title: ImpDet: Exploring Implicit Fields for 3D Object Detection

abstract:
  Conventional 3D object detection approaches concentrate on bounding boxes
representation learning with several parameters, i.e., localization, dimension,
and orientation. Despite its popularity and universality, such a
straightforward paradigm is sensitive to slight numerical deviations,
especially in localization. By exploiting the property that point clouds are
naturally captured on the surface of objects along with accurate location and
intensity information, we introduce a new perspective that views bounding box
regression as an implicit function. This leads to our proposed framework,
termed Implicit Detection or ImpDet, which leverages implicit field learning
for 3D object detection. Our ImpDet assigns specific values to points in
different local 3D spaces, thereby high-quality boundaries can be generated by
classifying points inside or outside the boundary. To solve the problem of
sparsity on the object surface, we further present a simple yet efficient
virtual sampling strategy to not only fill the empty region, but also learn
rich semantic features to help refine the boundaries. Extensive experimental
results on KITTI and Waymo benchmarks demonstrate the effectiveness and
robustness of unifying implicit fields into object detection.


title:
Bayesian optimization with known experimental and design constraints for
  chemistry applications

abstract:
  Optimization strategies driven by machine learning, such as Bayesian
optimization, are being explored across experimental sciences as an efficient
alternative to traditional design of experiment. When combined with automated
laboratory hardware and high-performance computing, these strategies enable
next-generation platforms for autonomous experimentation. However, the
practical application of these approaches is hampered by a lack of flexible
software and algorithms tailored to the unique requirements of chemical
research. One such aspect is the pervasive presence of constraints in the
experimental conditions when optimizing chemical processes or protocols, and in
the chemical space that is accessible when designing functional molecules or
materials. Although many of these constraints are known a priori, they can be
interdependent, non-linear, and result in non-compact optimization domains. In
this work, we extend our experiment planning algorithms Phoenics and Gryffin
such that they can handle arbitrary known constraints via an intuitive and
flexible interface. We benchmark these extended algorithms on continuous and
discrete test functions with a diverse set of constraints, demonstrating their
flexibility and robustness. In addition, we illustrate their practical utility
in two simulated chemical research scenarios: the optimization of the synthesis
of o-xylenyl Buckminsterfullerene adducts under constrained flow conditions,
and the design of redox active molecules for flow batteries under synthetic
accessibility constraints. The tools developed constitute a simple, yet
versatile strategy to enable model-based optimization with known experimental
constraints, contributing to its applicability as a core component of
autonomous platforms for scientific discovery.


title:
Automatic Detection of Expressed Emotion from Five-Minute Speech
  Samples: Challenges and Opportunities

abstract:
  We present a novel feasibility study on the automatic recognition of
Expressed Emotion (EE), a family environment concept based on caregivers
speaking freely about their relative/family member. We describe an automated
approach for determining the \textit{degree of warmth}, a key component of EE,
from acoustic and text features acquired from a sample of 37 recorded
interviews. These recordings, collected over 20 years ago, are derived from a
nationally representative birth cohort of 2,232 British twin children and were
manually coded for EE. We outline the core steps of extracting usable
information from recordings with highly variable audio quality and assess the
efficacy of four machine learning approaches trained with different
combinations of acoustic and text features. Despite the challenges of working
with this legacy data, we demonstrated that the degree of warmth can be
predicted with an $F_{1}$-score of \textbf{61.5\%}. In this paper, we summarise
our learning and provide recommendations for future work using real-world
speech samples.


title:
Effect of nonfactorizable background geometry on thermodynamics of
  clustering of galaxies

abstract:
  We study the effect of nonfactorizable background geometry on the
thermodynamics of the clustering of galaxies. A canonical partition function is
derived for the gravitating system of galaxies treated as point particles
contained in cells of appropriate dimensions. Various thermodynamic equations
of state, like Helmholtz free energy and entropy, among others, are also
obtained. We also estimate the effect of the corrected Newton law on the
distribution function of galaxies. Remarkably, the effect of the modified
Newton law is seen only in the clustering parameter while the standard
structure of the equations is preserved. A comparison of the modified
clustering parameter with that of the original clustering parameter is made to
visualize the effect of the correction on the time scale of clustering. The
possibility of system symmetry breaking is also analyzed by investigating the
behavior of the specific heat with increasing system temperature.


title:
Strategies for reducing frequency scatter in large arrays of
  superconducting resonators

abstract:
  Superconducting resonators are now found in a broad range of applications
that require high-fidelity measurement of low-energy signals. A common feature
across almost all of these applications is the need for increased numbers of
resonators to further improve sensitivity, and the ability to read out large
numbers of resonators without the need for additional cryogenic complexity is a
primary motivation. One of the major limitations of current resonator arrays is
the observed scatter in the resonator frequencies when compared to the initial
design. Here we present recent progress toward identifying one of the dominant
underlying causes of resonator scatter, inductor line width fluctuation. We
designed and fabricated an array of lumped-element resonators with inductor
line width changing from 1.8um to 2.2um in step of 0.1um defined with
electron-beam lithography to probe and quantify the systematic variation of
resonance frequency across a 6-inch wafer. The resonators showed a linear
frequency shift of 20MHz (140FWHM) and 30MHz (214FWHM), respectively, as they
are connected to two different capacitors. This linear relationship matches our
theoretical prediction. The widely used MLA photon lithography facility for
MKID fabrication has a resolution on the order of 600nm, which could cause
frequency fluctuation on the order of 100MHz or 710FWHM.


title: Random cubic planar graphs converge to the Brownian sphere

abstract:
  In this paper, the scaling limit of random connected cubic planar graphs
(respectively multigraphs) is shown to be the Brownian sphere.
  The proof consists in essentially two main steps. First, thanks to the known
decomposition of cubic planar graphs into their 3-connected components, the
metric structure of a random cubic planar graph is shown to be well
approximated by its unique 3-connected component of linear size, with modified
distances.
  Then, Whitney's theorem ensures that a 3-connected cubic planar graph is the
dual of a simple triangulation, for which it is known that the scaling limit is
the Brownian sphere. Curien and Le Gall have recently developed a framework to
study the modification of distances in general triangulations and in their
dual. By extending this framework to simple triangulations, it is shown that
3-connected cubic planar graphs with modified distances converge jointly with
their dual triangulation to the Brownian sphere.


title:
Origin of spin reorientation and intrinsic anomalous Hall effect in the
  kagome ferrimagnet TbMn6Sn6

abstract:
  TbMn$_6$Sn$_6$ has attracted a lot of recent interest for a variety of
reasons, most importantly, because of the hypothesis that it may support
quantum-limit Chern topological magnetism, derived from the kagome geometry.
Besides, TbMn$_6$Sn$_6$ features a highly unusual magnetic reorientation
transition about 100 K below the Curie point, whereby all spins in the system,
remaining collinear, rotate by 90$^\circ$. In this work, we address both issues
combining experiment, mean-field theory and first-principle calculations. Both
magnetic reorientation and the unusual temperature dependence of the anomalous
Hall conductivity (AHC) find quantitative explanation in the fact that Mn and
Tb, by virtue of the Mermin-Wagner theorem, have very different spin dynamics,
with Tb spins experiencing much more rapid fluctuation. We were able to cleanly
extract the intrinsic AHC from our experiment, and calculated the same
microscopically, with good semiquantitative agreement. We have identified the
points in the band structure responsible for the AHC and showed that they are
not the kagome-derived Dirac points at the K-corner of the Brillouin zone, as
conjectured previously.


title:
VL-InterpreT: An Interactive Visualization Tool for Interpreting
  Vision-Language Transformers

abstract:
  Breakthroughs in transformer-based models have revolutionized not only the
NLP field, but also vision and multimodal systems. However, although
visualization and interpretability tools have become available for NLP models,
internal mechanisms of vision and multimodal transformers remain largely
opaque. With the success of these transformers, it is increasingly critical to
understand their inner workings, as unraveling these black-boxes will lead to
more capable and trustworthy models. To contribute to this quest, we propose
VL-InterpreT, which provides novel interactive visualizations for interpreting
the attentions and hidden representations in multimodal transformers.
VL-InterpreT is a task agnostic and integrated tool that (1) tracks a variety
of statistics in attention heads throughout all layers for both vision and
language components, (2) visualizes cross-modal and intra-modal attentions
through easily readable heatmaps, and (3) plots the hidden representations of
vision and language tokens as they pass through the transformer layers. In this
paper, we demonstrate the functionalities of VL-InterpreT through the analysis
of KD-VLP, an end-to-end pretraining vision-language multimodal
transformer-based model, in the tasks of Visual Commonsense Reasoning (VCR) and
WebQA, two visual question answering benchmarks. Furthermore, we also present a
few interesting findings about multimodal transformer behaviors that were
learned through our tool.


title:
Dual Temperature Helps Contrastive Learning Without Many Negative
  Samples: Towards Understanding and Simplifying MoCo

abstract:
  Contrastive learning (CL) is widely known to require many negative samples,
65536 in MoCo for instance, for which the performance of a dictionary-free
framework is often inferior because the negative sample size (NSS) is limited
by its mini-batch size (MBS). To decouple the NSS from the MBS, a dynamic
dictionary has been adopted in a large volume of CL frameworks, among which
arguably the most popular one is MoCo family. In essence, MoCo adopts a
momentum-based queue dictionary, for which we perform a fine-grained analysis
of its size and consistency. We point out that InfoNCE loss used in MoCo
implicitly attract anchors to their corresponding positive sample with various
strength of penalties and identify such inter-anchor hardness-awareness
property as a major reason for the necessity of a large dictionary. Our
findings motivate us to simplify MoCo v2 via the removal of its dictionary as
well as momentum. Based on an InfoNCE with the proposed dual temperature, our
simplified frameworks, SimMoCo and SimCo, outperform MoCo v2 by a visible
margin. Moreover, our work bridges the gap between CL and non-CL frameworks,
contributing to a more unified understanding of these two mainstream frameworks
in SSL. Code is available at: https://bit.ly/3LkQbaT.


