Miracle-dz/UniFAR-FTUs huggingface dataset:
Ramsey Numbers for Non-trivial Berge Cycles

In this paper, we consider an extension of cycle-complete graph Ramsey numbers to Berge cycles in hypergraphs: for k ≥ 2, a non-trivial Berge k-cycle is a family of sets e1, e2, . , ek such that e1∩e2, e2∩e3, . , ek∩e1 has a system of distinct representatives and e1 ∩ e2 ∩ · · · ∩ ek = ∅. In the case that all the sets ei have size three, let Bk denotes the family of all non-trivial Berge k-cycles. The Ramsey numbers R(t,Bk) denote the minimum n such that every n-vertex 3-uniform hypergraph contains either a non-trivial Berge k-cycle or an independent set of size t. We prove R(t,B2k) ≤ t 1 2k−1 + 2 √ log t and moreover, we show that if a conjecture of Erdős and Simonovits [11] on girth in graphs is true, then this is tight up to a factor to(1) as t → ∞. E-mail: jin019@ucsd.edu Research supported by NSF Award DMS-1952786. E-mail: jacques@ucsd.edu
A Convex Optimization Approach to Learning Koopman Operators

Koopman operators provide tractable means of learning linear approximations of non-linear dynamics. Many approaches have been proposed to find these operators, typically based upon approximations using an a-priori fixed class of models. However, choosing appropriate models and bounding the approximation error is far from trivial. Motivated by these difficulties, in this paper we propose an optimization based approach to learning Koopman operators from data. Our results show that the Koopman operator, the associated Hilbert space of observables and a suitable dictionary can be obtained by solving two rank-constrained semi-definite programs (SDP). While in principle these problems are NP-hard, the use of standard relaxations of rank leads to convex SDPs. Further, these SDPs exhibit chordal sparsity leading to algorithms that scale linearly with the number of data points.
Learning Optimal Strategies for Temporal Tasks in Stochastic Games

Synthesis from linear temporal logic (LTL) specifications provides assured controllers for systems operating in stochastic and potentially adversarial environments. Automatic synthesis tools, however, require a model of the environment to construct controllers. In this work, we introduce a model-free reinforcement learning (RL) approach to derive controllers from given LTL specifications even when the environment is completely unknown. We model the problem as a stochastic game (SG) between the controller and the adversarial environment; we then learn optimal control strategies that maximize the probability of satisfying the LTL specifications against the worst-case environment behavior. We first construct a product game using the deterministic parity automaton (DPA) translated from the given LTL specification. By deriving distinct rewards and discount factors from the acceptance condition of the DPA, we reduce the maximization of the worst-case probability of satisfying the LTL specification into the maximization of a discounted reward objective in the product game; this enables the use of model-free RL algorithms to learn an optimal controller strategy. To deal with the common scalability problems when the number of sets defining the acceptance condition of the DPA (usually referred as colors), is large, we propose a lazy color generation method where distinct rewards and discount factors are utilized only when needed, and an approximate method where the controller eventually focuses on only one color. In several case studies, we show that our approach is scalable to a wide range of LTL formulas, significantly outperforming existing methods for learning controllers from LTL specifications in SGs.
Machine Learning Methods for Histopathological Image Analysis: A Review

Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.
Overcoming Bias in Community Detection Evaluation

Community detection is a key task to further understand the function and the structure of complex networks. Therefore, a strategy used to assess this task must be able to avoid biased and incorrect results that might invalidate further analyses or applications that rely on such communities. Two widely used strategies to assess this task are generally known as structural and functional. The structural strategy basically consists in detecting and assessing such communities by using multiple methods and structural metrics. On the other hand, the functional strategy might be used when ground truth data are available to assess the detected communities. However, the evaluation of communities based on such strategies is usually done in experimental configurations that are largely susceptible to biases, a situation that is inherent to algorithms, metrics and network data used in this task. Furthermore, such strategies are not systematically combined in a way that allows for the identification and mitigation of bias in the algorithms, metrics or network data to converge into more consistent results. In this context, the main contribution of this article is an approach that supports a robust quality evaluation when detecting communities in real-world networks. In our approach, we measure the quality of a community by applying the structural and functional strategies, and the combination of both, to obtain different pieces of evidence. Then, we consider the divergences and the consensus among the pieces of evidence to identify and overcome possible sources of bias in community detection algorithms, evaluation metrics, and network data. Experiments conducted with several real and synthetic networks provided results that show the effectiveness of our approach to obtain more consistent conclusions about the quality of the detected communities.
Challenges in Net Neutrality Violation Detection: A Case Study of Wehe Tool and Improvements

