title: A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification

abstract:
  The performance of automatic speaker verification (ASV) systems could be
degraded by voice spoofing attacks. Most existing works aimed to develop
standalone spoofing countermeasure (CM) systems. Relatively little work
targeted at developing an integrated spoofing aware speaker verification (SASV)
system. In the recent SASV challenge, the organizers encourage the development
of such integration by releasing official protocols and baselines. In this
paper, we build a probabilistic framework for fusing the ASV and CM subsystem
scores. We further propose fusion strategies for direct inference and
fine-tuning to predict the SASV score based on the framework. Surprisingly,
these strategies significantly improve the SASV equal error rate (EER) from
19.31% of the baseline to 1.53% on the official evaluation trials of the SASV
challenge. We verify the effectiveness of our proposed components through
ablation studies and provide insights with score distribution analysis.


title:
Deep Learning in Random Neural Fields: Numerical Experiments via Neural
  Tangent Kernel

abstract:
  A biological neural network in the cortex forms a neural field. Neurons in
the field have their own receptive fields, and connection weights between two
neurons are random but highly correlated when they are in close proximity in
receptive fields. In this paper, we investigate such neural fields in a
multilayer architecture to investigate the supervised learning of the fields.
We empirically compare the performances of our field model with those of
randomly connected deep networks. The behavior of a randomly connected network
is investigated on the basis of the key idea of the neural tangent kernel
regime, a recent development in the machine learning theory of
over-parameterized networks; for most randomly connected neural networks, it is
shown that global minima always exist in their small neighborhoods. We
numerically show that this claim also holds for our neural fields. In more
detail, our model has two structures: i) each neuron in a field has a
continuously distributed receptive field, and ii) the initial connection
weights are random but not independent, having correlations when the positions
of neurons are close in each layer. We show that such a multilayer neural field
is more robust than conventional models when input patterns are deformed by
noise disturbances. Moreover, its generalization ability can be slightly
superior to that of conventional models.


title:
Topogivity: A Machine-Learned Chemical Rule for Discovering Topological
  Materials

abstract:
  Topological materials present unconventional electronic properties that make
them attractive for both basic science and next-generation technological
applications. The majority of currently-known topological materials have been
discovered using methods that involve symmetry-based analysis of the quantum
wavefunction. Here we use machine learning to develop a simple-to-use heuristic
chemical rule that diagnoses with a high accuracy whether a material is
topological using only its chemical formula. This heuristic rule is based on a
notion that we term topogivity, a machine-learned numerical value for each
element that loosely captures its tendency to form topological materials. We
next implement a high-throughput strategy for discovering topological materials
based on the heuristic topogivity-rule prediction followed by ab initio
validation. This way, we discover new topological materials that are not
diagnosable using symmetry indicators, including several that may be promising
for experimental observation.


title: Conditional Diffusion Probabilistic Model for Speech Enhancement

abstract:
  Speech enhancement is a critical component of many user-oriented audio
applications, yet current systems still suffer from distorted and unnatural
outputs. While generative models have shown strong potential in speech
synthesis, they are still lagging behind in speech enhancement. This work
leverages recent advances in diffusion probabilistic models, and proposes a
novel speech enhancement algorithm that incorporates characteristics of the
observed noisy speech signal into the diffusion and reverse processes. More
specifically, we propose a generalized formulation of the diffusion
probabilistic model named conditional diffusion probabilistic model that, in
its reverse process, can adapt to non-Gaussian real noises in the estimated
speech signal. In our experiments, we demonstrate strong performance of the
proposed approach compared to representative generative models, and investigate
the generalization capability of our models to other datasets with noise
characteristics unseen during training.


title: Characterizing, Detecting, and Predicting Online Ban Evasion

abstract:
  Moderators and automated methods enforce bans on malicious users who engage
in disruptive behavior. However, malicious users can easily create a new
account to evade such bans. Previous research has focused on other forms of
online deception, like the simultaneous operation of multiple accounts by the
same entities (sockpuppetry), impersonation of other individuals, and studying
the effects of de-platforming individuals and communities. Here we conduct the
first data-driven study of ban evasion, i.e., the act of circumventing bans on
an online platform, leading to temporally disjoint operation of accounts by the
same user.
  We curate a novel dataset of 8,551 ban evasion pairs (parent, child)
identified on Wikipedia and contrast their behavior with benign users and
non-evading malicious users. We find that evasion child accounts demonstrate
similarities with respect to their banned parent accounts on several behavioral
axes - from similarity in usernames and edited pages to similarity in content
added to the platform and its psycholinguistic attributes. We reveal key
behavioral attributes of accounts that are likely to evade bans. Based on the
insights from the analyses, we train logistic regression classifiers to detect
and predict ban evasion at three different points in the ban evasion lifecycle.
Results demonstrate the effectiveness of our methods in predicting future
evaders (AUC = 0.78), early detection of ban evasion (AUC = 0.85), and matching
child accounts with parent accounts (MRR = 0.97). Our work can aid moderators
by reducing their workload and identifying evasion pairs faster and more
efficiently than current manual and heuristic-based approaches. Dataset is
available $\href{https://github.com/srijankr/ban_evasion}{\text{here}}$.


title: Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks

abstract:
  We give superpolynomial statistical query (SQ) lower bounds for learning
two-hidden-layer ReLU networks with respect to Gaussian inputs in the standard
(noise-free) model. No general SQ lower bounds were known for learning ReLU
networks of any depth in this setting: previous SQ lower bounds held only for
adversarial noise models (agnostic learning) or restricted models such as
correlational SQ.
  Prior work hinted at the impossibility of our result: Vempala and Wilmes
showed that general SQ lower bounds cannot apply to any real-valued family of
functions that satisfies a simple non-degeneracy condition.
  To circumvent their result, we refine a lifting procedure due to Daniely and
Vardi that reduces Boolean PAC learning problems to Gaussian ones. We show how
to extend their technique to other learning models and, in many well-studied
cases, obtain a more efficient reduction. As such, we also prove new
cryptographic hardness results for PAC learning two-hidden-layer ReLU networks,
as well as new lower bounds for learning constant-depth ReLU networks from
label queries.


title:
Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric
  Diodes

abstract:
  The deluge of sensors and data generating devices has driven a paradigm shift
in modern computing from arithmetic-logic centric to data centric processing.
At a hardware level, this presents an urgent need to integrate dense,
high-performance and low-power memory units with Si logic-processor units.
However, data-heavy problems such as search and pattern matching also require
paradigm changing innovations at the circuit and architecture level to enable
compute in memory (CIM) operations. CIM architectures that combine data storage
yet concurrently offer low-delay and small footprint are highly sought after
but have not been realized. Here, we present Aluminum Scandium Nitride (AlScN)
ferroelectric diode (FeD) memristor devices that allow for storage, search and
neural network-based pattern recognition in a transistor-free architecture. Our
devices can be directly integrated on top of Si processors in a scalable,
back-end-of-line process. We leverage the field-programmability, non-volatility
and non-linearity of FeDs to demonstrated circuit blocks that can support
search operations in-situ memory with search delay times < 0.1 ns and a cell
footprint < 0.12 um^2. In addition, we demonstrate matrix multiplication
operations with 4-bit operation of the FeDs. Our results highlight FeDs as
promising candidates for fast, efficient, and multifunctional CIM platforms.

categories: physics.app-ph cond-mat.dis-nn cond-mat.mes-hall cond-mat.mtrl-sci


title: Minimising Resources of Coherently Controlled Quantum Computations

abstract:
  Coherent control of quantum computation can be used to improve some quantum
protocols and algorithms. For instance, the complexity of implementing the
permutation of some given unitary transformations can be strictly decreased by
allowing coherent control, rather than using the standard quantum circuit
model. In this paper, we address the problem of optimising the resources of
coherently controlled quantum computations. We extend the PBS-calculus, a
graphical language for coherent control which is inspired by quantum optics. To
obtain a more resource-sensitive language, it manipulates abstract gates --
that can be interpreted as queries to an oracle -- and more importantly, it
avoids the representation of useless wires by allowing unsaturated polarising
beam splitters. Technically, the language forms a coloured prop. The language
is equipped with an equational theory that we show to be sound, complete, and
minimal.
  Regarding resource optimisation, we introduce an efficient procedure to
minimise the number of oracle queries of a given diagram. We also consider the
problem of minimising both the number of oracle queries and the number of
polarising beam splitters. We show that this optimisation problem is NP-hard in
general, but introduce an efficient heuristic that produces optimal diagrams
when at most one query to each oracle is required.


title: Holographic S-fold theories at one loop

abstract:
  A common feature of tree-level holography is that a correlator in one theory
can serve as a generating function for correlators in another theory with less
continuous symmetry. This is the case for a family of 4d CFTs with eight
supercharges which have protected operators dual to gluons in the bulk. The
most recent additions to this family were defined using S-folds which combine a
spatial identification with an action of the S-duality group in type IIB string
theory. Differences between these CFTs which have a dynamical origin first
become manifest at one loop. To explore this phenomenon at the level of
anomalous dimensions, we use the AdS unitarity method to bootstrap a one-loop
double discontinuity. Compared to previous studies, the subsequent analysis is
performed without any assumption about which special functions are allowed.
Instead, the Casimir singular and Casimir regular terms are extracted
iteratively in order to move from one Regge trajectory to the next. Our results
show that anomalous dimensions in the presence of an S-fold are no longer
rational functions of the spin.


title: Locating and Editing Factual Knowledge in GPT

abstract:
  We investigate the mechanisms underlying factual knowledge recall in
autoregressive transformer language models. First, we develop a causal
intervention for identifying neuron activations capable of altering a model's
factual predictions. Within large GPT-style models, this reveals two distinct
sets of neurons that we hypothesize correspond to knowing an abstract fact and
saying a concrete word, respectively. This insight inspires the development of
ROME, a novel method for editing facts stored in model weights. For evaluation,
we assemble CounterFact, a dataset of over twenty thousand counterfactuals and
tools to facilitate sensitive measurements of knowledge editing. Using
CounterFact, we confirm the distinction between saying and knowing neurons, and
we find that ROME achieves state-of-the-art performance in knowledge editing
compared to other methods. An interactive demo notebook, full code
implementation, and the dataset are available at https://rome.baulab.info/.


