Miracle-dz/UniFAR-FTUs huggingface dataset:
ResQ: Residual Quantization for Video Perception

This paper accelerates video perception, such as segmentation and human pose estimation, by levering cross-frame redundancies. Unlike the existing approaches, which avoid redundant computations by warping the past features using optical-flow or by performing sparse convolutions on frame differences, we approach the problem from a different perspective: low-bit quantization. We observe that residuals, as the difference in network activations between two neighboring frames, exhibit properties that make them highly quantizable. Based on this observation, we propose a novel quantization scheme for video networks coined as Res idual Quantization. ResQ extends the standard, frame-by-frame, quantization scheme by incorporating temporal dependencies that lead to better performance in terms of accuracy vs. bit-width. Furthermore, we extend our model to dynamically adjust the bit-width proportionally to the amount of changes in the video. We showcase the superiority of our model, against the standard quantization and existing efficient video perception models, using various architectures on semantic segmentation, video object segmentation and human pose estimation benchmarks.
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited 3D deep learning, mainly due to the limited availability of large-scale 3D datasets. Merging multiple available data sources and letting them collaboratively train a single model is a potential solution. However, due to the large domain gap between 3D point cloud datasets, such mixed supervision could adversely affect the model's performance and lead to degenerated performance (i.e., negative transfer) compared to single-dataset training. In view of this challenge, we introduce Point Prompt Training (PPT), a novel framework for multi-dataset synergistic learning in the context of 3D representation learning that supports multiple pre-training paradigms. Based on this framework, we propose Prompt-driven Normalization, which adapts the model to different datasets with domain-specific prompts and Language-guided Categorical Alignment that decently unifies the multiple-dataset label spaces by leveraging the relationship between label text. Extensive experiments verify that PPT can overcome the negative transfer associated with synergistic learning and produce generalizable representations. Notably, it achieves state-of-the-art performance on each dataset using a single weight-shared model with supervised multi-dataset training. Moreover, when served as a pre-training framework, it outperforms other pre-training approaches regarding representation quality and attains remarkable state-of-the-art performance across over ten diverse downstream tasks spanning both indoor and outdoor 3D scenarios.
PMET: Precise Model Editing in a Transformer

Model editing techniques modify a minor proportion of knowledge in Large Language Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing methods assume Transformer Layer (TL) hidden states are values of key-value memories of the Feed-Forward Network (FFN). They usually optimize the TL hidden states to memorize target knowledge and use it to update the weights of the FFN in LLMs. However, the information flow of TL hidden states comes from three parts: Multi-Head Self-Attention (MHSA), FFN, and residual connections. Existing methods neglect the fact that the TL hidden states contains information not specifically required for FFN. Consequently, the performance of model editing decreases. To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns. This implies that MHSA weights do not require updating when new knowledge is introduced. Based on above findings, we introduce PMET, which simultaneously optimizes Transformer Component (TC, namely MHSA and FFN) hidden states, while only using the optimized TC hidden states of FFN to precisely update FFN weights. Our experiments demonstrate that PMET exhibits state-of-the-art performance on both the \textsc{counterfact} and zsRE datasets. Our ablation experiments substantiate the effectiveness of our enhancements, further reinforcing the finding that the MHSA encodes certain general knowledge extraction patterns and indicating its storage of a small amount of factual knowledge. Our code is available at \url{https://github.com/xpq-tech/PMET}.
A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages

Large language models excel in text generation and generalization, however they face challenges in text editing tasks, especially in correcting spelling errors and mistyping.In this paper, we present a methodology for generative spelling correction (SC), tested on English and Russian languages and potentially can be extended to any language with minor changes. Our research mainly focuses on exploring natural spelling errors and mistyping in texts and studying how those errors can be emulated in correct sentences to enrich generative models’ pre-train procedure effectively. We investigate the effects of emulations in various text domains and examine two spelling corruption techniques: 1) first one mimics human behavior when making a mistake through leveraging statistics of errors from a particular dataset, and 2) second adds the most common spelling errors, keyboard miss clicks, and some heuristics within the texts.We conducted experiments employing various corruption strategies, models’ architectures, and sizes in the pre-training and fine-tuning stages and evaluated the models using single-domain and multi-domain test sets. As a practical outcome of our work, we introduce SAGE (Spell checking via Augmentation and Generative distribution Emulation).
Optimal Partitioning of Quantum Circuits Using Gate Cuts and Wire Cuts