title: Universalizing Analog Quantum Simulators

abstract:
  Unitary Sampling Expectation-Value Reconstruction (USER) is used to determine
expectation values that are not directly accessible on an analog quantum
simulator.


title: Generation and Simulation of Synthetic Datasets with Copulas

abstract:
  This paper proposes a new method to generate synthetic data sets based on
copula models. Our goal is to produce surrogate data resembling real data in
terms of marginal and joint distributions. We present a complete and reliable
algorithm for generating a synthetic data set comprising numeric or categorical
variables. Applying our methodology to two datasets shows better performance
compared to other methods such as SMOTE and autoencoders.


title: Continuous Scene Representations for Embodied AI

abstract:
  We propose Continuous Scene Representations (CSR), a scene representation
constructed by an embodied agent navigating within a space, where objects and
their relationships are modeled by continuous valued embeddings. Our method
captures feature relationships between objects, composes them into a graph
structure on-the-fly, and situates an embodied agent within the representation.
Our key insight is to embed pair-wise relationships between objects in a latent
space. This allows for a richer representation compared to discrete relations
(e.g., [support], [next-to]) commonly used for building scene representations.
CSR can track objects as the agent moves in a scene, update the representation
accordingly, and detect changes in room configurations. Using CSR, we
outperform state-of-the-art approaches for the challenging downstream task of
visual room rearrangement, without any task specific training. Moreover, we
show the learned embeddings capture salient spatial details of the scene and
show applicability to real world data. A summery video and code is available at
https://prior.allenai.org/projects/csr.


title:
Quantum simulation beyond Hamiltonian paradigm: categorical quantum
  simulation

abstract:
  With the development of topological field theory, the mathematical tool of
the tensor category was also introduced into physics. Traditional group theory
corresponds to a special category,group category. Tensor categories can
describe higher-order interactions and symmetric relations, while group theory
can only describe first-order interactions. In fact, the quantum circuit itself
constitutes a category. However, at present, the field of quantum computing
mainly uses group theory as a mathematical tool. If category theory is
introduced into the field of quantum simulation, the application scope of
quantum computers can be greatly expanded. This paper propose a new dynamic
simulation method,categorical quantum simulation. In our paradigm quantum
simulation is no longer based on the structure of the group theory, but based
on the structure of the tensor category. This could enable many systems that
could not be efficiently quantum simulated before.In this article we give an
concrete example of the categorical simulation of $SU(3)$ Yang-Mills theory. It
shows that categorical quantum simulation provides a new encoding
method,emergenism encoding, which saves more qubits resources than reductionism
quantum encoding. In addition, many domains can be described in the language of
category theory, which allows quantum circuits to directly encode and simulate
these domains.


title:
PLCverif: Status of a Formal Verification Tool for Programmable Logic
  Controller

abstract:
  Programmable Logic Controllers (PLC) are widely used for industrial
automation including safety systems at CERN. The incorrect behaviour of the PLC
control system logic can cause significant financial losses by damage of
property or the environment or even injuries in some cases, therefore ensuring
their correct behaviour is essential. While testing has been for many years the
traditional way of validating the PLC control system logic, CERN developed a
model checking platform to go one step further and formally verify PLC logic.
This platform, called PLCverif, first released internally for CERN usage in
2019, is now available to anyone since September 2020 via an open source
licence. In this paper, we will first give an overview of the PLCverif platform
capabilities before focusing on the improvements done since 2019 such as the
larger support coverage of the Siemens PLC programming languages, the better
support of the C Bounded Model Checker backend (CBMC) and the process of
releasing PLCverif as an open-source software.


title:
Entanglement Negativity and Mutual Information after a Quantum Quench:
  Exact Link from Space-Time Duality

abstract:
  We study the growth of entanglement between two adjacent regions in a
tripartite, one-dimensional many-body system after a quantum quench. Combining
a replica trick with a space-time duality transformation, we derive an exact,
universal relation between the entanglement negativity and Renyi-1/2 mutual
information which holds at times shorter than the sizes of all subsystems. Our
proof is directly applicable to any translationally invariant local quantum
circuit, i.e., any lattice system in discrete time characterised by local
interactions, irrespective of the nature of its dynamics. Our derivation
indicates that such a relation can be directly extended to any system where
information spreads with a finite maximal velocity.

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


title:
A Computational Architecture for Machine Consciousness and Artificial
  Superintelligence: Updating Working Memory Iteratively

abstract:
  This theoretical article examines how to construct human-like working memory
and thought processes within a computer. There should be two working memory
stores, one analogous to sustained firing in association cortex, and one
analogous to synaptic potentiation in the cerebral cortex. These stores must be
constantly updated with new representations that arise from either
environmental stimulation or internal processing. They should be updated
continuously, and in an iterative fashion, meaning that, in the next state,
some items in the set of coactive items should always be retained. Thus, the
set of concepts coactive in working memory will evolve gradually and
incrementally over time. This makes each state is a revised iteration of the
preceding state and causes successive states to overlap and blend with respect
to the set of representations they contain. As new representations are added
and old ones are subtracted, some remain active for several seconds over the
course of these changes. This persistent activity, similar to that used in
artificial recurrent neural networks, is used to spread activation energy
throughout the global workspace to search for the next associative update. The
result is a chain of associatively linked intermediate states that are capable
of advancing toward a solution or goal. Iterative updating is conceptualized
here as an information processing strategy, a computational and
neurophysiological determinant of the stream of thought, and an algorithm for
designing and programming artificial intelligence.


title:
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in
  Commercial Buildings

abstract:
  Modern buildings are densely equipped with smart energy meters, which
periodically generate a massive amount of time-series data yielding few million
data points every day. This data can be leveraged to discover the underlying
loads, infer their energy consumption patterns, inter-dependencies on
environmental factors, and the building's operational properties. Furthermore,
it allows us to simultaneously identify anomalies present in the electricity
consumption profiles, which is a big step towards saving energy and achieving
global sustainability. However, to date, the lack of large-scale annotated
energy consumption datasets hinders the ongoing research in anomaly detection.
We contribute to this effort by releasing a well-annotated version of a
publicly available ASHRAE Great Energy Predictor III data set containing 1,413
smart electricity meter time series spanning over one year. In addition, we
benchmark the performance of eight state-of-the-art anomaly detection methods
on our dataset and compare their performance.


title: Rethinking Video Salient Object Ranking

abstract:
  Salient Object Ranking (SOR) involves ranking the degree of saliency of
multiple salient objects in an input image. Most recently, a method is proposed
for ranking salient objects in an input video based on a predicted fixation
map. It relies solely on the density of the fixations within the salient
objects to infer their saliency ranks, which is incompatible with human
perception of saliency ranking. In this work, we propose to explicitly learn
the spatial and temporal relations between different salient objects to produce
the saliency ranks. To this end, we propose an end-to-end method for video
salient object ranking (VSOR), with two novel modules: an intra-frame adaptive
relation (IAR) module to learn the spatial relation among the salient objects
in the same frame locally and globally, and an inter-frame dynamic relation
(IDR) module to model the temporal relation of saliency across different
frames. In addition, to address the limited video types (just sports and
movies) and scene diversity in the existing VSOR dataset, we propose a new
dataset that covers different video types and diverse scenes on a large scale.
Experimental results demonstrate that our method outperforms state-of-the-art
methods in relevant fields. We will make the source code and our proposed
dataset available.


title:
More is Different: Non-Minimal Dark Sectors and their Implications for
  Particle Physics, Astrophysics, and Cosmology -- 13 Take-Away Lessons for
  Snowmass 2021

abstract:
  The phrase "more is different" is often used to refer to the new, unexpected
collective phenomena that can arise when the number of states in a given system
is large. In this contribution to the Snowmass 2021 Study, we describe 13
unexpected collective phenomena that can arise when the dark sector contains a
large number of states, contrary to the usual assumptions. These 13 take-away
lessons stretch across all of the domains of relevance for dark-matter physics,
including collider signatures, direct-detection signatures, indirect-detection
signatures, new perspectives on dark-matter complementarity, and even
unexpected astrophysical and cosmological phenomena that transcend those
normally associated with single-component dark-matter scenarios. These lessons
-- and the phenomena on which they are based -- thereby illustrate the need to
maintain a broad perspective when contemplating the possible signatures and
theoretical possibilities associated with non-minimal dark sectors.


title:
To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting
  Preprints Online

abstract:
  Double-blind conferences have engaged in debates over whether to allow
authors to post their papers online on arXiv or elsewhere during the review
process. Independently, some authors of research papers face the dilemma of
whether to put their papers on arXiv due to its pros and cons. We conduct a
study to substantiate this debate and dilemma via quantitative measurements.
Specifically, we conducted surveys of reviewers in two top-tier double-blind
computer science conferences -- ICML 2021 (5361 submissions and 4699 reviewers)
and EC 2021 (498 submissions and 190 reviewers). Our two main findings are as
follows. First, more than a third of the reviewers self-report searching online
for a paper they are assigned to review. Second, outside the review process, we
find that preprints from better-ranked affiliations see a weakly higher
visibility, with a correlation of 0.06 in ICML and 0.05 in EC. In particular,
papers associated with the top-10-ranked affiliations had a visibility of
approximately 11% in ICML and 22% in EC, whereas the remaining papers had a
visibility of 7% and 18% respectively.


title:
Generating High Fidelity Data from Low-density Regions using Diffusion
  Models

abstract:
  Our work focuses on addressing sample deficiency from low-density regions of
data manifold in common image datasets. We leverage diffusion process based
generative models to synthesize novel images from low-density regions. We
observe that uniform sampling from diffusion models predominantly samples from
high-density regions of the data manifold. Therefore, we modify the sampling
process to guide it towards low-density regions while simultaneously
maintaining the fidelity of synthetic data. We rigorously demonstrate that our
process successfully generates novel high fidelity samples from low-density
regions. We further examine generated samples and show that the model does not
memorize low-density data and indeed learns to generate novel samples from
low-density regions.


