Miracle-dz/UniFAR-FTUs huggingface dataset:
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability

We study the problem of learning an adversarially robust predictor to test time attacks in the semi-supervised PAC model. We address the question of how many labeled and unlabeled examples are required to ensure learning. We show that having enough unlabeled data (the size of a labeled sample that a fully-supervised method would require), the labeled sample complexity can be arbitrarily smaller compared to previous works, and is sharply characterized by a different complexity measure. We prove nearly matching upper and lower bounds on this sample complexity. This shows that there is a significant benefit in semi-supervised robust learning even in the worst-case distribution-free model, and establishes a gap between the supervised and semi-supervised label complexities which is known not to hold in standard non-robust PAC learning.
BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Emerging Autonomous Vehicles (AV) breed great potentials to exploit data-driven techniques for adaptive and personalized Human-Vehicle Interactions. However, the lack of high-quality and rich data supports limits the opportunities to explore the design space of data-driven techniques, and validate the effectiveness of concrete mechanisms. Our goal is to initialize the efforts to deliver the building block for exploring data-driven Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a multi-modal dataset with facial video records. We first brief our rationales to build BROOK dataset. Then, we elaborate how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present three example studies using BROOK to justify the applicability of BROOK dataset. We also identify key learning lessons from building BROOK dataset, and discuss about how BROOK dataset can foster an extensive amount of follow-up studies.
A Multi-Event Study of Early-Stage SEP Acceleration by CME-Driven Shocks—Sun to 1 AU

The solar corona between below 10 solar radii is an important region for early acceleration and transport of solar energetic particles (SEPs) by coronal mass ejection-driven shock waves. There, these waves propagate into a highly variable dynamic medium with steep gradients and rapidly expanding coronal magnetic fields, which modulates the particle acceleration near the shock/wave surfaces, and the way SEPs spread into the heliosphere. We present a study modeling the acceleration of SEPs in global coronal shock events in the corona, as well as their transport to 1 au, based on telescopic observations coupled with dynamic physical models. As part of the project Solar Particle Radiation Environment Analysis and Forecasting—Acceleration and Scattering Transport (SPREAdFAST), we model the interaction of observed off-limb coronal bright fronts (CBF) with the coronal plasma from synoptic magnetohydrodynamic (MHD) simulations. We then simulate the SEP acceleration in analytical diffusive shock acceleration (DSA) model. The simulated fluxes are used as time-dependent inner boundary conditions for modeling the particle transport to 1 au. Resulting flux time series are compared with 1 au observations for validation. We summarize our findings and present implications for nowcasting SEP acceleration and heliospheric connectivity.
Pregeometric first order Yang-Mills theory

The standard description of particles and fundamental interactions is crucially based on a regular metric background. In the language of differential geometry, this dependence is encoded into the action via Hodge star dualization. As a result, the conventional forms of the scalar and Yang-Mills actions break down in a pregeometric regime where the metric is degenerate. This suggests the use of first order formalism, where the metric may emerge from more fundamental constituents and the theory can be consistently extended to the pregeometric phase. We systematically explore different realizations and interpretations of first order formalism, finding that a fundamental vector or spinor substructure brings about continuum magnetization and polarization as integration constants. This effect is analogous to the description of the cosmological dark sector in a recent self-dual formulation of gravity, and the similar form obtained for the first order Yang-Mills theory suggests new paths toward unification.
From Symplectic to Poisson. A Study of Reduction and a Proposal Towards Implosion

The imploded cross-section of a symplectic manifold is a stratified space allowing for an abelianization of its symplectic reduction. After recalling symplectic and Poisson reduction and reviewing the basics of symplectic implosion, we prove a cross-section theorem for Poisson manifolds, generalizing the GuilleminSternberg theorem for symplectic manifolds, which constitutes a first step towards Poisson implosion. On our way, we find and fix a mistake in the proof of Guillemin-Sternberg’s theorem, and we identify Poisson transversals as the right analogue to symplectic submanifolds in this context.
Individual-based and continuum models of phenotypically heterogeneous growing cell populations

