Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Face Parsing via Surface Parameterization and 2D Semantic Segmentation Network

Face parsing assigns pixel-wise semantic labels as the face representation for computers, which is the fundamental part of many advanced face technologies. Compared with 2D face parsing, 3D face parsing shows more potential to achieve better performance and further application, but it is still challenging due to 3D mesh data computation. Recent works introduced different methods for 3D surface segmentation, while the performance is still limited. In this paper, we propose a method based on the"3D-2D-3D"strategy to accomplish 3D face parsing. The topological disk-like 2D face image containing spatial and textural information is transformed from the sampled 3D face data through the face parameterization algorithm, and a specific 2D network called CPFNet is proposed to achieve the semantic segmentation of the 2D parameterized face data with multi-scale technologies and feature aggregation. The 2D semantic result is then inversely re-mapped to 3D face data, which finally achieves the 3D face parsing. Experimental results show that both CPFNet and the"3D-2D-3D"strategy accomplish high-quality 3D face parsing and outperform state-of-the-art 2D networks as well as 3D methods in both qualitative and quantitative comparisons.
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks

Current state-of-the-art vision-and-language models are evaluated on tasks either individually or in a multi-task setting, overlooking the challenges of continually learning (CL) tasks as they arrive. Existing CL benchmarks have facilitated research on task adaptation and mitigating"catastrophic forgetting", but are limited to vision-only and language-only tasks. We present CLiMB, a benchmark to study the challenge of learning multimodal tasks in a CL setting, and to systematically evaluate how upstream continual learning can rapidly generalize to new multimodal and unimodal tasks. CLiMB includes implementations of several CL algorithms and a modified Vision-Language Transformer (ViLT) model that can be deployed on both multimodal and unimodal tasks. We find that common CL methods can help mitigate forgetting during multimodal task learning, but do not enable cross-task knowledge transfer. We envision that CLiMB will facilitate research on a new class of CL algorithms for this challenging multimodal setting.
Utility of probability density evolution method for experimental reliability‐based active vibration control

The utility of probability density evolution method for reliability‐based active vibration control of a cantilevered flexible beam is experimentally investigated. In this respect, an optimal linear quadratic regulator (LQR) is utilized together with an observer to design an online full‐state feedback controller. In order to design a well‐performing controller and to simulate the controller performance, a system model is obtained via identification techniques. Reliability tests are consequently performed to verify the effectiveness of the presented reliability assessment method as a foundation for reliability‐based control. Subsequently, a hybrid metaheuristic optimization scheme of continuous ant colony system is used for simultaneous integrated tuning of the LQR and observer parameters through all processes. Optimization of the controller and the observer is performed with the objective of system structural reliability. An optimized version of classical LQR controller is also developed for making comparisons between the two control methods. Finally, a comparison is made between the mean power consumption of the 2 controllers. It is realized that the reliability‐based controller decreases operating costs without compromising system safety.
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning

Negative sampling (NS) loss plays an important role in learning knowledge graph embedding (KGE) to handle a huge number of entities. However, the performance of KGE degrades without hyperparameters such as the margin term and number of negative samples in NS loss being appropriately selected. Currently, empirical hyperparameter tuning addresses this problem at the cost of computational time. To solve this problem, we theoretically analyzed NS loss to assist hyperparameter tuning and understand the better use of the NS loss in KGE learning. Our theoretical analysis showed that scoring methods with restricted value ranges, such as TransE and RotatE, require appropriate adjustment of the margin term or the number of negative samples different from those without restricted value ranges, such as RESCAL, ComplEx, and DistMult. We also propose subsampling methods specialized for the NS loss in KGE studied from a theoretical aspect. Our empirical analysis on the FB15k-237, WN18RR, and YAGO3-10 datasets showed that the results of actually trained models agree with our theoretical findings.
3D Object Detection for Autonomous Driving: A Review and New Outlooks

Autonomous driving, in recent years, has been receiv-ing increasing attention for its potential to relieve drivers’ bur-dens and improve the safety of driving. In modern autonomous driving pipelines, the perception system is an indispensable component, aiming to accurately estimate the status of surrounding environments and provide reliable observations for prediction and planning. 3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving. First, we introduce the background of 3D object detection and discuss the challenges in this task. Second, we conduct a comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR-based, camera-based, and multi-modal detection approaches. We also provide an in-depth analysis of the potentials and challenges in each category of methods. Additionally, we systematically investigate the applications of 3D object detection in driving systems. Finally, we conduct a performance analysis of the 3D object detection approaches, and we further summarize the research trends over the years and prospect the future directions of this area.
An Equal-Area Criterion Based Pulse Encoding Strategy for RF Switch-Mode Power Amplifier

