Papers made digestable

2023-02-02

- Detailed ablations also reveal the mechanism of our proposal
- It brings meaningful new challenges to the community
- Codes, data, and models are available at https://github.com/ucaszyp/STEPS.

Authors: Yupeng Zheng, Chengliang Zhong, Pengfei Li, Huan-ang Gao, Yuhang Zheng, Bu Jin, Ling Wang, Hao Zhao, Guyue Zhou, Qichao Zhang, Dongbin Zhao.

2023-02-02

- We establish strong PAC and regret lower bounds for learning in revealing POMDPs
- Technically, our hard instance construction adapts techniques in \emph{distribution testing}, which is new to the RL literature and may be of independent interest.

Authors: Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai.

2023-02-02

- We propose the first Bayesian encoder for metric learning
- We actualize this by first proving that the contrastive loss is a valid log-posterior
- We then propose three methods that ensure a positive definite Hessian
- Lastly, we present a novel decomposition of the Generalized Gauss-Newton approximation.

Authors: Frederik Warburg, Marco Miani, Silas Brack, Soren Hauberg.

2023-02-02

- We present evidence for a suppressed growth rate of large-scale structure during the dark-energy dominated era
- When combined, they yield $\gamma=0.633^{+0.025}_{-0.024}$, excluding $\gamma=0.55$ at a statistical significance of 3.7$\sigma$.

Authors: Nhat-Minh Nguyen, Dragan Huterer, Yuewei Wen.

2023-02-02

- Our framework is learned from in-the-wild 2D image collections only, without any 3D annotations
- The height field represents the surface elevation of 3D scenes, while the semantic field provides detailed scene semantics
- Lastly, a neural volumetric renderer, learned from 2D image collections through adversarial training, is employed to produce photorealistic images
- Extensive experiments demonstrate the effectiveness of SceneDreamer and superiority over state-of-the-art methods in generating vivid yet diverse unbounded 3D worlds.

Authors: Zhaoxi Chen, Guangcong Wang, Ziwei Liu.

2023-02-02

- Here we consider the application of a square optical lattice to such a system
- The 4-fold symmetry of the lattice, and the spacing set by it, competes with the intrinsic 6-fold symmetry and spacing of the dipolar droplets in the unperturbed ground state.

Authors: Eli J. Halperin, Shai Ronen, J. L. Bohn.

2023-02-02

- We study the universal coding under side-channel attacks posed and investigated by Santoso and Oohama (2021).

We study the universal coding under side-channel attacks posed and
investigated by Santoso and Oohama (2021). They proposed a theoretical security
model for Shannon cipher system under side-channel attacks, where the adversary
is not only allowed to collect ciphertexts by eavesdropping the public
communication channel, but is also allowed to collect the physical information
leaked by the devices where the cipher system is implemented on such as running
time, power consumption, electromagnetic radiation, etc. For any distributions
of the plain text, any noisy channels through which the adversary observe the
corrupted version of the key, and any measurement device used for collecting
the physical information, we can derive an achievable rate region for
reliability and security such that if we compress the ciphertext using an
affine encoder with rate within the achievable rate region, then: (1) anyone
with secret key will be able to decrypt and decode the ciphertext correctly,
but (2) any adversary who obtains the ciphertext and also the side physical
information will not be able to obtain any information about the hidden source
as long as the leaked physical information is encoded with a rate within the
rate region.

Authors: Yasutada Oohama, Bagus Santoso.

2023-02-02

- This work provides a formalization of Knowledge Graphs (KGs) as a new class of graphs that we denote doubly exchangeable attributed graphs, where node and pairwise (joint 2-node) representations must be equivariant to permutations of both node ids and edge (& node) attributes (relations & node features)
- Double-permutation equivariant KG representations open a new research direction in KGs.

Authors: Jianfei Gao, Yangze Zhou, Bruno Ribeiro.

2023-02-02

- The ensembles are created via sets of fixed dropout masks, making them less expensive than creating separate NF models
- In these experiments, we setup an active learning framework and evaluate each model's capability at measuring aleatoric and epistemic uncertainty.

In this work, we demonstrate how to reliably estimate epistemic uncertainty
while maintaining the flexibility needed to capture complicated aleatoric
distributions. To this end, we propose an ensemble of Normalizing Flows (NF),
which are state-of-the-art in modeling aleatoric uncertainty. The ensembles are
created via sets of fixed dropout masks, making them less expensive than
creating separate NF models. We demonstrate how to leverage the unique
structure of NFs, base distributions, to estimate aleatoric uncertainty without
relying on samples, provide a comprehensive set of baselines, and derive
unbiased estimates for differential entropy. The methods were applied to a
variety of experiments, commonly used to benchmark aleatoric and epistemic
uncertainty estimation: 1D sinusoidal data, 2D windy grid-world ($\it{Wet
Chicken}$), $\it{Pendulum}$, and $\it{Hopper}$. In these experiments, we setup
an active learning framework and evaluate each model's capability at measuring
aleatoric and epistemic uncertainty. The results show the advantages of using
NF ensembles in capturing complicated aleatoric while maintaining accurate
epistemic uncertainty estimates.

Authors: Lucas Berry, David Meger.

2023-02-02

- These structures are captured close to any annulus provided that its modulus belongs to a massive Borel set
- Compared to the scalar case, some technical issues emerge due to the interaction between the interfaces.

