Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection

LiDAR-produced point clouds are the major source for most state-of-the-art 3D object detectors. Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect. We present Sparse2Dense, a new framework to efficiently boost 3D detection performance by learning to densify point clouds in latent space. Specifically, we first train a dense point 3D detector (DDet) with a dense point cloud as input and design a sparse point 3D detector (SDet) with a regular point cloud as input. Importantly, we formulate the lightweight plug-in S2D module and the point cloud reconstruction module in SDet to densify 3D features and train SDet to produce 3D features, following the dense 3D features in DDet. So, in inference, SDet can simulate dense 3D features from regular (sparse) point cloud inputs without requiring dense inputs. We evaluate our method on the large-scale Waymo Open Dataset and the Waymo Domain Adaptation Dataset, showing its high performance and efficiency over the state of the arts.
Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification✱

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied ‘closed-set’ and ‘large-scale training samples per category’ logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks; and compare it against state-of-the-art methods. Further, the literature lacks a ‘very-large-scale’ collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale.
Inversion-Based Creativity Transfer with Diffusion Models

In this paper, we introduce the task of “Creativity Trans-fer”. The artistic creativity within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes including semantic elements, object shape, etc. Previous arbitrary example-guided artistic image generation meth-ods (e.g., style transfer) often fail to control shape changes or convey semantic elements. The pre-trained text-to-image synthesis diffusion probabilistic models have achieved remarkable quality, but they often require extensive textual descriptions to accurately portray attributes of a particular painting. We believe that the uniqueness of an artwork lies precisely in the fact that it cannot be adequately explained with normal language. Our key idea is to learn artistic creativity directly from a single painting and then guide the synthesis without providing complex textual descriptions. Speciﬁcally, we assume creativity as a learnable textual description of a painting. We propose an attention-based inversion method, which can efﬁciently and accurately learn the holistic and detailed information of an image, thus capturing the complete artistic creativity of a painting. We demonstrate the quality and efﬁciency of our method on numerous paintings of various artists and styles. Code and models are available at https://github.com/ zyxElsa/creativity-transfer .
Are micro-architectural features able to explain faulty executions in the presence of soft errors? A preliminary study

—Soft errors induced by radiations are one of the most challenging issues impacting the electronic systems’ reliability. In the embedded domain, system designers often rely on Double Modular Redundancy (DMR) to reach the desired level of reliability. This solution has increasing overhead, given the complexity achieved by modern microprocessors in all domains. This paper addresses the promising ﬁeld of using efﬁcient machine-learning powered cores for the detection of soft errors. To create such cores and make them general enough to work with different software applications, microarchitectural attributes are a fascinating option as candidate fault detection features. Several processors already track these features through dedicated Performance Monitoring Units. However, there is an open question to understand to what extent they are enough to detect faulty executions. This paper moves a step forward in this direction. Exploiting the capability of gem5 to simulate real computing systems, perform fault injection experiments and proﬁle microarchitectural attributes (i.e., gem5 Stats), this paper presents the results of a comprehensive analysis regarding the potential attributes to be used for soft error detection and associated models that can be trained with these features. Particular emphasis was devoted to understanding whether event timing could bring additional information to the detection task. This information is crucial to identify the best machine learning models to employ in this challenging task.
Calibration and Analysis of Underwater Earphones by Loudness‐Balance Method

Recent experiments in underwater directional communication necessitated the use of earphones with uniform response characteristics. Two earphones from headset P‐120‐PQC‐1 were calibrated underwater by loudness‐balance method at 50 dB above 1000‐cps threshold SPL. A 35‐dB range in response was observed in a frequency interval of 200–4000 cps, with principal resonance at 600 cps. Equalization was provided, after considering the role of the 50‐phon loudness contour, resulting in uniform response over the interval of 200–2500 cps. The experiment sheds some light on the role of aural vs bone‐conduction modes in underwater listening and suggests some approaches to improved underwater earphone and calibration methods. [Work supported, in part, by U. S. Navy Mine Defense Laboratory. ]
Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning

Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the typical FL paradigm ( e.g ., FedAvg), model weights are sent to and from the server each round to participating clients. Recently, the use of small pre-trained models has been shown effective in federated learning optimization and improving convergence. However, recent state-of-the-art pre-trained models are getting more capable but also have more parameters. In conventional FL, sharing the enormous model weights can quickly put a massive communication burden on the system, especially if more capable models are employed. Can we ﬁnd a solution to enable those strong and readily-available pre-trained models in FL to achieve excellent performance while simultaneously reducing the communication burden? To this end, we investigate the use of parameter-efﬁcient ﬁne-tuning in federated learning and thus introduce a new framework: FedPEFT. Speciﬁcally, we systemically evaluate the performance of FedPEFT across a variety of client stability, data distribution, and differential privacy settings. By only locally tuning and globally sharing a small portion of the model weights, signiﬁcant reductions in the total communication overhead can be achieved while maintaining competitive or even better performance in a wide range of federated learning scenarios, providing insight into a new paradigm for practical and effective federated systems.
QCD Axion Kinetic Misalignment without Prejudice