We consider the problem of detecting deliberate traffic discrimination on the Internet. Given the complex nature of the Internet, detection of deliberate discrimination is not easy to detect, and tools developed so far suffer from various limitations. We study challenges in detecting the violations (focusing on the HTTPS traffic) and discuss possible mitigation approaches. We focus on 'Wehe,’ the most recent tool developed to detect net-neutrality violations. Wehe hosts traffic from all services of interest in a common server and replays them to mimic the behavior of the traffic from original servers. Despite Wehe's vast utility and possible influences over policy decisions, its mechanisms are not yet validated by others. In this work, we highlight critical weaknesses in Wehe where its replay traffic is not being correctly classified as intended services by the network middleboxes. We validate this observation using a commercial traffic shaper. We propose a new method in which the SNI field is set appropriately in the initial TLS handshake to overcome this weakness. Using commercial traffic shapers, we validate that SNI makes the replay traffic gets correctly classified as the intended traffic by the middleboxes. Our new approach thus provides a more realistic method for detecting neutrality violations of HTTPS traffic.
Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks

Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the data-hungry training regime that requires millions of trial and error attempts, which is impractical when running experiments on robotic systems. Learning from Demonstrations (LfD) has been introduced to solve this issue by cloning the behavior of expert demonstrations. However, LfD requires a large number of demonstrations that are difficult to be acquired since dedicated complex setups are required. To overcome these limitations, we propose a multi-subtask reinforcement learning methodology where complex pick and place tasks can be decomposed into low-level subtasks. These subtasks are parametrized as expert networks and learned via DRL methods. Trained subtasks are then combined by a high-level choreographer to accomplish the intended pick and place task considering different initial configurations. As a testbed, we use a pick and place robotic simulator to demonstrate our methodology and show that our method outperforms a benchmark methodology based on LfD in terms of sample-efficiency. We transfer the learned policy to the real robotic system and demonstrate robust grasping using various geometric-shaped objects.
Symbolic Behaviour in Artificial Intelligence

The ability to use symbols is the pinnacle of human intelligence, but has yet to be fully replicated in machines. Here we argue that the path towards symbolically fluent artificial intelligence (AI) begins with a reinterpretation of what symbols are, how they come to exist, and how a system behaves when it uses them. We begin by offering an interpretation of symbols as entities whose meaning is established by convention. But crucially, something is a symbol only for those who demonstrably and actively participate in this convention. We then outline how this interpretation thematically unifies the behavioural traits humans exhibit when they use symbols. This motivates our proposal that the field place a greater emphasis on symbolic behaviour rather than particular computational mechanisms inspired by more restrictive interpretations of symbols. Finally, we suggest that AI research explore social and cultural engagement as a tool to develop the cognitive machinery necessary for symbolic behaviour to emerge. This approach will allow for AI to interpret something as symbolic on its own rather than simply manipulate things that are only symbols to human onlookers, and thus will ultimately lead to AI with more human-like symbolic fluency.
HGAN: Hybrid Generative Adversarial Network

In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines.
Ammonia-assisted dehydrofluorination between PVDF and Nafion for highly selective and low-cost proton exchange membranes: a possible way to further strengthen the commercialization of Nafion

