Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Generalizability of Fine-Tuned Models for Fake News Detection

The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous misinformation, denoted an `infodemic' by the CDC and WHO. Misinformation tied to the Covid-19 infodemic changes continuously;this can lead to performance degradation of fine-tuned models due to concept drift. Degredation can be mitigated if models generalize well-enough to capture some cyclical aspects of drifted data. In this paper, we explore generalizability of pre-trained and fine-tuned fake news detectors across 9 fake news datasets. We show that existing models often overfit on their training dataset and have poor performance on unseen data. However, on some subsets of unseen data that overlap with training data, models have higher accuracy. Based on this observation, we also present KMeans-Proxy, a fast and effective method based on K-Means clustering for quickly identifying these overlapping subsets of unseen data. KMeans-Proxy improves generalizability on unseen fake news datasets by 0.1-0.2 f1-points across datasets. We present both our generalizability experiments as well as KMeans-Proxy to further research in tackling the fake news problem.
How Different Groups Prioritize Ethical Values for Responsible AI

Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by. We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups: a representative sample of the US population (N=743), a sample of crowdworkers (N=755), and a sample of AI practitioners (N=175). Our results empirically confirm a common concern: AI practitioners’ value priorities differ from those of the general public. Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values. In contrast, self-identified women and black respondents found responsible AI values more important than other groups. Surprisingly, more liberal-leaning participants, rather than participants reporting experiences with discrimination, were more likely to prioritize fairness than other groups. Our findings highlight the importance of paying attention to who gets to define “responsible AI. ”
Policy Gradient Method For Robust Reinforcement Learning

This paper develops the first policy gradient method with global optimality guarantee and complexity analysis for robust reinforcement learning under model mismatch. Robust reinforcement learning is to learn a policy robust to model mismatch between simulator and real environment. We first develop the robust policy (sub-)gradient, which is applicable for any differentiable parametric policy class. We show that the proposed robust policy gradient method converges to the global optimum asymptotically under direct policy parameterization. We further develop a smoothed robust policy gradient method and show that to achieve an $\epsilon$-global optimum, the complexity is $\mathcal O(\epsilon^{-3})$. We then extend our methodology to the general model-free setting and design the robust actor-critic method with differentiable parametric policy class and value function. We further characterize its asymptotic convergence and sample complexity under the tabular setting. Finally, we provide simulation results to demonstrate the robustness of our methods.
Not to Overfit or Underfit? A Study of Domain Generalization in Question Answering

Machine learning models are prone to overﬁt-ting their source (training) distributions, which is commonly believed to be why they falter in novel target domains. Here we examine the contrasting view that multi-source domain generalization ( DG ) is in fact a problem of mitigating source domain underﬁtting: models not adequately learning the signal in their multi-domain training data. Experiments on a reading comprehension DG benchmark show that as a model gradually learns its source domains better—using known methods such as knowledge distillation from a larger model— its zero-shot out-of-domain accuracy improves at an even faster rate. Improved source domain learning also demonstrates superior generalization over three popular domain-invariant learning methods that aim to counter overﬁtting.
From Tool to Actor: How a project came to orchestrate its own life and that of others

Acknowledgments Chapter 1 Introducing subject matter . 1 1.1 Introducing the theme and background for the thesis.1 1.2 The relevance of this thesis . 3 1.3 Enacting a research idea . 4 1.4 A few concepts . 6 1.5 Outline of the thesis.9 Chapter 2 Positioning the research in the project management field . 11 2.1 A historical sketch of the project management discourse . 12 2.2 Project management perspectives.14 2.2.1 A traditional perspective on project management.15 2.2.2 An alternative perspective on project management . 16 2.3 Key issues of the alternative project management research . 19 2.3.1 Projects are evolving . 19 2.3.2 Projects are embedded.22 2.4 Who or what determines project action? 24 2.5 A comment . 27 Chapter 3 An approach to understanding projects . 29 3.1 It is all process . 30 3.2 It is about connecting . 31 3.2.1 Implication of connecting . 31 3.2.2 How actions and entities connect . 33 3.3 Heterogeneity . 34 3.4 Contingency.35 3.5 Process and the alternative perspective . 36 Chapter 4 Enacting the evolving project – on methodology . 41 4.1 Grappling with fluidity . 41
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses

We propose the Bayes-UCBVI algorithm for reinforcement learning in tabular, stage-dependent, episodic Markov decision process: a natural extension of the Bayes-UCB algorithm by Kaufmann et al. (2012) for multi-armed bandits. Our method uses the quantile of a Q-value function posterior as upper conﬁdence bound on the optimal Q-value function. For Bayes-UCBVI , we prove a regret bound of order (cid:101) O ( √ H 3 SAT ) where H is the length of one episode, S is the number of states, A the number of actions, T the number of episodes, that matches the lower-bound of Ω( √ H 3 SAT ) up to poly- log terms in H, S, A, T for a large enough T . To the best of our knowledge, this is the ﬁrst algorithm that obtains an optimal dependence on the horizon H (and S ) without the need of an involved Bernstein-like bonus or noise. explain can be easily extended beyond tabular setting, exhibiting a strong link between our algorithm and Bayesian bootstrap (Rubin, 1981).
Constraining feeble neutrino interactions with ultralight dark matter

If ultralight $(\ll$ eV), bosonic dark matter couples to right handed neutrinos, active neutrino masses and mixing angles depend on the ambient dark matter density. When the neutrino Majorana mass, induced by the dark matter background, is small compared to the Dirac mass, neutrinos are"pseudo-Dirac"fermions that undergo oscillations between nearly degenerate active and sterile states. We present a complete cosmological history for such a scenario and find severe limits from a variety of terrestrial and cosmological observables. For scalar masses in the"fuzzy"dark matter regime ($\sim 10^{-20}$ eV), these limits exclude couplings of order $10^{-30}$, corresponding to Yukawa interactions comparable to the gravitational force between neutrinos and surpassing equivalent limits on time variation in scalar-induced electron and proton couplings.
On Yang-Mills Stability Bounds and Plaquette Field Generating Function

We consider the Yang-Mills (YM) QFT with group U ( N ). We take a ﬁnite lattice regularization Λ ⊂ a Z d , d = 2 , 3 , 4, with a ∈ (0 , 1] and L (even) sites on a side. Each bond has a gauge variable U ∈ U ( N ). The Wilson partition function is used and the action is a sum of gauge-invariant plaquette (minimal square) actions times a d − 4 /g 2 , g 2 ∈ (0 , g 20 ], 0 < g 20 < ∞ . A plaquette action has the product of its four variables and the partition function is the integral of the Boltzmann factor with a product of U ( N ) Haar measures. Formally, when a ց 0 our action gives the usual YM continuum action. For free and periodic b.c., we show thermodynamic and stability bounds for a normalized partition function of any YM model deﬁned as before, with bound constants independent of L, a, g . The subsequential thermodynamic and ultraviolet limit of the free energy exist. To get our bounds, the Weyl integration formula is used and, to obtain the lower bound, a new quadratic global upper bound on the action is derived. We deﬁne gauge-invariant physical and scaled plaquette ﬁelds. Using periodic b.c. and the multi-reﬂection method, we bound the generating function of r − scaled plaquette correlations. A normalized generating function for the correlations of r scaled ﬁelds is absolutely bounded, for any L, a, g , and location of the external ﬁelds. From the joint analyticity on the ﬁeld sources, correlations are bounded. The bounds are new and we get a − d for the physical two-plaquette correlation at coincident points. Comparing with the a ց 0 singularity of the physical derivative massless scalar free ﬁeld two-point correlation, this is a measure of ultraviolet asymptotic freedom in the context of a lattice QFT. Our methods are an alternative and complete the more traditional ones.
High Performance of Gradient Boosting in Binding Affinity Prediction

Prediction of protein-ligand (PL) binding afﬁnity remains the key to drug discovery. Popular approaches in recent years involve graph neural networks (GNNs), which are used to learn the topology and geometry of PL complexes. How-ever, GNNs are computationally heavy and have poor scalability to graph sizes. On the other hand, traditional machine learning (ML) approaches, such as gradient-boosted decision trees (GBDTs), are lightweight yet extremely efﬁcient for tabular data. We propose to use PL interaction features along with PL graph-level features in GBDT. We show that this combination outperforms the existing solutions.
Deploying the high-power pulsed lasers in precision force metrology – Towards SI traceable and practical force quantization by photon momentum

Abstract Design and operational performance of table-top measurement apparatus is presented towards direct Planck constant traceable high accuracy and high precision small forces and optical power measurements within the SI unit system. Electromagnetic force compensation weighing balances, highly reflective mirrors and high-energy pulsed laser unit (static average power 20 W) are tailored together with a specially developed opto-electro-mechanical measurement infrastructure for cross-mapping the scale-systems of two different precision small force measurement methods. One of these methods obtains the force measurements by a state-of-the-art classical kinematic system employing the partial use of Kibble balance principle in the range of 10 nN to 4000 nN to be compared with forces generated due to quantum-mechanical effect namely the transfer of the momentum of photons from a macroscopic object. Detailed overview of the adapted measurement methodology, the static and the limits of dynamic measurement, the metrological traceability routes of the measurement parameters, quantities and their measurement uncertainties, parametric estimation of up (down)-scaling perspectives of the measurements are presented with respect to the state-of-the-art measurement principles and standard procedures within the newly redefined International System of Units (SI).
Pest Presence Prediction Using Interpretable Machine Learning

Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions. Meteo-climatic and vegetation conditions have been identified as key drivers of crop pest abundance. In this work, we applied an interpretable classifier, i.e., Explainable Boosting Machine, which uses earth observation vegetation indices, numerical weather predictions and insect trap catches to predict the onset of bollworm harmfulness in cotton fields in Greece. The glass-box nature of our approach provides significant insight on the main drivers of the model and the interactions among them. Model interpretability adds to the trustworthiness of our approach and therefore its potential for rapid uptake and context-based implementation in operational farm management scenarios. Our results are satisfactory and the importance of drivers, through our analysis on global and local explainability, is in accordance with the literature.
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion

Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos. However, compared to using appearance, it has some blind spots, such as the fact that objects become invisible if they do not move. In this work, we propose an approach that combines the strengths of motion-based and appearance-based segmentation. We propose to supervise an image segmentation network with the pretext task of predicting regions that are likely to contain simple motion patterns, and thus likely to correspond to objects. As the model only uses a single image as input, we can apply it in two settings: unsupervised video segmentation, and unsupervised image segmentation. We achieve state-of-the-art results for videos, and demonstrate the viability of our approach on still images containing novel objects. Additionally we experiment with different motion models and optical flow backbones and find the method to be robust to these change. Project page and code available at https://www.robots. ox.ac. uk/~vgg/research/gwm.
Generating Literal and Implied Subquestions to Fact-check Complex Claims

Verifying political claims is a challenging task, as politicians can use various tactics to subtly misrepresent the facts for their agenda. Existing automatic fact-checking systems fall short here, and their predictions like “half-true” are not very useful in isolation, since it is unclear which parts of a claim are true and which are not. In this work, we focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present CLAIMDECOMP, a dataset of decompositions for over 1000 claims. Given a claim and its verification paragraph written by fact-checkers, our trained annotators write subquestions covering both explicit propositions of the original claim and its implicit facets, such as asking about additional political context that changes our view of the claim’s veracity. We study whether state-of-the-art models can generate such subquestions, showing that these models generate reasonable questions to ask, but predicting the comprehensive set of subquestions from the original claim without evidence remains challenging. We further show that these subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers, suggesting that they can be useful pieces of a fact-checking pipeline.
Semantic Security with Infinite Dimensional Quantum Eavesdropping Channel

We propose a new proof method for direct coding theorems for wiretap channels where the eavesdropper has access to a quantum version of the transmitted signal on an infinite dimensional Hilbert space. This method yields errors that decay exponentially with increasing block lengths. Moreover, it provides a guarantee of a quantum version of semantic security, which is an established concept in classical cryptography and physical layer security. Semantic security has strong operational implications meaning essentially that the eavesdropper cannot use its quantum observation to gather any meaningful information about the transmitted signal. Therefore, it complements existing works which either do not prove the exponential error decay or use weaker notions of security. The main part of this proof method is a direct coding result on channel resolvability which states that there is only a doubly exponentially small probability that a standard random codebook does not solve the channel resolvability problem for the classical-quantum channel.
GraphSPME: Markov Precision Matrix Estimation and Asymptotic Stein-Type Shrinkage

GraphSPME is an open source Python, R and C++ header-only package implement-ing non-parametric sparse precision matrix estimation along with asymptotic Stein-type shrinkage estimation of the covariance matrix. The user defines a potential neighbourhood structure and provides data that potentially are p>>n. This paper introduces a novel approach for finding the optimal order (that data allows to estimate) of a potential Markov property. The algorithm is implemented in the package, alleviating the problem of users making Markov assumptions and implementing corresponding complex higher-order neighbourhood structures. Estimation is made accurate and stable by simultaneously utilising both Markov properties and Stein-type shrinkage. Asymptotic results on Stein-type shrinkage ensure that non-singular well conditioned matrices are obtained in an automatic manner. Final symmetry conversion creates symmetric positive definite estimates. Furthermore, the estimation routine is made efficient and scalable to very high-dimensional problems (~10^7) by utilising the sparse nature of the precision matrix under Markov assumptions. Implementation wise, the sparsity is exploited by employing the sparsity possibilities made available by the Eigen C++ linear-algebra library. The package and examples are available at https://github.com/equinor/GraphSPME
Error Exponents of the Dirty-Paper and Gel’fand–Pinsker Channels