The axion field, the angular direction of the complex scalar field associated with the spontaneous symmetry breaking of the Peccei–Quinn (PQ) symmetry, could have originated with initial non-zero velocity. The presence of a non-zero angular velocity resulting from additional terms in the potential that explicitly break the PQ symmetry has important phenomenological consequences such as a modification of the axion mass with respect to the conventional PQ framework or an explanation for the observed matter-antimatter asymmetry. We elaborate further on the consequences of the “kinetic misalignment” mechanism, assuming that axions form the entirety of the dark matter abundance. The kinetic misalignment mechanism possesses a weak limit in which the axion field starts to oscillate at the same temperature as in the conventional PQ framework, and a strong limit corresponding to large initial velocities which effectively delay the onset of oscillations. Following a UV-agnostic approach, we show how this scenario impacts the formation of axion miniclusters, and we sketch the details of these substructures along with potential detecting signatures.
Data2Model: Predicting Models from Training Data

—Understanding how changes in training data affect a trained model is critical to building trust in various stages of a machine learning pipeline: from cleaning poor-quality samples and tracking important ones to be collected during data preparation, to calibrating uncertainty of model prediction, to interpreting why certain behaviors of a model emerge during deployment. In this paper, we present a framework, D ATA 2M ODEL , for predicting the output model of a learning algorithm given the input data points. Specifically, D ATA 2M ODEL learns a parameterized function that takes a dataset S as the input and predicts the model obtained by training on S . Despite the potential complexity of the underlying end-to-end training process being approximated, we show that a neural network- based set function class can successfully predict the trained model from its training data. We introduce novel global and local regularization techniques for preventing overfitting and rigorously characterize the expressive power of neural networks (NN) in approximating the end-to-end training process. We perform extensive empirical investigations and demonstrate that D ATA 2M ODEL gives rise to a wide range of applications that boost the interpretability and accountability of machine learning (ML), such as data valuation, data selection, memorization quantification, and model calibration.
Periodic complexes and group actions

In this paper we show that the cohomology of a connected CW-complex is periodic if and only if it is the base space of a spherical fibration with total space that is homotopically finite dimensional. As applications we characterize those discrete groups that act freely and properly on In x Sm; we construct nonstandard free actions of rank-two simple groups on finite complexes Y Sn x Sm; and we prove that a finite p-group P acts freely on such a complex if and only if it does not contain a subgroup isomorphic to (Z/p)3.
Voxel-MAE: Masked Autoencoders for Self-supervised Pre-training Large-scale Point Clouds

Current perception models in autonomous driving greatly rely on large-scale labeled 3D data. However, it is expensive and time-consuming to annotate 3D data. In this work, we aim at facilitating research on self-supervised learning from the vast unlabeled 3D data in autonomous driving. We introduce a masked autoencoding framework for pre-training large-scale point clouds, dubbed Voxel-MAE. We take advantage of the geometric characteristics of large-scale point clouds, and propose the range-aware random masking strategy and binary voxel classiﬁcation task. Speciﬁcally, we transform point clouds into volumetric representations, and randomly mask voxels according to their distance to the capture device. Voxel-MAE reconstructs the occupancy values of masked voxels and distinguishes whether the voxels contain point clouds. This simple binary voxel classiﬁcation objective encourages Voxel-MAE to reason over high-level semantics to recover the masked voxel from only a small amount of visible voxels. Extensive experiments demonstrate the effectiveness of Voxel-MAE across several downstream tasks. For the 3D object detection task, Voxel-MAE reduces half labeled data for car detection on KITTI and boosts small object detection by around 2% mAP on Waymo. For the 3D semantic segmentation task, Voxel-MAE outperforms training from scratch by around 2% mIOU on nuScenes. For the ﬁrst time, our Voxel-MAE shows that it is feasible to pre-train unlabeled large-scale point clouds with masked autoencoding to enhance the 3D perception ability of autonomous driving. Codes are publicly available at https: //github.com/chaytonmin/Voxel-MAE .
Rational points on cubic hypersurfaces that split off two forms

We show that if X⊆ ℙn−1 is a cubic hypersurface defined over ℚ by a cubic form that splits off two forms, and n⩾11, then X(ℚ) is non‐empty. The same holds for an (m1, m2)‐form with m1⩾4 and m2⩾5.
Mixing quantum and classical mechanics and uniqueness of Planck's constant

