Miracle-dz/UniFAR-FTUs huggingface dataset:
Checking HateCheck: a cross-functional analysis of behaviour-aware learning for hate speech detection

Behavioural testing—verifying system capabilities by validating human-designed input-output pairs—is an alternative evaluation method of natural language processing systems proposed to address the shortcomings of the standard approach: computing metrics on held-out data. While behavioural tests capture human prior knowledge and insights, there has been little exploration on how to leverage them for model training and development. With this in mind, we explore behaviour-aware learning by examining several fine-tuning schemes using HateCheck, a suite of functional tests for hate speech detection systems. To address potential pitfalls of training on data originally intended for evaluation, we train and evaluate models on different configurations of HateCheck by holding out categories of test cases, which enables us to estimate performance on potentially overlooked system properties. The fine-tuning procedure led to improvements in the classification accuracy of held-out functionalities and identity groups, suggesting that models can potentially generalise to overlooked functionalities. However, performance on held-out functionality classes and i.i. d. hate speech detection data decreased, which indicates that generalisation occurs mostly across functionalities from the same class and that the procedure led to overfitting to the HateCheck data distribution.
Extremum Seeking With Intermittent Measurements: A Lie-Brackets Approach

Extremum seeking systems are powerful methods able to steer the input of a (dynamical) cost function toward an optimizer, without any prior knowledge of the cost function. To achieve their objective, they typically combine time-periodic signals with the online measurement of the cost. However, in some practical applications, the cost can only be measured during some regular time-intervals, and not continuously, contravening the classical extremum seeking framework. In this article, we first analyze how existing Lie-bracket-based extremum seeking systems behave when being fed with intermittent measurements, instead of continuous ones. We then propose two modifications of those schemes to improve both the convergence time and the steady-state accuracy in presence of intermittent measurements. The performances of the different schemes are compared on a case study.
Harmonic studies for offshore windfarms

This paper presents the methodology for conducting harmonic studies for offshore windfarms. The high level harmonic models used to represent wind turbine generators, dynamic reactive power compensation plant and background voltage distortions are explained. An example of the typical harmonic performance requirements imposed within wind farm connection conditions is given Simulation results of the background harmonic amplification and incremental harmonic generation for a real wind farm under construction are used to illustrate the described methodology. (6 pages)
A General Framework for Hierarchical Redundancy Resolution Under Arbitrary Constraints

The increasing interest in autonomous robots with a high number of degrees of freedom for industrial applications and service robotics demands control algorithms to handle multiple tasks as well as hard constraints efficiently. This article presents a general framework in which both kinematic (velocity- or acceleration-based) and dynamic (torque-based) control of redundant robots are handled in a unified fashion. The framework allows for the specification of redundancy resolution problems featuring a hierarchy of arbitrary (equality and inequality) constraints, arbitrary weighting of the control effort in the cost function and an additional input used to optimize possibly remaining redundancy. To solve such problems, a generalization of the saturation in the null space algorithm is introduced, which extends the original method according to the features required by our general control framework. Variants of the developed algorithm are presented, which ensure both efficient computation and optimality of the solution. Experiments on a KUKA LBRiiwa robotic arm, as well as simulations with a highly redundant mobile manipulator are reported.
RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining

This paper presents several BERT-based models for Russian language biomedical text mining (RuBioBERT, RuBioRoBERTa). The models are pre-trained on a corpus of freely available texts in the Russian biomedical domain. With this pre-training, our models demonstrate state-of-the-art results on RuMedBench - Russian medical language understanding benchmark that covers a diverse set of tasks, including text classification, question answering, natural language inference, and named entity recognition.
Learning to walk autonomously via reset-free quality-diversity

