Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability and instability issues of the Weinstock inequality

Given two planar, conformal, smooth open sets $\Omega$ and $\omega$, we prove the existence of a sequence of smooth sets $\Omega_n$ which geometrically converges to $\Omega$ and such that the (perimeter normalized) Steklov eigenvalues of $\Omega_n$ converge to the ones of $\omega$. As a consequence, we answer a question raised by Girouard and Polterovich on the stability of the Weinstock inequality and prove that the inequality is genuinely unstable. However, under some a priori knowledge of the geometry related to the oscillations of the boundaries, stability may occur.
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation

Successfully training a deep neural network demands a huge corpus of labeled data. However, each label only provides limited information to learn from, and collecting the requisite number of labels involves massive human effort. In this work, we introduce LEAN-LIFE, a web-based, Label-Efficient AnnotatioN framework for sequence labeling and classification tasks, with an easy-to-use UI that not only allows an annotator to provide the needed labels for a task but also enables LearnIng From Explanations for each labeling decision. Such explanations enable us to generate useful additional labeled data from unlabeled instances, bolstering the pool of available training data. On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances. Our framework is the first to utilize this enhanced supervision technique and does so for three important tasks – thus providing improved annotation recommendations to users and an ability to build datasets of (data, label, explanation) triples instead of the regular (data, label) pair.
Spin selection rule for {\it S} level transitions in atomic rubidium under paraxial and nonparaxial two-photon excitation

We report on an experimental test of the spin selection rule for two-photon transitions in atoms. In particular, we demonstrate that the $5S_{1/2}\to 6S_{1/2}$ transition rate in a rubidium gas follows a quadratic dependency on the helicity parameter linked to the polarization of the excitation light. For excitation via a single Gaussian beam or two counterpropagating beams in a hot vapor cell, the transition rate scales as the squared degree of linear polarization. The rate reaches zero when the light is circularly polarized. In contrast, when the excitation is realized via an evanescent field near an optical nanofiber, the two-photon transition cannot be completely extinguished (theoretically, not lower than 13\% of the maximum rate, under our experimental conditions) by only varying the polarization of the fiber-guided light. Our findings lead to a deeper understanding of the physics of multiphoton processes in atoms in strongly nonparaxial light.
Uncertainty quantification for the BGK model of the Boltzmann equation using multilevel variance reduced Monte Carlo methods

We propose a control variate multilevel Monte Carlo method for the kinetic BGK model of the Boltzmann equation subject to random inputs. The method combines a multilevel Monte Carlo technique with the computation of the optimal control variate multipliers derived from local or global variance minimization problems. Consistency and convergence analysis for the method equipped with a second-order positivity-preserving and asymptotic-preserving scheme in space and time is also performed. Various numerical examples confirm that the optimized multilevel Monte Carlo method outperforms the classical multilevel Monte Carlo method especially for problems with discontinuities.
Simulations of black hole accretion torus in various magnetic field configurations

Using axisymetric general relativistic magnetohydrodynamics simulations we study evolution of accretion torus around black hole endowed with different initial magnetic field configurations. Due to accretion of material onto black hole, parabolic magnetic field will develop in accretion torus funnel around vertical axis, for any initial magnetic field configuration.
Multi-Object Tracking with Siamese Track-RCNN

Multi-object tracking systems often consist of a combination of a detector, a short term linker, a re-identification feature extractor and a solver that takes the output from these separate components and makes a final prediction. Differently, this work aims to unify all these in a single tracking system. Towards this, we propose Siamese Track-RCNN, a two stage detect-and-track framework which consists of three functional branches: (1) the detection branch localizes object instances; (2) the Siamese-based track branch estimates the object motion and (3) the object re-identification branch re-activates the previously terminated tracks when they re-emerge. We test our tracking system on two popular datasets of the MOTChallenge. Siamese Track-RCNN achieves significantly higher results than the state-of-the-art, while also being much more efficient, thanks to its unified design.
Scheduling for Mobile Edge Computing With Random User Arrivals—An Approximate MDP and Reinforcement Learning Approach

In this paper, we investigate the scheduling design of a mobile edge computing (MEC) system, where active mobile devices with computation tasks randomly appear in a cell. Every task can be computed at either the mobile device or the MEC server. We jointly optimize the task offloading decision, uplink transmission device selection and power allocation by formulating the problem as an infinite-horizon Markov decision process (MDP). Compared with most of the existing literature, this is the first attempt to address the transmission and computation optimization with random device arrivals in an infinite time horizon to our best knowledge. Due to the uncertainty in the device number and location, the conventional approximate MDP approaches addressing the curse of dimensionality cannot be applied. An alternative and suitable low-complexity solution framework is proposed in this work. We first introduce a baseline scheduling policy, whose value function can be derived analytically with the statistics of random mobile device arrivals. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. The complexity of deriving the sub-optimal policy is reduced dramatically compared with conventional solutions of MDP by eliminating the complicated value iteration. To address a more general scenario where the statistics of random mobile device arrivals are unknown, a novel and efficient algorithm integrating reinforcement learning and stochastic gradient descent (SGD) is proposed to improve the system performance in an online manner. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant.
From quasi-incompressible to semi-compressible fluids

