Miracle-dz/UniFAR-FTUs huggingface dataset:
Breaking Down Accuracy with Subspace Optimization

This paper introduces a Python toolkit to break down accuracy of classification models, by identifying high-accuracy portions of the test dataset and thus facilitating a deeper understanding of where the model performs best and where it falls short. Given a dataset, a classification model and an accuracy threshold, our system returns a range query that selects the largest sub-space of the test dataset on which the classifier achieves higher accuracy. The toolkit allows users to interactively explore such sub-space, by adjusting the returned ranges with graphical elements and observing the change in the model’s accuracy and data distributions. Ranges can be manually initialized to highlight the strengths and weaknesses of the model in different scenarios. The core of our method consists of a mixed-integer optimization algorithm. Demonstration on real-world datasets and a selection of models show that our toolkit can serve as an effective way to understand performance across different data segments.
Cytokine Induced Phenotypic and Epigenetic Signatures Are Key to Establishing Specific Macrophage Phenotypes

Macrophages (MΦ) play an essential role in innate immune responses and can either display a pro-inflammatory, classically activated phenotype (M1) or undergo an alternative activation program (M2) promoting immune regulation. M-CSF is used to differentiate monocytes into MΦ and IFN-γ or IL-4+IL-13 to further polarize these cells towards M1 or M2, respectively. Recently, differentiation using only GM-CSF or M-CSF has been described to induce a M1- or M2-like phenotype, respectively. In this study, we combined both approaches by differentiating human MΦ in GM-CSF or M-CSF followed by polarization with either IFN-γ or IL-4+IL-13. We describe the phenotypic differences between CD14hi CD163hi CD206int FOLR2-expressing M-CSF MΦ and CD14lo CD163lo CD206hi GM-CSF MΦ but show that both macrophage populations reacted similarly to further polarization with IFN-γ or IL-4+IL-13 with up- and down-regulation of common M1 and M2 marker genes. We also show that high expression of the mannose receptor (CD206), a marker of alternative activation, is a distinct feature of GM-CSF MΦ. Changes of the chromatin structure carried out by chromatin modification enzymes (CME) have been shown to regulate myeloid differentiation. We analyzed the expression patterns of CME during MΦ polarization and show that M1 up-regulate the histone methyltransferase MLL and demethylase KDM6B, while resting and M2 MΦ were characterized by DNA methyltransferases and histone deacetylases. We demonstrate that MLL regulates CXCL10 expression and that this effect could be abrogated using a MLL-Menin inhibitor. Taken together we describe the distinct phenotypic differences of GM-CSF or M-CSF MΦ and demonstrate that MΦ polarization is regulated by specific epigenetic mechanisms. In addition, we describe a novel role for MLL as marker for classical activation. Our findings provide new insights into MΦ polarization that could be helpful to distinguish MΦ activation states.
Dataset Discovery and Exploration: State-of-the-art, Challenges and Opportunities

Dataset discovery and exploration involve identifying and understanding the available data, thereby informing users as to what data analyses may be possible. Discovering and exploring the relationships between datasets benefits from tool support, and in this tutorial, we specifically consider techniques that underpin dataset search , data navigation , dataset annotation and schema inference . Although there are are significant results in each of these areas, in practice they are far from independent of each other, and can share both objectives and underlying techniques. As a result, this tutorial not only seeks to provide insights into the challenges and opportunities of these areas in isolation, but also points out how they can complement and inform each other. The tutorial is associated with a Python Notebook to illustrate the concepts and techniques discussed in practice.
Predicting Fact Contributions from Query Logs with Machine Learning

A recent line of work has proposed to quantify the contribution of database tuples to query answers using Shapley values, a game theoretic function that has been extensively used as means of attribution in other areas, notably Machine Learning. In this paper we analyze and evaluate LearnShapley , a solu-tion that employs Machine Learning to rank input facts based on their estimated (Shapley-based) contribution to query answers. LearnShapley is trained on a corpus of SPJU queries, their output and the Shapley values of each input tuple with respect to each output tuple. At inference time, LearnShapley is given a new SPJU query over the same database schema, an output tuple of interest, and its lineage (i.e. the set of all facts that have contributed in some way to the generation of the tuple). Our experiments evaluate to what extent LearnShapley is able to leverage similarity measures applied to the query in hand and the queries stored in the repository, to compute a ranking of the facts in the lineage based on their contribution. Overall, our experiments indicate that a log of past queries, output tuples and their Shapley values includes a reasonably relevant signal for predicting the ranking of facts contributions for a new SPJU query over the same data-base. Both DBShap and our code are publicly available, and may serve for further investigation of Machine Learning approaches for explainability in databases.
"Please, Vadalog, tell me why": Interactive Explanation of Datalog-based Reasoning