title: Block-NeRF: Scalable Large Scene Neural View Synthesis

abstract:
  We present Block-NeRF, a variant of Neural Radiance Fields that can represent
large-scale environments. Specifically, we demonstrate that when scaling NeRF
to render city-scale scenes spanning multiple blocks, it is vital to decompose
the scene into individually trained NeRFs. This decomposition decouples
rendering time from scene size, enables rendering to scale to arbitrarily large
environments, and allows per-block updates of the environment. We adopt several
architectural changes to make NeRF robust to data captured over months under
different environmental conditions. We add appearance embeddings, learned pose
refinement, and controllable exposure to each individual NeRF, and introduce a
procedure for aligning appearance between adjacent NeRFs so that they can be
seamlessly combined. We build a grid of Block-NeRFs from 2.8 million images to
create the largest neural scene representation to date, capable of rendering an
entire neighborhood of San Francisco.


title: Periodically refreshed quantum thermal machines

abstract:
  We introduce unique class of cyclic quantum thermal machines (QTMs) which can
maximize their performance at the finite value of cycle duration $\tau$ where
they are most irreversible. These QTMs are based on single-stroke thermodynamic
cycles realized by the non-equilibrium steady state (NESS) of the so-called
Periodically Refreshed Baths (PReB) process. We find that such QTMs can
interpolate between standard collisional QTMs, which consider repeated
interactions with single-site environments, and autonomous QTMs operated by
simultaneous coupling to multiple macroscopic baths. We discuss the physical
realization of such processes and show that their implementation requires a
finite number of copies of the baths. Interestingly, maximizing performance by
operating in the most irreversible point as a function of $\tau$ comes at the
cost of increasing the complexity of realizing such a regime, the latter
quantified by the increase in the number of copies of baths required. We
demonstrate this physics considering a simple example. We also introduce an
elegant description of the PReB process for Gaussian systems in terms of a
discrete-time Lyapunov equation. Further, our analysis also reveals interesting
connections with Zeno and anti-Zeno effects.


title:
Image-to-Image Regression with Distribution-Free Uncertainty
  Quantification and Applications in Imaging

abstract:
  Image-to-image regression is an important learning task, used frequently in
biological imaging. Current algorithms, however, do not generally offer
statistical guarantees that protect against a model's mistakes and
hallucinations. To address this, we develop uncertainty quantification
techniques with rigorous statistical guarantees for image-to-image regression
problems. In particular, we show how to derive uncertainty intervals around
each pixel that are guaranteed to contain the true value with a user-specified
confidence probability. Our methods work in conjunction with any base machine
learning model, such as a neural network, and endow it with formal mathematical
guarantees -- regardless of the true unknown data distribution or choice of
model. Furthermore, they are simple to implement and computationally
inexpensive. We evaluate our procedure on three image-to-image regression
tasks: quantitative phase microscopy, accelerated magnetic resonance imaging,
and super-resolution transmission electron microscopy of a Drosophila
melanogaster brain.


title: First Principles Phenomenology of $H_0$

abstract:
  In this letter we discuss infinite statistics and motivate its role in
quantum gravity. Then, we connect infinite statistics to a dynamical form of
dark energy, and we obtain an expression for the evolution of the Hubble
parameter that we compare to observation. The equation of state parameter
$w_{eff} < -1$ in this framework.


title:
On Real-time Image Reconstruction with Neural Networks for MRI-guided
  Radiotherapy

abstract:
  MRI-guidance techniques that dynamically adapt radiation beams to follow
tumor motion in real-time will lead to more accurate cancer treatments and
reduced collateral healthy tissue damage. The gold-standard for reconstruction
of undersampled MR data is compressed sensing (CS) which is computationally
slow and limits the rate that images can be available for real-time adaptation.
Here, we demonstrate the use of automated transform by manifold approximation
(AUTOMAP), a generalized framework that maps raw MR signal to the target image
domain, to rapidly reconstruct images from undersampled radial k-space data.
The AUTOMAP neural network was trained to reconstruct images from a
golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on
lung cancer patient data and generic images from ImageNet. Model training was
subsequently augmented with motion-encoded k-space data derived from videos in
the YouTube-8M dataset to encourage motion robust reconstruction. We find that
AUTOMAP-reconstructed radial k-space has equivalent accuracy to CS but with
much shorter processing times after initial fine-tuning on retrospectively
acquired lung cancer patient data. Validation of motion-trained models with a
virtual dynamic lung tumor phantom showed that the generalized motion
properties learned from YouTube lead to improved target tracking accuracy. Our
work shows that AUTOMAP can achieve real-time, accurate reconstruction of
radial data. These findings imply that neural-network-based reconstruction is
potentially superior to existing approaches for real-time image guidance
applications.


title: HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging

abstract:
  In our previous work, $i.e.$, HNF-Net, high-resolution feature representation
and light-weight non-local self-attention mechanism are exploited for brain
tumor segmentation using multi-modal MR imaging. In this paper, we extend our
HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic
discrimination enhancing blocks to further exploit global semantic
discrimination for the obtained high-resolution features. We trained and
evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge
(BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2
achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as well
as the Hausdorff distances ($95\%$) of 8.9184, 16.2530, and 4.4895 for the
enhancing tumor, tumor core, and whole tumor, respectively. Our method won the
RSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task), which ranks 8th
out of all 1250 submitted results.


title:
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image
  Reconstruction using Pre-Trained Deep Denoisers

abstract:
  Current spatiotemporal deep learning approaches to Magnetic Resonance
Fingerprinting (MRF) build artefact-removal models customised to a particular
k-space subsampling pattern which is used for fast (compressed) acquisition.
This may not be useful when the acquisition process is unknown during training
of the deep learning model and/or changes during testing time. This paper
proposes an iterative deep learning plug-and-play reconstruction approach to
MRF which is adaptive to the forward acquisition process. Spatiotemporal image
priors are learned by an image denoiser i.e. a Convolutional Neural Network
(CNN), trained to remove generic white gaussian noise (not a particular
subsampling artefact) from data. This CNN denoiser is then used as a
data-driven shrinkage operator within the iterative reconstruction algorithm.
This algorithm with the same denoiser model is then tested on two simulated
acquisition processes with distinct subsampling patterns. The results show
consistent de-aliasing performance against both acquisition schemes and
accurate mapping of tissues' quantitative bio-properties. Software available:
https://github.com/ketanfatania/QMRI-PnP-Recon-POC


title:
A Deep Learning Approach for Digital Color Reconstruction of Lenticular
  Films

abstract:
  We propose the first accurate digitization and color reconstruction process
for historical lenticular film that is robust to artifacts. Lenticular films
emerged in the 1920s and were one of the first technologies that permitted to
capture full color information in motion. The technology leverages an RGB
filter and cylindrical lenticules embossed on the film surface to encode the
color in the horizontal spatial dimension of the image. To project the pictures
the encoding process was reversed using an appropriate analog device. In this
work, we introduce an automated, fully digital pipeline to process the scan of
lenticular films and colorize the image. Our method merges deep learning with a
model-based approach in order to maximize the performance while making sure
that the reconstructed colored images truthfully match the encoded color
information. Our model employs different strategies to achieve an effective
color reconstruction, in particular (i) we use data augmentation to create a
robust lenticule segmentation network, (ii) we fit the lenticules raster
prediction to obtain a precise vectorial lenticule localization, and (iii) we
train a colorization network that predicts interpolation coefficients in order
to obtain a truthful colorization. We validate the proposed method on a
lenticular film dataset and compare it to other approaches. Since no colored
groundtruth is available as reference, we conduct a user study to validate our
method in a subjective manner. The results of the study show that the proposed
method is largely preferred with respect to other existing and baseline
methods.


title: A Field of Experts Prior for Adapting Neural Networks at Test Time

abstract:
  Performance of convolutional neural networks (CNNs) in image analysis tasks
is often marred in the presence of acquisition-related distribution shifts
between training and test images. Recently, it has been proposed to tackle this
problem by fine-tuning trained CNNs for each test image. Such
test-time-adaptation (TTA) is a promising and practical strategy for improving
robustness to distribution shifts as it requires neither data sharing between
institutions nor annotating additional data. Previous TTA methods use a helper
model to increase similarity between outputs and/or features extracted from a
test image with those of the training images. Such helpers, which are typically
modeled using CNNs, can be task-specific and themselves vulnerable to
distribution shifts in their inputs. To overcome these problems, we propose to
carry out TTA by matching the feature distributions of test and training
images, as modelled by a field-of-experts (FoE) prior. FoEs model complicated
probability distributions as products of many simpler expert distributions. We
use 1D marginal distributions of a trained task CNN's features as experts in
the FoE model. Further, we compute principal components of patches of the task
CNN's features, and consider the distributions of PCA loadings as additional
experts. We validate the method on 5 MRI segmentation tasks (healthy tissues in
4 anatomical regions and lesions in 1 one anatomy), using data from 17 clinics,
and on a MRI registration task, using data from 3 clinics. We find that the
proposed FoE-based TTA is generically applicable in multiple tasks, and
outperforms all previous TTA methods for lesion segmentation. For healthy
tissue segmentation, the proposed method outperforms other task-agnostic
methods, but a previous TTA method which is specifically designed for
segmentation performs the best for most of the tested datasets. Our code is
publicly available.


title:
Single-channel speech enhancement by using psychoacoustical model
  inspired fusion framework

abstract:
  When the parameters of Bayesian Short-time Spectral Amplitude (STSA)
estimator for speech enhancement are selected based on the characteristics of
the human auditory system, the gain function of the estimator becomes more
flexible. Although this type of estimator in acoustic domain is quite effective
in reducing the back-ground noise at high frequencies, it produces more speech
distortions, which make the high-frequency contents of the speech such as
friciatives less perceptible in heavy noise conditions, resulting in
intelligibility reduction. On the other hand, the speech enhancement scheme,
which exploits the psychoacoustic evidence of frequency selectivity in the
modulation domain, is found to be able to increase the intelligibility of noisy
speech by a substantial amount, but also suffers from the temporal slurring
problem due to its essential design constraint. In order to achieve the joint
improvements in both the perceived speech quality and intelligibility, we
proposed and investigated a fusion framework by combining the merits of
acoustic and modulation domain approaches while avoiding their respective
weaknesses. Objective measure evaluation shows that the proposed speech
enhancement fusion framework can provide consistent improvements in the
perceived speech quality and intelligibility across different SNR levels in
various noise conditions, while compared to the other baseline techniques.