We derive various error exponents for communication channels with random states, which are available non-causally at the encoder only. For both the finite-alphabet Gel’fand–Pinsker channel and its Gaussian counterpart, the dirty-paper channel, we derive random coding exponents, error exponents of the typical random codes (TRCs), and error exponents of expurgated codes. For the two channel models, we analyze some sub-optimal bin-index decoders, which turn out to be asymptotically optimal, at least for the random coding error exponent. For the dirty-paper channel, we show explicitly via a numerical example, that both the error exponent of the TRC and the expurgated exponent strictly improve upon the random coding exponent, at relatively low coding rates, which is a known fact for discrete memoryless channels without random states. We also show that at rates below capacity, the optimal values of the dirty-paper design parameter $\alpha $ in the random coding sense and in the TRC exponent sense are different from one another, and they are both different from the optimal $\alpha $ that is required for attaining the channel capacity. For the Gel’fand–Pinsker channel, we allow for a variable-rate random binning code construction, and prove that the previously proposed maximum penalized mutual information decoder is asymptotically optimal within a given class of decoders, at least for the random coding error exponent.
Reynolds number dependence of Lagrangian dispersion in direct numerical simulations of anisotropic magnetohydrodynamic turbulence

Abstract Large-scale magnetic fields thread through the electrically conducting matter of the interplanetary and interstellar medium, stellar interiors and other astrophysical plasmas, producing anisotropic flows with regions of high-Reynolds-number turbulence. It is common to encounter turbulent flows structured by a magnetic field with a strength approximately equal to the root-mean-square magnetic fluctuations. In this work, direct numerical simulations of anisotropic magnetohydrodynamic (MHD) turbulence influenced by such a magnetic field are conducted for a series of cases that have identical resolution, and increasing grid sizes up to $2048^3$. The result is a series of closely comparable simulations at Reynolds numbers ranging from 1400 up to 21 000. We investigate the influence of the Reynolds number from the Lagrangian viewpoint by tracking fluid particles and calculating single-particle and two-particle statistics. The influence of Alfvénic fluctuations and the fundamental anisotropy on the MHD turbulence in these statistics is discussed. Single-particle diffusion curves exhibit mildly superdiffusive behaviours that differ in the direction aligned with the magnetic field and the direction perpendicular to it. Competing alignment processes affect the dispersion of particle pairs, in particular at the beginning of the inertial subrange of time scales. Scalings for relative dispersion, which become clearer in the inertial subrange for a larger Reynolds number, can be observed that are steeper than indicated by the Richardson prediction.
On the effective metric of axial black hole perturbations in DHOST gravity

We study axial (or odd-parity) perturbations about static and spherically symmetric hairy black hole (BH) solutions in shift-symmetric DHOST (Degenerate Higher-Order Scalar-Tensor) theories. We first extend to the family of DHOST theories the first-order formulation that we recently developed for Horndeski theories. Remarkably, we find that the dynamics of DHOST axial perturbations is equivalent to that of axial perturbations in general relativity (GR) evolving in a, distinct, effective metric. In the particular case of quadratic DHOST theories, this effective metric is derived from the background BH metric via a disformal transformation. We illustrate our general study with three examples of BH solutions. In some so-called stealth solutions, the effective metric is Schwarzschild with a shifted horizon. We also give an example of BH solution for which the effective metric is associated with a naked singularity.
The management of cardiovascular disease risk in patients with rheumatoid arthritis

ABSTRACT Introduction Rheumatoid Arthritis (RA) is a chronic inflammatory disorder associated with an increased incidence and prevalence of cardiovascular disease (CVD), including myocardial infarction and heart failure. In addition to traditional risk factors, evidence suggests inflammation is critical to the pathophysiology of both conditions. Despite the association being well-recognised, challenges remain in managing cardiovascular risk in RA. Areas Covered This manuscript analyses the association between CVD and RA and explores the limitations in evaluating cardiovascular risk in RA with available risk assessment tools. The authors review the optimal management of traditional risk factors such as hypertension and dyslipidaemia and contemporary risk factors such as inflammation and analyse the cardiovascular impact of RA medications. Expert opinion Analysis points to the critical role of inflammation in the pathogenesis of RA and CVD. It is well established that conventional disease-modifying anti-rheumatic drugs (DMARDs) improve cardiovascular outcomes; however, underlying risk often remains underappreciated. The authors suggest there remains an opportunity to improve mortality and morbidity with the early recognition and of at-risk populations and the timely initiation of appropriate cardiovascular and anti-inflammatory medications. More research is necessary into the role that imaging may play in stratifying risk and in the longer-term cardiovascular impact of biological DMARDs.