Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Comparison Procedures

1. Introduction Analysis of variance is used to test for the real treatment differences. When the null hypothesis that all the treatment means are equal is not rejected, it may seem that no further questions need to be asked. However, in some experimental situations, it may be an oversimplification of the problem. For example, consider an experiment in rice weed control with 15 treatments viz. 4 with hand weeding, 10 with herbicides and 1 with no weeding (control). The probable questions that may be raised and the specific mean comparisons that can provide their answers may be: i) Is any treatment effective in controlling the weeds? This question may be answered simply by comparing the mean of the control treatment with the mean of each of the 14 weed-control treatments. ii) Is there any difference between the group of hand-weeding treatments and the group of herbicide treatments? The comparison of the combined mean of the four hand weeding treatment effects with the combined mean of the 10-herbicide treatment effects may be able to answer the above question. iii) Are there differences between the 4 hand weeding treatments? To answer this question one should test the significant differences among the 4 hand weeding treatments. Similar question can be raised about the 10-herbicide treatments and can be answered in the above fashion. This illustrates the diversity in the types of treatment effects comparisons. Broadly speaking, these comparisons can be classified either as Pair Comparison or Group Comparison. In pair comparisons, we compare the treatment effects pairwise whereas in-group comparisons, the comparisons could be between group comparisons, within group comparisons, trend comparisons, and factorial comparisons. In the above example, question (i) is the example of the pair comparisons and question (ii) illustrates the between group comparison and question (iii) is within group comparison. Through trend comparisons, we can test the functional relationship (linear, quadratic, cubic, etc.) between treatment means and treatment levels using orthogonal polynomials. Factorial comparisons are related to the testing of means of levels of a factor averaged over levels of all other factors or average of treatment combinations of some factors averaged over all levels of other factors. For pairwise treatment comparisons there are many test procedures, however, for the group comparisons, the most commonly used test procedure is to partition the treatment sum of squares into meaningful comparisons. This can be done through contrast analysis either using single degrees …
Conditional Generative Adversarial Network for keystroke presentation attack

Cybersecurity is a crucial step in data protection to ensure user security and personal data privacy. In this sense, many companies have started to control and restrict access to their data using authentication systems. However, these traditional authentication methods, are not enough for ensuring data protection, and for this reason, behavioral biometrics have gained importance. Despite their promising results and the wide range of applications, biometric systems have shown to be vulnerable to malicious attacks, such as Presentation Attacks. For this reason, in this work, we propose to study a new approach aiming to deploy a presentation attack towards a keystroke authentication system. Our idea is to use Conditional Generative Adversarial Networks (cGAN) for generating synthetic keystroke data that can be used for impersonating an authorized user. These synthetic data are generated following two different real use cases, one in which the order of the typed words is known (ordered dynamic) and the other in which this order is unknown (no-ordered dynamic). Finally, both keystroke dynamics (ordered and no-ordered) are validated using an external keystroke authentication system. Results indicate that the cGAN can effectively generate keystroke dynamics patterns that can be used for deceiving keystroke authentication systems.
An Adjacency Matrix Perspective of Talented Monoids and Leavitt Path Algebras

In this article we establish relationships between Leavitt path algebras, talented monoids and the adjacency matrices of the underlying graphs. We show that indeed the adjacency matrix generates in some sense the group action on the generators of the talented monoid. With the help of this we de- duce a form of the aperiodicity index of a graph via the talented monoid. We classify hereditary and saturated subsets via the adjacency matrix. Moreover we give a formula to compute all paths of a given length in a Leavitt path algebra based on the adjacency matrix. In addition we discuss the number of cycles in a graph. In particular we give an equivalent characterization of acylic graphs via the adjacency matrix, the talented monoid and the Leavitt path algebra.
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension

The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of interest. As many legal services rely on processing and analyzing large collections of documents, automating such tasks with NLP tools emerges as a key challenge. Many popular language models, such as BERT or RoBERTa, are general-purpose models, which have limitations on processing specialized legal terminology and syntax. In addition, legal documents may contain specialized vocabulary from other domains, such as medical terminology in personal injury text. Here, we propose LegalRelectra, a legal-domain language model that is trained on mixed-domain legal and medical corpora. We show that our model improves over general-domain and single-domain medical and legal language models when processing mixed-domain (personal injury) text. Our training architecture implements the Electra framework, but utilizes Reformer instead of BERT for its generator and discriminator. We show that this improves the model's performance on processing long passages and results in better long-range text comprehension.
Modulated photothermal radiometry applied to semitransparent samples: Models and experiments

Mathematical modeling is presented of the combined conductive and radiative heat transfer occurring in a semitransparent material (STM) subjected to a periodic heat flux. The models rely on the quadrupole method, which is a very powerful tool to obtain analytical solutions in the Fourier or Laplace domain. Photoacoustic or photothermal radiometry techniques are reviewed. Two groups of methods are discussed depending on whether the sample has natural or opaque interfaces to simulate radiative exchanges with the surroundings. The metrological problem of measuring the phonic thermal diffusivity of semitransparent materials is investigated. Theoretical simulations are given. They explain some typical features of the phase-lag signal of temperature responses. Experimental measurements on pure silica validate the results and prove that these methods are efficient for the thermal characterization of STM.
Traffic sign detection and recognition using event camera image reconstruction

This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera. The solution used a FireNet deep convolutional neural network to reconstruct events into greyscale frames. Two YOLOv4 network models were trained, one based on greyscale images and the other on colour images. The best result was achieved for the model trained on the basis of greyscale images, achieving an efficiency of 87.03%.
Fine-grained Czech News Article Dataset: An Interdisciplinary Approach to Trustworthiness Analysis

We present the Verifee Dataset: a novel dataset of news articles with fine-grained trustworthiness annotations. We develop a detailed methodology that assesses the texts based on their parameters encompassing editorial transparency, journalist conventions, and objective reporting while penalizing manipulative techniques. We bring aboard a diverse set of researchers from social, media, and computer sciences to overcome barriers and limited framing of this interdisciplinary problem. We collect over $10,000$ unique articles from almost $60$ Czech online news sources. These are categorized into one of the $4$ classes across the credibility spectrum we propose, raging from entirely trustworthy articles all the way to the manipulative ones. We produce detailed statistics and study trends emerging throughout the set. Lastly, we fine-tune multiple popular sequence-to-sequence language models using our dataset on the trustworthiness classification task and report the best testing F-1 score of $0.52$. We open-source the dataset, annotation methodology, and annotators' instructions in full length at https://verifee.ai/research to enable easy build-up work. We believe similar methods can help prevent disinformation and educate in the realm of media literacy.
On the viability of f(Q) gravity models

In general relativity, the contracted Bianchi identity makes the field equation compatible with the energy conservation, likewise in f(R) theories of gravity. We show that this classical phenomenon is not guaranteed in the symmetric teleparallel theory, and rather generally f(Q) model specific. We further prove that the energy conservation criterion is equivalent to the affine connection’s field equation of f(Q) theory, and except the f(Q)=αQ+β model, the non-linear f(Q) models do not satisfy the energy conservation or, equivalently the second field equation in every spacetime geometry; unless Q itself is a constant. So the problem is deep-rooted in the theory, several physically motivated examples are provided in the support.
Discovering Structural Hole Spanners in Dynamic Networks via Graph Neural Networks