Integrating Large Language Models (LLMs) with logic-based Enterprise Knowledge Graphs (EKGs) and more generally with Knowledge Representation and Reasoning (KRR) approaches is currently at the forefront of research in many data-intensive areas, as language models may complement EKGs and ontological reasoning with flexibility and human orientation. Conversely, EKGs provide transparency and explainability on the conclusions drawn, a typical weak point of LLMs, which operate opaquely. In this demo, we integrate Llama 2 with our reasoning system Vadalog and use it to turn a chase graph, i.e., the trace of an ontological reasoning process, into a human-readable business report. In other words, we show the amazing capabilities of state-of-the-art LLMs in combination with a principled exploitation of the theoretical underpinnings of logic-based reasoning. We walk the audience through a visual environment, unfolding real-world reasoning settings from the Central Bank of Italy.
MIP: Advanced Data Processing and Analytics for Science and Medicine

We present the Medical Informatics Platform (MIP), an online collaborative platform for the scientific and medical community. It federates de-centralized patient data located in hospitals, helping clinicians, clinical scientists, and researchers identify patterns unique to diseases and provide clear diagnoses and personalized treatments. The platform enables users to access harmonized medical data from pre-processed neurophysiological and medical records, and research cohort datasets without the need to transfer original clinical data. This functionality facilitates exploration and analysis of medical data while preserving the privacy and security of sensitive patient information. The MIP blends data science and machine learning with data technology, and especially data integration, secure computation, decentralized distributed query execution, and low level, efficient execution of science pipelines exploiting features of modern data engines such as vectorization, parallelization, and JIT compilation. The MIP is the result of a multi-disciplinary, multi-year effort among computer scientists, clinical scientists and medical professionals. To date, it has been deployed and used in 40+ hospitals across Europe and another 12 installations are in process.
Deco: Fast and Accurate Decentralized Aggregation of Count-Based Windows in Large-Scale IoT Applications

In the realm of large-scale Internet-of-Things applications, ag-gregating data using count-based windows is a formidable challenge. Current methods, either centralized and slow or decentralized with potential inaccuracies, fail to strike a balance. This paper introduces Deco, a novel approach tailored for swift and precise aggregation in distributed stream processing systems. Ac-complishing this balance is complex due to the dynamic nature of event distribution: events arrive at varying rates, unordered, and at diverse times, making accurate window computation a challenge. To overcome this, we propose a lightweight prediction method that derives local window sizes based on the previously observed event rates and performs corrections when necessary to ensure accurate and fast query results. These windows are processed in a decentralized manner on local nodes, verified for correctness and then aggregated on a root node. Our evaluation showcases Deco’s superiority over centralized methods, outper-forming others significantly. Deco reduces network traffic by up to 99% and exhibits linear scalability with node count.
Under Consideration for Publication in J. Functional Programming Algebraic Fusion of Functions with an Accumulating Parameter and Its Improvement

This paper develops a new framework for fusion that is designed for eliminating the intermediate data structures involved in the composition of functions that have one accumulating parameter. The new fusion framework comprises two steps: algebraic fusion and its subsequent improvement process. The key idea in our development is to regard functions with an accumulating parameter as functions that operate over the monoid of data contexts. Algebraic fusion composes each such function with a monoid homomorphism that is derived from the definition of the consumer function to obtain a higher-order function that computes over the monoid of endofunctions. The transformation result may be further refined by an improvement process, which replaces the operation over the monoid of endofunctions (i.e., function closures) with another monoid operation over a monoid structure other than function closures. Using our framework, one can formulate a particular solution to the fusion problem by devising appropriate monoids and monoid homomorphisms. This provides a unified exposition of a variety of fusion methods that have been developed so far in different formalisms. Furthermore, the cleaner formulation makes it possible to argue about some delicate issues on a firm mathematical basis. We demonstrate that algebraic fusion and improvement in the world of CPOs and continuous functions can correctly fuse functions that operate on partial and infinite data structures. We also show that subtle differences in termination behaviors of transformed programs caused by certain different fusion methods can be cleanly explained by corresponding improvement processes that have different underlying monoid structures.
SAGED: Few-Shot Meta Learning for Tabular Data Error Detection

