Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning

We consider the problem of defense against reward-poisoning attacks in reinforcement learning and formulate it as a game in T rounds be-tween a defender and an adaptive attacker in an adversarial environment. To address this problem, we design two novel defense algorithms. First, we propose Exp3-DARP , a defense algorithm that uses Exp3 as a hyperparameter learning sub-routine, and show that it achieves order-optimal ˜Θ( T 1 / 2 ) bounds on our notion of regret with respect to a defense that always picks the optimal parameter in hindsight. We show that the order of T in the bounds cannot be improved when the reward arrival process is adversarial, even if the feedback model of the defense is stronger. However, assuming that the environment is stochastic, we propose OMDUCB-DARP that uses estimates of costs as proxies to update the randomized strategy of the learner and are able to substantially improve the bounds proportional to how smoothly the attacker’s strategy changes. Furthermore, we show that weaker types of defense, that do not take into account the attack structure and the poisoned rewards, suffer linear regret with respect to a defender that always selects the optimal parameter in hindsight when faced with an adaptive attacker that uses a no-regret algorithm to learn the behavior of the defense. Finally, we support our theoretical results with experimental evaluations on three different environments, showcasing the efficiency of our methods.
Sparse Spectral Bayesian Permanental Process with Generalized Kernel

We introduce a novel scheme for Bayesian inference on permanental processes which models the Poisson intensity as the square of a Gaussian process. Combining generalized kernels and a Fourier features-based representation of the Gaussian process with a Laplace approximation to the posterior, we achieve a fast and efficient inference that does not require numerical integration over the input space, allows kernel design and scales linearly with the number of events. Our method builds and improves upon the state-of-the-art Laplace Bayesian point process benchmark of Walder and Bishop (2017), demonstrated on both synthetic, real-world temporal and large spatial data sets.
Reasoning-Learning Systems Based on Non-Axiomatic Reasoning System Theory

In this paper a strong motivation for real-time reasoning-learning systems based on Non-Axiomatic Reasoning System (NARS) Theory as an approach to build intelligent systems with agency is given. This contains the requirement to work under the Assumption of Insufficient Knowledge and Resources which demands open-ended adaptation while obeying to strict computational resource restrictions to allow for real-time response. We show how this aligns with the phenomenon of intelligence as found in nature, allowing for systems which can both react instantly, and plan ahead deliberately dependent on implicitly outcome-dependent time pressures. In this context a specific implementation design is considered, OpenNARS for Applications (ONA), and how its learning and reasoning abilities lead to data-efficient adaptation in novel circumstances in various domains, whereby we compare with a reinforcement learning method, Q-Learning, in Space Invaders, Pong and a grid robot environment. We will see that both techniques perform comparably well for reactive tasks in Markovian environments, while the uncertainty reasoner performs better when the Markov property is violated, with the additional property that it can plan ahead to exploit task compositionality, also taking explicit background knowledge into account.
Mode-Seeking Divergences: Theory and Applications to GANs

Generative adversarial networks (GANs) represent a game between two neural network machines designed to learn the distribution of data. It is commonly observed that different GAN formulations and divergence/distance measures used could lead to considerably different performance results, especially when the data distribution is multi-modal. In this work, we give a theoretical characterization of the mode-seeking behavior of general f -divergences and Wasserstein distances, and prove a performance guarantee for the setting where the underlying model is a mixture of multiple symmetric quasiconcave distributions. This can help us understand the trade-off between the quality and diversity of the trained GANs’ output samples. Our theoretical results show the mode-seeking nature of the Jensen-Shannon (JS) divergence over standard KL-divergence and Wasserstein distance measures. We subsequently demonstrate that a hybrid of JS-divergence and Wasserstein distance measures minimized by Lip-schitz GANs mimics the mode-seeking behavior of the JS-divergence. We present numerical re-sults showing the mode-seeking nature of the JS-divergence and its hybrid with the Wasserstein distance while highlighting the mode-covering properties of KL-divergence and Wasserstein distance measures. Our numerical experiments indicate the different behavior of several standard GAN formulations in application to benchmark Gaussian mixture and image datasets.
Cardiomyocyte Ploidy, Metabolic Reprogramming and Heart Repair