A new concept of semi-compressible fluids is introduced for slightly compressible visco-elastic fluids (typically rather liquids than gasses) where mass density variations are negligible in some sense, while being directly controlled by pressure which is very small in comparison with the elastic bulk modulus. The physically consistent fully Eulerian models with specific dispersion of pressure-wave speed are devised. This contrasts to the so-called quasi-incompressible fluids which are described not physically consistently and, in fact, only approximate ideally incompressible ones in the limit. After surveying and modifying models for the quasi-incompressible fluids, we eventually devise some fully convective models complying with energy conservation and capturing phenomena as pressure-wave propagation with wave-length (and possibly also pressure) dependent velocity.
Exploiting Categorical Structure Using Tree-Based Methods

Standard methods of using categorical variables as predictors either endow them with an ordinal structure or assume they have no structure at all. However, categorical variables often possess structure that is more complicated than a linear ordering can capture. We develop a mathematical framework for representing the structure of categorical variables and show how to generalize decision trees to make use of this structure. This approach is applicable to methods such as Gradient Boosted Trees which use a decision tree as the underlying learner. We show results on weather data to demonstrate the improvement yielded by this approach.
Differentially Private Linear Regression over Fully Decentralized Datasets

This paper presents a differentially private algorithm for linear regression learning in a decentralized fashion. Under this algorithm, privacy budget is theoretically derived, in addition to that the solution error is shown to be bounded by $O(t)$ for $O(1/t)$ descent step size and $O(\exp(t^{1-e}))$ for $O(t^{-e})$ descent step size.
Single upper limb pose estimation method based on improved stacked hourglass network

At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human-machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model was designed.Deconvolution was employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression was used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments showed that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.
Effects of In-medium \(k_{\mathrm {T}}\) Broadening on Di-jet Observables

We study the influence of in-medium transverse momentum broadening on observables of jet pairs produced in nuclear collisions. These dijets were obtained numerically via the Monte-Carlo simulations of nuclear collisions, where the partons within the colliding nuclei were described via unintegrated parton densities, and their subsequent propagation within the medium of a quark-gluon plasma (QGP). For jet-evolution in the QGP both, kicks transverse to the momentum of the incoming particle as well as medium induced radiation were considered. After favorable comparison of our results with experimental LHC-data on jet-quenching we make predictions for the decorrelation of dijets. In particular, we study deviations from a transverse momentum broadening that follows a Gaussian distribution.
Stability of doubly-intractable distributions

Doubly-intractable distributions appear naturally as posterior distributions in Bayesian inference frameworks whenever the likelihood contains a normalizing function $Z$. Having two such functions $Z$ and $\widetilde Z$ we provide estimates of the total variation and Wasserstein distance of the resulting posterior probability measures. As a consequence this leads to local Lipschitz continuity w.r. t. $Z$. In the more general framework of a random function $\widetilde Z$ we derive bounds on the expected total variation and expected Wasserstein distance. The applicability of the estimates is illustrated within the setting of two representative Monte Carlo recovery scenarios.
Where can I drive? Deep Ego-Corridor Estimation for Robust Automated Driving

Lane detection is an essential part of the perception module of any automated driving (AD) or advanced driver assistance system (ADAS). So far, model-driven approaches for the detection of lane markings proved sufficient. More recently, however data-driven approaches have been proposed that show superior results. These deep learning approaches typically propose a classification of the free-space using for example semantic segmentation. While these examples focus and optimize on unmarked inner-city roads, we believe that mapless driving in complex highway scenarios is still not handled with sufficient robustness and availability. Especially in challenging weather situations such as heavy rain, fog at night or reflections in puddles, the reliable detection of lane markings will decrease significantly or completely fail with low-cost video-only AD systems. Therefore, we propose to specifically classify a drivable corridor in the ego-lane on a pixel level with a deep learning approach. Our approach is intentionally kept simple with only 660k parameters. Thus, we were able to easily integrate our algorithm into an online AD system of a test vehicle. We demonstrate the performance of our approach under challenging conditions qualitatively and quantitatively in comparison to a state-of-the-art model-driven approach. We see the current approach as a fallback method whenever a model-driven approach cannot cope with a specific scenario. Due to this, a fallback method does not have to fulfill the same requirements on comfort in lateral control as the primary algorithm: Its task is to catch the temporal shortcomings of the main perception task.
HST/FGS Trigonometric Parallaxes of M-dwarf Eclipsing Binaries

Hubble Space Telescope (HST) Fine Guidance Sensor (FGS) trigonometric parallax observations were obtained to directly determine distances to five nearby M-dwarf/M-dwarf eclipsing binary systems. These systems are intrinsically interesting as benchmark systems for establishing basic physical parameters for low-mass stars, such as luminosity L, and radius R. HST/FGS distances are also one of the few direct checks on Gaia trigonometric parallaxes, given the comparable sensitivity in both magnitude limit and determination of parallactic angles. A spectral energy distribution (SED) fit of each system’s blended flux output was carried out, allowing for estimation of the bolometric flux from the primary and secondary components of each system. From the stellar M, L, and R values, the low-mass star relationships between L and M, and R and M, are compared against idealized expectations for such stars. An examination on the inclusion of these close M-dwarf/M-dwarf pairs in higher-order common proper motion (CPM) pairs is analyzed; each of the 5 systems has indications of being part of a CPM system. Unexpected distances on interesting objects found within the grid of parallactic reference stars are also presented, including a nearby M dwarf and a white dwarf.
Sustainable Recipes. A Food Recipe Sourcing and Recommendation System to Minimize Food Miles

