Miracle-dz/UniFAR-FTUs huggingface dataset:
CLseg: Contrastive Learning of Story Ending Generation

Story Ending Generation (SEG) is a challenging task in natural language generation. Recently, methods based on Pre-trained Language Models (PLM) have achieved great prosperity, which can produce fluent and coherent story endings. However, the pre-training objective of PLM-based methods is unable to model the consistency between story context and ending. The goal of this paper is to adopt contrastive learning to generate endings more consistent with story context, while there are two main challenges in contrastive learning of SEG. First is the negative sampling of wrong endings inconsistent with story contexts. The second challenge is the adaptation of contrastive learning for SEG. To address these two issues, we propose a novel Contrastive Learning framework for Story Ending Generation (CLseg)†, which has two steps: multi-aspect sampling and story-specific contrastive learning. Particularly, for the first issue, we utilize novel multi-aspect sampling mechanisms to obtain wrong endings considering the consistency of order, causality, and sentiment. To solve the second issue, we well-design a story-specific contrastive training strategy that is adapted for SEG. Experiments show that CLseg outperforms baselines and can produce story endings with stronger consistency and rationality.
Rank-Sensitive Computation of the Rank Profile of a Polynomial Matrix

Consider a matrix F ε K [x]^mxn of univariate polynomials over a field K. We study the problem of computing the column rank profile of F. To this end we first give an algorithm which improves the minimal kernel basis algorithm of Zhou, Labahn, and Storjohann (Proceedings ISSAC 2012). We then provide a second algorithm which computes the column rank profile of F with a rank-sensitive complexity of O~ (rw-2n(m+d)) operations in K. Here, D is the sum of row degrees of F, w is the exponent of matrix multiplication, and O~ (.) hides logarithmic factors.
Joint Learning of Frequency and Spatial Domains for Dense Predictions

Current artificial neural networks mainly conduct the learning process in the spatial domain but neglect the frequency domain learning. However, the learning course performed in the frequency domain can be more efficient than that in the spatial domain. In this paper, we fully explore frequency domain learning and propose a joint learning paradigm of frequency and spatial domains. This paradigm can take full advantage of the preponderances of frequency learning and spatial learning; specifically, frequency and spatial domain learning can effectively capture global and local information, respectively. Exhaustive experiments on two dense prediction tasks, i.e., self-supervised depth estimation and semantic segmentation, demonstrate that the proposed joint learning paradigm can 1) achieve performance competitive to those of state-of-the-art methods in both depth estimation and semantic segmentation tasks, even without pretraining; and 2) significantly reduce the number of parameters compared to other state-of-the-art methods, which provides more chance to develop real-world applications. We hope that the proposed method can encourage more research in cross-domain learning.
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models

Automatic emotion recognition plays a key role in computer-human interaction as it has the potential to enrich the next-generation artificial intelligence with emotional intelligence. It finds applications in customer and/or representative behavior analysis in call centers, gaming, personal assistants, and social robots, to mention a few. Therefore, there has been an increasing demand to develop robust automatic methods to analyze and recognize the various emotions. In this paper, we propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. More specifically, we i) adapt a residual network (ResNet) based model trained on a large-scale speaker recognition task using transfer learning along with a spectrogram augmentation approach to recognize emotions from speech, and ii) use a fine-tuned bidirectional encoder representations from transformers (BERT) based model to represent and recognize emotions from the text. The proposed system then combines the ResNet and BERT-based model scores using a late fusion strategy to further improve the emotion recognition performance. The proposed multimodal solution addresses the data scarcity limitation in emotion recognition using transfer learning, data augmentation, and fine-tuning, thereby improving the generalization performance of the emotion recognition models. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture (IEMOCAP) dataset. Experimental results indicate that both audio and text-based models improve the emotion recognition performance and that the proposed multimodal solution achieves state-of-the-art results on the IEMOCAP benchmark.
Análise de interações de alunos do ensino secundário em ambientes virtuais

In this study an environment was created to make teachers and students "believe" they were in a classroom, even though they were physically separated. We designed teaching-learning activities related to programming, in this case, 3D object modelling, with the purpose of analysing the interactions between students from three vocational education classes from three secondary schools, in a Virtual Reality environment. This exploratory study (case study) consisted in teaching three classes in a Virtual Reality environment, in the subject Programming and Information Systems of the first and second years of the Professional Course of Management and Programming of Computer Systems (TGPSI). The students programmed objects in 3D modelling. The motivation for using this environment is related to the idea of providing teachers and students with a new teaching and learning environment that facilitates the exploration of the initial concepts of programming and algorithms, which can be extended to any level of education. Simultaneously, it is intended to explore new environments, which can be customised. Starting from the experimentation and exploration of a classroom (identical to the real one), the study focused on discovering the benefits and challenges regarding the potential of these environments, considering the students' perspective. The aim was to understand how these environments can be facilitators of the teaching/learning process and promoters of socialisation, capable of involving and motivating the students. Based on the analysis of the results obtained, these environments were considered promising and innovative, with the potential to be applied not only in the current context of the pandemic, but also later when returning to classroom teaching.
Algorithmic Experiments in the Design of Brutalist Web Pages