The adult heart is made up of cardiomyocytes (CMs) that maintain pump function but are unable to divide and form new myocytes in response to myocardial injury. In contrast, the developmental cardiac tissue is made up of proliferative CMs that regenerate injured myocardium. In mammals, CMs during development are diploid and mononucleated. In response to cardiac maturation, CMs undergo polyploidization and binucleation associated with CM functional changes. The transition from mononucleation to binucleation coincides with unique metabolic changes and shift in energy generation. Recent studies provide evidence that metabolic reprogramming promotes CM cell cycle reentry and changes in ploidy and nucleation state in the heart that together enhances cardiac structure and function after injury. This review summarizes current literature regarding changes in CM ploidy and nucleation during development, maturation and in response to cardiac injury. Importantly, how metabolism affects CM fate transition between mononucleation and binucleation and its impact on cell cycle progression, proliferation and ability to regenerate the heart will be discussed.
Federated Learning as Enabler for Collaborative Security between not Fully-Trusting Distributed Parties

Literature shows that trust typically relies on knowledge about the communication partner. Federated learning is an approach for collaboratively improving machine learning models. It allows collaborators to share Machine Learning models without revealing secrets, as only the abstract models and not the data used for their creation is shared. Federated learning thereby provides a mechanism to create trust without revealing secrets, such as specificities of local industrial systems. A fundamental challenge, however, is determining how much trust is justified for each contributor to collaboratively optimize the joint models. By assigning equal trust to each contribution, divergence of a model from its optimum can easily happen—caused by errors, bad observations, or cyberattacks. Trust also depends on how much an aggregated model contributes to the objectives of a party. For example, a model trained for an OT system is typically useless for monitoring IT systems. This paper shows first directions how heterogeneous distributed data sources could be integrated using federated learning methods. With an extended abstract, it shows current research directions and open issues from a cyber-analyst’s perspective.
Graph Alignment Kernels using Weisfeiler and Leman Hierarchies

Graph kernels have become a standard approach for tackling the graph similarity and learning tasks at the same time. Most graph kernels proposed so far are instances of the R -convolution framework. These kernels decompose graphs into their substructures and sum over all pairs of these substructures. However, considerably less attention has been paid to other types of kernels. In this paper, we propose a new kernel between graphs which reorders the adjacency matrix of each graph based on soft permutation matrices, and then compares those aligned adjacency matrices to each other using a linear kernel. To compute the permutation matrices, the kernel ﬁnds corresponding vertices in different graphs. Two vertices match with each other if the Weisfeiler-Leman test of isomorphism assigns the same label to both of them. The proposed kernel is evaluated on several graph clas-siﬁcation and graph regression datasets. Our re-sults indicate that the kernel is competitive with traditional and state-of-the-art methods. The code is available at https://github.com/ giannisnik/gawl .
Improved Bound on Generalization Error of Compressed KNN Estimator

This paper studies the generalization capability of the compressed k -nearest neighbor (KNN) estimator, where randomly-projected low-dimensional data are put into the KNN estimator rather than the high-dimensional raw data. Considering both regression and classiﬁcation, we give improved bounds on its generalization errors, to put more speciﬁc, ` 2 error for regression and mis-classiﬁcation rate for classiﬁcation. As a byproduct of our analysis, we prove that ordered distance is almost preserved with random projections, which we believe is for the ﬁrst time. In addition, we provide numerical experiments on various public datasets to verify our theorems.
A Fact Extraction and Verification Framework for Social Media Posts on Covid-19

Social media has become popular for spreading and consuming information online. On the other hand, the high number of posts has increased the need for fact checking. In the COVID-19 pandemic, the lack of information on the disease paved the way for the spread of false information, negatively affecting public health and society. In this paper, a new zero-shot fact extraction and verification framework for informal user posts on COVID-19 against medical articles is proposed. The framework includes five main steps, which are pre-processing user posts, claim extraction, document & evidence extraction, and verdict assignment. The framework aims to classify user posts while presenting the related evidence set extracted from peer-reviewed medical articles about each claim in user posts, making it interpretable for end users. The proposed framework obtains on-par and stable performance compared with the state-of-the-art supervised techniques for classifying raw user posts (Coaid) and rumors collected from social media (COVID-19 Rumors Dataset). By utilizing the zero-shot capabilities of the present models in the literature, it achieves superior performance detecting newly emerged misinformation posts and topics.
Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare

Cardinal objectives serve as intuitive targets in fair machine learning by summarizing utility (welfare) or disutility (malfare) u over g groups. Under standard axioms, all welfare and malfare functions are w -weighted p -power-means , i.e., M p ( u ; w ) . = p (cid:112)(cid:80) gi =1 w i u p i , with p ≤ 1 for welfare, or p ≥ 1 for malfare. We show the same under weaker axioms, and also identify stronger axioms that naturally restrict p . It is known that power-mean malfare functions are Lipschitz continuous, and thus statistically easy to estimate or learn. We show that all power means are locally H ¨ older continuous, i.e., (cid:12)(cid:12) M( u ; w ) − M( u ′ ; w ) (cid:12)(cid:12) ≤ λ (cid:13)(cid:13) u − u ′ (cid:13)(cid:13) α for some λ, α, ∥·∥ . In particular, λ and 1 α are bounded except as p → 0 or min i w i → 0 , and via this analysis we bound the sample complexity of optimizing welfare. This yields a novel concept of fair-PAC learning , wherein welfare functions are only polynomially harder to optimize than malfare functions, except when p ≈ 0 or min i w i ≈ 0 , which is exponentially harder .
Crux of the MATTR: Voynichese Morphological Complexity