title:
R2L: Distilling Neural Radiance Field to Neural Light Field for
  Efficient Novel View Synthesis

abstract:
  Recent research explosion on Neural Radiance Field (NeRF) shows the
encouraging potential to represent complex scenes with neural networks. One
major drawback of NeRF is its prohibitive inference time: Rendering a single
pixel requires querying the NeRF network hundreds of times. To resolve it,
existing efforts mainly attempt to reduce the number of required sampled
points. However, the problem of iterative sampling still exists. On the other
hand, Neural Light Field (NeLF) presents a more straightforward representation
over NeRF in novel view synthesis -- the rendering of a pixel amounts to one
single forward pass without ray-marching. In this work, we present a deep
residual MLP network (88 layers) to effectively learn the light field. We show
the key to successfully learning such a deep NeLF network is to have sufficient
data, for which we transfer the knowledge from a pre-trained NeRF model via
data distillation. Extensive experiments on both synthetic and real-world
scenes show the merits of our method over other counterpart algorithms. On the
synthetic scenes, we achieve 26-35x FLOPs reduction (per camera ray) and 28-31x
runtime speedup, meanwhile delivering significantly better (1.4-2.8 dB average
PSNR improvement) rendering quality than NeRF without any customized
implementation tricks.


title: Length L-function for Network-Constrained Point Data

abstract:
  Network constrained points are referred to as points restricted to road
networks, such as taxi pick up and drop off locations. A significant pattern of
network constrained points is referred to as an aggregation; e.g., the
aggregation of pick up points may indicate a high taxi demand in a particular
area. Although the network K function using the shortest path network distance
has been proposed to detect point aggregation, its statistical unit is still
radius based. R neighborhood, in particular, has inconsistent network length
owing to the complex configuration of road networks which cause unfair counts
and identification errors in networks (e.g., the length of the r neighborhood
located at an intersection is longer than that on straight roads, which may
include more points). In this study, we derived the length L function for
network constrained points to identify the aggregation by designing a novel
neighborhood as the statistical unit; the total length of this is consistent
throughout the network. Compared to the network K function, our method can
detect a true to life aggregation scale, identify the aggregation with higher
network density, as well as identify the aggregations that the network K
function cannot. We validated our method using taxi trips pick up location data
within Zhongguancun Area in Beijing, analyzing differences in maximal
aggregation between workdays and weekends to understand taxi demand in the
morning and evening peak.


title:
Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement
  by Re-Synthesis

abstract:
  Since facial actions such as lip movements contain significant information
about speech content, it is not surprising that audio-visual speech enhancement
methods are more accurate than their audio-only counterparts. Yet,
state-of-the-art approaches still struggle to generate clean, realistic speech
without noise artifacts and unnatural distortions in challenging acoustic
environments. In this paper, we propose a novel audio-visual speech enhancement
framework for high-fidelity telecommunications in AR/VR. Our approach leverages
audio-visual speech cues to generate the codes of a neural speech codec,
enabling efficient synthesis of clean, realistic speech from noisy signals.
Given the importance of speaker-specific cues in speech, we focus on developing
personalized models that work well for individual speakers. We demonstrate the
efficacy of our approach on a new audio-visual speech dataset collected in an
unconstrained, large vocabulary setting, as well as existing audio-visual
datasets, outperforming speech enhancement baselines on both quantitative
metrics and human evaluation studies. Please see the supplemental video for
qualitative results at
https://github.com/facebookresearch/facestar/releases/download/paper_materials/video.mp4.


title:
Growth of R\'enyi Entropies in Interacting Integrable Models and the
  Breakdown of the Quasiparticle Picture

abstract:
  R\'enyi entropies are conceptually valuable and experimentally relevant
generalisations of the celebrated von Neumann entanglement entropy. After a
quantum quench in a clean quantum many-body system they generically display a
universal linear growth in time followed by saturation. While a finite
subsystem is essentially at local equilibrium when the entanglement saturates,
it is genuinely out-of-equilibrium in the growth phase. In particular, the
slope of the growth carries vital information on the nature of the system's
dynamics, and its characterisation is a key objective of current research. Here
we show that the slope of R\'enyi entropies can be determined by means of a
spacetime duality transformation. In essence, we argue that the slope coincides
with the stationary density of entropy of the model obtained by exchanging the
roles of space and time. Therefore, very surprisingly, the slope of the
entanglement is expressed as an equilibrium quantity. We use this observation
to find an explicit exact formula for the slope of R\'enyi entropies in all
integrable models treatable by thermodynamic Bethe ansatz and evolving from
integrable initial states. Interestingly, this formula can be understood in
terms of a quasiparticle picture only in the von Neumann limit.


title: A 23 MW data centre is all you need

abstract:
  The field of machine learning has achieved striking progress in recent years,
witnessing breakthrough results on language modelling, protein folding and
nitpickingly fine-grained dog breed classification. Some even succeeded at
playing computer games and board games, a feat both of engineering and of
setting their employers' expectations. The central contribution of this work is
to carefully examine whether this progress, and technology more broadly, can be
expected to continue indefinitely. Through a rigorous application of
statistical theory and failure to extrapolate beyond the training data, we
answer firmly in the negative and provide details: technology will peak at 3:07
am (BST) on 20th July, 2032. We then explore the implications of this finding,
discovering that individuals awake at this ungodly hour with access to a
sufficiently powerful computer possess an opportunity for myriad forms of
long-term linguistic 'lock in'. All we need is a large (>> 1W) data centre to
seize this pivotal moment. By setting our analogue alarm clocks, we propose a
tractable algorithm to ensure that, for the future of humanity, the British
spelling of colour becomes the default spelling across more than 80% of the
global word processing software market.


title:
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable
  Facial Editing

abstract:
  Recent advances like StyleGAN have promoted the growth of controllable facial
editing. To address its core challenge of attribute decoupling in a single
latent space, attempts have been made to adopt dual-space GAN for better
disentanglement of style and content representations. Nonetheless, these
methods are still incompetent to obtain plausible editing results with high
controllability, especially for complicated attributes. In this study, we
highlight the importance of interaction in a dual-space GAN for more
controllable editing. We propose TransEditor, a novel Transformer-based
framework to enhance such interaction. Besides, we develop a new dual-space
editing and inversion strategy to provide additional editing flexibility.
Extensive experiments demonstrate the superiority of the proposed framework in
image quality and editing capability, suggesting the effectiveness of
TransEditor for highly controllable facial editing.


title: Variational Quantum Pulse Learning

abstract:
  Quantum computing is among the most promising emerging techniques to solve
problems that are computationally intractable on classical hardware. A large
body of existing works focus on using variational quantum algorithms on the
gate level for machine learning tasks, such as the variational quantum circuit
(VQC). However, VQC has limited flexibility and expressibility due to limited
number of parameters, e.g. only one parameter can be trained in one rotation
gate. On the other hand, we observe that quantum pulses are lower than quantum
gates in the stack of quantum computing and offers more control parameters.
Inspired by the promising performance of VQC, in this paper we propose
variational quantum pulses (VQP), a novel paradigm to directly train quantum
pulses for learning tasks. The proposed method manipulates variational quantum
pulses by pulling and pushing the amplitudes of pulses in an optimization
framework. Similar to variational quantum algorithms, our framework to train
pulses maintains the robustness to noise on Noisy Intermediate-Scale Quantum
(NISQ) computers. In an example task of binary classification, VQP learning
achieves up to 11% and 9% higher accuracy compared with VQC learning on the
qiskit noise simulators (with noise model from real machine) and ibmq-jarkata,
respectively, demonstrating its effectiveness and feasibility. Stability for
VQP to obtain reliable results has also been verified in the presence of noise.


title:
On the simplicity of the tensor product of two simple modules of quantum
  affine algebras

abstract:
  Lapid and M\'{i}nguez gave a criterion of the irreducibility of the parabolic
induction $\sigma \times \pi$, where $\sigma$ is a ladder representation and
$\pi$ is an arbitrary irreducible representation of the general linear group
over a non-archimedean field. Through quantum affine Schur-Weyl duality, when
$k$ is large enough, this gives a criterion of the irreducibility of the tensor
product of a snake module $L(M)$ and any simple module $L(N)$ of the quantum
affine algebra $U_q(\widehat{\mathfrak{sl}_k})$.
  The goal of this paper is to add conditions to their criterion such that it
works for any $k \geq 1$. We prove the criterion in the case that $L(M)$,
$L(N)$ are snake modules or $M=Y_{1,s}$ for some $s \in \mathbb{Z}$, $L(N)$ is
any simple module. We also defined a similar criterion in terms of two tableaux
and show that for any $k \geq 1$, two ladders in the Grassmannian cluster
algebra $\mathbb{C}[\mathrm{Gr}(k,n, \sim)]$ are compatible if and only if the
corresponding tableaux satisfy the criterion. This generalizes Leclerc and
Zelevinsky's result that two Pl\"{u}cker coordinates are compatible if and only
if they are weakly separated.


title: A Closer Look at Rehearsal-Free Continual Learning

abstract:
  Continual learning describes a setting where machine learning models learn
novel concepts from continuously shifting training data, while simultaneously
avoiding degradation of knowledge on previously seen classes (a phenomenon
known as the catastrophic forgetting problem) which may disappear from the
training data for extended periods of time. Current approaches for continual
learning of a single expanding task (aka class-incremental continual learning)
require extensive rehearsal of previously seen data to avoid this degradation
of knowledge. Unfortunately, rehearsal comes at a sharp cost to memory and
computation, and it may also violate data-privacy. Instead, we explore
combining knowledge distillation and parameter regularization in new ways to
achieve strong continual learning performance without rehearsal. Specifically,
we take a deep dive into common continual learning techniques: prediction
distillation, feature distillation, L2 parameter regularization, and EWC
parameter regularization. We first disprove the common assumption that
parameter regularization techniques fail for rehearsal-free continual learning
of a single, expanding task. Next, we explore how to leverage knowledge from a
pre-trained model in rehearsal-free continual learning and find that vanilla L2
parameter regularization outperforms EWC parameter regularization and feature
distillation. We then highlight the impact of the rehearsal-free continual
learning settings with a classifier expansion benchmark, showing that a
strategy based on our findings combined with a positive/negative label
balancing heuristic can close the performance gap between the upper bound and
the existing strategies by up to roughly 50%. Finally, we show that a simple
method consisting of pre-training, L2 regularization, and prediction
distillation can even outperform rehearsal-based methods on the common
CIFAR-100 benchmark.