Quality-Diversity (QD) algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. However, the generation of behavioural repertoires has mainly been limited to simulation environments instead of real-world learning. This is because existing QD algorithms need large numbers of evaluations as well as episodic resets, which require manual human supervision and intervention. This paper proposes Reset-Free QD (RF-QD) as a step towards autonomous learning for robotics in open-ended environments. We build on Dynamics-Aware QD (DA-QD) and introduce a behaviour selection policy that leverages the diversity of the imagined repertoire and environmental information to intelligently select of behaviours that can act as automatic resets. We demonstrate this through a task of learning to walk within defined training zones with obstacles. Our experiments show that we can learn repertoires of locomotion controllers autonomously without manual resets and with high sample efficiency in spite of harsh safety constraints. Finally, using an ablation of different target objectives, we show that it is important for RF-QD to have diverse types solutions available for the behaviour selection policy over solutions optimised with a specific objective. Videos and code available at https://sites.google. com/view/rf-qd.
Trajectory Tracking Control Of A Two-Link Robot Manipulator Using Variable Structure System Theory

The control of robots, mobile robots and manipulator arms has fascinated control engineers for several decades. Robots are complex mechanical systems with highly nonlinear dynamics. Hence high-performance operation requires nonlinear control designs to fully exploit a robot's capabilities. After describing the dynamic model for robot, in this paper, we discuss sliding mode control design alternative for the classic trajectory tracking problem, in which the robot is asked to follow a prescribed trajectory. Finally, we will illustrated the control method on a computer simulated example of a two-link robot manipulator.
Quantifying relation between mobility patterns and socioeconomic status of dockless sharing-bike users

Bikes are among the healthiest, greenest, and most aﬀordable means of transportation for a better future city, but mobility patterns of riders with diﬀerent income were rarely studied due to limitation on collecting data. Newly emergent dockless bike-sharing platforms that record detailed information regarding each trip provide us a unique opportunity. Attribute to its better usage ﬂexibility and accessibility, dockless bike-sharing platforms are booming over the past a few years worldwide and reviving the riding fashion in cities. In this work, by exploiting massive riding records in two megacities from a dockless bike-sharing platform, we reveal that individual mobility patterns, including radius of gyration and average travel distance, are similar among users with diﬀerent income, which indicates that human beings all follow similar physical rules. However, collective mobility patterns, including average range and diversity of visitation, and commuting directions, all exhibit diﬀerent behaviors and spatial patterns across income categories. Hotspot locations that attract more cycling activities are quite diﬀerent over groups, and locations where users reside are of a low user ratio for both higher and lower income groups. Lower income groups are inclined to visit less ﬂourishing locations, and commute towards the direction to the city center in both cities, and of a smaller mobility diversity in Beijing but a larger diversity in Shanghai. In addition, diﬀerences on mobility patterns among socioeconomic categories are more evident in Beijing than in Shanghai. Our ﬁndings would be helpful on designing better promotion strategies for dockless bike-sharing platforms and towards the transition to a more sustainable green transportation.
T cell morphodynamics reveal periodic shape oscillations in 3D migration

T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralise infected and cancerous cells. However, there is limited quantitative understanding of the migration process in 3D extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the 3D morphodynamics of migrating T cells at high spatiotemporal resolution. We ﬁrst developed a new shape descriptor based on spherical harmonics, incorporating key polarisation information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at ∼ 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, ﬁnding ‘stereo-typed’ motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every ∼ 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across diﬀerent conditions (e.g. diﬀerentiation, activation, tissues, and drug treatments), and improve the precision of immunotherapeutic development. to the (wavelet-speciﬁc) with of corresponding to the approximate timescale of organisation found from the autocorrelation The width was found by each with a square to ﬁnd where edge eﬀects begin. When repeating this method for only the four run mode datasets, we used for the adaptive binarisation parameters square of 15 and a bias of 50, again found with a grid search.
KGI: An Integrated Framework for Knowledge Intensive Language Tasks

In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking. Moreover, given a user query, we show how the output from these different models can be combined to cross-examine the outputs of each other. Particularly, we show how accuracy in dialogue can be improved using the question answering model. We are also releasing all models used in the demo as a contribution of this paper. A short video demonstrating the system is available at https://ibm.box. com/v/emnlp2022-demo.
The Lyapunov spectrum for conditioned random dynamical systems

