Miracle-dz/UniFAR-FTUs huggingface dataset:
SGMM: Stochastic Approximation to Generalized Method of Moments

We introduce a new class of algorithms, Stochastic Generalized Method of Moments (SGMM), for estimation and inference on (overidentified) moment restriction models. Our SGMM is a novel stochastic approximation alternative to the popular Hansen (1982) (offline) GMM, and offers fast and scalable implementation with the ability to handle streaming datasets in real time. We establish the almost sure convergence, and the (functional) central limit theorem for the inefficient online 2SLS and the efficient SGMM. Moreover, we propose online versions of the Durbin-Wu-Hausman and Sargan-Hansen tests that can be seamlessly integrated within the SGMM framework. Extensive Monte Carlo simulations show that as the sample size increases, the SGMM matches the standard (offline) GMM in terms of estimation accuracy and gains over computational efficiency, indicating its practical value for both large-scale and online datasets. We demonstrate the efficacy of our approach by a proof of concept using two well known empirical examples with large sample sizes.
Some issues in robust clustering

Some key issues in robust clustering are discussed with focus on Gaussian mixture model based clustering, namely the formal definition of outliers, ambiguity between groups of outliers and clusters, the interaction between robust clustering and the estimation of the number of clusters, the essential dependence of (not only) robust clustering on tuning decisions, and shortcomings of existing measurements of cluster stability when it comes to outliers.
Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models

To translate well, machine translation (MT) systems and general-purposed language models (LMs) need a deep understanding of both source and target languages and cultures. Therefore, idioms, with their non-compositional nature, pose particular challenges for Transformer-based systems, as literal translations often miss the intended meaning. Traditional methods, which replace idioms using existing knowledge bases (KBs), often lack scale and context-awareness. Addressing these challenges, our approach prioritizes context-awareness and scalability, allowing for offline storage of idioms in a manageable KB size. This ensures efficient serving with smaller models and provides a more comprehensive understanding of idiomatic expressions. We introduce a multilingual idiom KB (IdiomKB) developed using large LMs to address this. This KB facilitates better translation by smaller models, such as BLOOMZ (7.1B), Alpaca (7B), and InstructGPT (6.7B), by retrieving idioms' figurative meanings. We present a novel, GPT-4-powered metric for human-aligned evaluation, demonstrating that IdiomKB considerably boosts model performance. Human evaluations further validate our KB's quality.
Chiral and flavor oscillations in a hyperentangled neutrino state

By means of the Dirac bispinor formalism, we show that the state of a (massive) oscillating neutrino produced by a weak interaction process, is an hyperentangled state of flavor, chirality, and spin. Since chirality is not a conserved quantity, chiral oscillations take place and affects the flavor transition probability. By means of the complete complementarity relations, we analyze how correlations and coherence are redistributed in time between the different degrees-of-freedoms of the system. In a similar way, we consider a spin entangled lepton-antineutrino pair and describe the redistribution of the spin-spin entanglement into correlations between the other degrees-of-freedom. In both cases the effects of chiral oscillations are relevant in the non-relativistic regime. Our analysis provides a complete characterization of the quantum correlations involved in lepton-antineutrino pairs and in single particle neutrino evolution, and provides a further insight on possible routes to interpret and measure chiral oscillations.
Global well-posedness for the one-phase Muskat problem in 3D

This paper is concerned with the long time dynamics of the free boundary of a Darcy fluid in three space dimensions, also known as the one-phase Muskat problem. The dynamics of the free boundary is governed by a nonlocal fully nonlinear parabolic partial differential equation. It is proven that for any periodic Lipschitz graph given as initial data, the problem has a unique global-in-time solution which satisfies the equation in the strong sense. Moreover, all H\"older norms of the solution decay exponentially in time. These results have been previously established in two space dimensions. This paper addresses new challenges to extend the results to the more difficult three dimensional setting. The approach developed is critical in three space dimensions and crucially relies on Dahlberg-Kenig's $W^{1, 2+\varepsilon}$ optimal regularity for layer potentials together with delicate structures of the Dirichlet-to-Neumann operator and layer potentials in Lipschitz domains of $\mathbb{T}^2\times \mathbb{R}$.
Yarrowia lipolytica: an industrial workhorse