title:
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera
  Images via Spatiotemporal Transformers

abstract:
  3D visual perception tasks, including 3D detection and map segmentation based
on multi-camera images, are essential for autonomous driving systems. In this
work, we present a new framework termed BEVFormer, which learns unified BEV
representations with spatiotemporal transformers to support multiple autonomous
driving perception tasks. In a nutshell, BEVFormer exploits both spatial and
temporal information by interacting with spatial and temporal space through
predefined grid-shaped BEV queries. To aggregate spatial information, we design
a spatial cross-attention that each BEV query extracts the spatial features
from the regions of interest across camera views. For temporal information, we
propose a temporal self-attention to recurrently fuse the history BEV
information. Our approach achieves the new state-of-the-art 56.9\% in terms of
NDS metric on the nuScenes test set, which is 9.0 points higher than previous
best arts and on par with the performance of LiDAR-based baselines. We further
show that BEVFormer remarkably improves the accuracy of velocity estimation and
recall of objects under low visibility conditions. The code will be released at
https://github.com/zhiqi-li/BEVFormer.


title: Do Vision-Language Pretrained Models Learn Primitive Concepts?

abstract:
  Vision-language pretrained models have achieved impressive performance on
multimodal reasoning and zero-shot recognition tasks. Many of these VL models
are pretrained on unlabeled image and caption pairs from the internet. In this
paper, we study whether the notion of primitive concepts, such as color and
shape attributes, emerges automatically from these pretrained VL models. We
propose to learn compositional derivations that map primitive concept
activations into composite concepts, a task which we demonstrate to be
straightforward given true primitive concept annotations. This compositional
derivation learning (CompDL) framework allows us to quantitively measure the
usefulness and interpretability of the learned derivations, by jointly
considering the entire set of candidate primitive concepts. Our study reveals
that state-of-the-art VL pretrained models learn primitive concepts that are
highly useful as visual descriptors, as demonstrated by their strong
performance on fine-grained visual recognition tasks, but those concepts
struggle to provide interpretable compositional derivations, which highlights
limitations of existing VL models. Code and models will be released.


title: MyStyle: A Personalized Generative Prior

abstract:
  We introduce MyStyle, a personalized deep generative prior trained with a few
shots of an individual. MyStyle allows to reconstruct, enhance and edit images
of a specific person, such that the output is faithful to the person's key
facial characteristics. Given a small reference set of portrait images of a
person (~100), we tune the weights of a pretrained StyleGAN face generator to
form a local, low-dimensional, personalized manifold in the latent space. We
show that this manifold constitutes a personalized region that spans latent
codes associated with diverse portrait images of the individual. Moreover, we
demonstrate that we obtain a personalized generative prior, and propose a
unified approach to apply it to various ill-posed image enhancement problems,
such as inpainting and super-resolution, as well as semantic editing. Using the
personalized generative prior we obtain outputs that exhibit high-fidelity to
the input images and are also faithful to the key facial characteristics of the
individual in the reference set. We demonstrate our method with fair-use images
of numerous widely recognizable individuals for whom we have the prior
knowledge for a qualitative evaluation of the expected outcome. We evaluate our
approach against few-shots baselines and show that our personalized prior,
quantitatively and qualitatively, outperforms state-of-the-art alternatives.


title: FindIt: Generalized Localization with Natural Language Queries

abstract:
  We propose FindIt, a simple and versatile framework that unifies a variety of
visual grounding and localization tasks including referring expression
comprehension, text-based localization, and object detection. Key to our
architecture is an efficient multi-scale fusion module that unifies the
disparate localization requirements across the tasks. In addition, we discover
that a standard object detector is surprisingly effective in unifying these
tasks without a need for task-specific design, losses, or pre-computed
detections. Our end-to-end trainable framework responds flexibly and accurately
to a wide range of referring expression, localization or detection queries for
zero, one, or multiple objects. Jointly trained on these tasks, FindIt
outperforms the state of the art on both referring expression and text-based
localization, and shows competitive performance on object detection. Finally,
FindIt generalizes better to out-of-distribution data and novel categories
compared to strong single-task baselines. All of these are accomplished by a
single, unified and efficient model. The code will be released.


title: Visual Prompting: Modifying Pixel Space to Adapt Pre-trained Models

abstract:
  Prompting has recently become a popular paradigm for adapting language models
to downstream tasks. Rather than fine-tuning model parameters or adding
task-specific heads, this approach steers a model to perform a new task simply
by adding a text prompt to the model's inputs. In this paper, we explore the
question: can we create prompts with pixels instead? In other words, can
pre-trained vision models be adapted to a new task solely by adding pixels to
their inputs? We introduce visual prompting, which learns a task-specific image
perturbation such that a frozen pre-trained model prompted with this
perturbation performs a new task. We discover that changing only a few pixels
is enough to adapt models to new tasks and datasets, and performs on par with
linear probing, the current de facto approach to lightweight adaptation. The
surprising effectiveness of visual prompting provides a new perspective on how
to adapt pre-trained models in vision, and opens up the possibility of adapting
models solely through their inputs, which, unlike model parameters or outputs,
are typically under an end-user's control. Code is available at
http://hjbahng.github.io/visual_prompting .


title:
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable
  Object Manipulations with Tools

abstract:
  We consider the problem of sequential robotic manipulation of deformable
objects using tools. Previous works have shown that differentiable physics
simulators provide gradients to the environment state and help trajectory
optimization to converge orders of magnitude faster than model-free
reinforcement learning algorithms for deformable object manipulation. However,
such gradient-based trajectory optimization typically requires access to the
full simulator states and can only solve short-horizon, single-skill tasks due
to local optima. In this work, we propose a novel framework, named DiffSkill,
that uses a differentiable physics simulator for skill abstraction to solve
long-horizon deformable object manipulation tasks from sensory observations. In
particular, we first obtain short-horizon skills using individual tools from a
gradient-based optimizer, using the full state information in a differentiable
simulator; we then learn a neural skill abstractor from the demonstration
trajectories which takes RGBD images as input. Finally, we plan over the skills
by finding the intermediate goals and then solve long-horizon tasks. We show
the advantages of our method in a new set of sequential deformable object
manipulation tasks compared to previous reinforcement learning algorithms and
compared to the trajectory optimizer.


title: Bringing Old Films Back to Life

abstract:
  We present a learning-based framework, recurrent transformer network (RTN),
to restore heavily degraded old films. Instead of performing frame-wise
restoration, our method is based on the hidden knowledge learned from adjacent
frames that contain abundant information about the occlusion, which is
beneficial to restore challenging artifacts of each frame while ensuring
temporal coherency. Moreover, contrasting the representation of the current
frame and the hidden knowledge makes it possible to infer the scratch position
in an unsupervised manner, and such defect localization generalizes well to
real-world degradations. To better resolve mixed degradation and compensate for
the flow estimation error during frame alignment, we propose to leverage more
expressive transformer blocks for spatial restoration. Experiments on both
synthetic dataset and real-world old films demonstrate the significant
superiority of the proposed RTN over existing solutions. In addition, the same
framework can effectively propagate the color from keyframes to the whole
video, ultimately yielding compelling restored films. The implementation and
model will be released at
https://github.com/raywzy/Bringing-Old-Films-Back-to-Life.


title: The Mu3e Experiment

abstract:
  The Mu3e experiment at the Paul Scherrer Institut will search for the
lepton-number-violating decay \mueee, extending the sensitivity by four orders
of magnitude compared to existing limits. This probe of new physics is
complementary to the existing collider, dark matter and neutrino particle
physics programmes, and part of a global programme investigating the charged
lepton flavour sector. As well as the main \mueee search, Mu3e will also extend
the sensitivity to low-mass dark photons, and additional flavour-violating
decays involving long-lived or stable particles.


title: Moosinesq Convection in the Cores of Moosive Stars

abstract:
  Stars with masses $\gtrsim 4 \times 10^{27}M_{\rm{moose}} \approx 1.1
M_\odot$ have core convection zones during their time on the main sequence. In
these moosive stars, convection introduces many uncertainties in stellar
modeling. In this Letter, we build upon the Boussinesq approximation to present
the first-ever simulations of Moosinesq convection, which captures the complex
geometric structure of the convection zones of these stars. These flows are
bounded in a manner informed by the majestic terrestrial Alces alces (moose)
and could have important consequences for the evolution of these stars. We find
that Moosinesq convection results in very interesting flow morphologies and
rapid heat transfer, and posit this as a mechanism of biomechanical
thermoregulation.


title: Ball 3D Localization From A Single Calibrated Image

abstract:
  Ball 3D localization in team sports has various applications including
automatic offside detection in soccer, or shot release localization in
basketball. Today, this task is either resolved by using expensive multi-views
setups, or by restricting the analysis to ballistic trajectories. In this work,
we propose to address the task on a single image from a calibrated monocular
camera by estimating ball diameter in pixels and use the knowledge of real ball
diameter in meters. This approach is suitable for any game situation where the
ball is (even partly) visible. To achieve this, we use a small neural network
trained on image patches around candidates generated by a conventional ball
detector. Besides predicting ball diameter, our network outputs the confidence
of having a ball in the image patch. Validations on 3 basketball datasets
reveals that our model gives remarkable predictions on ball 3D localization. In
addition, through its confidence output, our model improves the detection rate
by filtering the candidates produced by the detector. The contributions of this
work are (i) the first model to address 3D ball localization on a single image,
(ii) an effective method for ball 3D annotation from single calibrated images,
(iii) a high quality 3D ball evaluation dataset annotated from a single
viewpoint. In addition, the code to reproduce this research is be made freely
available at https://github.com/gabriel-vanzandycke/deepsport.