title: Towards a Guideline for Evaluation Metrics in Medical Image Segmentation

abstract:
  In the last decade, research on artificial intelligence has seen rapid growth
with deep learning models, especially in the field of medical image
segmentation. Various studies demonstrated that these models have powerful
prediction capabilities and achieved similar results as clinicians. However,
recent studies revealed that the evaluation in image segmentation studies lacks
reliable model performance assessment and showed statistical bias by incorrect
metric implementation or usage. Thus, this work provides an overview and
interpretation guide on the following metrics for medical image segmentation
evaluation in binary as well as multi-class problems: Dice similarity
coefficient, Jaccard, Sensitivity, Specificity, Rand index, ROC curves, Cohen's
Kappa, and Hausdorff distance. As a summary, we propose a guideline for
standardized medical image segmentation evaluation to improve evaluation
quality, reproducibility, and comparability in the research field.


title: Motion Puzzle: Arbitrary Motion Style Transfer by Body Part

abstract:
  This paper presents Motion Puzzle, a novel motion style transfer network that
advances the state-of-the-art in several important respects. The Motion Puzzle
is the first that can control the motion style of individual body parts,
allowing for local style editing and significantly increasing the range of
stylized motions. Designed to keep the human's kinematic structure, our
framework extracts style features from multiple style motions for different
body parts and transfers them locally to the target body parts. Another major
advantage is that it can transfer both global and local traits of motion style
by integrating the adaptive instance normalization and attention modules while
keeping the skeleton topology. Thus, it can capture styles exhibited by dynamic
movements, such as flapping and staggering, significantly better than previous
work. In addition, our framework allows for arbitrary motion style transfer
without datasets with style labeling or motion pairing, making many publicly
available motion datasets available for training. Our framework can be easily
integrated with motion generation frameworks to create many applications, such
as real-time motion transfer. We demonstrate the advantages of our framework
with a number of examples and comparisons with previous work.


title: Leptoquarks with Exactly Stable Protons

abstract:
  A simple TeV-scale extension of the Standard Model by scalar leptoquarks
recently gained popularity due to the flavor anomalies. But the non-observation
of proton decay and charged lepton flavour violation severely constrain the
leptoquark interactions, meaning that their ultraviolet (UV) realisation must
be rather special. Using a lepton-flavored $\mathrm{U}(1)_X$ gauge symmetry to
restrict the leptoquark interactions, we discover a novel mechanism for
rendering the proton exactly stable to all orders in the effective field theory
expansion. Introducing a scalar condensate to break the $\mathrm{U}(1)_X$ in
the UV and generate realistic neutrino masses with a type-I seesaw leaves a
linearly realised discrete $\mathbb{Z}_9$ or $\mathbb{Z}_{18}$ gauge symmetry
in the infrared (IR). This symmetry exactly prohibits all $\Delta B = 1$
processes and can easily be adapted to many other new-physics models. Charged
lepton flavour violation is forbidden at the renormalisable level, leaving the
leptoquarks with approximately preserved muon number. We speculate that $
\mathrm{U}(1)_X $ can emerge from a gauge-flavour unified theory with gauge
group $\mathrm{SU}(12) \times \mathrm{SU}(2) \times \mathrm{SU}(2)$ even deeper
in the UV.


title: Massive vector form factors to three loops

abstract:
  We compute the three-loop non-singlet corrections to the photon-quark form
factors taking into account the full dependence on the virtuality of the photon
and the quark mass. We combine the method of differential equations in an
effective way with expansions around regular and singular points. This allows
us to obtain results for the form factors with an accuracy of about eight to
twelve digits in the whole kinematic range.


title:
The impact of galaxy selection on the splashback boundaries of galaxy
  clusters

abstract:
  We explore how the splashback radius ($R_{\rm sp}$) of galaxy clusters,
measured using the number density of the subhalo population, changes based on
various selection criteria using the IllustrisTNG cosmological galaxy formation
simulation. We identify $R_{\rm sp}$ by extracting the steepest radial gradient
in a stacked set of clusters in 0.5 dex wide mass bins, with our clusters
having halo masses $10^{13} \leq M_{\rm 200, mean} / {\rm M}_\odot \leq
10^{15}$. We apply cuts in subhalo mass, galaxy stellar mass, $i$-band absolute
magnitude and specific star formation rate. We find that, generally, galaxies
of increasing mass and luminosity trace smaller measured splashback radii
relative to the intrinsic dark matter radius. We also show that quenched
galaxies may be used to reliably reconstruct the dark matter splashback radius.
This trend is likely due to changes in the galaxy population. Additionally, we
are able to reconcile different observational predictions that $R_{\rm sp}$
based upon galaxy number counts and dark matter may either align or show
significant offset (e.g. those using optically- or SZ-selected clusters)
through the selection functions that these studies employ. Finally, we
demonstrate that changes in $R_{\rm sp}$ measured through number counts are not
due to a simple change in galaxy abundance inside and outside of the cluster.


title: Islands in Closed and Open Universes

abstract:
  We show that spatial curvature has a significant effect on the existence of
entanglement islands in cosmology. We consider a homogeneous, isotropic
universe with thermal radiation purified by a reference spacetime. Arbitrarily
small positive curvature guarantees that the entire universe is an island.
Proper subsets of the time-symmetric slice of a closed or open universe can be
islands, but only if the cosmological constant is negative and sufficiently
large in magnitude.


title:
Sensitive Chandra coverage of a representative sample of weak-line
  quasars: revealing the full range of X-ray properties

abstract:
  We present deeper Chandra observations for weak-line quasars (WLQs) in a
representative sample that previously had limited X-ray constraints, and
perform X-ray photometric analyses to reveal the full range of X-ray properties
of WLQs. Only 5 of the 32 WLQs included in this representative sample remain
X-ray undetected after these observations, and a stacking analysis shows that
these 5 have an average X-ray weakness factor of > 85. One of the WLQs in the
sample that was known to have extreme X-ray variability, SDSS J1539+3954,
exhibited dramatic X-ray variability again: it changed from an X-ray normal
state to an X-ray weak state within ~ 3 months in the rest frame. This short
timescale for an X-ray flux variation by a factor of $\gtrsim$ 9 further
supports the thick disk and outflow (TDO) model proposed to explain the X-ray
and multiwavelength properties of WLQs. The overall distribution of the
X-ray-to-optical properties of WLQs suggests that the TDO has an average
covering factor of the X-ray emitting region of ~ 0.5, and the column density
of the TDO can range from $N_{\rm H}$ $\sim 10^{23-24}~{\rm cm}^{-2}$ to
$N_{\rm H}$ $\gtrsim 10^{24}~{\rm cm}^{-2}$, which leads to different levels of
absorption and Compton reflection (and/or scattering) among WLQs.


title: Engineered Dissipation for Quantum Information Science

abstract:
  Quantum information processing relies on precise control of non-classical
states in the presence of many uncontrolled environmental degrees of freedom --
requiring careful orchestration of how the relevant degrees of freedom interact
with that environment. These interactions are often viewed as detrimental, as
they dissipate energy and decohere quantum states. Nonetheless, when
controlled, dissipation is an essential tool for manipulating quantum
information: Dissipation engineering enables quantum measurement, quantum state
preparation, and quantum state stabilization. The progress of quantum device
technology, marked by improvements of characteristic coherence times and
extensible architectures for quantum control, has coincided with the
development of such dissipation engineering tools which interface quantum and
classical degrees of freedom. This Review presents dissipation as a fundamental
aspect of the measurement and control of quantum devices and highlights the
role of dissipation engineering for quantum error correction and quantum
simulation that enables quantum information processing on a practical scale.

categories: quant-ph cond-mat.mes-hall cond-mat.quant-gas physics.atom-ph


title:
SCAT Uncovers ATLAS's First Tidal Disruption Event ATLAS18mlw: A Faint
  and Fast TDE in a Quiescent Balmer Strong Galaxy

abstract:
  We present the discovery that ATLAS18mlw was a tidal disruption event (TDE)
in the galaxy WISEA J073544.83+663717.3, at a distance of 334 Mpc. Initially
discovered by the Asteroid Terrestrial Impact Last Alert System (ATLAS) on 2018
March 17.3, the TDE nature of the transient was uncovered only recently with
the re-reduction of a SuperNova Integral Field Spectrograph (SNIFS) spectrum.
This spectrum, taken by the Spectral Classification of Astronomical Transients
(SCAT) survey, shows a strong blue continuum and a broad H$\alpha$ emission
line. Here we present roughly six years of optical survey photometry beginning
before the TDE to constrain AGN activity, optical spectroscopy of the
transient, and a detailed study of the host galaxy properties through analysis
of archival photometry and a host spectrum. ATLAS18mlw was detected in
ground-based light curves for roughly two months. From a blackbody fit to the
transient spectrum and bolometric correction of the optical light curve, we
conclude that ATLAS18mlw is likely a low-luminosity TDE with a peak luminosity
of log(L [erg s$^{-1}$]) = $43.5 \pm 0.2$. The TDE classification is further
supported by the quiescent Balmer strong nature of the host galaxy. We also
calculated the TDE decline rate from the bolometric light curve and find
$\Delta L_{40} = -0.7 \pm 0.2$ dex, making ATLAS18mlw a member of the growing
class of "faint and fast" TDEs with low peak luminosities and fast decline
rates.


