Miracle-dz/UniFAR-FTUs huggingface dataset:
GOOD: General Optimization-based Fusion for 3D Object Detection via LiDAR-Camera Object Candidates

3D object detection serves as the core basis of the perception tasks in autonomous driving. Recent years have seen the rapid progress of multi-modal fusion strategies for more robust and accurate 3D object detection. However, current researches for robust fusion are all learning-based frameworks, which demand a large amount of training data and are inconvenient to implement in new scenes. In this paper, we propose GOOD, a general optimization-based fusion framework that can achieve satisfying detection without training additional models and is available for any combinations of 2D and 3D detectors to improve the accuracy and robustness of 3D detection. First we apply the mutual-sided nearest-neighbor probability model to achieve the 3D-2D data association. Then we design an optimization pipeline that can optimize different kinds of instances separately based on the matching result. Apart from this, the 3D MOT method is also introduced to enhance the performance aided by previous frames. To the best of our knowledge, this is the first optimization-based late fusion framework for multi-modal 3D object detection which can be served as a baseline for subsequent research. Experiments on both nuScenes and KITTI datasets are carried out and the results show that GOOD outperforms by 9.1\% on mAP score compared with PointPillars and achieves competitive results with the learning-based late fusion CLOCs.
Deep author name disambiguation using DBLP data

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.
The Cascaded Forward Algorithm for Neural Network Training

Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning. However, there exist some limitations associated with this algorithm, such as getting stuck in local minima and experiencing vanishing/exploding gradients, which have led to questions about its biological plausibility. To address these limitations, alternative algorithms to backpropagation have been preliminarily explored, with the Forward-Forward (FF) algorithm being one of the most well-known. In this paper we propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples and thus leads to a more efficient process at both training and testing. Moreover, in our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems. The proposed method is evaluated on four public image classification benchmarks, and the experimental results illustrate significant improvement in prediction accuracy in comparison with the baseline.
Turbulent bores and hydraulic jumps

A theoretical model for the velocity field and the surface profile of bores and hydraulic jumps is developed. The turbulence is assumed to be concentrated in a wedge that originates at the toe of the front and spreads towards the bottom, and the turbulent closure used is a simplified k-ε model allowing for non-equilibrium in the turbulent kinetic energy. The flow equations are satisfied in depth-integrated form (method of weighted residuals), and measured deviations from static pressure are analysed and shown to have a negligible effect on the results. Comparison with measurements shows good agreement, but there is a clear need for further experimental results in the highly turbulent region near the free surface. Some basic mechanisms of the flow are discussed and explained from the theory.
Counter-Example Guided Inductive Synthesis of Control Lyapunov Functions for Uncertain Systems

We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems affected by parametric uncertainty with arbitrary shape. In the CEGIS framework, a learner iteratively proposes a candidate control Lyapunov function and a tailored controller by solving a linear matrix inequality (LMI) feasibility problem, while a verifier either falsifies the current candidate by producing a counter-example to be considered at the next iteration, or it certifies that the tentative control Lyapunov function actually enjoys such feature. We investigate the Lipschitz continuity of the objective function of the global optimization problem solved by the verifier, which is key to establish the convergence of our method in a finite number of iterations. Numerical simulations confirm the effectiveness of the proposed approach.
Decentralized Riemannian natural gradient methods with Kronecker-product approximations

With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problems. We study the natural gradient methods for the large-scale decentralized optimization problems on Riemannian manifolds, where the local objective function defined by the local dataset is of a log-probability type. By utilizing the structure of the Riemannian Fisher information matrix (RFIM), we present an efficient decentralized Riemannian natural gradient descent (DRNGD) method. To overcome the communication issue of the high-dimension RFIM, we consider a class of structured problems for which the RFIM can be approximated by a Kronecker product of two low-dimension matrices. By performing the communications over the Kronecker factors, a high-quality approximation of the RFIM can be obtained in a low cost. We prove that DRNGD converges to a stationary point with the best-known rate of $\mathcal{O}(1/K)$. Numerical experiments demonstrate the efficiency of our proposed method compared with the state-of-the-art ones. To the best of our knowledge, this is the first Riemannian second-order method for solving decentralized manifold optimization problems.
Can we observe the ion-neutral drift velocity in prestellar cores?

