Miracle-dz/UniFAR-FTUs huggingface dataset:
Harnessing Explanations to Bridge AI and Humans

Machine learning models are increasingly integrated into societally critical applications such as recidivism prediction and medical diagnosis, thanks to their superior predictive power. In these applications, however, full automation is often not desired due to ethical and legal concerns. The research community has thus ventured into developing interpretable methods that explain machine predictions. While these explanations are meant to assist humans in understanding machine predictions and thereby allowing humans to make better decisions, this hypothesis is not supported in many recent studies. To improve human decision-making with AI assistance, we propose future directions for closing the gap between the efficacy of explanations and improvement in human performance.
SuperModel predictions in the outskirts of the galaxy cluster Zwicky 3146

The relaxed galaxy cluster Zwicky 3146 is analyzed via the SuperModel, a tool already tested on many clusters since 2009. In particular, this analysis is focused on the intracluster medium X-ray temperature data measured by XMM-Newton up to r_{500}. A previous analysis was based on the temperature profile derived from the Sunyaev-Zeldovich effect pressure data. The gas mass fraction f_{gas} is obtained from the resulting SuperModel temperature profile extrapolated up to the virial radius R, that turns out in agreement with the steep temperature profiles observed by Suzaku, and from the gas density profile observed by XMM-Newton. The comparison between f_{gas} with the universal value indicates a non-thermal pressure component, p_{nth}, in the cluster outskirts. The SuperModel analysis shows a ratio alpha(R) (about 50%) of p_{nth} to the total pressure greater than the values found by simulations, highlighting the possible presence of accreting substructures and inhomogeneities in the gas density profile. Once that this profile is corrected for clumpiness, the level of p_{nth} is considerably reduced. However, a significant turbulence (alpha(R) about 20%) and entropy flattening are still present in the outskirts of the galaxy cluster Zwicky 3146.
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond

With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for explainable AI. Interpretability and explanation methods for gaining a better understanding about the problem solving abilities and strategies of nonlinear Machine Learning such as Deep Learning (DL), LSTMs, and kernel methods are therefore receiving increased attention. In this work we aim to (1) provide a timely overview of this active emerging field and explain its theoretical foundations, (2) put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations, (3) outline best practice aspects i.e. how to best include interpretation methods into the standard usage of machine learning and (4) demonstrate successful usage of explainable AI in a representative selection of application scenarios. Finally, we discuss challenges and possible future directions of this exciting foundational field of machine learning.
Semi-Supervised Learning on Graphs with Feature-Augmented Graph Basis Functions

For semi-supervised learning on graphs, we study how initial kernels in a supervised learning regime can be augmented with additional information from known priors or from unsupervised learning outputs. These augmented kernels are constructed in a simple update scheme based on the Schur-Hadamard product of the kernel with additional feature kernels. As generators of the positive definite kernels we will focus on graph basis functions (GBF) that allow to include geometric information of the graph via the graph Fourier transform. Using a regularized least squares (RLS) approach for machine learning, we will test the derived augmented kernels for the classification of data on graphs.
Cytology Image Analysis Techniques Toward Automation

Cytology is a branch of pathology that deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. In the present work, the term cytology is used to indicate solid organ cytology. Automation in cytology started in the early 1950s with an aim to reduce manual efforts in the diagnosis of cancer. The influx of intelligent systems with high computational power and improved specimen collection techniques helped to achieve technological heights in the cytology automation process. In the present survey, we focus on image analysis techniques paving the way to automation in cytology. We take a short tour of 17 types of solid organ cytology to explore various segmentation and/or classification techniques that evolved during the past three decades to automate cytology image analysis. It is observed that most of the works are aligned toward three types of cytology: Cervical, Breast, and Respiratory tract cytology. These are discussed elaborately in the article. Commercial systems developed during the period are also summarized to comprehend the overall growth in respective domains. Finally, we discuss different state-of-the-art methods and related challenges to provide prolific and competent future research directions in bringing cytology-based commercial systems into the mainstream.
Foundations of Explainable Knowledge-Enabled Systems

Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.
Prepared for submission to JHEP Semileptonic D → V ( ω , ρ , K ∗ ) ` + ν ` decays within QCD light-cone sum rule approach