This study examines Voynichese at the word level, using a corpus of language samples and word-level statistics in order to identify the most plausible language families for Voynichese, and to exclude statistically improbable language families. The results narrow down the possibilities for a source language and emphasize the likelihood that Voynichese does in fact encode meaningful language. Comparison texts include samples from 160 modern languages in ten major language families, as well as historical manuscripts written in Hebrew, Italian, Old Church Slavonic, and Welsh. The methodology employs two particular word distribution statistics, the Moving Average Type-Token Ratio (MATTR) and the Most Common Words percentage (MCW). A graph of the corpus language samples shows a clustering by language family and correlates with the presence of agglutinating or isolating morphology in a given language family. The methodology ranks plausible language families and excludes statistically improbable language families based on their distribution, and indicates that Voynichese possesses a middle range of morphological complexity that most closely resembles that of medieval Germanic languages. By contrast, Semitic languages like Hebrew and Arabic, and languages from the Slavic and Celtic families are possible but less likely candidates. Families that feature heavy agglutinating morphology like Turkic and Uralic can be effectively excluded.
An analysis of approach to the fake news assessment based on the graph neural networks

The experience of Russia’s war against Ukraine demonstrates the relevance and necessity of understanding the problems of constant disinformation, the spread of propaganda, and the implementation of destructive negative psychological influence. The issue of dissemination in online media informational messages containing negative psychological influence was researched. Ways of improving the system of monitoring online media using the graph neural networks are considered. The methods of automated fake news detection, based on graph neural networks, were reviewed. The purpose of the article is the analysis of existing approaches that allow identifying destructive signs of influence in text data. It is found that the best way to automate the content analysis process is to use the latest machine learning methods. It was determined and substantiated that graph neural networks are the most reliable and effective solution for the specified task. An approach to automating this procedure based on graph neural networks has been designed and analyzed, which will allow timely and efficient detection and analysis of fake news in the information space of our country. During the research, the process of detecting fake news was simulated. The obtained results showed that the described models of graph neural networks can provide good results in solving the tasks of timely detection and response to threats posed by fake news spread by Russia.
On the Accelerated Noise-Tolerant Power Method

We revisit the acceleration of the noise-tolerant power method for which, despite previous studies, the results remain unsatisfactory as they are either wrong or suboptimal, also lacking generality. In this work, we present a simple yet general and optimal analysis via noise-corrupted Cheby-shev polynomials, which allows a larger iteration rank p than the target rank k , requires less noise conditions in a new form, and achieves the optimal iteration complexity Θ (cid:18)q λ k
Evaluating the Robustness of Biomedical Concept Normalization

Biomedical concept normalization involves linking entity mentions in text to standard concepts in knowledge bases. It aids in resolving challenges to standardising ambiguous, variable terms in text or handling missing links. Therefore, it is one of the essential tasks of text mining that helps in effective information access and finds its utility in biomedical decision-making. Pre-trained language models (e.g., BERT) achieve impressive performance on this task. It has been observed that such models are insensitive to word order permutations and vulnerable to adversarial attacks on tasks like Text Classification, Natural Language Inference. However, the effect of such attacks is unknown for the task of Normalization, especially in the biomedical domain. In this paper, we propose heuristic-based Input Transformations (word level modifications and word order variations) and Adversarial attacks to study the robustness of BERT-based normalization models across various datasets consisting of different biomedical entity types. We conduct experiments across three datasets: NCBI disease, BC5CDR Disease, and BC5CDR Chemical. We observe that for Input Transformations, pre-trained models often fail to detect invalid input. On the other hand, our proposed Adversarial attacks that add imperceptible perturbations result in affecting the ranking of a concept list for a given mention (or vice versa). We also generate natural adversarial examples that lead to performance degradation of ∼ 30% in the F1-score. Additionally, we explore existing mitigation strategies to help a model recognize invalid inputs.
Towards Reducing Misinformation and Toxic Content Using Cross-Lingual Text Summarization

