Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Pretrained Models for Sequential Decision-Making: Synthesis, Verification, Grounding, and Perception

Recently developed pretrained models can encode rich world knowledge expressed in multiple modalities, such as text and images. However, the outputs of these models cannot be integrated into algorithms to solve sequential decision-making tasks. We develop an algorithm that utilizes the knowledge from pretrained models to construct and verify controllers for sequential decision-making tasks, and to ground these controllers to task environments through visual observations. In particular, the algorithm queries a pretrained model with a user-provided, text-based task description and uses the model's output to construct an automaton-based controller that encodes the model's task-relevant knowledge. It then verifies whether the knowledge encoded in the controller is consistent with other independently available knowledge, which may include abstract information on the environment or user-provided specifications. If this verification step discovers any inconsistency, the algorithm automatically refines the controller to resolve the inconsistency. Next, the algorithm leverages the vision and language capabilities of pretrained models to ground the controller to the task environment. It collects image-based observations from the task environment and uses the pretrained model to link these observations to the text-based control logic encoded in the controller (e.g., actions and conditions that trigger the actions). We propose a mechanism to ensure the controller satisfies the user-provided specification even when perceptual uncertainties are present. We demonstrate the algorithm's ability to construct, verify, and ground automaton-based controllers through a suite of real-world tasks, including daily life and robot manipulation tasks.
Two Novel Approaches to Detect Community: A Case Study of Omicron Lineage Variants PPI Network

The capacity to identify and analyze protein-protein interactions, along with their internal modular organization, plays a crucial role in comprehending the intricate mechanisms underlying biological processes at the molecular level. We can learn a lot about the structure and dynamics of these interactions by using network analysis. We can improve our understanding of the biological roots of disease pathogenesis by recognizing network communities. This knowledge, in turn, holds significant potential for driving advancements in drug discovery and facilitating personalized medicine approaches for disease treatment. In this study, we aimed to uncover the communities within the variant B.1. 1.529 (Omicron virus) using two proposed novel algorithm (ABCDE and ALCDE) and four widely recognized algorithms: Girvan-Newman, Louvain, Leiden, and Label Propagation algorithm. Each of these algorithms has established prominence in the field and offers unique perspectives on identifying communities within complex networks. We also compare the networks by the global properties, statistic summary, subgraph count, graphlet and validate by the modulaity. By employing these approaches, we sought to gain deeper insights into the structural organization and interconnections present within the Omicron virus network.
Comparison Theorem for Infinite-Dimensional Linear Impulsive Systems

We consider a linear impulsive system in an infinite-dimensional Banach space. It is assumed that the moments of impulsive action satisfy the averaged dwell-time condition and the linear operator on the right side of the differential equation generates an analytic semigroup in the state space. Using commutator identities, we prove a comparison theorem that reduces the problem of asymptotic stability of the original system to the study of a simpler system with constant dwell-times. An illustrative example of a linear impulsive system of parabolic type in which the continuous and discrete dynamics are both unstable is given.
A review on the questions of spin and spin quantum correlations in the relativistic regime

The majority of current understanding of the quantum correlations is in the field of non-relativistic quantum mechanics. To develop quantum information and computation tasks fully, one must inevitably take into account the relativistic effects. In this regard, the spin is one of the central tools. For this purpose, it is of paramount importance to understand and characterize fully the theory of spin in relativistic quantum information theory where the spin states act as qubit. This area is still far from being resolved. As a result, this article will explore the recent studies of the concepts of the spin and spin quantum correlations in inertial frames and some apparent paradoxes regarding this concept. We will mainly focus on the problem of characterizing the spin, reduced spin density matrices and spin quantum correlations in inertial reference frames and the apparent paradoxes involved therein. Another important aspect is the use of tools of quantum field theory to extend several concepts in non-relativistic domain to relativistic one. In this regard, we analyze the development of the theory of relativistic secret sharing and a correlation measure namely the entanglement of purification.
Quantum Mechanics with Real Numbers: Entanglement, Superselection Rules and Gauges