We establish the existence of a full spectrum of Lyapunov exponents for memoryless random dynamical systems with absorption. To this end, we crucially embed the process conditioned to never being absorbed, the Q -process, into the framework of random dynamical systems, allowing us to study multiplicative ergodic properties. We show that the ﬁnite-time Lyapunov exponents converge in conditioned probability and apply our results to iterated function systems and stochastic diﬀerential equations.
Figures of merit for a stochastic gravitational-wave background measurement by LISA: Implications of LISA Pathfinder noise correlations

An important goal of the Laser Interferometer Space Antenna (LISA) is to observe a stochastic gravitational-wave background (SGWB). A study of possible correlated noise in LISA is relevant to establish limits for this future measurement. To test noise investigation methods under somewhat realistic conditions, we use the data of LISA Pathﬁnder. We calculate the coherence between the LISA Pathﬁnder diﬀerential acceleration of the two test masses with magnetic ﬁelds, temperature, and micronewton cold gas thruster activity in the spacecraft. We apply our observed correlations to LISA, and estimate how the presence of such correlated noise would aﬀect its search for a SGWB. In the context of a ﬁgure of merit, we estimate the eﬀect of noise on the LISA SGWB search.
Quantitative Evaluation of Common Cause Failures in High Safety-significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants

Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework’s capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety. FT was quantified with SAPHIRE using a truncation level of 1E-12; RTS failure probability is 4.288E-6 with five cut sets. Results indicate hardware CCFs are the main concerns for the failure analog safety-related redundant I&C systems. Compared with the original RTS-FT, the total failure probability of integrated four-division RTS-FT is reduced about 50%.
Q-learning with online random forests

Q -learning is the most fundamental model-free reinforcement learning algorithm. Deployment of Q -learning requires approximation of the state-action value function (also known as the Q -function). In this work, we provide online random forests as Q -function approximators and propose a novel method wherein the random forest is grown as learning proceeds (through expanding forests). We demonstrate improved performance of our methods over state-of-the-art Deep Q -Networks in two OpenAI gyms (‘blackjack’ and ‘inverted pendulum’) but not in the ‘lunar lander’ gym. We suspect that the resilience to overﬁtting enjoyed by random forests recommends our method for common tasks that do not require a strong representation of the problem domain. We show that expanding forests (in which the number of trees increases as data comes in) improve performance, suggesting that expanding forests are viable for other applications of online random forests beyond the reinforcement learning setting.
CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction

In this paper, we present CrudeOilNews, a corpus of English Crude Oil news for event extraction. It is the first of its kind for Commodity News and serves to contribute towards resource building for economic and financial text mining. This paper describes the data collection process, the annotation methodology, and the event typology used in producing the corpus. Firstly, a seed set of 175 news articles were manually annotated, of which a subset of 25 news was used as the adjudicated reference test set for inter-annotator and system evaluation. The inter-annotator agreement was generally substantial, and annotator performance was adequate, indicating that the annotation scheme produces consistent event annotations of high quality. Subsequently, the dataset is expanded through (1) data augmentation and (2) Human-in-the-loop active learning. The resulting corpus has 425 news articles with approximately 11k events annotated. As part of the active learning process, the corpus was used to train basic event extraction models for machine labeling; the resulting models also serve as a validation or as a pilot study demonstrating the use of the corpus in machine learning purposes. The annotated corpus is made available for academic research purpose at https://github.com/meisin/CrudeOilNews-Corpus
Measurement and characterization of DNS over HTTPS traffic