Given the low ionization fraction of molecular clouds, ambipolar diffusion is thought to be an integral process in star formation. However, chemical and radiative-transfer effects, observational challenges, and the fact that the ion-neutral drift velocity is inherently very small render a definite detection of ambipolar diffusion extremely non-trivial. Here, we study the ion-neutral drift velocity in a suite of chemodynamical, non-ideal magnetohydrodynamic (MHD), two-dimensional axisymmetric simulations of prestellar cores where we alter the temperature, cosmic-ray ionization rate, visual extinction, mass-to-flux ratio, and chemical evolution. Subsequently, we perform a number of non-local thermodynamic equilibrium (non-LTE) radiative-transfer calculations considering various idealized and non-idealized scenarios in order to assess which factor (chemistry, radiative transfer and/or observational difficulties) is the most challenging to overcome in our efforts to detect the ion-neutral drift velocity. We find that temperature has a significant effect in the amplitude of the drift velocity with the coldest modelled cores (T = 6 K) exhibiting drift velocities comparable to the sound speed. Against expectations, we find that in idealized scenarios (where two species are perfectly chemically co-evolving) the drift velocity ``survives"radiative-transfer effects and can in principle be observed. However, we find that observational challenges and chemical effects can significantly hinder our view of the ion-neutral drift velocity. Finally, we propose that $\rm{HCN}$ and $\rm{HCNH^+}$, being chemically co-evolving, could be used in future observational studies aiming to measure the ion-neutral drift velocity.
HIVE: Harnessing Human Feedback for Instructional Visual Editing

Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human feedback, as their outputs may not adhere to the correct instructions and preferences of users. In this paper, we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically, we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides, to mitigate the bias brought by the limitation of data, we contribute a new 1M training dataset, a 3.6K reward dataset for rewards learning, and a 1K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively, showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin.
Scribble-Supervised RGB-T Salient Object Detection

Salient object detection segments attractive objects in scenes. RGB and thermal modalities provide complementary information and scribble annotations alleviate large amounts of human labor. Based on the above facts, we propose a scribble-supervised RGB-T salient object detection model. By a four-step solution (expansion, prediction, aggregation, and supervision), label-sparse challenge of scribble-supervised method is solved. To expand scribble annotations, we collect the superpixels that foreground scribbles pass through in RGB and thermal images, respectively. The expanded multi-modal labels provide the coarse object boundary. To further polish the expanded labels, we propose a prediction module to alleviate the sharpness of boundary. To play the complementary roles of two modalities, we combine the two into aggregated pseudo labels. Supervised by scribble annotations and pseudo labels, our model achieves the state-of-the-art performance on the relabeled RGBT-S dataset. Furthermore, the model is applied to RGB-D and video scribble-supervised applications, achieving consistently excellent performance.1
On a Frank-Wolfe approach for abs-smooth functions

We propose an algorithm which appears to be the first bridge between the fields of conditional gradient methods and abs-smooth optimization. Our problem setting is motivated by various applications that lead to nonsmoothness, such as $\ell_1$ regularization, phase retrieval problems, or ReLU activation in machine learning. To handle the nonsmoothness in our problem, we propose a generalization to the traditional Frank-Wolfe gap and prove that first-order minimality is achieved when it vanishes. We derive a convergence rate for our algorithm which is {\em identical} to the smooth case. Although our algorithm necessitates the solution of a subproblem which is more challenging than the smooth case, we provide an efficient numerical method for its partial solution, and we identify several applications where our approach fully solves the subproblem. Numerical and theoretical convergence is demonstrated, yielding several conjectures.
Measurement Optimization under Uncertainty using Deep Reinforcement Learning

Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many uncertainties in system input and parameters that affect system response sensor measurements. Accounting for the uncertainties in this typically expensive optimization is challenging due to computational intractability. This study proposes a stochastic environment in the form of a Markov Decision Process and a sensor placement agent that aims to maximize the information gain from placing a particular number of sensors of different types within the system. The agent is trained to maximize its reward based on an information-theoretic reward function. To verify the efficacy, the approach is applied to place a set of heterogeneous sensors in a shear building model. This methodology can be used to accommodate uncertainties in the sensor placement problem in real-world systems.
Multi‐step tangential versus transversal linearizations in non‐linear dynamics

A family of multi‐step tangential linearization (MTnL) techniques is developed for numeric–analytic and geometric integration of non‐linear oscillators under deterministic loading. A family of multi‐step transversal linearization (MTrL) schemes is also explored and a comparison between the two linearization families is provided with a view of bringing out their relative advantages and deficiencies. The MTnL methodology essentially works through an application of the concept of MTrL to non‐linear rate equations. Different variants of the MTnL are obtained and it turns out that they are specific cases of a generalized MTnL method. Local error estimates are also provided. Moreover, higher order MTrL and MTnL methods are successfully implemented through Chebyshev nodes instead of equidistant nodes. This helps to improve accuracy even with larger step sizes. A comparison of relative errors through the MTrL and MTnL techniques consistently indicates a superior quality of approximations via the MTrL route. Whilst the usage of the tangential linearization is very common in non‐linear computational mechanics, it is demonstrated that the transversal version offers, in addition to higher numerical accuracy, an easier implementation and avoids computation of Jacobian matrices. This allows the MTrL family to be applicable even to non‐linear oscillators having C0 vector fields. Copyright © 2007 John Wiley & Sons, Ltd.
Decentralized output feedback controller design for nonlinear interconnected systems with unknown control direction and time‐varying delays

The decentralized output feedback control problem is considered for a class of large‐scale systems with unknown time‐varying delays. The uncertain interconnections are bounded by general nonlinear functions with unknown coefficients. The control direction parameters are unknown for each subsystem, which brings a challenging issue for decentralized controller design. To deal with this problem, we propose a new decentralized control scheme with the help of Nussbaum function. The decentralized filter is designed at first. By constructing Lyapunov–Krasovskii functional, we design the dynamic output feedback controller. It is rigorously proved that the closed‐loop system is asymptotically stable. Finally, the simulation is performed, and the results verify the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.
Calibration of a scanning probe microscope by the use of an interference-holographic position measurement system

Nonlinearities and hysteretic behaviours of piezoelectric actuators used in scanning probe microscopes (SPM) lead to uncertain position determinations of the moving probe in all directions. Therefore, probes must be controlled with nanometre resolution and the three dimensions must be traceable to the unit of length for metrological investigations. Many positioning systems exist but only a few are implementable into SPM or have unsatisfying dynamic properties as well as Abbe errors.A hybrid implementation of a capacitive and a novel three-dimensional interferometric position measurement system has been developed to overcome these problems. The theory and technique of this holographic interferometry are given and the characteristics of the components used for implementation in the final set-up are analysed. A realized SPM head including this technique, in addition to a capacitive position system, as well as its properties are discussed.
Large Sample Confidence Regions Based on Subsamples under Minimal Assumptions

In this article, the construction of confidence regions by approximating the sampling distribution of some statistic is studied. The true sampling distribution is estimated by an appropriate normalisation of the values of the statistic computed over subsamples of the data. In the i.i. d. context, the method has been studied by Wu in regular situations where the statistic is asymptotically normal. The goal of the present work is to prove the method yields asymptotically valid confidence regions under minimal conditions. Essentially, all that is required is that the statistic, suitably normalized, possesses a limit distribution under the true model. Unlike the bootstrap, the convergence to the limit distribution need not be uniform in any sense. The method is readily adapted to parameters of stationary time series or, more generally, homogeneous random fields. For example, an immediate application is the construction of a confidence interval for the spectral density function of a homogeneous random field
GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation

Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. However, the tight coupling between the current text encoder and image decoder in T2I models makes it challenging to replace or upgrade. Such changes often require massive fine-tuning or even training from scratch with the prohibitive expense. To address this problem, we propose GlueGen, which applies a newly proposed GlueNet model to align features from single-modal or multi-modal encoders with the latent space of an existing T2I model. The approach introduces a new training objective that leverages parallel corpora to align the representation spaces of different encoders. Empirical results show that GlueNet can be trained efficiently and enables various capabilities beyond previous state-of-the-art models: 1) multilingual language models such as XLM-Roberta can be aligned with existing T2I models, allowing for the generation of high-quality images from captions beyond English; 2) GlueNet can align multi-modal encoders such as AudioCLIP with the Stable Diffusion model, enabling sound-to-image generation; 3) it can also upgrade the current text encoder of the latent diffusion model for challenging case generation. By the alignment of various feature representations, the GlueNet allows for flexible and efficient integration of new functionality into existing T2I models and sheds light on X-to-image (X2I) generation.1
Thermal Radiation Effects on Squeezing Flow Casson Fluid between Parallel Disks

In this paper, we investigate the thermal radiation effects in a time-dependent two-dimensional flow of a Casson fluid between two parallel disks when upper disk is taken to be impermeable and lower one is porous. Suitable similarity transforms are employed to convert governing partial differential equations into system of ordinary differential equations. Well known Homotopy Analysis Method (HAM) is employed to obtain the expressions for velocity and temperature profiles. Effects of different physical parameters such as squeeze number $S$, Prandtl number $Pr$, Eckert number $Ec$ and the dimensionless length on the flow are also discussed with the help of graphs for velocity and temperature coupled with a comprehensive discussions. The skin friction coefficient and local Nusselt number along with convergence of the series solutions obtained by HAM are presented in tabulated form, while numerical solution is obtained by $RK-4$ method and comparison shows an excellent agreement between both the solutions.
An efficient semi‐implicit subgrid method for free‐surface flows on hierarchical grids

We present a new modelling strategy for improving the efficiency of computationally intensive flow problems in environmental free‐surface flows. The approach combines a recently developed semi‐implicit subgrid method with a hierarchical grid solution strategy. The method allows the incorporation of high‐resolution data on subgrid scale to obtain a more accurate and efficient hydrodynamic model. The subgrid method improves the efficiency of the hierarchical grid method by providing better solutions on coarse grids. The method is applicable to both steady and unsteady flows, but we particularly focus on river flows with steady boundary conditions. There, the combined hierarchical grid–subgrid method reduces the computational effort to obtain a steady state with factors up to 43. For unsteady models, the method can be used for efficiently generating accurate initial conditions on high‐resolution grids. Additionally, the method provides automatic insight in grid convergence. We demonstrate the efficiency and applicability of the method using a schematic test for the vortex shedding around a circular cylinder and a real‐world river case study. Copyright © 2015 John Wiley & Sons, Ltd.
Some Key Research Problems in Automated Theorem Proving for Hardware and Software Verification

This paper sketches the state of the art in the application of mechanical theorem provers to the verification of commercial computer hardware and software. While the paper focuses on the theorem proving system ACL2, developed by the two authors, it references much related work in formal methods. The paper is intended to satisfy the curiosity of readers interested in logic and artificial intelligence as to the role of mechanized theorem proving in hardware and software design today. In addition, it points out some of the key research topics in the area. These topics transcend any one particular theorem proving system. Algunos problemas claves de investigaci ´ on en la demostraci ´ on mec´ anica de teoremas para la verificaci´ on de hardware y software