We show how imaginary numbers in quantum physics can be eliminated by enlarging the Hilbert space followed by an imposition of—what effectively amounts to—a superselection rule. We illustrate this procedure with a qubit and apply it to the Mach–Zehnder interferometer. The procedure is somewhat reminiscent of the constrained quantization of the electromagnetic field, where, in order to manifestly comply with relativity, one enlarges the Hilbert Space by quantizing the longitudinal and scalar modes, only to subsequently introduce a constraint to make sure that they are actually not directly observable.Quanta 2023; 12: 164–170.
The philosophy of causal set theory

This article presents the most interesting philosophical issues as they arise in causal set theory. The first concerns the apparent disappearance of spacetime at the fundamental level. It shows how the looming empirical incoherence is averted if we adopt spacetime functionalism. Second, classical sequential growth dynamics rekindles hope for a fundamental passage of physical time compatible with relativistic physics. The article argues that this hope is faint at best, as a block view offers the most natural interpretation of dynamical causal set theory. Third, causal set theory admits a very natural structuralist interpretation, enabling a fruitful interaction between debates in philosophy of science concerning structural realism and the metaphysics of causal sets.
Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual Translation of Dravidian Languages

Current research in zero-shot translation is plagued by several issues such as high compute requirements, increased training time and off target translations. Proposed remedies often come at the cost of additional data or compute requirements. Pivot based neural machine translation is preferred over a single-encoder model for most settings despite the increased training and evaluation time. In this work, we overcome the shortcomings of zero-shot translation by taking advantage of transliteration and linguistic similarity. We build a single encoder-decoder neural machine translation system for Dravidian-Dravidian multilingual translation and perform zero-shot translation. We compare the data vs zero-shot accuracy tradeoff and evaluate the performance of our vanilla method against the current state of the art pivot based method. We also test the theory that morphologically rich languages require large vocabularies by restricting the vocabulary using an optimal transport based technique. Our model manages to achieves scores within 3 BLEU of large-scale pivot-based models when it is trained on 50\% of the language directions.
Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface

This paper proposes a technique for efficiently modeling dynamic humans by explicifying the implicit neural fields via a Neural Explicit Surface (NES). Implicit neural fields have advantages over traditional explicit representations in modeling dynamic 3D content from sparse observations and effectively representing complex geometries and appearances. Implicit neural fields defined in 3D space, however, are expensive to render due to the need for dense sampling during volumetric rendering. Moreover, their memory efficiency can be further optimized when modeling sparse 3D space. To overcome these issues, the paper proposes utilizing Neural Explicit Surface (NES) to explicitly represent implicit neural fields, facilitating memory and computational efficiency. To achieve this, the paper creates a fully differentiable conversion between the implicit neural fields and the explicit rendering interface of NES, leveraging the strengths of both implicit and explicit approaches. This conversion enables effective training of the hybrid representation using implicit methods and efficient rendering by integrating the explicit rendering interface with a newly proposed rasterization-based neural renderer that only incurs a texture color query once for the initial ray interaction with the explicit surface, resulting in improved inference efficiency. NES describes dynamic human geometries with pose-dependent neural implicit surface deformation fields and their dynamic neural textures both in 2D space, which is a more memory-efficient alternative to traditional 3D methods, reducing redundancy and computational load. The comprehensive experiments show that NES performs similarly to previous 3D approaches, with greatly improved rendering speed and reduced memory cost.
A Comparative Assessment of Multi-View Fusion Learning For Crop Classification

With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitude, and noise of RS data. The typical approach for merging multiple RS sources has been input-level fusion, but other - more advanced - fusion strategies may outperform this traditional approach. This work assesses different fusion strategies for crop classification in the CropHarvest dataset. The fusion methods proposed in this work outperform models based on individual views and previous fusion methods. We do not find one single fusion method that consistently outperforms all other approaches. Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance. Despite this, we suggest a preliminary criterion for the selection of fusion methods.
TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities

Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness.
Multi-Visual-Inertial System: Analysis, Calibration and Estimation

