Miracle-dz/UniFAR-FTUs huggingface dataset:
Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors

In the wake of responsible AI, interpretability methods, which attempt to provide an explanation for the predictions of neural models have seen rapid progress. In this work, we are concerned with explanations that are applicable to natural language processing (NLP) models and tasks, and we focus specifically on the analysis of counterfactual, contrastive explanations. We note that while there have been several explainers proposed to produce counterfactual explanations, their behaviour can vary significantly and the lack of a universal ground truth for the counterfactual edits imposes an insuperable barrier on their evaluation. We propose a new back translation-inspired evaluation methodology that utilises earlier outputs of the explainer as ground truth proxies to investigate the consistency of explainers. We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models, and infer patterns that would be otherwise obscured. Using this methodology, we conduct a thorough analysis and propose a novel metric to evaluate the consistency of counterfactual generation approaches with different characteristics across available performance indicators.
NeuroX Library for Neuron Analysis of Deep NLP Models

Neuron analysis provides insights into how knowledge is structured in representations and discovers the role of neurons in the network. In addition to developing an understanding of our models, neuron analysis enables various applications such as debiasing, domain adaptation and architectural search. We present NeuroX, a comprehensive open-source toolkit to conduct neuron analysis of natural language processing models. It implements various interpretation methods under a unified API, and provides a framework for data processing and evaluation, thus making it easier for researchers and practitioners to perform neuron analysis. The Python toolkit is available at https://www.github. com/fdalvi/NeuroX.Demo Video available at: https://youtu.be/mLhs2YMx4u8
Sensitivity prospects for lepton-trijet signals in the $\nu$SMEFT at the LHeC

The observation of neutrino oscillations and masses motivates the extension of the standard model with right handed neutrinos, leading to heavy neutrino states possibly in the electroweak scale, which could be impacted by new high-scale weakly coupled physics. A systematic tool for studying these interactions is the neutrino-extended standard model effective field theory $\nu$SMEFT. In this work we study the prospects of the future LHeC electron-proton collider to discover or constrain the $\nu$SMEFT interactions, performing the first dedicated and realistic analysis of the well known lepton-trijet signals, both for the lepton flavor violating $p ~ e^{-} \rightarrow \mu^{-} + 3 \mathrm{j}$ (LFV) and the lepton number violating $p ~ e^{-} \rightarrow \mu^{+} + 3 \mathrm{j}$ (LNV) channels, for HNLs masses in the electroweak scale range: $100 ~\rm GeV \leq m_N \leq 500 ~\rm GeV$. The obtained sensitivity prospects show that the LHeC with $100 ~\rm fb^{-1}$ luminosity could be able to probe the scenario of a heavy $N$ and constrain the effective couplings to a region of the parameter space as tight as the bounds that are currently considered for the $\mathcal{O}(10)$GeV scale masses, with effective couplings of $\mathcal{O}(10^{-1})$ for NP scale $\Lambda=1 \rm TeV$.
Data Owner Benefit-Driven Design of People Analytics

With increasingly digitalized workplaces, the potential for sophisticated analyses of employee data rises. This increases the relevance of people analytics (PA), which are tools for the behavioral analysis of employees. Despite this potential, the successful usage of PA is hindered by employee concerns. Especially in Europe, where the GDPR or equivalent laws apply, employee consent is required before data can be processed in PA. Therefore, PA can only provide relevant insights if employees are willing to share their data. One potential way of achieving this is the use of appeal strategies. In the design of PA, the core strategy that can be used is the inclusion of data owner benefits, such as automated feedback, that are given to employees in exchange for sharing their own data. In this paper, we examine benefits as an appeal strategy and develop four design principles for the inclusion of benefits in PA. Then, we describe an exemplary set of analyses and benefits, demonstrating how our principles may be put into practice. Based on this exemplary implementation, we describe and discuss the results of a user study (n = 46) among employees in the EU and UK. Our study investigates the factors that foster or hinder employees' consent to sharing their data with PA. Then, we introduce our data owner benefits and analyze whether they can positively influence this consent decision. Our introduced data owner benefits were, contrary to our expectations, not suited to motivate our participants to consent to sharing their data. We therefore analyze how participants judge the benefits. Participants generally appreciate having them, confirming the value of including data owner benefits when designing PA. Some of our introduced benefits negatively influenced participants' sharing decision, though, meaning that careful consideration of potential risks is required when conceptualizing them.
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging

Massively multilingual language models have displayed strong performance in zero-shot (ZS-XLT) and few-shot (FS-XLT) cross-lingual transfer setups, where models fine-tuned on task data in a source language are transferred without any or with only a few annotated instances to the target language(s). However, current work typically overestimates model performance as fine-tuned models are frequently evaluated at model checkpoints that generalize best to validation instances in the target languages. This effectively violates the main assumptions of ‘true’ ZS-XLT and FS-XLT. Such XLT setups require robust methods that do not depend on labeled target language data for validation and model selection. In this work, aiming to improve the robustness of ‘true’ ZS-XLT and FS-XLT, we propose a simple and effective method that averages different checkpoints (i.e., model snapshots) during task fine-tuning. We conduct exhaustive ZS-XLT and FS-XLT experiments across higher-level semantic tasks (NLI, extractive QA) and lower-level token classification tasks (NER, POS). The results indicate that averaging model checkpoints yields systematic and consistent performance gains across diverse target languages in all tasks. Importantly, it simultaneously substantially desensitizes XLT to varying hyperparameter choices in the absence of target language validation. We also show that checkpoint averaging benefits performance when further combined with run averaging (i.e., averaging the parameters of models fine-tuned over independent runs).
Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs

Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains underexplored. In this research, we specialize in harnessing the potential of LLMs to comprehend critical information from financial reports, which are hybrid long-documents. We propose an Automated Financial Information Extraction (AFIE) framework that enhances LLMs' ability to comprehend and extract information from financial reports. To evaluate AFIE, we develop a Financial Reports Numerical Extraction (FINE) dataset and conduct an extensive experimental analysis. Our framework is effectively validated on GPT-3.5 and GPT-4, yielding average accuracy increases of 53.94% and 33.77%, respectively, compared to a naive method. These results suggest that the AFIE framework offers accuracy for automated numerical extraction from complex, hybrid documents.
Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring

Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt as used in training and assign only a holistic score. However, such settings conflict with real-education situations; pre-graded essays for a particular prompt are lacking, and detailed trait scores of sub-rubrics are required. Thus, predicting various trait scores of unseen-prompt essays (called cross-prompt essay trait scoring) is a remaining challenge of AES. In this paper, we propose a robust model: prompt- and trait relation-aware cross-prompt essay trait scorer. We encode prompt-aware essay representation by essay-prompt attention and utilizing the topic-coherence feature extracted by the topic-modeling mechanism without access to labeled data; therefore, our model considers the prompt adherence of an essay, even in a cross-prompt setting. To facilitate multi-trait scoring, we design trait-similarity loss that encapsulates the correlations of traits. Experiments prove the efficacy of our model, showing state-of-the-art results for all prompts and traits. Significant improvements in low-resource-prompt and inferior traits further indicate our model's strength.
DiffusionNAG: Task-guided Neural Architecture Generation with Diffusion Models

Neural Architecture Search (NAS) has emerged as a powerful technique for automating neural architecture design. However, existing NAS methods either require an excessive amount of time for repetitive training or sampling of many task-irrelevant architectures. Moreover, they lack generalization across different tasks and usually require searching for optimal architectures for each task from scratch without reusing the knowledge from the previous NAS tasks. To tackle such limitations of existing NAS methods, we propose a novel transferable task-guided Neural Architecture Generation (NAG) framework based on diffusion models, dubbed DiffusionNAG. With the guidance of a surrogate model, such as a performance predictor for a given task, our DiffusionNAG can generate task-optimal architectures for diverse tasks, including unseen tasks. DiffusionNAG is highly efficient as it generates task-optimal neural architectures by leveraging the prior knowledge obtained from the previous tasks and neural architecture distribution. Furthermore, we introduce a score network to ensure the generation of valid architectures represented as directed acyclic graphs, unlike existing graph generative models that focus on generating undirected graphs. Extensive experiments demonstrate that DiffusionNAG significantly outperforms the state-of-the-art transferable NAG model in architecture generation quality, as well as previous NAS methods on four computer vision datasets with largely reduced computational cost.
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation

While $\mathcal{H}_\infty$ methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastically reduced. Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in quantitative terms. Toward addressing this issue, we borrow the increasingly ubiquitous notion of adversarial training from machine learning to construct a class of controllers which are optimized for disturbances consisting of mixed stochastic and worst-case components. We find that this problem admits a linear time invariant optimal controller that has a form closely related to suboptimal $\mathcal{H}_\infty$ solutions. We then provide a quantitative performance-robustness tradeoff analysis in two analytically tractable cases: state feedback control, and state estimation. In these special cases, we demonstrate that the severity of the tradeoff depends in an interpretable manner upon system-theoretic properties such as the spectrum of the controllability gramian, the spectrum of the observability gramian, and the stability of the system. This provides practitioners with general guidance for determining how much robustness to incorporate based on a priori system knowledge. We empirically validate our results by comparing the performance of our controller against standard baselines, and plotting tradeoff curves.
Robust design of multiple-input multiple-output radar waveform covariance matrix in the presence of clutter

