Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Transformers for On-Line Handwritten Signature Verification

The application of mobile biometrics as a user-friendly authentication method has increased in the last years. Recent studies have proposed novel behavioral biometric recognition systems based on Transformers, which currently outperform the state of the art in several application scenarios. On-line handwritten signature verification aims to verify the identity of subjects, based on their biometric signatures acquired using electronic devices such as tablets or smartphones. This paper investigates the suitability of architectures based on recent Transformers for on-line signature verification. In particular, four different configurations are studied, two of them rely on the Vanilla Transformer encoder, and the two others have been successfully applied to the tasks of gait and activity recognition. We evaluate the four proposed configurations according to the experimental protocol proposed in the SVC-onGoing competition. The results obtained in our experiments are promising, and promote the use of Transformers for on-line signature verification.
Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRI

Limited by imaging systems, the reconstruction of Magnetic Resonance Imaging (MRI) images from partial measurement is essential to medical imaging research. Benefiting from the diverse and complementary information of multi-contrast MR images in different imaging modalities, multi-contrast Super-Resolution (SR) reconstruction is promising to yield SR images with higher quality. In the medical scenario, to fully visualize the lesion, radiologists are accustomed to zooming the MR images at arbitrary scales rather than using a fixed scale, as used by most MRI SR methods. In addition, existing multi-contrast MRI SR methods often require a fixed resolution for the reference image, which makes acquiring reference images difficult and imposes limitations on arbitrary scale SR tasks. To address these issues, we proposed an implicit neural representations based dual-arbitrary multi-contrast MRI super-resolution method, called Dual-ArbNet. First, we decouple the resolution of the target and reference images by a feature encoder, enabling the network to input target and reference images at arbitrary scales. Then, an implicit fusion decoder fuses the multi-contrast features and uses an Implicit Decoding Function~(IDF) to obtain the final MRI SR results. Furthermore, we introduce a curriculum learning strategy to train our network, which improves the generalization and performance of our Dual-ArbNet. Extensive experiments in two public MRI datasets demonstrate that our method outperforms state-of-the-art approaches under different scale factors and has great potential in clinical practice.
NMS Threshold matters for Ego4D Moment Queries - 2nd place solution to the Ego4D Moment Queries Challenge 2023

This report describes our submission to the Ego4D Moment Queries Challenge 2023. Our submission extends ActionFormer, a latest method for temporal action localization. Our extension combines an improved ground-truth assignment strategy during training and a refined version of SoftNMS at inference time. Our solution is ranked 2nd on the public leaderboard with 26.62% average mAP and 45.69% Recall@1x at tIoU=0.5 on the test set, significantly outperforming the strong baseline from 2023 challenge. Our code is available at https://github.com/happyharrycn/actionformer_release.
OrthoBoXY: A Simple Way to Compute True Self-Diffusion Coefficients from MD Simulations with Periodic Boundary Conditions without Prior Knowledge of the Viscosity.

Recently, an analytical expression for the system size dependence and direction-dependence of self-diffusion coefficients for neat liquids due to hydrodynamic interactions has been derived for molecular dynamics (MD) simulations using orthorhombic unit cells. Based on this description, we show that for systems with a "magic" box length ratio of Lz/Lx = Lz/Ly = 2.7933596497 the computed self-diffusion coefficients Dx and Dy in the x- and y-direction become system-size independent and represent the true self-diffusion coefficient D0 = (Dx + Dy)/2. Moreover, by using this particular box geometry, the viscosity can be determined with a reasonable degree of accuracy from the difference of components of the diffusion coefficients in x-, y-, and z-directions using the simple expression η = kBT × 8.1711245653/[3πLz(Dx + Dy - 2Dz)], where kB denotes Boltzmann's constant and T represents the temperature. MD simulations of TIP4P/2005 water for various system sizes using both orthorhombic and cubic box geometries are used to test the approach.
Molecular Outflow in the Reionization-epoch Quasar J2054-0005 Revealed by OH 119 μm Observations

