Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural selection of mutants that modify population structure

Evolution occurs in populations of reproducing individuals. It is well known that population structure can affect evolutionary dynamics. Traditionally, natural selection is studied between mutants that differ in reproductive rate, but are subject to the same population structure. Here we study how natural selection acts on mutants that have the same reproductive rate, but experience different population structures. In our framework, mutation alters population structure, which is given by a graph that specifies the dispersal of offspring. Reproduction can be either genetic or cultural. Competing mutants disperse their offspring on different graphs. A more connected graph implies higher motility. We show that enhanced motility tends to increase an invader’s fixation probability, but there are interesting exceptions. For island models, we show that the magnitude of the effect depends crucially on the exact layout of the additional links. Finally, we show that for low-dimensional lattices, the effect of altered motility is comparable to that of altered fitness: in the limit of large population size, the invader’s fixation probability is either constant or exponentially small, depending on whether it is more or less motile than the resident.
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks

In this paper, we present a novel deep neural network architecture for joint class-agnostic object segmentation and grasp detection for robotic picking tasks using a parallel-plate gripper. We introduce depth-aware Coordinate Convolution (CoordConv), a method to increase accuracy for point proposal based object instance segmentation in complex scenes without adding any additional network parameters or computation complexity. Depth-aware CoordConv uses depth data to extract prior information about the location of an object to achieve highly accurate object instance segmentation. These resulting segmentation masks, combined with predicted grasp candidates, lead to a complete scene description for grasping using a parallel-plate gripper. We evaluate the accuracy of grasp detection and instance segmentation on challenging robotic picking datasets, namely Sil\'eane and OCID_grasp, and show the benefit of joint grasp detection and segmentation on a real-world robotic picking task.
Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data

A hybrid two-stage machine-learning architecture that addresses the problem of excessive false positives (false alarms) in solar flare prediction systems is investigated. The first stage is a convolutional neural network (CNN) model based on the VGG-16 architecture that extracts features from a temporal stack of consecutive Solar Dynamics Observatory Helioseismic and Magnetic Imager magnetogram images to produce a flaring probability. The probability of flaring is added to a feature vector derived from the magnetograms to train an extremely randomized trees (ERT) model in the second stage to produce a binary deterministic prediction (flare/no-flare) in a 12 hr forecast window. To tune the hyperparameters of the architecture, a new evaluation metric is introduced: the “scaled True Skill Statistic. ” It specifically addresses the large discrepancy between the true positive rate and the false positive rate in the highly unbalanced solar flare event training data sets. Through hyperparameter tuning to maximize this new metric, our two-stage architecture drastically reduces false positives by ≈48% without significantly affecting the true positives (reduction by ≈12%), when compared with predictions from the first-stage CNN alone. This, in turn, improves various traditional binary classification metrics sensitive to false positives, such as the precision, F1, and the Heidke Skill Score. The end result is a more robust 12 hr flare prediction system that could be combined with current operational flare-forecasting methods. Additionally, using the ERT-based feature-ranking mechanism, we show that the CNN output probability is highly ranked in terms of flare prediction relevance.
Distinguishing Engagement Facets: An Essential Component for AI-based Healthcare

Engagement in Human-Machine Interaction is the process by which entities participating in the interaction establish, maintain, and end their perceived connection. It is essential to monitor the engagement state of patients in various AI-based healthcare paradigms. This includes medical conditions that alter social behavior such as Autism Spectrum Disorder (ASD) or Attention-Deﬁcit/Hyperactivity Disorder (ADHD). Engagement is a multifaceted construct which is composed of behavioral, emotional, and mental components. Previous research has neglected the multi-faceted nature of engagement. In this paper, a system is presented to distinguish these facets using contextual and relational features. This can facilitate further ﬁne-grained analysis. Several machine learning classiﬁers including traditional and deep learning models are compared for this task. A highest accuracy of 74 . 57% with an F-Score and mean absolute error of 0 . 74 and 0 . 23 respectively was obtained on a balanced dataset of 22242 instances with neural network-based classiﬁcation.
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective

Recently, deep learning (DL) approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance and ultrafast inference times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. In this article, we provide an overview of these approaches from a coherent perspective in the context of classical inverse problems and discuss their applications to biological imaging, including electron, fluorescence, deconvolution microscopy, optical diffraction tomography (ODT), and functional neuroimaging.
Heat Flux Pipe in Large-scale Fire Tests

