Miracle-dz/UniFAR-FTUs huggingface dataset:
“Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity”

Notations. For a matrix X ∈ Rm×n, X ≥ c for some scalar c ∈ R means that each element of X is no-less than c. For a vector x, we use (x), √ x, |x| to denote the component-wise square, square-root, and absolute value of x. We use P(s,a,b),s′ to denote the transition probability P (s′ | s, a, b), and Ps,a,b to denote the vector P (· | s, a, b). We use ‖u − v‖TV to denote the total variation distance between two probability distributions u, v ∈ ∆(S) over a finite space S , which is defined as ‖u−v‖TV := 1 2 ∑ s∈S |u(s)−v(s)|. We also use P to denote the transition probability of state-action pairs induced by the policy pair (μ, ν), which is defined as P (s,a,b),(s′,a′,b′) = μ(a ′ | s′)ν(b′ | s′)P (s′ | s, a, b).
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration

Learning-to-optimize (L2O) is an emerging research area in large-scale optimization with applications in data science. Recently, researchers have proposed a novel L2O framework called learned mirror descent (LMD), based on the classical mirror descent (MD) algorithm with learnable mirror maps parameterized by input-convex neural networks. The LMD approach has been shown to significantly accelerate convex solvers while inheriting the convergence properties of the classical MD algorithm. This work proposes several practical extensions of the LMD algorithm, addressing its instability, scalability, and feasibility for high-dimensional problems. We first propose accelerated and stochastic variants of LMD, leveraging classical momentum-based acceleration and stochastic optimization techniques for improving the convergence rate and per-iteration computational complexity. Moreover, for the particular application of training neural networks, we derive and propose a novel and efficient parameterization for the mirror potential, exploiting the equivariant structure of the training problems to significantly reduce the dimensionality of the underlying problem. We provide theoretical convergence guarantees for our schemes under standard assumptions and demonstrate their effectiveness in various computational imaging and machine learning applications such as image inpainting, and the training of support vector machines and deep neural networks.
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters

Congestion Control (CC) plays a fundamental role in optimizing traffic in Data Center Networks (DCN). Currently, DCNs mainly implement two main CC protocols: DCTCP and DCQCN. Both protocols -- and their main variants -- are based on Explicit Congestion Notification (ECN), where intermediate switches mark packets when they detect congestion. The ECN configuration is thus a crucial aspect on the performance of CC protocols. Nowadays, network experts set static ECN parameters carefully selected to optimize the average network performance. However, today's high-speed DCNs experience quick and abrupt changes that severely change the network state (e.g., dynamic traffic workloads, incast events, failures). This leads to under-utilization and sub-optimal performance. This paper presents GraphCC, a novel Machine Learning-based framework for in-network CC optimization. Our distributed solution relies on a novel combination of Multi-agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN), and it is compatible with widely deployed ECN-based CC protocols. GraphCC deploys distributed agents on switches that communicate with their neighbors to cooperate and optimize the global ECN configuration. In our evaluation, we test the performance of GraphCC under a wide variety of scenarios, focusing on the capability of this solution to adapt to new scenarios unseen during training (e.g., new traffic workloads, failures, upgrades). We compare GraphCC with a state-of-the-art MARL-based solution for ECN tuning -- ACC -- and observe that our proposed solution outperforms the state-of-the-art baseline in all of the evaluation scenarios, showing improvements up to $20\%$ in Flow Completion Time as well as significant reductions in buffer occupancy ($38.0-85.7\%$).
Survival analysis of longitudinal data: the case of English population aged 50 and over

Abstract This study considers data from 5 waves of the English Longitudinal Study of Ageing (ELSA). We aim to study the impact of demographic and self-rated health variables including disability and diseases on the survival of the population aged 50+. The disability variables that we consider are mobility impairment, difficulties in performing Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). One of the problems with the survey study is missing observations. This may happen due to different reasons, such as errors, nonresponse and temporary withdrawals. We address this problem by applying single and multiple imputation methods. We then fit a Generalized Linear model (GLM) and Generalized Linear Mixed model (GLMM) to our data and show that a GLMM performs better than a GLM in terms of information criteria. We also look at the predictability of our models in terms of the time-dependent receiver operating characteristic (ROC) and the area of ROC, i.e. AUC. We conclude that among the disability factors, IADL and among the diseases, cancer significantly affect the survival of the English population aged 50 and older.
Correction: Decision factors influencing new variety adoption in western Canada by the seed industry

