Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Learning With Euler Particle Transport

We propose an Euler particle transport (EPT) approach for generative learning. The proposed approach is motivated by the problem of finding an optimal transport map from a reference distribution to a target distribution characterized by the Monge-Ampere equation. Interpreting the infinitesimal linearization of the Monge-Ampere equation from the perspective of gradient flows in measure spaces leads to a stochastic McKean-Vlasov equation. We use the forward Euler method to solve this equation. The resulting forward Euler map pushes forward a reference distribution to the target. This map is the composition of a sequence of simple residual maps, which are computationally stable and easy to train. The key task in training is the estimation of the density ratios or differences that determine the residual maps. We estimate the density ratios (differences) based on the Bregman divergence with a gradient penalty using deep density-ratio (difference) fitting. We show that the proposed density-ratio (difference) estimators do not suffer from the "curse of dimensionality" if data is supported on a lower-dimensional manifold. Numerical experiments with multi-mode synthetic datasets and comparisons with the existing methods on real benchmark datasets support our theoretical results and demonstrate the effectiveness of the proposed method.
Advanced Algorithms in Heterogeneous and Uncertain Networking Environments

Communication networks are used today everywhere and on every scale: starting from small Internet of Things (IoT) networks at home, via campus and enterprise networks, and up to tier-one networks of Internet providers. Accordingly, network devices should support a plethora of tasks with highly heterogeneous characteristics in terms of processing time, bandwidth energy consumption, deadlines and so on. Evaluating these characteristics and the amount of currently available resources for handling them requires analyzing all the arriving inputs, gathering information from numerous remote devices, and integrating all this information. Performing all these tasks in real time is very challenging in today's networking environments, which are characterized by tight bounds on the latency, and always-increasing data rates. Hence, network algorithms should typically make decisions under uncertainty. This work addresses optimizing performance in heterogeneous and uncertain networking environments. We begin by detailing the sources of heterogeneity and uncertainty and show that uncertainty appears in all layers of network design, including the time required to perform a task; the amount of available resources; and the expected gain from successfully completing a task. Next, we survey current solutions and show their limitations. Based on these insights we develop general design concepts to tackle heterogeneity and uncertainty, and then use these concepts to design practical algorithms. For each of our algorithms, we provide rigorous mathematical analysis, thus showing worst-case performance guarantees. Finally, we implement and run the suggested algorithms on various input traces, thus obtaining further insights as to our algorithmic design principles.
Distant Domain Transfer Learning for Medical Imaging

Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than “non-transfer” algorithms, and 8% higher than existing transfer and distant transfer algorithms.
Automatic test suite generation for key-points detection DNNs using many-objective search (experience paper)

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time---where each individual key-point may be critical in the targeted application---and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining.
Random cones in high dimensions I: Donoho-Tanner and Cover-Efron cones.

Two models of random cones in high dimensions are considered, together with their duals. The Donoho-Tanner random cone $D_{n,d}$ can be defined as the positive hull of $n$ independent $d$-dimensional Gaussian random vectors. The Cover-Efron random cone $C_{n,d}$ is essentially defined as the same positive hull, conditioned on the event that it is not the whole space. We consider expectations of various combinatorial and geometric functionals of these random cones and prove that they satisfy limit theorems, as $d$ and $n$ tend to infinity in a suitably coordinated way. This includes, for example, large deviation principles and central as well as non-central limit theorems for the expected number of $k$-faces and the $k$-th conic intrinsic volumes, as $n$, $d$ and possibly also $k$ tend to infinity simultaneously. Furthermore, we determine the precise high-dimensional asymptotic behaviour of the expected statistical dimension for both models of random cones, uncovering thereby another high-dimensional phase transition. As an application, limit theorems for the number of $k$-faces of high-dimensional polytopes generated by random Gale diagrams are discussed as well.
Newtonian potential in higher-derivative quantum gravity