title:
Translation and Rotation Equivariant Normalizing Flow (TRENF) for
  Optimal Cosmological Analysis

abstract:
  Our universe is homogeneous and isotropic, and its perturbations obey
translation and rotation symmetry. In this work we develop Translation and
Rotation Equivariant Normalizing Flow (TRENF), a generative Normalizing Flow
(NF) model which explicitly incorporates these symmetries, defining the data
likelihood via a sequence of Fourier space-based convolutions and pixel-wise
nonlinear transforms. TRENF gives direct access to the high dimensional data
likelihood p(x|y) as a function of the labels y, such as cosmological
parameters. In contrast to traditional analyses based on summary statistics,
the NF approach has no loss of information since it preserves the full
dimensionality of the data. On Gaussian random fields, the TRENF likelihood
agrees well with the analytical expression and saturates the Fisher information
content in the labels y. On nonlinear cosmological overdensity fields from
N-body simulations, TRENF leads to significant improvements in constraining
power over the standard power spectrum summary statistic. TRENF is also a
generative model of the data, and we show that TRENF samples agree well with
the N-body simulations it trained on, and that the inverse mapping of the data
agrees well with a Gaussian white noise both visually and on various summary
statistics: when this is perfectly achieved the resulting p(x|y) likelihood
analysis becomes optimal. Finally, we develop a generalization of this model
that can handle effects that break the symmetry of the data, such as the survey
mask, which enables likelihood analysis on data without periodic boundaries.


title:
An accurate treatment of scattering and diffusion in piecewise power-law
  models for cosmic ray and radiation/neutrino transport

abstract:
  A popular numerical method to model the dynamics of a 'full spectrum' of
cosmic rays (CRs), also applicable to radiation/neutrino hydrodynamics (RHD),
is to discretize the spectrum at each location/cell as a piecewise power law in
'bins' of momentum (or frequency) space. This gives rise to a pair of conserved
quantities (e.g. CR number and energy) which are exchanged between cells or
bins, that in turn give the update to the normalization and slope of the
spectrum in each bin. While these methods can be evolved exactly in
momentum-space (considering injection, absorption, continuous losses/gains),
numerical challenges arise dealing with spatial fluxes, if the scattering rates
depend on momentum. This has often been treated by either by neglecting
variation of those rates 'within the bin,' or sacrificing conservation --
introducing significant errors. Here, we derive a rigorous treatment of these
terms, and show that the variation within the bin can be accounted for
accurately with a simple set of scalar correction coefficients that can be
written entirely in terms of other, explicitly-evolved 'bin-integrated'
quantities. This eliminates the relevant errors without added computational
cost, has no effect on the numerical stability of the method, and retains
manifest conservation. We derive correction terms both for methods which
explicitly integrate flux variables (two-moment or M1-like) methods, as well as
single-moment (advection-diffusion, FLD-like) methods, and approximate
corrections valid in various limits.

categories: astro-ph.HE astro-ph.GA astro-ph.IM physics.plasm-ph physics.space-ph


title: A pointed Prym-Petri Theorem

abstract:
  We construct pointed Prym-Brill-Noether varieties parametrizing line bundles
assigned to an irreducible \'etale double covering of a curve with a prescribed
minimal vanishing at a fixed point. We realize them as degeneracy loci in type
D and deduce their classes in case of expected dimension. Thus, we determine a
pointed Prym-Petri map and prove a pointed version of the Prym-Petri theorem
implying that the expected dimension holds in the general case. These results
build on work of Welters and De Concini-Pragacz on the unpointed case. Finally,
we show that Prym varieties are Prym-Tyurin varieties for Prym-Brill-Noether
curves of exponent enumerating standard shifted tableaux, extending to the Prym
setting work of Ortega.


title:
Pre-thermalization via self-driving and external driving of extensive
  subsystems

abstract:
  We investigate the non-equilibrium states of an interacting multi-component
quantum system when only an extensive subsystem is quantum-quenched or driven
from the ground state. As a concrete example, we consider a system where two
XXZ spin chains are coupled to a transverse field Ising (TFI) chain, and only
the transverse field in the TFI chain is quantum-quenched or periodically
driven in time, starting from an initially ordered state. This system is
studied using density matrix renormalization group (DMRG) simulations and
various entanglement entropy diagnostics. In the case of quantum quenching,
when the transverse field is suddenly switched on to become the largest energy
scale, the resulting internal dynamics leads to a pre-thermal steady state with
persistent oscillating magnetization (`self-driving') and emergent conservation
laws. Upon applying the time-dependent drive to the TFI chain (`external
driving'), sufficiently fast drive gives rise to a pre-thermal steady state
with finite magnetization, whereas a slow drive generates a high-temperature
disordered state. We briefly discuss the experimental implementation of our
protocol in organic materials with quantum-tunneling hydrogen atoms.


title:
The origin of the unfocused XMM-Newton background, its variability and
  lessons learned for ATHENA

abstract:
  We analyzed the unexposed to the sky outFOV region of the MOS2 detector on
board XMM-Newton covering 15 years of data amounting to 255 Ms. We show
convincing evidence that the origin of the unfocused background in XMM-Newton
is due to energetic protons, electrons and hard X-ray photons. Galactic Cosmic
Rays are the main contributors as shown by the tight correlation (2.6% of total
scatter) with 1 GeV protons data of the SOHO EPHIN detector. Tight correlations
are found with a proxy of the Chandra background rate, revealing the common
source of background for detectors in similar orbits, and with the data of the
EPIC Radiation Monitor (ERM), only when excluding Solar Energetic Particles
events (SEPs). The entrance to the outer electron belts is associated to a
sudden increase in the outFOV MOS2 rate and a spectral change. These facts
support the fact that MeV electrons can generate an unfocused background
signal. The correlation between MOS2 outFOV data and the SOHO EPHIN data
reveals a term constant in time and isotropic similar to the one found in the
study of the pn data. The most plausible origin of this component is hard
unfocused X-ray photons of the Cosmic X-ray Background (CXB) Compton-scattering
in the detector as supported by the strength of the signal in the two detectors
with different thicknesses. Based on this physical understanding a particle
radiation monitor on board ATHENA has been proposed and it is currently under
study. It will be able to track different species with the necessary accuracy
and precision to guarantee the challenging requirement of 2% reproducibility of
the background.


title: ACC for minimal log discrepancies of terminal threefolds

abstract:
  We prove that the ACC conjecture for minimal log discrepancies holds for
threefolds in $[1-\delta,+\infty)$, where $\delta>0$ only depends on the
coefficient set. We also study Reid's general elephant for pairs, and show
Shokurov's conjecture on the existence of $(\epsilon,n)$-complements for
threefolds for any $\epsilon\geq 1$. As a key important step, we prove the
uniform boundedness of divisors computing minimal log discrepancies for
terminal threefolds. We show the ACC for threefold canonical thresholds, and
that the set of accumulation points of threefold canonical thresholds is equal
to $\{0\}\cup\{\frac{1}{n}\}_{n\in\mathbb Z_{\ge 2}}$ as well.


title:
The mechanism of efficient electron acceleration at parallel
  non-relativistic shocks

abstract:
  Thermal electrons cannot directly participate in the process of diffusive
acceleration at electron-ion shocks because their Larmor radii are smaller than
the shock transition width: this is the well-known electron injection problem
of diffusive shock acceleration. Instead, an efficient pre-acceleration process
must exist that scatters electrons off of electromagnetic fluctuations on
scales much shorter than the ion gyro radius. The recently found
intermediate-scale instability provides a natural way to produce such
fluctuations in parallel shocks. The instability drives comoving (with the
upstream plasma) ion-cyclotron waves at the shock front and only operates when
the drift speed is smaller than half of the electron Alfven speed. Here, we
perform particle-in-cell simulations with the SHARP code to study the impact of
this instability on electron acceleration at parallel non-relativistic,
electron-ion shocks. To this end, we compare a shock simulation in which the
intermediate-scale instability is expected to grow to simulations where it is
suppressed. In particular, the simulation with an Alfvenic Mach number large
enough to quench the intermediate instability shows a great reduction (by two
orders of magnitude) of the electron acceleration efficiency. Moreover, the
simulation with a reduced ion-to-electron mass ratio (where the intermediate
instability is also suppressed) not only artificially precludes electron
acceleration but also results in erroneous electron and ion heating in the
downstream and shock transition regions. This finding opens up a promising
route for a plasma physical understanding of diffusive shock acceleration of
electrons, which necessarily requires realistic mass ratios in simulations of
collisionless electron-ion shocks.

categories: astro-ph.HE astro-ph.SR physics.comp-ph physics.plasm-ph


title:
Acoustic waves and g-mode turbulence as energy carriers in a viscous
  intracluster medium

abstract:
  Many recent works on the observed galaxy clusters in the X-rays highlight
broadly two classes of exclusive energy carriers - sound waves and turbulence.
In order to understand this dichotomy, we design an idealized three-dimensional
hydrodynamic simulation of a cluster, to assess which of these carriers can
dissipate energy in and around the core ($\gtrsim 100$ kpc) . Specifically, we
explore how gentle (long-duration outbursts) and intermediate (shorter duration
outbursts) feedback modes can function efficiently mediated by compressible
(sound waves) and incompressible (g-modes/instabilities/turbulence)
disturbances. Since g-modes are confined tightly to the central core, we
attempt to maximise the flux of fast sound waves to distribute the feedback
energy over a large distance. We find that the contribution to heat dissipation
from sound and turbulence varies on the basis of the aforementioned feedback
modes, namely: turbulence contributes relatively more than sound in the
slow-piston regime and vice versa for the intermediate regime. For the first
time in a 3D simulation, we show that up to $\sim 20\%$ of the injected power
can be carried away by sound flux in the intermediate feedback but it reduces
to $\sim 12 \%$ in the slow-piston regime. Lastly, we find that sound waves can
be elusive if we deduce the equation-of-state (isobaric/isentropic) of the
fluctuations from X-ray observations.


title:
Inverse problems for semilinear elliptic PDE with measurements at a
  single point

abstract:
  We consider the inverse problem of determining a potential in a semilinear
elliptic equation from the knowledge of the Dirichlet-to-Neumann map. For
bounded Euclidean domains we prove that the potential is uniquely determined by
the Dirichlet-to-Neumann map measured at a single boundary point, or integrated
against a fixed measure. This result is valid even when the Dirichlet data is
only given on a small subset of the boundary. We also give related uniqueness
results on Riemannian manifolds.