In the agricultural sector, innovation is a vital economic driver for increasing food production. New crop varieties are developed and commercialized, greatly contributing to improved global food security through higher yields, improved nutrition and climate resiliency. Canada is a competitive and innovative actor in the global seed market. This article quantiﬁes the degree of improvement for numerous crop traits required for commercialization success. We use empirical data from seed producers in the prairies to identify their adoption criteria to multiply new seed varieties. Results show that yield potential, disease resistance and lodging resistance are the key traits for pedigreed seed growers regardless of crop type, while other agronomic traits depend on the crop type. Quality factors such as malting or milling properties for cereals, protein content for pulses and oil content for oilseeds are also part of the variety selection decision process for prairie pedigreed seed growers.
Balanced resuscitation and earlier mortality end points: bayesian post hoc analysis of the PROPPR trial

Introduction The Pragmatic Randomized Optimal Platelet and Plasma Ratios (PROPPR) trial failed to demonstrate a mortality difference for hemorrhaging patients receiving a balanced (1:1:1) vs a 1:1:2 resuscitation at 24 hours and 30 days. Recent guidelines recommend earlier mortality end points for hemorrhage-control trials, and the use of contemporary statistical methods. The aim of this post hoc analysis of the PROPPR trial was to evaluate the impact of a balanced resuscitation strategy at early resuscitation time points using a Bayesian analytical framework. Methods Bayesian hierarchical models were created to assess mortality differences at the 1, 3, 6, 12, 18, and 24 hours time points between study cohorts. Posterior probabilities and Bayes factors were calculated for each time point. Results A 1:1:1 resuscitation displayed a 96%, 99%, 94%, 92%, 96%, and 94% probability for mortality benefit at 1, 3, 6, 12, 18, and 24 hours, respectively, when compared with a 1:1:2 approach. Associated Bayes factors for each respective time period were 21.2, 142, 14.9, 11.4, 26.4, and 15.5, indicating ‘strong’ to ‘decisive’ supporting evidence in favor of balanced transfusions. Conclusion This analysis provides evidence in support that a 1:1:1 resuscitation has a high probability of mortality benefit when compared with a 1:1:2 strategy, especially at the newly defined more proximate time points during the resuscitative period. Researchers should consider using Bayesian approaches, along with more proximate end points when assessing hemorrhage-related mortality, for the analysis of future clinical trials. Level of evidence Level III/Therapeutic.
Characterizing the Use of Code Obfuscation in Malicious and Benign Android Apps

Obfuscation is frequently used by both benign and malicious Android apps. Since static analysis of obfuscated apps often produces incomplete or misleading results, the problems of identifying and quantifying the use of specific obfuscation techniques in apps has received significant attention. Even though several existing works have addressed these problems, most studies focus on data obfuscation methods such as identifier renaming and string obfuscation, while more advanced code obfuscation methods, such as reflection and control-flow obfuscation, have received less attention. Moreover, existing approaches to detecting Android code obfuscation have significant limitations, as shown by a detailed survey that we present as part of this paper. This is in part due to a fundamental “bootstrapping” problem: since, on one hand, the landscape of Android code obfuscation is poorly known, researchers have very little guidance when designing new detection methods. On the other hand, the lack of detection methods mean that the obfuscation landscape is bound to remain largely unexplored. In this work, we aim to take the first steps towards addressing this “bootstrapping” problem. To this end, we propose two novel approaches to obfuscation detection and perform a study on over 200,000 malicious apps, in addition to 13,436 apps from Google Play. In particular, we propose a new anomaly-detection-based method for identifying likely control-flow obfuscation, and use it to perform what is, to the best of our knowledge, the first empirical study of control-flow obfuscation in Android apps. In addition to presenting new insights into the use of control-flow obfuscation, we also propose a new approach to characterizing the use of reflection-based obfuscation, which allows us to corroborate earlier findings indicating that this type of obfuscation is much more common in malware than in benign apps.
Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making

Background Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them. Findings The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action. Conclusion The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.
ReSG: A Data Structure for Verification of Majority based In-Memory Computing on ReRAM Crossbars

