Miracle-dz/UniFAR-FTUs huggingface dataset:
Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification

Learning from set-structured data, such as point clouds, has gained significant attention from the community. Geometric deep learning provides a blueprint for designing effective set neural networks by incorporating permutation symmetry. Of our interest are permutation invariant networks, which are composed of a permutation equivariant backbone, permutation invariant global pooling, and regression/classification head. While existing literature has focused on improving permutation equivariant backbones, the impact of global pooling is often overlooked. In this paper, we examine the interplay between permutation equivariant backbones and permutation invariant global pooling on three benchmark point cloud classification datasets. Our findings reveal that: 1) complex pooling methods, such as transport-based or attention-based poolings, can significantly boost the performance of simple backbones, but the benefits diminish for more complex backbones, 2) even complex backbones can benefit from pooling layers in low data scenarios, 3) surprisingly, the choice of pooling layers can have a more significant impact on the model's performance than adjusting the width and depth of the backbone, and 4) pairwise combination of pooling layers can significantly improve the performance of a fixed backbone. Our comprehensive study provides insights for practitioners to design better permutation invariant set neural networks.
A Weak Blind Signature Based on Quantum Key Distribution

Recently, the laws of quantum physics have amazed classical cryptography and aided researchers to provide secure communications in presence of adversaries. In this paper we present a novel weak blind signature scheme whose security is guaranteed by fundamental principles of quantum physics. Despite previous schemes which are taking advantage of quantum entangled states, our proposed quantum blind signature relies only on Quantum Key Distribution (QKD) protocol. We show throughout this paper that our proposed quantum weak blind signature achieves a good position in security and reliability. In addition, it is feasible for the proposed scheme to become commercialized with current technology. Hence, it can be widely used for e-payment, e-government, e-business and etc.
A Guided Tour Study of the Untidy but Inspirational PIM of Visual Artists

While all individuals deal with increasingly large amounts of digital information in their everyday lives and professionally, prior works suggest visual artists have unique information management practices and challenges. This study therefore examined the personal information management (PIM) practices and challenges of six practising visual artists using guided tours and short interviews. It was found that the visual artists had some unique practices connected to their strong emphasis on serendipity, inspiration, and visual dimensions of information. Like non‐artists, the participants faced challenges across all phases of PIM, chiefly an excess of information and fragmented organisation, and they found it especially hard to assess how personal and valuable their information could be. After characterising this rarely discussed PIM demographic, we draw on the findings to provide concrete recommendations for artists doing PIM, for information and cultural heritage institutions, and for designers of PIM software.
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions

Learning in MDPs with highly complex state representations is currently possible due to multiple advancements in reinforcement learning algorithm design. However, this incline in complexity, and furthermore the increase in the dimensions of the observation came at the cost of volatility that can be taken advantage of via adversarial attacks (i.e. moving along worst-case directions in the observation space). To solve this policy instability problem we propose a novel method to detect the presence of these non-robust directions via local quadratic approximation of the deep neural policy loss. Our method provides a theoretical basis for the fundamental cut-off between safe observations and adversarial observations. Furthermore, our technique is computationally efficient, and does not depend on the methods used to produce the worst-case directions. We conduct extensive experiments in the Arcade Learning Environment with several different adversarial attack techniques. Most significantly, we demonstrate the effectiveness of our approach even in the setting where non-robust directions are explicitly optimized to circumvent our proposed method.
Quartile-Based Seasonality Decomposition for Time Series Forecasting and Anomaly Detection

The timely detection of anomalies is essential in the telecom domain as it facilitates the identification and characterization of irregular patterns, abnormal behaviors, and network anomalies, contributing to enhanced service quality and operational efficiency. Precisely forecasting and eliminating predictable time series patterns constitutes a vital component of time series anomaly detection. While the state-of-the-art methods aim to maximize forecasting accuracy, the computational performance takes a hit. In a system composed of a large number of time series variables, e.g., cell Key Performance Indicators (KPIs), the time and space complexity of the forecasting employed is of crucial importance. Quartile-Based Seasonality Decomposition (QBSD) is a live forecasting method proposed in this paper to make an optimal trade-off between computational complexity and forecasting accuracy. This paper compares the performance of QBSD to the state-of-the-art forecasting methods and their applicability to practical anomaly detection. To demonstrate the efficacy of the proposed solution, experimental evaluation was conducted using publicly available datasets as well as a telecom KPI dataset.
Closed quantum surfaces from the Toeplitz extension