title: Reproducibility Issues for BERT-based Evaluation Metrics

abstract:
  Reproducibility is of utmost concern in machine learning and natural language
processing (NLP). In the field of natural language generation (especially
machine translation), the seminal paper of Post (2018) has pointed out problems
of reproducibility of the dominant metric, BLEU, at the time of publication.
Nowadays, BERT-based evaluation metrics considerably outperform BLEU. In this
paper, we ask whether results and claims from four recent BERT-based metrics
can be reproduced. We find that reproduction of claims and results often fails
because of (i) heavy undocumented preprocessing involved in the metrics, (ii)
missing code and (iii) reporting weaker results for the baseline metrics. (iv)
In one case, the problem stems from correlating not to human scores but to a
wrong column in the csv file, inflating scores by 5 points. Motivated by the
impact of preprocessing, we then conduct a second study where we examine its
effects more closely (for one of the metrics). We find that preprocessing can
have large effects, especially for highly inflectional languages. In this case,
the effect of preprocessing may be larger than the effect of the aggregation
mechanism (e.g., greedy alignment vs. Word Mover Distance).


title:
Graph-based Active Learning for Semi-supervised Classification of SAR
  Data

abstract:
  We present a novel method for classification of Synthetic Aperture Radar
(SAR) data by combining ideas from graph-based learning and neural network
methods within an active learning framework. Graph-based methods in machine
learning are based on a similarity graph constructed from the data. When the
data consists of raw images composed of scenes, extraneous information can make
the classification task more difficult. In recent years, neural network methods
have been shown to provide a promising framework for extracting patterns from
SAR images. These methods, however, require ample training data to avoid
overfitting. At the same time, such training data are often unavailable for
applications of interest, such as automatic target recognition (ATR) and SAR
data. We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to
embed SAR data into a feature space, and then construct a similarity graph from
the embedded data and apply graph-based semi-supervised learning techniques.
The CNNVAE feature embedding and graph construction requires no labeled data,
which reduces overfitting and improves the generalization performance of graph
learning at low label rates. Furthermore, the method easily incorporates a
human-in-the-loop for active learning in the data-labeling process. We present
promising results and compare them to other standard machine learning methods
on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset
for ATR with small amounts of labeled data.


title: Data Sampling Affects the Complexity of Online SGD over Dependent Data

abstract:
  Conventional machine learning applications typically assume that data samples
are independently and identically distributed (i.i.d.). However, practical
scenarios often involve a data-generating process that produces highly
dependent data samples, which are known to heavily bias the stochastic
optimization process and slow down the convergence of learning. In this paper,
we conduct a fundamental study on how different stochastic data sampling
schemes affect the sample complexity of online stochastic gradient descent
(SGD) over highly dependent data. Specifically, with a $\phi$-mixing model of
data dependence, we show that online SGD with proper periodic data-subsampling
achieves an improved sample complexity over the standard online SGD in the full
spectrum of the data dependence level. Interestingly, even subsampling a subset
of data samples can accelerate the convergence of online SGD over highly
dependent data. Moreover, we show that online SGD with mini-batch sampling can
further substantially improve the sample complexity over online SGD with
periodic data-subsampling over highly dependent data. Numerical experiments
validate our theoretical results.


title:
Low-fat diets and testosterone in men: Systematic review and
  meta-analysis of intervention studies

abstract:
  Background: Higher endogenous testosterone levels are associated with reduced
chronic disease risk and mortality. Since the mid-20th century, there have been
significant changes in dietary patterns, and men's testosterone levels have
declined in western countries. Cross-sectional studies show inconsistent
associations between fat intake and testosterone in men.
  Methods: Studies eligible for inclusion were intervention studies, with
minimal confounding variables, comparing the effect of low-fat vs high-fat
diets on men's sex hormones. 9 databases were searched from their inception to
October 2020, yielding 6 eligible studies, with a total of 206 participants.
Random effects meta-analyses were performed using Cochrane's Review Manager
software. Cochrane's risk of bias tool was used for quality assessment.
  Results: There were significant decreases in sex hormones on low-fat vs
high-fat diets. Standardised mean differences with 95% confidence intervals
(CI) for outcomes were: total testosterone [-0.38 (95% CI -0.75 to -0.01) P =
0.04]; free testosterone [-0.37 (95% CI -0.63 to -0.11) P = 0.005]; urinary
testosterone [-0.38 (CI 95% -0.66 to -0.09) P = 0.009], and dihydrotestosterone
[-0.3 (CI 95% -0.56 to -0.03) P = 0.03]. There were no significant differences
for luteinising hormone or sex hormone binding globulin. Subgroup analysis for
total testosterone, European and American men, showed a stronger effect [-0.52
(95% CI -0.75 to -0.3) P < 0.001].
  Conclusions: Low-fat diets appear to decrease testosterone levels in men, but
further randomised controlled trials are needed to confirm this effect. Men
with European ancestry may experience a greater decrease in testosterone, in
response to a low-fat diet.


title:
Improving Adversarial Transferability via Neuron Attribution-Based
  Attacks

abstract:
  Deep neural networks (DNNs) are known to be vulnerable to adversarial
examples. It is thus imperative to devise effective attack algorithms to
identify the deficiencies of DNNs beforehand in security-sensitive
applications. To efficiently tackle the black-box setting where the target
model's particulars are unknown, feature-level transfer-based attacks propose
to contaminate the intermediate feature outputs of local models, and then
directly employ the crafted adversarial samples to attack the target model. Due
to the transferability of features, feature-level attacks have shown promise in
synthesizing more transferable adversarial samples. However, existing
feature-level attacks generally employ inaccurate neuron importance
estimations, which deteriorates their transferability. To overcome such
pitfalls, in this paper, we propose the Neuron Attribution-based Attack (NAA),
which conducts feature-level attacks with more accurate neuron importance
estimations. Specifically, we first completely attribute a model's output to
each neuron in a middle layer. We then derive an approximation scheme of neuron
attribution to tremendously reduce the computation overhead. Finally, we weight
neurons based on their attribution results and launch feature-level attacks.
Extensive experiments confirm the superiority of our approach to the
state-of-the-art benchmarks.


title: On products of symmetries in von Neumann algebras

abstract:
  Let $\mathscr{R}$ be a type $II_1$ von Neumann algebra. We show that every
unitary in $\mathscr{R}$ may be decomposed as the product of six symmetries
(that is, self-adjoint unitaries) in $\mathscr{R}$, and every unitary in
$\mathscr{R}$ with finite spectrum may be decomposed as the product of four
symmetries in $\mathscr{R}$. Consequently, the set of products of four
symmetries in $\mathscr{R}$ is norm-dense in the unitary group of
$\mathscr{R}$. Furthermore, we show that the set of products of three
symmetries in a von Neumann algebra $\mathscr{M}$ is not norm-dense in the
unitary group of $\mathscr{M}$. This strengthens a result of Halmos-Kakutani
which asserts that the set of products of three symmetries in
$\mathcal{B}(\mathscr{H})$, the ring of bounded operators on a Hilbert space
$\mathscr{H}$, is not the full unitary group of $\mathcal{B}(\mathscr{H})$.


title: Transition edge sensor based detector: from X-ray to $\gamma$-ray

abstract:
  The Transition Edge Sensor is extremely sensitive to the change of
temperature, combined with the high-Z metal of a certain thickness, it can
realize the high energy resolution measurement of particles such as X-rays.
X-rays with energies below 10 keV have very weak penetrating ability, so only a
few microns thick of gold or bismuth can obtain quantum efficiency higher than
70\%. Therefore, the entire structure of the TES X-ray detector in this energy
range can be realized in the microfabrication process. However, for X-rays or
gamma rays from 10 keV to 200 keV, sub-millimeter absorber layers are required,
which cannot be realized by microfabrication process. This paper first briefly
introduces a set of TES X-ray detectors and their auxiliary systems built by
ShanghaiTech University, then focus on the introduction of the TES $\gamma$-ray
detector, with absorber based on an sub-millimeter lead-tin alloy sphere. The
detector has a quantum efficiency above 70\% near 100 keV, and an energy
resolution of about 161.5eV@59.5keV.


title: Leveraging Privacy Profiles to Empower Users in the Digital Society

abstract:
  Privacy and ethics of citizens are at the core of the concerns raised by our
increasingly digital society. Profiling users is standard practice for software
applications triggering the need for users, also enforced by laws, to properly
manage privacy settings. Users need to manage software privacy settings
properly to protect personally identifiable information and express personal
ethical preferences. AI technologies that empower users to interact with the
digital world by reflecting their personal ethical preferences can be key
enablers of a trustworthy digital society. We focus on the privacy dimension
and contribute a step in the above direction through an empirical study on an
existing dataset collected from the fitness domain. We find out which set of
questions is appropriate to differentiate users according to their preferences.
The results reveal that a compact set of semantic-driven questions (about
domain-independent privacy preferences) helps distinguish users better than a
complex domain-dependent one. This confirms the study's hypothesis that moral
attitudes are the relevant piece of information to collect. Based on the
outcome, we implement a recommender system to provide users with suitable
recommendations related to privacy choices. We then show that the proposed
recommender system provides relevant settings to users, obtaining high
accuracy.


title: Winding Tachyons and Stringy Black Holes

abstract:
  We study string theory on $\mathbb{R}^d\times \mathbb{S}^1$. For applications
to thermodynamics, the circumference of the $\mathbb{S}^1$ is the inverse
temperature, $\beta$. We show that for $d=6$, the low energy effective field
theory at the inverse Hagedorn temperature, $\beta=\beta_H$, has a one
parameter family of normalizable spherically symmetric solutions that break the
winding symmetry around the $\mathbb{S}^1$. The resulting backgrounds exhibit
an enhanced symmetry, with the symmetry breaking pattern $SU(2)_L\times
SU(2)_R\to SU(2)_{\rm diagonal}$. The effective field theory analysis of these
backgrounds is reliable for some range of parameters. More generally, they are
described by a worldsheet CFT, which corresponds to the free theory on
$\mathbb{R}^6\times \mathbb{S}^1$ perturbed by a non-abelian Thirring
deformation with an $r$-dependent coupling. We propose that, in a certain
scaling limit, string theory in these backgrounds is described by the
$SL(2,\mathbb{R})/U(1)$ cigar, and provides a thermodynamic description of
weakly coupled highly excited fundamental strings. We also discuss the relation
of these backgrounds to Euclidean black holes with near-Hagedorn Hawking
temperature, and possible generalizations to other $d$.