Observables of quantum or classical mechanics form algebras called quantum or classical Hamilton algebras (HAs), respectively (Grgin and Petersen 1974 J. Math. Phys. 15 764, Sahoo 1977 Pramana 8 545). We show that the tensor product of two quantum HAs, each characterized by a different Planck's constant (PC), is an algebra of the same type characterized by yet another PC. The algebraic structure of mixed quantum and classical systems is then analysed by taking the limit of vanishing PC in one of the component algebras. This approach provides new insight into failures of various formalisms dealing with mixed quantum? classical systems. It shows that in the interacting mixed quantum? classical description, there can be no back-reaction of the quantum system on the classical system. A natural algebraic requirement involving restriction of the tensor product of two quantum HAs to their components proves that PC is unique.
Front fluctuations for the stochastic Cahn-Hilliard equation

We consider the Cahn-Hilliard equation in one space dimension, perturbed by the derivative of a space and time white noise of intensity $\epsilon^{\frac 12}$, and we investigate the effect of the noise, as $\epsilon \to 0$, on the solutions when the initial condition is a front that separates the two stable phases. We prove that, given $\gamma< \frac 23$, with probability going to one as $\epsilon \to 0$, the solution remains close to a front for times of the order of $\epsilon^{-\gamma}$, and we study the fluctuations of the front in this time scaling. They are given by a one dimensional continuous process, self similar of order $\frac 14$ and non Markovian, related to a fractional Brownian motion and for which a couple of representations are given.
Binomial-Poisson entropic inequalities and the M/M/$\infty$ queue

This article provides entropic inequalities for binomial-Poisson distributions, derived from the two point space. They appear as local inequalities of the M/M/$\infty$ queue. They describe in particular the exponential dissipation of $\Phi$-entropies along this process. This simple queueing process appears as a model of ``constant curvature'', and plays for the simple Poisson process the role played by the Ornstein-Uhlenbeck process for Brownian Motion. Some of the inequalities are recovered by semi-group interpolation. Additionally, we explore the behaviour of these entropic inequalities under a particular scaling, which sees the Ornstein-Uhlenbeck process as a fluid limit of M/M/$\infty$ queues. Proofs are elementary and rely essentially on the development of a ``$\Phi$-calculus''.
Toric degenerations and symplectic geometry of smooth projective varieties

Let X be an n ‐dimensional smooth complex projective variety embedded in CPN . We construct a smooth family X over C with an embedding in CPN×C such that its generic fiber is X and its special fiber is the torus (C∗)n sitting in CPN via a monomial embedding. We use this to show that if ω is a Kähler form on X with rational cohomology class, then for any ε>0 there is an open subset Uε⊂X such that vol(X∖Uε)
Parallel velocity shear instabilities in an inhomogeneous plasma with a sheared magnetic field

Injection of fast neutrals with a component of the beam velocity along the magnetic field B0 results in a plasma having a sheared mean velocity along B0. If this parallel velocity shear U′ and the density inhomogeneity scale length Ln are large enough so that U ′|Ln |/(τ + 1) 1/2υi > 1 then a parallel Kelvin‐Helmholtz instability is excited, where vi is the ion thermal speed and τ is the electron temperature/ion temperature. Magnetic shear stabilization requires a shear length Ls comparable to or less than |Ln |, namely, τL s /3 (τ + 1) 172| (Ln / Ls)3/2 | provided |Ln /Ls| 600 and U ′ /Ωi ≪ 1, where ai and Ωi are the ion gyration radius and .
On an extension problem for density matrices

We investigate the problem of the existence of a density matrix ρ123 on a Hilbert space H1⊗H2⊗H3 with given partial traces ρ12 = Tr3 ρ123 and ρ23 = Tr1 ρ123. While we do not solve this problem completely, we offer partial results in the form of some necessary and some sufficient conditions on ρ12 and ρ23. The quantum case differs markedly from the classical (commutative) case, where the obvious necessary compatibility condition suffices, namely, Tr1 ρ12 = Tr3ρ23.
The Orlik-Solomon model for hypersurface arrangements

We develop a model for the cohomology of the complement of a hypersurface arrangement inside a smooth projective complex variety. This generalizes the case of normal crossing divisors, discovered by P. Deligne in the context of the mixed Hodge theory of smooth complex varieties. Our model is a global version of the Orlik-Solomon algebra, which computes the cohomology of the complement of a union of hyperplanes in an affine space. The main tool is the complex of logarithmic forms along a hypersurface arrangement, and its weight filtration. Connections with wonderful compactifications and the configuration spaces of points on curves are also studied.
The Schur multiplier, the non-abelian tensor and exterior square of some Heisenberg Lie algebras