This paper presents the results from a series of commodity classification tests conducted with a unique flame heat flux instrument, i.e., Heat Flux Pipe. In the early 1990’s, FM Global established the ranking of combustible products with a commodity classification test using benchmark commodities. The methodology used heat release rate calorimetry to determine the hazard level of a commodity. It is well known that flame heat flux is closely linked to flame spread and heat release rates associated with various commodities. In the present test series, the initial sprinklered tests were conducted with Class II and Standard Plastic commodities and a distinct difference in maximum flame heat flux was measured, i.e., 114 kW/m 2 and 192 kW/m 2 respectively. Subsequent freeburn commodity classification tests with Class II, wood pallet and Standard Plastic showed large measured maximum flame heat fluxes, 140 kW/m 2 , 190 kW/m 2 and 440 kW/m 2 respectively. This difference in flame heat flux between commodities can be exploited to better quantify the commodity hazards and hence the corresponding protection requirements. Also, the Heat Flux Pipe has been shown to be a robust instrument in large fires, up to 30 MW, and withstanding flame heat flux level of up to 440 kW/m 2 . The Heat Flux Pipe can clearly be used in other fire scenario where flame
Predictive maintenance on event logs: Application on an ATM fleet

Predictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance. In most cases, predictive maintenance applications use output from sensors, recording physical phenomenons such as temperature or vibration which can be directly linked to the degradation process of the machine. However, in some applications, outputs from sensors are not available, and event logs generated by the machine are used instead. We first study the approaches used in the literature to solve predictive maintenance problems and present a new public dataset containing the event logs from 156 machines. After this, we define an evaluation framework for predictive maintenance systems, which takes into account business constraints, and conduct experiments to explore suitable solutions, which can serve as guidelines for future works using this new dataset.
Symmetric Sparse Boolean Matrix Factorization and Applications

In this work, we study a variant of nonnegative matrix factorization where we wish to find a symmetric factorization of a given input matrix into a sparse, Boolean matrix. Formally speaking, given $\mathbf{M}\in\mathbb{Z}^{m\times m}$, we want to find $\mathbf{W}\in\{0,1\}^{m\times r}$ such that $\| \mathbf{M} - \mathbf{W}\mathbf{W}^\top \|_0$ is minimized among all $\mathbf{W}$ for which each row is $k$-sparse. This question turns out to be closely related to a number of questions like recovering a hypergraph from its line graph, as well as reconstruction attacks for private neural network training. As this problem is hard in the worst-case, we study a natural average-case variant that arises in the context of these reconstruction attacks: $\mathbf{M} = \mathbf{W}\mathbf{W}^{\top}$ for $\mathbf{W}$ a random Boolean matrix with $k$-sparse rows, and the goal is to recover $\mathbf{W}$ up to column permutation. Equivalently, this can be thought of as recovering a uniformly random $k$-uniform hypergraph from its line graph. Our main result is a polynomial-time algorithm for this problem based on bootstrapping higher-order information about $\mathbf{W}$ and then decomposing an appropriate tensor. The key ingredient in our analysis, which may be of independent interest, is to show that such a matrix $\mathbf{W}$ has full column rank with high probability as soon as $m = \widetilde{\Omega}(r)$, which we do using tools from Littlewood-Offord theory and estimates for binary Krawtchouk polynomials.
Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks

High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transferred in register-level. With the help of HLS, the hardware designers must describe only a high-level behavioral flow of the design. Despite this, it still can take weeks to develop a high-performance architecture mainly because there are many design choices at a higher level that requires more time to explore. It also takes several minutes to hours to get feedback from the HLS tool on the quality of each design candidate. In this paper, we propose to solve this problem by modeling the HLS tool with a graph neural network (GNN) that is trained to be used for a wide range of applications. The experimental results demonstrate that by employing the GNN-based model, we are able to estimate the quality of design in milliseconds with high accuracy which can help us search through the solution space very quickly.
Theoretical Development of an Adaptive Secondary Task to Measure Pilot Workload for Flight Evaluations

A series of research projects were reviewed that formulated the basis for the theoretical development and evaluation of an adaptive secondary task to measure pilot workload. The final flight project established the technical feasability of using a visual and auditory item-recognition (Sternberg) task as a measure of sensory-response loading and reserve information processing capacity while flying precision pitch maneuvers simulating terrain profiles. The discrete item secondary task presented letters of the alphabet at a rate driven by a scoring algorithm that adapted to the pitch error scores i.e., cross-coupled to the primary task. The preliminary results indicate an appreciable increase in reaction time and errors for the visual secondary task while flying the terrain avoidance primary task as compared to flying the same task under auditory task loading. Preliminary conclusions support the multiple resource model of information processing.
EVOLUTIONS OF EVASION TECHNIQUES AGAINST NETWORK INTRUSION DETECTION SYSTEMS

