{MX}_2$ and
graphene are often combined with each other to make van der Waals
heterostructures. Here, the $\mathrm{MX}_2/\mathrm{Gr}$ heterostructures are
investigated theoretically based on the Density Functional Theory (DFT). The
electronic structure and the optical properties of four different
$\mathrm{MX}_2/\mathrm{Gr}$ heterostructures are computed. We systematically
compare these $\mathrm{MX}_2/\mathrm{Gr}$ heterostructures for their complex
permittivity, absorption coefficient, reflectivity and refractive index.


title:
Quantum steganography with large payload based on dense coding and
  entanglement swapping of GHZ states

abstract:
  A quantum steganography protocol with large payload is proposed, based on
dense coding and entanglement swapping of GHZ states. Its super quantum channel
is formed by building up the hidden channel within the original quantum secure
direct communication (QSDC) scheme. Based on the original QSDC, secrets
messages are transmitted by integrating dense coding of GHZ states and
entanglement swapping of GHZ states. Its capacity of super quantum channel
achieves six bits per round covert communication, much higher than the previous
quantum steganography protocols. Its imperceptibility is good, since the
information and secrets messages can be regarded to be random or pseudo-random.
Moreover, its security is proved to be reliable.


title:
SIMD$^2$: A Generalized Matrix Instruction Set for Accelerating Tensor
  Computation beyond GEMM

abstract:
  Matrix-multiplication units (MXUs) are now prevalent in every computing
platform. The key attribute that makes MXUs so successful is the semiring
structure, which allows tiling for both parallelism and data reuse.
Nonetheless, matrix-multiplication is not the only algorithm with such
attributes. We find that many algorithms share the same structure and differ in
only the core operation; for example, using add-minimum instead of
multiply-add. Algorithms with a semiring-like structure therefore have
potential to be accelerated by a general-purpose matrix operation architecture,
instead of common MXUs.
  In this paper, we propose SIMD$^2$, a new programming paradigm to support
generalized matrix operations with a semiring-like structure. SIMD$^2$
instructions accelerate eight more types of matrix operations, in addition to
matrix multiplications. Since SIMD$^2$ instructions resemble a
matrix-multiplication instruction, we are able to build SIMD$^2$ architecture
on top of any MXU architecture with minimal modifications. We developed a
framework that emulates and validates SIMD$^2$ using NVIDIA GPUs with Tensor
Cores. Across 8 applications, SIMD2 provides up to 38.59$\times$ speedup and
more than 10.63$\times$ on average over optimized CUDA programs, with only 5%
of full-chip area overhead.


title:
Storyteller: The papers co-citing Sleeping Beauty and Prince before
  awakening

abstract:
  In the Cumulative Advantage(CA) model, which is one of the most fundamental
approaches to understand the mechanism of citation dynamics, papers receive
citations depending on how much they have been already cited. On the other
hand, a substantial effect not included in CA is that some surprising
discoveries suddenly acquire citations after a long time from publishing. This
phenomenon is known as Sleeping Beauty(SB). Since disrupting discoveries need
long-time discussion by the research community to accept, SBs can capture
innovative findings and reveal the nature of disruptive scientific knowledge
production. To research SBs citation burst mechanism, bibliometricians consider
the existence of the Prince(PR) for each SBs, which can be the trigger of SBs
awakeness. For example, the discovery of Green Fluorescent Protein(GFP), which
got Nobel prize in chemistry, had been overlooked for 30 years until Chalfie
and Tsien, who also received the prize, developed a method to use GFP as a
marker protein in genetic engineering. However, how does Chalfies and Tsiens
research relight the hidden knowledge in the research community? If we can
clarify such a mechanism rediscovering from nearly nothing, it can be helpful
in science support and policy decision-making. This study proposes a
Storyteller that focuses on the connection between SB and PR before SB gets
citation burst by co-citation. PR is found to be the paper awakening SB in
retrospect, but it is not easy to detect it as the trigger of SBs awakeness at
the time of PR submission. We named the papers which co-cites SB and PR before
the citation burst of SB as Storyteller(ST) and analyze (1) how ST contributes
to broadening the novelty of SB&PR connections and (2) how much ST leads the
citation burst after awakening.


title: Deep API Learning Revisited

abstract:
  Understanding the correct API usage sequences is one of the most important
tasks for programmers when they work with unfamiliar libraries. However,
programmers often encounter obstacles to finding the appropriate information
due to either poor quality of API documentation or ineffective query-based
searching strategy. To help solve this issue, researchers have proposed various
methods to suggest the sequence of APIs given natural language queries
representing the information needs from programmers. Among such efforts, Gu et
al. adopted a deep learning method, in particular an RNN Encoder-Decoder
architecture, to perform this task and obtained promising results on common
APIs in Java. In this work, we aim to reproduce their results and apply the
same methods for APIs in Python. Additionally, we compare the performance with
a more recent Transformer-based method, i.e., CodeBERT, for the same task. Our
experiment reveals a clear drop in performance measures when careful data
cleaning is performed. Owing to the pretraining from a large number of source
code files and effective encoding technique, CodeBERT outperforms the method by
Gu et al., to a large extent.