Closed quantum surfaces of any genus are defined as subalgebras of the Toeplitz algebra by mimicking the classical construction of identifying arcs on the boundary of the (quantum) unit disk. Isomorphism classes obtained from different arrangements of arcs are classified. It is shown that the K-groups are isomorphic to the classical counterparts and explicit generators of the C*-algebras and of the K-groups are given.
Branching via Cutting Plane Selection: Improving Hybrid Branching

Cutting planes and branching are two of the most important algorithms for solving mixed-integer linear programs. For both algorithms, disjunctions play an important role, being used both as branching candidates and as the foundation for some cutting planes. We relate branching decisions and cutting planes to each other through the underlying disjunctions that they are based on, with a focus on Gomory mixed-integer cuts and their corresponding split disjunctions. We show that selecting branching decisions based on quality measures of Gomory mixed-integer cuts leads to relatively small branch-and-bound trees, and that the result improves when using cuts that more accurately represent the branching decisions. Finally, we show how the history of previously computed Gomory mixed-integer cuts can be used to improve the performance of the state-of-the-art hybrid branching rule of SCIP. Our results show a 4% decrease in solve time, and an 8% decrease in number of nodes over affected instances of MIPLIB 2017.
Turbulent boundary layer with strong favorable pressure gradient and curvature effects: Streamline coordinate and scaling analysis

Direct numerical simulation (DNS) of a turbulent boundary layer over the Gaussian (Boeing) bump is performed. This boundary layer exhibits a series of adverse and favorable pressure gradients and convex and concave curvature effects before separating. These effects on turbulent boundary layers are characterized and compared to a lower Reynolds number flow over the same geometry. The momentum budgets are analyzed in the streamline-aligned coordinate system upstream of the separation region. These momentum budgets allow the simplification of equations to facilitate an integral analysis. Integral analysis-based scalings for Reynolds stresses in the inner and outer regions of the boundary layer are also formulated. These proposed scalings exhibit a better collapse of Reynolds stress profiles compared to friction velocity scaling and Zagarola-Smits scaling in the strong favorable pressure gradient region and in the mild adverse pressure region that precedes it in this flow.
Strain-stiffening elastomers fail from the edge

The accurate measurement of fracture resistance in elastomers is essential for predicting the mechanical limits of soft devices. Usually, this is achieved by performing tearing or peeling experiments on thin-sheet samples. Here, we show that these tests can be surprisingly thickness-dependent, with thicker samples being significantly stronger than thinner ones. Even for a simple geometry, direct imaging of the fracture surface shows that the fracture process actually involves three distinct cracks: an inner crack, and two edge cracks. Ultimately, samples fail when two edge cracks meet at the sample's mid-plane. The opening angle of edge crack, $2 \theta$, determines how far the sample has to be stretched before the edge cracks meet. Conveniently, $\theta$ is a material property that can be inferred from the elastomer's non-linear elastic response. To yield thickness-independent fracture-test results, sample thickness should be much smaller than the smallest lateral sample dimension divided by $\tan \theta$. Our results have direct implications for characterizing, understanding, and modelling fracture in soft elastomers.
Dormant entanglement that can be activated or destroyed by the basis choice of measurements on an external system

We propose a new form of entanglement called the dormant entanglement that can be activated or destroyed by the basis choice of measurements on an external system. The dormant entanglement without activation cannot be used as a quantum communication resource and has reduced correlation as compared to the Bell states. A particular form of the dormant entanglement is so weak that, without activation, no correlation in any basis can be observed between the entangled qubits. The dormant entanglement showcases a unique quantum behavior that the physical description of a local system remains incomplete until the information on all external systems entangled with the local system becomes available. For a potential application, we propose an n-party collective quantum communication channel that allows any 2 out of the n parties to activate an entanglement pair with the complete consensus of all other parties.
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://zenodo.org/record/6525446, baseline models and code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description at https://aircraftverse.onrender. com/.
Hidden Hydroxides in KOH-Grown BaNiO3 Crystals: A Potential Link to Their Catalytic Behavior