Molecular outflows are expected to play a key role in galaxy evolution at high redshift. To study the impact of outflows on star formation at the epoch of reionization, we performed sensitive Atacama Large Millimeter/submillimeter Array observations of OH 119 μm toward J2054-0005, a luminous quasar at z = 6.04. The OH line is detected and exhibits a P-Cygni profile that can be fitted with a broad blueshifted absorption component, providing unambiguous evidence of an outflow, and an emission component at near-systemic velocity. The mean and terminal outflow velocities are estimated to be v out ≈ 670 and 1500 km s−1, respectively, making the molecular outflow in this quasar one of the fastest at the epoch of reionization. The OH line is marginally spatially resolved for the first time in a quasar at z > 6, revealing that the outflow extends over the central 2 kpc region. The mass outflow rate is comparable to the star formation rate ( M ̇ out / SFR ∼ 2 ), indicating rapid (∼107 yr) quenching of star formation. The mass outflow rate in a sample of star-forming galaxies and quasars at 4 < z < 6.4 exhibits a positive correlation with the total infrared luminosity, although the scatter is large. Owing to the high outflow velocity, a large fraction (up to ∼50%) of the outflowing molecular gas may be able to escape from the host galaxy into the intergalactic medium.
Convergence Analysis for Restarted Anderson Mixing and Beyond

Anderson mixing (AM) is a classical method that can accelerate fixed-point iterations by exploring historical information. Despite the successful application of AM in scientific computing, the theoretical properties of AM are still under exploration. In this paper, we study the restarted version of the Type-I and Type-II AM methods, i.e., restarted AM. With a multi-step analysis, we give a unified convergence analysis for the two types of restarted AM and justify that the restarted Type-II AM can locally improve the convergence rate of the fixed-point iteration. Furthermore, we propose an adaptive mixing strategy by estimating the spectrum of the Jacobian matrix. If the Jacobian matrix is symmetric, we develop the short-term recurrence forms of restarted AM to reduce the memory cost. Finally, experimental results on various problems validate our theoretical findings.
Reaction diffusion equation in heterogeneous media : persistance, propagation and effect of the geometry

In this thesis we are interested in reaction diffusion equations and their applications in biology and medical sciences. In particular we study the existence or non-existence of propagation phenomena in non homogeneous media through the existence of traveling waves or more generally the existence of transition fronts.First we study a bistable reaction diffusion equation in exterior domain modelling the evolution of the density of a population facing an obstacle. We prove that when the obstacle satisfies some regularity properties and is close to a star shaped or directionally convex domain then the population invades the entire domain. We also investigate the optimal regularity conditions that allow a complete invasion of the population. In a second work, we look at a reaction diffusion equation with forced speed, modelling the evolution of the density of a population facing an unfavourable climate change. We prove that depending on the speed of the climate change the population keeps track with the climate change or goes extinct. We also prove that the population, when it survives, propagates with a constant profile at a constant speed at large time. Lastly we consider a bistable reaction diffusion equation in various cylindrical domains, modelling the evolution of a depolarisation wave in the brain. We prove that this wave is blocked when the domain goes from a thin channel to a cylinder, whose diameter is of order 1 and we give general conditions on the geometry of the domain that allow propagation. We also study this problem numerically and prove that for curved cylinders the curvature can block the wave for particular boundary conditions.
Local coefficients and normalization of intertwining operators for $GL(n)$

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Hierarchical Planning and Policy Shaping Shared Autonomy for Articulated Robots

In this work, we propose a novel shared autonomy framework to operate articulated robots. We provide strategies to design both the task-oriented hierarchical planning and policy shaping algorithms for efficient human-robot interactions in context-aware operation of articulated robots. Our framework for interplay between the human and the autonomy, as the participating agents in the system, is particularly influenced by the ideas from multi-agent systems, game theory, and theory of mind for a sliding level of autonomy. We formulate the sequential hierarchical human-in-the-loop decision making process by extending MDPs and Options framework to shared autonomy, and make use of deep RL techniques to train an uncertainty-aware shared autonomy policy. To fine-tune the formulation to a human, we use history of the system states, human actions, and their error with respect to a surrogate optimal model to encode human's internal state embeddings, beyond the designed values, by using conditional VAEs. We showcase the effectiveness of our formulation for different human skill levels and degrees of cooperativeness by using a case study of a feller-buncher machine in the challenging tasks of timber harvesting. Our framework is successful in providing a sliding level of autonomy from fully autonomous to fully manual, and is particularly successful in handling a noisy non-cooperative human agent in the loop. The proposed framework advances the state-of-the-art in shared autonomy for operating articulated robots, but can also be applied to other domains where autonomous operation is the ultimate goal.
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers

Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classification accuracy. It is imperative to have methods in place that can compare different models and identify over-reliances on specific concepts. We consider three well-known abusive language classifiers trained on large English datasets and focus on the concept of negative emotions, which is an important signal but should not be learned as a sufficient feature for the label of abuse. Motivated by the definition of global sufficiency, we first examine the unwanted dependencies learned by the classifiers by assessing their accuracy on a challenge set across all decision thresholds. Further, recognizing that a challenge set might not always be available, we introduce concept-based explanation metrics to assess the influence of the concept on the labels. These explanations allow us to compare classifiers regarding the degree of false global sufficiency they have learned between a concept and a label.
High-Strength Amorphous Silicon Carbide for Nanomechanics.

For decades, mechanical resonators with high sensitivity have been realized using thin-film materials under high tensile loads. Although there have been remarkable strides in achieving low-dissipation mechanical sensors by utilizing high tensile stress, the performance of even the best strategy is limited by the tensile fracture strength of the resonator materials. In this study, a wafer-scale amorphous thin film is uncovered, which has the highest ultimate tensile strength ever measured for a nanostructured amorphous material. This silicon carbide (SiC) material exhibits an ultimate tensile strength of over 10 GPa, reaching the regime reserved for strong crystalline materials and approaching levels experimentally shown in graphene nanoribbons. Amorphous SiC strings with high aspect ratios are fabricated, with mechanical modes exceeding quality factors 108 at room temperature, the highest value achieved among SiC resonators. These performances are demonstrated faithfully after characterizing the mechanical properties of the thin film using the resonance behaviors of free-standing resonators. This robust thin-film material has significant potential for applications in nanomechanical sensors, solar cells, biological applications, space exploration and other areas requiring strength and stability in dynamic environments. The findings of this study open up new possibilities for the use of amorphous thin-film materials in high-performance applications. This article is protected by copyright. All rights reserved.
Wholesale Market Participation of DERA: Competitive DER Aggregation

We consider the aggregation of distributed energy resources (DERs) by a profit-seeking DER aggregator (DERA), participating directly in the wholesale electricity market. We propose a competitive DER aggregation that maximizes the DERA's profit subject to that each customer of the DERA gains no less surplus and pays no higher energy cost than that under the regulated retail tariff. The DERA participates in the wholesale electricity market as virtual storage with optimized generation offers and consumption bids derived from the DERA's competitive aggregation. Also derived are DERA's bid curves for the distribution network access. We show that, with sufficiently high network access, the proposed DERA's wholesale market participation achieves the same welfare-maximizing outcome as when its customers participate directly in the wholesale electricity market. Numerical studies compare the proposed competitive DER aggregation with existing benchmarks on surpluses of DERA's customers, DERA profits, and the overall social welfare as functions of the DER adoption level in the distribution system.
Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?

The rapid evolution of high-end smartphones with advanced high-resolution cameras has resulted in contactless capture of fingerprint biometrics that are more reliable and suitable for verification. Similar to other biometric systems, contactless fingerprint-verification systems are vulnerable to presentation attacks. In this paper, we present a comparative study on the generalizability of seven different pre-trained Convolutional Neural Networks (CNN) and a Vision Transformer (ViT) to reliably detect presentation attacks. Extensive experiments were carried out on publicly available smartphone-based presentation attack datasets using four different Presentation Attack Instruments (PAI). The detection performance of the eighth deep feature technique was evaluated using the leave-one-out protocol to benchmark the generalization performance for unseen PAI. The obtained results indicated the best generalization performance with the ResNet50 CNN.
Improving Address Matching using Siamese Transformer Networks

Matching addresses is a critical task for companies and post offices involved in the processing and delivery of packages. The ramifications of incorrectly delivering a package to the wrong recipient are numerous, ranging from harm to the company's reputation to economic and environmental costs. This research introduces a deep learning-based model designed to increase the efficiency of address matching for Portuguese addresses. The model comprises two parts: (i) a bi-encoder, which is fine-tuned to create meaningful embeddings of Portuguese postal addresses, utilized to retrieve the top 10 likely matches of the un-normalized target address from a normalized database, and (ii) a cross-encoder, which is fine-tuned to accurately rerank the 10 addresses obtained by the bi-encoder. The model has been tested on a real-case scenario of Portuguese addresses and exhibits a high degree of accuracy, exceeding 95% at the door level. When utilized with GPU computations, the inference speed is about 4.5 times quicker than other traditional approaches such as BM25. An implementation of this system in a real-world scenario would substantially increase the effectiveness of the distribution process. Such an implementation is currently under investigation.
Classical Yang-Baxter equation for vertex operator algebras and its operator forms

