Social media platforms offer an invaluable wealth of data to understand what is taking place in our society. However, social media data hides demographic biases related to characteristics such as gender or age. Therefore, considering social media data as representative of the population can lead to fallacious interpretations. For instance, in France in 2021, women represent 51.6% of the population 1 1 https://www.insee.fr/fr/statistiques/6024136 . , whereas on Twitter they represent only 33.5% of French users 2 2 https://datareportal.com/reports/digital-2021-france . . With such a significant difference between social network user demographics and the actual population, detecting the gender or age before delving into a deeper analysis of social phenomena becomes a priority. In this paper, we tackle the gender detection problem on Twitter. We introduce miniAM2, which is an assemblage model of an enriched distillation with weak-supervised learning. Our contributions are threefold: (i) a novel multilingual model that outperforms existing models in both accuracy and speed, allowing for real-time gender detection and organization status on Twitter based on their name , screen_name , and description , making it lighter and faster than state-of-the-art; (ii) an innovative assemblage multi-language strategy that enriches a distillation process with weak-supervised learning using minimal annotated data, and (iii) a unique method to adapt the model to similar languages without requiring annotated data in the target language, which provides significant advancements in handling resource-poor languages in gender detection tasks. We provide our model on demand so social scientists can use it for their analysis. • Over the SOTA multilingual model detects gender/org. from Twitter names and descriptions. • Multi-language approach enhances distillation, weak supervision, and low data use. • Adapts model to similar languages without annotated target language data..

Optimizing drug dosages is essential for effective treatment. Clinical protocols may not suit all types of patients evenly, due to many drug trials not being designed to account for all comorbities or clinically relevant outcomes. Methodologies to optimize drug policies with observational data exist, but struggle due to limited data completeness in clinical settings. Computational methods can help overcome these challenges by leveraging field knowledge. This paper proposes an Individualized Doser (IDoser), a core dosing model that links drug dose to relevant covariates via a set of coefficients and includes a loss function to code needed assumptions and requirements. Coordinate descent is used to obtain a fitted model with minimal loss. The loss function also measures performance when validating the model with unseen data. We validated the proposed approach using the case of follicle-stimulating hormone (FSH) dosing for controlled ovarian stimulation (COS). When compared to clinical practice, IDoser achieved a net improvement of up to 31.97% in the validation cases. We present a simple but effective method to bridge the gap between current clinical dosing policies and gold policies based on the true underlying and often unknown dose–response functions.

Artificial intelligence, and the ability to learn optimized solutions that comply with a set of safety rules, could facilitate the human-based design process of safety-critical systems. However, the reconciliation of state-of-the-art artificial intelligence technology with current safety standards and safety engineering processes is a challenge to be addressed. In this article, this publication describes a method based on optimization and on formal verification for the design of safety-critical systems that are defined by Boolean algebra. Several diverse optimization techniques and a hybrid of these approaches are used to find an optimized design that considers performance requirements, availability rules, and complies with all defined safety rules. Subsequently, this solution is translated into an alternative knowledge representation that can be formally verified and developed in compliance with currently considered safety standards. This method is evaluated with a simplified safety-critical case study.

Peer-to-peer ridesharing enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic). Despite these very promising potential advantages, the percentage of users who currently adopt ridesharing solutions is very low, well below the adoption rate required to achieve said benefits. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who are not able to estimate the costs and the benefits of a given ridesharing adoption policy. Here we address these issues by (i) developing a novel algorithm that makes large-scale, real-time peer-to-peer ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing scenarios in terms of environmental benefits (i.e., reduction of CO2 emissions, noise pollution, and traffic congestion) and quality of service for the users. Our analysis on a real-world dataset shows that major societal benefits are expected from deploying peer-to-peer ridesharing depending on the trade-off between environmental benefits and quality of service. Results on a real-world dataset show that our approach can produce reductions up to a 70.78% in CO2 emissions and up to 80.08% in traffic congestion.

MOTIVATION: The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily. RESULTS: Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting. AVAILABILITY AND IMPLEMENTATION: The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo. CONTACT: : [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously. RESULTS: Here, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1. CONCLUSION: Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

Summary The coalition structure generation problem is well known in the area of multi‐agent systems. Its goal is to establish coalitions between agents while maximizing the global welfare. Among the existing different algorithms designed to solve the coalition structure generation problem, DP and IDP are the ones with smaller temporal complexity. After analyzing the operation of the dynamic programming and improved dynamic programming algorithms, we have identified which are the most frequent operations and propose an optimized method. In addition, we study and implement a method for dividing the work into different threads. To describe incremental improvements of the algorithm design, we first compare performance of an improved single central processing unit core version where we obtain speedups ranging from 7 × to 11 × . Then, we describe the best resource use in a multi‐thread optimized version where we obtain an additional 7.5 × speedup running in a 12‐core machine.

Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this article, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (Coalition Formation for Sparse Synergies (CFSS)), which is particularly efficient when applied to a general class of characteristic functions called m + a functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a nonredundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is four orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12-core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2,700 agents).

This paper introduces Accelerated Logistic Regression : a hybrid generative-discriminative approach to training Logistic Regression with high-order features. We present two main results: (1) that our combined generative-discriminative approach significantly improves the efficiency of Logistic Regression and (2) that incorporating higher order features (i.e. features that are the Cartesian products of the original features) reduces the bias of Logistic Regression, which in turn significantly reduces its error on large datasets. We assess the efficacy of Accelerated Logistic Regression by conducting an extensive set of experiments on 75 standard datasets. We demonstrate its competitiveness, particularly on large datasets, by comparing against state-of-the-art classifiers including Random Forest and Averaged n -Dependence Estimators.

