Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicción de Múltiples Puntos de Series de Tiempo Utilizando Support Vector Machines

SE PRESENTA LA EVALUACION DE LA PREDICCION DE MULTIPLES PUNTOS DE SERIES DE TIEMPO, MEDIANTE UN CORRIMIENTO DE VENTANA PARA SUPPORT VECTOR MACHINES (SVM) CON DOS FUNCIONES DE KERNEL DISTINTAS (LINEAL Y CON BASE RADIAL). PARA LA EVALUACION SE UTILIZO UN CONJUNTO DE TREINTA SERIES DE DIFERENTE ORIGEN Y COMPORTAMIENTO DINAMICO. SE ENCUENTRA QUE LA SVM POSEE UNA BUENA CAPACIDAD PARA AJUSTARSE A LAS DIFERENTES DINAMICAS DE LAS SERIES DE TIEMPO Y PRESENTA UN BUEN DESEMPENO PARA LA PREDICCION DE LOS PRIMEROS PUNTOS DE LAS SERIES UTILIZANDO LA FUNCION DE KERNEL RADIAL, A PESAR DEL PROCESO DE EXPANSION DEL ERROR DE PREDICCION.
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners

We explore an efficient approach to establish a foundational video-text model. We present VideoCoCa that maximally reuses a pretrained image-text contrastive captioner (CoCa) model and adapt it to video-text tasks with minimal extra training. While previous works adapt image-text models with various cross-frame fusion modules, we find that the generative attentional pooling and contrastive attentional pooling layers in CoCa are instantly adaptable to flattened frame embeddings, yielding state-of-the-art results on zero-shot video classification and zero-shot text-to-video retrieval. Furthermore, we explore lightweight finetuning on top of VideoCoCa, and achieve strong results on video question-answering and video captioning.
Controllability of complex networks: input node placement restricting the longest control chain

The minimum number of inputs needed to control a network is frequently used to quantify its controllability. Control of linear dynamics through a minimum set of inputs, however, often has prohibitively large energy requirements and there is an inherent trade-off between minimizing the number of inputs and control energy. To better understand this trade-off, we study the problem of identifying a minimum set of input nodes such that controllabililty is ensured while restricting the length of the longest control chain. The longest control chain is the maximum distance from input nodes to any network node, and recent work found that reducing its length significantly reduces control energy. We map the longest control chain-constraint minimum input problem to finding a joint maximum matching and minimum dominating set. We show that this graph combinatorial problem is NP-complete, and we introduce and validate a heuristic approximation. Applying this algorithm to a collection of real and model networks, we investigate how network structure affects the minimum number of inputs, revealing, for example, that for many real networks reducing the longest control chain requires only few or no additional inputs, only the rearrangement of the input nodes.
A New OFDM System for IIR Channels

In this letter, we propose a new orthogonal frequency division multiplexing (OFDM) system for an infinite impulse response (IIR) channel with the form of <inline-formula> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>)/<inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>) for two polynomials <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>) and <inline-formula> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>). Different from the conventional OFDM transmission over a finite impulse response (FIR) channel, a guard interval of an OFDM symbol is added such that the corresponding part at receiver is the cyclic prefix (CP) of the received OFDM symbol. The guard interval and CP lengths are the same and not smaller than the orders of polynomials <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>) and <inline-formula> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>). The OFDM symbol without the guard interval is the same as the conventional OFDM symbol without the CP. At the receiver, the IIR channel is then converted to <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> intersymbol interference (ISI) free subchannels, where <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> is the number of subcarriers of an OFDM symbol.
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization

In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization. Prior art has tackled each of these components individually, purportedly aiming to alleviate difficulties in effectively train a holistic network. We design a self-supervised image warping correspondence loss for both feature detection and matching, a weakly-supervised epipolar constraints loss on relative camera pose learning, and a directional matching scheme that detects key-point features in a source image and performs coarse-to-fine correspondence search on the target image. We leverage this framework to enforce cycle consistency in our matching module. In addition, we propose a new loss to robustly handle both definite inlier/outlier matches and less-certain matches. The integration of these learning mechanisms enables end-to-end training of a single network performing all three localization components. Bench-marking our approach on public data-sets, exemplifies how such an end-to-end framework is able to yield more accurate localization that out-performs both traditional methods as well as state-of-the-art weakly supervised methods.
Penalization of stationary Navier-Stokes equations and applications in topology optimization