A limited number of qubits, high error rates, and limited qubit connectivity are major challenges for effective near-term quantum computations. Quantum circuit partitioning divides a quantum computation into classical postprocessing steps and a set of smaller scale quantum computations that individually require fewer qubits, lower qubit connectivity, and typically incur less error. However, as partitioning generally increases the duration of a quantum computation exponentially in the required partitioning effort, it is crucial to select optimal partitioning points, so-called cuts, and to use optimal cut realizations. In this work, we develop the first optimal partitioning method relying on quantum circuit knitting for optimal cut realizations and an optimal selection of wire cuts and gate cuts that trades off ancilla qubit insertions for a decrease in quantum computing time. Using this combination, the developed method demonstrates a reduction in quantum computing runtime by 41% on average compared to previous quantum circuit partitioning methods. Furthermore, the qubit requirement of the evaluated quantum circuits was reduced by 40% on average for a runtime budget of one hour and a sampling frequency of 1 kHz. These results highlight the optimality gap of previous quantum circuit partitioning methods and the possible extension in the computational reach of near-term quantum computers.
High-frequency homogenization for periodic dispersive media

High-frequency homogenization is used to study dispersive media, containing inclusions placed periodically, for which the properties of the material depend on the frequency (Lorentz or Drude model with damping, for example). Effective properties are obtained near a given point of the dispersion diagram in frequency-wavenumber space. The asymptotic approximations of the dispersion diagrams, and the wavefields, so obtained are then cross-validated via detailed comparison with finite element method simulations in both one and two dimensions.
From a vortex gas to a vortex crystal in instability-driven two-dimensional turbulence

We study structure formation in two-dimensional turbulence driven by an external force, interpolating between linear instability forcing and random stirring, subject to nonlinear damping. Using extensive direct numerical simulations, we uncover a rich parameter space featuring four distinct branches of stationary solutions: large-scale vortices, hybrid states with embedded shielded vortices (SVs) of either sign, and two states composed of many similar SVs. Of the latter, the first is a dense vortex gas where all SVs have the same sign and diffuse across the domain. The second is a hexagonal vortex crystal forming from this gas when the instability is sufficiently weak. These solutions coexist stably over a wide parameter range. The late-time evolution of the system from small-amplitude initial conditions is nearly self-similar, involving three phases: initial inverse cascade, random nucleation of SVs from turbulence and, once a critical number of vortices is reached, a phase of explosive nucleation of SVs, leading to a statistically stationary state. The vortex gas is continued in the forcing parameter, revealing a sharp transition towards the crystal state as the forcing strength decreases. This transition is analysed in terms of the diffusion of individual vortices and tools from statistical physics. The crystal can also decay via an inverse cascade resulting from the breakdown of shielding or insufficient nonlinear damping acting on SVs. Our study highlights the importance of the forcing details in two-dimensional turbulence and reveals the presence of nontrivial SV states in this system, specifically the emergence and melting of a vortex crystal.
Uplift Modeling: From Causal Inference to Personalization

Uplift modeling is a collection of machine learning techniques for estimating causal effects of a treatment at the individual or subgroup levels. Over the last years, causality and uplift modeling have become key trends in personalization at online e-commerce platforms, enabling the selection of the best treatment for each user in order to maximize the target business metric. Uplift modeling can be particularly useful for personalized promotional campaigns, where the potential benefit caused by a promotion needs to be weighed against the potential costs. In this tutorial we will cover basic concepts of causality and introduce the audience to state-of-the-art techniques in uplift modeling. We will discuss the advantages and the limitations of different approaches and dive into the unique setup of constrained uplift modeling. Finally, we will present real-life applications and discuss challenges in implementing these models in production.
3D particle-in-cell simulations of negative and positive streamers in C4F7N–CO2 mixtures

We investigate negative and positive streamers in C4F7N–CO2 mixtures through simulations. These mixtures are considered to be more environmentally friendly than the insulating gas SF6 that is widely used in high voltage technology. Simulations are performed using a 3D particle-in-cell model. Negative streamers can propagate when the background field is close to the critical field. We relate this to their short conductive channels, due to rapid electron attachment, which limits their field enhancement. Positive streamers also require a background field close to the critical field, and in addition a source of free electrons ahead of them. In our simulations these electrons are provided through an artificial stochastic background ionization process as no efficient photoionization process is known for these gases. In 3D, we can only simulate the early inception stage of positive discharges, due to the extremely high electric fields and electron densities that occur. Qualitative 2D Cartesian simulations show that the growth of these discharges is highly irregular, resulting from incoming negative streamers that connect to existing channels. The inclusion of a stochastic background ionization process also has an interesting effect on negative discharges: new streamers can be generated behind previous ones, thereby forming a chain of negative streamers.
An Eigenvalue-Free Implementation of the Log-Conformation Formulation