Abstract - With the rise of the information society, new types of attacks and strategies to make detection more difficult are discovered every day. Currently, Network Intrusion Detection Systems can be avoided in different ways, so it is essential to know the different existing evasion techniques when planning the defense perimeter of a network. This paper explains the most historically representative evasion strategies. It also analyzes the current impact of these threats, emphasizing those that currently still pose a risk, thus allowing researchers to develop more effective defense mechanisms.
Automatic grammar repair

We describe the first approach to automatically repair bugs in context-free grammars: given a grammar that fails some tests in a given test suite, we iteratively and gradually transform the grammar until it passes all tests. Our core idea is to build on spectrum-based fault localization to identify promising repair sites (i.e., specific positions in rules), and to apply grammar patches at these sites whenever they satisfy explicitly formulated pre-conditions necessary to potentially improve the grammar. We have implemented this approach in the gfixr system, and successfully used it to fix grammars students submitted as homeworks in a compiler engineering course, and to map one Pascal dialect grammar against another dialect. gfixr can be configured to explore the repair space in different ways, and can also take advantage of counterexamples to enable restriction patches that make the grammar less permissive.
The development of weeds sugar beet (Beta vulgaris L.) fields depending on the weeding method used

The process of weed development in a sugar beet field was followed under the climatic conditions of mideastern Poland in 1977. The sugar beet was cultivated on loess soil and weeded using two methods: mechanical and chemical (Pyramin 5 kg/ha just after sowing). The results of the study revealed a specific developmental rythm of the particular weed species on each of the weeded objects. The lise of the herbicide delayed the germination of weeds and limited their repeatability, eliminated sonic species from the weed community as well as delaying the start of generative development of some weeds. It also decreased the participation of the fruit and seed shedding phase of the weeds before harvest of the cultivated crop.
E-Learning Performance Prediction Based on Attention Mechanism

One of the current research hotspots in educational data mining is to predict their online academic performance. The prediction results can provide personalized guidance for learners and teaching strategies. At present, performance prediction methods often ignore the fact that different behavior characteristics have different effects on performance. Therefore, this paper proposed an e-Learning performance prediction method based on attention mechanism. This method calculated the attention score of each behavior feature, then assigned the corresponding attention weight to each behavior feature. So, by this way, different behavior features have different influence on academic performance. The experimental results show that this method can predict e-Learning performance more accurately than other methods.
Management of Physical Education Classroom Teaching in the Context of Big Data

In the era of big data (BD), education management is a key area facing reform in China, and the value of physical education management data is more important in the era of BD. The current problems faced in the development of physical education classroom management have emerged as new solution ideas in the context of BD, and the application of BD thinking to solve classroom management problems is a proven means. The purpose of this paper is to study the management of physical education classroom teaching in the context of BD. From the perspective of education policy and education management, this paper explores the problems that arise in China's physical education classroom and the corresponding solutions based on three aspects: cognition, mechanism and management mode in the context of BD. This paper adopts various analytical methods such as SWOT analysis, field interview method and questionnaire survey method to explore the problems in the development of physical education classroom teaching management, and proposes the use of BD technology to promote the construction of physical education informatization, so as to improve the teaching mode and learning mode of physical education classroom and enhance the quality of physical education teaching. The experimental results show that only 35% of physical education teachers feel that school sports facilities and necessary sports equipment can be fully satisfied in teaching in the era of BD, but more than 70% of students like physical education classes, so school administrators and physical education teachers need to improve the importance of physical education classes.
Boundary layer dynamics and bottom friction in combined wave–current flows over large roughness elements

Abstract In the coastal ocean, interactions of waves and currents with large roughness elements, similar in size to wave orbital excursions, generate drag and dissipate energy. These boundary layer dynamics differ significantly from well-studied small-scale roughness. To address this problem, we derived spatially and phase-averaged momentum equations for combined wave–current flows over rough bottoms, including the canopy layer containing obstacles. These equations were decomposed into steady and oscillatory parts to investigate the effects of waves on currents, and currents on waves. We applied this framework to analyse large-eddy simulations of combined oscillatory and steady flows over hemisphere arrays (diameter $D$), in which current ($U_c$), wave velocity ($U_w$) and period ($T$) were varied. In the steady momentum budget, waves increase drag on the current, and this is balanced by the total stress at the canopy top. Dispersive stresses from oscillatory flow around obstacles are increasingly important as $U_w/U_c$ increases. In the oscillatory momentum budget, acceleration in the canopy is balanced by pressure gradient, added-mass and form drag forces; stress gradients are small compared to other terms. Form drag is increasingly important as the Keulegan–Carpenter number $KC=U_wT/D$ and $U_c/U_w$ increase. Decomposing the drag term illustrates that a quadratic relationship predicts the observed dependences of steady and oscillatory drag on $U_c/U_w$ and $KC$. For large roughness elements, bottom friction is well represented by a friction factor ($f_w$) defined using combined wave and current velocities in the canopy layer, which is proportional to drag coefficient and frontal area per unit plan area, and increases with $KC$ and $U_c/U_w$.
Refuting FPT Algorithms for Some Parameterized Problems Under Gap-ETH

