Miracle-dz/UniFAR-FTUs huggingface dataset:
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer

Although autoregressive models have achieved promising results on image generation, their unidirectional generation process prevents the resultant images from fully reflecting global contexts. To address the issue, we propose an effective image generation framework of Draft-and-Revise with Contextual RQ-transformer to consider global contexts during the generation process. As a generalized VQ-VAE, RQ-VAE first represents a high-resolution image as a sequence of discrete code stacks. After code stacks in the sequence are randomly masked, Contextual RQ-Transformer is trained to infill the masked code stacks based on the unmasked contexts of the image. Then, Contextual RQ-Transformer uses our two-phase decoding, Draft-and-Revise, and generates an image, while exploiting the global contexts of the image during the generation process. Specifically. in the draft phase, our model first focuses on generating diverse images despite rather low quality. Then, in the revise phase, the model iteratively improves the quality of images, while preserving the global contexts of generated images. In experiments, our method achieves state-of-the-art results on conditional image generation. We also validate that the Draft-and-Revise decoding can achieve high performance by effectively controlling the quality-diversity trade-off in image generation.
Thermally-induced qubit coherence in quantum electromechanics

Quantum coherence, the ability of a quantum system to be in a superposition of orthogonal quantum states, is a distinct feature of the quantum mechanics, thus marking a deviation from classical physics. Coherence finds its applications in quantum sensing and metrology, quantum thermodynamics and computation. A particularly interesting is the possibility to observe coherence arising in counter-intuitive way from thermal energy that is without implementation of intricate protocols involving coherent driving sequences. In this manuscript, we investigate quantum coherence emerging in a hybrid system composed of a two-level system (qubit) and a thermal quantum harmonic oscillator (a material mechanical oscillator), inspired by recent experimental progress in fabrication of such systems. We show that quantum coherence is created in such a composite system solely from the interaction of the parts and persists under relevant damping. Implementation of such scheme will demonstrate previously unobserved mechanisms of coherence generation and can be beneficial for hybrid quantum technologies with mechanical oscillators and qubits.
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons

Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to input perturbations. Vector Neurons (VN) is a recently developed framework offering a simple yet effective approach for deriving rotation-equivariant analogs of standard machine learning operations by extending one-dimensional scalar neurons to three-dimensional"vector neurons. "We introduce a novel"VN-Transformer"architecture to address several shortcomings of the current VN models. Our contributions are: $(i)$ we derive a rotation-equivariant attention mechanism which eliminates the need for the heavy feature preprocessing required by the original Vector Neurons models; $(ii)$ we extend the VN framework to support non-spatial attributes, expanding the applicability of these models to real-world datasets; $(iii)$ we derive a rotation-equivariant mechanism for multi-scale reduction of point-cloud resolution, greatly speeding up inference and training; $(iv)$ we show that small tradeoffs in equivariance ($\epsilon$-approximate equivariance) can be used to obtain large improvements in numerical stability and training robustness on accelerated hardware, and we bound the propagation of equivariance violations in our models. Finally, we apply our VN-Transformer to 3D shape classification and motion forecasting with compelling results.
Security–Reliability Tradeoff Analysis for SWIPT- and AF-Based IoT Networks With Friendly Jammers

Radio-frequency (RF) energy harvesting (EH) in wireless relaying networks has attracted considerable recent interest, especially for supplying energy to relay nodes in the Internet of Things (IoT) systems to assist the information exchange between a source and a destination. Moreover, limited hardware, computational resources, and energy availability of IoT devices have raised various security challenges. To this end, physical-layer security (PLS) has been proposed as an effective alternative to cryptographic methods for providing information security. In this study, we propose a PLS approach for simultaneous wireless information and power transfer (SWIPT)-based half-duplex (HD) amplify-and-forward (AF) relaying systems in the presence of an eavesdropper. Furthermore, we take into account both static power splitting relaying (SPSR) and dynamic power splitting relaying (DPSR) to thoroughly investigate the benefits of each one. To further enhance secure communication, we consider multiple friendly jammers to help prevent wiretapping attacks from the eavesdropper. More specifically, we provide a reliability and security analysis by deriving closed-form expressions of outage probability (OP) and intercept probability (IP), respectively, for both the SPSR and DPSR schemes. Then, simulations are also performed to validate our analysis and the effectiveness of the proposed schemes. Specifically, numerical results illustrate the nontrivial tradeoff between reliability and security of the proposed system. In addition, we conclude from the simulation results that the proposed DPSR scheme outperforms the SPSR-based scheme in terms of OP and IP under the influences of different parameters on system performance.
BigVGAN: A Universal Neural Vocoder with Large-Scale Training

Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder that generalizes well for various out-of-distribution scenarios without fine-tuning. We introduce periodic activation function and anti-aliased representation into the GAN generator, which brings the desired inductive bias for audio synthesis and significantly improves audio quality. In addition, we train our GAN vocoder at the largest scale up to 112M parameters, which is unprecedented in the literature. We identify and address the failure modes in large-scale GAN training for audio, while maintaining high-fidelity output without over-regularization. Our BigVGAN, trained only on clean speech (LibriTTS), achieves the state-of-the-art performance for various zero-shot (out-of-distribution) conditions, including unseen speakers, languages, recording environments, singing voices, music, and instrumental audio. We release our code and model at: https://github.com/NVIDIA/BigVGAN
{\Lambda}CDM with baryons vs. MOND: the time evolution of the universal acceleration scale in the Magneticum simulations

MOdiﬁed Newtonian Dynamics (MOND) is an alternative to the standard Cold Dark Matter (CDM) paradigm which proposes an alteration of Newton’s laws of motion at low accelerations, characterized by a universal acceleration scale 𝑎 0 . It attempts to explain observations of galactic rotation curves and predicts a speciﬁc scaling relation of the baryonic and total acceleration in galaxies, referred to as the Rotational Acceleration Relation (RAR), which can be equivalently formulated as a Mass Discrepancy Acceleration Relation (MDAR). The appearance of these relations in observational data such as SPARC has lead to investigations into the existence of similar relations in cosmological simulations using the standard Λ CDM model. Here, we report the existence of an RAR and MDAR similar to that predicted by MOND in Λ CDM using a large sample of galaxies extracted from a cosmological, hydrodynamical simulation ( Magneticum ). Furthermore, by using galaxies in Magneticum at diﬀerent redshifts, a prediction for the evolution of the inferred acceleration parameter 𝑎 0 with cosmic time is derived by ﬁtting a MOND force law to these galaxies. In Magneticum , the best ﬁt for 𝑎 0 is found to increase by a factor (cid:39) 3 from redshift 𝑧 = 0 to 𝑧 = 2 . 3. This oﬀers a powerful test from cosmological simulations to distinguish between MOND and Λ CDM observationally.
Overcoming the Spectral Bias of Neural Value Approximation

Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm. While multi-layer perceptron networks are universal function approximators, recent works in neural kernel regression suggest the presence of a spectral bias, where fitting high-frequency components of the value function requires exponentially more gradient update steps than the low-frequency ones. In this work, we re-examine off-policy reinforcement learning through the lens of kernel regression and propose to overcome such bias via a composite neural tangent kernel. With just a single line-change, our approach, the Fourier feature networks (FFN) produce state-of-the-art performance on challenging continuous control domains with only a fraction of the compute. Faster convergence and better off-policy stability also make it possible to remove the target network without suffering catastrophic divergences, which further reduces TD}(0)'s estimation bias on a few tasks.
Sequential Analysis of Chunks of Pure Qubits

We investigate an advantage for information processing of ordering a set of states over making a global quantum measurement with a ﬁxed number of copies. The setting that we want to investigate is clearer if we introduce Alice and Bob, as usual. Suppose Alice has N < ∞ copies of one of two quantum states σ 0 or σ 1 and she gives this states to Bob. We ﬁnd that there are situations where Bob has to make chunks of l states and process them sequentially to optimally distinguish the two hypotheses. We ﬁnd an expression for the optimal l given some desired error constraints in the case of pure qubits. The results presented here also have relevance in the “it from qubit” program in the sense that they suggest that time arises from information processing.
QED as a many-body theory of worldlines: General formalism and infrared structure

We discuss a reformulation of QED in which matter and gauge fields are integrated out explicitly, resulting in a many-body Lorentz covariant theory of 0+1 dimensional worldlines describing super-pairs of spinning charges interacting through Lorentz forces. This provides a powerful, string inspired definition of amplitudes to all loop orders. In particular, one obtains a more general formulation of Wilson loops and lines, with exponentiated dynamical fields and spin precession contributions, and worldline contour averages exactly defined through first quantized path integrals. We discuss in detail the attractive features of this formalism for high order perturbative computations. We show that worldline S-matrix elements, to all loop orders in perturbation theory, can be constructed to be manifestly free of soft singularities, with infrared (IR) divergences captured and removed by endpoint photon exchanges at infinity that are equivalent to the soft coherent dressings of the Dyson S-matrix proposed by Faddeev and Kulish. We discuss these IR structures and make connections with soft theorems, the Abelian exponentiation of IR divergences and cusp anomalous dimensions. Follow-up papers will discuss the efficient computation of cusp anomalous dimensions and universal features of soft theorems. 1 ar X iv :2 20 6. 04 18 8v 1 [ he pth ] 8 J un 2 02 2
Efficient Human Pose Estimation via 3D Event Point Cloud