title:
Incorporating Financial Hardship in Measuring the Mental Health Impact
  of Housing Stress

abstract:
  Housing expenditure tends to be sticky and costly to adjust, and makes up a
large proportion of household expenditure. Additionally, the loss of housing
can have catastrophic consequences. These specific features of housing
expenditure imply that housing stress could cause negative mental health
impacts. This research investigates the effects of housing stress on mental
health, contributing to the literature by nesting housing stress within a
measure of financial hardship, thus improving robustness to omitted variables
and creating a natural comparison group for matching. Fixed effects (FE)
regressions and a difference-in-differences (DID) methodology are estimated
utilising data from the Household Income and Labour Dynamics in Australia
(HILDA) Survey. The results show that renters who are in housing stress have a
significant decline in self-reported mental health, with those in prior
financial hardship being more severely affected. In contrast, there is little
to no evidence of housing stress impacting on owners with a mortgage. The
results also suggest that the mental health impact of housing stress is more
important than some, but not all, aspects of financial hardship.


title:
Hybrid Finite Difference Schemes for Elliptic Interface Problems with
  Discontinuous and High-Contrast Variable Coefficients

abstract:
  For elliptic interface problems with discontinuous coefficients, the maximum
accuracy order for compact 9-point finite difference scheme in irregular points
is three [7]. The discontinuous coefficients usually have abrupt jumps across
the interface curve in the porous medium of realistic problems, causing the
pollution effect of numerical methods. So, to obtain a reasonable numerical
solution of the above problem, the higher order scheme and its effective
implementation are necessary. In this paper, we propose an efficient and
flexible way to achieve the implementation of a hybrid (9-point scheme with
sixth order accuracy for interior regular points and 13-point scheme with fifth
order accuracy for interior irregular points) finite difference scheme in
uniform meshes for the elliptic interface problems with discontinuous and
high-contrast piecewise smooth coefficients in a rectangle $\Omega$. We also
derive the $6$-point and $4$-point finite difference schemes in uniform meshes
with sixth order accuracy for the side points and corner points of various
mixed boundary conditions (Dirichlet, Neumann and Robin) of elliptic equations
in a rectangle. Our numerical experiments confirm the flexibility and the sixth
order accuracy in $l_2$ and $l_{\infty}$ norms of the proposed hybrid scheme.


title: Norm-Agnostic Linear Bandits

abstract:
  Linear bandits have a wide variety of applications including recommendation
systems yet they make one strong assumption: the algorithms must know an upper
bound $S$ on the norm of the unknown parameter $\theta^*$ that governs the
reward generation. Such an assumption forces the practitioner to guess $S$
involved in the confidence bound, leaving no choice but to wish that
$\|\theta^*\|\le S$ is true to guarantee that the regret will be low. In this
paper, we propose novel algorithms that do not require such knowledge for the
first time. Specifically, we propose two algorithms and analyze their regret
bounds: one for the changing arm set setting and the other for the fixed arm
set setting. Our regret bound for the former shows that the price of not
knowing $S$ does not affect the leading term in the regret bound and inflates
only the lower order term. For the latter, we do not pay any price in the
regret for now knowing $S$. Our numerical experiments show standard algorithms
assuming knowledge of $S$ can fail catastrophically when $\|\theta^*\|\le S$ is
not true whereas our algorithms enjoy low regret.


title:
Universal Optimality and Robust Utility Bounds for Metric Differential
  Privacy

abstract:
  We study the privacy-utility trade-off in the context of metric differential
privacy. Ghosh et al. introduced the idea of universal optimality to
characterise the best mechanism for a certain query that simultaneously
satisfies (a fixed) $\epsilon$-differential privacy constraint whilst at the
same time providing better utility compared to any other
$\epsilon$-differentially private mechanism for the same query. They showed
that the Geometric mechanism is "universally optimal" for the class of counting
queries. On the other hand, Brenner and Nissim showed that outside the space of
counting queries, and for the Bayes risk loss function, no such universally
optimal mechanisms exist. In this paper we use metric differential privacy and
quantitative information flow as the fundamental principle for studying
universal optimality. Metric differential privacy is a generalisation of both
standard (i.e., central) differential privacy and local differential privacy,
and it is increasingly being used in various application domains, for instance
in location privacy and in privacy preserving machine learning. Using this
framework we are able to clarify Nissim and Brenner's negative results, showing
(a) that in fact all privacy types contain optimal mechanisms relative to
certain kinds of non-trivial loss functions, and (b) extending and generalising
their negative results beyond Bayes risk specifically to a wide class of
non-trivial loss functions. We also propose weaker universal benchmarks of
utility called "privacy type capacities". We show that such capacities always
exist and can be computed using a convex optimisation algorithm.