Structural Hole (SH) theory states that the node which acts as a connecting link among otherwise disconnected communities gets positional advantages in the network. These nodes are called Structural Hole Spanners (SHS). SHSs have many applications, including viral marketing, information dissemination, community detection, etc. Numerous solutions are proposed to discover SHSs; however, most of the solutions are only applicable to static networks. Since real-world networks are dynamic networks; consequently, in this study, we aim to discover SHSs in dynamic networks. Discovering SHSs is an NPhard problem, due to which, instead of discovering exact k SHSs, we adopt a greedy approach to discover top-k SHSs. Motivated from the success of Graph Neural Networks (GNNs) on various graph mining problems, we design a Graph Neural Network-based model, GNN-SHS, to discover SHSs in dynamic networks, aiming to reduce the computational cost while achieving high accuracy. We analyze the efficiency of the proposed model through exhaustive experiments, and our results show that the proposed GNN-SHS model is at least 31.8 times faster and, on an average 671.6 times faster than the comparative method, providing a considerable efficiency advantage.
NetRPC: Enabling In-Network Computation in Remote Procedure Calls

People have shown that in-network computation (INC) significantly boosts performance in many application scenarios include distributed training, MapReduce, agreement, and network monitoring. However, existing INC programming is unfriendly to the normal application developers, demanding tedious network engineering details like flow control, packet organization, chip-specific programming language, and ASIC architecture with many limitations. We propose a general INC-enabled RPC system, NetRPC. NetRPC provides a set of familiar and lightweight interfaces for software developers to describe an INC application using a traditional RPC programming model. NetRPC also proposes a general-purpose INC implementation together with a set of optimization techniques to guarantee the efficiency of various types of INC applications running on a shared INC data plane. We conduct extensive experiments on different types of applications on the real testbed. Results show that using only about 5% or even fewer human-written lines of code, NetRPC can achieve performance similar to the state-of-the-art INC solutions.
On Human Visual Contrast Sensitivity and Machine Vision Robustness: A Comparative Study

It is well established in neuroscience that color vision plays an essential part in the human visual perception system. Meanwhile, many novel designs for computer vision inspired by human vision have achieved success in a wide range of tasks and applications. Nonetheless, how color differences affect machine vision has not been well explored. Our work tries to bridge this gap between the human color vision aspect of visual recognition and that of the machine. To achieve this, we curate two datasets: CIFAR10-F and CIFAR100-F, which are based on the foreground colors of the popular CIFAR datasets. Together with CIFAR10-B and CIFAR100-B, the existing counterpart datasets with information on the background colors of CIFAR test sets, we assign each image based on its color contrast level per its foreground and background color labels and use this as a proxy to study how color contrast affects machine vision. We first conduct a proof-of-concept study, showing the effect of color difference and validate our datasets. Furthermore, on a broader level, an important characteristic of human vision is its robustness against ambient changes; therefore, drawing inspirations from ophthalmology and the robustness literature, we analogize contrast sensitivity from the human visual aspect to machine vision and complement the current robustness study using corrupted images with our CIFAR-CoCo datasets. In summary, motivated by neuroscience and equipped with the datasets we curate, we devise a new framework in two dimensions to perform extensive analyses on the effect of color contrast and corrupted images: (1) model architecture, (2) model size, to measure the perception ability of machine vision beyond total accuracy. We also explore how task complexity and data augmentation play a role in this setup. Our results call attention to new evaluation approaches for human-like machine perception.
Reconstruction of a compact object motion in the vicinity of a black hole by its electromagnetic radiation

A method of motion reconstruction of an isotropic compact source in the vicinity of a Schwarzschild black hole is described. The method is based on the analysis of the redshift and the luminous intensity of several images of the source and is most effective for the source located at the distance of several Schwarzschild radii to the black hole. The cases of three and two observed images are considered. The uniqueness of motion reconstruction is investigated for each case. The results are illustrated by an example of direct and inverse problem solution. An observational scenario that allows to obtain the information necessary for motion reconstruction is suggested.
Evolution of Pt Clusters on Graphene Induced by Electron Irradiation