title:
Luminosity function and event rate density of XMM-Newton-selected
  supernova shock-breakout candidates

abstract:
  A dozen X-ray supernova shock breakout (SN SBO) candidates were reported
recently based on XMM-Newton archival data, which increased the X-ray selected
SN SBO sample by an order of magnitude. Assuming they are genuine SN SBOs, we
study the luminosity function (LF) by improving upon the method used in our
previous work. The light curves and the spectra of the candidates were used to
derive the maximum volume within which these objects could be detected with
XMM-Newton by simulation. The results show that the SN SBO LF can be described
by either a broken power law (BPL) with indices (at the 68$\%$ confidence
level) of $0.48 \pm 0.28$ and $2.11 \pm 1.27$ before and after the break
luminosity at $\log (L_b/\rm erg\,s^{-1})=$ $45.32 \pm 0.55$ or a single power
law (SPL) with index of $0.80 \pm 0.16$. The local event rate densities of SN
SBOs above $5\times 10^{42}$ $\rm erg\,s^{-1}$ are consistent for two models,
i.e., $4.6 ^{+1.7}_{-1.3} \times 10^4$ and $4.9 ^{+1.9}_{-1.4} \times 10^4$
$\rm Gpc^{-3}\,yr^{-1}$ for BPL and SPL models, respectively. The number of
fast X-ray transients of SN SBO origin can be significantly increased by the
wide-field X-ray telescopes such as the Einstein Probe.


title: On One-Bit Quantization

abstract:
  We consider the one-bit quantizer that minimizes the mean squared error for a
source living in a real Hilbert space. The optimal quantizer is a projection
followed by a thresholding operation, and we provide methods for identifying
the optimal direction along which to project. As an application of our methods,
we characterize the optimal one-bit quantizer for a continuous-time random
process that exhibits low-dimensional structure. We numerically show that this
optimal quantizer is found by a neural-network-based compressor trained via
stochastic gradient descent.


title: Pattern formation and coarsening dynamics in apparent competition models

abstract:
  Apparent competition is an indirect interaction between species that share
natural resources without any mutual aggression but negatively affect each
other if there is a common enemy. The negative results of the apparent
competition are reflected in the species spatial segregation, which impacts the
dynamics of their populations. Performing a series of stochastic simulations,
we study a model where organisms of two prey species do not compete for space
but share a common predator. Our outcomes elucidate the central role played by
the predator in the pattern formation and coarsening dynamics in apparent
competition models. Investigating the effects of predator mortality on the
persistence of the species, we find a crossover between a curvature driven
scaling regime and a coexistence scenario. For low predator mortality, spatial
domains mainly inhabited by one type of prey arise, surrounded by interfaces
that mostly contain predators. We demonstrate that the dynamics of the
interface network are curvature driven whose coarsening follows a scaling law
common to other nonlinear systems. The effects of the apparent competition
decrease for high predator mortality, allowing organisms of two prey species to
share a more significant fraction of lattice. Finally, our results reveal that
predation capacity in single-prey domains influences the scaling power law that
characterises the coarsening dynamics. Our findings may be helpful to
biologists to understand the pattern formation and dynamics of biodiversity in
systems with apparent competition.

categories: q-bio.PE nlin.AO nlin.PS physics.bio-ph q-bio.QM


title:
Universal Learning Waveform Selection Strategies for Adaptive Target
  Tracking

abstract:
  Online selection of optimal waveforms for target tracking with active sensors
has long been a problem of interest. Many conventional solutions utilize an
estimation-theoretic interpretation, in which a waveform-specific
Cram\'{e}r-Rao lower bound on measurement error is used to select the optimal
waveform for each tracking step. However, this approach is only valid in the
high SNR regime, and requires a rather restrictive set of assumptions regarding
the target motion and measurement models. Further, due to computational
concerns, many traditional approaches are limited to near-term, or myopic,
optimization, even though radar scenes exhibit strong temporal correlation.
More recently, reinforcement learning has been proposed for waveform selection,
in which the problem is framed as a Markov decision process (MDP), allowing for
long-term planning. However, a major limitation of reinforcement learning is
that the memory length of the underlying Markov process is often unknown for
realistic target and channel dynamics, and a more general framework is
desirable. This work develops a universal sequential waveform selection scheme
which asymptotically achieves Bellman optimality in any radar scene which can
be modeled as a $U^{\text{th}}$ order Markov process for a finite, but unknown,
integer $U$. Our approach is based on well-established tools from the field of
universal source coding, where a stationary source is parsed into variable
length phrases in order to build a context-tree, which is used as a
probabalistic model for the scene's behavior. We show that an algorithm based
on a multi-alphabet version of the Context-Tree Weighting (CTW) method can be
used to optimally solve a broad class of waveform-agile tracking problems while
making minimal assumptions about the environment's behavior.


title: Non-stationary Anderson acceleration with optimized damping

abstract:
  Anderson acceleration (AA) has a long history of use and a strong recent
interest due to its potential ability to dramatically improve the linear
convergence of the fixed-point iteration. Most authors are simply using and
analyzing the stationary version of Anderson acceleration (sAA) with a constant
damping factor or without damping. Little attention has been paid to
nonstationary algorithms. However, damping can be useful and is sometimes
crucial for simulations in which the underlying fixed-point operator is not
globally contractive. The role of this damping factor has not been fully
understood. In the present work, we consider the non-stationary Anderson
acceleration algorithm with optimized damping (AAoptD) in each iteration to
further speed up linear and nonlinear iterations by applying one extra
inexpensive optimization. We analyze this procedure and develop an efficient
and inexpensive implementation scheme. We also show that, compared with the
stationary Anderson acceleration with fixed window size sAA(m), optimizing the
damping factors is related to dynamically packaging sAA(m) and sAA(1) in each
iteration (alternating window size $m$ is another direction of producing
non-stationary AA). Moreover, we show by extensive numerical experiments that
the proposed non-stationary Anderson acceleration with optimized damping
procedure often converges much faster than stationary AA with constant damping
or without damping.


title:
Comparison of Pebble Bed Velocity Profiles Between High-Fidelity and
  Intermediate-Fidelity Codes

abstract:
  Recent interest for the development of high-temperature gas reactors has
increased the need for more advanced understanding of flow characteristics in
randomly packed pebble beds. A proper understanding of these flow
characteristics can provide a better idea of the cooling capabilities of the
system in both normal operation and accident scenarios. In order to enhance the
accuracy of computationally efficient, intermediate fidelity modeling,
high-fidelity simulation may be used to generate correlative data. For this
research, NekRS, a GPU-enabled spectral-element computational fluid dynamics
code, was used in order to produce the high-fidelity flow data for beds of
1,568 and 45,000 pebbles. Idaho National Lab's Pronghorn porous media code was
used as the intermediate fidelity code. The results of the high-fidelity model
were separated into multiple concentric regions in order to extract porosity
and velocity averages in each region. The porosity values were input into the
Pronghorn model and the resulting velocity profile was compared with that from
NekRS. Both cases were run with a Reynolds number of 20,000 based on pebble
diameter. The Pronghorn results were found to significantly overestimate the
velocity in the outermost region indicating that changes in the porosity alone
do not cause the difference in fluid velocity. We conclude that further work is
necessary to develop a more effective drag coefficient correlation for the
near-wall region and improve predictive capabilities of intermediate fidelity
models.


title:
Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application
  to Face Recognition

abstract:
  Systems that analyse faces have seen significant improvements in recent years
and are today used in numerous application scenarios. However, these systems
have been found to be negatively affected by facial alterations such as
tattoos. To better understand and mitigate the effect of facial tattoos in
facial analysis systems, large datasets of images of individuals with and
without tattoos are needed. To this end, we propose a generator for
automatically adding realistic tattoos to facial images. Moreover, we
demonstrate the feasibility of the generation by training a deep learning-based
model for removing tattoos from face images. The experimental results show that
it is possible to remove facial tattoos from real images without degrading the
quality of the image. Additionally, we show that it is possible to improve face
recognition accuracy by using the proposed deep learning-based tattoo removal
before extracting and comparing facial features.


title: Dissolving salt is not equivalent to applying a pressure on water

abstract:
  Salt water is ubiquitous, playing crucial roles in geological and
physiological processes. Despite centuries of investigations, whether or not
water's structure is drastically changed by dissolved ions is still debated.
Based on density functional theory, we employ machine learning based molecular
dynamics to model sodium chloride, potassium chloride, and sodium bromide
solutions at different concentrations. The resulting reciprocal-space structure
factors agree quantitatively with neutron diffraction data. Here we provide
clear evidence that the ions in salt water do not distort the structure of
water in the same way as neat water responds to elevated pressure. Rather, the
computed structural changes are restricted to the ionic first solvation shells
intruding into the hydrogen bond network, beyond which the oxygen
radial-distribution function does not undergo major change relative to neat
water. Our findings suggest that the widely cited pressure-like effect on the
solvent in Hofmeister series ionic solutions should be carefully revisited.


title:
Characterization of matrices with bounded Graver bases and depth
  parameters and applications to integer programming

abstract:
  An intensive line of research on fixed parameter tractability of integer
programming is focused on exploiting the relation between the sparsity of a
constraint matrix $A$ and the norm of the elements of its Graver basis. In
particular, integer programming is fixed parameter tractable when parameterized
by the primal tree-depth and the entry complexity of $A$, and when
parameterized by the dual tree-depth and the entry complexity of $A$; both
these parameterization imply that $A$ is sparse, in particular, the number of
its non-zero entries is linear in the number of columns or rows, respectively.
  We study preconditioners transforming a given matrix to an equivalent sparse
matrix if it exists and provide structural results characterizing the existence
of a sparse equivalent matrix in terms of the structural properties of the
associated column matroid. In particular, our results imply that the
$\ell_1$-norm of the Graver basis is bounded by a function of the maximum
$\ell_1$-norm of a circuit of $A$. We use our results to design a parameterized
algorithm that constructs a matrix equivalent to an input matrix $A$ that has
small primal/dual tree-depth and entry complexity if such an equivalent matrix
exists.
  Our results yield parameterized algorithms for integer programming when
parameterized by the $\ell_1$-norm of the Graver basis of the constraint
matrix, when parameterized by the $\ell_1$-norm of the circuits of the
constraint matrix, when parameterized by the smallest primal tree-depth and
entry complexity of a matrix equivalent to the constraint matrix, and when
parameterized by the smallest dual tree-depth and entry complexity of a matrix
equivalent to the constraint matrix.


