Miracle-dz/UniFAR-FTUs huggingface dataset:
Shifted coisotropic structures for differentiable stacks

We introduce a notion of coisotropics on 1-shifted symplectic Lie groupoids (i.e. quasi-symplectic groupoids) using twisted Dirac structures and show that it satisfies properties analogous to the corresponding derived-algebraic notion in shifted Poisson geometry. In particular, intersections of 1-coisotropics are 0-shifted Poisson. We also show that 1-shifted coisotropic structures transfer through Morita equivalences, giving a well-defined notion for differentiable stacks. Most results are formulated with clean-intersection conditions weaker than transversality while avoiding derived geometry. Examples of 1-coisotropics that are not necessarily Lagrangians include Hamiltonian actions of quasi-symplectic groupoids on Dirac manifolds, and this recovers several generalizations of Marsden-Weinstein-Meyer's symplectic reduction via intersection and Morita transfer.
Aerial STAR-RIS Empowered MEC: A DRL Approach for Energy Minimization

Multi-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establishing line-of-sight connections, integrating unmanned aerial vehicles (UAVs) has proven beneficial, offering enhanced data exchange, rapid deployment, and mobility. The utilization of reconfigurable intelligent surfaces (RIS), specifically simultaneously transmitting and reflecting RIS (STAR-RIS) technology, further extends coverage capabilities and introduces flexibility in MEC. This study explores the integration of UAV and STAR-RIS to facilitate communication between IoT devices and an MEC server. The formulated problem aims to minimize energy consumption for IoT devices and aerial STAR-RIS by jointly optimizing task offloading, aerial STAR-RIS trajectory, amplitude and phase shift coefficients, and transmit power. Given the non-convexity of the problem and the dynamic environment, solving it directly within a polynomial time frame is challenging. Therefore, deep reinforcement learning (DRL), particularly proximal policy optimization (PPO), is introduced for its sample efficiency and stability. Simulation results illustrate the effectiveness of the proposed system compared to benchmark schemes in the literature.
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions

We examine a private ADMM variant for (strongly) convex objectives which is a primal-dual iterative method. Each iteration has a user with a private function used to update the primal variable, masked by Gaussian noise for local privacy, without directly adding noise to the dual variable. Privacy amplification by iteration explores if noises from later iterations can enhance the privacy guarantee when releasing final variables after the last iteration. Cyffers et al. explored privacy amplification by iteration for the proximal ADMM variant, where a user's entire private function is accessed and noise is added to the primal variable. In contrast, we examine a private ADMM variant requiring just one gradient access to a user's function, but both primal and dual variables must be passed between successive iterations. To apply Balle et al.'s coupling framework to the gradient ADMM variant, we tackle technical challenges with novel ideas. First, we address the non-expansive mapping issue in ADMM iterations by using a customized norm. Second, because the dual variables are not masked with any noise directly, their privacy guarantees are achieved by treating two consecutive noisy ADMM iterations as a Markov operator. Our main result is that the privacy guarantee for the gradient ADMM variant can be amplified proportionally to the number of iterations. For strongly convex objective functions, this amplification exponentially increases with the number of iterations. These amplification results align with the previously studied special case of stochastic gradient descent.
Factorization Vision Transformer: Modeling Long Range Dependency with Local Window Cost