In situ low-voltage transmission electron microscopy (TEM) was performed to study the evolution of small Pt clusters on suspended graphene. Pt clusters, trapped by the edge of holes, generally take a stable shape of truncated octahedron for sizes ranging from sub-1 to � 5nm. The interaction to the graphene dots takes in charge when they form composite nanostructures embedded in graphene. The Pt clusters are slowly flattened due to hole enlargement under electron irradiation. The planar structure is maintained by the peripheral Pt-C bonds and instantly collapses into a three-dimensional (3D) cluster if one side is detached from the edge. Based on the heat transfer model, the thermal effect can be excluded under the experimental condition. Atomistic evolution can be attributed to the electron irradiation. Molecular dynamics simulations revealed that the evolution kinetics was found to be dominated by the surface diffusion (characterized by the migration barrier Em), the temperature (the thermal activation energy � 5kBT), and the scattering from electrons (the maximum transferred energy Emax). The corresponding energies are comparable for the Pt cluster system, leading to similar evolution behaviors. A different scenario in graphene systems is due to the large difference in agitations, i.e., Emax � Em � 5kBT at 3000K. This unique behavior comes from TEM observation, implying that electron beam irradiation can be utilized as a unique tool in shaping carbon nanostructures. [DOI: 10.1115/1.4024168]
A GIT construction of moduli spaces of sheaves of length 2

Let $\Bbbk$ be an algebraically closed field of characteristic zero. Let $\mathrm{Sch}/\Bbbk$ denote the category of schemes of finite type over $\Bbbk$. Let $B$ be a connected projective scheme over $\Bbbk$ and let $\mathcal L$ be an ample line bundle on $B$. Let $\tau$ be a Harder-Narasimhan type of length 2, and let $\delta\in\mathbb N$. We say a pure sheaf $\mathcal E$ on $B$ is $(\tau,\delta)$-stable if its Harder-Narasimhan filtration $0=\mathcal E_{\leq 0}\subsetneq\mathcal E_{\leq 1}\subsetneq\mathcal E_{\leq 2}=\mathcal E$ is non-splitting, of type $\tau$, with stable subquotients, and $\delta=\dim_\Bbbk\mathrm{Hom}_{\mathcal O_B}(\mathcal E_2,\mathcal E_1)$ for $\mathcal E_i:=\mathcal E_{\leq i}/\mathcal E_{\leq i-1}$. We define a moduli functor $\mathbf M'_{\tau,\delta}$ classifying $(\tau,\delta)$-stable sheaves on $B$ and construct its coarse moduli space by non-reductive geometric invariant theory (GIT). We extend the non-reductive GIT in arXiv:1607.04181 and arXiv:1601.00340 to linear actions on non-reduced schemes, and apply our non-reductive GIT to prove that the sheafification $(\mathbf M'_{\tau,\delta})^\sharp$ on $(\mathrm{Sch}/\Bbbk)_{\'etale}$ is represented by a quasi-projective scheme. Our methods generalise Jackson's construction of moduli spaces of $(\tau,\delta)$-stable sheaves in arXiv:2111.07428 in the category of varieties, to allow non-reduced moduli schemes.
Cyclic symmetry induced pitchfork bifurcations in the diblock copolymer model

The Ohta-Kawasaki model for diblock copolymers exhibits a rich equilibrium bifurcation structure. Even on one-dimensional base domains the bifurcation set is characterized by high levels of multi-stability and numerous secondary bifurcation points. Many of these bifurcations are of pitchfork type. In previous work, the authors showed that if pitchfork bifurcations are induced by a simple $\mathbb{Z}_2$ symmetry-breaking, then computer-assisted proof techniques can be used to rigorously validate them using extended systems. However, many diblock copolymer pitchfork bifurcations cannot be treated in this way. In the present paper, we show that in these more involved cases, a cyclic group action is responsible for their existence, based on cyclic groups of even order. We present theoretical results establishing such bifurcation points and show that they can be characterized as nondegenerate solutions of a suitable extended nonlinear system. Using the latter characterization, we also demonstrate that computer-assisted proof techniques can be used to validate such bifurcations. While the methods proposed in this paper are only applied to the diblock copolymer model, we expect that they will also apply to other parabolic partial differential equations.
Towards Robust Calculation of index-$k$ Saddle Point: Iterative Proximal-Minimization and Differential Game Model