Web Design and Web Development have been in a continuous evolution state ever since the first web page was made publicly available, enabling the development of innovative ways to inter-act and communicate with people. This paper proposed an installation that continuously presents automatically generated web pages based on a computational design system that explores the use of algorithmic design processes for brutalist web page generation reflecting the semantic meaning of its content. The content is gathered from the Wikipedia API, from randomly selected words available in the API. Although conceptually and visually the outputs are Brutalism Websites, this approach effortlessly can be modified to generate outputs taking into consideration other visual and conceptual styles. This way, the proposed installation reflects how the Artificial Intelligence approaches may be impactful in the future of Web Design and Web Development.
Power Chess: Robot-to-Robot Nonverbal Emotional Expression Applied to Competitive Play

Human-machine communication has evolved from one-to-one to multi-agent systems where the interplay between machines themselves interacts with human perception and behavior, complicated by unconstrained emotion-based variables in social systems. To investigate Human-Robot and Robot-Robot-Human interaction while constraining the interaction variables in a rule-based system, we developed an artistic intervention using competitive game performance between robotic arms. Two robots play chess with each other while expressively making gestures like thinking, examining, hesitating, shows of satisfaction and bewilderment, breathing, etc. These nonverbal behaviors and evolving rules between games tell a narrative of power struggle between two robots of aggressive vs. reflective personalities. We used recorded videos to assay audience interpretations of individual and robot-to-robot expressions, finding that gestures like standing and confirming were perceived as aggressive, while head turns, deliberation, and audience alerts were seen as curious. Human perception of robot play-style and their own intended play strategies were influenced by robot-robot interactions, such as holding defensive strategies when the robot was deemed aggressive. Robotic movements caused audiences to attribute personality characteristics to them, modifying their intended strategy in patterns like pretending to be friendly first to lull the robot opponent. Our work uses artistic metaphors to study multi-agent environments that cannot be easily controlled for in scientific settings.
Variable Image Patterns

Modularity has served as a key in design and art to help reimagine new visual artifacts. Modular systems achieve this by presenting a simpler way to describe a mechanism, by breaking it into smaller and more, manageable parts. This way, they provide an easy way to get different combinations and possible results. With recent developments in technology and the rise of programming languages for art and design, we can portray this concept in a new mode. In this paper, we propose a modular computational system that focuses on a generative design standpoint to create images using tiles capable of changing their shape to form different styles and patterns. Our goal is to build a generative system that adapts to different visual scenarios and purposes. We achieve this by implementing programmable tiles and by allowing the user to combine different values for the given settings. The presented system can be employed to generate stylized images or set an interactive mirror for new media art installations. It can also be used to adapt images to printing purposes or specific displays, produce laser cuts, artifacts for a graphic identity, or even build modular typefaces.
Multi-eGO: An in silico lens to look into protein aggregation kinetics at atomic resolution

Protein aggregation into amyloid fibrils is the archetype of aberrant biomolecular self-assembly processes, with more than 50 diseases associated that are mostly uncurable. Understanding aggregation mechanisms is thus of fundamental importance and goes in parallel with the characterization of the structures of the transient oligomers formed in the process. Oligomers have been proven elusive to high-resolution structural techniques, while the large sizes and long-time scales typical of aggregation processes have limited, so far, the use of computational methods. To surmount these limitations, we introduce here multi-eGO, an atomistic, hybrid structure-based model, which leveraging on the knowledge of monomers conformational dynamics and of fibril structures, can efficiently capture the essential structural and kinetics aspects of protein aggregation. Multi-eGO molecular dynamics simulations can describe the aggregation kinetics of thousands of monomers. The concentration dependence of the simulated kinetics, as well as the structural features of the resulting fibrils, are in qualitative agreement with in vitro experiments on an amyloidogenic peptide of Transthyretin, a protein responsible for one of the most common cardiac amyloidosis. Multi-eGO simulations allow to observe in time and at atomic resolution the formation of primary nuclei in a sea of transient lower order oligomers, to follow their growth and the subsequent secondary nucleation events, till the maturation of multiple fibrils. Multi-eGO, combined with the many experimental techniques deployed to study protein aggregation, can provide the structural basis needed to advance the design of molecules targeting amyloidogenic diseases. Significance Statement Alzheimer’s and Parkinson’s diseases are uncurable pathologies associated to the aberrant aggregation of specific proteins into amyloid fibrils. Understanding the mechanism leading to protein aggregation, by characterizing the structures of the oligomeric species populated in the process, would have a tremendous impact on the design of therapeutic molecules. We propose that a structure-based approach to molecular dynamics simulations can allow following at high resolution the aggregation kinetics of thousands of monomers. Having shown that simulations can describe the aggregation of a Transthyretin amyloidogenic peptide, we demonstrate how their efficiency allows acquiring a wealth of structural information. We foresee that integrating the latter with the many techniques developed to study protein aggregation will support the design of molecules to modulate amyloidogenesis.
Babesia divergens egress from host cells is orchestrated by essential and druggable kinases and proteases