In the paper, the physical observables for D → V (ω, ρ,K)`ν` semileptonic decays are investigated thoroughly with the SSE extrapolated HFFs, which are calculated within the framework of QCD LCSR and up to twist-4. For the decay width parts, we find that 1/|Vcq|×Γ(D → V `ν`) decrease with the increase of final meson mass, and the absolute change number of the transverse and longitudinal are almost the same. The transverse differential decay width dominates in the small q2 region, while the longitudinal differential decay width dominates in the large q2 region, and the dominant alternate point is near the midpoint of the whole physically feasible region. We then calculate the branching fractions by applying the D0(D+) meson lifetime from the Particle Data Group, i.e. B(D0 → ρeνe, ρμνμ) = ( 1.440 −0.250, 1.432 +0.274 −0.248 ) × 10−3 and B(D+ → ρeνe, ρμνμ) = ( 1.827 −0.317, 1.816 +0.348 −0.314 ) × 10−3 for ρ meson channel; B(D+ → ωeνe, ωμνμ) = ( 1.740 −0.399, 1.728 +0.479 −0.397 ) × 10−3 for ω meson channel; B(D0 → Keνe,Kμνμ) = ( 2.082 −0.314, 2.066 +0.330 −0.310 ) × 10−2 and B(D+ → K̄eνe, K̄μνμ) = ( 5.282 −0.796, 5.242 +0.838 −0.787 ) × 10−2 for K∗ meson channel. All those branching fraction results are lower compared to other theories, but fits well with BESIII predictions within errors. For the polarization parts, we study in detail the q2 dependence of longitudinal and transverse polarizations fractions of the charged lepton and the vector meson in the final state, i.e. P ` L,T and F ` L,T, the forward-backward asymmetry AFB and the lepton-side convexity parameters C` F. In the small q2 region, all those polarization observations have a singularity due to the δ` factor, expect for F ` L,T. With the increase of q 2, all polarization values tend to be more stable, thus the polarization dependence on q2 is smaller. Our predictions for their corresponding average values have agreement with the theoretical group for Covariant Confining Quark Model results within the errors. ar X iv :2 00 3. 07 62 6v 1 [ he pph ] 1 7 M ar 2 02 0
Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice

This paper analyzes how candidate choice prediction improves by different psychological predictors. To investigate this question, it collected an original survey dataset featuring the popular TV series “Game of Thrones”. The respondents answered which character they anticipated to win in the final episode of the series, and explained their choice of the final candidate in free text from which sentiments were extracted. These sentiments were compared to feature sets derived from candidate likeability and candidate personality ratings. In our benchmarking of 10-fold cross-validation in 100 repetitions, all feature sets except the likeability ratings yielded a 10–11% improvement in accuracy on the holdout set over the base model. Treating the class imbalance with synthetic minority oversampling (SMOTE) increased holdout set performance by 20-34% but surprisingly not testing set performance. Taken together, our study provides a quantified estimation of the additional predictive value of psychological predictors. Likeability ratings were clearly outperformed by the feature sets based on personality, emotional valence, and basic emotions.
Phase diagram of the $\mathbb{Z}_3$-Fock parafermion chain with pair hopping

We study a tight binding model of \mathbb{Z}_3ℤ3-Fock parafermions with single-particle and pair-hopping terms. The phase diagram has four different phases: a gapped phase, a gapless phase with central charge \boldsymbol{c=2}𝐜=2, and two gapless phases with central charge \boldsymbol{c=1}𝐜=1. We characterise each phase by analysing the energy gap, entanglement entropy and different correlation functions. The numerical simulations are complemented by analytical arguments.
Genotype-environment interaction and the maintenance of genetic variation: an empirical study of Lobelia inflata (Campanulaceae)

High levels of genetic variation are often observed in natural populations, suggesting the action of processes such as frequency-dependent selection, heterozygote advantage and variable selection. However, the maintenance of genetic variation in fitness-related traits remains incompletely explained. The extent of genetic variation in obligately self-fertilizing populations of Lobelia inflata (Campanulaceae L.) strongly implies balancing selection. Lobelia inflata thus offers an exceptional opportunity for an empirical test of genotype-environment interaction (G × E) as a variance-maintaining mechanism under fluctuating selection: L. inflata is monocarpic and reproduces only by seed, facilitating assessment of lifetime fitness; genome-wide homozygosity precludes some mechanisms of balancing selection, and microsatellites are, in effect, genotypic lineage markers. Here, we find support for the temporal G × E hypothesis using a manipulated space-for-time approach across four environments: a field environment, an outdoor experimental plot and two differing growth-chamber environments. High genetic variance was confirmed: 83 field-collected individuals consisted of 45 distinct microsatellite lineages with, on average, 4.5 alleles per locus. Rank-order fitness, measured as lifetime fruit production in 16 replicated multilocus genotypes, changed significantly across environments. Phenotypic differences among microsatellite lineages were detected. Results thus support the G × E hypothesis in principle. However, the evaluation of the effect size of this mechanism and fitness effects of life-history traits will require a long-term study of fluctuating selection on labelled genotypes in the field.
Multi-level modelling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development

