Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories

Systems of interacting particles or agents have wide applications in many disciplines such as Physics, Chemistry, Biology and Economics. These systems are governed by interaction laws, which are often unknown: estimating them from observation data is a fundamental task that can provide meaningful insights and accurate predictions of the behaviour of the agents. In this paper, we consider the inverse problem of learning interaction laws given data from multiple trajectories, in a nonparametric fashion, when the interaction kernels depend on pairwise distances. We establish a condition for learnability of interaction kernels, and construct estimators that are guaranteed to converge in a suitable $L^2$ space, at the optimal min-max rate for 1-dimensional nonparametric regression. We propose an efficient learning algorithm based on least squares, which can be implemented in parallel for multiple trajectories and is therefore well-suited for the high dimensional, big data regime. Numerical simulations on a variety examples, including opinion dynamics, predator-swarm dynamics and heterogeneous particle dynamics, suggest that the learnability condition is satisfied in models used in practice, and the rate of convergence of our estimator is consistent with the theory. These simulations also suggest that our estimators are robust to noise in the observations, and produce accurate predictions of dynamics in relative large time intervals, even when they are learned from data collected in short time intervals.
Data-driven discovery of free-form governing differential equations

We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation. The input to our method is a dataset (or ensemble of datasets) corresponding to a particular solution (or ensemble of particular solutions) of a differential equation. The output is a human-readable differential equation with parameters calibrated to the individual particular solutions provided. The key to our method is to learn differentiable models of the data that subsequently serve as inputs to a genetic programming algorithm in which graphs specify computation over arbitrary compositions of functions, parameters, and (potentially differential) operators on functions. Differential operators are composed and evaluated using recursive application of automatic differentiation, allowing our algorithm to explore arbitrary compositions of operators without the need for human intervention. We also demonstrate an active learning process to identify and remedy deficiencies in the proposed governing equations.
Named entity recognition model for Indonesian tweet using CRF classifier

Named Entity Recognition (NER) is a part of Natural Language Processing (NLP) that acts to recognize the existing word entity in the document. By using NER, it is possible to perform activities such as information extraction and text summary. One of the data sources for the NLP process is tweets which are real time, occurred frequently, but limited by the number of words per tweet. In Indonesia, twitter is one of the most popular social media with various topics, so, it is necessary to provide models, train data, and test data for Indonesian tweet. In this study, the models were built using Conditional Random Field classification from 8,000 tweets that have been grouped to formal tweets and informal tweets. By testing the models to 2,000 training data, it provided recall and precision results of 62% and 87% respectively for formal tweets, 36% and 90% respectively for informal tweets, and 60% and 86% respectively for mixed tweets. These results indicate that the created Indonesian tweet models can be used for automatic NER.
Predicting dynamical system evolution with residual neural networks

Forecasting time series and time-dependent data is a common problem in many applications. One typical example is solving ordinary differential equation (ODE) systems $\dot{x}=F(x)$. Oftentimes the right hand side function $F(x)$ is not known explicitly and the ODE system is described by solution samples taken at some time points. Hence, ODE solvers cannot be used. In this paper, a data-driven approach to learning the evolution of dynamical systems is considered. We show how by training neural networks with ResNet-like architecture on the solution samples, models can be developed to predict the ODE system solution further in time. By evaluating the proposed approaches on three test ODE systems, we demonstrate that the neural network models are able to reproduce the main dynamics of the systems qualitatively well. Moreover, the predicted solution remains stable for much longer times than for other currently known models.
Characterization of material around the centaur (2060) Chiron from a visible and near-infrared stellar occultation in 2011

The centaur (2060) Chiron exhibits outgassing behaviour and possibly hosts a ring system. On 2011 November 29, Chiron occulted a fairly bright star (R ∼ 15 mag) as observed from the 3-m NASA Infrared Telescope Facility (IRTF) on Mauna Kea and the 2-m Faulkes Telescope North (FTN) at Haleakala. Data were taken as visible wavelength images and simultaneous, low-resolution, near-infrared (NIR) spectra. Here, we present a detailed examination of the light-curve features in the optical data and an analysis of the NIR spectra. We place a lower limit on the spherical diameter of Chiron's nucleus of 160.2 ± 1.3 km. Sharp, narrow dips were observed between 280 and 360 km from the centre (depending on event geometry). For a central chord and assumed ring plane, the separated features are 298.5–302 and 308–310.5 km from the nucleus, with normal optical depth ∼0.5–0.9, and a gap of 9.1 ± 1.3 km. These features are similar in equivalent depth to Chariklo's inner ring. The absence of absorbing/scattering material near the nucleus suggests that these sharp dips are more likely to be planar rings than a shell of material. The region of relatively increased transmission is within the 1:2 spin-orbit resonance, consistent with the proposed clearing pattern for a non-axisymmetric nucleus. Characteristics of possible azimuthally incomplete features are presented, which could be transient, as well as a possible shell from ∼900–1500 km: future observations are needed for confirmation. There are no significant features in the NIR light curves, nor any correlation between optical features and NIR spectral slope.
Live Demonstration: On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor

This live demonstration will show real-time, embedded learning of gestures shown to a dynamics vision sensor in neuromorphic hardware. A multi-layer spiking neural network implemented in the Loihi neuromorphic processor partially trained on 11 classes of gestures will be able to learn new classes of gestures shown to the vision sensor by using a combination of transfer learning and local synaptic plasticity. Visitors will experience real-time learning of new classes of gestures they show to the vision sensor whose data is processed in real-time by the network on a connected neuromorphic chip.
Fairness in Clustering with Multiple Sensitive Attributes

A clustering may be considered as fair on pre-specified sensitive attributes if the proportions of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we consider the task of fair clustering for scenarios involving multiple multi-valued or numeric sensitive attributes. We propose a fair clustering method, \textit{FairKM} (Fair K-Means), that is inspired by the popular K-Means clustering formulation. We outline a computational notion of fairness which is used along with a cluster coherence objective, to yield the FairKM clustering method. We empirically evaluate our approach, wherein we quantify both the quality and fairness of clusters, over real-world datasets. Our experimental evaluation illustrates that the clusters generated by FairKM fare significantly better on both clustering quality and fair representation of sensitive attribute groups compared to the clusters from a state-of-the-art baseline fair clustering method.
GREASE: A Generative Model for Relevance Search over Knowledge Graphs

Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBpedia which supports a wide range of different semantics of relevance based on numerous types of relations and attributes. As users may lack the expertise to formalize the desired semantics, supervised methods have emerged to learn the hidden user-defined relevance from user-provided examples. Along this line, in this paper we propose a novel generative model over KGs for relevance search, named GREASE. The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities. It is also extended to support properties that constrain answer entities. Extensive experiments on two large-scale KGs demonstrate that GREASE has advanced the state of the art in effectiveness, expressiveness, and efficiency.
Keeping an eye on DBI: power-counting for small-cs cosmology

Inflationary mechanisms for generating primordial fluctuations ultimately compute them as the leading contributions in a derivative expansion, with corrections controlled by powers of derivatives like the Hubble scale over Planck mass: H/Mp. At face value this derivative expansion breaks down for models with a small sound speed, cs, to the extent that cs ≪ 1 is obtained by having higher-derivative interactions like Leff ∼ (∂ Φ)4 compete with lower-derivative propagation. This concern arises more generally for models whose lagrangian is given as a function P(X) for X = −∂μ Φ ∂μ Φ—including in particular DBI models for which P(X) ∝ √1−kX—since these keep all orders in ∂ Φ while dropping ∂n Φ for n > 1. We here find a sensible power-counting scheme for DBI models that gives a controlled expansion in powers of three types of small parameters: H/Mp, slow-roll parameters (possibly) and cs ≪ 1. We do not find a similar expansion framework for generic small-cs or P(X) models. Our power-counting result quantifies the theoretical error for any prediction (such as for inflationary correlation functions) by fixing the leading power of these small parameters that is dropped when not computing all graphs (such as by restricting to the classical approximation); a prerequisite for meaningful comparisons with observations. The new power-counting regime arises because small cs alters the kinematics of free fluctuations in a way that changes how interactions scale at low energies, in particular allowing 1−cs to be larger than derivative-measuring quantities like (H/Mp)2.
Discovery of Metastable He I* λ10830 Mini-broad Absorption Lines and Very Narrow Paschen α Emission Lines in the ULIRG Quasar IRAS F11119+3257