We consider the steady Navier-Stokes system with mixed boundary conditions, in subdomains of a holdall domain. We study, via the penalization method, its approximation properties. Error estimates, obtained using the extension operator, other evaluations and the uniqueness of the solution, when the viscosity may be arbitrarily small in certain subdomains, are also discussed. Numerical tests, including topological optimization applications, are presented. A general convergence result for the approximation of this type of geometric inverse problems and of the associated optimal control problems, is investigated in the last part of the paper.
Valence-dependent analytic bond-order potential for transition metals

An analytic interatomic bond-order potential is derived that depends explicitly on the valence of the transition-metal element. It generalizes the second-moment Finnis-Sinclair and fourth-moment Carlsson potentials to include higher moments. We find that the sixth-moment approximation predicts not only the structural trend from $\mathrm{hcp}\ensuremath{\rightarrow}\mathrm{bcc}\ensuremath{\rightarrow}\mathrm{hcp}\ensuremath{\rightarrow}\mathrm{fcc}$ that is observed across the nonmagnetic $4d$ and $5d$ transition-metal series, but also the different ferromagnetic moments of the bcc, fcc, and hcp phases of the $3d$ transition-metal iron. An analytic expression for the force is obtained and proved to converge to the Hellmann-Feynman force as higher moments are included.
Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey

Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement. As such, researchers have long attempted to create systems that can generate stories automatically. In recent years, powered by deep learning and massive data resources, automatic story generation has shown significant advances. However, considerable challenges, like the need for global coherence in generated stories, still hamper generative models from reaching the same storytelling ability as human narrators. To tackle these challenges, many studies seek to inject structured knowledge into the generation process, which is referred to as structured knowledge-enhanced story generation. Incorporating external knowledge can enhance the logical coherence among story events, achieve better knowledge grounding, and alleviate over-generalization and repetition problems in stories. This survey provides the latest and comprehensive review of this research field: (i) we present a systematic taxonomy regarding how existing methods integrate structured knowledge into story generation; (ii) we summarize involved story corpora, structured knowledge datasets, and evaluation metrics; (iii) we give multidimensional insights into the challenges of knowledge-enhanced story generation and cast light on promising directions for future study.
Risk management against extremes in a changing environment: a risk‐layer approach using copulas

Assessing and managing extreme risk have to be carried out differently compared with frequent event risk. Especially dependencies among risks are important to be incorporated here. Additionally, dynamics that may alter risk in the future are important to be included in modeling approaches so that strategies to reduce risk are sustainable in the long run. However, little is known about changing risks under dynamic conditions and corresponding advantages and limits of options to decrease or hedge risks. We use this knowledge gap as our starting point to lay out an approach how to model dependencies with the use of copulas and how to manage and prevent increases in risk because of dynamic changes over time. The uniqueness of these approaches is that changes in anticipated losses over different spatial scales (or risks) compared with the current situation are separated into different risk layers, and these different risk layers can be expressed simultaneously with loss distributions. Because risk‐reduction and risk‐transfer measures are suitable for different risk layers, this distinction can serve in determining the most appropriate risk management measures over different scales. We apply our method to flood risk in Hungary, specifically within the Tisza region. The approach may be particularly useful if dissimilar changes in risk at the local level can be expected, for example, an increase in low‐probability events in one region and an increase in the frequency of small magnitude events in a neighboring region. Copyright © 2012 John Wiley & Sons, Ltd.
Neural Volume Super-Resolution

Neural volumetric representations have become a widely adopted model for radiance fields in 3D scenes. These representations are fully implicit or hybrid function approximators of the instantaneous volumetric radiance in a scene, which are typically learned from multi-view captures of the scene. We investigate the new task of neural volume super-resolution - rendering high-resolution views corresponding to a scene captured at low resolution. To this end, we propose a neural super-resolution network that operates directly on the volumetric representation of the scene. This approach allows us to exploit an advantage of operating in the volumetric domain, namely the ability to guarantee consistent super-resolution across different viewing directions. To realize our method, we devise a novel 3D representation that hinges on multiple 2D feature planes. This allows us to super-resolve the 3D scene representation by applying 2D convolutional networks on the 2D feature planes. We validate the proposed method by super-resolving multi-view consistent views on a diverse set of unseen 3D scenes, confirming qualitative and quantitatively favorable quality over existing approaches.
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction

With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and diverse environments. In this paper, we present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Extensive experiments and in-depth analysis show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation. Our dataset and source code are publicly available at https://andytang15.github. io/FLAG3D.
Ponderomotive effects in the femtosecond plasmon-assisted photoelectric effect in bulk metals : Evidence for coupling between surface and interface plasmons

The existence of ponderomotive acceleration effects in the metal bulk has been experimentally demonstrated in the context of the femtosecond plasmon-assisted multiphoton photoelectric effect in metal systems. The resulting electron energy spectra show that these effects essentially depend on the coupling between the surface and interface plasmons. While the essential part played by the ponderomotive force of the surface plasmon is to accelerate the photoelectrons in the vacuum, the dramatic enhancement of the photoelectron production and the angular dependence in the photoemission process mainly result from ponderomotive effects in the metal bulk.
Plaquette valence bond state in spin-1/2 J1-J2 XY model on square lattice

We studied the ground state phase diagram of spin-1/2 J1-J2 XY model on the square lattice with first- J1 and second-neighbor J2 antiferromagnetic interactions using both iDMRG and DMRG approaches. We show that a plaquette valence bond phase is realized in an intermediate region 0.50<= J2/J1<= 0.54 between a N\'eel magnetic ordered phase at J2/J1<0.50 and a stripy magnetic ordered phase at J2/J1>= 0.54. The plaquette valence bond phase is characterized by finite dimer orders in both the horizontal and vertical directions. Contrary to the spin-1/2 J1-J2 Heisenberg model, we do not find numerical evidence for a quantum spin liquid phase in the J1-J2 XY model.
Spin-Momentum Properties in the Paraxial Optical Systems

Spin angular momentum, an elementary dynamical property of classical electromagnetic fields, plays an important role in spin-orbit and light-matter interactions, especially in near-field optics. The research on optical spins has led to the discovery of phenomena such as optical spin-momentum locking and photonic topological quasiparticles, as well as applications in high-precision detection and nanometrology. Here, we investigate spin-momentum relations in paraxial optical systems and show that the optical spin angular momentum contains transverse and longitudinal spin components simultaneously. The transverse spin originates from inhomogeneities of field and governed by the vorticity of the kinetic momentum density, whereas the longitudinal spin parallel to the local canonical momentum is proportional to the polarization ellipticity of light. Moreover, the skyrmionlike spin textures arise from the optical transverse spin can be observed in paraxial beams, and their topologies are maintained free from the influence of the Gouy phase during propagation. Interestingly, the optical singularities, including both phase and polarization singularities, can also affect the spin-momentum properties significantly. Our findings describe the intrinsic spin-momentum properties in paraxial optical systems and apply in the analysis of the properties of spin-momentum in optical focusing, imaging, and scattering systems.
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations

Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research field known as scientific machine learning in which techniques such as deep neural networks and statistical learning are applied to classical problems of applied mathematics. In this paper, we develop a novel numerical algorithm that incorporates machine learning and artificial intelligence to solve PDEs. Based on the Legendre-Galerkin framework, we propose the {\it unsupervised machine learning} algorithm to learn {\it multiple instances} of the solutions for different types of PDEs. Our approach overcomes the limitations of data-driven and physics-based methods. The proposed neural network is applied to general 1D and 2D PDEs with various boundary conditions as well as convection-dominated {\it singularly perturbed PDEs} that exhibit strong boundary layer behavior.
Neural Vector Fields for Implicit Surface Representation and Inference