Recent advancements in the fabrication of Resistive Random Access Memory (ReRAM) devices have led to the development of large scale crossbar structures. In-memory computing architectures relying on ReRAM crossbars aim to mitigate the processor-memory bottleneck that exists with current CMOS technology. With this motivation, several synthesis and mapping approaches focusing on the realizations of Boolean functions in the ReRAM crossbars have been proposed earlier. Thus far, the verification of the designs realized on ReRAM crossbars is done either through manual inspection or using simulation based approaches. Since manual inspections and simulation based approaches are limited to smaller designs, they cannot be applied to the verification of complex designs on large-scale ReRAM crossbars. Motivated by this, we propose, for the first time, an automatic equivalence checking flow that determines the equivalence between the original function specification (e.g., Majority Inverter Graph (MIG)) and the crossbar micro-operations file formats. We consider two crossbar structures, zero-transistor, one-memristor (0T1R) and one-transistor, one-memristor (1T1R) to implement the micro-operations. While the micro-operations file format exists for 0T1R crossbar structures, no representations for micro-operations to be executed in 1T1R crossbars exist till date. In this work, we introduce the micro-operation file format for 1T1R crossbar structures to efficiently represent the micro-operations as ReRAM crossbar netlists. Afterwards, we introduce two intermediate data structures, ReRAM Sequence Graph for 0T1R crossbars (ReSG-0T1R) and for 1T1R crossbars (ReSG-1T1R), that are derived from the 0T1R and 1T1R crossbar micro-operations file formats, respectively. These ReSGs are then translated into Boolean Satisfiability (SAT) formula, and then the verification is done by checking the generated SAT formulae against the golden functional specification (represented in Verilog) using Z3 Satisfiability solver. Experimental evaluations confirm the effectiveness of the proposed verification methodology on MCNC and ISCAS benchmarks.
Invasive fungal diseases in patients with autoimmune diseases: a case series from the French RESSIF network

Objectives We aimed to describe patients with autoimmune diseases (AID) developing invasive fungal disease (IFD) and identify factors associated with short-term mortality. Methods We analysed cases of IFD associated with AID from the surveillance network of invasive fungal diseases (Réseau de surveillance des infections fongiques invasives, RESSIF) registry of the French national reference centre for invasive mycoses. We studied association of AID-specific treatments with 30-day mortality. We analysed total lymphocyte and CD4-T cell counts in patients with Pneumocystis jirovecii pneumonia (PCP). Results From 2012 to 2018, 549 individuals with IFD and AID were included, mainly with PCP (n=227, 41.3%), fungemia (n=167, 30.4%) and invasive aspergillosis (n=84, 15.5%). Rheumatoid arthritis (RA) and anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitides (AAV) were the most frequent AID in PCP (n=55 and 25, respectively) and invasive aspergillosis (n=15 and 10, respectively), inflammatory bowel diseases (IBDs) were predominant in fungemia (n=36). At IFD diagnosis, 365 (66.5%) patients received glucocorticoids (GCs), 285 (51.9%) immunosuppressants, 42 (7.7%) tumor necrosis factor (TNF)-α blockers, 75 (13.7%) other biologics. Mortality at 30 days was 28.1% (143/508). Fungemia and high-dose GCs were independently associated with higher 30-day mortality. In PCP patients, lymphopenia <1500/mm3 was frequent (132/179, 73.7%) even if CD4+T cell count exceeded 200/mm3 in 56/78 patients (71.8%) (median 472.5/mm3, IQR 160–858). Conclusion IFD associated with AID occurs primarily in RA, AAV and IBD, especially when treated with GCs and immunosuppressants. Mortality is high, especially for patients on high-dose GCs. Lymphopenia may help identify risk of PCP, but normal CD4+T cell count does not rule out the risk. Further studies are needed to assess the individual risk factors for IFD.
Artificial Intelligence for next generation cybersecurity: The AI4CYBER framework

Artificial Intelligence (AI) is playing a crucial role both in the technological advances and emerging advanced threats in cybersecurity. Despite efforts by competent authorities in Europe to regulate the use of AI in a way that aligns with the ethics and individuals’ fundamental rights, there are still challenges to be tackled, not to mention the malicious use of AI by cybercriminals. In this paper we present a novel framework that is composed of innovative cybersecurity services that leverage AI to provide support in the management of the incident response and recovery lifecycle of the critical entities’ systems against advanced attacks. The paper describes the main components and architecture of the AI4CYBER framework and provides a clear understanding of the application of the autonomous intelligent cybersecurity services and their role in enforcing defensive actions throughout the entire lifecycle of the systems.
Real-Time Defensive Strategy Selection via Deep Reinforcement Learning

