Miracle-dz/UniFAR-FTUs huggingface dataset:
A sufficient condition for the super-linearization of polynomial systems

We provide in this paper a sufficient condition for a polynomial dynamical system $\dot x(t) = f(x(t))$ to be super-linearizable, i.e., to be such that all its trajectories are linear projections of the trajectories of a linear dynamical system. The condition is expressed in terms of the hereby introduced weighted dependency graph $G$, whose nodes $v_i$ correspond to variables $x_i$ and edges $v_iv_j$ have weights $\frac{\partial f_j}{\partial x_i}$. We show that if the product of the edge weights along any cycle in $G$ is a constant, then the system is super-linearizable. The proof is constructive, and we provide an algorithm to obtain super-linearizations and illustrate it on an example.
Complex balanced distributions for chemical reaction networks

Stationary distributions of continuous time Markov chains (CTMCs) are of-ten a main interest, but hard to ﬁnd. We consider CTMCs modelling reaction networks, and characterise complex balanced distributions (provided they exist) of reaction networks with arbitrary transition functions through conditions on the cycles of their corresponding digraph. The proof works by constructing a dynamically equivalent reaction network of disjoint cycles. We further derive a suﬃcient condition for the existence of a complex balanced distribution, and give precise conditions on when it is necessary. The suﬃcient condition holds for mass-action kinetics or if the digraph consists of only cyclic connected components. Hence, we fully characterise the existence and form of complex balanced distributions. Moreover, these results can be used to design complex balanced reaction networks with a given stationary distribution.
The energy-momentum complex in non-local gravity

In General Relativity, the issue of defining the gravitational energy contained in a given spatial region is still unresolved, except for particular cases of localized objects where the asymptotic flatness holds for a given spacetime. In principle, a theory of gravity is not self-consistent, if the whole energy content is not uniquely defined in a specific volume. Here we generalize the Einstein gravitational energy-momentum pseudotensor to non-local theories of gravity where analytic functions of the non-local integral operator $\Box^{-1}$ are taken into account. We apply the Noether theorem to a gravitational Lagrangian, supposed invariant under the one-parameter group of diffeomorphisms, that is, the infinitesimal rigid translations. The invariance of non-local gravitational action under global translations leads to a locally conserved Noether current, and thus, to the definition of a gravitational energy-momentum pseudotensor, which is an affine object transforming like a tensor under affine transformations. Furthermore, the energy-momentum complex remains locally conserved, thanks to the non-local contracted Bianchi identities. The continuity equations for the gravitational pseudotensor and the energy-momentum complex, taking into account both gravitational and matter components, can be derived. Finally, the weak field limit of pseudotensor is performed to lowest order in metric perturbation in view of astrophysical applications.
Robust web element identification for evolving applications by considering visual overlaps

Fragile (i.e., non-robust) test execution is a common challenge for automated GUI-based testing of web applications as they evolve. Despite recent progress, there is still room for improvement since test execution failures caused by technical limitations result in unnecessary maintenance costs that limit its effectiveness and efficiency. One of the most reported technical challenges for web-based tests concerns how to reliably locate a web element used by a test script.This paper proposes the novel concept of Visually Overlapping Nodes (VON) that reduces fragility by utilizing the phenomenon that visual web elements (observed by the user) are constructed from multiple web-elements in the Document Object Model (DOM) that overlaps visually.We demonstrate the approach in a tool, VON Similo, which extends the state-of-the-art multi-locator approach (Similo) that is also used as the baseline for an experiment. In the experiment, a ground truth set of 1163 manually collected web element pairs, from different releases of the 40 most popular web applications on the internet, are used to compare the approaches’ precision, recall, and accuracy.Our results show that VON Similo provides 94.7% accuracy in identifying a web element in a new release of the same SUT. In comparison, Similo provides 83.8% accuracy.These results demonstrate the applicability of the visually overlapping nodes concept/tool for web element localization in evolving web applications and contribute a novel way of thinking about web element localization in future research on GUI-based testing.
Advertising Visual Attention to Facebook Social Network: Evidence from Eye Movements