High data quality is paramount for the success of machine learning (ML) applications, as it broadly impacts model performance and decision outcomes. In domains like medical diagnosis and financial systems, inaccuracies or data incompleteness can result in unreliable predictions, posing risks to system users. Unfortunately, real-world data commonly exhibits noise, errors, missing values, and inconsistencies, posing challenges to ML model accuracy. Data cleaning plays a vital role in addressing these issues before model training, yet identifying dirty data instances can be a cumbersome and time-consuming process. In this context, numerous automated error detection tools for tabular data have been introduced. Nevertheless, these tools suffer from several shortcomings, encompassing the necessity for domain-specific expertise and substantial time requirements. To address these shortcomings, we introduce a novel error detection tool, denoted as SAGED 12 , which leverage meta-learning principles. Specifically, SAGED exploits an ensemble of pre-trained models derived from historical datasets to facilitate error detection in new data with limited labeled instances. The method consists of two key phases: knowledge extraction and error detection. In the knowledge extraction phase, ML models are trained to distinguish erroneous instances within historical datasets, thereby accumulating valuable insights. In the error detection phase, pre-trained base models, chosen through rigorous matching, generate a comprehensive feature vector based on predictions, facilitating the role of a meta-classifier in pinpointing errors efficiently. As a proof of concept, we conduct a comparative study of SAGED against ten state-of-the-art error detection tools, employing a set of 14 real-world datasets. The findings reveal the superior performance of SAGED in error detection tasks, with limited user intervention. SAGED’s promising results demonstrate its effectiveness and efficiency in real-world scenarios
Appearance preserving octree-textures

Because of their geometric complexity, high resolution 3D models, either designed in high-end modeling packages or acquired with range scanning devices, cannot be directly used in applications that require rendering at interactive framerates. One clever method to overcome this limitation is to perform an appearance preserving geometry simplification, by replacing the original model with a low resolution mesh equipped with high resolution normal maps. This process visually preserves small scale features from the initial geometry, while only requiring a reduced set of polygons. However, this conversion usually relies on some kind of global or piecewise parameterization, combined with the generation of a texture atlas, a process that is computationally expensive and requires precise user supervision. In this paper, we propose an alternative method in which the normal field of a high resolution model is adaptively sampled and encoded in an octree-based data structure, that we call appearance preserving octree-texture (APO). Our main contributions are: a normal-driven octree generation, a compact encoding and an efficient look-up algorithm. Our method is efficient, totally automatic, and avoids the expensive creation of a parameterization with its corresponding texture atlas.
Aion: Efficient Temporal Graph Data Management

Modern graph database management systems (DBMSs) can process highly dynamic labeled property graphs (LPGs) with many billions of relationships comfortably, but those systems often ignore the temporal dimension of data, how a graph evolved over time. Temporal analytics allow users to query and compute over the graph throughout its history so that valuable line-of-business data is always accessible and never lost. However, existing approaches tend to be ad-hoc and vary in performance depending on the size of the effective graph workload, such as local pattern matching or global graph algorithms. In this work, we describe Aion, a transactional temporal graph DBMS that generalizes previous approaches for LPGs. Aion extends Neo4j, a modern graph DBMS, incurring minimal performance overhead by decoupling the graph’s history from the latest graph version. To support efficient temporal analytics independently of workload characteristics, Aion adopts a hybrid temporal storage approach: (i) for fast full graph restoration at arbitrary time points, it uses TimeStore that indexes updates by time; (ii) for fine-grained graph history accesses, it uses LineageStore that indexes updates by entity identifiers. To enable incremental graph computations for improved latency, Aion introduces a compute-efficient in-memory LPG representation. Our experiments show that Aion achieves comparable or better performance versus existing non-transactional temporal systems and provides up to an order of magnitude speedup over classic Neo4j.
BOLD: Knowledge Graph Exploration and Analysis Platform

The linked open data (LOD) cloud maintains several interlinked knowledge graphs. These graphs span various domains such as government, media, life sciences, etc. The graphs are often manually curated or automatically extracted (e.g. YAGO—Yet Another Great Ontology) using information extraction techniques. They are used in various applications such as data governance, fraud detection, fact checking, etc. Although the graphs in LOD are widely used, they do not contain metadata about their represen-tativeness (distribution of key features). Since most of the graphs are automatically curated, bias can manifest due to sensitive features and their causal influences, or through under (over)- representation of certain entities (e.g. people) and relations (e.g. president-of, works-for). The aim of this work is to develop a system to automatically generate bias profiles (metadata about the representativeness of data) for knowledge graphs. As a result, the metadata can be used as a guide for users to choose bias free (balanced) datasets for their studies. Moreover, it enables researchers to quickly gauge the relevance of a graph for a problem at hand (e.g. classification task).
Traffic-aware virtual machine migration in topology-adaptive DCN