title:
Detection of a dense group of hyper-compact radio sources in the central
  parsec of the Galaxy

abstract:
  Using the JVLA, we explored the Galactic center (GC) with a resolution of
0.05" at 33.0 and 44.6 GHz. We detected 64 hyper-compact radio sources (HCRs)
in the central parsec. The dense group of HCRs can be divided into three
spectral types: 38 steep-spectrum ($\alpha\le-0.5$) sources; 10 flat-spectrum
($-0.5<\alpha\le0.2$) sources; and 17 inverted-spectrum sources having
$\alpha>0.2$, assuming $S\propto\nu^\alpha$. The steep-spectrum HCRs are likely
represent a population of massive stellar remnants associated with nonthermal
compact radio sources powered by neutron stars and stellar black holes. The
surface-density distribution of the HCRs as function of radial distance ($R$)
from Sgr~A* can be described as a steep power-law $\Sigma (R) \propto
R^{-\Gamma}$, with $\Gamma=1.6\pm0.2$, along with presence of a localized
order-of-magnitude enhancement in the range 0.1-0.3 pc. The steeper profile of
the HCRs relative to that of the central cluster might result from the
concentration massive stellar remnants by mass segregation at the GC. The GC
magnetar SGR~J1745-2900 belongs to the inverted-spectrum sub-sample. We find
that two spectral components present in the averaged radio spectrum of
SGR~J1745-2900, separated at $\nu\sim30$ GHz. The centimeter-component is
fitted to a power-law with $\alpha_{cm}=-1.5\pm0.6$. The enhanced
millimeter-component shows a rising spectrum $\alpha_{mm}=1.1\pm0.2$. Based on
the ALMA observations at 225 GHz, we find that the GC magnetar is highly
variable on a day-to-day time scale, showing variations up to a factor of 6.
Further JVLA and ALMA observations of the variability, spectrum, and
polarization of the HCRs are critical for determining whether they are
associated with stellar remnants.


title: Anisotropic Infall and Substructure formation in Embedded Disks

abstract:
  The filamentary nature of accretion streams found around embedded sources
suggest that protostellar disks experience heterogenous infall from the
star-forming environment, consistent with the accretion behavior onto
star-forming cores in top-down star-cluster formation simulations. This may
produce disk substructures in the form of rings, gaps, and spirals continuing
to be identified by high-resolution imaging surveys in both embedded Class 0/I
and later Class II sources. We present a parameter study of anisotropic infall,
informed by the properties of accretion flows onto protostellar cores in
numerical simulations, and varying the relative specific angular momentum of
incoming flows as well as their flow geometry. Our results show that
anisotropic infall perturbs the disk and readily launches the Rossby wave
instability (RWI). It forms vortices at the inner and outer edge of the infall
zone where material is deposited. These vortices drive spiral waves and angular
momentum transport, with some models able to drive stresses corresponding to a
viscosity parameter on the order of $\alpha \sim 10^{-2}$. The resulting
azimuthal shear forms robust pressure bumps that act as barriers to radial
drift of dust grains, as demonstrated by post-processing calculations of
drift-dominated dust evolution. We discuss how a self-consistent model of
anisotropic infall can account for the formation of millimeter rings in the
outer disk as well as producing compact dust disks, consistent with
observations of embedded sources.


title:
Trust in AI: Interpretability is not necessary or sufficient, while
  black-box interaction is necessary and sufficient

abstract:
  The problem of human trust in artificial intelligence is one of the most
fundamental problems in applied machine learning. Our processes for evaluating
AI trustworthiness have substantial ramifications for ML's impact on science,
health, and humanity, yet confusion surrounds foundational concepts. What does
it mean to trust an AI, and how do humans assess AI trustworthiness? What are
the mechanisms for building trustworthy AI? And what is the role of
interpretable ML in trust? Here, we draw from statistical learning theory and
sociological lenses on human-automation trust to motivate an AI-as-tool
framework, which distinguishes human-AI trust from human-AI-human trust.
Evaluating an AI's contractual trustworthiness involves predicting future model
behavior using behavior certificates (BCs) that aggregate behavioral evidence
from diverse sources including empirical out-of-distribution and out-of-task
evaluation and theoretical proofs linking model architecture to behavior. We
clarify the role of interpretability in trust with a ladder of model access.
Interpretability (level 3) is not necessary or even sufficient for trust, while
the ability to run a black-box model at-will (level 2) is necessary and
sufficient. While interpretability can offer benefits for trust, it can also
incur costs. We clarify ways interpretability can contribute to trust, while
questioning the perceived centrality of interpretability to trust in popular
discourse. How can we empower people with tools to evaluate trust? Instead of
trying to understand how a model works, we argue for understanding how a model
behaves. Instead of opening up black boxes, we should create more behavior
certificates that are more correct, relevant, and understandable. We discuss
how to build trusted and trustworthy AI responsibly.


title:
A Novel Silicon Photomultiplier Readout Architecture for Low-light
  Applications

abstract:
  In this article we describe the photon detection readout electronics for the
nEDM@SNS experiment. The chosen "photon counting" architecture, which utilizes
high-efficiency silicon photomultipliers (SiPMs) and is appropriate for
low-light applications, allows the use of a relatively high SiPM operating
voltage. This maximizes photon detection efficiency and minimizes
gain/efficiency voltage-dependence while eliminating optical cross-talk.


title:
Spin conductivity of the XXZ chain in the antiferromagnetic massive
  regime

abstract:
  We present a series representation for the dynamical two-point function of
the local spin current for the XXZ chain in the antiferromagnetic massive
regime at zero temperature. From this series we can compute the correlation
function with very high accuracy up to very long times and large distances.
Each term in the series corresponds to the contribution of all scattering
states of an even number of excitations. These excitations can be interpreted
in terms of an equal number of particles and holes. The lowest term in the
series comprises all scattering states of one hole and one particle. This term
determines the long-time large-distance asymptotic behaviour which can be
obtained explicitly from a saddle-point analysis. The space-time Fourier
transform of the two-point function of currents at zero momentum gives the
optical spin conductivity of the model. We obtain highly accurate numerical
estimates for this quantity by numerically Fourier transforming our data. For
the one-particle, one-hole contribution, equivalently interpreted as a
two-spinon contribution, we obtain an exact and explicit expression in terms of
known special functions. For large enough anisotropy, the two-spinon
contribution carries most of the spectral weight, as can be seen by calculating
the f-sum rule.

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


title: Wilkie's conjecture for Pfaffian structures

abstract:
  We prove an effective form of Wilkie's conjecture in the structure generated
by restricted sub-Pfaffian functions: the number of rational points of height
$H$ lying in the transcendental part of such a set grows no faster than some
power of $\log H$. Our bounds depend only on the Pfaffian complexity of the
sets involved. As a corollary we deduce Wilkie's original conjecture for
$\mathbb{R}_{\mathrm{exp}}$ in full generality.


title:
Characterizing and overcoming the greedy nature of learning in
  multi-modal deep neural networks

abstract:
  We hypothesize that due to the greedy nature of learning in multi-modal deep
neural networks, these models tend to rely on just one modality while
under-fitting the other modalities. Such behavior is counter-intuitive and
hurts the models' generalization, as we observe empirically. To estimate the
model's dependence on each modality, we compute the gain on the accuracy when
the model has access to it in addition to another modality. We refer to this
gain as the conditional utilization rate. In the experiments, we consistently
observe an imbalance in conditional utilization rates between modalities,
across multiple tasks and architectures. Since conditional utilization rate
cannot be computed efficiently during training, we introduce a proxy for it
based on the pace at which the model learns from each modality, which we refer
to as the conditional learning speed. We propose an algorithm to balance the
conditional learning speeds between modalities during training and demonstrate
that it indeed addresses the issue of greedy learning. The proposed algorithm
improves the model's generalization on three datasets: Colored MNIST, Princeton
ModelNet40, and NVIDIA Dynamic Hand Gesture.


title: Sub-cycle time-resolved nondipole dynamics in tunneling ionization

abstract:
  The electron nondipole dynamics in tunneling ionization in an elliptically
polarized laser field is investigated theoretically using a relativistic
Coulomb-corrected strong-field approximation (SFA) based on the eikonal
approximation of the Klein-Gordon equation. We calculate attoclock
angle-resolved light-front momentum distributions at different ellipticities of
the laser field in quasistatic and nonadiabatic regimes and analyze them with
an improved Simpleman model. The nondipole correlations between longitudinal
and transverse momentum components are examined. Deviations of the
photoelectron momentum distribution calculated via SFA with respect to the
available experimental results as well as with the improved Simpleman model are
discussed and interpreted in terms of nonadiabatic as well as Coulomb effects
in the continuum and under-the-barrier. The favorable prospects of an
experimental observation are discussed.


title: Preventing congestion in crowd dynamics caused by reversing flow

abstract:
  In this paper we devise a microscopic (agent-based) mathematical model for
reproducing crowd behaviour in a specific scenario: a number of pedestrians,
consisting of numerous social groups, flow along a corridor until a gate
located at the end of the corridor closes. People are not informed about the
closure of the gate and perceive the blockage observing dynamically the local
crowd conditions. Once people become aware of the new conditions, they stop and
then decide either to stay, waiting for reopening, or to go back and leave the
corridor for ever. People going back hit against newly incoming people who are
not yet aware of the blockage or have already decided to stay. This creates a
dangerous counter-flow which can easily lead to accidents. We run several
numerical simulations varying parameters which control the crowd behaviour, in
order to understand the factors which have the greatest impact on the dynamics.
We conclude with some useful suggestions directed to the organizers of mass
events.