title:
Quantum spin liquids bootstrapped from Ising criticality in Rydberg
  arrays

abstract:
  Arrays of Rydberg atoms constitute a highly tunable, strongly interacting
venue for the pursuit of exotic states of matter. We develop a new strategy for
accessing a family of fractionalized phases known as quantum spin liquids in
two-dimensional Rydberg arrays. We specifically use effective field theory
methods to study arrays assembled from Rydberg chains tuned to an Ising phase
transition that famously hosts emergent fermions propagating within each chain.
This highly entangled starting point allows us to naturally access spin liquids
familiar from Kitaev's honeycomb model, albeit from an entirely different
framework. In particular, we argue that finite-range repulsive Rydberg
interactions, which frustrate nearby symmetry-breaking orders, can enable
coherent propagation of emergent fermions between the chains in which they were
born. Delocalization of emergent fermions across the full two-dimensional
Rydberg array yields a gapless Z2 spin liquid with a single massless Dirac
cone. Here, the Rydberg occupation numbers exhibit universal power-law
correlations that provide a straightforward experimental diagnostic of this
phase. We further show that explicitly breaking symmetries perturbs the gapless
spin liquid into gapped, topologically ordered descendants: Breaking lattice
symmetries generates toric-code topological order, whereas introducing
chirality generates non-Abelian Ising topological order. In the toric-code
phase, we analytically construct microscopic incarnations of non-Abelian
defects, which can be created and transported by dynamically controlling the
atom positions in the array. Our work suggests that appropriately tuned Rydberg
arrays provide a cold-atoms counterpart of solid-state 'Kitaev materials' and,
more generally, spotlights a new angle for pursuing experimental platforms for
Abelian and non-Abelian fractionalization.

categories: cond-mat.str-el cond-mat.quant-gas physics.atom-ph quant-ph


title: Non-integer conformal dimensions for type IIA flux vacua

abstract:
  We study the conformal dimensions of the would-be dual operators to the
stabilized moduli for two non-supersymmetric DGKT vacua. For the first of them,
related to the standard SUSY DGKT vacuum by $G_4=-G_4^{\text{SUSY}}$, we obtain
integer conformal dimensions. For the second of them, which has a non-zero
harmonic component in $G_2$, we obtain both integers and real numbers.


title: Measuring magic on a quantum processor

abstract:
  Magic states are the resource that allows quantum computers to attain an
advantage over classical computers. This resource consists in the deviation
from a property called stabilizerness which in turn implies that stabilizer
circuits can be efficiently simulated on a classical computer. Without magic,
no quantum computer can do anything that a classical computer cannot do. Given
the importance of magic for quantum computation, it would be useful to have a
method for measuring the amount of magic in a quantum state. In this work, we
propose and experimentally demonstrate a protocol for measuring magic based on
randomized measurements. Our experiments are carried out on two IBM Quantum
Falcon processors. This protocol can provide a characterization of the
effectiveness of a quantum hardware in producing states that cannot be
effectively simulated on a classical computer. We show how from these
measurements one can construct realistic noise models affecting the hardware.


title: The magneto-rotational instability in massive stars

abstract:
  The interaction between weak magnetic fields and rotation can lead to
instabilities that transport angular momentum (AM) and chemical elements
affecting the evolution of massive stars. We explore the effects of the
magneto-rotational instability (MRI) in massive stars to determine its impact
on stellar evolution. We use the GENEC code to simulate the evolution of a 15
$M_\odot$ model at solar metallicity up to the end of oxygen burning. The MRI
is computed with different trigger conditions, (depending on the weighting of
chemical gradients through an arbitrary but commonly used factor), and with
different treatments of meridional circulation as either advective or
diffusive. We also compare the MRI with the Tayler-Spruit (TS) dynamo, in
models that included both instabilities interacting linearly. The MRI triggers
throughout stellar evolution. Its activation is highly sensitive to the
treatment of meridional circulation and the existence of chemical gradients.
The MRI is very efficient at transporting both matter and AM, leading to
noticeable differences in rotation rates and chemical structure, which may be
observable in young main sequence stars. While the TS dynamo is the dominant
mechanism for transferring AM, the MRI remains relevant in models where both
instabilities are included. Extrapolation of our results suggests that models
including the MRI tend to develop more compact cores, which likely produce
failed explosions and black holes, than models where only the TS dynamo is
included (where explosions an neutron stars may be more frequent. The MRI is an
important factor in massive star evolution but is very sensitive to the
implementation ofother processes in the model. The transport of AM and chemical
elements due to the MRI alters the rotation rates and the chemical make-up of
the star from the core to the surface, and may change the explodability
properties of massive stars.


title:
Evaluating the feasibility of interpretable machine learning for
  globular cluster detection

abstract:
  Extragalactic globular clusters (GCs) are important tracers of galaxy
formation and evolution. Obtaining GC catalogues from photometric data involves
several steps which will likely become too time-consuming to perform on the
large data volumes that are expected from upcoming wide-field imaging projects
such as Euclid. In this work, we explore the feasibility of various machine
learning (ML) methods to aid the search for GCs. We use archival Hubble Space
Telescope data in the F475W and F850LP bands of 141 early-type galaxies in the
Fornax and Virgo galaxy clusters. Using existing GC catalogues to label the
data, we obtain an extensive data set of 84929 sources containing 18556 GCs and
we train several ML methods both on image and tabular data containing
physically relevant features extracted from the images. We find that our
evaluated ML models are capable of producing catalogues of similar quality as
the existing ones. The best performing methods, ensemble-based models like
random forests and convolutional neural networks, recover ~ 90-94 % of GCs
while producing an acceptable amount of false detections (~ 6-8 %) - with some
falsely detected sources being identifiable as GCs that have not been labelled
as such in the used catalogues. In the magnitude range 22 < m4_g < 24.5 mag, 98
- 99 % of GCs are recovered. We even find such high performance levels when
training on Virgo and evaluating on Fornax data (and vice versa), illustrating
that the models are transferable to environments with different conditions such
as different distances than in the used training data. Additionally, we
demonstrate how interpretable methods can be used to better understand model
predictions, recovering that magnitudes, colours, and sizes are important for
identifying GCs. These are encouraging results, indicating that similar methods
can be applied for creating GC catalogues for a large number of galaxies.


title: Chaos enhancement in large-spin chains

abstract:
  We study the chaotic properties of a large-spin XXZ chain with onsite
disorder and a small number of excitations above the fully polarized state. We
show that while the classical limit, which is reached for large spins, is
chaotic, enlarging the spin suppresses quantum chaos features. We attribute
this suppression to the occurrence of large and slow clusters of onsite
excitations, and propose a way to facilitate their fragmentation by introducing
additional decay channels. We numerically verify that the introduction of such
relaxation channels restores chaoticity for large spins, so that only three
excitations are required to achieve strong level repulsion and ergodic
eigenstates.


title: Kepler-1656b's Extreme Eccentricity: Signature of a Gentle Giant

abstract:
  Highly eccentric orbits are one of the major surprises of exoplanets relative
to the Solar System and indicate rich and tumultuous dynamical histories. One
system of particular interest is Kepler-1656, which hosts a sub-Jovian planet
with an eccentricity of 0.8. Sufficiently eccentric orbits will shrink in
semi-major axis due to tidal dissipation of orbital energy during periastron
passage. Here our goal was to assess whether Kepler-1656b is currently
undergoing such high-eccentricity migration, and to further understand the
system's origins and architecture. We confirm a second planet in the system
with $M_{\rm c}= 0.40 \pm 0.09M_{\rm jup}$ and P$_{\rm c}= 1919\pm 27\,$days.
We simulated the dynamical evolution of planet b in the presence of planet c
and find a variety of possible outcomes for the system, such as tidal migration
and engulfment. The system is consistent with an in situ dynamical origin of
planet b followed by subsequent Eccentric Kozai Lidov (EKL) perturbations that
excite Kepler-1656b's eccentricity gently, i.e. without initiating tidal
migration. Thus, despite its high eccentricity, we find no evidence that planet
b is or has migrated through the high-eccentricity channel. Finally, we predict
the outer orbit to be mutually inclined in a nearly perpendicular configuration
with respect to the inner planet orbit based on the outcomes of our
simulations, and make observable predictions for the inner planet's spin-orbit
angle. Our methodology can be applied to other eccentric or tidally locked
planets to constrain their origins, orbital configurations and properties of a
potential companion.


title:
Spectacular 240 kpc double-sided relativistic jets in a spiral-hosted
  narrow-line Seyfert 1 galaxy

abstract:
  Narrow-line Seyfert 1 (NLS1) galaxies are a peculiar subclass of active
galactic nuclei (AGN). They have demonstrated that the presence of relativistic
jets in an AGN is not strictly related to its radio-loudness, the black hole
mass, or the host galaxy type. Here we present a remarkable example of a
radio-quiet NLS1, 6dFGS gJ035432.8-134008 (J0354-1340). In our Karl G. Jansky
Very Large Array observations at 5.5 GHz the source shows a bright core with a
flat spectral index, and extended emission corresponding to very elongated
jets. These are the largest double-sided radio jets found to date in an NLS1,
with a deprojected linear size of almost 250 kpc. We also analysed
near-infrared and optical images obtained by the Magellan Baade and the
European Southern Observatory New Technology Telescope. By means of photometric
decomposition and colour maps, we determined that J0354-1340 is hosted by a
spiral/disk galaxy. Fully evolved relativistic jets have traditionally been
associated with high-mass elliptical galaxies hosting the most massive black
holes, but our results confirm that also less massive black holes in spiral
galaxies can launch and sustain powerful jets, implying that the launching of
the jets is governed by factors other than previously believed.