In this article we study a well-known problem, called Bipartite Token Jumping and not-so-well known problem(s), which we call, Half (Induced-) Subgraph , and show that under Gap-ETH, these problems do not admit FPT algorithms. The problem Bipartite Token Jumping takes as input a bipartite graph G and two independent sets S, T in G , where | S | = | T | = k , and the objective is to test if there is a sequence of exactly k -sized independent sets ⟨ I 0 , I 1 , · · · , I ℓ ⟩ in G , such that: i) I 0 = S and I ℓ = T , and ii) for every j ∈ [ ℓ ], I j is obtained from I j − 1 by replacing a vertex in I j − 1 by a vertex in V ( G ) \ I j − 1 . We show that, assuming Gap-ETH, Bipartite Token Jumping does not admit an FPT algorithm. We note that this result resolves one of the (two) open problems posed by Bartier et al. (ISAAC 2020), under Gap-ETH. Most of the known reductions related to Token Jumping exploit the property given by triangles (i.e., C 3 s), to obtain the correctness, and our results refutes FPT algorithm for Bipartite Token Jumping , where the input graph cannot have any triangles. For an integer k ∈ N , the half graph S k,k is the graph with vertex set V ( S k,k ) = A k ∪ B k , where A k = { a 1 , a 2 , · · · , a k } and B k = { b 1 , b 2 , · · · , b k } , and for i, j ∈ [ k ], { a i , b j } ∈ E ( T k,k ) if and only if j ≥ i . We also study the Half (Induced-)Subgraph problem where we are given a graph G and an integer k , and the goal is to check if G contains S k,k as an (induced-)subgraph. Again under Gap-ETH, we show that Half (Induced-)Subgraph does not admit an FPT algorithm, even when the input is a bipartite graph. We believe that the above problem (and its negative) result maybe of independent interest and could be useful obtaining new fixed parameter intractability results. There are very few reductions known in the literature which refute FPT algorithms for a parameterized problem based on assumptions like Gap-ETH. Thus our technique (and simple reductions) exhibits the potential of such conjectures in obtaining new (and possibly easier) proofs for refuting FPT algorithms for parameterized problems.
Intelligent English automatic translation system based on artificial intelligence support vector machine

Learning is the most fundamental characteristic of all intelligent systems. Machine learning is one of the most intelligent and cutting-edge research fields of AI. Machine learning studies how to make machines acquire new knowledge and skills by recognizing and utilizing existing knowledge. In order to improve the intelligent level of English translation software and the accuracy of English machine translation, an intelligent English automatic translation system (ATS) design method based on artificial intelligence (AI) and support vector machine (SVM) is proposed. Search module is used to search the basic meaning and subject content of vocabulary to be proofread, and the user’s behavior data is grasped through the behavior log to optimize the system. In the software part of the system, SVM-based computer intelligent proofreading method is used to find the correct words to replace the words to be proofread, so as to realize the intelligent proofreading of English translation. With the help of AI, machine translation has been continuously developed and improved, and today’s translation industry has entered a new stage.
Collaboration in editing world wide news in four Wikipedia language communities

With the popularity of social media, there has been a shift in the ways we gather and share news. Wikipedia uniquely brings together collective efforts to report on current events. While there is strong evidence of English Wikipedia attention to news events, it is not clear how does this effort exists across the different Wikipedia language communities. We study the collaboration process and the resulting content quality of the 6,730 Wikipedia articles related to 2,064 news events in English and three of the most spoken languages in the world: Arabic, Chinese, and Spanish. Our results provide evidence that while limited resources in smaller Wikipedia communities can lead to different collaboration patterns, their efforts lead to very comparable quality of contribution. However, this similar quality level is achieved with a delay corresponding to size of their Wikipedia and potential availability of resources.