We propose an alternative parameterization of Logistic Regression (LR) for the categorical data, multi-class setting. LR optimizes the conditional log-likelihood over the training data and is based on an iterative optimization procedure to tune this objective function. The optimization procedure employed may be sensitive to scale and hence an effective pre-conditioning method is recommended. Many problems in machine learning involve arbitrary scales or categorical data (where simple standardization of features is not applicable). The problem can be alleviated by using optimization routines that are invariant to scale such as (second-order) Newton methods. However, computing and inverting the Hessian is a costly procedure and not feasible for big data. Thus one must often rely on first-order methods such as gradient descent (GD), stochastic gradient descent (SGD) or approximate second-order such as quasi-Newton (QN) routines, which are not invariant to scale. This paper proposes a simple yet effective pre-conditioner for speeding-up LR based on naive Bayes conditional probability estimates. The idea is to scale each attribute by the log of the conditional probability of that attribute given the class. This formulation substantially speeds-up LR's convergence. It also provides a weighted naive Bayes formulation which yields an effective framework for hybrid generative-discriminative classification.

Research on optimization in multi-agent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multi-agent application domains. Multi-agent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multi-agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multi-agent optimization.

Supply Chain Formation (SCF) is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized SCF appears as a highly intricate task because agents only possess local information and have limited knowledge about the capabilities of other agents. The decentralized SCF problem has been recently cast as an optimization problem that can be efficiently approximated using max-sum loopy belief propagation. Along this direction, in this paper we propose a novel encoding of the problem into a binary factor graph (containing only binary variables) as well as an alternative algorithm. We empirically show that our approach allows to significantly increase scalability, hence allowing to form supply chains in market scenarios with a large number of participants and high competition.

Mixed multi-unit combinatorial auctions (MMUCAs) offer a high potential to be employed for the automated assembly of supply chains of agents. However, in order for mixed auctions to be effectively applied to supply chain formation, we must ensure computational tractability and reduce bidders' uncertainty. With this aim, we introduce Sequential Mixed Auctions (SMAs), a novel auction model conceived to help bidders collaboratively discover supply chain structures. Thus, an SMA allows bidders progressively build a supply chain structure through successive auction rounds. Moreover, the incremental nature of an SMA provides its participants with valuable information at the end of each auction round to guide their bidding. Finally, we empirically show that SMAs significantly reduce the computational effort required by MMUCA at the expense of a slight decrease in the auctioneer's revenue.

Sensor networks (SNs) have arisen as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical systems ease the development and proliferation of this kind of networks in a wide range of actual-world applications. Multiagent systems (MAS) have been identified as one of the most suitable technologies to contribute to the deployment of SNs that exhibit flexibility, robustness and autonomy. The purpose of this survey is 2-fold. On the one hand, we review the most relevant contributions of agent technologies to this emerging application domain. On the other hand, we identify the challenges that researchers must address to establish MAS as the key enabling technology for SNs.

In this paper we formulate a novel Divide-and-Coordinate (DaC) algorithm, the so-called Egalitarian Utilities Divide-and-Coordinate (EU-DaC) algorithm. The Divide-and-Coordinate (DaC) framework [3] is a family of bounded DCOP algorithms that solve DCOPs by exploiting the concept of agreement. The intuition behind EU-DaC is that agents get closer to an agreement, on the optimal solution, when they communicate the local max-marginals utilities for their assignments instead of only their preferred assignments. We provide empirical evidence supporting this hypothesis as well as illustrating the competitiveness of EU-DaC.

We study worst-case bounds on the quality of any fixed point assignment of the max-product algorithm for Markov Random Fields (MRF). We start proving a bound independent of the MRF structure and parameters. Afterwards, we show how this bound can be improved for MRFs with particular structures such as bipartite graphs or grids. Our results provide interesting insight into the behavior of max-product. For example, we prove that max-product provides very good results (at least 90% of the optimal) on MRFs with large variable-disjoint cycles (MRFs in which all cycles are variable-disjoint, namely that they do not share any edge and in which each cycle contains at least 20 variables).

In this paper, we introduce a novel auction-based decision support system to help a firm readily assemble supply chains. At this aim, we tackle the problem of deciding whether to outsource some production processes or not-the so-called make-or-buy decision problem. The solution to the make-or-buy decision problem is an optimal supply chain whose operations are either performed in-house or outsourced. We propose to employ a novel extension of combinatorial reverse auctions, the so-called Multiunit Combinatorial Reverse Auction with Transformability Relationships Among Goods, which allows the following: 1) a buyer/auctioneer to express transformability relationships among goods (some goods can be transformed into others at some transformation cost) and 2) bidders to express their preferences over bundles of goods.

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Free Augmented Naive Bayes (TAN) has shown to be competitive with state-of-the-art machine learning algorithms [3].However, the TAN induction algorithm that appears in [3] can be improved in several ways.In this paper we identify three shortcomings in it and introduce two ideas to overcome those problems: the multinomial sampling approach to learning bayesian networks and local bayesian model averaging.These ideas approaches are generic and can thus be reused to improve other learning algorithms.We empirically test the new algorithms, and conclude that in most of the cases they lead to an improvement in accuracy in the classification and in the quality of the probabilities given as predictions.'The work was partially developed at the Institut de Investi-gaci6 en Intel.ligBnciaArtificial, CSIC, Spain.