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks (DCNs). Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. When the VM demands change, however, a great cost will be paid on the VM migration. With the advance of software-defined network (SDN), recent studies have shown great potential to implement an adaptive network topology at a low cost. Taking advantage of the topology adaptability, in this paper, we propose a new paradigm for VM migration by dynamically constructing a topology based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. Then we develop a novel progressive-decompose-rounding (PDR) algorithm to solve this problem in polynomial time with a proved approximation ratio. Extensive trace-based simulations show that PDR can achieve higher flow throughput among VMs with only a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solution.
Multi-source fusion in subjective logic

Belief fusion consists of taking into account multiple sources of belief about a domain of interest. This paper describes cumulative and averaging multi-source belief fusion in the formalism of subjective logic, which represent generalisations of binary-source belief fusion operators previously described. The advantage of this approach is that we can model and analyse belief fusion situations involving an arbitrary number of sources.
Physiologically Based Pharmacokinetic Modeling of Intestinal First-Pass Metabolism of CYP3A Substrates with High Intestinal Extraction

Prediction of intestinal availability (FG), in conjunction with hepatic metabolism, is of considerable importance in drug disposition to assess oral clearance and liability to drug-drug interactions. In the current study, FG predictions were performed within a physiologically based pharmacokinetic (PBPK) model using in vitro permeability and clearance data. The prediction success was assessed in comparison with the QGut model. In addition, apparent oral clearance values, predicted using the PBPK model, were compared with in vivo observations from meta-analyses. Finally, unbound intrinsic clearance values (CLuint) were determined for 12 CYP3A substrates in eight individual human jejunal microsome (HJM) samples to assess interindividual variability in intestinal intrinsic clearance and subsequent FG predictions. Overall, the PBPK model improved FG predictions in comparison with the QGut model; this was apparent by a reduced bias and increased precision. In particular, FG predictions of indinavir, saquinavir, and terfenadine were model-dependent. The predicted oral clearance values of the drugs investigated ranged from 8.79 to 6320 l/h for tacrolimus and simvastatin, respectively, and were overall within 3-fold of the observed data with the exception of indinavir, atorvastatin, and buspirone. The individual HJM CLuint values ranged from 17 to 14,000 μl · min−1 · mg−1 for atorvastatin and saquinavir, respectively, and corresponding interindividual variability in CLuint estimates ranged from 41 to 67%. These in vitro data resulted in predicted FG values ranging from 0.03 to 0.94 for simvastatin and indinavir, respectively. The largest interindividual variability of FG was predicted for terfenadine (65%) in contrast with the low variability in the case of indinavir (3%).
Formation of Trans-Activation Competent HIV-1 Rev:RRE Complexes Requires the Recruitment of Multiple Protein Activation Domains

The HIV-1 Rev trans-activator is a nucleocytoplasmic shuttle protein that is essential for virus replication. Rev directly binds to unspliced and incompletely spliced viral RNA via the cis-acting Rev Response Element (RRE) sequence. Subsequently, Rev oligomerizes cooperatively and interacts with the cellular nuclear export receptor CRM1. In addition to mediating nuclear RNA export, Rev also affects the stability, translation and packaging of Rev-bound viral transcripts. Although it is established that Rev function requires the multimeric assembly of Rev molecules on the RRE, relatively little is known about how many Rev monomers are sufficient to form a trans-activation competent Rev:RRE complex, or which specific activity of Rev is affected by its oligomerization. We here analyzed by functional studies how homooligomer formation of Rev affects the trans-activation capacity of this essential HIV-1 regulatory protein. In a gain-of-function approach, we fused various heterologous dimerization domains to an otherwise oligomerization-defective Rev mutant and were able to demonstrate that oligomerization of Rev is not required per se for the nuclear export of this viral trans-activator. In contrast, however, the formation of Rev oligomers on the RRE is a precondition to trans-activation by directly affecting the nuclear export of Rev-regulated mRNA. Moreover, experimental evidence is provided showing that at least two protein activation domains are required for the formation of trans-activation competent Rev:RRE complexes. The presented data further refine the model of Rev trans-activation by directly demonstrating that Rev oligomerization on the RRE, thereby recruiting at least two protein activation domains, is required for nuclear export of unspliced and incompletely spliced viral RNA.