title:
Bifurcation and chaos for a new model of trigonal centrifugal governor
  with nonsmooth control

abstract:
  The flywheel ball and hexagonal structures in the design of the classical
centrifugal governor systems lead to both modeling and analytical difficulties.
In the present paper, a new trigonal centrifugal governor is proposed in an
attempt to overcome both of these difficulties by introducing the radical
nonlinearity and nonsmooth control strategy in the simple and clear formula.
The nonlinear dynamical behaviors of this new model are investigated for both
the autonomous and the non-autonomous cases. The three equations of motion of
the TCG are presented based on Euler-Lagrange equation and the theorem of
angular momentum. The velocity, nonlinear restoring force and nonsmooth torque
surfaces are plotted to display the complex relationship of parameter change
dependence. Secondely, the equilibrium bifurcation and stability analysis for
autonomous system are investigated to show the pitchork bifurcation phenomena
and the saddle-focus point respectively. It is found that the system bears
significant similarities to the Duffing system with mono-stable and bi-stable
characteristic. Finally, the three-dimensional Melnikov method is defined and
employed to obtained the analytical chaotic thresholds for non-autonomous
centrifugal governor system, and numerical results of chaotic behaviors verify
the proposed theoretical criteria of the non-autonomous system. The
experimental studies are carried out to validate the theoretical and numerical
results.


title: A Model of Financial Market Control

abstract:
  This study investigates the prevention of market manipulation using a
price-impact model of financial market trading as a linear system. First, I
define a trading game between speculators such that they implement a
manipulation trading strategy that exploits momentum traders. Second, I
identify market intervention by a controller (e.g., a central bank) with a
control of the system. The main result shows that there is a control strategy
that prevents market manipulation as a subgame perfect equilibrium outcome of
the trading game. On the equilibrium path, no intervention is realized. This
study also characterizes the set of manipulation-proof linear pricing rules of
the system. The set is very restrictive if there is no control, while the
presence of control drastically expands the set.


title:
Mismatched Disturbance Rejection Control for Second-Order Discrete-Time
  Systems

abstract:
  This paper is concerned with mismatched disturbance rejection control for the
second-order discrete-time systems.Different from previous work, the
controllability of the system is applied to design the disturbance compensation
gain, which does not require any coordinate transformations. Via this new idea,
it is shown that disturbance in the regulated output is immediately and
directly compensated in the case that the disturbance is known. When the
disturbance is unknown, an extra generalized extended state observer is applied
to design the controller. Two examples are given to show the effectiveness of
the proposed methods. Numerical simulation shows that the designed controller
has excellent disturbance rejection effect when the disturbance is known. The
example with respect to the permanent-magnet direct current motor illustrates
that the proposed control method for unknown disturbance rejection is
effective.


title: Probing fundamental aspects of quantum mechanics with quantum computers

abstract:
  IBM quantum computers are used to perform a recently-proposed experiment
testing the necessity of complex numbers in the standard formulation of quantum
mechanics. While the noisier devices are incapable of delivering definitive
results, it is shown that certain devices possess sufficiently small error
rates to yield convincing evidence that a faithful description of quantum
phenomena must involve complex numbers. The results are consistent with
previous experiments and robust against daily calibration of these devices.
This work demonstrates the feasibility of using freely-available, noisy,
intermediate-scale quantum devices to test foundational features of quantum
mechanics.


title: How Do Captions Affect Visualization Reading?

abstract:
  Captions help readers better understand visualizations. However, if the
visualization is intended to communicate specific features, should the caption
be statistical, and focus on specific values, or perceptual, and focus on
general patterns? Prior work has shown that when captions mention visually
salient features, users tend to recall those features. Still, we lack explicit
guidelines for how to compose the appropriate caption. Further, what if the
author wishes to emphasize a less salient feature?
  In this paper, we study how the visual salience of the feature described in a
caption, and the semantic level of the caption description, affect a reader's
takeaways from line charts. For each single- or multi-line chart, we generate 4
captions that 1) describe either the primary or secondary most salient feature
in a chart, and 2) describe the feature either at the statistical or perceptual
levels. We then show users random chart-caption pairs and record their
takeaways.
  We find that the primary salient feature is more memorable for single-line
charts when the caption is expressed at the statistical level; for secondary
salient features in single- and multi-line charts, the perceptual level is more
memorable. We also find that many users will tend to rely on erroneous data in
the caption and not double-check its veracity against the data in the chart.