Saddle point with a given Morse index on a potential energy surface is an important object related to energy landscape in physics and chemistry. Efﬁcient numerical methods based on iterative minimization formulation have been proposed in the forms of the sequence of minimization subproblems or the continuous dynamics. We here present a differential game interpretation of this formulation and theoretically investigate the Nash equilibrium of the proposed game and the original saddle point on potential energy surface. To deﬁne this differential game, a new proximal function growing faster than quadratic is introduced to the cost function in the game and a robust Iterative Proximal-Minimization algorithm (IPM) is then derived to compute the saddle points. We prove that the Nash equilibrium of the game is exactly the saddle point in concern and show that the new algorithm is more robust than the previous iterative minimization algorithm without proximity, while the same convergence rate and the computational cost still hold. A two dimensional problem and the Cahn-Hillard problem are tested to demonstrate this numerical advantage.
Adaptive sensor scheduling for target tracking in wireless sensor network

Target tracking is an essential capability for Wireless Sensor Networks (WSNs) and is used as a canonical problem for collaborative signal and information processing to dynamically manage sensor resources and efficiently process distributed sensor measurements. In existing work for target tracking in WSNs, such as the information-driven sensor query (IDSQ) approach, the tasking sensors are scheduled based on uniform sampling interval, ignoring the changing of the target dynamics and obtained estimation accuracy. This paper proposes the adaptive sensor scheduling strategy by jointly selecting the tasking sensor and determining the sampling interval according to the predicted tracking accuracy and tracking cost. The sensors are scheduled in two tracking modes, i.e., the fast tracking approaching mode when the predicted tracking accuracy is not satisfactory, and the tracking maintenance mode when the predicted tracking accuracy is satisfactory. The approach employs an Extended Kalman Filter (EKF) based estimation technique to predict the tracking accuracy, and adopts a linear energy model to predict the energy consumption. Simulation results demonstrate that, compared to the non-adaptive approach, the proposed approach can achieve significant improvement on energy consumption without degrading the tracking accuracy.
Error estimates of a theta-scheme for second-order mean field games

We introduce and analyze a new finite-difference scheme, relying on the theta-method, for solving monotone second-order mean field games. These games consist of a coupled system of the Fokker-Planck and the Hamilton-Jacobi-Bellman equation. The theta-method is used for discretizing the diffusion terms: we approximate them with a convex combination of an implicit and an explicit term. On contrast, we use an explicit centered scheme for the first-order terms. Assuming that the running cost is strongly convex and regular, we first prove the monotonicity and the stability of our theta-scheme, under a CFL condition. Taking advantage of the regularity of the solution of the continuous problem, we estimate the consistency error of the theta-scheme. Our main result is a convergence rate of order $\mathcal{O}(h^r)$ for the theta-scheme, where $h$ is the step length of the space variable and $r \in (0,1)$ is related to the H\"older continuity of the solution of the continuous problem and some of its derivatives.
Offline Reinforcement Learning for Visual Navigation

Reinforcement learning can enable robots to navigate to distant goals while optimizing user-specified reward functions, including preferences for following lanes, staying on paved paths, or avoiding freshly mowed grass. However, online learning from trial-and-error for real-world robots is logistically challenging, and methods that instead can utilize existing datasets of robotic navigation data could be significantly more scalable and enable broader generalization. In this paper, we present ReViND, the first offline RL system for robotic navigation that can leverage previously collected data to optimize user-specified reward functions in the real-world. We evaluate our system for off-road navigation without any additional data collection or fine-tuning, and show that it can navigate to distant goals using only offline training from this dataset, and exhibit behaviors that qualitatively differ based on the user-specified reward function.