Traditionally, previous studies computed the click-through rate (CTR) to estimate a number of people who reviewed the advertisements placed online or used a self-report indicator to measure the effectiveness. However, these research results couldn't reveal potential customers for lack of valid common view on Internet advertising. This study recruited 65 participants whose eye movements were tracked and recorded by the eye tracking system, of which 35 were assigned to Facebook friend group and the other 30 were assigned to non-Facebook friend group according to the interpersonal relationships with the researcher. Eye tracking measurements, including total fixation duration (TFD) and latency of first (LFF) on the defined regions of interest (ROIs) of Facebook page were compared to indicate their visual attentions. The experimental results showed that 1) participants of the two groups spent less time viewing the ads at the right hand side (RHS) of Facebook based on TFD and 2) participants of non-Facebook friend group spent much time than participants of Facebook friend group while viewing the ads in the desktop news feed (DNF) of Facebook based on TFD. 3) Participants of the two groups have the same sequences of viewing ROIs placed ads at the right hand side (RHS). 4) Participants of the two groups have different sequences of viewing ROIs placed ads in the desktop news feed (DNF).
Stream-K: Work-Centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU

We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra. Whereas contemporary decompositions are primarily tile-based, our method operates by partitioning an even share of the aggregate inner loop iterations among physical processing elements. This provides a near-perfect utilization of computing resources, regardless of how efficiently the output tiling for any given problem quantizes across the underlying processing elements.
Total energy-shaping control for mechanical systems via Control-by-Interconnection

Application of IDA-PBC to mechanical systems has received much attention in recent decades, but its application is still limited by the solvability of the so-called matching conditions. In this work, it is shown that total energy-shaping control of under-actuated mechanical systems has a control-by-interconnection interpretation. Using this interpretation, alternate matching conditions are formulated that defines constraints on the added energy, rather then the total closed-loop energy. It is additionally shown that, for systems that are under-actuated degree one with the mass matrix depending on a single coordinate, the kinetic energy matching conditions resolve to ODEs which can be evaluated numerically. Using this approach controllers are proposed for the benchmark cart-pole and acrobot systems.
The Cosmological Simulation Code OpenGadget3 -- Implementation of Meshless Finite Mass

Subsonic turbulence plays a major role in determining properties of the intra cluster medium (ICM). We introduce a new Meshless Finite Mass (MFM) implementation in OpenGadget3 and apply it to this specific problem. To this end, we present a set of test cases to validate our implementation of the MFM framework in our code. These include but are not limited to: the soundwave and Kepler disk as smooth situations to probe the stability, a Rayleigh-Taylor and Kelvin-Helmholtz instability as popular mixing instabilities, a blob test as more complex example including both mixing and shocks, shock tubes with various Mach numbers, a Sedov blast wave, different tests including self-gravity such as gravitational freefall, a hydrostatic sphere, the Zeldovich-pancake, and the nifty cluster as cosmological application. Advantages over SPH include increased mixing and a better convergence behavior. We demonstrate that the MFM-solver is robust, also in a cosmological context. We show evidence that the solver preforms extraordinarily well when applied to decaying subsonic turbulence, a problem very difficult to handle for many methods. MFM captures the expected velocity power spectrum with high accuracy and shows a good convergence behavior. Using MFM or SPH within OpenGadget3 leads to a comparable decay in turbulent energy due to numerical dissipation. When studying the energy decay for different initial turbulent energy fractions, we find that MFM performs well down to Mach numbers $\mathcal{M}\approx 0.007$. Finally, we show how important the slope limiter and the energy-entropy switch are to control the behavior and the evolution of the fluids.
Sensing the Environment with 5G Scattered Signals (5G-CommSense): A Feasibility Analysis

By making use of the sensors and AI (SensAI) algorithms for a specialized task, Application Specific INstrumentation (ASIN) framework uses less computational overhead and gives a good performance. This work evaluates the feasibility of the ASIN framework dependent Communication based Sensing (CommSense) system using 5th Generation New Radio (5G NR) infrastructure. Since our proposed system is backed up by 5G NR infra, this system is termed as 5G-CommSense. In this paper, we have used NR channel models specified by the 3rd Generation Partnership Project (3GPP) and added white Gaussian noise (AWGN) to vary the signal to noise ratio at the receiver. Finally, from our simulation result, we conclude that the proposed system is practically feasible.
White-box Inference Attacks against Centralized Machine Learning and Federated Learning

With the development of information science and technology, various industries have generated massive amounts of data, and machine learning is widely used in the analysis of big data. However, if the privacy of machine learning applications’ customers cannot be guaranteed, it will cause security threats and losses to users' personal privacy information and service providers. Therefore, the issue of privacy protection of machine learning has received wide attention. For centralized machine learning models, we evaluate the impact of different neural network layers, gradient, gradient norm, and fine-tuned models on member inference attack performance with prior knowledge; For the federated learning model, we discuss the location of the attacker in the target model and its attack mode. The results show that the centralized machine learning model shows more serious member information leakage in all aspects, and the accuracy of the attacker in the central parameter server is significantly higher than the local Inference attacks as participants.
One-particle density matrix and momentum distribution of the out-of-equilibrium one-dimensional Tonks-Girardeau gas: Analytical results at large 
N