For the first time, dehydrofluorination reaction was detected between PVDF and Nafion in the presence of ammonia. Cross-linked networks were hence formed in the ammonia-modified Nafion + PVDF composite proton exchange membrane (PEM). They significantly enhanced the fuel resistance, thermal and mechanical stabilities, as well as the structural maintenance of the composite PEM. Meanwhile, at 80 °C, a slight increase in the proton conductivity of the composite PEM was also observed due to its improved water retention capability and the newly formed oxygen-containing functional groups. More importantly, such a highly selective Nafion-based PEM may have strong cost competitiveness in the market of commercial PEMs by virtue of the extremely low cost of PVDF (a dosage of up to 30–50 wt%) and ammonia.
Dynamic movement primitives in robotics: A tutorial survey

Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally leads to the formulation of the motor primitives’ theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems and are well suited to generate motor commands for artificial systems like robots. In the last decades, DMPs have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human–robot co-working, resulting in a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMP formulations in rigorous mathematical terms and discuss the advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state-of-the-art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.
A Data Augmented Bayesian Network for Node Failure Prediction in Optical Networks

Failures in optical network backbone can cause significant interruption in internet data traffic. Hence, it is very important to reduce such network outages. Prediction of such failures would be a step forward to avoid such disruption of internet services for users as well as operators. Several research proposals are available in the literature which are applications of data science and machine learning techniques. Most of the techniques rely on significant amount of real time data collection. Network devices are assumed to be equipped to collect data and these are then analysed by different algorithms to predict failures. Every network element which is already deployed in the field may not have these data gathering or analysis techniques designed into them initially. However, such mechanisms become necessary later when they are already deployed in the field. This paper proposes a Bayesian network based failure prediction of network nodes, g. , routers etc., using very basic information from the log files of the devices and applying power law based data augmentation to complement for scarce real time information. Numerical results show that network node failure prediction can be performed with high accuracy using the proposed mechanism.
A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising

Creative plays a great important role in e-commerce for exhibiting products. Sellers usually create multiple creatives for comprehensive demonstrations, thus it is crucial to display the most appealing design to maximize the Click-Through Rate (CTR). For this purpose, modern recommender systems dynamically rank creatives when a product is proposed for a user. However, this task suffers more cold-start problem than conventional products recommendation since the user-click data is more scarce and creatives potentially change more frequently. In this paper, we propose a hybrid bandit model with visual priors which first makes predictions with a visual evaluation, and then naturally evolves to focus on the specialities through the hybrid bandit model. Our contributions are three-fold: 1) We present a visual-aware ranking model (called VAM) that incorporates a list-wise ranking loss for ordering the creatives according to the visual appearance. 2) Regarding visual evaluation as a prior, the hybrid bandit model (called HBM) is proposed to evolve consistently to make better posteriori estimations by taking more observations into consideration for online scenarios. 3) A first large-scale creative dataset, CreativeRanking1, is constructed, which contains over 1.7M creatives of 500k products as well as their real impression and click data. Extensive experiments have also been conducted on both our dataset and public Mushroom dataset, demonstrating the effectiveness of the proposed method.
A Note on Argumentative Topology: Circularity and Syllogisms as Unsolved Problems

In the last couple of years there were a few attempts to apply topological data analysis to text, and in particular to natural language inference. A recent work by Tymochko et al. suggests the possibility of capturing `the notion of logical shape in text,' using `topological delay embeddings,' a technique derived from dynamical systems, applied to word embeddings. In this note we reconstruct their argument and show, using several old and new examples, that the problem of connecting logic, topology and text is still very much unsolved. We conclude that there is no clear answer to the question: ``Can we find a circle in a circular argument? '' We point out some possible avenues of exploration. The code used in our experiment is also shown.
Coherent spin states and stochastic hybrid path integrals