Recently, the authors obtained the Schur multiplier, the tensor square and the non-abelian exterior square of $d$-generator generalized Heisenberg Lie algebras of rank $ \frac{1}{2}d(d-1). $ Here, we intend to obtain the same for a $d$-generator generalized Heisenberg Lie algebra of rank $ \frac{1}{2}d(d-1)-1$ and $ \frac{1}{2}d(d-1)-2. $ Moreover, we show that all of such Lie algebras are capable.
A gyroscope-free visual-inertial flight control and wind sensing system for 10-mg robots

Tiny “gnat robots,” weighing just a few milligrams, were first conjectured in the 1980s. How to stabilize one if it were to hover like a small insect has not been answered. Challenges include the requirement that sensors be both low mass and high bandwidth and that silicon-micromachined rate gyroscopes are too heavy. The smallest robot to perform controlled hovering uses a sensor suite weighing hundreds of milligrams. Here, we demonstrate that an accelerometer represents perhaps the most direct way to stabilize flight while satisfying the extreme size, speed, weight, and power constraints of a flying robot even as it scales down to just a few milligrams. As aircraft scale reduces, scaling physics dictates that the ratio of aerodynamic drag to mass increases. This results in reduced noise in an accelerometer’s airspeed measurement. We show through simulation and experiment on a 30-gram robot that a 2-milligram off-the-shelf accelerometer is able in principle to stabilize a 10-milligram robot despite high noise in the sensor itself. Inspired by wind-vision sensory fusion in the flight controller of the fruit fly Drosophila melanogaster, we then added a tiny camera and efficient, fly-inspired autocorrelation-based visual processing to allow the robot to estimate and reject wind as well as control its attitude and flight velocity using a Kalman filter. Our biology-inspired approach, validated on a small flying helicopter, has a wind gust response comparable to the fruit fly and is small and efficient enough for a 10-milligram flying vehicle (weighing less than a grain of rice). Description A visual-inertial flight control and wind sensing system is small and efficient enough for a 10-milligram aerial robot.
Generating Landmark-based Manipulation Instructions from Image Pairs

We investigate the problem of generating landmark-based manipulation instructions (e.g. move the blue block so that it touches the red block on the right ) from image pairs showing a before and an after state in a visual scene. We present a transformer model with difference attention heads that learns to attend to target and landmark objects in consecutive images via a difference key. Our model outperforms the state-of-the-art for instruction generation on the BLOCKS dataset and particularly improves the accuracy of generated target and landmark references. Furthermore, our model outperforms state-of-the-art models on a difference spotting dataset.
Sharp Poincaré inequalities in a class of non-convex sets

Let $\gamma$ be a smooth curve whose image is symmetric with respect to the y-axis, and let D be a planar domain consisting of the points on one side of $\gamma$, within a suitable distance $\delta$ of $\gamma$. Denote by $\mu_1^{odd}(D)$ the smallest nontrivial Neumann eigenvalue having a corresponding eigenfunction that is odd with respect to the y-axis. If $\gamma$ satisfies some simple geometric conditions, then $\mu_1^{odd}(D)$ can be sharply estimated from below in terms of the length of $\gamma$, its curvature, and $\delta$. Moreover, we give explicit conditions on $\delta$ that ensure $\mu_1^{odd}(D)=\mu_1(D)$. Finally, we can extend our bound on $\mu_1^{odd}(D)$ to a certain class of three-dimensional domains. In both the two- and three-dimensional settings, our domains are generically non-convex.
Securing the Ethereum from Smart Ponzi Schemes: Identification Using Static Features

Malware detection approaches have been extensively studied for traditional software systems. However, the development of blockchain technology has promoted the birth of a new type of software system–decentralized applications. Composed of smart contracts, a type of application that implements the Ponzi scheme logic (called smart Ponzi schemes) has caused irreversible loss and hindered the development of blockchain technology. These smart contracts generally had a short life but involved a large amount of money. Whereas identification of these Ponzi schemes before causing financial loss has been significantly important, existing methods suffer from three main deficiencies, i.e., the insufficient dataset, the reliance on the transaction records, and the low accuracy. In this study, we first build a larger dataset. Then, a large number of features from multiple views, including bytecode, semantic, and developers, are extracted. These features are independent of the transaction records. Furthermore, we leveraged machine learning methods to build our identification model, i.e., Multi-view Cascade Ensemble model (MulCas). The experiment results show that MulCas can achieve higher performance and robustness in the scope of our dataset. Most importantly, the proposed method can identify smart Ponzi scheme at the creation time.