Abstract. In this work, the issue of robust waveform optimization is addressed in the presence of clutter to improve the worst-case estimation accuracy for collocated multiple-input multiple-output (MIMO) radar. Robust design is necessary due to the fact that waveform design may be sensitive to uncertainties in the initial parameter estimates. Following the min–max approach, the robust waveform covariance matrix design is formulated here on the basis of Cramér–Rao Bound to ease this sensitivity systematically for improving the worst-case accuracy. To tackle the resultant complicated and nonlinear problem, a new diagonal loading (DL)-based iterative approach is developed, in which the inner optimization problem can first be decomposed to some independent subproblems by using the Hadamard’s inequality, and then these subproblems can be reformulated into convex issues by using DL method, as well as the outer optimization problem can also be relaxed to a convex issue by translating the nonlinear function into a linear one, and, hence, both of them can be solved very effectively. An optimal solution to the original problem can be obtained via the least-squares fitting of the solution acquired by the iterative approach. Numerical simulations show the efficiency of the proposed method.
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution

Adversarial attacks can mislead automatic speech recognition (ASR) systems into predicting an arbitrary target text, thus posing a clear security threat. To prevent such attacks, we propose DistriBlock, an efficient detection strategy applicable to any ASR system that predicts a probability distribution over output tokens in each time step. We measure a set of characteristics of this distribution: the median, maximum, and minimum over the output probabilities, the entropy of the distribution, as well as the Kullback-Leibler and the Jensen-Shannon divergence with respect to the distributions of the subsequent time step. Then, by leveraging the characteristics observed for both benign and adversarial data, we apply binary classifiers, including simple threshold-based classification, ensembles of such classifiers, and neural networks. Through extensive analysis across different state-of-the-art ASR systems and language data sets, we demonstrate the supreme performance of this approach, with a mean area under the receiver operating characteristic for distinguishing target adversarial examples against clean and noisy data of 99% and 97%, respectively. To assess the robustness of our method, we show that adaptive adversarial examples that can circumvent DistriBlock are much noisier, which makes them easier to detect through filtering and creates another avenue for preserving the system's robustness.
Fact-Driven Logical Reasoning for Machine Reading Comprehension

Recent years have witnessed an increasing interest in training machines with reasoning ability, which deeply relies on accurately and clearly presented clue forms. The clues are usually modeled as entity-aware knowledge in existing studies. However, those entity-aware clues are primarily focused on commonsense, making them insufficient for tasks that require knowledge of temporary facts or events, particularly in logical reasoning for reading comprehension. To address this challenge, we are motivated to cover both commonsense and temporary knowledge clues hierarchically. Specifically, we propose a general formalism of knowledge units by extracting backbone constituents of the sentence, such as the subject-verb-object formed ``facts''. We then construct a supergraph on top of the fact units, allowing for the benefit of sentence-level (relations among fact groups) and entity-level interactions (concepts or actions inside a fact). Experimental results on logical reasoning benchmarks and dialogue modeling datasets show that our approach improves the baselines substantially, and it is general across backbone models. Code is available at https://github.com/ozyyshr/FocalReasoner.
AN ALGORITHMIC APPROACH TO NETWORK LOCATION PROBLEMS.

Problems of finding p-centers and dominating sets of radius r in networks are discussed in this paper. Let n be the number of vertices and |El be the number of edges of a network. With the assumption that the distance-matrix of the network is available, we design an 0(I|EI n *lg n) algorithm for finding an absolute 1-center of a vertex-weighted network and an O(|E| * n + n2 * lg n) algorithm for finding an absolute 1 -center of a vertex-unweighted network (the problem of finding a vertex 1-center of a network is trivial). We show that the problem of finding a (vertex or absolute) p-center (for 1< p < n) of a (vertex-weighted or vertex-unweighted) network, and the problem of finding a dominating set of radius r are NP-hard even in the case where the network has a simple structure (e.g., a planar graph of maximum vertex degree 3). However, we describe an algorithm of complexity 0((tEI| * n2p l/(p _ 1)! ) lg n) (respectively, 0(IEIP * n2p l/(p _ 1)! )) for finding an absolute p-center in a vertex-weighted (respectively, vertex-unweighted) network. We proceed by discussing the problems of finding p-centers and dominating sets of networks whose underlying graphs are trees. When the network is a vertex-weighted tree, we obtain the following algorithms: An 0(n * lg n) algorithm for finding the (vertex or absolute) 1-center; an 0(n) algorithm for finding a (vertex or absolute) dominating set of radius r; an 0(n2 * lg n) algorithm for finding a (vertex or absolute) p-center for any 1 < p < n. Some generalizations of these problems are discussed. When the network is a vertex-unweighted tree, 0(n) algorithms for finding the (vertex or absolute) 1-center and an abWolute 2-center are already known; we extend these results by giving an 0(n * lg,-2n) algorithm for finding an absolute p-center (where 3 p < n) and an 0(n * lgP 1n) algorithm for finding a vertex p-center (where 2 p < n). In part II we treat the p-median problem.
Panini - Anonymous Anycast and an Instantiation