Stochastic hybrid systems involve a coupling between a discrete Markov chain and a continuous stochastic process. If the latter evolves deterministically between jumps in the discrete state, then the system reduces to a piecewise deterministic Markov process. Well known examples include stochastic gene expression, voltage fluctuations in neurons, and motor-driven intracellular transport. In this paper we use coherent spin states to construct a new path integral representation of the probability density functional for stochastic hybrid systems, which holds outside the weak noise regime. We use the path integral to derive a system of Langevin equations in the semi-classical limit, which extends previous diffusion approximations based on a quasi-steady-state reduction. We then show how in the weak noise limit the path integral is equivalent to an alternative representation that was previously derived using Doi–Peliti operators. The action functional of the latter is related to a large deviation principle for stochastic hybrid systems.
Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

Successful visual navigation depends upon capturing images that contain sufficient useful information. In this letter, we explore a data-driven approach to account for environmental lighting changes, improving the quality of images for use in visual odometry (VO) or visual simultaneous localization and mapping (SLAM). We train a deep convolutional neural network model to predictively adjust camera gain and exposure time parameters such that consecutive images contain a maximal number of matchable features. The training process is fully self-supervised: our training signal is derived from an underlying VO or SLAM pipeline and, as a result, the model is optimized to perform well with that specific pipeline. We demonstrate through extensive real-world experiments that our network can anticipate and compensate for dramatic lighting changes (e.g., transitions into and out of road tunnels), maintaining a substantially higher number of inlier feature matches than competing camera parameter control algorithms.
Kinetic Features for the Identification of Kelvin–Helmholtz Vortices in In Situ Observations

The boundaries identification of Kelvin–Helmholtz vortices in observational data has been addressed by searching for single-spacecraft small-scale signatures. A recent hybrid Vlasov–Maxwell simulation of Kelvin–Helmholtz instability has pointed out clear kinetic features that uniquely characterize the vortex during both the nonlinear and turbulent stage of the instability. We compare the simulation results with in situ observations of Kelvin–Helmholtz vortices by the Magnetospheric Multiscale satellites. We find good agreement between simulation and observations. In particular, the edges of the vortex are associated with strong current sheets, while the center is characterized by a low value for the magnitude of the total current density and strong deviation of the ion distribution function from a Maxwellian distribution. We also find a significant temperature anisotropy parallel to the magnetic field inside the vortex region and strong agyrotropies near the edges. We suggest that these kinetic features can be useful for the identification of Kelvin–Helmholtz vortices in in situ data.
Integrating PRISM theory and Monte Carlo simulation to study polymer-functionalised particles and polymer nanocomposites

Polymer nanocomposites consist of nanoscale additives embedded in a polymer matrix and are widely used in the automobiles, optics and microelectronics industries. Since the composition and the morphology of the polymer nanocomposite impact its macroscopic properties, significant efforts have been made to understand how parameters, such as polymer and nanoparticle chemistries, molecular weight of the matrix polymers and nanoparticle size, help tune the morphology. Theory and simulations have proven to be useful tools in this field due to their ability to link molecular level interactions, the morphology and the macroscopic properties. Due to the computational intensity of molecular simulations of a dense polymer matrix, there has been a strong effort on the theoretical front to develop methodologies that map out equilibrium structure and phase behaviour of polymer nanocomposites over a large parameter space. In this paper, we review the details of the self-consistent polymer reference interaction site model (PRISM)–Monte Carlo (MC) simulation method which integrates theory and simulation to study phase behaviour in polymer nanocomposites. We discuss two specific cases of polymer nanocomposites containing polymer-grafted nanoparticles with chemical and physical heterogeneity in grafts in which this self-consistent PRISM–MC approach has been used to study effective inter-filler interactions and phase behaviour.
Cosine Series Representation

This short paper is based on Chung et al. (2010), where the cosine series representation (CSR) is used in modeling the shape of white matter fiber tracts in diffusion tensor imaging(DTI) and Wang et al. (2018), where the method is used to denoise EEG. The proposed explicit analytic approach offers far superior flexibility in statistical modeling compared to the usual implicit Fourier transform methods such as the discrete cosine transforms often used in signal processing. The MATLAB codes and sample data can be obtained from http: //brainimaging.waisman. wisc.edu/ ̃chung/tracts.