The hexagonal perovskite BaNiO3, prepared via non-ceramic approaches, is known to act as a good catalyst for the oxygen-evolution reaction (OER) in alkaline media. Here we report our observation that BaNiO3 synthesized via KOH flux growth and high O2 pressure ceramic synthesis have different magnetic properties. We show that this is because the KOH flux-grown crystals made in open-air are actually a hydroxide-containing form of BaNiO3 that can be dried upon annealing in O2 flow. This work not only unveils a previously unknown aspect of the BaNiO3 OER catalyst and offers some insights into the underlying mechanism, but also suggests that hydroxide ions may be present in other hexagonal perovskite oxides prepared in wet conditions.
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning

The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated definitions may also consist of unfamiliar words for language learners. In this work, we propose a novel task of Trans-Lingual Definition Generation (TLDG), which aims to generate definitions in another language, i.e., the native speaker’s language. Initially, we explore the unsupervised manner of this task and build up a simple implementation of fine-tuning the multi-lingual machine translation model. Then, we develop two novel methods, Prompt Combination and Contrastive Prompt Learning, for further enhancing the quality of the generation. Our methods are evaluated against the baseline Pipeline method in both rich- and low-resource settings, and we empirically establish its superiority in generating higher-quality trans-lingual definitions.
Rational $p$-adic Hodge theory for rigid-analytic varieties

We study a cohomology theory for rigid-analytic varieties over $\mathbb{C}_p$, without properness or smoothness assumptions, taking values in filtered quasi-coherent complexes over the Fargues-Fontaine curve, which compares to other rational $p$-adic cohomology theories for rigid-analytic varieties $-$ namely, the rational $p$-adic pro-\'etale cohomology, the Hyodo-Kato cohomology, and the infinitesimal cohomology over the positive de Rham period ring. In particular, this proves a conjecture of Le Bras. Such comparison results are made possible thanks to the systematic use of the condensed and solid formalisms developed by Clausen-Scholze. As applications, we deduce some general comparison theorems that describe the rational $p$-adic pro-\'etale cohomology in terms of de Rham data, thereby recovering and extending results of Colmez-Niziol.
McFIL: Model Counting Functionality-Inherent Leakage

Protecting the confidentiality of private data and using it for useful collaboration have long been at odds. Modern cryptography is bridging this gap through rapid growth in secure protocols such as multi-party computation, fully-homomorphic encryption, and zero-knowledge proofs. However, even with provable indistinguishability or zero-knowledgeness, confidentiality loss from leakage inherent to the functionality may partially or even completely compromise secret values without ever falsifying proofs of security. In this work, we describe McFIL, an algorithmic approach and accompanying software implementation which automatically quantifies intrinsic leakage for a given functionality. Extending and generalizing the Chosen-Ciphertext attack framework of Beck et al. with a practical heuristic, our approach not only quantifies but maximizes functionality-inherent leakage using Maximum Model Counting within a SAT solver. As a result, McFIL automatically derives approximately-optimal adversary inputs that, when used in secure protocols, maximize information leakage of private values.
Cycles through two edges in signed graphs

We give a characterization of when a signed graph $G$ with a pair of distinguished edges $e_1, e_2 \in E(G)$ has the property that all cycles containing both $e_1$ and $e_2$ have the same sign. This answers a question of Zaslavsky.
Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approach

The objective of this paper is to investigate the finite-time analysis of a Q-learning algorithm applied to two-player zero-sum Markov games. Specifically, we establish a finite-time analysis of both the minimax Q-learning algorithm and the corresponding value iteration method. To enhance the analysis of both value iteration and Q-learning, we employ the switching system model of minimax Q-learning and the associated value iteration. This approach provides further insights into minimax Q-learning and facilitates a more straightforward and insightful convergence analysis. We anticipate that the introduction of these additional insights has the potential to uncover novel connections and foster collaboration between concepts in the fields of control theory and reinforcement learning communities.
Towards An Empirical Theory of Ideologies in the Open Source Software Movement