Transformers have astounding representational power but typically consume considerable computation which is quadratic with image resolution. The prevailing Swin transformer reduces computational costs through a local window strategy. However, this strategy inevitably causes two drawbacks: 1) the local window-based self-attention (WSA) hinders global dependency modeling capability and 2) recent studies point out that local windows impair robustness. To overcome these challenges, we pursue a preferable trade-off between computational cost and performance. Accordingly, we propose a novel factorization self-attention (FaSA) mechanism that enjoys both the advantages of local window cost and long-range dependency modeling capability. By factorizing the conventional attention matrix into sparse subattention matrices, FaSA captures long-range dependencies, while aggregating mixed-grained information at a computational cost equivalent to the local WSA. Leveraging FaSA, we present the factorization vision transformer (FaViT) with a hierarchical structure. FaViT achieves high performance and robustness, with linear computational complexity concerning input image spatial resolution. Extensive experiments have shown FaViT's advanced performance in classification and downstream tasks. Furthermore, it also exhibits strong model robustness to corrupted and biased data and hence demonstrates benefits in favor of practical applications. In comparison to the baseline model Swin-T, our FaViT-B2 significantly improves classification accuracy by 1% and robustness by 7% , while reducing model parameters by 14% . Our code will soon be publicly available: at https://github.com/q2479036243/FaViT.
Motion Flow Matching for Human Motion Synthesis and Editing

Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawbacks in terms of slow sampling speeds and error accumulation. In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications. Our method reduces the sampling complexity from thousand steps in previous diffusion models to just ten steps, while achieving comparable performance in text-to-motion and action-to-motion generation benchmarks. Noticeably, our approach establishes a new state-of-the-art Fr\'echet Inception Distance on the KIT-ML dataset. What is more, we tailor a straightforward motion editing paradigm named \emph{sampling trajectory rewriting} leveraging the ODE-style generative models and apply it to various editing scenarios including motion prediction, motion in-between prediction, motion interpolation, and upper-body editing. Our code will be released.
Pseudorandomness from Subset States

We show it is possible to obtain quantum pseudorandomness and pseudoentanglement from random subset states -- i.e. quantum states which are equal superpositions over (pseudo)random subsets of strings. This answers an open question of Aaronson et al. [arXiv:2211.00747], who devised a similar construction augmented by pseudorandom phases. Our result follows from a direct calculation of the trace distance between $t$ copies of random subset states and the Haar measure, via the representation theory of the symmetric group. We show that the trace distance is negligibly small, as long as the subsets are of an appropriate size which is neither too big nor too small. In particular, we analyze the action of basis permutations on the symmetric subspace, and show that the largest component is described by the Johnson scheme: the double-cosets of the symmetric group $\mathbb{S}_N$ by the subgroup $\mathbb{S}_t \times \mathbb{S}_{N-t}$. The Gelfand pair property of this setting implies that the matrix eigenbasis coincides with the symmetric group irreducible blocks, with the largest eigenblock asymptotically approaching the Haar average. An immediate corollary of our result is that quantum pseudorandom and pseudoentangled state ensembles do not require relative phases.
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training

In the acceleration of deep neural network training, the GPU has become the mainstream platform. GPUs face substantial challenges on GNNs, such as workload imbalance and memory access irregularities, leading to underutilized hardware. Existing solutions such as PyG, DGL with cuSPARSE, and GNNAdvisor frameworks partially address these challenges but memory traffic is still significant. We argue that drastic performance improvements can only be achieved by the vertical optimization of algorithm and system innovations, rather than treating the speedup optimization as an"after-thought"(i.e., (i) given a GNN algorithm, designing an accelerator, or (ii) given hardware, mainly optimizing the GNN algorithm). In this paper, we present MaxK-GNN, an advanced high-performance GPU training system integrating algorithm and system innovation. (i) We introduce the MaxK nonlinearity and provide a theoretical analysis of MaxK nonlinearity as a universal approximator, and present the Compressed Balanced Sparse Row (CBSR) format, designed to store the data and index of the feature matrix after nonlinearity; (ii) We design a coalescing enhanced forward computation with row-wise product-based SpGEMM Kernel using CBSR for input feature matrix fetching and strategic placement of a sparse output accumulation buffer in shared memory; (iii) We develop an optimized backward computation with outer product-based and SSpMM Kernel. We conduct extensive evaluations of MaxK-GNN and report the end-to-end system run-time. Experiments show that MaxK-GNN system could approach the theoretical speedup limit according to Amdahl's law. We achieve comparable accuracy to SOTA GNNs, but at a significantly increased speed: 3.22/4.24 times speedup (vs. theoretical limits, 5.52/7.27 times) on Reddit compared to DGL and GNNAdvisor implementations.
EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment

This work presents the network architecture EVP (Enhanced Visual Perception). EVP builds on the previous work VPD which paved the way to use the Stable Diffusion network for computer vision tasks. We propose two major enhancements. First, we develop the Inverse Multi-Attentive Feature Refinement (IMAFR) module which enhances feature learning capabilities by aggregating spatial information from higher pyramid levels. Second, we propose a novel image-text alignment module for improved feature extraction of the Stable Diffusion backbone. The resulting architecture is suitable for a wide variety of tasks and we demonstrate its performance in the context of single-image depth estimation with a specialized decoder using classification-based bins and referring segmentation with an off-the-shelf decoder. Comprehensive experiments conducted on established datasets show that EVP achieves state-of-the-art results in single-image depth estimation for indoor (NYU Depth v2, 11.8% RMSE improvement over VPD) and outdoor (KITTI) environments, as well as referring segmentation (RefCOCO, 2.53 IoU improvement over ReLA). The code and pre-trained models are publicly available at https://github.com/Lavreniuk/EVP.
A Multi-Harmonic NIR-UV Dual-Comb Spectrometer

Dual-comb spectroscopy in the ultraviolet (UV) and visible would enable broad bandwidth electronic spectroscopy with unprecedented frequency resolution. However, there are significant challenges in generation, detection and processing of dual-comb data that have restricted its progress in this spectral region. In this work, we leverage robust 1550 nm few-cycle pulses to generate frequency combs in the UV-visible. We couple this source to a wavelength multiplexed dual-comb spectrometer and simultaneously retrieve 100 MHz comb-mode-resolved spectra over three distinct harmonics spanning 380-800 nm. The experiments highlight the path to continuous dual-comb coverage spanning 200-750 nm, offering extensive access to electronic transitions in atoms, molecules, and solids.
Space-Time Approximation with Shallow Neural Networks in Fourier Lebesgue spaces

Approximation capabilities of shallow neural networks (SNNs) form an integral part in understanding the properties of deep neural networks (DNNs). In the study of these approximation capabilities some very popular classes of target functions are the so-called spectral Barron spaces. This spaces are of special interest when it comes to the approximation of partial differential equation (PDE) solutions. It has been shown that the solution of certain static PDEs will lie in some spectral Barron space. In order to alleviate the limitation to static PDEs and include a time-domain that might have a different regularity than the space domain, we extend the notion of spectral Barron spaces to anisotropic weighted Fourier-Lebesgue spaces. In doing so, we consider target functions that have two blocks of variables, among which each block is allowed to have different decay and integrability properties. For these target functions we first study the inclusion of anisotropic weighted Fourier-Lebesgue spaces in the Bochner-Sobolev spaces. With that we can now also measure the approximation error in terms of an anisotropic Sobolev norm, namely the Bochner-Sobolev norm. We use this observation in a second step where we establish a bound on the approximation rate for functions from the anisotropic weighted Fourier-Lebesgue spaces and approximation via SNNs in the Bochner-Sobolev norm.
Occurrence of Brachyuran Larvae in the Surf Zone of Fukiage Beach, Kagoshima Prefecture, Japan