Abstract A novel pulse encoding strategy for RF switch-mode power amplifier (SMPA) is proposed in this paper to achieve benefits of multi-level delta-sigma modulators (DSM) while alleviating challenges that multi-level DSM brings to SMPA. The pulse encoder consists of a multi-level delta-sigma modulators (DSM) followed by a level transformation module (LTM). Based on the equal-area criterion, LTM directly maps the multi-level DSM pulse train into a binary pulse train which enables a simple system configuration. The feasibility and effectiveness of proposed strategy are verified by numerical simulation and experimental measurement. Results show that, with a similar coding efficiency, the proposed strategy obtains more than 7 dB improvement in average in-band signal to noise ratio (SNR) compared to traditional two-level DSM.
Work-loop techniques for optimising nonlinear forced oscillators

Linear and nonlinear resonant states can be restrictive: they exist at particular discrete states in frequency and/or elasticity, under particular (e.g., simple-harmonic) waveforms. In forced oscillators, this restrictiveness is an obstacle to system design and control modulation: altering the system elasticity, or modulating the response, would both appear to necessarily incur a penalty to efficiency. In this work, we describe an approach for bypassing this obstacle. Using novel work-loop techniques, we prove and illustrate how certain classes of resonant optimisation problem lead to non-unique solutions. In a structural optimisation context, several categories of energetically-optimal elasticity are non-unique. In an optimal control context, several categories of energetically-optimal frequency are non-unique. For these classes of non-unique optimum, we can derive simple bounds defining the optimal region. These novel theoretical results have practical implications for the design and control of a range of biomimetic propulsion systems, including flapping-wing micro-air-vehicles: using these results, we can generate efficient forms of wingbeat modulation for flight control.
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval

Unsupervised image retrieval aims to learn an efﬁcient retrieval system without expensive data annotations, but most existing methods rely heavily on handcrafted feature descriptors or pre-trained feature extractors. To minimize human supervision, recent advance proposes deep fully unsupervised image retrieval aiming at training a deep model from scratch to jointly optimize visual features and quantization codes. However, existing approach mainly focuses on instance contrastive learning without considering underlying semantic structure information, resulting in sub-optimal performance. In this work, we propose a novel self-supervised consistent quantization approach to deep fully unsupervised image retrieval, which consists of part consistent quantization and global consistent quantization. In part consistent quantization , we devise part neighbor semantic consistency learning with codeword diversity regularization. This allows to discover underlying neighbor structure information of sub-quantized representations as self-supervision. In global consistent quantization , we employ contrastive learning for both embedding and quantized representations and fuses these representations for consistent contrastive regularization between instances. This can make up for the loss of useful representation information during quantization and regularize consistency between instances. With a uniﬁed learning objective of part and global consistent quantization, our approach exploits richer self-supervision cues to facilitate model learning. Extensive experiments on three benchmark datasets show the superiority of our approach over the state-of-the-art methods.
Bioinspired random projections for robust, sparse classification

Inspired by the use of random projections in biological sensing systems, we present a new algorithm for processing data in classification problems. This is based on observations of the human brain and the fruit fly's olfactory system and involves randomly projecting data into a space of greatly increased dimension before applying a cap operation to truncate the smaller entries. This leads to a simple algorithm that is very computationally efficient and can be used to either give a sparse representation with minimal loss in classification accuracy or give improved robustness, in the sense that classification accuracy is improved when noise is added to the data. This is demonstrated with numerical experiments, which supplement theoretical results demonstrating that the resulting signal transform is continuous and invertible, in an appropriate sense.
Pattern bounds for principal specializations of $\beta$-Grothendieck Polynomials

There has been recent interest in lower bounds for the principal specializations of Schubert polynomials ν w := S w (1 , . . . , 1). We prove a conjecture of Yibo Gao in the setting of 1243-avoiding permutations that gives a lower bound for ν w in terms of the permutation patterns contained in w . We extended this result to principal specializations of β -Grothendieck polynomials ν ( β ) w := G ( β ) w (1 , . . . , 1) by restricting to the class of vexillary 1243-avoiding permutations. Our methods are bijective, oﬀering a combinatorial interpretation of the coeﬃcients c w and c ( β ) w appearing in these conjectures.
Overcoming High Frequency Limitations of Current-Mode Control Using a Control Conditioning Approach - Part II: Implementation and Hardware