In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial measurement units (IMUs) or gyroscopes and global and(or) rolling shutter cameras. We are especially interested in the full calibration of the associated visual-inertial sensors, including the IMU or camera intrinsics and the IMU-IMU(or camera) spatiotemporal extrinsics as well as the image readout time of rolling-shutter cameras (if used). To this end, we develop a new analytic combined IMU integration with intrinsics-termed ACI3-to preintegrate IMU measurements, which is leveraged to fuse auxiliary IMUs and(or) gyroscopes alongside a base IMU. We model the multi-inertial measurements to include all the necessary inertial intrinsic and IMU-IMU spatiotemporal extrinsic parameters, while leveraging IMU-IMU rigid-body constraints to eliminate the necessity of auxiliary inertial poses and thus reducing computational complexity. By performing observability analysis of MVIS, we prove that the standard four unobservable directions remain - no matter how many inertial sensors are used, and also identify, for the first time, degenerate motions for IMU-IMU spatiotemporal extrinsics and auxiliary inertial intrinsics. In addition to the extensive simulations that validate our analysis and algorithms, we have built our own MVIS sensor rig and collected over 25 real-world datasets to experimentally verify the proposed calibration against the state-of-the-art calibration method such as Kalibr. We show that the proposed MVIS calibration is able to achieve competing accuracy with improved convergence and repeatability, which is open sourced to better benefit the community.
Field theory of a superconductor with repulsion

A superconductor emerges as a condensate of electron pairs, which bind despite their strong Coulomb repulsion. Eliashberg's theory elucidates the mechanisms enabling them to overcome this repulsion and predicts the transition temperature and pairing correlations. However, a comprehensive understanding of how repulsion impacts the phenomenology of the resulting superconductor remains elusive. We present a formalism that addresses this challenge by applying the Hubbard-Stratonovich transformation to an interaction including instantaneous repulsion and retarded attraction. We first decompose the interaction into frequency scattering channels and then integrate out the fermions. The resulting bosonic action is complex and the saddle point corresponding to Eliashberg's equations generally extends into the complex plane and away from the physical axis. We numerically determine this saddle point using the gradient descent method, which is particularly well-suited for the case of strong repulsion. We then turn to consider fluctuations around this complex saddle point. The matrix controlling fluctuations about the saddle point is found to be a non-Hermitian symmetric matrix, which generally suffers from exceptional points that are tuned by different parameters. These exceptional points may influence the thermodynamics of the superconductor. For example, within the quadratic approximation the upper critical field sharply peaks at a critical value of the repulsion strength related to an exceptional point appearing at $T_c$. Our work facilitates the mapping between microscopic and phenomenological theories of superconductivity, particularly in the presence of strong repulsion. It has the potential to enhance the accuracy of theoretical predictions for experiments in systems where the pairing mechanism is unknown.
3D Scene Diffusion Guidance using Scene Graphs

Guided synthesis of high-quality 3D scenes is a challenging task. Diffusion models have shown promise in generating diverse data, including 3D scenes. However, current methods rely directly on text embeddings for controlling the generation, limiting the incorporation of complex spatial relationships between objects. We propose a novel approach for 3D scene diffusion guidance using scene graphs. To leverage the relative spatial information the scene graphs provide, we make use of relational graph convolutional blocks within our denoising network. We show that our approach significantly improves the alignment between scene description and generated scene.
DNA droplets for intelligent and dynamical artificial cells: from the viewpoint of computation and non-equilibrium systems

Living systems are molecular assemblies whose dynamics are maintained by non-equilibrium chemical reactions. To date, artificial cells have been studied from such physical and chemical viewpoints. This review briefly gives a perspective on using DNA droplets in constructing artificial cells. A DNA droplet is a coacervate composed of DNA nanostructures, a novel category of synthetic DNA self-assembled systems. The DNA droplets have programmability in physical properties based on DNA base sequence design. The aspect of DNA as an information molecule allows physical and chemical control of nanostructure formation, molecular assembly and molecular reactions through the design of DNA base pairing. As a result, the construction of artificial cells equipped with non-equilibrium behaviours such as dynamical motions, phase separations, molecular sensing and computation using chemical energy is becoming possible. This review mainly focuses on such dynamical DNA droplets for artificial cell research in terms of computation and non-equilibrium chemical reactions.
Understanding the Role of Time-Varying Unobserved Ability Heterogeneity in Education Production