ISMID, MOHAMAD, HIROSHI SUZUKI and TOSHIO SAISHO (Laboratory of Marine Biology, Faculty of Fisheries, Kagoshima University). 1994. Occurrence of Brachyuran Larvae in the Surf Zone of Fukiage Beach, Kagoshima Prefecture, Japan. II. Families Leucosiidae, Xanthidae and Pinnotheridae. Benthos Research, 46: 25-32. This paper considers Brachyuran larvae of the families Leucosiidae, Xanthidae and Pinnotheridae collected from the surf zone of Fukiage beach, Kagoshima Prefecture. Descriptions are given of zoea I of Ebalia sp. , Xanthinae sp. , Pilumnus sp. , Pinnotheres , pholadis, and P. ? sinensis. Seasonal abundance of these zoeae throughout the 18-month study period showed high concentrations in the summer months (June to August) followed by a steady decline in autumn. The occurrence of only zoea I in the surf zone suggests that the larvae of these species are transient and not dependent on the surf zone for their development.
Using micrometeorological methods and modeling to determine greenhouse gas budgets over agricultural systems in the inland Pacific Northwest

by Sarah Rachel Waldo, Ph.D. Washington State University May 2016 Chair: Brian K. Lamb Agriculture can be a sink for carbon dioxide (CO2) if climate and management conditions are conducive for the build-up of soil organic matter. At the same time, agricultural soils are the main anthropogenic source of nitrous oxide (N2O), which is both a greenhouse gas and an ozone depleting substance. The work presented herein investigates the dynamics of CO2 and N2O fluxes over terrestrial systems in the Pacific Northwest (PNW) using measurements and modeling. The first study used the eddy covariance micrometeorological method to monitor fluxes of CO2 for two years at two agricultural sites representing the lowand high-end of the regional precipitation gradient. Combining the measured net ecosystem exchange of CO2 with information on carbon (C) export during harvest, we found that both sites were net carbon sinks. The high-rainfall site had a two-year C balance of 202 ± 60 g C m, and the low-rainfall site had a C balance of 444 ± 34 g C m. The second study used the flux gradient micrometeorological technique and automated static chambers to measure N2O emissions over one year at two agricultural sites, one under no-tillage management (CAF-NT) and one with conventional tillage
Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification

We provide a systematic investigation of using physics-informed neural networks to compute Lyapunov functions. We encode Lyapunov conditions as a partial differential equation (PDE) and use this for training neural network Lyapunov functions. We analyze the analytical properties of the solutions to the Lyapunov and Zubov PDEs. In particular, we show that employing the Zubov equation in training neural Lyapunov functions can lead to approximate regions of attraction close to the true domain of attraction. We also examine approximation errors and the convergence of neural approximations to the unique solution of Zubov's equation. We then provide sufficient conditions for the learned neural Lyapunov functions that can be readily verified by satisfiability modulo theories (SMT) solvers, enabling formal verification of both local stability analysis and region-of-attraction estimates in the large. Through a number of nonlinear examples, ranging from low to high dimensions, we demonstrate that the proposed framework can outperform traditional sums-of-squares (SOS) Lyapunov functions obtained using semidefinite programming (SDP).
Hamiltonian Truncation Crafted for UV-divergent QFTs

We develop the theory of Hamiltonian Truncation (HT) to systematically study RG flows that require the renormalization of coupling constants. This is a necessary step towards making HT a fully general method for QFT calculations. We apply this theory to a number of QFTs defined as relevant deformations of $d=1+1$ CFTs. We investigated three examples of increasing complexity: the deformed Ising, Tricritical-Ising, and non-unitary minimal model $M(3,7)$. The first two examples provide a crosscheck of our methodologies against well established characteristics of these theories. The $M(3,7)$ CFT deformed by its $Z_2$-even operators shows an intricate phase diagram that we clarify. At a boundary of this phase diagram we show that this theory flows, in the IR, to the $M(3,5)$ CFT.
Quantifying Divergence for Human-AI Collaboration and Cognitive Trust