title:
Dynamics of charge-imbalance-resolved entanglement negativity after a
  quench in a free-fermion model

abstract:
  The presence of a global internal symmetry in a quantum many-body system is
reflected in the fact that the entanglement between its subparts is endowed
with an internal structure, namely it can be decomposed as sum of contributions
associated to each symmetry sector. The symmetry resolution of entanglement
measures provides a formidable tool to probe the out-of-equilibrium dynamics of
quantum systems. Here, we study the time evolution of charge-imbalance-resolved
negativity after a global quench in the context of free-fermion systems,
complementing former works for the symmetry-resolved entanglement entropy.
  We find that the charge-imbalance-resolved logarithmic negativity shows an
effective equipartition in the scaling limit of large times and system size,
with a perfect equipartition for early and infinite times. We also derive and
conjecture a formula for the dynamics of the charged R\'enyi logarithmic
negativities. We argue that our results can be understood in the framework of
the quasiparticle picture for the entanglement dynamics, and provide a
conjecture that we expect to be valid for generic integrable models.


title: Does charge matter in high-energy collisions of black holes?

abstract:
  We perform numerical-relativity simulations of high-energy head-on collisions
of charged black holes with the same charge-to-mass ratio $\lambda$. We find
that electromagnetic interactions have subdominant effects already at low
Lorentz factors $\gamma$, supporting the conjecture that the details of the
properties of black holes (e.g., their spin or charge) play a secondary role in
these phenomena. Using this result and conservation of energy, we argue these
events cannot violate cosmic censorship.


title:
Mining the manifolds of deep generative models for multiple
  data-consistent solutions of ill-posed tomographic imaging problems

abstract:
  Tomographic imaging is in general an ill-posed inverse problem. Typically, a
single regularized image estimate of the sought-after object is obtained from
tomographic measurements. However, there may be multiple objects that are all
consistent with the same measurement data. The ability to generate such
alternate solutions is important because it may enable new assessments of
imaging systems. In principle, this can be achieved by means of posterior
sampling methods. In recent years, deep neural networks have been employed for
posterior sampling with promising results. However, such methods are not yet
for use with large-scale tomographic imaging applications. On the other hand,
empirical sampling methods may be computationally feasible for large-scale
imaging systems and enable uncertainty quantification for practical
applications. Empirical sampling involves solving a regularized inverse problem
within a stochastic optimization framework in order to obtain alternate
data-consistent solutions. In this work, we propose a new empirical sampling
method that computes multiple solutions of a tomographic inverse problem that
are consistent with the same acquired measurement data. The method operates by
repeatedly solving an optimization problem in the latent space of a style-based
generative adversarial network (StyleGAN), and was inspired by the Photo
Upsampling via Latent Space Exploration (PULSE) method that was developed for
super-resolution tasks. The proposed method is demonstrated and analyzed via
numerical studies that involve two stylized tomographic imaging modalities.
These studies establish the ability of the method to perform efficient
empirical sampling and uncertainty quantification.


title: Posets for which Verdier duality holds

abstract:
  We discuss two known sheaf-cosheaf duality theorems: Curry's for the face
posets of finite regular CW complexes and Lurie's for compact Hausdorff spaces,
i.e., covariant Verdier duality. We provide a uniform formulation for them and
prove their generalizations. Our version of the former works over the sphere
spectrum and for more general finite posets, which we characterize in terms of
the Gorenstein* condition. Our version of the latter says that the
stabilization of a proper separated $\infty$-topos is rigid in the sense of
Gaitsgory. As an application, for stratified topological spaces, we clarify the
relation between these two duality equivalences.


title:
Integrating Testing and Operation-related Quantitative Evidences in
  Assurance Cases to Argue Safety of Data-Driven AI/ML Components

abstract:
  In the future, AI will increasingly find its way into systems that can
potentially cause physical harm to humans. For such safety-critical systems, it
must be demonstrated that their residual risk does not exceed what is
acceptable. This includes, in particular, the AI components that are part of
such systems' safety-related functions. Assurance cases are an intensively
discussed option today for specifying a sound and comprehensive safety argument
to demonstrate a system's safety. In previous work, it has been suggested to
argue safety for AI components by structuring assurance cases based on two
complementary risk acceptance criteria. One of these criteria is used to derive
quantitative targets regarding the AI. The argumentation structures commonly
proposed to show the achievement of such quantitative targets, however, focus
on failure rates from statistical testing. Further important aspects are only
considered in a qualitative manner -- if at all. In contrast, this paper
proposes a more holistic argumentation structure for having achieved the
target, namely a structure that integrates test results with runtime aspects
and the impact of scope compliance and test data quality in a quantitative
manner. We elaborate different argumentation options, present the underlying
mathematical considerations, and discuss resulting implications for their
practical application. Using the proposed argumentation structure might not
only increase the integrity of assurance cases but may also allow claims on
quantitative targets that would not be justifiable otherwise.


title:
Mosaics of Combinatorial Designs for Semantic Security on Quantum
  Wiretap Channels

abstract:
  We study semantic security for classical-quantum channels. Our security
functions are functional forms of mosaics of combinatorial designs. We extend
methods for classical channels to classical-quantum channels to demonstrate
that mosaics of designs ensure semantic security for classical-quantum
channels, and are also capacity achieving coding scheme. The legitimate channel
users share an additional public resource, more precisely, a seed chosen
uniformly at random. An advantage of these modular wiretap codes is that we
provide explicit code constructions that can be implemented in practice for
every channels, giving an arbitrary public code.


title:
Audio Matters Too: How Audial Avatar Customization Enhances Visual
  Avatar Customization

abstract:
  Avatar customization is known to positively affect crucial outcomes in
numerous domains. However, it is unknown whether audial customization can
confer the same benefits as visual customization. We conducted a preregistered
2 x 2 (visual choice vs. visual assignment x audial choice vs. audial
assignment) study in a Java programming game. Participants with visual choice
experienced higher avatar identification and autonomy. Participants with audial
choice experienced higher avatar identification and autonomy, but only within
the group of participants who had visual choice available. Visual choice led to
an increase in time spent, and indirectly led to increases in intrinsic
motivation, immersion, time spent, future play motivation, and likelihood of
game recommendation. Audial choice moderated the majority of these effects. Our
results suggest that audial customization plays an important enhancing role
vis-\`a-vis visual customization. However, audial customization appears to have
a weaker effect compared to visual customization. We discuss the implications
for avatar customization more generally across digital applications.


title: Argonne Pixel Tracking Telescope at the Fermilab Test Beam Facility

abstract:
  The Argonne Pixel Tracking Telescope is installed at the Fermilab Test Beam
Facility. The telescope consists of six planar n^{+}-in-n silicon sensors with
a pixel size of 250 {\times} 50 {\mu}m^{2}. The instrumentation of the
telescope is described including the electrical and mechanical setups. A 120
GeV proton beam is used to evaluate the telescope performance using criteria
such as the cluster size, pixel tracking efficiency, and spatial resolution.
The spatial resolution of the telescope is measured to be 72 {\mu}m {\times} 13
{\mu}m.


title: A Multi-task Learning Framework for Product Ranking with BERT

abstract:
  Product ranking is a crucial component for many e-commerce services. One of
the major challenges in product search is the vocabulary mismatch between query
and products, which may be a larger vocabulary gap problem compared to other
information retrieval domains. While there is a growing collection of neural
learning to match methods aimed specifically at overcoming this issue, they do
not leverage the recent advances of large language models for product search.
On the other hand, product ranking often deals with multiple types of
engagement signals such as clicks, add-to-cart, and purchases, while most of
the existing works are focused on optimizing one single metric such as
click-through rate, which may suffer from data sparsity. In this work, we
propose a novel end-to-end multi-task learning framework for product ranking
with BERT to address the above challenges. The proposed model utilizes
domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs
multi-task learning to optimize multiple objectives simultaneously, which
yields a general end-to-end learning framework for product search. We conduct a
set of comprehensive experiments on a real-world e-commerce dataset and
demonstrate significant improvement of the proposed approach over the
state-of-the-art baseline methods.


title:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated
  Optimization

abstract:
  Large-scale machine learning systems often involve data distributed across a
collection of users. Federated optimization algorithms leverage this structure
by communicating model updates to a central server, rather than entire
datasets. In this paper, we study stochastic optimization algorithms for a
personalized federated learning setting involving local and global models
subject to user-level (joint) differential privacy. While learning a private
global model induces a cost of privacy, local learning is perfectly private. We
show that coordinating local learning with private centralized learning yields
a generically useful and improved tradeoff between accuracy and privacy. We
illustrate our theoretical results with experiments on synthetic and real-world
datasets.


title: Strong persistence and associated prime of powers of monomial ideals

abstract:
  Let $R=K[x_1,\ldots, x_n]$ be the polynomial ring in $n$ variables over a
field $K$ and $I$ be a monomial ideal of degree $d\leq 2$. We show that
$(I^{k+1}:I)=I^k$ for all $k\geq 1$ and we disprove a motivation question that
was appeared in \cite[Question 2.51]{CHHV} by providing of a counter-example.
Also, by this counter-example, we give a negative answer to the question that
depth function of square-free monomial ideals are non-increasing.


title: Denoising Convolutional Networks to Accelerate Detector Simulation

abstract:
  The high accuracy of detector simulation is crucial for modern particle
physics experiments. However, this accuracy comes with a high computational
cost, which will be exacerbated by the large datasets and complex detector
upgrades associated with next-generation facilities such as the High Luminosity
LHC. We explore the viability of regression-based machine learning (ML)
approaches using convolutional neural networks (CNNs) to "denoise" faster,
lower-quality detector simulations, augmenting them to produce a higher-quality
final result with a reduced computational burden. The denoising CNN works in
concert with classical detector simulation software rather than replacing it
entirely, increasing its reliability compared to other ML approaches to
simulation. We obtain promising results from a prototype based on photon
showers in the CMS electromagnetic calorimeter. Future directions are also
discussed.


title: Markovian Repeated Interaction Quantum Systems

abstract:
  We study a class of dynamical semigroups $(\mathbb{L}^n)_{n\in\mathbb{N}}$
that emerge, by a Feynman--Kac type formalism, from a random quantum dynamical
system
$(\mathcal{L}_{\omega_n}\circ\cdots\circ\mathcal{L}_{\omega_1}(\rho_{\omega_0}))_{n\in\mathbb{N}}$
driven by a Markov chain $(\omega_n)_{n\in\mathbb{N}}$. We show that the almost
sure large time behavior of the system can be extracted from the large $n$
asymptotics of the semigroup, which is in turn directly related to the spectral
properties of the generator $\mathbb{L}$. As a physical application, we
consider the case where the $\mathcal{L}_\omega$'s are the reduced dynamical
maps describing the repeated interactions of a system $\mathcal{S}$ with
thermal probes $\mathcal{C}_\omega$. We study the full statistics of the
entropy in this system and derive a fluctuation theorem for the heat exchanges
and the associated linear response formulas.