Sustainable Recipes is a tool that (1) connects food recipes ingredient lists with the closest organic providers to minimize the distance that food travels from farm to food preparation site and (2) recommends recipes given a GPS coordinate to minimize food miles. Sustainable Recipes provides consumers, entrepreneurs, cooking enthusiasts, and restauranteurs in the United States and elsewhere with an easy to use interface to help them (1) connect with organic ingredient producers to source ingredients to produce food recipes minimizing food miles and (2) recommend recipes using locally grown food. The main academic contribution of Sustainable Recipes is to bridge the gap between two streams of literature in data science of food recipes: studies of food recipes and studies of food supply chains. The outcomes of the interphase are (1) a map visualization that highlights the location of the producers that can supply the ingredients for a food recipe along with a ticket consisting of their contact addresses and the food miles used to produce a recipe and (2) a list of recipes that minimize food miles for a given GPS coordinate in which the recipe is going to be produced.
AMPSO: Artificial Multi-Swarm Particle Swarm Optimization

In this paper we propose a novel artificial multi-swarm PSO which consists of an exploration swarm, an artificial exploitation swarm and an artificial convergence swarm. The exploration swarm is a set of equal-sized sub-swarms randomly distributed around the particles space, the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, and the convergence swarm is artificially generated from a Gaussian perturbation of the best particle in the exploitation swarm as it is stagnated. The exploration and exploitation operations are alternatively carried out until the evolution rate of the exploitation is smaller than a threshold or the maximum number of iterations is reached. An adaptive inertia weight strategy is applied to different swarms to guarantee their performances of exploration and exploitation. To guarantee the accuracy of the results, a novel diversity scheme based on the positions and fitness values of the particles is proposed to control the exploration, exploitation and convergence processes of the swarms. To mitigate the inefficiency issue due to the use of diversity, two swarm update techniques are proposed to get rid of lousy particles such that nice results can be achieved within a fixed number of iterations. The effectiveness of AMPSO is validated on all the functions in the CEC2015 test suite, by comparing with a set of comprehensive set of 16 algorithms, including the most recently well-performing PSO variants and some other non-PSO optimization algorithms.
Online Social Deception and Its Countermeasures for Trustworthy Cyberspace: A Survey

We are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building a trustworthy SNSs. In this paper, we conducted an extensive survey, covering (i) the multidisciplinary concepts of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; (iv) datasets/metrics used for validation and verification; and (v) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons from existing literature. We conclude this survey paper with an in-depth discussions on the limitations of the state-of-the-art and recommend future research directions in this area.
Voice-Indistinguishability: Protecting Voiceprint In Privacy-Preserving Speech Data Release

With the development of smart devices, such as the Amazon Echo and Apple’s HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may hinder the collection and sharing of real-world speech data, which contain the speaker’s identifiable information, i.e., voiceprint, which is considered a type of biometric identifier. Current studies on voiceprint privacy protection do not provide either a meaningful privacy-utility trade-off or a formal and rigorous definition of privacy. In this study, we design a novel and rigorous privacy metric for voiceprint privacy, which is referred to as voice-indistinguishability, by extending differential privacy. We also propose mechanisms and frameworks for privacy-preserving speech data release satisfying voice-indistinguishability. Experiments on public datasets verify the effectiveness and efficiency of the proposed methods.
Ownership at Large: Open Problems and Challenges in Ownership Management

Software-intensive organizations rely on large numbers of software assets of different types, e.g., source-code files, tables in the data warehouse, and software configurations. Who is the most suitable owner of a given asset changes over time, e.g., due to reorganization and individual function changes. New forms of automation can help suggest more suitable owners for any given asset at a given point in time. By such efforts on ownership health, accountability of ownership is increased. The problem of finding the most suitable owners for an asset is essentially a program comprehension problem: how do we automatically determine who would be best placed to understand, maintain, evolve (and thereby assume ownership of) a given asset. This paper introduces the Facebook Ownesty system, which uses a combination of ultra large scale data mining and machine learning and has been deployed at Facebook as part of the company's ownership management approach. Ownesty processes many millions of software assets (e.g., source-code files) and it takes into account workflow and organizational aspects. The paper sets out open problems and challenges on ownership for the research community with advances expected from the fields of software engineering, programming languages, and machine learning.
If deep learning is the answer, then what is the question?

Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence (AI) research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This perspective has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterise computations or neural codes, or who wish to understand perception, attention, memory, and executive functions? In this Perspective, our goal is to offer a roadmap for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics, and neural representation in artificial and biological systems. We highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.