—This article is the second part of a paper series about interference in extremum (i.e., peak or valley) current-mode control, which applies to both ﬁxed and variable switching frequency power converters. Speciﬁcally, this part presents three control conditioning methods that mitigate the adverse effect of interference. These methods are new ways to use: (i) slope compensation; (ii) low-pass ﬁltering; and (iii) the phenomenon of comparator-overdrive-delay, for control conditioning. The stability criterion, closed-loop dynamics, and transient performance are derived with mathematical rigor for each method. The design tradeoffs are illustrated, discussed, and compared. The effectiveness of all three methods are demonstrated and validated in hardware using a power converter operating at multi-MHz switching frequencies.
Pantograph–Catenary Arcing Detection Based on Electromagnetic Radiation

Currently, the detection equipment for offline arcing on high-speed trains is of great interest because it can evacuate the contact state between the contact wire and the strip. In order to ensure the safety of the train under the effect of the arcing, the self-designed fourth-order Hilbert curve fractal antenna is chosen to detect the arcing, in this paper, based on its electromagnetic radiation signal. Aspects of time-, frequency-, and joint time–frequency-domain analyses of the signals were considered. Meanwhile, the experiments were conducted several times with different circuit currents and different electrode gaps, and the electromagnetic pulses under different moments were studied. Results indicated that the electromagnetic radiation pulses were corresponding to the abrupt variations of voltage during arcing, and all the characteristic frequencies were located at similar values (18 MHz). Mechanisms of using the short-time Fourier transform method to obtain an accurate arcing moment, extinguished time, and arcing duration have been explained in detail. Thus, the characteristics of arcing electromagnetic radiation could become the significant and efficient parameters to detect the pantograph–catenary operation state of high-speed trains.
Reliability of capacitive RF MEMS switches at high and low temperatures

Some applications of RF MEMS switches, such as aircraft condition monitoring and distributed satellite communication, present a unique challenge for device design and reliability. This article examines these switches when operational temperatures in the range −60°C to 100°C are envisioned. The basic operation of a capacitive MEMS switch is described and two tools for examining device reliability, modeling, and on‐chip experimentation, are discussed in the case of capacitive MEMS switches. 1D, 2D, and 3D models are presented with emphasis on 3D coupled‐field finite‐element analysis, including temperature effects. Results and findings from the 3D simulations are reported. In particular, the advantages of employing corrugated membranes in the design of RF MEMS switches are assessed. Their performance in terms of reliability as a function of temperature is quantified. The effects of corrugation on the geometric parameters are discussed in the context of device‐design optimization. In order to assess reliability experimentally, the M‐test and the membrane deflection experiment (MDE) are reviewed due to their on‐chip characteristic and simplicity. Ways in which these experimental/computational methodologies can be combined for identifying material properties and device performance is also highlighted. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14: 317–328, 2004.
Strange quark matter from a baryonic approach

We construct a model for dense matter based on low-density nuclear matter properties that exhibits a chiral phase transition and that includes strangeness through hyperonic degrees of freedom. Empirical constraints from nuclear matter alone allow for various scenarios, from a strong first-order chiral transition at relatively low densities through a weaker transition at higher densities, even up to a smooth crossover not far beyond the edge of the allowed range. The model parameters can be chosen such that at asymptotically large densities the chirally restored phase contains strangeness and the speed of sound approaches the conformal limit, resulting in a high-density phase that resembles deconfined quark matter. Additionally, if the model is required to reproduce sufficiently massive compact stars, the allowed parameter range is significantly narrowed down, resulting for instance in a very narrow range for the poorly known slope parameter of the symmetry energy, $L\simeq (88-92)\, {\rm MeV}$. We also find that for the allowed parameter range strangeness does not appear in the form of hyperons in the chirally broken phase and the chiral transition is of first order. Due to its unified approach and relative simplicity - here we restrict ourselves to zero temperature and the mean-field approximation - the model can be used in the future to study dense matter under compact star conditions in the vicinity of the chiral phase transition, for instance to compute the surface tension or to investigate spatially inhomogeneous phases.
(Certified!!) Adversarial Robustness for Free!

