Miracle-dz/UniFAR-FTUs huggingface dataset:
High resolution models of transcription factor-DNA anities improve in vitro and in vivo binding predictions: supplementary methods

We previously showed that SVR in vitro predictions outperformed the maximum E-score approach (E-max) across yeast and mouse TFs (see Results, Figure 2). Supplementary Figure 1 shows that (a) PBM-derived PSSMs and E-max give comparable performance in cross-validation on probe designs, while (b) PBM-trained SVR models clearly outperform PSSMs. In Figure 2, we compare the performance obtained by scoring PBM probe sequences using the maximum E-scores (“E-max” method) or Z-scores (“Z-max” method) over constituent k-mer patterns, where Z-scores are the median of the transformed probe intensities (see Badis et al. (2009)). As in the paper, we evaluated performance on TFs for which PBMs experiments on two array designs are available, using E-scores or Z-scores estimated on one array to make predictions for the other array. Using the detection of the top 100 as our evaluation measure, we found no statistically significant performance difference between Emax and Zmax (Emax is equivalent or superior to Zmax in 68 out of 114 cases, and the mean score for each is 20.7 and 20.3 respectively). A signed rank test shows no significant improvement of one method over the other for the detection of the top 100 probes using a significance threshold of p < 0.05. These results indicate that either E-max or Z-max can be used as essentially equivalent baseline methods. We chose the E-max approach since E-scores were readily available for all the data at the time of processing. To examine the choice of Seed-n-Wobble as our baseline method for deriving PSSMs from PBM experiments, we ran the RankMotif algorithm on PBM data for mouse TFs for which two array designs are available. As before, we learned PSSMs on one array and tested on the other. Since RankMotif requires selection of a motif length parameter, we tested lengths 6-13, and we chose the length with the highest likelihood for each PBM array using only the training data. We also constrained our search to PSSMs of length 10 to determine the accuracy of a single parameter setting. Figure 3 shows the comparison of the performance of Seed-n-Wobble PSSMs, SVR models, and RankMotif. We found that Seed-n-Wobble slightly outperforms RankMotif when a fixed length of 10bp is used, whereas RankMotif slightly outperforms Seed-n-Wobble when using the optimal (on
Diagnostic Equations for the Stable Boundary Layer Height: Evaluation and Dimensional Analysis

Abstract The performance of diagnostic equations for the stable boundary layer height h is evaluated with four observational datasets that represent a broad range of latitudes, land use, and surface roughness. In addition, large-eddy simulation results are used. Special care is given to data-quality selection. The diagnostic equations evaluated are so-called multilimit equations as derived by Zilitinkevich and coworkers in a number of papers. It appears that these equations show a serious negative bias, especially for h < 100 m, and it was found that the parameters involved could not be determined uniquely with calibration. As an alternative, dimensional analysis is used here to derive a formulation for h that is more robust. The formulation depends on the surface friction velocity u*, surface buoyancy flux Bs, Coriolis parameter, and the free-flow stability N. The relevance of the Coriolis parameter for the boundary layer height estimation in practice is also discussed. If the Coriolis parameter is ignor.
Near-Real-Time Applications of a Mesoscale Analysis System to Complex Terrain

Abstract Several mesoscale data analysis systems are reviewed, of which one is then adapted and applied to the complex terrain of northwest Utah and the western United States. The analysis system relies on the simple, but computationally efficient, successive correction methodology. Near-real-time three-dimensional mesoscale analyses are produced hourly over northwest Utah at 1-km horizontal resolution while analyses are produced every 15 min for surface fields over northwest Utah and the western United States. Surface analyses over the western United States are also generated at 0000 and 1200 UTC to help to initialize 36-h mesoscale model forecasts. Comparisons between the 1-km three-dimensional analyses and the background three-dimensional analysis provided by the National Centers for Environmental Prediction Rapid Update Cycle, version 2 (RUC-2), indicate that, where surface and upper-air observations are abundant, the local analysis adds information beyond that of simply interpolating the background (.
Bogdanov-Takens Bifurcation of Codimension 3 in the Gierer-Meinhardt Model

Bifurcation of the local Gierer–Meinhardt model is analyzed in this paper. It is found that the degenerate Bogdanov–Takens bifurcation of codimension 3 exists in the model, except for the saddle-node bifurcation and the Hopf bifurcation. That was not reported in the literature about this model. The existence of equilibria, their stability, the bifurcation and the induced complicated and interesting dynamics are explored in detail, by using stability analysis, normal form method and bifurcation theory. Numerical results are also presented to validate the theoretical results.
A combined VOF-RANS approach for studying the evolution of incipient wind-generated waves over a viscous liquid