A unique aspect of apicomplexan biology is the requirement for egress from and invasion into host red blood cells (RBCs). The cellular mechanisms and molecular mediators of RBC egress and invasion remain poorly characterized in Babesia spp. , a group of parasites of veterinary importance and emerging cause of zoonotic disease. Through the use of video microscopy, transcriptomics, and chemical genetics we have implicated signaling, proteases and gliding motility in egress and/or invasion by Babesia divergens. We developed CRISPR/Cas9 and two inducible knockdown systems to perform a genetic screen of putative mediators of egress. We found that proteases ASP2 and ASP3 are required for invasion, and the latter is also required for egress. Strikingly, parasites continue to replicate intracellularly in the absence of the protein kinases, PKG or CDPK4, indicating that they are required for exit from the replication cycle and egress. These essential molecules present druggable targets for Babesia spp. All together we have established a molecular framework for the spread of infection through host RBCs, with egress of B. divergens more closely resembling T. gondii than the more evolutionarily related Plasmodium spp. Highlights Egress in Babesia divergens requires host cell lysis and parasite motility Transcriptomics can be used to identify egress and invasion proteins Knockdown of the proteases, ASP2 and ASP3, inhibit egress and invasion Inhibition of PKG or CDPK4 signaling results in continued intracellular replication
Rapid GPR183-mediated recruitment of eosinophils to the lung after Mycobacterium tuberculosis infection

Influx of eosinophils into the lungs is typically associated with type-II responses during allergy, fungal and parasitic infections. However, we previously reported that eosinophils accumulate in lung lesions during type-I inflammatory responses to Mycobacterium tuberculosis (Mtb) in humans, macaques, and mice where they contribute to host resistance. Here we show eosinophils migrate into the lungs of macaques and mice as early as one week after Mtb-exposure. In mice this influx was CCR3 independent and instead required cell-intrinsic expression of the oxysterol-receptor GPR183, which is highly expressed on human and macaque eosinophils. Murine eosinophils interacted directly with bacilli-laden alveolar macrophages, which upregulated the oxysterol-synthesizing enzyme Ch25h, and eosinophil recruitment was impaired in Ch25h deficient mice. Our findings show that eosinophils are among the first cells from circulation to sense and respond to Mtb infection of alveolar macrophages and reveal a novel role for GPR183 in the migration of eosinophils into lung tissue. HIGHLIGHTS In mice and macaques eosinophils accumulate early in Mtb-infected lungs preceding neutrophils Eosinophils interact with Mtb-infected cells in the alveoli in mice Early pulmonary eosinophil migration occurs independently of CCR3 in mice Early lung migration in mice requires Ch25h and eosinophil-intrinsic GPR183 expression
Design and experimental research of the MOEMS low frequency sensor

The MOEMS acoustic sensor is a new technology which combines acoustic sensor technology with MEMS technology and micro-optics technology.The sensors have high sensitivity and may be used in heavy magnetic,electric fields.In case of warning and detecting antisubmarine aircraft by submarine,the acoustic sensor array which has high sensitivity,good response at low frequency and miniaturized is needed.To meet the requirement,a low frequency MOEMS acoustic sensor based on light intensity modulation is designed.Static performances of membranes with several sizes are simulated with Finite Element modeling,and the parameters of membrane are given out.The characteristic of reflected light intensity is simulated and experimented.The low frequency MOEMS acoustic sensor is made of membrane,the wavelength of the LED is 850 nm.Experiment shows that the sensor has good response in low frequency and its sensitivity is 200 mV/Pa,which reach the aim of the design.
Characterizing and Modeling Breathing Dynamics: Flow Rate, Rhythm, Period, and Frequency