categories: math-ph cond-mat.stat-mech math.MP quant-ph


title: Homotopy coherent representations

abstract:
  Homotopy coherence has a considerable history, albeit also by other names.
  For this volume highlighting symmetries, the appropriate use is: Homotopy
coherence of representations, at one time known as strong homotopy
representations. We provide a brief semi-historical survey providing some links
that may not be common knowledge.


title:
An Application of Viscosity Theory to Higher Order Differential
  Equations

abstract:
  We directly apply the viscosity theory for fully nonlinear second-order
differential equations to higher order differential operators. We prove
existence and uniqueness theorems for equations of the general form: $$\epsilon
\Delta v+G(D(\Delta v), D^2(\Delta v)) = f(x)$$ when we have appropriate
conditions on the functional $G$. As an explicit application, we show that the
inhomogeneous $\infty-$Bilaplacian equation on a ball $B_R\subset
\mathbb{R}^n$: $$\Delta_\infty^2 u=(\Delta u)^3 |D(\Delta u)|^2 =f(x)$$ with
Navier Boundary Conditions ($u=g\in C(\partial B_R)$, $\Delta u=0$ on $\partial
B_R$) admits solutions in $W^{2,\infty}(B_R)$ under some mild conditions on
$f(x)$ (e.g. H\"older continuity).


title:
Understanding the Digital News Consumption Experience During the COVID
  Pandemic

abstract:
  During the COVID-19 pandemic, people sought information through digital news
platforms. To investigate how to design these platforms to support users' needs
in a crisis, we conducted a two-week diary study with 22 participants across
the United States. Participants' news-consumption experience followed two
stages: in the \textbf{seeking} stage, participants increased their general
consumption, motivated by three common informational needs -- specifically, to
find, understand and verify relevant news pieces. Participants then moved to
the \textbf{sustaining} stage, and coping with the news emotionally became as
important as their informational needs. We elicited design ideas from
participants and used these to distill six themes for creating digital news
platforms that provide better informational and emotional support during a
crisis. Thus, we contribute, first, a model of users' needs over time with
respect to engaging with crisis news, and second, example design concepts for
supporting users' needs in each of these stages.


title:
Exact finite-size scaling for the random-matrix representation of bond
  percolation on square lattice

abstract:
  We report on the exact treatment of a random-matrix representation of bond
percolation model on a square lattice in two dimensions with occupation
probability $p$. The percolation problem is mapped onto a random complex matrix
composed of two random real-valued matrices of elements $+1$ and $-1$ with
probability $p$ and $1-p$, respectively. We find that the onset of percolation
transition can be detected by the emergence of power-law divergences due to the
coalescence of the first two extreme eigenvalues in the thermodynamic limit. We
develop a universal finite-size scaling law that fully characterizes the
scaling behavior of the extreme eigenvalue's fluctuation in terms of a set of
universal scaling exponents and amplitudes. We make use of the relative entropy
as an index of the disparity between two distributions of the first and
second-largest extreme eigenvalues, to show that its minimum underlies the
scaling framework. Our study may provide an inroad for developing new methods
and algorithms with diverse applications in machine learning, complex systems,
and statistical physics.

categories: cond-mat.stat-mech cond-mat.dis-nn math-ph math.MP math.PR


title:
Robust Policy Selection and Harvest Risk Quantification for Natural
  Resources Management under Model Uncertainty

abstract:
  In this work the problem of optimal harvesting policy selection for natural
resources management under model uncertainty is investigated. Under the
framework of the neoclassical growth model dynamics, the associated optimal
control problem is investigated by introducing the concept of model uncertainty
on the initial conditions of the operational procedure. At this stage, the
notion of convex risk measures, and in particular the class of Fr\'echet risk
measures, is employed in order to quantify the total operational and marginal
risk, whereas simultaneously obtaining robust to model uncertainty harvesting
strategies.


title: Three-dimensional graph products with unbounded stack-number

abstract:
  We prove that the stack-number of the strong product of three $n$-vertex
paths is $\Theta(n^{1/3})$. The best previously known upper bound was $O(n)$.
No non-trivial lower bound was known. This is the first explicit example of a
graph family with bounded maximum degree and unbounded stack-number.
  The main tool used in our proof of the lower bound is the topological overlap
theorem of Gromov. We actually prove a stronger result in terms of so-called
triangulations of Cartesian products. We conclude that triangulations of
three-dimensional Cartesian products of any sufficiently large connected graphs
have large stack-number.
  The upper bound is a special case of a more general construction based on
families of permutations derived from Hadamard matrices.
  The strong product of three paths is also the first example of a bounded
degree graph with bounded queue-number and unbounded stack-number. A natural
question that follows from our result is to determine the smallest $\Delta_0$
such that there exist a graph family with unbounded stack-number, bounded
queue-number and maximum degree $\Delta_0$. We show that $\Delta_0\in \{6,7\}$.


title: Forward Build Systems, Formally

abstract:
  Build systems are a fundamental part of software construction, but their
correctness has received comparatively little attention, relative to more
prominent parts of the toolchain. In this paper, we address the correctness of
\emph{forward build systems}, which automatically determine the dependency
structure of the build, rather than having it specified by the programmer.
  We first define what it means for a forward build system to be correct -- it
must behave identically to simply executing the programmer-specified commands
in order. Of course, realistic build systems avoid repeated work, stop early
when possible, and run commands in parallel, and we prove that these
optimizations, as embodied in the recent forward build system \textsc{Rattle},
preserve our definition of correctness. Along the way, we show that other
forward build systems, such as \textsc{Fabricate} and \textsc{Memoize}, are
also correct.
  We carry out all of our work in \Agda, and describe in detail the assumptions
underlying both \textsc{Rattle} itself and our modeling of it.


title: Spork: Structured Merge for Java with Formatting Preservation

abstract:
  The highly parallel workflows of modern software development have made
merging of source code a common activity for developers. The state of the
practice is based on line-based merge, which is ubiquitously used with "git
merge". Line-based merge is however a generalized technique for any text that
cannot leverage the structured nature of source code, making merge conflicts a
common occurrence. As a remedy, research has proposed structured merge tools,
which typically operate on abstract syntax trees instead of raw text.
Structured merging greatly reduces the prevalence of merge conflicts but
suffers from important limitations, the main ones being a tendency to alter the
formatting of the merged code and being prone to excessive running times. In
this paper, we present SPORK, a novel structured merge tool for JAVA. SPORK is
unique as it preserves formatting to a significantly greater degree than
comparable state-of-the-art tools. SPORK is also overall faster than the state
of the art, in particular significantly reducing worst-case running times in
practice. We demonstrate these properties by replaying 1740 real-world file
merges collected from 119 open-source projects, and further demonstrate several
key differences between SPORK and the state of the art with in-depth case
studies.


title: Data-driven sensor placement with shallow decoder networks

abstract:
  Sensor placement is an important and ubiquitous problem across the
engineering and physical sciences for tasks such as reconstruction, forecasting
and control. Surprisingly, there are few principled mathematical techniques
developed to date for optimizing sensor locations, with the leading sensor
placement algorithms often based upon the discovery of linear, low-rank
sub-spaces and the QR algorithm. QR is a computationally efficient greedy
search algorithm which selects sensor locations from candidate positions with
maximal variance exhibited in a training data set. More recently, neural
networks, specifically shallow decoder networks (SDNs), have been shown to be
very successful in mapping sensor measurements to the original high-dimensional
state space. SDNs outperform linear subspace representations in reconstruction
accuracy, noise tolerance, and robustness to sensor locations. However, SDNs
lack principled mathematical techniques for determining sensor placement. In
this work, we develop two algorithms for optimizing sensor locations for use
with SDNs: one which is a linear selection algorithm based upon QR (Q-SDN), and
one which is a nonlinear selection algorithm based upon neural network pruning
(P-SDN). Such sensor placement algorithms promise to enhance the already
impressive reconstruction capabilities of SDNs. We demonstrate our sensor
selection algorithms on two example data sets from fluid dynamics. Moreover, we
provide a detailed comparison between our linear (Q-SDN) and nonlinear (P-SDN)
algorithms with traditional linear embedding techniques (proper orthogonal
decomposition) and QR greedy selection. We show that QR selection with SDNs
enhances performance. QR even out-performs our nonlinear selection method that
uses magnitude-based pruning. Thus, the combination of a greedy linear
selection (QR) with nonlinear encoding (SDN) provides a synergistic
combination.


title:
Describing image focused in cognitive and visual details for visually
  impaired people: An approach to generating inclusive paragraphs

abstract:
  Several services for people with visual disabilities have emerged recently
due to achievements in Assistive Technologies and Artificial Intelligence
areas. Despite the growth in assistive systems availability, there is a lack of
services that support specific tasks, such as understanding the image context
presented in online content, e.g., webinars. Image captioning techniques and
their variants are limited as Assistive Technologies as they do not match the
needs of visually impaired people when generating specific descriptions. We
propose an approach for generating context of webinar images combining a dense
captioning technique with a set of filters, to fit the captions in our domain,
and a language model for the abstractive summary task. The results demonstrated
that we can produce descriptions with higher interpretability and focused on
the relevant information for that group of people by combining image analysis
methods and neural language models.


title:
An Initial Description of Capabilities and Constraints for a
  Computational Auditory System (an Artificial Ear) for Cognitive Architectures

abstract:
  We present an initial set of factors, features, and constraints for
developing a Computational Auditory System (CAS, aka less formally an
artificial ear, AE) for use by cognitive architectures. We start to define a
CAS and what tasks it should be able to perform. We then outline the features
of a CAS for use by a cognitive architecture and factors that influence its
performance. We conclude with an update on what has been created so far and
insights on how to create and use a CAS in a cognitive architecture and include
a set of functionalities for an artificial ear.