Encompassing a diverse population of developers, non-technical users, organizations, and many other stakeholders, open source software (OSS) development has expanded to broader social movements from the initial product development aims. Ideology, as a coherent system of ideas, offers value commitments and normative implications for any social movement, so does OSS ideology for the open source movement. However, the literature on open source ideology is often fragile, or lacking in empirical evidence. In this paper, we sought to develop a comprehensive empirical theory of ideologies in open source software movement. Following a grounded theory procedure, we collected and analyzed data from 22 semi-structured interviews and 41 video recordings of Open Source Initiative (OSI) board members' public speeches. An empirical theory of OSS ideology emerged in our analysis, with six key categories: membership, norms/values, goals, activities, resources, and positions/group relations; each consists of a number of themes and subthemes. We discussed a subset of carefully selected themes and subthemes in detail based on their theoretical significance. With this ideological lens, we examined the implications and insights into open source development, and shed light on the research into open source as a social-cultural construction in the future.
More than programming? The impact of AI on work and skills

This chapter explores the ways in which organisational readiness and scientific advances in Artificial Intelligence have been affecting the demand for skills and their training in Australia and other nations leading in the promotion, use or development of AI. The consensus appears that having adequate numbers of qualified data scientists and machine learning experts is critical for meeting the challenges ahead. The chapter asks what this may mean for Australia's education and training system, what needs to be taught and learned, and whether technical skills are all that matter.
Partitioning Strategies for Distributed SMT Solving

For many users of Satisfiability Modulo Theories (SMT) solvers, the solver’s performance is the main bottleneck in their application. One promising approach for improving performance is to leverage the increasing availability of parallel and cloud computing. However, despite many efforts, the best parallel approach to date consists of running a portfolio of solvers, meaning that performance is still limited by the best possible sequential performance. In this paper, we revisit divide-and-conquer approaches to parallel SMT, in which a challenging problem is partitioned into several subproblems. We introduce several new partitioning strategies and evaluate their performance, both alone as well as within portfolios, on a large set of difficult SMT benchmarks. We show that hybrid portfolios that include our new strategies can significantly outperform traditional portfolios for parallel SMT.
Active stop and go motion: a strategy to improve spatial exploration?

We consider active Brownian particles switching between run and stop states. Such intermittent dynamics are ubiquitous at all scales, from bacteria to animals and in artificial active systems. We provide exact solutions for their transport properties, e.g., velocity autocorrelations and diffusion coefficients. The spread of particles depends on minute details, such as if the memory of active orientation is retained across a stop event. We predict dynamic schemes maximizing the effective diffusivity, potentially utilized by active agents for better access to distributed food.
AI based Out-Of-Distribution Analysis of Sea Surface Height Data

We performed Out-Of-Distribution (OOD) analysis of 7.8 million Sea Surface Topography Merged Altimeter L4 cdr grid cutouts in an effort to identify rare (possibly unknown) physical phenomenon sea surface height (SSH) data. The algorithm used for the project is Ulmo which is a probabilistic autoencoder (PAE), originally developed for sea surface temperature data. A PAE is made of an autoencoder for taking the extracted images and encoding them into a latent representation of the data, and a normalizing flow which takes the encoding and maps it to a normal distribution for probabilistic interpretation. A Log-Likelihood (LL) value for each cutout was calculated from this normal distribution and we defined the images with the lowest 0.1 percentile of LL values as anomalies. Ulmo successfully identifies outliers and distinguishes the ocean's most dynamic regions being Western boundary currents.
Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction

Generating realistic human 3D reconstructions using image or video data is essential for various communication and entertainment applications. While existing methods achieved impressive results for body and facial regions, realistic hair modeling still remains challenging due to its high mechanical complexity. This work proposes an approach capable of accurate hair geometry reconstruction at a strand level from a monocular video or multi-view images captured in uncontrolled lighting conditions. Our method has two stages, with the first stage performing joint reconstruction of coarse hair and bust shapes and hair orientation using implicit volumetric representations. The second stage then estimates a strand-level hair reconstruction by reconciling in a single optimization process the coarse volumetric constraints with hair strand and hairstyle priors learned from the synthetic data. To further increase the reconstruction fidelity, we incorporate image-based losses into the fitting process using a new differentiable renderer. The combined system, named Neural Haircut, achieves high realism and personalization of the reconstructed hairstyles. For video results, please refer to our project page †.