Misinformation has long been considered a major problem in our digital world, but automatically identifying it still remains a challenging issue. It becomes even more of a problem when tackling content written in languages other than English. We also note that much progress has been made in classifying short social media posts, but there are many other types of misinformation. We present steps towards addressing the problem by adopting ideas that have shown to be promising in related prior work, namely applying extractive and abstractive text summarization methods so that we can process documents of any length and by incorporating machine translation as part of our overall architecture. We consider misinformation as just one out of many types of content that should be identified automatically on the way to a healthier digital ecosystem and see toxic content such as hate speech as naturally falling within the same scope of our work. We demonstrate on several benchmark collections covering both misinformation and toxic content that our approach is robust and achieves competitive performance on these datasets. This offers plenty of scope for future work. To foster reproducibility, we make all code and models available to the community via GitHub and Hugging Face.
Building Disease Prediction Model Using Machine Learning Algorithms on Electronic Health Records' Logs

The number of tasks devoted to predicting the incidence of infectious diseases is growing rapidly due to the availability of statistical data supporting the analysis. This article describes the main solutions currently available for creating short and long-term disease forecasts. Their limitations and practical applications are shown. Much attention is given to the Naïve Bayes classification, logistic regression, artificial neural network algorithm and k-means artificial neural networks as methods of model analysis based on machine learning. This article provides an overview of two popular machine learning algorithms used to predict diseases. The standard dataset is used for a wide range of diseases including fungal infection, allergy, GERD, chronic cholestasis, peptic ulcer disease, diabetes, bronchial asthma
A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data

We introduce a Bayesian multi-task Gaussian process model for estimating treatment effects from panel data, where an intervention outside the observer’s control influences a subset of the observed units. Our model encodes structured temporal dynamics both within and across the treatment and control groups and incorporates a flexible prior for the evolution of treatment effects over time. These innovations aid in inferring posteriors for dynamic treatment effects that encode our uncertainty about the likely trajectories of units in the absence of treatment. We also discuss the asymptotic properties of the joint posterior over counterfactual outcomes and treatment effects, which exhibits intuitive behavior in the large-sample limit. In experiments on both synthetic and real data, our approach performs no worse than existing methods and significantly better when standard assumptions are violated.
Response of Ammodendron bifolium Seedlings Inoculated with AMF to Drought Stress

Drought has become a serious environmental problem affecting people all around the world as a result of rising atmospheric pollution and global warming. Through symbiosis with Arbuscular mycorrhizal fungus (AMF), plants may mitigate the impacts of drought stress on growth and development as well as physiological metabolism. As a pioneer plant for windbreak and sand fixation, the symbiosis between AMF and Ammodendron bifolium can improve its drought resistance, which is of great significance for species protection and desertification control. In this study, A. bifolium seedlings were used as the test subject in a pot experiment with four drought stress gradients and two inoculation treatments to examine the effects of water treatment and AMF inoculation on the growth of the seedlings. The results showed that drought stress significantly inhibited the growth indexes of A. bifolium seedlings such as the height, basal diameter, blades number, and biomass, and that inoculation with AMF could promote the growth of A. bifolium seedlings and help mitigate the damage caused by drought stress. Drought stress increased the antioxidant enzyme activity and proline (Pro) accumulation in A. bifolium plants, and AMF inoculation induced higher antioxidant enzyme activity and lower malondialdehyde (MDA) and Pro contents in A. bifolium seedlings compared to non-AMF-inoculated plants. Drought stress harmed the chloroplast structure, reduced the chlorophyll concentration, and decreased the photosynthetic efficiency in A. bifolium seedlings. The ability of AMF-inoculated plants to withstand drought was enhanced by increased levels of photosynthetic pigments, higher photosynthetic activity, and increased photosynthetic product accumulation in the roots. These results suggest that AMF inoculation can alleviate drought-induced damage by promoting plant growth and improving plant antioxidant, osmoregulation, and photosynthetic capacity. In the context of increasing drought due to global warming, AMF inoculation can be an excellent way to enhance A. bifolium drought resistance.
Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics

This paper provides a uniﬁed perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE). Both the KL-divergence and the IPMs are widely used in various ﬁelds in applications such as generative modeling. However, a uniﬁed understanding of these concepts has still been unexplored. In this paper, we show that the KL-divergence and the IPMs can be represented as maximal likelihoods differing only by sampling schemes, and use this result to derive a uniﬁed form of the IPMs and a relaxed estimation method. To develop the estimation problem, we construct an unconstrained maximum likelihood estimator to perform DRE with a stratiﬁed sampling scheme. We further propose a novel class of probability divergences, called the Density Ratio Metrics (DRMs), that interpolates the KL-divergence and the IPMs. In addition to these ﬁndings, we also introduce some applications of the DRMs, such as DRE and generative adversarial networks. In experiments, we validate the effectiveness of our proposed methods.