Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Modelling of the Well-Made Surprise

The “well-made” surprise is a narrative pattern of setting up and executing a surprise in a way that is generally perceived as enjoyable and rewarding. It leverages biases in human cognition to manipulate the audience’s state of belief, and is commonly found in western culture as early as Aristotle’s Poetics. We propose a novel framework to model the audience’s beliefs of a narrative world using approximate Bayesian inference over Markov Logic Networks. We operationalise three qualitative attributes of the well-made surprise (consistency, divergence and certainty) as quantitative functions of the outputs of inference. This work follows the paradigm from computational narrative of operational-ising qualitative concepts from literary theory in order to model and generate narratives, either autonomously or cooperatively with a human author. We demonstrate the proposed framework on ten short narratives, and test it with a study on 91 participants. We find that for consistency and divergence, a change in the model’s prediction corresponds with a significant change in the participants’ rating. Our results suggest that the proposed framework may have meaningful predictive power and potential for future applications to narrative generation, plot analysis, and computer-aided creativity.
Medical Schema Matching using Knowledge Graph Embedding (short paper)

Heterogeneous medical schema matching is important to realize the data sharing between medical databases. Traditional medical schema matching algorithms which usually employ hand-crafted features created by medical experts fail to handle increasingly giant data with the continuous development of medical digitization. In this paper, we propose a new medical schema matching method which extracts equivalent relations between columns of relational tables in heterogeneous databases using knowledge graph embedding. Our preliminary experiments on a real-world medical dataset show the superiority and competitiveness of our proposed method.
Evaluation of Curriculum Learning Algorithms using Computational Creativity Inspired Metrics

Curriculum learning, especially in robotics, is an active research field aiming to devise algorithms that speed up knowledge acquisition by proposing sequences of tasks an agent should train on. We focus on curriculum generation in reinforcement learning, where various meth-ods are currently compared based on the agent’s performance in terms of rewards on a predefined distribution of target tasks. We want to extend this singular characterization of existing algorithms by introducing metrics inspired by notions from the field of computational creativity. Namely, we introduce surprise, novelty, interest-ingness, and typicality that quantify various aspects of tasks stochastically proposed by the curriculum learning algorithms for the learner to train on. We model proposed tasks with Gaussian mixture models which enable their probabilistic interpretation, and use Hellinger distances between distributions and training rewards in formulation of the proposed metrics. Results are presented for eight curriculum learning algorithms show-casing differences in prioritization of various aspects of task creation and statistically different mean metric values when comparing agent’s best and worst training runs. The latter finding is not only useful for analysis of existing algorithms, but potentially also provides guidance for design of future curriculum learning methods.
Witscript 2: A System for Generating Improvised Jokes Without Wordplay

A previous paper presented Witscript, a system for generating conversational jokes that rely on wordplay. This paper extends that work by presenting Witscript 2, which uses a large language model to generate conversational jokes that rely on common sense instead of wordplay. Like Witscript, Witscript 2 is based on joke-writing algorithms created by an expert comedy writer. Human evaluators judged Witscript 2's responses to input sentences to be jokes 46% of the time, compared to 70% of the time for human-written responses. This is evidence that Witscript 2 represents another step toward giving a chatbot a humanlike sense of humor.
SciGraph: A Knowledge Graph Constructed by Function and Topic Annotation of Scientific Papers (poster)

In researchers’ literature exploration, a knowledge structure of certain domain helps those without clear purposes gain a quick understanding of new topics. Besides that, a paper set classified by function save the exploration time of those with clear purposes. This paper proposes a solution to annotate the function and topics of scientific papers, and construct a knowledge graph, named SciGraph, which helps researchers obtain both purpose-oriented papers and the knowledge structure of a domain. A dataset of 0.9 million scientific papers from different disciplines is collected, and the proposed solutions are applied to form SciGraph, within which a total of ca. 1.9 million nodes and ca. 16.4 million relations are generated. The contribution of this study includes, (1) organizing the functions and topics of scientific papers within a unified knowledge graph, which may support explorative retrieval; (2) proposing a DF-ITF method to identify the candidate feature words in the function definition; and (3) applying a method based on term co-occurrence to extract and rank the hyponymy relation of topic
Aeolian Dust Transportation on Fine Days over the Slopes in Mountainous Areas around the Taklimakan Desert, China( ADEC-Aeolian Dust Experiment on Climate Impact-)

To observe aeolian dust in high mountainous areas, two sets of automatic weather station including visibility meters have been installed on the slopes of the Tianshan Mountains and the Kunlun Mountains. The two observation sites are at a height of about 2500 m above sea level, and those slopes face to the Taklimakan Desert. The aeolian dust climbs the slopes with anabatic wind in daytime. Horizontal mass transportation of dust over the slopes on a fine day was estimated using the data set of visibility and wind speed, and its daily and seasonal variations were analyzed. Before this analysis, visibility was converted to dust concentration via a relationship determined by visibility meter and an optical particle counter in a laboratory. It has been recognized that the mass transportation over the slopes reflects activity of the dust outbreak at the foot. areas.
Training GPT-2 to represent two Romantic-era authors: challenges, evaluations and pitfalls