Existing studies comparing individual-based models of growing cell populations and their continuum counterparts have mainly focused on homogeneous populations, in which all cells have the same phenotypic characteristics. However, significant intercellular phenotypic variability is commonly observed in cellular systems. Therefore, we develop here an individual-based model for the growth of phenotypically heterogeneous cell populations. In this model, the phenotypic state of each cell is described by a structuring variable that captures intercellular variability in cell proliferation and migration rates. The model tracks the spatial evolutionary dynamics of single cells, which undergo pressure-dependent proliferation, heritable phenotypic changes and directional movement in response to pressure differentials. We formally show that the continuum limit of this model comprises a non-local partial differential equation for the cell population density, which generalises earlier models of growing cell populations. Results of the individual-based model illustrate how proliferation-migration tradeoffs shaping the evolution of single cells can lead to the formation of travelling waves at the population level where highly-mobile cells locally dominate at the invasive front, while more-proliferative cells are found at the rear. We demonstrate that there is an excellent quantitative agreement between these results and the results of numerical simulations and formal travelling-wave analysis of the continuum model, when sufficiently large cell numbers are considered. We provide numerical evidence of scenarios in which the predictions of the two models may differ due to demographic stochasticity, which cannot be captured by the continuum model. This indicates the importance of integrating individual-based and continuum approaches when modelling the growth of phenotypically heterogeneous cell populations.
Solving Large-Scale Security Constrained AC Optimal Power Flow Problems

In this paper, we study efﬁcient and robust computational methods for solving the security-constrained alternating current optimal power ﬂow (SC-ACOPF) problem, a two-stage nonlinear mixed-integer optimization problem central to the operation of electric power grids. The ﬁrst-stage problem in SC-ACOPF determines the operation of the power grid in normal condition, while the second-stage problem responds to various contingencies of losing generators, transmission lines, and transformers. The two stages are coupled through disjunctive constraints, which model genera-tors’ active and reactive power output changes responding to system-wide active power imbalance and voltage deviations after contingencies. Real-world SC-ACOPF problems may involve power grids with more than 30 k buses and 22 k contingencies and need to be solved within 10-45 minutes to get a base case solution with high feasibility and reasonably good generation cost. We develop a comprehensive algorithmic framework to solve SC-ACOPF that meets the challenge of speed, solution quality, and computation robustness. In particular, we develop a smoothing technique to approximate disjunctive constraints into a smooth structure which can be handled by interior-point solvers; we design a distributed optimization algorithm with convergence guarantee to solve the two-stage SC-ACOPF model; we propose a screening procedure to prioritize contin-gencies; and ﬁnally, we develop a reliable and parallel architecture that integrates all algorithmic components. Extensive tests on industry-scale systems demonstrate the superior performance of the proposed algorithms.
NeuVV: Neural Volumetric Videos with Immersive Rendering and Editing

Some of the most exciting experiences that Metaverse promises to offer, for instance, live interactions with virtual characters in virtual environments, require real-time photo-realistic rendering. 3D reconstruction approaches to rendering, active or passive, still require extensive cleanup work to fix the meshes or point clouds. In this paper, we present a neural volumography technique called neural volumetric video or NeuVV to support immersive, interactive, and spatial-temporal rendering of volumetric video contents with photo-realism and in real-time. The core of NeuVV is to efficiently encode a dynamic neural radiance field (NeRF) into renderable and editable primitives. We introduce two types of factorization schemes: a hyper-spherical harmonics (HH) decomposition for modeling smooth color variations over space and time and a learnable basis representation for modeling abrupt density and color changes caused by motion. NeuVV factorization can be integrated into a Video Octree (VOctree) analogous to PlenOctree to significantly accelerate training while reducing memory overhead. Real-time NeuVV rendering further enables a class of immersive content editing tools. Specifically, NeuVV treats each VOctree as a primitive and implements volume-based depth ordering and alpha blending to realize spatial-temporal compositions for content re-purposing. For example, we demonstrate positioning varied manifestations of the same performance at different 3D locations with different timing, adjusting color/texture of the performer's clothing, casting spotlight shadows and synthesizing distance falloff lighting, etc, all at an interactive speed. We further develop a hybrid neural-rasterization rendering framework to support consumer-level VR headsets so that the aforementioned volumetric video viewing and editing, for the first time, can be conducted immersively in virtual 3D space.
Gamekins: Gamifying Software Testing in Jenkins

Developers have to write thorough tests for their software in order to find bugs and to prevent regressions. Writing tests, however, is not every developer’s favourite occupation, and if a lack of motivation leads to a lack of tests, then this may have dire consequences, such as programs with poor quality or even project failures. This paper introduces Gamekins, a tool that uses gamification to motivate developers to write more and better tests. Gamekins is integrated into the Jenkins continuous integration platform where game elements are based on commits to the source code repository: Developers can earn points for completing test challenges and quests posed by Gamekins, compete with other developers or developer teams on a leaderboard, and are rewarded for their test-related achievements. A demo video of Gamekins is available at https://youtu.be/qnRWEQim12E; The tool, documentation, and source code are available at https://gamekins.org.
A characterization of $g_2$-minimal normal 3-pseudomanifolds with at most four singularities