In this paper we introduce an analog of the (classical) Yang-Baxter equation (CYBE) for vertex operator algebras (VOAs) in its tensor form, called the vertex operator Yang-Baxter equation (VOYBE). When specialized to level one of a vertex operator algebra, the VOYBE reduces to the CYBE for Lie algebras. To give an operator form of the VOYBE, we also introduce the notion of relative Rota-Baxter operators (RBOs) as the VOA analog of relative RBOs (classically called $\mathcal{O}$-operators) for Lie algebras. It is shown that skewsymmetric solutions $r$ to the VOYBE in a VOA $U$ are characterized by the condition that their corresponding linear maps $T_r:U'\to U$ from the graded dual $U'$ of $U$ are relative RBOs. On the other hand, strong relative RBOs on a VOA $V$ associated to an ordinary $V$-module $W$ are characterized by the condition that their antisymmetrizers are solutions to the $0$-VOYBE in the semidirect product VOA $V\rtimes W'$. Specializing to the first level of a VOA, these relations between the solutions of the VOYBE and the relative RBOs for VOAs recover the classical relations between the solutions of the CYBE and the relative RBOs for Lie algebras.
High Reynolds Number tests of the NASA SC(2)-0012 airfoil in the Langley 0.3-meter transonic cryogenic tunnel

A wind-tunnel investigation of the NASA SC(2)-0012 airfoil has been conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel. This investigation supplements the two-dimensional airfoil studies of the Advanced Technology Airfoil Test Program. The Mach number was varied from 0.60 to 0.84. The stagnation temperature and pressure were varied to provide a Reynolds number range from 6 to 40 x 10 to the 6th power based on a 6.0-in. (15.24-cm) airfoil chord. No corrections for wind-tunnel wall interference have been made to the data. The aerodynamic results are presented as integrated force and moment coefficients and pressure distributions without any analysis.
Universality in the tripartite information after global quenches: spin flip and semilocal charges

We study stationary states emerging after global quenches in which the time evolution is under local Hamiltonians that possess semilocal conserved operators. In particular, we study a model that is dual to quantum XY chain. We show that a localized perturbation in the initial state can turn an exponential decay of spatial correlations in the stationary state into an algebraic decay. We investigate the consequences on the behavior of the (Rényi-α) entanglement entropies, focusing on the tripartite information of three adjacent subsystems. In the limit of large subsystems, we show that in the stationary state with the algebraic decay of correlations the tripartite information exhibits a non-zero value with a universal dependency on the cross ratio, while it vanishes in the stationary state with the exponential decay of correlations.
Exact and Parameterized Algorithms for the Independent Cutset Problem

The Independent Cutset problem asks whether there is a set of vertices in a given graph that is both independent and a cutset. Such a problem is $\textsf{NP}$-complete even when the input graph is planar and has maximum degree five. In this paper, we first present a $\mathcal{O}^*(1.4423^{n})$-time algorithm for the problem. We also show how to compute a minimum independent cutset (if any) in the same running time. Since the property of having an independent cutset is MSO$_1$-expressible, our main results are concerned with structural parameterizations for the problem considering parameters that are not bounded by a function of the clique-width of the input. We present $\textsf{FPT}$-time algorithms for the problem considering the following parameters: the dual of the maximum degree, the dual of the solution size, the size of a dominating set (where a dominating set is given as an additional input), the size of an odd cycle transversal, the distance to chordal graphs, and the distance to $P_5$-free graphs. We close by introducing the notion of $\alpha$-domination, which allows us to identify more fixed-parameter tractable and polynomial-time solvable cases.
Modeling Tag Prediction based on Question Tagging Behavior Analysis of CommunityQA Platform Users

In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of such platforms. To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users' tagging behavior in 17 StackExchange communities. We found various common inherent properties of this behavior in those diverse domains. We used the findings to develop a flexible neural tag prediction architecture, which predicts both popular tags and more granular tags for each question. Our extensive experiments and obtained performance show the effectiveness of our model
Physics-based Motion Retargeting from Sparse Inputs

Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.