Human Pose Estimation (HPE) based on RGB images has experienced a rapid development benefiting from deep learning. However, event-based HPE has not been fully studied, which remains great potential for applications in extreme scenes and efficiency-critical conditions. In this paper, we are the first to estimate 2D human pose directly from 3D event point cloud. We propose a novel representation of events, the rasterized event point cloud, aggregating events on the same position of a small time slice. It maintains the 3D features from multiple statistical cues and significantly reduces memory consumption and computation complexity, proved to be efficient in our work. We then leverage the rasterized event point cloud as input to three different back-bones, PointNet, DGCNN, and Point Transformer, with two linear layer decoders to predict the location of human key-points. We find that based on our method, PointNet achieves promising results with much faster speed, whereas Point Transfomer reaches much higher accuracy, even close to previous event-frame-based methods. A comprehensive set of results demonstrates that our proposed method is consistently effective for these 3D backbone models in event-driven human pose estimation. Our method based on PointNet with 2048 points input achieves 82.46mm in MPJPE3D on the DHP19 dataset, while only has a latency of 12.29ms on an NVIDIA Jetson Xavier NX edge computing platform, which is ideally suitable for real-time detection with event cameras. Code is available at https: //github.com/ MasterHow/EventPointPose.
Neural Prompt Search

The size of vision models has grown exponentially over the last few years, especially after the emergence of Vision Transformer. This has motivated the development of parameter-efficient tuning methods, such as learning adapter layers or visual prompt tokens, which allow a tiny portion of model parameters to be trained whereas the vast majority obtained from pre-training are frozen. However, designing a proper tuning method is non-trivial: one might need to try out a lengthy list of design choices, not to mention that each downstream dataset often requires custom designs. In this paper, we view the existing parameter-efficient tuning methods as"prompt modules"and propose Neural prOmpt seArcH (NOAH), a novel approach that learns, for large vision models, the optimal design of prompt modules through a neural architecture search algorithm, specifically for each downstream dataset. By conducting extensive experiments on over 20 vision datasets, we demonstrate that NOAH (i) is superior to individual prompt modules, (ii) has a good few-shot learning ability, and (iii) is domain-generalizable. The code and models are available at https://github.com/Davidzhangyuanhan/NOAH.
Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective

The graph neural network (GNN) has demonstrated its superior performance in various applications. The working mechanism behind it, however, remains mysterious. GNN models are designed to learn effective representations for graph-structured data, which intrinsically coincides with the principle of graph signal denoising (GSD). Algorithm unrolling, a"learning to optimize"technique, has gained increasing attention due to its prospects in building efficient and interpretable neural network architectures. In this paper, we introduce a class of unrolled networks built based on truncated optimization algorithms (e.g., gradient descent and proximal gradient descent) for GSD problems. They are shown to be tightly connected to many popular GNN models in that the forward propagations in these GNNs are in fact unrolled networks serving specific GSDs. Besides, the training process of a GNN model can be seen as solving a bilevel optimization problem with a GSD problem at the lower level. Such a connection brings a fresh view of GNNs, as we could try to understand their practical capabilities from their GSD counterparts, and it can also motivate designing new GNN models. Based on the algorithm unrolling perspective, an expressive model named UGDGNN, i.e., unrolled gradient descent GNN, is further proposed which inherits appealing theoretical properties. Extensive numerical simulations on seven benchmark datasets demonstrate that UGDGNN can achieve superior or competitive performance over the state-of-the-art models.
CLIP-Actor: Text-Driven Recommendation and Stylization for Animating Human Meshes

We propose CLIP-Actor, a text-driven motion recommendation and neural mesh stylization system for human mesh animation. CLIP-Actor animates a 3D human mesh to conform to a text prompt by recommending a motion sequence and optimizing mesh style attributes. We build a text-driven human motion recommendation system by leveraging a large-scale human motion dataset with language labels. Given a natural language prompt, CLIP-Actor suggests a text-conforming human motion in a coarse-to-fine manner. Then, our novel zero-shot neural style optimization detailizes and texturizes the recommended mesh sequence to conform to the prompt in a temporally-consistent and pose-agnostic manner. This is distinctive in that prior work fails to generate plausible results when the pose of an artist-designed mesh does not conform to the text from the beginning. We further propose the spatio-temporal view augmentation and mask-weighted embedding attention, which stabilize the optimization process by leveraging multi-frame human motion and rejecting poorly rendered views. We demonstrate that CLIP-Actor produces plausible and human-recognizable style 3D human mesh in motion with detailed geometry and texture solely from a natural language prompt.
Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers

Advances in artificial intelligence (AI) are shaping modern life, from transportation, health care, science, finance, to the military. Forecasts of AI development could help improve policyand decisionmaking. We report the results from a large survey of AI and machine learning (ML) researchers on their beliefs about progress in AI. The survey, fielded in late 2019, elicited forecasts for near-term AI development milestones and highor human-level machine intelligence, defined as when machines are able to accomplish every or almost every task humans are able to do currently. As part of this study, we recontacted respondents from a highly-cited study by Grace et al. (2018) in which AI/ML researchers gave forecasts about high-level machine intelligence and near-term milestones in AI development. Results from our 2019 survey show that, in aggregate, AI/ML researchers surveyed placed a 50% likelihood of human-level machine intelligence being achieved by 2060. The results show researchers newly contacted in 2019 expressed similar beliefs about the progress of advanced AI as respondents in the Grace et al. (2018) survey. For the recontacted participants from the Grace et al. (2018) study, the aggregate forecast for a 50% likelihood of high-level machine intelligence shifted from 2062 to 2076, although this change is not statistically significant likely due to the small size of our panel sample. Forecasts of several near-term AI milestones have reduced in time, suggesting more optimism about AI progress. Finally, AI/ML researchers also exhibited significant optimism about how human-level machine intelligence will impact society.
IT AT THE LOCAL LEVEL IN SUPPORT OF SOCIO-ECONOMIC DEVELOPMENT - THE EGYPTIAN EXPERIENCE

Dr. Sherif Kamel American University in Cairo 1101 Corniche El-Nil Street, Garden City, Cairo, Egypt Abstract: This paper describes the case of Egypt in introducing, using and diffusing the use of information technology at the local level in socioeconomic development and rationalizing the decision making process. The paper describes the case of the Governorates Information and Decision Support Centers Project (GIDSC). The project was initiated by the government of Egypt to help improve the managerial, administrative and technological infrastructure for the establishment of a cutting edge platform for information and decision support at the local level. The paper will demonstrate the establishment of GIDSCs in Egypt's 26 governorates with its various base building, diffusion, expansion and sustained growth phases with their strategies and impacts. Keywords: Information systems, developing countries, decision support systems, IT in local administration, IT transfer, decision-making process.
Young Children's Developing Understanding of Geometric Shapes.

FEBRUARY 1999 How can we improve geometry instruction at the preschool and primary levels? To answer that question, I conducted research to analyze young children’s understanding of the geometric concepts of triangle and rectangle and to determine patterns in the development of this understanding from ages 3 through 6. The research suggests that early childhood educators need to rethink the way that basic shapes are introduced to young children. Since a basic understanding of shapes is essential to a future study of geometry, teachers need to focus on how best to help children develop that initial understanding of shape categories. After a brief explanation of the research, specific ways to present developmentally appropriate activities designed to enhance children’s understanding of basic shapes are discussed.
A ROBUST NONLINEAR CONTROLLER FOR AUTONOMOUS HELICOPTER LANDING

ABSTARCT We consider the problem of controlling the vertical motion of a nonlinear model of a helicopter during landing, while stabilizing the lateral and longitudinal position and maintaining a constant attitude. The dynamics of the helicopter main and tail rotors are often neglected to simplify the control design, but this reduction has the disadvantage of an unreal choice of the control inputs. We have solved this contradiction by choosing the main controller required for performing the secure autonomous landing by neglecting the complex rotors dynamics, and then we have included these important dynamics in a closed loop model having real controls determined from an auxiliary controller. Simulation results show the effectiveness of the method to cope with uncertainties on the plant and actuator model.
Open and Distance Learning: Benefits and Challenges of Technology Usage for Online Teaching and Learning in Africa

Open and Distance Learning (ODL) is gaining prominence in African Universities. Universities in Africa use distance learning mode where materials are sent to learners, supplemented with a fewer contact sessions through study centers. With the advent of new technologies for teaching and learning, African Universities are also moving towards online teaching and learning. This paper presents the benefits and challenges associated with the use of technology in ODL mode in Botswana Higher Education context. Web based surveys were used to collect the data for this study. The study was carried out among the online lecturers and learners in two institutions of Higher Learning in Botswana. 9 experienced lecturers and 25 learners who had used technology for their ODL teaching and learning participated in this study. Findings indicate that, major benefits of using technology for online teaching and learning include interaction and student engagement; access to latest information; content sharing and communication. The major challenges faced are access of technology, affordability and technophobia. Emerging technologies recommended include Virtual learning platforms (Blackboard, Moodle, Edmodo, Schoology), Social networking platforms (Facebook, WhatsApp) and Content sharing software (Camtasia). The survey results will provide insightful guidelines to ODL lecturers, ODL learners and the institutions who are offering online learning modes of learning, especially in Africa.