Poetry generation within style constraints has many creative challenges, despite the recent advances in Trans-former models for text generation. We study 1) how overfitting of various versions of GPT-2 models affects the quality of the generated text, and 2) which model is better at generating text in a specific style. For that purpose, we propose a novel setup for text evaluation with neural networks. Our GPT-2 models are trained on datasets of collected works of the two Romantic-era po-ets: Byron and Shelley. With some models, overfitting manifests by producing malformed samples, with others, the samples are always well-formed, but contain increasingly higher levels of n-grams duplicated from the original corpus. This behaviour can lead to incorrect evaluations of generated text because the plagia-rised output can deceive neural network classifiers and even human judges. To determine which model is better at preserving style before it becomes overfitted, we conduct two series of experiments with BERT-based classifiers. Overall, our results provide a novel way of selecting the right models for fine-tuning on a specific dataset, while highlighting the pitfalls that come with overfitting, like reordering and replicating text, towards more credible creative text generation.
Creative Walk Adversarial Networks: Novel Art Generation with Probabilistic Random Walk Deviation from Style Norms

We propose Creative Walk Adversarial Networks (CWAN) for novel art generation. Quality learning representation of unseen art styles is critical to facilitate generation of new meaningful artworks. CWAN learns an improved metric space for generative art by exploring unseen visual spaces with probabilistic random walks. CWAN constructs a dynamic graph that includes the seen art style centers and generated samples in the current minibatch. We then initiate a random walk from each art style center through the generated artworks in the current minibatch. As a deviation signal, we encourage the random walk to eventually land after T steps in a feature representation that is difficult to classify as any of the seen art styles. We investigate the ability of the proposed loss to generate meaningful novel visual art on the WikiArt dataset. Our experimental re-sults and human evaluations demonstrate that CWAN can generate novel art that is significantly more preferable compared to strong state-of-the-art methods, including StyleGAN2 and StyleCAN2. The code is publicly available at: https://vision-cair.github. io/CWAN/
Getting Smaller by Denaturation: Acid-Induced Compaction of Antibodies

Protein denaturation is a ubiquitous process that occurs both in vitro and in vivo. While the molecular understanding of the denatured structures of proteins is limited, it is commonly accepted that the loss of unique intramolecular contacts makes proteins larger. Herein, we report compaction of the immunoglobulin G1 (IgG1) protein upon acid denaturation. Small-angle X-ray scattering coupled with size exclusion chromatography revealed that IgG1 radii of gyration at pH 2 were ∼75% of those at a neutral pH. Scattering profiles showed a compact globular shape, supported by analytical ultracentrifugation. The acid denaturation of proteins with size reduction is energetically costly, and acid-induced compaction requires an attractive force for domain reorientation. Such intramolecular aggregation may be widespread in immunoglobulin proteins as non-canonical structures. Herein, we discuss the potential biological significance of these non-canonical structures of antibodies.
APOE ε4 gene dose effect on imaging and blood biomarkers of glial reactivity and β-amyloid pathology

Increased reactivity of microglia and astrocytes is known to be present at various stages of the Alzheimer’s continuum but their relationship with core Alzheimer’s disease pathology in the preclinical stages is less clear. We investigated glial reactivity and β-amyloid pathology in cognitively unimpaired APOE ε4 homozygotes, heterozygotes and non-carriers using 11C-PK11195 PET (targeting 18-kDa translocator protein), 11C-PiB PET (targeting β-amyloid), brain MRI, and a preclinical cognitive composite (APCC). Plasma glial fibrillary acidic protein (GFAP) by and plasma Aβ1-42/1-40 were measured using single molecule array and immunoprecipitation combined with mass spectrometry, respectively. We observed that (i) 11C-PiB-binding was significantly higher in APOE ε4 homozygotes compared with non-carriers in all evaluated regions, (ii) regional 11C-PK11195-binding did not differ between the APOE ε4 gene doses or between Aβ-positive and -negative individuals, and (iii) higher 11C-PK11195-binding and plasma GFAP were associated with lower hippocampal volume, and elevated 11C-PiB-binding and plasma GFAP concentration with lower APCC scores. Increased glial reactivity might emerge in later stages of preclinical Alzheimer’s disease in parallel with early neurodegenerative changes.
A bipartite function of ESRRB can integrate signaling over time to balance self-renewal and differentiation