In one-dimensional (1D) quantum gases, the momentum distribution (MD) of the atoms is a standard experimental observable, routinely measured in various experimental setups. The MD is sensitive to correlations, and it is notoriously hard to compute theoretically for large numbers of atoms $N$, which often prevents direct comparison with experimental data. Here we report significant progress on this problem for the 1D Tonks-Girardeau (TG) gas in the asymptotic limit of large $N$, at zero temperature and driven out of equilibrium by a quench of the confining potential. We find an exact analytical formula for the one-particle density matrix $\langle \hat{\Psi}^\dagger(x) \hat{\Psi}(x') \rangle$ of the out-of-equilibrium TG gas in the $N \rightarrow \infty$ limit, valid on distances $|x-x'| $ much larger than the interparticle distance. By comparing with time-dependent Bose-Fermi mapping numerics, we demonstrate that our analytical formula can be used to compute the out-of-equilibrium MD with great accuracy for a wide range of momenta (except in the tails of the distribution at very large momenta). For a quench from a double-well potential to a single harmonic well,which mimics a `quantum Newton cradle' setup, our method predicts the periodic formation of peculiar, multiply peaked, momentum distributions.
Ensemble Learning Approaches for Retinal Vessel Segmentation

Retinal vessel analysis of fundus images is an important practice for the screening and diagnosis of related diseases. Yet, automatic segmentation remains a challenging task. It is well known that ensemble learning methods show great effectiveness improving models performance in a number of applications. Bearing this in mind, in this paper, we explore the implementation of two ensemble techniques, Stochastic Weight Averaging and Snapshot Ensembles, for retinal vessel segmentation. The proposed methods are verified on DRIVE database and it shows higher performance, in terms of Acc, when compared with other state-of-the-art methods. Also, our results hint that may be possible to further improve the segmentation performance, tuning these ensemble methods.
Causal Inference for Recommendation: Foundations, Methods and Applications

Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, relying solely on correlation without considering the underlying causal mechanism may lead to various practical issues such as fairness, explainability, robustness, bias, echo chamber and controllability problems. Therefore, researchers in related area have begun incorporating causality into recommendation systems to address these issues. In this survey, we review the existing literature on causal inference in recommender systems. We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems. Finally, we discuss open problems and future directions in the field of causal inference for recommendations.
Improving unlinkability in C-ITS: a methodology for optimal obfuscation

In this paper, we develop a new methodology to provide high assurance about privacy in Cooperative Intelligent Transport Systems (C-ITS). Our focus lies on vehicle-to-everything (V2X) communications enabled by Cooperative Awareness Basic Service. Our research motivation is developed based on the analysis of unlinkability provision methods indicating a gap. To address this, we propose a Hidden Markov Model (HMM) to express unlinkability for the situation where two cars are communicating with a Roadside Unit (RSU) using Cooperative Awareness Messages (CAMs). Our HMM has labeled states specifying distinct origins of the CAMs observable by a passive attacker. We then demonstrate that a high assurance about the degree of uncertainty (e.g., entropy) about labeled states can be obtained for the attacker under the assumption that he knows actual positions of the vehicles (e.g., hidden states in HMM). We further demonstrate how unlinkability can be increased in C-ITS: we propose a joint probability distribution that both drivers must use to obfuscate their actual data jointly. This obfuscated data is then encapsulated in their CAMs. Finally, our findings are incorporated into an obfuscation algorithm whose complexity is linear in the number of discrete time steps in HMM.
Unsupervised Mandarin-Cantonese Machine Translation

Advancements in unsupervised machine translation have enabled the development of machine translation systems that can translate between languages for which there is not an abundance of parallel data available. We explored unsupervised machine translation between Mandarin Chinese and Cantonese. Despite the vast number of native speakers of Cantonese, there is still no large-scale corpus for the language, due to the fact that Cantonese is primarily used for oral communication. The key contributions of our project include: 1. The creation of a new corpus containing approximately 1 million Cantonese sentences, and 2. A large-scale comparison across different model architectures, tokenization schemes, and embedding structures. Our best model trained with character-based tokenization and a Transformer architecture achieved a character-level BLEU of 25.1 when translating from Mandarin to Cantonese and of 24.4 when translating from Cantonese to Mandarin. In this paper we discuss our research process, experiments, and results.
A Multi-Level Framework for the AI Alignment Problem