The log-conformation formulation, although highly successful, was from the beginning formulated as a partial differential equation that contains an, for PDEs unusual, eigenvalue decomposition of the unknown field. To this day, most numerical implementations have been based on this or a similar eigenvalue decomposition, with Knechtges et al. (2014) being the only notable exception for two-dimensional flows. In this paper, we present an eigenvalue-free algorithm to compute the constitutive equation of the log-conformation formulation that works for two- and three-dimensional flows. Therefore, we first prove that the challenging terms in the constitutive equations are representable as a matrix function of a slightly modified matrix of the log-conformation field. We give a proof of equivalence of this term to the more common log-conformation formulations. Based on this formulation, we develop an eigenvalue-free algorithm to evaluate this matrix function. The resulting full formulation is first discretized using a finite volume method, and then tested on the confined cylinder and sedimenting sphere benchmarks.
Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images

Many medical or pharmaceutical processes have strict guidelines regarding continuous hygiene monitoring. This often involves the labor-intensive task of manually counting microorganisms in Petri dishes by trained personnel. Automation attempts often struggle due to major challenges: significant scaling differences, low separation, low contrast, etc. To address these challenges, we introduce AttnPAFPN, a high-resolution detection pipeline that leverages a novel transformer variation, the efficient-global self-attention mechanism. Our streamlined approach can be easily integrated in almost any multi-scale object detection pipeline. In a comprehensive evaluation on the publicly available AGAR dataset, we demonstrate the superior accuracy of our network over the current state-of-the-art. In order to demonstrate the task-independent performance of our approach, we perform further experiments on COCO and LIVECell datasets.
Dr.Bokeh: DiffeRentiable Occlusion-aware Bokeh Rendering

Bokeh is widely used in photography to draw attention to the subject while effectively isolating distractions in the background. Computational methods simulate bokeh effects without relying on a physical camera lens. However, in the realm of digital bokeh synthesis, the two main challenges for bokeh synthesis are color bleeding and partial occlusion at object boundaries. Our primary goal is to overcome these two major challenges using physics principles that define bokeh formation. To achieve this, we propose a novel and accurate filtering-based bokeh rendering equation and a physically-based occlusion-aware bokeh renderer, dubbed Dr.Bokeh, which addresses the aforementioned challenges during the rendering stage without the need of post-processing or data-driven approaches. Our rendering algorithm first preprocesses the input RGBD to obtain a layered scene representation. Dr.Bokeh then takes the layered representation and user-defined lens parameters to render photo-realistic lens blur. By softening non-differentiable operations, we make Dr.Bokeh differentiable such that it can be plugged into a machine-learning framework. We perform quantitative and qualitative evaluations on synthetic and real-world images to validate the effectiveness of the rendering quality and the differentiability of our method. We show Dr.Bokeh not only outperforms state-of-the-art bokeh rendering algorithms in terms of photo-realism but also improves the depth quality from depth-from-defocus.
3D Analytics: Opportunities and Guidelines for Information Systems Research

Progress in sensor technologies has made three-dimensional (3D) representations of the physical world available at a large scale. Leveraging such 3D representations with analytics has the potential to advance Information Systems (IS) research in several areas. However, this novel data type has rarely been incorporated. To address this shortcoming, this article first presents two showcases of 3D analytics applications together with general modeling guidelines for 3D analytics, in order to support IS researchers in implementing research designs with 3D components. Second, the article presents several promising opportunities for 3D analytics to advance behavioral and design-oriented IS research in several contextual areas, such as healthcare IS, human-computer interaction, mobile commerce, energy informatics and others. Third, we investigate the nature of the benefits resulting from the application of 3D analytics, resulting in a list of common tasks of research projects that 3D analytics can support, regardless of the contextual application area. Based on the given showcases, modeling guidelines, research opportunities and task-related benefits, we encourage IS researchers to start their journey into this largely unexplored third spatial dimension.
Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion Recognition from Physiological Signals