Anycast messaging (i.e., sending a message to an unspecified receiver) has long been neglected by the anonymous communication community. An anonymous anycast prevents senders from learning who the receiver of their message is, allowing for greater privacy in areas such as political activism and whistleblowing. While there have been some protocol ideas proposed, formal treatment of the problem is absent. Formal definitions of what constitutes anonymous anycast and privacy in this context are however a requirement for constructing protocols with provable guarantees. In this work, we define the anycast functionality and use a game-based approach to formalize its privacy and security goals. We further propose Panini, the first anonymous anycast protocol that only requires readily available infrastructure. We show that Panini allows the actual receiver of the anycast message to remain anonymous, even in the presence of an honest but curious sender. In an empirical evaluation, we find that Panini adds only minimal overhead over regular unicast: Sending a message anonymously to one of eight possible receivers results in an end-to-end latency of 0.76s.
Structural and electronic characterization of GaN on MgAl2O4 (111) substrates

We report the structural and electronic characterization of GaN on magnesium aluminate (MgAl2O4) (111) substrates using first‐principles calculations. Atomic structures and surface energies of eight stoichiometric MgAl2O4 (111) surfaces with different terminations are investigated after surface relaxations. The results of surface energy indicate that the Al (Mg)‐termination is relatively more energetically stable than other terminations. The initial stage of growth is studied based on adsorption of the Ga or N atoms on the optimized MgAl2O4 (111) surface. The results of adsorption energy reveal that N atoms are more stable than Ga atoms on the surface. The bonding characteristics of the GaO and NO adsorption bonds are examined by the COHP technique, which confirms the strong bonding interactions between N atom and the surface. The first atomic layer for GaN growth should be a nitrogen layer. It leads to the experimentally‐confirmed epitaxial relationships for GaN/MgAl2O4. Additionally, the polarity determination of GaN on MgAl2O4 (111) is also studied based on adsorption of Ga atom on the nitrogen layer covered MgAl2O4 surface. The results indicate that Ga atom preferentially adsorbs at the central site among three N atoms on the surface, which leads to the formation of Ga‐polar GaN.
Robustness results of LQR-based systems with structured uncertainty

It is well known that linear quadratic regulator can have excellent stability robustness. However, some authors have recommended caution, by producing examples where the linear quadratic regulator may suffer from poor robustness. Specifically, small changes in the parameters of the system can cause fast unstable closed-loop poles. However, by comparing the bounds on allowable perturbations it is shown that the stability robustness properties are preserved in a general sense. The high parameter sensitivity gives a warning to those designers who favour high gain controllers.
From self-regulation to co-regulation: refining learning presence in a community of inquiry in interprofessional education

ABSTRACT Introduction Online interprofessional education is a collaborative process that emphasizes both individual reflection and shared discourses. A useful analytical tool for understanding the complex dynamics of online collaborative learning is the community of inquiry (CoI) framework, which originally held that there are three types of presence in such learning: teaching, cognitive, and social. However, it was later revised to include learning presence, which is characterized by self-regulated learning. Our study aims to refine the construct of learning presence through a clearer understanding of how self- and co-regulation jointly influence learning outcomes. Methods We surveyed 110 people involved with an online interprofessional medical-education curriculum at a university in Hong Kong. Path analysis was adopted to explore the relationships among 1) the three original presences of CoI; 2) learning presence (i.e., for this purpose, a combination of self-regulation and co-regulation); and 3) two learning outcomes: perceived progress and learner satisfaction. Results The results of path analysis indicated that teaching presence had a significant indirect effect, through co-regulation, on perceived progress. In terms of direct relationships, co-regulation significantly and positively influenced both self-regulation and cognitive presence; and social presence had both positive influence on learners’ satisfaction and perceived progress. Discussion This study’s findings suggest the important role of co-regulation in supporting self-regulation, especially in online collaborative-learning environments. Learners’ self-regulation skills are shaped by their social interactions and regulatory activities with others. This further implies that health-professions educators and instructional designers should create learning activities that facilitate the development of co-regulatory skills, as a means of improving learning outcomes. As self-regulation is an important skill for health professions learners’ lifelong learning, and because their future workplaces will be interdisciplinary in nature, it is critical to provide interactive and collaborative learning environments that will promote co-regulation and self-regulation.