What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. We analyse a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949–2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by country but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines in years when countries become wealthier. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Multi-level modelling also confirms that the within-country effect of pathogen prevalence remains robust even after considering a region-level analysis. Longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. Overall, our findings illuminate the dynamic process by which environment and democracy shape each other.
Looking Beyond the Horizon

The Internet exposes us to cyberthreats attacking information, services, and the Internet infrastructure itself. Such attacks are typically detected in a reactive fashion. The downside of this approach is that alerts of an attack are issued as it is happening. In this article, we advocate that the security community could benefit by complementing traditional reactive solutions with a proactive threat detection approach, as this would enable us to provide early warnings by analyzing and detecting threat indicators in actively collected data. By describing three use cases from the DNS domain, we highlight the strengths and limitations of proactive threat detection and discuss how we could integrate those with existing solutions.
An Enhanced Neural Network Approach to Person-Job Fit in Talent Recruitment

The widespread use of online recruitment services has led to an information explosion in the job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is the bridge for adapting the right candidates to the right positions. Existing studies on Person-Job Fit usually focus on measuring the matching degree between talent qualification and job requirements mainly based on the manual inspection of human resource experts, which could be easily misguided by the subjective, incomplete, and inefficient nature of human judgment. To that end, in this article, we propose a novel end-to-end Topic-based Ability-aware Person-Job Fit Neural Network (TAPJFNN) framework, which has a goal of reducing the dependence on manual labor and can provide better interpretability about the fitting results. The key idea is to exploit the rich information available in abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, two hierarchical topic-based ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measure the different contribution of each job experience to a specific ability requirement. In addition, we design a refinement strategy for Person-Job Fit prediction based on historical recruitment records. Furthermore, we introduce how to exploit our TAPJFNN framework for enabling two specific applications in talent recruitment: talent sourcing and job recommendation. Particularly, in the application of job recommendation, a novel training mechanism is designed for addressing the challenge of biased negative labels. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness and interpretability of the TAPJFNN and its variants compared with several baselines.
A Single ssRNA Segment Encoding RdRp Is Sufficient for Replication, Infection, and Transmission of Ourmia-Like Virus in Fungi

Recently, an increasing number of ourmia-like viruses have been found in fungi; however, the features of these viruses remain unknown. Here, we report a novel ourmia-like virus isolated from Sclerotinia sclerotiorum. This virus, named S. sclerotiorum ourmia-like virus 4 (SsOLV4), has a genome 2,982 nt in length with a G-pentamer (GGGGG) at the 5′-terminus and a C-pentamer (CCCCC) at the 3′-terminus. The SsOLV4 genome has only one large putative open reading frame (ORF) predicted with both standard codes and mitochondrial codes and encodes an RNA-dependent RNA polymerase (RdRp). SsOLV4 is closely phylogenetically related to Pyricularia oryzae ourmia-like virus 1, with 42% identity between the RdRp amino acid sequences. We constructed full-length cDNA of SsOLV4 and synthesized RNA in vitro using the T7 RNA polymerase. The synthesized RNA could transfect S. sclerotiorum protoplasts efficiently. We further found that viral RNA could infect mycelia when mixed with PEG buffer. Our study suggests that a novel genus in family Botourmiaviridae should be established for SsOLV4 and other related viruses and demonstrates that one single-stranded RNA segment encoding RdRp is sufficient for ourmia-like viruses in fungi.
Design of a Guided Internet- and Mobile-Based Intervention for Internet Use Disorder—Study Protocol for a Two-Armed Randomized Controlled Trial