Recent laboratory experiments have revealed that important insights into the physical processes involved in the wind-driven generation of surface waves may be obtained by varying the viscosity of the carrying liquid over several orders of magnitude. The present paper reports on the development of a companion approach aimed at studying similar phenomena through numerical simulation. After reviewing available options, we select and approach based on the combination of a volume of fluid technique to track the evolution of the air-liquid interface, and a two-dimensional Reynolds-averaged version of the Navier-Stokes equations supplemented with a turbulence model to predict the velocity and pressure fields in both fluids. We examine the formal and physical frameworks in which such a formulation is meaningful, and close the governing momentum equations with the one-equation Spalart-Allmaras model which directly solves a transport equation for the eddy viscosity. For this purpose, we assume the interface to behave as a rigid wall with respect to turbulent fluctuations in the air, and implement a versatile algorithm to compute the local distance to the interface whatever its shape. We first assess the performance of this model in the single-phase configuration of flow over a wavy rigid wall, which is of specific relevance with respect to wind-wave generation. Then, we discuss the initialization protocol used in two-phase simulations, which involves a white noise distribution applied to the interface position. We finally present some examples of interface evolutions obtained at several wind speeds with liquids of various viscosities.
Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control

Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback. Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback. First, implicit data just includes positive feedback. Therefore, we are not sure whether the non-interacted items are really negative or positive but not displayed to the corresponding user. Moreover, the relevance of rare items is usually underestimated since much fewer positive feedback of rare items is collected compared with popular ones. To tackle such difficulties, both pointwise and pairwise solutions are proposed before for unbiased relevance learning. As pairwise learning suits well for the ranking tasks, the previously proposed unbiased pairwise learning algorithm already achieves state-of-the-art performance. Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias. In this work, we propose an unbiased pairwise learning method, named UPL, with much lower variance to learn a truly unbiased recommender model. Extensive offline experiments on real world datasets and online A/B testing demonstrate the superior performance of our proposed method.
A Comprehensive Study on Object Detection Techniques in Unconstrained Environments

Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches. Additionally, we present a comparative analysis of the methods and highlight their strengths and weaknesses. Finally, we provide some future research directions to further improve object detection in unconstrained environments.
PERFORMANCE EVALUATION OF CORNER DETECTORS: A SURVEY

An image may contains number of important features, such as closed-boundary regions, edges, contours, line intersections, Corners, etc. Among them corner is one of the important feature, which can be identified by the change of intensity gradient in at least two-directions. Corner detectors have many applications in computer vision, object tracking or recognition. The corner detectors are classified into three clauses: contour based, intensity based and parametric based model. The performances of corner detector are presented in terms of consistency, accuracy, matching score, information rate, ground truth, visual inspection. The objective of this paper is to provide a comprehensive study of corner detection methods and their evaluation.
Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives

Abstract Collectiveness is an important property of many systems—both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals or even to produce intelligent collective behavior out of not-so-intelligent individuals. Indeed, collective intelligence, namely, the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems—motivated by recent technoscientific trends like the Internet of Things, swarm robotics, and crowd computing, to name only a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognized research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this article considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.
SocioEconomicMag Meets a Platform for SES-Diverse College Students: A Case Study

Emerging research shows that individual differences in how people use technology sometimes cluster by socioeconomic status (SES) and that when technology is not socioeconomically inclusive, low-SES individuals may abandon it. To understand how to improve technology's SES-inclusivity, we present a multi-phase case study on SocioEconomicMag (SESMag), an emerging inspection method for socio+economic inclusivity. In our 16-month case study, a software team developing a learning management platform used SESMag to evaluate and then to improve their platform's SES-inclusivity. The results showed that (1) the practitioners identified SES-inclusivity bugs in 76% of the features they evaluated; (2) these inclusivity bugs actually arise among low-SES college students; and (3) the SESMag process pointed ways towards fixing these bugs. Finally, (4) a user study with SES-diverse college students showed that the platform's SES-inclusivity eradicated 45-54% of the bugs; for some types of bugs, the bug instance eradication rate was 80% or higher.
Accelerating Globally Optimal Consensus Maximization in Geometric Vision

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an n-1 dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to four fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, point set registration, and rotation and focal length estimation. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios.
Biological Factor Regulatory Neural Network

Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their promising performance, it is hard for deep neural networks to provide biological insights for humans due to their black-box nature. Recently, some works integrated biological knowledge with neural networks to improve the transparency and performance of their models. However, these methods can only incorporate partial biological knowledge, leading to suboptimal performance. In this paper, we propose the Biological Factor Regulatory Neural Network (BFReg-NN), a generic framework to model relations among biological factors in cell systems. BFReg-NN starts from gene expression data and is capable of merging most existing biological knowledge into the model, including the regulatory relations among genes or proteins (e.g., gene regulatory networks (GRN), protein-protein interaction networks (PPI)) and the hierarchical relations among genes, proteins and pathways (e.g., several genes/proteins are contained in a pathway). Moreover, BFReg-NN also has the ability to provide new biologically meaningful insights because of its white-box characteristics. Experimental results on different gene expression-based tasks verify the superiority of BFReg-NN compared with baselines. Our case studies also show that the key insights found by BFReg-NN are consistent with the biological literature.
Curriculum-Based Imitation of Versatile Skills