title: Looking for structure in the cobordism conjecture

abstract:
  The cobordism conjecture of the swampland program states that the bordism
group of quantum gravity must be trivial. We investigate this statement in
several directions, on both the mathematical and physical side. We consider the
Whitehead tower construction as a possible organising principle for the
topological structures entering the formulation of the conjecture. We discuss
why and how to include geometric structures in bordism groups, such as higher
U(1)-bundles with connection. The inclusion of magnetic defects is also
addressed in some detail. We further elaborate on how the conjecture could
predict Kaluza--Klein monopoles, and we study the gravity decoupling limit in
the cobordism conjecture, with a few observations on NSNS string backgrounds.
We end with comments in relation to T-duality, as well as the finiteness
conjecture.


title:
Hubble constant and nuclear equation of state from kilonova
  spectro-photometric light curves

abstract:
  The merger of two compact objects of which at least one is a neutron star is
signalled by transient electromagnetic emission in a kilonova. This event is
accompanied by gravitational waves and possibly other radiation messengers such
as neutrinos or cosmic rays. The electromagnetic emission arises from the
radioactive decay of heavy $r-$process elements synthesised in the material
ejected during and after the merger. In this paper we show that the analysis of
kilonovae light curves can provide cosmological distance measurements and
constrain the properties of the ejecta. In this respect, MAAT, the new Integral
Field Unit in the OSIRIS spectrograph on the $10.4$ m Gran Telescopio CANARIAS
(GTC), is well suited for the study of kilonovae by performing absolute
spectro-photometry over the entire $360-1000$ nm spectral range. Here, we study
the most representative cases regarding the scientific interest of kilonovae
from binary neutron stars, and evaluate the observational prospects and
performance of MAAT on the GTC to: a) study the impact of the equation of state
on the kilonovae light curves, and to what extent bounds on neutron star radii
or compactness deriving from their peak magnitudes can be identified and b)
measure the Hubble constant, $H_0$, with improved precision, up to $40\%$, when
both gravitational wave data and photometric light curves are used. In this
context we discuss how the equation of state, the viewing-angle, and the
distance affect the precision and estimated value of $H_0$.

categories: astro-ph.CO astro-ph.HE astro-ph.IM gr-qc


title: Field-Based Physical Inference From Peculiar Velocity Tracers

abstract:
  We present a Bayesian hierarchical modelling approach to reconstruct the
initial cosmic matter density field constrained by peculiar velocity
observations. As our approach features a model for the gravitational evolution
of dark matter to connect the initial conditions to late-time observations, it
reconstructs the final density and velocity fields as natural byproducts. We
implement this field-based physical inference approach by adapting the Bayesian
Origin Reconstruction from Galaxies (BORG) algorithm, which explores the
high-dimensional posterior through the use of Hamiltonian Monte Carlo sampling.
We test the self-consistency of the method using random sets of mock tracers,
and assess its accuracy in a more complex scenario where peculiar velocity
tracers are non-linearly evolved mock haloes. We find that our framework
self-consistently infers the initial conditions, density and velocity fields,
and shows some robustness to model mis-specification. As compared to the
state-of-the-art approach of constrained Gaussian random fields/Wiener
filtering, our method produces more accurate final density and velocity field
reconstructions. It also allows us to constrain the initial conditions by
peculiar velocity observations, complementing in this aspect previous
field-based approaches based on other cosmological observables.


title:
Darker and brighter branes, suppression and enhancement of photon
  production in a strongly coupled magnetized plasma

abstract:
  We extend our holographic analysis of the emission of photons by a strongly
coupled plasma subject to a very intense external magnetic field. We previously
showed that in our model, any photon produced by the plasma has to be in its
only polarization state parallel to the reaction plane. In this paper we
present the results and details of the numerical calculation for the
differential rate of emitted photons. One of our main results is that the
production of photons is increased by the introduction of non-vanishing
magnetic field with an intensity up to a value $B_{\vartheta}$, above which the
effect is reversed and said production becomes lower than the $B=0$ case. The
characteristic intensity $B_{\vartheta}$ depends on the propagation direction
and tends to zero as the photon momentum becomes aligned with the magnetic
field. Additionally, we also show that the magnetic field has the effect of
increasing the value of the elliptic flow, providing a possible explanation for
the excess measured in collision experiments. The holographic model is
constructed by means of a five-dimensional truncation of 10-dimensional type
IIB supergravity and includes a scalar field in addition to the constant
magnetic field.


title:
Stripped-envelope stars in different metallicity environments. II. Type
  I supernovae and compact remnants

abstract:
  Stripped-envelope stars can be observed as Wolf-Rayet (WR) stars, or as less
luminous hydrogen-poor stars with low mass loss rates and transparent winds.
Both types are potential progenitors of Type I core-collapse supernovae (SNe).
We use grids of core-collapse models obtained from helium stars at different
metallicities to study the effects of metallicity on the transients and
remnants these stars produce. We characterise the surface and core properties
of our core collapse models, and investigate their explodability employing
three criteria. In cases where explosions are predicted, we estimate the ejecta
mass, explosion energy, nickel mass and neutron star (NS) mass. Otherwise, we
predict the mass of the resulting black hole (BH). We construct a simplified
population model, and find that the properties SNe and compact objects depend
strongly on metallicity. Ejecta masses and explosion energies for Type Ic SNe
are best reproduced by models with Z=0.04 which exhibit strong winds during
core helium burning. This implies that either their mass loss rates are
underestimated, or that Type Ic SN progenitors experience mass loss through
other mechanisms before exploding. The distributions of ejecta masses,
explosion energies and nickel mass for Type Ib SNe are not well reproduced by
progenitor models with WR mass loss, but are better reproduced if we assume no
mass loss in progenitors with luminosities below the minimum WR star
luminosity. We find that Type Ic SNe become more common as metallicity
increases, and that the vast majority of progenitors of Type Ib SNe must be
transparent-wind stripped-envelope stars. We find several models with
pre-collapse CO-masses of up to $\sim 30 M_{\odot}$ may form $\sim 3 M_{\odot}$
BHs in fallback SNe. This may carry important consequences for our
understanding of SNe, binary BH and NS systems, X-ray binary systems and
gravitational wave transients.


title:
Constraining black-hole binary spin precession and nutation with
  sequential prior conditioning

abstract:
  We investigate the detectability of sub-dominant spin effects in merging
black-hole binaries using current gravitational-wave data. Using a
phenomenological model that separates the spin dynamics into precession
(azimuthal motion) and nutation (polar motion), we present constraints on the
resulting amplitudes and frequencies. We also explore current constraints on
the spin morphologies, indicating if binaries are trapped near spin-orbit
resonances. We dissect such weak effects from the signals using a sequential
prior conditioning approach, where parameters are progressively re-sampled from
their posterior distribution. This allows us to investigate whether the data
contain additional information beyond what is already provided by quantities
that are better measured, namely the masses and the effective spin. For the
current catalog of events, we find no significant measurements of weak spin
effects such as nutation and spin-orbit locking. We synthesize a source with a
high nutational amplitude and show that near-future detections will allow us to
place powerful constraints, hinting that we may be at the cusp of detecting
spin nutations in gravitational-wave data.


title: Soft gamma-ray polarimetry with COSI using maximum likelihood analysis

abstract:
  Measurements of the linear polarization of high-energy emission from pulsars,
accreting black holes, and gamma-ray bursts (GRBs) provide an opportunity for
constraining the emission mechanisms and geometries (e.g., of the accretion
disk, jet, magnetic field, etc.) in the sources. For photons in the soft (MeV)
gamma-ray band, Compton scattering is the most likely interaction to occur in
detectors. Compton telescopes detect multiple interactions from individual
incoming photons, allowing for scattering angles to be measured. After many
photons are detected from a source, the distribution of azimuthal angles
provides polarization information. While the standard method relies on binning
the photons to produce and fit an azimuthal scattering angle distribution,
improved polarization sensitivity is obtained by using additional information
to more accurately weight each event's contribution to the likelihood
statistic. In this chapter, we describe the Compton Spectrometer and Imager
(COSI) and its capabilities for polarization measurements. We also describe the
maximum likelihood technique, its application to COSI data analysis, and plans
for its future use.


title:
Gravitational Wave Signals from Finite Size Effects in Spinning Binary
  Inspirals Including Parity Violating Constituents

abstract:
  We generalize the world line EFT formalism to account for parity violating
finite size effects. Results are presented for potentials and radiating moments
of a binary inspiral for the parity conserving sector, and agreement is found
with, previous calculations. Furthermore, we generate new results in this
sector, calculating the current quadrupole moment induced by finite size
gravitomagnetic effects. We also present novel results for parity violating
sources, which might be due to beyond standard model physics, and show that
they generate GW signals with the unique signature that the current-moment
appears at 0.5PN order earlier relative to the mass-moment in the PN expansion.
Parity violation also induces a new type of potential, which is proportional to
the $\textbf{S}\cdot \textbf{r}$. Finally, we present new results for the
dissipative force for parity violating constituents, which leads to the curious
signature of a force normal to the orbit.


title:
Spin chain on a metallic surface: Dissipation-induced order vs. Kondo
  entanglement

abstract:
  We explore the physics of a spin-1/2 Heisenberg chain with Kondo interaction,
$J_k$, to a two-dimensional electron gas. At weak $J_k$ the problem maps onto a
Heisenberg chain locally coupled to a dissipative Ohmic bath. At the decoupled
fixed point, the dissipation is a marginally relevant perturbation and drives
long-range antiferromagnetic order along the chain. In the dynamical spin
structure factor we observe a quadratic low-energy dispersion akin to
Landau-damped Goldstone modes. At large $J_k$ Kondo screening dominates, and
the spin correlations of the chain inherit the power law of the host metal,
akin to a paramagnetic heavy Fermi liquid. In both phases we observe heavy
bands near the Fermi energy in the composite-fermion spectral function. Our
results, obtained from auxiliary-field quantum Monte Carlo simulations, provide
a unique negative-sign-free realization of a quantum transition between an
antiferromagnetic metal and a heavy-fermion metal. We discuss the relevance of
our results in the context of scanning tunneling spectroscopy experiments of
magnetic adatom chains on metallic surfaces.