Domain name system communication may provide sensitive information on users' Internet activity. DNS-over-TLS and DNS-over-HTTPS are proposals aiming at increasing the privacy of Internet end users. In this paper we present an overview of the current state in the deployment of DNS-over-HTTPS (DoH) implementations complemented by measurements of DoH traffic in terms of the incurred overhead and the possibility of the DoH detection based on the inherent characteristics of the communication patterns.
IA-GCN: Interactive Graph Convolutional Network for Recommendation

Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on a user-item bipartite graph, and then provide the users with personalized item suggestions based on the representations. Despite effectiveness, existing algorithms neglect precious interactive features between user-item pairs in the embedding process. When predicting a user's preference for different items, they still aggregate the user tree in the same way, without emphasizing target-related information in the user neighborhood. Such a uniform aggregation scheme easily leads to suboptimal user and item representations, limiting the model expressiveness to some extent. In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN). Specifically, when learning the user representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item. Correspondingly, when learning the item representation, we pay more attention to those neighbors resembling the target user. This leads to interactive and interpretable features, effectively distilling target-specific information through each graph convolutional operation. Our model is built on top of LightGCN, a state-of-the-art GCN model for CF, and can be combined with various GCN-based CF architectures in an end-to-end fashion. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of IA-GCN.
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems

The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite major research effort on LPV data-driven modeling, a key shortcoming of the current identification theory is that often the scheduling variable is assumed to be a given measured signal in the data set. In case of identifying an LPV model of a NL system, the selection of the scheduling map, which describes the relation to the measurable scheduling signal, is put on the users’ shoulder, with only limited supporting tools available. This choice however greatly affects the usability and complexity of the resulting LPV model. This paper presents a deep-learning-based approach to provide joint estimation of a scheduling map and an LPV state-space model of a NL system from input-output data, and has consistency guarantees under general innovation-type noise conditions. Its efficiency is demonstrated on a realistic identification problem.
Quantum Linear Algorithm for Edit Distance Using the Word QRAM Model

Many problems that can be solved in quadratic time have bit-parallel speed-ups with factor $w$, where $w$ is the computer word size. For example, edit distance of two strings of length $n$ can be solved in $O(n^2/w)$ time. In a reasonable classical model of computation, one can assume $w=\Theta(\log n)$. There are conditional lower bounds for such problems stating that speed-ups with factor $n^\epsilon$ for any $\epsilon>0$ would lead to breakthroughs in complexity theory. However, these conditional lower bounds do not cover quantum models of computing. Indeed, Boroujeni et al. (J. ACM, 2021) showed that edit distance can be approximated within a factor $3$ in sub-quadratic time $O(n^{1.81})$ using quantum computing. They also showed that, in their chosen model of quantum computing, the approximation factor cannot be improved using sub-quadractic time. To break through the aforementioned classical conditional lower bounds and this latest quantum lower bound, we enrich the model of computation with a quantum random access memory (QRAM), obtaining what we call the word QRAM model. Under this model, we show how to convert the bit-parallelism of quadratic time solvable problems into quantum algorithms that attain speed-ups with factor $n$. The technique we use is simple and general enough to apply to many bit-parallel algorithms that use Boolean logics and bit-shifts. To apply it to edit distance, we first show that the famous $O(n^2/w)$ time bit-parallel algorithm of Myers (J. ACM, 1999) can be adjusted to work without arithmetic + operations. As a direct consequence of applying our technique to this variant, we obtain linear time edit distance algorithm under the word QRAM model for constant alphabet. We give further results on a restricted variant of the word QRAM model to give more insights to the limits of the model.
On the Enumeration and Asymptotic Analysis of Fibonacci Compositions

We study Fibonacci compositions, which are compositions of natural numbers that only use Fibonacci numbers, in two different contexts. We ﬁrst prove inequalities comparing the number of Fibonacci compositions to regular compositions where summands have a maximum possible value. Then, we consider asymptotic properties of Fibonacci compositions, comparing them to compositions whose terms come from positive linear recurrence sequences. Finally, we consider analogues of these results where we do not allow the use of a certain number of consecutive Fibonacci numbers starting from F 2 = 1