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning methods that are based on such a maximum likelihood (ML) objective. The ML objective forces the model to cover all data, it prevents specialization in the context space and can cause mode-averaging in the behavior space, leading to suboptimal or potentially catastrophic behavior. Here, we alleviate those issues by introducing a curriculum using a weight for each data point, allowing the model to specialize on data it can represent while incentivizing it to cover as much data as possible by an entropy bonus. We extend our algorithm to a Mixture of (linear) Experts (MoE) such that the single components can specialize on local context regions, while the MoE covers all data points. We evaluate our approach in complex simulated and real robot control tasks and show it learns from versatile human demonstrations and significantly outperforms current SOTA methods. 11A reference implementation can be found at https://github.com/intuitive-robots/ML-Cur
A family of total Lagrangian Petrov–Galerkin Cosserat rod finite element formulations

The standard in rod finite element formulations is the Bubnov–Galerkin projection method, where the test functions arise from a consistent variation of the ansatz functions. This approach becomes increasingly complex when highly nonlinear ansatz functions are chosen to approximate the rod's centerline and cross‐section orientations. Using a Petrov–Galerkin projection method, we propose a whole family of rod finite element formulations where the nodal generalized virtual displacements and generalized velocities are interpolated instead of using the consistent variations and time derivatives of the ansatz functions. This approach leads to a significant simplification of the expressions in the discrete virtual work functionals. In addition, independent strategies can be chosen for interpolating the nodal centerline points and cross‐section orientations. We discuss three objective interpolation strategies and give an in‐depth analysis concerning locking and convergence behavior for the whole family of rod finite element formulations.
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning

No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems. Accordingly, these theorems are often referenced in support of the notion that individual problems require specially tailored inductive biases. While virtually all uniformly sampled datasets have high complexity, real-world problems disproportionately generate low-complexity data, and we argue that neural network models share this same preference, formalized using Kolmogorov complexity. Notably, we show that architectures designed for a particular domain, such as computer vision, can compress datasets on a variety of seemingly unrelated domains. Our experiments show that pre-trained and even randomly initialized language models prefer to generate low-complexity sequences. Whereas no free lunch theorems seemingly indicate that individual problems require specialized learners, we explain how tasks that often require human intervention such as picking an appropriately sized model when labeled data is scarce or plentiful can be automated into a single learning algorithm. These observations justify the trend in deep learning of unifying seemingly disparate problems with an increasingly small set of machine learning models.
Multi-scale Fusion Fault Diagnosis Method Based on Two-Dimensionaliztion Sequence in Complex Scenarios

Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to analyze time series data, but in real-life scenarios, sensor data is often noisy and cannot be accurately characterized in the time domain, leading to mode collapse in trained models. Two-dimensionalization methods such as the Gram angle field method (GAF) or interval sampling have been proposed, but they lack mathematical derivation and interpretability. This paper proposes an improved GAF combined with grayscale images for convolution scenarios. The main contributions include illustrating the feasibility of the approach in complex scenarios, widening the data set, and introducing an improved convolutional neural network method with a multi-scale feature fusion diffusion model and deep learning compression techniques for deployment in industrial scenarios.
Effect of Inversion Asymmetry on Bilayer Graphene's Superconducting and Exciton Condensates

Inversion asymmetry in bilayer graphene can be tuned by the displacement field. As a result, the band dispersion in biased bilayer graphene acquires flat band regions near the Dirac points along with a non-trivial band geometry. We analyze the effect of inversion symmetry on the critical temperature and superfluid stiffness of the superconducting state of AB-stacked graphene bilayer and on the exciton condensate in double layers formed by two AB-stacked graphene bilayers. The geometric superfluid stiffness in bilayer graphene superconductors is found to be negligible due to the small superconducting gap. Furthermore, we show that the geometric superfluid stiffness is maximized for a constant order parameter. Therefore, it can be neglected in biased bilayer graphene superconductors with any pairing symmetry. However, the displacement field enhances the geometric superfluid stiffness in exciton condensates. It is most prominent at low densities and high displacement fields. A consequence of the geometric superfluid stiffness is a modest enhancement of the Berezinskii-Kosterlitz-Thouless transition temperature in bilayer graphene's exciton condensate.
Feudal Graph Reinforcement Learning

Graph-based representations and weight-sharing modular policies constitute prominent approaches to tackling composable control problems in Reinforcement Learning (RL). However, as shown by recent graph deep learning literature, message-passing operators can create bottlenecks in information propagation and hinder global coordination. The issue becomes dramatic in tasks where high-level planning is needed. In this work, we propose a novel methodology, named Feudal Graph Reinforcement Learning (FGRL), that addresses such challenges by relying on hierarchical RL and a pyramidal message-passing architecture. In particular, FGRL defines a hierarchy of policies where high-level commands are propagated from the top of the hierarchy down through a layered graph structure. The bottom layers mimic the morphology of the physical system, while the upper layers capture more abstract sub-modules. The resulting agents are then characterized by a committee of policies where actions at a certain level set goals for the level below, thus implementing a hierarchical decision-making structure that encompasses task decomposition. We evaluate the proposed framework on locomotion tasks on benchmark MuJoCo environments and show that FGRL compares favorably against relevant baselines. Furthermore, an in-depth analysis of the command propagation mechanism provides evidence that the introduced message-passing scheme favors the learning of hierarchical decision-making policies.