Let $\Delta$ be a $g_2$-minimal normal 3-pseudomanifold. A vertex in $\Delta$ whose link is not a sphere is called a singular vertex. When $\Delta$ contains at most two singular vertices, its combinatorial characterization is known [9]. In this article, we present a combinatorial characterization of such a $\Delta$ when it has three singular vertices, including one $\mathbb{RP}^2$-singularity, or four singular vertices, including two $\mathbb{RP}^2$-singularities. In both cases, we prove that $\Delta$ is obtained from a one-vertex suspension of a surface, and some boundary complexes of $4$-simplices by applying the combinatorial operations of types connected sums, vertex foldings, and edge foldings.
Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection

Object recognition from images means to automatically find object(s) of interest and to return their category and location information. Benefiting from research on deep learning, like convolutional neural networks~(CNNs) and generative adversarial networks, the performance in this field has been improved significantly, especially when training and test data are drawn from similar distributions. However, mismatching distributions, i.e., domain shifts, lead to a significant performance drop. In this paper, we build domain-invariant detectors by learning domain classifiers via adversarial training. Based on the previous works that align image and instance level features, we mitigate the domain shift further by introducing a domain adaptation component at the region level within Faster \mbox{R-CNN}. We embed a domain classification network in the region proposal network~(RPN) using adversarial learning. The RPN can now generate accurate region proposals in different domains by effectively aligning the features between them. To mitigate the unstable convergence during the adversarial learning, we introduce a balanced domain classifier as well as a network learning rate adjustment strategy. We conduct comprehensive experiments using four standard datasets. The results demonstrate the effectiveness and robustness of our object detection approach in domain shift scenarios.
Off-Policy Evaluation for Large Action Spaces via Embeddings

Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of new policies using only historic log data. Unfortunately, when the number of actions is large, existing OPE estimators -- most of which are based on inverse propensity score weighting -- degrade severely and can suffer from extreme bias and variance. This foils the use of OPE in many applications from recommender systems to language models. To overcome this issue, we propose a new OPE estimator that leverages marginalized importance weights when action embeddings provide structure in the action space. We characterize the bias, variance, and mean squared error of the proposed estimator and analyze the conditions under which the action embedding provides statistical benefits over conventional estimators. In addition to the theoretical analysis, we find that the empirical performance improvement can be substantial, enabling reliable OPE even when existing estimators collapse due to a large number of actions.
Lower Bounds for Unambiguous Automata via Communication Complexity

We use results from communication complexity, both new and old ones, to prove lower bounds for unambiguous finite automata (UFAs). We show three results. $\textit{Complement:}$ There is a language $L$ recognised by an $n$-state UFA such that the complement language $\overline{L}$ requires NFAs with $n^{\tilde{\Omega}(\log n)}$ states. This improves on a lower bound by Raskin. $\textit{Union:}$ There are languages $L_1$, $L_2$ recognised by $n$-state UFAs such that the union $L_1\cup L_2$ requires UFAs with $n^{\tilde{\Omega}(\log n)}$ states. $\textit{Separation:}$ There is a language $L$ such that both $L$ and $\overline{L}$ are recognised by $n$-state NFAs but such that $L$ requires UFAs with $n^{\Omega(\log n)}$ states. This refutes a conjecture by Colcombet.
A Variational Formula for Infinity-Rényi Divergence with Applications to Information Leakage

We present a variational characterization for the Rényi divergence of order infinity. Our characterization is related´ to guessing: the objective functional is a ratio of maximal expected values of a gain function applied to the probability of correctly guessing an unknown random variable. An important aspect of our variational characterization is that it remains agnostic to the particular gain function considered, as long as it satisfies some regularity conditions. Also, we define two variants of a tunable measure of information leakage, the maximal αleakage, and obtain closed-form expressions for these information measures by leveraging our variational characterization.
A 2.4 GHz polarization-diversity planar printed dipole antenna for WLAN and wireless communication applications

This article presents the design simulation, fabrication and measured performance of a 2.4 GHz polarization-diversity printed dipole antenna for wireless communication applications. Two orthogonal printed dipole antennas, each with a microstrip via-hole balun for vertical and horizontal polarization, an combined and fabricated on a PCB substrate. PIN diodes are used as switches to select the desired antenna polarization. The 3D finite-element-method (FEM) electromagnetic EM simulator, HFSS, is used in the design simulation of this planar antenna structure. Numerical and measured results of the antenna radiation characteristics, including input SWR, radiation pattern coverage and polarization-diversity, are presented and compared.
On the Importance of Building High-quality Training Datasets for Neural Code Search