Implicit fields have recently shown increasing success in representing and learning 3D shapes accurately. Signed distance fields and occupancy fields are decades old and still the preferred representations, both with well-studied properties, despite their restriction to closed surfaces. With neural networks, several other variations and training principles have been proposed with the goal to represent all classes of shapes. In this paper, we develop a novel and yet a fundamental representation considering unit vectors in 3D space and call it Vector Field (VF): at each point in $\mathbb{R}^3$, VF is directed at the closest point on the surface. We theoretically demonstrate that VF can be easily transformed to surface density by computing the flux density. Unlike other standard representations, VF directly encodes an important physical property of the surface, its normal. We further show the advantages of VF representation, in learning open, closed, or multi-layered as well as piecewise planar surfaces. We compare our method on several datasets including ShapeNet where the proposed new neural implicit field shows superior accuracy in representing any type of shape, outperforming other standard methods. Code is available at https://github.com/edomel/ImplicitVF.
Comparative Study of Sentiment Analysis for Multi-Sourced Social Media Platforms

There is a vast amount of data generated every second due to the rapidly growing technology in the current world. This area of research attempts to determine the feelings or opinions of people on social media posts. The dataset we used was a multi-source dataset from the comment section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were employed to perform sentiment analysis on the obtained dataset. In this paper, we provide a comparative analysis using techniques of lexicon-based, machine learning and deep learning approaches. The Machine Learning algorithm used in this work is Naive Bayes, the Lexicon-based approach used in this work is TextBlob, and the deep-learning algorithm used in this work is LSTM.
Formulation of the undertow using linear wave theory

The undertow is one of the most important mechanisms for sediment transport in nearshore regions. As such, its formulation has been an active subject of research for at least the past 40 years. Still, much debate persists on the exact nature of the forcing and theoretical expression of this current. Here, assuming linear wave theory and keeping most terms in the wave momentum equations, a solution to the undertow in the surf zone is derived, and it is shown that it is unique. It is also shown that, unless they are erroneous, most solutions presented in the literature are identical, albeit simplified versions of the solution presented herein. Finally, it is demonstrated that errors in past derivations of the undertow profile stem from inconsistencies between (1) the treatment of advective terms in the momentum equations and the wave action equation, (2) the expression of the mean current equation and the surface shear stress, and (3) the omission of bottom shear stress in the momentum equation.
Explosion Limits of Hydrogen-Oxygen Mixture as a Stochastic Phenomenon

The fluctuation in the induction period of ignition as well as the randomness of the ignition position in a hydrogen-oxygen mixture behind reflected shock waves was statistically investigated. It was proved that ignition is a stochastic phenomenon by comparing the ignition probability of the mixture obtained from a histogram of the induction period and that obtained from a histogram of the ignition position. The explosion limits of the hydrogen-oxygen mixture, the so-called explosion peninsula, was explained by the stochastic theory in which the mixture has a certain constant ignition probability.
2022 ASSBI PRESIDENTIAL ADDRESS. Reflections on language and primary progressive aphasias

Abstract Primary progressive aphasias are rare younger-onset dementias. As the label denotes, these dementias are characterised clinically by marked changes in language skills. Evidence over the years has shown that individuals with primary progressive aphasia experience widespread cognitive and behavioural changes that extend beyond language. This evidence, however, seems to be largely ignored or downplayed. This article proposes that linguistic relativity which induces a cognitive bias may be responsible for this omission; it also indicates that a revision of the current diagnostic criteria may need to be revised.
Molecular insights into endolysosomal microcompartment formation and maintenance

Abstract The endolysosomal system of eukaryotic cells has a key role in the homeostasis of the plasma membrane, in signaling and nutrient uptake, and is abused by viruses and pathogens for entry. Endocytosis of plasma membrane proteins results in vesicles, which fuse with the early endosome. If destined for lysosomal degradation, these proteins are packaged into intraluminal vesicles, converting an early endosome to a late endosome, which finally fuses with the lysosome. Each of these organelles has a unique membrane surface composition, which can form segmented membrane microcompartments by membrane contact sites or fission proteins. Furthermore, these organelles are in continuous exchange due to fission and fusion events. The underlying machinery, which maintains organelle identity along the pathway, is regulated by signaling processes. Here, we will focus on the Rab5 and Rab7 GTPases of early and late endosomes. As molecular switches, Rabs depend on activating guanine nucleotide exchange factors (GEFs). Over the last years, we characterized the Rab7 GEF, the Mon1-Ccz1 (MC1) complex, and key Rab7 effectors, the HOPS complex and retromer. Structural and functional analyses of these complexes lead to a molecular understanding of their function in the context of organelle biogenesis.