IRAS F11119+3257 is a quasar-dominated ultraluminous infrared galaxy, with a partially obscured narrow-line Seyfert 1 nucleus. In this paper, we present the near-IR (NIR) spectroscopy of F11119+3257, in which we find unusual Paschen emission lines and metastable He i* λ10830 absorption associated with the previously reported atomic sodium and molecular OH mini-BAL (broad absorption line) outflow. Photoionization diagnosis confirms previous findings that the outflows are at kiloparsec scales. Such large-scale outflows should produce emission lines. We indeed find that high-ionization emission lines ([O III], [Ne III], and [Ne V]) are dominated by blueshifted components at similar speeds to the mini-BALs. The blueshifted components are also detected in some low-ionization emission lines, such as [O II] λ3727 and some Balmer lines (Hα, Hβ, and Hγ), even though their cores are dominated by narrow (FWHMNEL = 570 ± 40 km s−1) or broad components at the systemic redshift of z = 0.18966 ± 0.00006. The mass flow rate (230–730 M⊙ yr−1) and the kinetic luminosity ( erg s−1) are then inferred jointly from the blueshifted emission and absorption lines. In the NIR spectrum of F11119+3257, we also find that the Paschen emission lines are unique, in which a very narrow (FWHM = 260 ± 20 km s−1) component is shown in only Paα. This narrow component most probably comes from heavily obscured star formation. Based on the Paα and Paβ emissions, we obtain an extinction at the H band, AH > 2.1 (or a reddenning of > 3.7), and a star formation rate of SFR > 130 M⊙ yr−1 that resembles the estimates inferred from the far-IR emissions (SFRFIR = 190 ± 90 M⊙ yr−1).
Preconditioned Multiple Orthogonal Least Squares and Applications in Ghost Imaging via Sparsity Constraint

Ghost imaging via sparsity constraint (GISC) can recover objects from the intensity fluctuation of light fields even when the sampling rate is far below the Nyquist sampling rate. In this paper, we develop an efficient algorithm called the preconditioned multiple orthogonal least squares (PmOLS) for solving the GISC reconstruction problem. Our analysis shows that the PmOLS algorithm perfectly recovers any $n$-dimensional $K$-sparse signal from $m$ random linear samples of the signal with probability exceeding $1-3n^2 e^{-cm/K^2}$. Simulations and experiments demonstrate that the proposed algorithm has very competitive imaging quality compared to the state-ofthe-art methods.
Optimal percolation in correlated multilayer networks with overlap

We show that the size of the optimal percolation set of a multilayer network is substantially affected by the presence of interlayer degree correlations and edge overlap. We provide extensive numerical evidence to confirm that the state-of-the-art optimal percolation strategies normally fail to identify minimal percolation sets in synthetic and real-world correlated multilayer networks, thus underestimating their fragility. We propose a family of Pareto-efficient strategies for optimal multilayer percolation, that take into account both intralayer and interlayer heuristics, and can be easily extended to multiplex networks with an arbitrary number of layers. We show that these strategies consistently outperform existing attacking algorithms, providing interesting insights about the interplay of correlations and overlap in determining the hyperfragility of real-world multilayer networks.
An Empirical Analysis of Hazardous Uses of Android Shared Storage

Android shared storage is shared with all the applications (apps for short) and the user. It is common to see that a large amount of apps store different kinds of files on it. It is well known that apps granted the read or write permissions can freely access any files in the shared storage. As a consequence, the shared storage has been demonstrated to expose sensitive information and jeopardize users’ privacy. In this paper, we systematically study a simple but overlooked threat related to the shared storage—the lack of input validation (e.g., integrity verifications) when consuming files on the shared storage. We argue that the untrusted input from the shared storage is a much ubiquitous problem. By undertaking an empirically study through a static analysis tool we develop, we find over 30 percent of the 13,746 analyzed popular apps on the market suffer from such problem. By investigating the types of files consumed, we find shockingly a large fraction of apps store and consume sensitive files, which allows us to construct end-to-end attacks. Considering the ubiquity of this class of vulnerabilities, we finally define better access control policies for external storage to eliminate them for most apps.
Privacy-Preserving Contextual Bandits

Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm. They use the reward as a learning signal and aim to maximize the total reward over the inputs. Contextual bandits are commonly used to solve recommendation or ranking problems. This paper considers a learning setting in which multiple parties aim to train a contextual bandit together in a private way: the parties aim to maximize the total reward but do not want to share any of the relevant information they possess with the other parties. Specifically, multiple parties have access to (different) features that may benefit the learner but that cannot be shared with other parties. One of the parties pulls the arm but other parties may not learn which arm was pulled. One party receives the reward but the other parties may not learn the reward value. This paper develops a privacy-preserving contextual bandit algorithm that combines secure multi-party computation with a differential private mechanism based on epsilon-greedy exploration in contextual bandits.
Decay of a quasistable quantum system and quantum backflow