The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived. A large corpus of high-quality query and code pairs is demanded to establish a precise mapping from the natural language to the programming language. Due to the limited availability, most widely-used code search datasets are established with compromise, such as using code comments as a replacement of queries. Our empirical study on a famous code search dataset reveals that over one-third of its queries contain noises that make them deviate from natural user queries. Models trained through noisy data are faced with severe performance degradation when applied in real-world scenarios. To improve the dataset quality and make the queries of its samples semantically identical to real user queries is critical for the practical usability of neural code search. In this paper, we propose a data cleaning framework consisting of two subsequent filters: a rule-based syntactic filter and a model-based semantic filter. This is the first framework that applies semantic query cleaning to code search datasets. Experimentally, we evaluated the effectiveness of our framework on two widely-used code search models and three manually-annotated code retrieval benchmarks. Training the popular DeepCS model with the filtered dataset from our framework improves its performance by 19.2% MRR and 21.3% Answer@l, on average with the three validation benchmarks.
Effect of infill pattern and build orientation on mechanical properties of FDM printed parts: An experimental modal analysis approach

Fused Deposition Modeling (FDM) is one of the most widely explored additive manufacturing method that uses thermoplastic materials to manufacture products. Mechanical properties of parts manufactured using FDM are influenced by different process parameters involved during manufacturing as they impact the bonding among different layers of cross-section. In this study, the effect of infill patterns and build orientations on the mechanical properties of PLA based parts manufactured using FDM method is studied. Six different infill patterns (triangles, cubic, concentric, tetrahedral, lines, and zigzag) and three different orientations (flatwise, edgewise, and upright) are considered for rectangular beam type parts. For determining mechanical properties of parts with different infill patterns and orientations, flexural bending test is performed. Experimental modal analysis of manufactured parts is performed to observe the relation of natural frequencies with elastic modulus of the parts obtained from flexural bending test. The possibility of experimental modal analysis as an alternative non-destructive method for testing mechanical properties of FDM 3D printed parts is explored.
Gradient-Free Optimization for Non-Smooth Minimax Problems with Maximum Value of Adversarial Noise

This paper investigates zeroth-order methods for non-smooth convex-concave saddle point proble ms (with r -growth condition for duality gap). We assume that a black-box gradient-free oracle returns an inexact function value corrupted by an adversarial noise. In this work we prove that the standard zeroth-order version of the mirror descent method is optimal in terms of the oracle calls complexity and the maximum admissible noise.
Gain-loss ratio of storing intermediate data from workflows

Sequentially, the systematic processing of a significant amount of data can be necessary for input datasets to get desired outputs. In a workflow management system(WMS), usually, users build workflows by manually selecting and interconnecting various modules concerning some particular tasks. Thus, a system of automatically suggesting the appropriate intermediate datasets for modules and a suggestion for the decision of saving intermediate states will be helpful in a pipeline building process. This work investigates a technique for both automatically suggesting the intermediate datasets to use and store through mining and analyzing association rules from the previously developed workflows. Investigation on workflows shows that the association rule mining technique can help us to suggest subsequent modules for retrieving and storing data and explain them with gain-loss ratios.
Detection and parameter estimation challenges of type-II lensed binary black hole signals

Strong lensing of {gravitational-wave signals} can produce three types of images, denoted as Type-I, Type-II and Type-III, corresponding to the minima, saddle and maxima of the lensing potential of the lensed images. Type-II images, in particular, receive a non-trivial phase shift of $\pi/2$. This phase shift can introduce additional distortions in the strains produced by the Type-II image of the binary black hole signals depending on the morphology of the signals, e.g., when they have contributions from higher harmonics, precession, eccentricity, etc. {The probability of observing Type-II images is nearly the same as that of strong lensing itself, and thus these signals are likely to be observed in the near future. } In this work, we investigate the potential applicability of these distortions in helping identify Type-II signals from a single detection and the systematic biases that could arise in the inference of parameters if they are recovered with gravitational-wave templates that do not take the distortion into account. We show that the lensing distortions will allow us to confidently identify the Type-II images for highly inclined binaries: at network signal-to-noise ratio (SNR) $\rho=20(50)$, individual Type-II images should be identifiable with ln Bayes factor $\ln \mathcal{B}>2$ for inclinations $ \iota>5 \pi/12 (\pi/3) $. Furthermore, based on the trends we observe in these results, we predict that, at high SNRs ($\gtrsim 100$), individual Type-II images would be identifiable even when the inclination angle is much lower ($\sim \pi/6$). We then show that neglecting physical effects arising from these identifiable Type-II images can significantly bias the estimates of source parameters. Thus, in the future, using templates that take into account the lensing deformation would be necessary to extract source parameters from Type-II lensed signals.