Unobserved ability heterogeneity has long been postulated to play a key role in human capital development. Traditional strategies to estimate education production functions do not allow for varying role or development of unobserved ability as a child ages. Such restrictions are highly inconsistent with a growing body of scientific evidence; moreover, in order to obtain unbiased parameter estimates of observed educational inputs, researchers must properly account for unobserved skills that may be correlated with other inputs to the production process. To illustrate our empirical strategy we use experimental data from Tennessee's Student/Teacher Achievement Ratio experiment, known as Project STAR. We find that unobserved ability is endogenously developed over time and its impact on cognitive achievement varies significantly between grades in all subject areas. Moreover, we present evidence that accounting for time-varying unobserved ability across individuals and a more general depreciating pattern of observed inputs are both important when estimating education production functions.
Research on redundant transmission method of distribution network protection data based on device direct connection

The reliability of data transmission in traditional protection systems mainly depends on the redundant realization of the communication network. The protection devices in the system do not have the capability and transmission mechanism for redundant data transmission. In order to solve this problem, this paper proposes a data redundancy transmission method based on protection devices, so that the protection devices in the system can be directly connected to each other and data transmission can be realized. Through the repeated transmission and reception of data by different communication ports of each protection device, a set of multi-port data dynamic transmission strategy based on protection device is established, forming a redundant transmission method that does not depend on network communication equipment. This method reduces equipment costs while improving communication efficiency and reliability.
Fast reaction of soluble surfactant can remobilize a stagnant cap

Abstract Analytical solutions are derived showing that a stagnant cap of surfactant at the interface between two viscous fluids caused by a linear extensional flow can be remobilized by fast kinetic exchange of surfactant with one of the fluids. Using a complex variable formulation of this multiphysics problem at zero capillary number, zero Reynolds number and zero bulk Péclet number, and assuming a linear equation of state, it is shown that the system is governed by a forced complex Burgers equation at arbitrary surface Péclet number. Consequently, this nonlinear system is shown to be linearizable using a complex analogue of the Cole–Hopf transformation. Steady equilibria of the system at any finite value of the surface Péclet number are found explicitly in terms of parabolic cylinder functions. While surface diffusion is naturally expected to mollify sharp gradients associated with stagnant caps and to remobilize the interface, this work gives an analytical demonstration of the less intuitive result that fast kinetic exchange has a similar effect. Indeed, the analytical approach here imposes no limit on the surface Péclet number, which can be taken to be infinitely large so that surface diffusion is completely absent. Mathematically, the solution structure is then very rich allowing a theoretical investigation of this extreme case where it is seen that fast surfactant exchange with the bulk can alone remobilize a stagnant cap. Remarkably, it is also possible to track explicitly the time evolution of the system to these remobilized equilibria by finding time-evolving exact solutions.
Wind turbine bearing defect identification method based on improved SOLOv2

Aiming at the problems of low recognition rate of complex shape defects on wind turbine bearing surface and inaccurate localization of small target defects by traditional inspection methods, this paper proposes an improved SOLOv2-based bearing surface defect recognition method. The method selects ResNeXt-101-FPN as the backbone network on the basis of SOLOv2 model framework, combines deformable convolution (DCNv2) to strengthen the learning of defect morphological features by the network, which is more adaptable to complex defect objects; proposes improved mask feature branching, adds adaptive attention module and feature enhancement module, which not only reduces the loss of feature information, but also enhances the feature expression and change the way of feature fusion. Experiments show that the mean average precision (mAP) of the improved SOLOv2 network for bearing surface defect recognition is 96.6%, which is 2.8% higher than that before the improvement. The average recall (AR) is 96.8%, which is 1.6% higher than before the improvement; the improved model is less affected by the variable shape of the defects, locates small targets more accurately, and effectively solves the problems of partial missed segmentation and under-segmentation.
Multi-feature short-term load forecasting based on stacking ensemble learning

Short-term power load forecasting plays an important role in power system dispatching. To improve forecasting accuracy, a short-term load forecasting model based on stacking ensemble learning was proposed. Firstly, add effective multi-feature variables, and establishes a Stacking ensemble learning model for the load data and feature, which was ensembles by Light Gradient Boosting Machine (abbr. LightGBM) and eXtreme Gradient Boosting (abbr. XGBoost) for prediction. Finally, the comparison and experimental results show that the forecasting error of the proposed model is less than that of the comparative model.