Yarrowia lipolytica is one of the most extensively studied ‘‘non-conventional’’ yeasts, being a strictly aerobic microorganism capable of producing important metabolites and having an intense secretory activity, which justifies efforts to use it in industry (as a biocatalyst), in molecular biology and in genetics studies. Moreover, Y. lipolytica has been considered an adequate model for dimorphism studies in yeasts. Yarrowia lipolytica presents the ability to grow on Olive Mill Wastewater (OMW) as well as to degradate organic compounds, including aliphatic and aromatic hydrocarbons, often accompanied by biosurfactants production. One of the most important products secreted by this microorganism is lipase which can be exploited for several applications in the detergent, food, pharmaceutical, and environmental industries. In addition, Y. lipolytica is able to produce citric acid and aroma from a variety of carbon sources, including sugars, alkanes, plant oils, starch hydrolysates, ethanol, and glycerol. Thus, this chapter presents an overview of Yarrowia lipolytica features and its major biotechnological applications.
ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges

The increasing scale and complexity of workloads in modern cloud services highlight a crucial challenge in automated index tuning: recommending high-quality indexes while ensuring scalability. This is further complicated by the need for these automated solutions to minimize query performance regressions in production deployments. This paper directs attention to some of these challenges in automated index tuning and explores ways in which machine learning (ML) techniques provide new opportunities in their mitigation. In particular, we reflect on our recent efforts in developing ML techniques for workload selection, candidate index filtering, speeding up index configuration search, reducing the amount of query optimizer calls, and lowering the chances of performance regressions. We highlight the key takeaways from these efforts and underline the gaps that need to be closed for their effective functioning within the traditional index tuning framework. Additionally, we present a preliminary cross-platform design aimed at democratizing index tuning across multiple SQL-like systems-an imperative in today's continuously expanding data system landscape. We believe our findings will help provide context and impetus to the research and development efforts in automated index tuning.
InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models

Music editing primarily entails the modification of instrument tracks or remixing in the whole, which offers a novel reinterpretation of the original piece through a series of operations. These music processing methods hold immense potential across various applications but demand substantial expertise. Prior methodologies, although effective for image and audio modifications, falter when directly applied to music. This is attributed to music's distinctive data nature, where such methods can inadvertently compromise the intrinsic harmony and coherence of music. In this paper, we develop InstructME, an Instruction guided Music Editing and remixing framework based on latent diffusion models. Our framework fortifies the U-Net with multi-scale aggregation in order to maintain consistency before and after editing. In addition, we introduce chord progression matrix as condition information and incorporate it in the semantic space to improve melodic harmony while editing. For accommodating extended musical pieces, InstructME employs a chunk transformer, enabling it to discern long-term temporal dependencies within music sequences. We tested InstructME in instrument-editing, remixing, and multi-round editing. Both subjective and objective evaluations indicate that our proposed method significantly surpasses preceding systems in music quality, text relevance and harmony. Demo samples are available at https://musicedit.github. io/
Party Prediction for Twitter

A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a predictive model for inferring their political affiliation. The accuracy of this model can change the conclusions of a downstream analysis significantly, yet the choice between different models seems to be made arbitrarily. In this paper, we provide a comprehensive survey and an empirical comparison of the current party prediction practices and propose several new approaches which are competitive with or outperform state-of-the-art methods, yet require less computational resources. Party prediction models rely on the content generated by the users (e.g., tweet texts), the relations they have (e.g., who they follow), or their activities and interactions (e.g., which tweets they like). We examine all of these and compare their signal strength for the party prediction task. This paper lets the practitioner select from a wide range of data types that all give strong performance. Finally, we conduct extensive experiments on different aspects of these methods, such as data collection speed and transfer capabilities, which can provide further insights for both applied and methodological research.
Internal Hopf algebroid

We introduce a natural generalization of the definition of a symmetric Hopf algebroid, internal to any symmetric monoidal category with coequalizers that commute with the monoidal product. Motivation for this is the study of Heisenberg doubles of countably dimensional Hopf algebras $A$ as internal Hopf algebroids over a (noncommutative) base $A$ in the category $\mathrm{indproVect}$ of filtered cofiltered vector spaces introduced by the author. One example of such Heisenberg double is internal Hopf algebroid $U(\mathfrak{g}) \sharp U(\mathfrak{g})^*$ over universal enveloping algebra $U(\mathfrak{g})$ of a finite-dimesional Lie algebra $\mathfrak{g}$ that is a properly internalized version of a completed Hopf algebroid previously studied as a Lie algebra type noncommutative phase space.
Quantifying the Influence of User Behaviors on the Dissemination of Fake News on Twitter with Multivariate Hawkes Processes