AI alignment considers how we can encode AI systems in a way that is compatible with human values. The normative side of this problem asks what moral values or principles, if any, we should encode in AI. To this end, we present a framework to consider the question at four levels: Individual, Organizational, National, and Global. We aim to illustrate how AI alignment is made up of value alignment problems at each of these levels, where values at each level affect the others and effects can flow in either direction. We outline key questions and considerations of each level and demonstrate an application of this framework to the topic of AI content moderation.
Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching

Reaching disabilities affect the quality of life. Functional Electrical Stimulation (FES) can restore lost motor functions. Yet, there remain challenges in controlling FES to induce desired movements. Neuromechanical models are valuable tools for developing FES control methods. However, focusing on the upper extremity areas, several existing models are either overly simplified or too computationally demanding for control purposes. Besides the model-related issues, finding a general method for governing the control rules for different tasks and subjects remains an engineering challenge. Here, we present our approach toward FES-based restoration of arm movements to address those fundamental issues in controlling FES. Firstly, we present our surface-FES-oriented neuromechanical models of human arms built using well-accepted, open-source software. The models are designed to capture significant dynamics in FES controls with minimal computational cost. Our models are customisable and can be used for testing different control methods. Secondly, we present the application of reinforcement learning (RL) as a general method for governing the control rules. In combination, our customisable models and RL-based control method open the possibility of delivering customised FES controls for different subjects and settings with minimal engineering intervention. We demonstrate our approach in planar and 3D settings.
Midtropospheric closed cyclone formation over the Southwestern United States, the Eastern United States, and the Alps

Abstract Observational composites of midtropospheric closed cyclone formation are constructed and diagnosed for three regions: the southwestern United States, the eastern United States, and the southern lee of the Alps. The spatial scales upon which closed cyclone formation occurs are then examined by zonally decomposing the composite 500-hPa height fields into three distinct wave groups: the planetary scale (zonal waves 1–3), the large synoptic scale (zonal waves 4-9), and the small synoptic scale (zonal waves 10-25). This analysis leads to a description of closed cyclogenesis as a combined wave interaction and wave superposition process involving both wave groups 4–9 and 10–25, which is intimately linked to preexisting along-stream speed variations and flow curvature. This description is inconsistent with modal and nonmodal analytical instability theories of cyclogenesis. The essence of the closed cyclogenesis process is contained in the relative positioning of, and interaction between, preexisting jets.
Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature Refinement

Video semantic segmentation aims to generate accurate semantic maps for each video frame. To this end, many works dedicate to integrate diverse information from consecutive frames to enhance the features for prediction, where a feature alignment procedure via estimated optical ﬂow is usually required. However, the optical ﬂow would inevitably suﬀer from inaccuracy, and then introduce noises in feature fusion and further result in unsatisfactory segmentation results. In this paper, to tackle the misalignment issue, we propose a spatial-temporal fusion (STF) module to model dense pairwise relationships among multi-frame features. Diﬀerent from previous methods, STF uniformly and adaptively fuses features at diﬀerent spatial and temporal positions, and avoids error-prone optical ﬂow estimation. Besides, we further exploit feature reﬁnement within a single frame and propose a novel memory-augmented reﬁnement (MAR) module to tackle diﬃcult predictions among semantic boundaries. Speciﬁcally, MAR can store the boundary features and prototypes extracted from the training samples, which together form the task-speciﬁc memory, and then use them to reﬁne the features during inference. Essentially, MAR can move the hard features closer to the most likely category and thus make them more discriminative. We conduct extensive experiments on Cityscapes and CamVid, and the results show that our proposed methods signiﬁcantly outperform previous methods and achieves
Local Convergence Behaviour of Generalized Gauss-Newton Multiple Shooting, Single Shooting and Differential Dynamic Programming

We revisit three classical numerical methods for solving unconstrained optimal control problems - multiple shooting, single shooting, and differential dynamic programming - and examine their local convergence behaviour. In particular, we show that all three methods converge with the same linear rate if a Gauss-Newton (GN), or more general a Generalized Gauss-Newton (GGN), Hessian approximation is used, which is the case in widely used implementations such as iLQR.
Adversarial Policies Beat Superhuman Go AIs

We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a>97% win rate against KataGo running at superhuman settings. Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other superhuman Go-playing AIs, and is comprehensible to the extent that human experts can implement it without algorithmic assistance to consistently beat superhuman AIs. The core vulnerability uncovered by our attack persists even in KataGo agents adversarially trained to defend against our attack. Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. Example games are available https://goattack.far. ai/.