title: A misfired outburst in the neutron star X-ray binary Centaurus X-4

abstract:
  We report on a long-term optical monitoring of the neutron star X-ray binary
Centaurus X-4 performed during the last 13.5 years. This source has been in
quiescence since its outburst in 1979. Our monitoring reveals the overall
evolution of the accretion disc; we detect short-duration flares, likely
originating also in the disc, superimposed with a small-amplitude (< 0.1 mag)
ellipsoidal modulation from the companion star due to geometrical effects. A
long-term (~2300 days) downward trend, followed by a shorter (~1000 days)
upward one, is observed in the disc light curve. Such a rise in the optical has
been observed for other X-ray binaries preceding outbursts, as predicted by the
disc instability model. For Cen X-4, the rise of the optical flux proceeded for
~3 years, and culminated in a flux increase at all wavelengths
(optical-UV-X-rays) at the end of 2020. This increase faded after ~2 weeks,
without giving rise to a full outburst. We suggest that the propagation of an
inside-out heating front was ignited due to a partial ionization of hydrogen in
the inner disc. The propagation might have stalled soon after the ignition due
to the increasing surface density in the disc that the front encountered while
propagating outwards. The stall was likely eased by the low level irradiation
of the outer regions of the large accretion disc, as shown by the slope of the
optical/X-ray correlation, suggesting that irradiation does not play a strong
role in the optical, compared to other sources of emission.


title: Conformal Dirac-Einstein equations on manifolds with boundary

abstract:
  In this paper we study Dirac-Einstein equations on manifolds with boundary,
restricted to a conformal class with constant boundary volume, under chiral bag
boundary conditions for the Dirac operator. We characterize the bubbling
phenomenon, also classifying ground state bubbles. Finally, we prove an
Aubin-type inequality and a related existence result.


title: Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets

abstract:
  We introduce a new class of attacks on machine learning models. We show that
an adversary who can poison a training dataset can cause models trained on this
dataset to leak significant private details of training points belonging to
other parties. Our active inference attacks connect two independent lines of
work targeting the integrity and privacy of machine learning training data.
  Our attacks are effective across membership inference, attribute inference,
and data extraction. For example, our targeted attacks can poison <0.1% of the
training dataset to boost the performance of inference attacks by 1 to 2 orders
of magnitude. Further, an adversary who controls a significant fraction of the
training data (e.g., 50%) can launch untargeted attacks that enable 8x more
precise inference on all other users' otherwise-private data points.
  Our results cast doubts on the relevance of cryptographic privacy guarantees
in multiparty computation protocols for machine learning, if parties can
arbitrarily select their share of training data.


title: Self-interacting dark baryons

abstract:
  Using results from lattice QCD, it is possible to quantitatively design
models of dark baryons leading to velocity-dependent self-interaction cross
sections that match the values needed for solving small-scale structure
problems of standard cold dark matter. However it is not obvious that the main
dark matter component in such models will be nucleons rather than large nuclei,
or dark pions or atoms, whose scattering properties would be different. We
first identify the parameters of a dark SU(3) sector analogous to QCD -- the
confinement scale $\Lambda$ and pion mass $m_\pi$ -- needed to reproduce
desired self-interaction cross sections. Then we show that these values can
generically be compatible with the absence of a sufficiently stable deuteron
bound state, and hence leading to no heavier nuclei, thus establishing the
consistency of the scenario for self-interacting dark nucleons. The range of
dark photon masses needed to avoid dominant pion or atomic dark matter is
determined, as well as allowed values for the kinetic mixing parameter. The
dark proton might be detected directly in future searches, by dark photon
exchange.


title: Parallel Proof-of-Work with Concrete Bounds

abstract:
  Authorization is challenging in distributed systems that cannot rely on the
identification of nodes. Proof-of-work offers an alternative gate-keeping
mechanism, but its probabilistic nature is incompatible with conventional
security definitions. Recent related work establishes concrete bounds for the
failure probability of Bitcoin's sequential proof-of-work mechanism. We propose
a family of state replication protocols using parallel proof-of-work. Our
bottom-up design from an agreement sub-protocol allows us to give concrete
bounds for the failure probability in adversarial synchronous networks. After
the typical interval of 10 minutes, parallel proof-of-work offers two orders of
magnitude more security than sequential proof-of-work. This means that state
updates can be sufficiently secure to support commits after one block (i.e.,
after 10 minutes), removing the risk of double-spending in many applications.
We offer guidance on the optimal choice of parameters for a wide range of
network and attacker assumptions. Simulations show that the proposed
construction is robust against violations of design assumptions.


title: Curiosity Driven Self-supervised Tactile Exploration of Unknown Objects

abstract:
  Intricate behaviors an organism can exhibit is predicated on its ability to
sense and effectively interpret complexities of its surroundings. Relevant
information is often distributed between multiple modalities, and requires the
organism to exhibit information assimilation capabilities in addition to
information seeking behaviors. While biological beings leverage multiple
sensing modalities for decision making, current robots are overly reliant on
visual inputs. In this work, we want to augment our robots with the ability to
leverage the (relatively under-explored) modality of touch. To focus our
investigation, we study the problem of scene reconstruction where touch is the
only available sensing modality. We present Tactile Slam (tSLAM) -- which
prepares an agent to acquire information seeking behavior and use implicit
understanding of common household items to reconstruct the geometric details of
the object under exploration. Using the anthropomorphic `ADROIT' hand, we
demonstrate that tSLAM is highly effective in reconstructing objects of varying
complexities within 6 seconds of interactions. We also established the
generality of tSLAM by training only on 3D Warehouse objects and testing on
ContactDB objects.


title:
A Statistical Decision-Theoretical Perspective on the Two-Stage Approach
  to Parameter Estimation

abstract:
  One of the most important problems in system identification and statistics is
how to estimate the unknown parameters of a given model. Optimization methods
and specialized procedures, such as Empirical Minimization (EM) can be used in
case the likelihood function can be computed. For situations where one can only
simulate from a parametric model, but the likelihood is difficult or impossible
to evaluate, a technique known as the Two-Stage (TS) Approach can be applied to
obtain reliable parametric estimates. Unfortunately, there is currently a lack
of theoretical justification for TS. In this paper, we propose a statistical
decision-theoretical derivation of TS, which leads to Bayesian and Minimax
estimators. We also show how to apply the TS approach on models for independent
and identically distributed samples, by computing quantiles of the data as a
first step, and using a linear function as the second stage. The proposed
method is illustrated via numerical simulations.


title: Higher-order interactions promote chimera states

abstract:
  Since the discovery of chimera states, the presence of a nonzero phase lag
parameter turns out to be an essential attribute for the emergence of chimeras
in a nonlocally coupled identical Kuramoto phase oscillators' network with
pairwise interactions. In this letter, we report the emergence of chimeras
without phase lag in nonlocally coupled identical Kuramoto network owing to the
introduction of non-pairwise interactions. The influence of added nonlinearity
in the coupled system dynamics in the form of simplicial complexes mitigates
the requisite of a nonzero phase lag parameter for the emergence of chimera
states. Chimera states stimulated by the reciprocity of the pairwise and
non-pairwise interaction strengths and their multistable nature are
characterized with appropriate measures and are demonstrated in the parameter
spaces.


title:
Optimal Resource Scheduling and Allocation under Allowable
  Over-Scheduling

abstract:
  This paper studies optimal scheduling and resource allocation under allowable
over-scheduling. Formulating an optimisation problem where over-scheduling is
embedded, we derive an optimal solution that can be implemented by means of a
new additive increase multiplicative decrease (AIMD) algorithm. After
describing the AIMD-like scheduling mechanism as a switching system, we show
convergence of the scheme, based on the joint spectral radius of symmetric
matrices, and propose two methods for fitting an optimal AIMD tuning to the
optimal solution derived. Finally, we demonstrate the overall optimal design
strategy via an illustrative example.


title: Lattice QCD and the Computational Frontier

abstract:
  The search for new physics requires a joint experimental and theoretical
effort. Lattice QCD is already an essential tool for obtaining precise
model-free theoretical predictions of the hadronic processes underlying many
key experimental searches, such as those involving heavy flavor physics, the
anomalous magnetic moment of the muon, nucleon-neutrino scattering, and rare,
second-order electroweak processes. As experimental measurements become more
precise over the next decade, lattice QCD will play an increasing role in
providing the needed matching theoretical precision. Achieving the needed
precision requires simulations with lattices with substantially increased
resolution. As we push to finer lattice spacing we encounter an array of new
challenges. They include algorithmic and software-engineering challenges,
challenges in computer technology and design, and challenges in maintaining the
necessary human resources. In this white paper we describe those challenges and
discuss ways they are being dealt with. Overcoming them is key to supporting
the community effort required to deliver the needed theoretical support for
experiments in the coming decade.


title:
Integrating Biological Knowledge in Kernel-Based Analyses of
  Environmental Mixtures and Health

abstract:
  A key goal of environmental health research is to assess the risk posed by
mixtures of pollutants. As epidemiologic studies of mixtures can be expensive
to conduct, it behooves researchers to incorporate prior knowledge about
mixtures into their analyses. This work extends the Bayesian multiple index
model (BMIM), which assumes the exposure-response function is a non-parametric
function of a set of linear combinations of pollutants formed with a set of
exposure-specific weights. The framework is attractive because it combines the
flexibility of response-surface methods with the interpretability of linear
index models. We propose three strategies to incorporate prior toxicological
knowledge into construction of indices in a BMIM: (a) constraining index
weights, (b) structuring index weights by exposure transformations, and (c)
placing informative priors on the index weights. We propose a novel prior
specification that combines spike-and-slab variable selection with informative
Dirichlet distribution based on relative potency factors often derived from
previous toxicological studies. In simulations we show that the proposed priors
improve inferences when prior information is correct and can protect against
misspecification suffered by naive toxicological models when prior information
is incorrect. Moreover, different strategies may be mixed-and-matched for
different indices to suit available information (or lack thereof). We
demonstrate the proposed methods on an analysis of data from the National
Health and Nutrition Examination Survey and incorporate prior information on
relative chemical potencies obtained from toxic equivalency factors available
in the literature.