We hereby derive the Newtonian metric potentials for the fourth-derivative gravity including the one-loop logarithm quantum corrections. It is explicitly shown that the behavior of the modified Newtonian potential near the origin is improved respect to the classical one, but this is not enough to remove the curvature singularity in $r=0$. Our result is grounded on a rigorous proof based on numerical and analytic computations.
A New Bound on Approximate Support Recovery

Orthogonal matching pursuit (OMP) is a greedy algorithm popularly being used for the recovery of sparse signals. In this paper, we study the performance of OMP for support recovery of sparse signal under noise. Our analysis shows that under mild constraint on the minimum-to-average ratio of nonzero entries in the sparse signal and the signal-to-noise ratio, the OMP algorithm can recover the support of signal with an error rate that can be arbitrarily small. Our result offers an affirmative answer to the conjecture of [Wang, TSP 2015] that the error rate of support recovery via OMP has no dependence on the maximum element of the signal.
Solute Mass Transfer Effects in Two‐Dimensional Dual‐Permeability Modeling of Bromide Leaching From a Tile‐Drained Field

Preferential flow (PF) depends on processes and structures in soil at the small scale and can affect flow and transport processes at much larger scales. For studying PF processes, the discharge and solute effluent from subsurface drained experimental fields has frequently been used as a field‐integrated signal that included combined effects of macropore flow and lateral transport toward the drain. The objective of this study was to better understand effects of the mass transfer coefficients on bromide (Br) leaching in a two‐dimensional (2D) dual‐permeability concept. The Br leaching was simulated for data of a Br tracer irrigation experiment on a drained field (5000 m2 area) at Bokhorst (Germany), where soils developed from glacial till sediments. Flow and transport in 2D vertical cross‐sections was described using a numerical 2D dual‐permeability model. For applied Br, influx of Br only in the soil matrix (SM) domain, only in the preferential flow (PF) domain, and proportional to the water influx in both domains was considered to assess the impact of small‐scale redistribution processes occurring at the structured soil surface on field‐ and plot‐scale transport. Three values of the water and four of the solute mass transfer rate coefficients were tested to imitate local effects (e.g., of clay‐organic coatings) on the inter‐domain mass transfer. The local‐scale solute mass transfer between PF and SM domains had a clear impact on Br concentrations in drain effluent at the field scale; concentrations mainly increased more rapidly with smaller values of the diffusive mass transfer coefficient. In the 2D flow domain, representing the plot scale, mass transfer rates were temporally and spatially variable with varying importance of diffusive and advective components depending on the influx rates; local effects were still significant at the field scale. Small‐scale properties and processes such as domain‐specific infiltration and mass transfer in structured soil seem important for improving the description of larger‐scale flow and transport processes.
Excitable actin dynamics and amoeboid cell migration

Amoeboid cell migration is characterized by frequent changes of the direction of motion and resembles a persistent random walk on long time scales. Although it is well known that cell migration is typically driven by the actin cytoskeleton, the cause of this migratory behavior remains poorly understood. We analyze the spontaneous dynamics of actin assembly due to nucleation promoting factors, where actin filaments lead to an inactivation of these factors. We show that this system exhibits excitable dynamics and can spontaneously generate waves, which we analyze in detail. By using a phase-field approach, we show that these waves can generate cellular random walks. We explore how the characteristics of these persistent random walks depend on the parameters governing the actin-nucleator dynamics. In particular, we find that the effective diffusion constant and the persistence time depend strongly on the speed of filament assembly and the rate of nucleator inactivation. Our findings point to a deterministic origin of the random walk behavior and suggest that cells could adapt their migration pattern by modifying the pool of available actin.
SEGSys: A mapping system for segmentation analysis in energy