Cooperative DNA binding of transcription factors (TFs) integrates external stimuli and context across tissues and time. Naïve mouse embryonic stem cells are derived from early development and can sustain the pluripotent identity indefinitely. Here we ask whether TFs associated with pluripotency evolved to directly support this state, or if the state emerges from their combinatorial action. NANOG and ESRRB are key pluripotency factors that co-bind DNA. We find that when both factors are expressed, ESRRB supports pluripotency. However, when NANOG is not present, ESRRB supports a bistable culture of cells with an embryo-like primitive endoderm identity ancillary to pluripotency. The stoichiometry between NANOG and ESRRB quantitatively influences differentiation, and in silico modeling of bipartite TF activity suggests ESRRB safeguards plasticity in differentiation. Thus, the concerted activity of cooperative TFs can transform their effect to sustain intermediate cell identities and allow ex vivo expansion of highly stable stem cell models.
Cancer-associated SMARCAL1 loss-of-function mutations promote alternative lengthening of telomeres and tumorigenesis in telomerase-negative glioblastoma cells

Telomere maintenance mechanisms are a hallmark of cancer and are required to enable the replicative immortality of malignant cells. While most cancers activate the enzyme telomerase for telomere maintenance, a subset of cancers (∼10-15%) use telomerase-independent mechanisms termed alternative lengthening of telomeres (ALT). ALT is characterized by elevated replication stress at telomeres, telomere synthesis via homology directed-repair mechanisms, and is frequently associated with mutations in the ATRX gene. Because ALT is absent in non-malignant proliferating cells, therapeutic strategies targeting ALT-mediated telomere synthesis is an area of significant translational and clinical interest. We previously showed that a subset of adult GBM patients with ATRX-expressing ALT-positive tumors harbored loss-of-function mutations in the SMARCAL1 gene. SMARCAL1 is an annealing helicase involved in replication fork remodeling and the resolution of replication stress. In this study, we used a patient-derived ALT-positive GBM cell line with native SMARCAL1 deficiency to investigate the role of SMARCAL1 in ALT-mediated telomere synthesis and gliomagenesis in vivo. Our results show that inducible rescue of SMARCAL1 expression suppresses ALT indicators and inhibits de novo telomere synthesis in GBM and osteosarcoma cells, suggesting that SMARCAL1 deficiency plays a functional role in ALT induction in cancers that natively lack SMARCAL1 function. Further, SMARCAL1-deficient ALT-positive cells can be serially propagated in vivo in the absence of detectable telomerase activity, suggesting that the SMARCAL1-deficient ALT phenotype maintains telomeres in a manner that promotes tumorigenesis. In summary, we show that SMARCAL1 loss-of-function mutations are permissive to ALT and promote gliomagenesis. We also established isogenic model systems that permit the dynamic modulation of ALT activity, which will be valuable for future studies aimed at understanding the molecular mechanisms of ALT and for identifying novel anti-cancer therapeutics that target the ALT phenotype.
Bi-orthogonal polynomial sequences and the asymmetric simple exclusion process

We state the diffusion algebra equations of the stationary state of the three parameter (α, β and q) asymmetric simple exclusion Process as a linear functional  , ? > acting on a tensor algebra. From  ? > we construct a pair of sequences, { P n } ? > and { Q m } ? > , of monic polynomials which are bi-orthogonal, that is, they satisfy  ( P n Q m ) = Λ n δ n , m ? > (where Λ n ? > is a scalar). The uniqueness and existence of the pair of sequences arises from the determinant of the bi-moment matrix whose elements satisfy a pair of q-recurrence relations. The determinant is evaluated using an LDU-decomposition. If the linear functional is represented as an inner product,  ( · ) = ⟨ W ∣ · ∣ V ⟩ ? > then the action of the polynomials Qn on the boundary vector ∣ V ⟩ ? > generate a basis ∣ V n ⟩ = Q n ∣ V ⟩ ? > whose orthogonal dual vectors are given by the action of Pn on the dual boundary vector ⟨ W ∣ ? > , that is ⟨ W n ∣ = ⟨ W ∣ P n ? > . This basis gives the representation of the algebra which is associated with the Al-Salam–Chihara polynomials obtained by Sasamoto.
Air Traffic Complexity: Towards an Intrinsic Metric

This paper address the problem of measuring the air traffic complexity, given the observed positions and speeds of aircraft present in air space. Different studies have been conducted following various approaches like dynamic density and cognitive models. However, an intrinsic measure of the traffic complexity is still to be introduced in order to complete those previous works. Two classes of indicators are investigated. The first one uses geometrical properties in order to build a new complexity coordinate system in which sector complexity evolution through time is represented. The second one uses a representation of air traffic as a dynamical system, yielding, through the topological Kolmogorov entropy, an intrinsic measure of complexity.
A Turkish Hate Speech Dataset and Detection System