In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2-norm bounded perturbations by relying exclusively on off-the-shelf pretrained models. To do so, we instantiate the denoised smoothing approach of Salman et al. 2020 by combining a pretrained denoising diffusion probabilistic model and a standard high-accuracy classifier. This allows us to certify 71% accuracy on ImageNet under adversarial perturbations constrained to be within an 2-norm of 0.5, an improvement of 14 percentage points over the prior certified SoTA using any approach, or an improvement of 30 percentage points over denoised smoothing. We obtain these results using only pretrained diffusion models and image classifiers, without requiring any fine tuning or retraining of model parameters.
Shallow and Deep Nonparametric Convolutions for Gaussian Processes

A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function. The moving average, or process convolutions, construction of GPs allows some additional flexibility, but still requires choosing a proper smoothing kernel, which is non-trivial. Previous approaches have built covariance functions by using GP priors over the smoothing kernel, and by extension the covariance, as a way to bypass the need to specify it in advance. However, such models have been limited in several ways: they are restricted to single dimensional inputs, e.g. time; they only allow modelling of single outputs and they do not scale to large datasets since inference is not straightforward. In this paper, we introduce a nonparametric process convolution formulation for GPs that alleviates these weaknesses by using a functional sampling approach based on Matheron's rule to perform fast sampling using interdomain inducing variables. Furthermore, we propose a composition of these nonparametric convolutions that serves as an alternative to classic deep GP models, and allows the covariance functions of the intermediate layers to be inferred from the data. We test the performance of our model on benchmarks for single output GPs, multiple output GPs and deep GPs and find that our approach can provide improvements over standard GP models, particularly for larger datasets.
Ideal Polytopes for Representations of $GL_n(\mathbb{C})$

In this paper we use the superpotential Wq for the flag variety GLn/B and particular coordinate systems that we call ‘ideal coordinates for i’, to construct polytopes P i λ inside R+ , associated to highest weight representations Vλ of GLn(C). Here i is a reduced expression of the longest element of the Weyl group and R+ is the set of positive roots of GLn. The lattice points of P i λ can be used to encode a basis of the representation Vλ. In particular, for a specific choice of i, the polytope P i λ is unimodularly equivalent to a Gelfand-Tsetlin polytope. The construction of the polytopes involves tropicalisation of the superpotential, namely P i λ is { Trop ( W i tλ ) ≥ 0 } written in a tropical version of the ideal coordinates for i. Using work of Judd ([12]) we have that there is a unique ‘positive’ critical point of Wtλ over the field of Puiseux series. Its coordinates, in terms of the ideal coordinates for i, are positive in the sense of having positive leading term. The remarkable property of our new polytopes relates to the tropical version of this critical point, which, for every choice of i, gives a point in R+ that lies in the interior of the polytope P i λ. We prove that this tropical critical point is independent of the reduced expression i, and that it is given by a pattern called the ‘ideal filling’ for λ that was introduced by Judd. Finally, combining these results with work of Rietsch ([21]) relating critical points of the superpotential with Toeplitz matrices, we show that for a totally positive lower-triangular Toeplitz matrix over the field of Puiseux series factorized into simple root subgroups, the valuations of the factors give an ideal filling.
The Indian Pulsar Timing Array: First data release

Abstract We present the pulse arrival times and high-precision dispersion measure estimates for 14 millisecond pulsars observed simultaneously in the 300 $-$ 500 MHz and 1260 $-$ 1460 MHz frequency bands using the upgraded Giant Metrewave Radio Telescope. The data spans over a baseline of 3.5 years (2018-2021), and is the first official data release made available by the Indian Pulsar Timing Array collaboration. This data release presents a unique opportunity for investigating the interstellar medium effects at low radio frequencies and their impact on the timing precision of pulsar timing array experiments. In addition to the dispersion measure time series and pulse arrival times obtained using both narrowband and wideband timing techniques, we also present the dispersion measure structure function analysis for selected pulsars. Our ongoing investigations regarding the frequency dependence of dispersion measures have been discussed. Based on the preliminary analysis for five millisecond pulsars, we do not find any conclusive evidence of chromaticity in dispersion measures. Data from regular simultaneous two-frequency observations are presented for the first time in this work. This distinctive feature leads us to the highest precision dispersion measure estimates obtained so far for a subset of our sample. Simultaneous multi-band upgraded Giant Metrewave Radio Telescope observations in 300 $-$ 500 MHz and 1260 $-$ 1460 MHz are crucial for high-precision dispersion measure estimation and for the prospect of expanding the overall frequency coverage upon the combination of data from the various Pulsar Timing Array consortia in the near future. Parts of the data presented in this work are expected to be incorporated into the upcoming third data release of the International Pulsar Timing Array.
Preference Change in Persuasive Robotics

—Human-robot interaction exerts inﬂuence towards the human, which often changes behavior. This article explores an externality of this changed behavior - preference change. It expands on previous work on preference change in AI systems. Speciﬁcally, this article will explore how a robot’s adaptive behavior, personalized to the user, can exert inﬂuence through social interactions, that in turn change a user’s preference. It argues that the risk of this is high given a robot’s unique ability to inﬂuence behavior compared to other pervasive technologies . Persuasive Robotics thus runs the risk of being manipulative.