Authors: Zineb Hassainia, Taoufik Hmidi, Emeric Roulley.

2023-02-02

- In some cases, it is possible to evaluate the objectives separately, and a different latency or evaluation cost can be associated with each objective
- We prove consistency of the algorithm and show empirically that it significantly outperforms a benchmark algorithm which always evaluates both objectives.

Multi-objective Bayesian optimization aims to find the Pareto front of
optimal trade-offs between a set of expensive objectives while collecting as
few samples as possible. In some cases, it is possible to evaluate the
objectives separately, and a different latency or evaluation cost can be
associated with each objective. This presents an opportunity to learn the
Pareto front faster by evaluating the cheaper objectives more frequently. We
propose a scalarization based knowledge gradient acquisition function which
accounts for the different evaluation costs of the objectives. We prove
consistency of the algorithm and show empirically that it significantly
outperforms a benchmark algorithm which always evaluates both objectives.

Authors: Jack M. Buckingham, Sebastian Rojas Gonzalez, Juergen Branke.

2023-02-02

- After quantifying the performance by detecting short-circuit faults in a multi-layer silicon die, we assess how a QDM would detect faults in a heterogeneously integrated (HI) die stack
- This work establishes QDM-based magnetic imaging as a competitive technique for electronics FA, offering high spatial resolution, high sensitivity, and robust instrumentation
- We anticipate these advantages to be especially useful for finding faults deep within chip-stack ICs with many metal layers, optically-opaque layers, or optically-scattering layers.

Authors: P. Kehayias, J. Walraven, A. L. Rodarte, A. M. Mounce.

2023-02-02

- Understanding the extent to which the perceptual world can be recovered from language is a fundamental problem in cognitive science
- We also explore meaningful divergences between LLM and human representations.

Understanding the extent to which the perceptual world can be recovered from
language is a fundamental problem in cognitive science. We reformulate this
problem as that of distilling psychophysical information from text and show how
this can be done by combining large language models (LLMs) with a classic
psychophysical method based on similarity judgments. Specifically, we use the
prompt auto-completion functionality of GPT3, a state-of-the-art LLM, to elicit
similarity scores between stimuli and then apply multidimensional scaling to
uncover their underlying psychological space. We test our approach on six
perceptual domains and show that the elicited judgments strongly correlate with
human data and successfully recover well-known psychophysical structures such
as the color wheel and pitch spiral. We also explore meaningful divergences
between LLM and human representations. Our work showcases how combining
state-of-the-art machine models with well-known cognitive paradigms can shed
new light on fundamental questions in perception and language research.

Authors: Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Thomas L. Griffiths.

2023-02-02

- Many disc galaxies host galactic bars, which exert time-dependent, non-axisymmetric forces that can alter the orbits of stars
- We find that the bar induces both azimuth angle- and radius-dependent trends in the median distance that stars have travelled to enter a given annulus
- In the inner zone, stars generally originated at larger radii and their orbits evolved inwards
- Stars in the outer zone likely originated at smaller radii and their orbits evolved outwards
- In the intermediate zone, there is no net inwards or outwards evolution of orbits.

Authors: Carrie Filion, Rachel L. McClure, Martin D. Weinberg, Elena D'Onghia, Kathryne J. Daniel.

2023-02-02

- Denote the Grundy and {\rm b}-chromatic number of $G$ by $\Gamma(G)$ and ${\rm b}(G)$, respectively
- The $z$-coloring is an improvement over both the Grundy and b-coloring of graphs
- We show that acyclic graphs are $z$-monotonic and $z$-continuous.

By a $z$-coloring of a graph $G$ we mean any proper vertex coloring
consisting of the color classes $C_1, \ldots, C_k$ such that $(i)$ for any two
colors $i$ and $j$ with $1 \leq i < j \leq k$, any vertex of color $j$ is
adjacent to a vertex of color $i$, $(ii)$ there exists a set $\{u_1, \ldots,
u_k\}$ of vertices of $G$ such that $u_j \in C_j$ for any $j \in \{1, \ldots,
k\}$ and $u_k$ is adjacent to $u_j$ for each $1 \leq j \leq k$ with $j \not=k$,
and $(iii)$ for each $i$ and $j$ with $i \not= j$, the vertex $u_j$ has a
neighbor in $C_i$. Denote by $z(G)$ the maximum number of colors used in any
$z$-coloring of $G$. Denote the Grundy and {\rm b}-chromatic number of $G$ by
$\Gamma(G)$ and ${\rm b}(G)$, respectively. The $z$-coloring is an improvement
over both the Grundy and b-coloring of graphs. We prove that $z(G)$ is much
better than $\min\{\Gamma(G), {\rm b}(G)\}$ for infinitely many graphs $G$ by
obtaining an infinite sequence $\{G_n\}_{n=3}^{\infty}$ of graphs such that
$z(G_n)=n$ but $\Gamma(G_n)={\rm b}(G_n)=2n-1$ for each $n\geq 3$. We show that
acyclic graphs are $z$-monotonic and $z$-continuous. Then it is proved that to
decide whether $z(G)=\Delta(G)+1$ is $NP$-complete even for bipartite graphs
$G$. We finally prove that to recognize graphs $G$ satisfying $z(G)=\chi(G)$ is
$coNP$-complete, improving a previous result for the Grundy number.

Authors: Abbas Khaleghi, Manouchehr Zaker.