Fake news has emerged as a pervasive problem within Online Social Networks, leading to a surge of research interest in this area. Understanding the dissemination mechanisms of fake news is crucial in comprehending the propagation of disinformation/misinformation and its impact on users in Online Social Networks. This knowledge can facilitate the development of interventions to curtail the spread of false information and inform affected users to remain vigilant against fraudulent/malicious content. In this paper, we specifically target the Twitter platform and propose a Multivariate Hawkes Point Processes model that incorporates essential factors such as user networks, response tweet types, and user stances as model parameters. Our objective is to investigate and quantify their influence on the dissemination process of fake news. We derive parameter estimation expressions using an Expectation Maximization algorithm and validate them on a simulated dataset. Furthermore, we conduct a case study using a real dataset of fake news collected from Twitter to explore the impact of user stances and tweet types on dissemination patterns. This analysis provides valuable insights into how users are influenced by or influence the dissemination process of disinformation/misinformation, and demonstrates how our model can aid in intervening in this process.
Turnkey Technology: A Powerful Tool for Cyber Warfare

Turnkey technology has emerged as a game-changing tool in cyber warfare, offering state and non-state actors unprecedented access to advanced cyber capabilities. The advantages of turnkey technology include rapid deployment and adaptability, lower costs and resource requirements, the democratization of cyber warfare capabilities, and enhanced offensive and defensive strategies. However, turnkey technology also introduces significant risks, such as the proliferation of cyber weapons, ethical considerations, potential collateral damage, escalation of conflicts, and legal ramifications. This paper provides a unique perspective on the implications of turnkey technology in cyber warfare, highlighting its advantages, risks, and challenges, as well as the potential strategies for mitigating these concerns. The research's novelty lies in examining real-world examples and proposing a multifaceted approach to address the challenges associated with turnkey technology in cyber warfare. This approach focuses on developing effective cybersecurity measures, establishing international norms and regulations, promoting responsible use and development of turnkey technology, and enhancing global cooperation on cyber warfare issues. By adopting this accountable and balanced approach, governments, organizations, and the international community can work together to create a more secure and stable digital environment, leveraging the benefits of turnkey technology while minimizing the associated risks and challenges.
Zip to Zip-It: Compression to Achieve Local Differential Privacy

Local differential privacy techniques for numerical data typically transform a dataset to ensure a bound on the likelihood that, given a query, a malicious user could infer information on the original samples. Queries are often solely based on users and their requirements, limiting the design of the perturbation to processes that, while privatizing the results, do not jeopardize their usefulness. In this paper, we propose a privatization technique called Zeal, where perturbator and aggregator are designed as a unit, resulting in a locally differentially private mechanism that, by-design, improves the compressibility of the perturbed dataset compared to the original, saves on transmitted bits for data collection and protects against a privacy vulnerabilities due to floating point arithmetic that affect other state-of-the-art schemes. We prove that the utility error on querying the average is invariant to the bias introduced by Zeal in a wide range of conditions, and that under the same circumstances, Zeal also guarantee protection against the aforementioned vulnerability. Our numerical results show up to 94 % improvements in compression and up to 95 % more efficient data transmissions, while keeping utility errors within 2 %.
ACUTE UPPER GASTROINTESTINAL HEMORRHAGE: PHARMACOLOGIC MANAGEMENT

EVIDENCE DEFINITIONS  Class I: Prospective randomized controlled trial.  Class II: Prospective clinical study or retrospective analysis of reliable data. Includes observational, cohort, prevalence, or case control studies.  Class III: Retrospective study. Includes database or registry reviews, large series of case reports, expert opinion.  Technology assessment: A technology study which does not lend itself to classification in the above-mentioned format. Devices are evaluated in terms of their accuracy, reliability, therapeutic potential, or cost effectiveness.
Exploring gravitational lensing

In this article, we discuss the idea of gravitational lensing, from a systematic, historical and didactic point of view. We show how the basic lensing equation together with the concepts of geometrical optics opens a space of implications that can be explored along different dimensions. We argue that Einstein explored the idea along different pathways in this space of implication, and that these explorations are documented by different calculational manuscripts. The conceptualisation of the idea of gravitational lensing as a space of exploration also shows the feasibility of discussing the idea in the classroom using some of Einstein’s manuscripts.
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of black-box Algorithmic rankers

Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high consequences. For example, a university can be deemed less prestigious if not featured in a top-k list, and consumers might not even explore products that do not get recommended to buyers. At the heart of most of these decisions are opaque ranking schemes, which dictate the ordering of data entities, but their internal logic is inaccessible or proprietary. Drawing inferences about the ranking differences is like a guessing game to the stakeholders, like, the rankees (i.e., the entities who are ranked, like product companies) and the decision-makers (i.e., who use the rankings, like buyers). In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods. To realize this aim, we leverage the exploration–explanation paradigm of human–data interaction to let human stakeholders explore subsets and groupings of complex multi-attribute ranking data using visual explanations of model fit and attribute influence on rankings. We realize this explanation paradigm for transparent ranking interpretation in TRIVEA , a visual analytic system that is fueled by: (i) visualizations of model fit derived from algorithmic rankers that learn the associations between attributes and rankings from available data and (ii) visual explanations derived from XAI methods that help abstract important patterns, like, the relative influence of attributes in different ranking ranges. Using TRIVEA, end users not trained in data science have the agency to transparently reason about the global and local behavior of the rankings without the need to open black-box ranking models and develop confidence in the resulting attribute-based inferences. We demonstrate the efficacy of TRIVEA using multiple usage scenarios and subjective feedback from researchers with diverse domain expertise.
Generalized stability conditions for binary bose mixtures