The decay of quasi-stable quantum system involves primarily an outgoing probability current density. However, during the transition from exponential to inverse-power-law decay there are time intervals during which this current, although small, is inward. In this paper this inward flow is associated with quantum backflow. Furthermore substantial backflow exists for time-evolving free wave packets which are initially confined in space.
SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Prediction

The typical multi-task learning methods for spatio-temporal data prediction involve low-rank tensor computation. However, such a method have relatively weak performance when the task number is small, and we cannot integrate it into non-linear models. In this paper, we propose a two-step suboptimal unitary method (SUM) to combine a meta-learning strategy into multi-task models. In the first step, it searches for a global pattern by optimising the general parameters with gradient descents under constraints, which is a geological regularizer to enable model learning with less training data. In the second step, we derive an optimised model on each specific task from the global pattern with only a few local training data. Compared with traditional multi-task learning methods, SUM shows advantages of generalisation ability on distant tasks. It can be applied on any multi-task models with the gradient descent as its optimiser regardless if the prediction function is linear or not. Moreover, we can harness the model to enable traditional prediction model to make coKriging. The experiments on public datasets have suggested that our framework, when combined with current multi-task models, has a conspicuously better prediction result when the task number is small compared to low-rank tensor learning, and our model has a quite satisfying outcome when adjusting the current prediction models for coKriging.
Fast and Bayes-consistent nearest neighbors

Research on nearest-neighbor methods tends to focus somewhat dichotomously either on the statistical or the computational aspects -- either on, say, Bayes consistency and rates of convergence or on techniques for speeding up the proximity search. This paper aims at bridging these realms: to reap the advantages of fast evaluation time while maintaining Bayes consistency, and further without sacrificing too much in the risk decay rate. We combine the locality-sensitive hashing (LSH) technique with a novel missing-mass argument to obtain a fast and Bayes-consistent classifier. Our algorithm's prediction runtime compares favorably against state of the art approximate NN methods, while maintaining Bayes-consistency and attaining rates comparable to minimax. On samples of size $n$ in $\R^d$, our pre-processing phase has runtime $O(d n \log n)$, while the evaluation phase has runtime $O(d\log n)$ per query point.
Neural Generation for Czech: Data and Baselines

We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.
Vector-borne epidemics driven by human mobility

Vector-borne epidemics are the result of the combination of different factors such as the crossed contagions between humans and vectors, their demographic distribution and human mobility among others. The current availability of information about the former ingredients demands their incorporation to current mathematical models for vector-borne disease transmission. Here, relying on metapopulation dynamics, we propose a framework whose results are in fair agreement with those obtained from mechanistic simulations. This framework allows us to derive an expression of the epidemic threshold capturing with high accuracy the conditions leading to the onset of epidemics. Driven by these insights, we obtain a prevalence indicator to rank the patches according to the risk of being affected by a vector-borne disease. We illustrate the utility of this epidemic risk indicator by reproducing the spatial distribution Dengue cases reported in the city of Santiago de Cali (Colombia) from 2015 to 2016.
Shape Constrained Network for Eye Segmentation in the Wild

Semantic segmentation of eyes has long been a vital pre-processing step in many biometric applications. Majority of the works focus only on high resolution eye images, while little has been done to segment the eyes from low quality images in the wild. However, this is a particularly interesting and meaningful topic, as eyes play a crucial role in conveying the emotional state and mental well-being of a person. In this work, we take two steps toward solving this problem: (1) We collect and annotate a challenging eye segmentation dataset containing 8882 eye patches from 4461 facial images of different resolutions, illumination conditions and head poses; (2) We develop a novel eye segmentation method, Shape Constrained Network (SCN), that incorporates shape prior into the segmentation network training procedure. Specifically, we learn the shape prior from our dataset using VAE-GAN, and leverage the pre-trained encoder and discriminator to regularise the training of SegNet. To improve the accuracy and quality of predicted masks, we replace the loss of SegNet with three new losses: Intersection-over-Union (IoU) loss, shape discriminator loss and shape embedding loss. Extensive experiments shows that our method outperforms state-of-the-art segmentation and landmark detection methods in terms of mean IoU (mIoU) accuracy and the quality of segmentation masks. The dataset is available at https://ibug.doc. ic.ac. uk/resources/ibug-eye-segmentation-dataset/