Context: Internet Use Disorder (IUD), characterized as the inability to control one's internet use, is emerging as an increasing societal concern as it is associated with reduced quality of life and mental health comorbidities. Evidence-based treatment options are, however, scarce due to the novelty of the diagnosis. Internet- and mobile-based interventions may be an effective means to deliver psychological treatment to individuals with IUD as they address individuals affected in their online setting. The aim of the study is to evaluate the efficacy of a newly developed, guided internet- and mobile-based intervention for IUD. Methods: In a two-armed randomized controlled trial (N = 130), individuals showing problematic internet use patterns (Internet Addiction Test ≥ 49) will be randomly allocated to the internet- and mobile-based intervention or a waiting control group. Assessments will take place at baseline, 7 weeks, 6- and 12 months after randomization. The primary outcome is internet addiction symptom severity (IAT) at 7 weeks. Secondary outcomes include quality of life, depressive symptoms, anxiety, and other psychosocial variables associated with IUD. Intervention: The intervention consists of seven sessions: Goal setting and motivational interviewing, impulse control, problem solving, cognitive restructuring, self-worth, relapse prevention, and a booster session. Participants are supported by an eCoach who provides individual feedback after completion of each session. Participants can choose between several elective sessions based on individual need. Conclusions: This is the first study to evaluate an internet- and mobile-based intervention for IUD, which could be a promising first step to reduce individuals' disease burden. Trial Registration: DRKS00015314. The study is currently ongoing. First participants were enrolled in the study on September 14th 2018. Recruitment will continue approximately through March 2020.
Antioxidant Nanotherapies for the Treatment of Inflammatory Diseases

Reactive oxygen species (ROS) are essential in regulating various physiological functions. However, overproduction of ROS is implicated in the pathogenesis of various inflammatory diseases. Antioxidant therapy has thus represented an effective strategy for the treatment of oxidative stress relevant inflammatory diseases. Conventional anti-oxidative agents showed limited in vivo effects owing to their non-specific distribution and low retention in disease sites. Over the past decades, significant achievements have been made in the development of antioxidant nanotherapies that exhibit multiple advantages such as excellent pharmacokinetics, stable anti-oxidative activity, and intrinsic ROS-scavenging properties. This review provides a comprehensive overview on recent advances in antioxidant nanotherapies, including ROS-scavenging inorganic nanoparticles, organic nanoparticles with intrinsic antioxidant activity, and drug-loaded anti-oxidant nanoparticles. We highlight the biomedical applications of antioxidant nanotherapies in the treatment of different inflammatory diseases, with an emphasis on inflammatory bowel disease, cardiovascular disease, and brain diseases. Current challenges and future perspectives to promote clinical translation of antioxidant nanotherapies are also briefly discussed.
Antimicrobial Activity of Isolated Bacterial Endophytes from Cichorium intybus L , Pelargonium hortorum , and Portulaca oleracea Against Human Nosocomial Bacterial Pathogens

Background: Bacterial endophytes are colonizers of the inner plant tissues in which they do not normally cause any substantial morphological changes or disease symptoms. Endophytic bacteria are safe microorganisms that reside within the plant hosts and are known to enhance the growth and development of host plants, probably by secreting growth hormones. These bacteria are known to enhance growth and products of plants by ﬁxing atmospheric nitrogen, solubilization of phosphate, production of phy-tohormonesandsiderophores,andpossessionof antagonisticactivity,aswellasreducingthelevelof stressethyleneinhostplants. Objectives: In this descriptive study, we focused on the isolation of bacterial endophytes from three medicinal plants Cichorium intybusL ,Pelargoniumhortorum,and Portulacaoleracea andscreenedthemforactivitiesagainstnosocomialisolatesof Staphylococcus aureus , Enterococcus faecalis , Pseudomonas aeroginosa , and Acinetobacter baumannii . Methods: Random samples from asymptomatic leaves and branches of three medicinal plants ( Cichorium intybus L , Pelargonium hortorum , and Portulaca oleracea ) were collected. To isolate the endophytic bacteria, the disinfected portions of the plants were distributed onto the isolation media. To examine endophytic bacterial contents, bioassays were conducted using growing colonies in PA and YEA, inactivating them by chloroform. To test the antibacterial activity of the endophytic bacterial culture broth, ﬁlter-sterilized supernatants were poured into cylinders on each bacterial plate. Results: A total of 24 phenotypically distinguishable bacterial endophytes were isolated in pure form from three medicinal plants. In part of the chloroform-inactivated colonies of all 24 isolated endophytes, the most eﬀective herb was C. intybus L , followed by Po. oleracea , and in part of the supernatant culture broth, the most eﬀective herb was Po. oleracea , followed by C. intybus L . Conclusions: Endophytic microorganisms residing in Cichorium intybus L , Portulaca oleracea , and Pelargonium hortorum are a very promising source for production of bioactive compounds. In general, most isolated endophytes had an acceptable eﬀect against indicator bacterial pathogens.