The characterization of breathing dynamics provides researchers and clinicians the ability to differentiate respiratory compensation, impairment, disease progression, ventilator assistance, and the onset of respiratory failure. However, within many sub-fields of respiratory physiology, we still have challenges identifying changes within the breathing dynamics and critical respiratory states. We discuss one fundamental modeling of breathing and how modeling imprecise assumptions decades ago regarding breathing are still propagating into our quantitative analysis today, limiting our characterization and modeling of breathing. The assumption that breathing is a continuous sinusoidal wave that can consist of a single frequency which is composed of a stationary time-invariant process has limited our expanded discussion of breathing dynamics, modeling, functional testings, and metrics. Therefore, we address major misnomers regarding breathing dynamics, specifically rate, rhythm, frequency, and period. We demonstrate how these misnomers impact the characterization and modeling through the force equations that are linked to the Work of Breathing (WoB) and our interpretation of breathing dynamics through the fundamental models and create possible erroneous evaluations of work of breathing. This discussion and simplified non-periodic WoB models ultimately sets the foundation for improved quantitative approaches needed to further our understanding of breathing dynamics, compensation, and adaptation.
Clinical Correlates Identify ProBDNF and Thrombo-Inflammatory Markers as Key Predictors of Circulating p75NTR Extracellular Domain Levels in Older Adults

The p75NTR receptor binds all neurotrophins and is mostly known for its role in neuronal survival and apoptosis. Recently, the extracellular domain (ECD) of p75NTR has been reported in plasma, its levels being dysregulated in numerous neurological diseases. However, the factors associated with p75NTR ECD levels remain unknown. We investigated clinical correlates of plasma p75NTR ECD levels in older adults without clinically manifested neurological disorders. Circulating p75NTR levels were measured by enzyme-linked immunosorbent assay in plasma obtained from participants in the BEL-AGE cohort (n = 1,280). Determinants of plasma p75NTR ECD levels were explored using linear and non-linear statistical models. Plasma p75NTR ECD levels were higher in male participants; were positively correlated with circulating concentrations of pro-brain-derived neurotrophic factor, and inflammatory markers interleukin-6 and CD40 Ligand; and were negatively correlated with the platelet activation marker P-selectin. While most individuals had p75NTR levels ranging from 43 to 358 pg/ml, high p75NTR levels reaching up to 9,000 pg/ml were detectable in a subgroup representing 15% of the individuals studied. In this cohort of older adults without clinically manifested neurological disorders, there was no association between plasma p75NTR ECD levels and cognitive performance, as assessed by the Montreal Cognitive Assessment score. The physiological relevance of high p75NTR ECD levels in plasma warrants further investigation. Further research assessing the source of circulating p75NTR is needed for a deeper understanding of the direction of effect, and to investigate whether high p75NTR ECD levels are predictive biomarkers or consequences of neuropathology.
Roles of the Cannabinoid System in the Basal Ganglia in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disease usually caused by neuroinflammation, oxidative stress and other etiologies. Recent studies have found that the cannabinoid system present in the basal ganglia has a strong influence on the progression of PD. Altering the cannabinoid receptor activation status by modulating endogenous cannabinoid (eCB) levels can exert an anti-movement disorder effect. Therefore, the development of drugs that modulate the endocannabinoid system may be a novel strategy for the treatment of PD. However, eCB regulation is complex, with diverse cannabinoid receptor functions and the presence of dopaminergic, glutamatergic, and γ-aminobutyric signals interacting with cannabinoid signaling in the basal ganglia region. Therefore, the study of eCB is challenging. Here, we have described the function of the cannabinoid system in the basal ganglia and its association with PD in three parts (eCBs, cannabinoid receptors, and factors regulating the cannabinoid metabolism) and summarized the mechanisms of action related to the cannabinoid analogs currently aimed at treating PD. The shortcomings identified from previous studies and the directions that should be explored in the future will provide insights into new approaches and ideas for the future development of cannabinoid-based drugs and the treatment of PD.
Distribution and Controlling Growth Factors of Ooids in Qinghai Lake, Northern Tibet Plateau, China

Ooids are coated carbonate grains, which exist in shallow water marine and lacustrine environments. There is an ongoing debate about whether the origin of ooids is inorganic or organic. Qinghai Lake is the largest inland lake in China, and ooids are seen on the lake shore. This paper focuses on whether environmental energy has an impact on the growth and size of ooids. Through hydrochemical analysis, thin section observation, and scanning electron microscope, the carbonate coats of beach sands from Qinghai Lake were studied. The research shows that the carbonate-coated grain content from the different shores of the lake present variations. The hydrodynamics and particularly the waves seem to control the distribution of carbonate coats in the lake shore, not the hydrochemical condition. In addition, the integrity and thickness of carbonate coats from the shores with a strong hydrodynamic force are high and thick, respectively. The carbonate coats are often observed on medium-grained sands, and the maximum carbonate-coated grain occurred under the strongest waves, indicating that ooids can be produced only when hydrodynamic force and particle size are well matched. Bacteria or extracellular polymeric substances are not observed within the ooid cortices by scanning electron microscopy. So, bacteria may not be a major factor in the formation and growth of ooids, but hydrodynamic forces appear to play a great role in carbonate grain coat distribution, integrity, thickness, and ooid grain size.