Emotions play a significant role in the cognitive processes of the human brain, such as decision making, learning and perception. The use of physiological signals has shown to lead to more objective, reliable and accurate emotion recognition combined with raising machine learning methods. Supervised learning methods have dominated the attention of the research community, but the challenge in collecting needed labels makes emotion recognition difficult in large-scale semi- or uncontrolled experiments. Unsupervised methods are increasingly being explored, however sub-optimal signal feature selection and label identification challenges unsupervised methods' accuracy and applicability. This article proposes an unsupervised deep cluster framework for emotion recognition from physiological and psychological data. Tests on the open benchmark data set WESAD show that deep k-means and deep c-means distinguish the four quadrants of Russell's circumplex model of affect with an overall accuracy of 87%. Seeding the clusters with the subject's subjective assessments helps to circumvent the need for labels.
Diversifying AI: Towards Creative Chess with AlphaZero

In recent years, Artificial Intelligence (AI) systems have surpassed human intelligence in a variety of computational tasks. However, AI systems, like humans, make mistakes, have blind spots, hallucinate, and struggle to generalize to new situations. This work explores whether AI can benefit from creative decision-making mechanisms when pushed to the limits of its computational rationality. In particular, we investigate whether a team of diverse AI systems can outperform a single AI in challenging tasks by generating more ideas as a group and then selecting the best ones. We study this question in the game of chess, the so-called drosophila of AI. We build on AlphaZero (AZ) and extend it to represent a league of agents via a latent-conditioned architecture, which we call AZ_db. We train AZ_db to generate a wider range of ideas using behavioral diversity techniques and select the most promising ones with sub-additive planning. Our experiments suggest that AZ_db plays chess in diverse ways, solves more puzzles as a group and outperforms a more homogeneous team. Notably, AZ_db solves twice as many challenging puzzles as AZ, including the challenging Penrose positions. When playing chess from different openings, we notice that players in AZ_db specialize in different openings, and that selecting a player for each opening using sub-additive planning results in a 50 Elo improvement over AZ. Our findings suggest that diversity bonuses emerge in teams of AI agents, just as they do in teams of humans and that diversity is a valuable asset in solving computationally hard problems.
Interior and boundary mixed norm derivative estimates for nonstationary Stokes equations

We obtain weighted mixed norm Sobolev estimates in the whole space for nonstationary Stokes equations in divergence and nondivergence form with variable viscosity coefficients that are merely measurable in time variable and have small mean oscillation in spatial variables in small cylinders. As an application, we prove interior mixed norm derivative estimates for solutions to both equations. We also discuss boundary mixed norm Hessian estimates for solutions to equations in nondivergence form under the Lions boundary conditions.
$U(1)$ Defects on Domain Lines

Based on recent experimental results, we give field-theoretic description of $U(1)$ defects localized on the domain lines on thin films. We describe topology of our model and solve this model in the adiabatic approximation. It turns out that such a model naturally provides periodic structure observed in experiment. The effective theory turns out to be the sine-Gordon model, but unlike the previous theoretical considerations we argue that in this case it is favorable for sine-Gordon kinks to merge into one defect with a uniform winding. We consider a system of adjacent domain lines and anti-lines and explain the experimental fact that the appearance of defects on a domain line prevents defect creation on the adjacent anti-lines. We also quantize the model and investigate possible effects of finite transverse dimension of the film.
Spin-wave dispersion and magnon chirality in multiferroic 
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Inelastic neutron scattering experiments combining time-of-flight and polarized techniques yield a comprehensive picture of the magnon dispersion in multiferroic TbMnO3 including the dynamic chirality. Taking into account only Mn3+ moments, spin-wave calculations including nearest-neighbor interactions, frustrating next-nearest neighbor exchange as well as single-ion anisotropy and antisymmetric terms describe the energy dispersion and the distribution of neutron scattering intensity in the multiferroic state very well. Polarized neutron scattering reveals strong dynamic chirality of both signs that may be controlled by external electric fields in the multiferroic phase. Also above the onset of long-range multiferroic order in zero electric field, a small inelastic chiral component can be inverted by an electric field. The microscopic spin-wave calculations fully explain also the dynamic chirality of magnetic excitations, which is imprinted by the static chirality of the multiferroic phase. The ordering of Tb3+ moments at lower temperature reduces the broadening of magnons but also renders the magnon dispersion more complex.