Social media posts containing hate speech are reproduced and redistributed at an accelerated pace, reaching greater audiences at a higher speed. We present a machine learning system for automatic detection of hate speech in Turkish, along with a hate speech dataset consisting of tweets collected in two separate domains. We first adopted a definition for hate speech that is in line with our goals and amenable to easy annotation; then designed the annotation schema for annotating the collected tweets. The Istanbul Convention dataset consists of tweets posted following the withdrawal of Turkey from the Istanbul Convention. The Refugees dataset was created by collecting tweets about immigrants by filtering based on commonly used keywords related to immigrants. Finally, we have developed a hate speech detection system using the transformer architecture (BERTurk), to be used as a baseline for the collected dataset. The binary classification accuracy is 77% when the system is evaluated using 5-fold cross-validation on the Istanbul Convention dataset and 71% for the Refugee dataset. We also tested a regression model with 0.66 and 0.83 RMSE on a scale of [0-4], for the Istanbul Convention and Refugees datasets.
The effectiveness of teaching thinking & media literacy book on the ability of optimal media consumption and criticism analysis of its messages

Background and Objectives: Thesedays, people all over the world are faced down with the public media as new communication technologies in societies. A phenomenon that has been able to cover a large part of their daily lives and as the most important communication way with the world, affect on all private and public areas such as religion, ethics, politics, society and culture. Media literacy creates the opportunity to achieve a framework of criticism view in this field by educating and raising the audience knowledge level. Therefore, media literacy could be introduced as the ability of the audience to analyze the message of various media with a criticism view of the content of these messages. Therefore, teaching media literacy to students, as individuals who will play the most important role in the future of any society, can have a significant impact on creating a correct and efficient understanding of the atmosphere of today's societies. Therefore, the purpose of this study is to determine the effect of teaching thinking and media literacy book on the ability of optimal media consumption and criticism analysis of its messages in the tenth grade students in the Khomein city. Methods: The present study is an applied research and quasi-experimental in terms of data collection method. In the present study, the pre-test & post-test designs has been used by performing four steps. The statistical population included 1173 students in the tenth grade of Khomein city in the 1398-99 academic year. Sampling method used in this research is available sampling. The sample size of this study in the experimental group is a class by 30 student. To evaluate the criticism analysis of media messages and its components,
Operating the Dual-Orbiter GRAIL Mission to Measure the Moon's Gravity

NASA's mission to measure the Moon's gravity and determine the interior structure, from crust to core, has almost completed its 3-month science data collection phase. The twin orbiters of the Gravity Recovery and Interior Laboratory (GRAIL) mission were launched from Florida on September 10, 2011, on a Delta-II launch vehicle. After traveling for nearly four months on a low energy trajectory to the Moon, they were inserted into lunar orbit on New Year's Eve and New Year's Day. In January 2012 a series of circularization maneuvers brought the orbiters into co-planar near-circular polar orbits. In February a distant (75- km) rendezvous was achieved and the science instruments were turned on. A dual- frequency (Ka and S-band) inter-orbiter radio link provides a precise orbiter-to-orbiter range measurement that enables the gravity field estimation. NASA's Jet Propulsion Laboratory in Pasadena, CA, manages the GRAIL project. Mission management, mission planning and sequencing, and navigation are conducted at JPL. Lockheed Martin, the flight system manufacturer, operates the orbiters from their control center in Denver, Colorado. The orbiters together have performed 28 propulsive maneuvers to reach and maintain the science phase configuration. Execution of these maneuvers, as well as the payload checkout and calibration activities, has gone smoothly due to extensive pre-launch operations planning and testing. The key to the operations success has been detailed timelines for product interchange between the operations teams and proven procedures from previous JPL/LM planetary missions. Once in science phase, GRAIL benefitted from the payload operational heritage of the GRACE mission that measures the Earth's gravity.
Développement et analyse de méthodes adaptatives pour les équations de transport

Les resultats presentes dans cette these portent sur l'approximation adaptative de deux problemes de transport non-lineaire : le systeme de Vlasov-Poisson et les lois de conservation scalaires. Pour le premier, et dans une approche semi-lagrangienne, on a propose un schema adaptatif original a base d'elements finis hierarchiques ou l'evolution des maillages est realisee par une etape de prediction tres simple suivie d'une etape de correction plus classique. En introduisant la notion de courbure totale pour etendre la semi-norme W2,1(R2) aux fonctions affines par morceaux, on a alors etabli une estimation d'erreur a priori prouvant la convergence de ce schema en distance L∞, et donne des elements de preuve concernant sa complexite optimale. Les lois de conservations scalaire ne pouvant etre approchees en distance L∞, on a considere leur analyse en distance uniforme de Hausdorff, moins repandue bien que plus geometrique. Apres avoir montre que les solutions de ces equations etaient stables vis-a-vis de cette distance, on a etabli un resultat d'approximation adaptative d'ordre eleve.