Predicting the collaboration likelihood and measuring cognitive trust to AI systems is more important than ever. To do that, previous research mostly focus solely on the model features (e.g., accuracy, confidence) and ignore the human factor. To address that, we propose several decision-making similarity measures based on divergence metrics (e.g., KL, JSD) calculated over the labels acquired from humans and a wide range of models. We conduct a user study on a textual entailment task, where the users are provided with soft labels from various models and asked to pick the closest option to them. The users are then shown the similarities/differences to their most similar model and are surveyed for their likelihood of collaboration and cognitive trust to the selected system. Finally, we qualitatively and quantitatively analyze the relation between the proposed decision-making similarity measures and the survey results. We find that people tend to collaborate with their most similar models -- measured via JSD -- yet this collaboration does not necessarily imply a similar level of cognitive trust. We release all resources related to the user study (e.g., design, outputs), models, and metrics at our repo.
Automated Process Planning Based on a Semantic Capability Model and SMT

In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. Approaches in this research area develop information models that capture all information relevant to interpret the requirements, effects and behavior of functions. These approaches are intended to overcome the heterogeneity resulting from the various types of processes and from the large number of different vendors. However, these models and associated methods do not offer solutions for automated process planning, i.e. finding a sequence of individual capabilities required to manufacture a certain product or to accomplish a mission using autonomous robots. Instead, this is a typical task for AI planning approaches, which unfortunately require a high effort to create the respective planning problem descriptions. In this paper, we present an approach that combines these two topics: Starting from a semantic capability model, an AI planning problem is automatically generated. The planning problem is encoded using Satisfiability Modulo Theories and uses an existing solver to find valid capability sequences including required parameter values. The approach also offers possibilities to integrate existing human expertise and to provide explanations for human operators in order to help understand planning decisions.
Joint reducing subspaces and orthogonal decompositions of operators in an annulus

A commuting tuple of Hilbert space operators $(T_1, \dotsc, T_n)$ is said to be an \textit{$\mathbb{A}_r^n$-contraction} if the closure of the polyannulus \[ \mathbb A_r^n=\left\{(z_1, \dotsc, z_n) \ : \ r<|z_i|<1, \ 1 \leq i \leq n \right\} \subseteq \mathbb{C}^n \qquad \quad (0<r<1) \] is a spectral set for $(T_1, \dotsc, T_n)$. We find characterizations for the $\mathbb A_r^n$-unitaries and $\mathbb A_r^n$-isometries and decipher their structures. We find Wold type decompositions for any number of commuting and doubly commuting $\mathbb A_r$-isometries. Then we generalize these results to any family of commuting and doubly commuting $\mathbb A_r$-contractions.
Enhancing Hybrid Eye Typing Interfaces with Word and Letter Prediction: A Comprehensive Evaluation

Eye typing interfaces enable a person to enter text into an interface using only their own eyes. But despite the inherent advantages of touchless operation and intuitive design, such eye-typing interfaces often suffer from slow typing speeds, resulting in slow words per minute (WPM) counts. In this study, we add word and letter prediction to the eye-typing interface and investigate users' typing performance as well as their subjective experience while using the interface. In experiment 1, we compared three typing interfaces with letter prediction (LP), letter+word prediction (L+WP), and no prediction (NoP), respectively. We found that the interface with L+WP achieved the highest average text entry speed (5.48 WPM), followed by the interface with LP (3.42 WPM), and the interface with NoP (3.39 WPM). Participants were able to quickly understand the procedural design for word prediction and perceived this function as very helpful. Compared to LP and NoP, participants needed more time to familiarize themselves with L+WP in order to reach a plateau regarding text entry speed. Experiment 2 explored training effects in L+WP interfaces. Two moving speeds were implemented: slow (6.4{\deg}/s same speed as in experiment 1) and fast (10{\deg}/s). The study employed a mixed experimental design, incorporating moving speeds as a between-subjects factor, to evaluate its influence on typing performance throughout 10 consecutive training sessions. The results showed that the typing speed reached 6.17 WPM for the slow group and 7.35 WPM for the fast group after practice. Overall, the two experiments show that adding letter and word prediction to eye-typing interfaces increases typing speeds. We also find that more extended training is required to achieve these high typing speeds.