Customer segmentation analysis can give valuable insights into the energy efficiency of residential buildings. This paper presents a mapping system, SEGSys that enables segmentation analysis at the individual and the neighborhood levels. SEGSys supports the online and offline classification of customers based on their daily consumption patterns and consumption intensity. It also supports the segmentation analysis according to the social characteristics of customers of individual households or neighborhoods, as well as spatial geometries. SEGSys uses a three-layer architecture to model the segmentation system, including the data layer, the service layer, and the presentation layer. The data layer models data into a star schema within a data warehouse, the service layer provides data service through a RESTful interface, and the presentation layer interacts with users through a visual map. This paper showcases the system on the segmentation analysis using an electricity consumption data set and validates the effectiveness of the system.
Unsupervised Learning of slow features for Data Efficient Regression

Research in computational neuroscience suggests that the human brain's unparalleled data efficiency is a result of highly efficient mechanisms to extract and organize slowly changing high level features from continuous sensory inputs. In this paper, we apply this slowness principle to a state of the art representation learning method with the goal of performing data efficient learning of down-stream regression tasks. To this end, we propose the slow variational autoencoder (S-VAE), an extension to the $\beta$-VAE which applies a temporal similarity constraint to the latent representations. We empirically compare our method to the $\beta$-VAE and the Temporal Difference VAE (TD-VAE), a state-of-the-art method for next frame prediction in latent space with temporal abstraction. We evaluate the three methods against their data-efficiency on down-stream tasks using a synthetic 2D ball tracking dataset, a dataset from a reinforcent learning environment and a dataset generated using the DeepMind Lab environment. In all tasks, the proposed method outperformed the baselines both with dense and especially sparse labeled data. The S-VAE achieved similar or better performance compared to the baselines with $20\%$ to $93\%$ less data.
Novel synthesis of biodegradable amphiphilic linear and star block copolymers based on poly(ε-caprolactone) and poly(ethylene glycol)

Biodegradable, amphiphilic, diblock poly(e-caprolactone)-block-poly(ethylene glycol) (PCL-b-PEG), triblock poly(e-caprolactone)-block-poly(ethylene glycol)-block-poly(e-caprolactone) (PCL-b-PEG-b-PCL), and star shaped copolymers were synthesized by ring opening polymerization of e-caprolactone in the presence of poly(ethylene glycol) methyl ether or poly(ethylene glycol) or star poly(ethylene glycol) and potassium hexamethyldisilazide as a catalyst. Polymerizations were carried out in toluene at room temperature to yield monomodal polymers of controlled molecular weight. The chemical structure of the copolymers was investigated by 1H and 13C NMR. The formation of block copolymers was confirmed by 13C NMR and DSC investigations. The effects of copolymer composition and molecular structure on the physical properties were investigated by GPC and DSC. For the same PCL chain length, the materials obtained in the case of linear copolymers are viscous whereas in the case of star copolymer solid materials are obtained with low Tg and Tm temperatures. © 2007 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 45: 3975–3985, 2007
Crowd Vetting: Rejecting Adversaries via Collaboration With Application to Multirobot Flocking

In this article, we characterize the advantage of using a robot’s neighborhood to find and eliminate adversarial robots in the presence of a Sybil attack. We show that by leveraging the opinions of their neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize the number of communication rounds required to be a function of the communication quality and of the proportion of legitimate to malicious robots. This result enables increased resiliency of many multirobot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems of a time critical nature. We develop two algorithms, FindSpoofedRobots that determines trusted neighbors with high probability, and FindResilientAdjacencyMatrix that enables distributed computation of graph properties in an adversarial setting. We apply our methods to a flocking problem where a team of robots must track a moving target in the presence of adversarial robots. We show that by using our algorithms, the team of robots are able to maintain tracking ability of the dynamic target.
Quantifying Synchronization in a Biologically Inspired Neural Network