Considering both interspecies and intraspecies fluctuations in ultracold bose-bose mixtures, we derive generalized stability conditions against collapse and phase separation. Furthermore, by examining the energy density of the system, we show that new LHY-like effects may enforce or destroy the stability according to the excitation branch, therefore, permitting or prohibiting the existence of droplet states.
Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition

The most common type of lung cancer, lung adenocarcinoma (LUAD), has been increasingly detected since the advent of low-dose computed tomography screening technology. In clinical practice, pre-invasive LUAD (Pre-IAs) should only require regular follow-up care, while invasive LUAD (IAs) should receive immediate treatment with appropriate lung cancer resection, based on the cancer subtype. However, prior research on diagnosing LUAD has mainly focused on classifying Pre-IAs/IAs, as techniques for distinguishing different subtypes of IAs have been lacking. In this study, we proposed a multi-head attentional feature fusion (MHA-FF) model for not only distinguishing IAs from Pre-IAs, but also for distinguishing the different subtypes of IAs. To predict the subtype of each nodule accurately, we leveraged both radiomics and deep features extracted from computed tomography images. Furthermore, those features were aggregated through an adaptive fusion module that can learn attention-based discriminative features. The utility of our proposed method is demonstrated here by means of real-world data collected from a multi-center cohort.
A systematic review of Hepatitis B virus (HBV) prevalence and genotypes in Kenya: Data to inform clinical care and health policy

Sub-Saharan Africa accounts for around 20% of the global disease burden from chronic hepatitis B infection (CHB), however there is minimal high quality seroprevalence data from individual countries and little viral sequencing data available. We undertook a systematic review of the prevalence and genetic data available for hepatitis B virus (HBV) in Kenya using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2020 checklist. We identified 23 studies reporting HBV prevalence and 25 studies that included HBV genetic data published in English between January 2000 and December 2021. We assessed study quality using the Joanna Briggs Institute critical appraisal checklist. Due to study heterogeneity, we divided the studies to represent low, moderate, high and very high-risk for HBV infection. We calculated pooled HBV prevalence within each group and evaluated available sequencing data. We also assessed whether reported HBV biomarkers could be applied to determine treatment eligibility. Eight studies were identified in the low-risk group, seven in the moderate risk group, five in the high-risk group and three in the very high-risk group for HBV infection. Pooled HBV prevalence was 3.31% (95% CI 2.62-4.01%), 5.58% (95% CI 3.46-7.7%), 6.17% (95% CI 4.4-9.94) and 31.39% (95% CI 9.5-53.09) respectively. Study quality was overall low, representing a small geographical location or a limited population subset. Only three studies detailed sample size calculation and 17/23 studies were cross sectional. Eight studies included genetic information on HBV, representing 247 individuals. Six studies sequenced one or two genes; two undertook whole genome sequencing, representing 22 participants. 92% people were infected with genotype A. Other genotypes included genotype D (6%), D/E recombinants (1%) or mixed populations (1%). Drug resistance mutations were reported by two studies. Seven studies presented additional biomarkers alongside HBsAg, however none provided sufficient information to deduce treatment eligibility.
MM-AU:Towards Multimodal Understanding of Advertisement Videos

Advertisement videos (ads) play an integral part in the domain of Internet e-commerce, as they amplify the reach of particular products to a broad audience or can serve as a medium to raise awareness about specific issues through concise narrative structures. The narrative structures of advertisements involve several elements like reasoning about the broad content (topic and the underlying message) and examining fine-grained details involving the transition of perceived tone due to the sequence of events and interaction among characters. In this work, to facilitate the understanding of advertisements along the three dimensions of topic categorization, perceived tone transition, and social message detection, we introduce a multimodal multilingual benchmark called MM-AU comprised of 8.4 K videos (147hrs) curated from multiple web-based sources. We explore multiple zero-shot reasoning baselines through the application of large language models on the ads transcripts. Further, we demonstrate that leveraging signals from multiple modalities, including audio, video, and text, in multimodal transformer-based supervised models leads to improved performance compared to unimodal approaches.