As computer networks face increasingly sophisticated attacks there is a need to create adaptive defensive systems that can select appropriate countermeasures to thwart attacks. The use of Deep Reinforcement Learning to train defensive agents is an avenue to study to meet this demand. In this paper we describe a simulated computer network environment wherein we conduct attacks and train defensive agents that employ Moving Target Defense and Deception strategies. We train an attacking agent, using Proximal Policy Optimization, to learn a policy to extract sensitive network data as quickly as possible from the environment. We then train a defending agent to prevent the attacker from reaching its objective. Our results demonstrate how the defender is able to learn a policy to inhibit the attacker.
A Qualitative Analysis of Illicit Arms Trafficking on Darknet Marketplaces

During the last decade, the dark web has become the playground for criminal and underground activities, such as marketplaces of drugs and guns, as well as illegal content sharing. The dark web is one of the top crime environments presented in EUROPOL’s Internet Organised Crime Threat Assessment 2021. This paper provides a qualitative study on the darknet marketplaces of illegal arms trafficking. For this purpose, we implemented a crawler based on the ACHE Python library to collect hidden web pages (onion services) on the Tor network. We gathered data from ten marketplaces recommended by dark web search engines – Ahmia, Deep Search, and Onion Land Search. We provide a first report of the overall landscape of illicit arms trafficking, discussing the range of weapons such as military drones, explosives, and other related products, together with the payment and shipping methods provided by the vendors. The findings verify previous reports from reputable institutions (United Nations and RAND Europe). Most of these illicit marketplaces are easily accessible to the average user; they are well-organized with a large variety of firearms and also provide extensive customer support.
A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection

Intrusion detection systems (IDS) play a key role to assure security properties of modern computer networks. IDS are often based on machine and deep learning techniques; as such, IDS are vulnerable to various forms of adversarial attacks. This paper presents an initial case study on the robustness of machine learning for network intrusion detection against adversarial attacks. Experiments are based on a recent fix of the widely-used CICIDS2017 benchmark dataset, two well-known machine learning techniques for intrusion detection (i.e., deep autoencoders and decision trees), and the virtual adversarial method (VAM) to generate the adversarial examples. Based on the data and experiments at hand, the results provide many interesting findings on the robustness of the IDS models assessed. The autoencoder-based IDS is more robust to evasion rather than overstimulation. On the contrary, the decision tree is vulnerable to evasion; moreover, changes to the learning parameters can strongly affect the robustness of the decision tree against the VAM attack.
Content-Based Relevance Estimation in Retrieval Settings with Ranking-Incentivized Document Manipulations

In retrieval settings such as the Web, many document authors are ranking incentivized: they opt to have their documents highly ranked for queries of interest. Consequently, they often respond to rankings by modifying their documents. These modifications can hurt retrieval effectiveness even if the resultant documents are of high quality. We present novel content-based relevance estimates which are "ranking-incentives aware"; that is, the underlying assumption is that content can be the result of ranking incentives rather than of pure authorship considerations. The suggested estimates are based on inducing information from past dynamics of the document corpus. Empirical evaluation attests to the clear merits of our most effective methods. For example, they substantially outperform state-of-the-art approaches that were not designed to address ranking-incentivized document manipulations.
Heterogeneity Analysis of Glioblastoma Tumor Cell Population Based on Single-Cell Rna Sequencing Data Analysis

Gliomas are lethal cancers that originate in the central nervous system. Glioblastoma multiforme (GBM) is the most aggressive and commonly occurring malignant brain glioma, accounting for just under 50% of all cases of malignant brain tumours in adults. In this paper, glioblastoma tumour cell single-cell RNA sequencing data were analyzed with Seurat, and cell groups were identified by employing various marker genes. Diverse populations of cell types were revealed, including tumour-associated macrophages, microglia, monocytes, t-cells, oligodendrocytes, glioblastoma stem cells, and other progenitor cells. Glioblastoma heterogeneity was also observed, as different samples of glioblastoma possessed distinct cellular compositions. Analysis of phagocytic cell clusters revealed the presence of microglia-like cells that resulted from monocyte differentiation. The upregulation of TIGIT and STAT3 in t-cell clusters was observed in cases with especially low t-cell counts, which demonstrates glioblastoma's immunosuppressive abilities. Furthermore, stem cell count was shown to be exceedingly low in cases of recurrent glioblastoma in comparison to cases of newly-diagnosed glioblastoma. Presumably, tumour recurrence should be caused by stem cells, but the exceptionally low stem cell count in cases of recurrent glioblastoma proves otherwise. This reveals that treatment or surgery should target stem cells in cases of newly developed glioblastoma but should target other factors in cases of recurrent glioblastoma—examples of which include the tumour microenvironment. These results can be used to help create more innovative and effective treatments for glioblastoma multiforme.