We present a collated set of algorithms to obtain objective measures of synchronization in brain time-series data. The algorithms are implemented in MATLAB; we refer to our collated set of ‘tools' as SyncBox. Our motivation for SyncBox is to understand the underlying dynamics in an existing population neural network, commonly referred to as neural mass models, that mimic Local Field Potentials of the visual thalamic tissue. Specifically, we aim to measure the phase synchronization objectively in the network response to periodic stimuli; this is to mimic the condition of Steady-state-visually-evoked-potentials (SSVEP), which are scalp Electroencephalograph (EEG) corresponding to periodic stimuli. We showcase the use of SyncBox on our existing neural mass model of the visual thalamus. Following our successful testing of SyncBox, it is currently being used for further research on understanding the underlying dynamics in enhanced neural networks of the visual pathway. Link to SyncBox: https://github.com/PranavMahajan25/SyncBox
Detection of Adversarial Supports in Few-shot Classifiers Using Feature Preserving Autoencoders and Self-Similarity

Few-shot classifiers excel under limited training samples, making it useful in real world applications. However, the advent of adversarial samples threatens the efficacy of such classifiers. For them to remain reliable, defences against such attacks must be explored. However, closer examination to prior literature reveals a big gap in this domain. Hence, in this work, we propose a detection strategy to highlight adversarial support sets, aiming to destroy a few-shot classifier's understanding of a certain class of objects. We make use of feature preserving autoencoder filtering and also the concept of self-similarity of a support set to perform this detection. As such, our method is attack-agnostic and also the first to explore detection for few-shot classifiers to the best of our knowledge. Our evaluation on the miniImagenet and CUB datasets exhibit optimism when employing our proposed approach, showing high AUROC scores for detection in general.
The Deep Parametric PDE Method: Application to Option Pricing

We propose the deep parametric PDE method to solve high-dimensional parametric partial differential equations. A single neural network approximates the solution of a whole family of PDEs after being trained without the need of sample solutions. As a practical application, we compute option prices in the multivariate Black-Scholes model. After a single training phase, the prices for different time, state and model parameters are available in milliseconds. We evaluate the accuracy in the price and a generalisation of the implied volatility with examples of up to 25 dimensions. A comparison with alternative machine learning approaches, confirms the effectiveness of the approach.
A sentiment analysis approach to the prediction of market volatility

Prediction and quantification of future volatility and returns play an important role in financial modeling, both in portfolio optimisation and risk management. Natural language processing today allows one to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. We found that there is evidence of correlation between sentiment and stock market movements. Moreover, the sentiment captured from news headlines could be used as a signal to predict market returns; we also found that the same does not apply for volatility. However, for the sentiment found in Twitter comments we obtained, in a surprising finding, a correlation coefficient of –0.7 (p < 0.0 5), which indicates a strong negative correlation between negative sentiment captured from the tweets on a given day and the volatility observed the next day. It is important to keep in mind that stock volatility rises greatly when the market collapses but not symmetrically so when it goes up (the so-called leverage effect). We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modeling, based on Latent Dirichlet Allocation, in order to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modeling even on modest (essentially personal) architecture our classifier achieved a directional prediction accuracy for volatility of 63%.
Testing 
f(R)
 gravity with scale dependent cosmic void velocity profiles

We study the impact of cosmological scale modifications to General Relativity on the dynamics of halos within voids by comparing N-body simulations incorporating Hu-Sawicki $f(R)$ gravity, with $|f_{R0}|=10^{-6}$ and $10^{-5}$, to those of $\Lambda$CDM. By examining the radial velocity statistics within voids classified based on their size and density-profile, as "rising" ($R$-type) or "shell" ($S$-type), we find that halo motions in small $R$-type voids, with effective radius $<15 Mpc/h$, reveal distinctive differences between $f(R)$ and $\Lambda$CDM cosmologies. To understand this observed effect, we study the linear and nonlinear fifth forces, and develop an iterative algorithm to accurately solve the non-linear fifth force equation. We use this to characterize the Chameleon screening mechanism in voids and contrast the behavior with that observed in gravitationally collapsed objects. The force analysis underscores how smaller $R$-type voids exhibit the highest ratios of fifth force to Newtonian force, which source distinguishable differences in the velocity profiles and thereby provide rich environments in which to constrain gravity.