Miracle-dz/UniFAR-FTUs huggingface dataset:
A Measure for Linguistic Coherence in Spatial Language Variation

Based on historical dialect data we introduce a local measure of linguistic coherence in spatial language variation aiming at the identification of regions which are particularly sensitive to language variation and change. Besides, we use a measure of global coherence for the automated detection of linguistic items (e.g., sounds or morphemes) with higher or lesser language variation. The paper describes both the data and the method and provides analyses examples.
A weakly supervised textual entailment approach to zero-shot text classification

Zero-shot text classification is a widely studied task that deals with a lack of annotated data. The most common approach is to reformulate it as a textual entailment problem, enabling classification into unseen classes. This work explores an effective approach that trains on a weakly supervised dataset generated from traditional classification data. We empirically study the relation between the performance of the entailment task, which is used as a proxy, and the target zero-shot text classification task. Our findings reveal that there is no linear correlation between both tasks, to the extent that it can be detrimental to lengthen the fine-tuning process even when the model is still learning, and propose a straightforward method to stop training on time. As a proof of concept, we introduce a domain-specific zero-shot text classifier that was trained on Microsoft Academic Graph data. The model, called SCIroShot, achieves state-of-the-art performance in the scientific domain and competitive results in other areas. Both the model and evaluation benchmark are publicly available on HuggingFace and GitHub.
Prediction of BBB permeation based on molecular indices

Aim To quantitatively predict the BBB permeation(logBB) and determine the key descriptors largely influencing the BBB permeation in structure.Methods A dataset composed of 190 diversified compounds,was studied by principal component analysis and multivariate linear regression analysis methods.Results and conclusion The resulting model showed satisfactory statistical results (R2:0.642,SEP:0.496) with 10 descriptors,and proper predictability were validated by the test set(R2:0.642,SEE:0.495). Those key descriptors were identified,and they are valuable and helpful for aiding further screening and development of drugs.
Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning

Multi-hop reasoning, a prevalent approach for query answering, aims at inferring new facts along reasonable paths over a knowledge graph.Reinforcement learning methods can be adopted by formulating the problem into a Markov decision process.However, common suffering within RL-based reasoning models is that the agent can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation.In this work, we take a deep dive into this phenomenon and define a metric named Path Spuriousness (PS), to quantitatively estimate to what extent a path is spurious.Guided by the definition of PS, we design a model with a new reward that considers both answer accuracy and path reasonableness.We test our method on four datasets and experiments reveal that our method considerably enhances the agent’s capacity to prevent spurious paths while keeping comparable to state-of-the-art performance.
Multimodal Graph Transformer for Multimodal Question Answering

Despite the success of Transformer models in vision and language tasks, they often learn knowledge from enormous data implicitly and cannot utilize structured input data directly. On the other hand, structured learning approaches such as graph neural networks (GNNs) that integrate prior information can barely compete with Transformer models.In this work, we aim to benefit from both worlds and propose a novel Multimodal Graph Transformer for question answering tasks that requires performing reasoning across multiple modalities. We introduce a graph-involved plug-and-play quasi-attention mechanism to incorporate multimodal graph information, acquired from text and visual data, to the vanilla self-attention as effective prior. In particular, we construct the text graph, dense region graph, and semantic graph to generate adjacency matrices, and then compose them with input vision and language features to perform downstream reasoning. Such a way of regularizing self-attention with graph information significantly improves the inferring ability and helps align features from different modalities.We validate the effectiveness of Multimodal Graph Transformer over its Transformer baselines on GQA, VQAv2, and MultiModalQA datasets.
Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation

Recent task-oriented dialogue systems are trained on annotated dialogues, which, in turn, reflect certain domain information (e.g., restaurants or hotels in a given region). However, when such domain knowledge changes (e.g., new restaurants open), the initial dialogue model may become obsolete, decreasing the overall performance of the system. Through a number of experiments, we show, for instance, that adding 50{% of new slot-values reduces of about 55{% the dialogue state-tracker performance. In light of such evidence, we suggest that automatic adaptation of training dialogues is a valuable option for re-training obsolete models. We experimented with a dialogue adaptation approach based on fine-tuning a generative language model on domain changes, showing that a significant reduction of performance decrease can be obtained.
Yu Sheng: Human-in-Loop Classical Chinese Poetry Generation System

The development of poetry generation system mainly focuses on enhancing the capacity of generation model. However, the demands of customization and polishing are generally ignored, which highly reduces the scope of application. In this work, we present Yu Sheng, a web-based poetry generation system that is featured a human-in-loop generation framework, providing various customization options for users with different backgrounds to engage in the process of poetry composition. To this end, we propose two methods and train the models that can perform constrained generation and fine-grained polishing. The automatic and human evaluation results show that our system has a strong ability to generate and polish poetry compared to other vanilla models. Our system is publicly accessible at: https://yusheng.cis. um.edu. mo.
ITMT: Interactive Topic Model Trainer

Topic Modeling is a commonly used technique for analyzing unstructured data in various fields, but achieving accurate results and useful models can be challenging, especially for domain experts who lack the knowledge needed to optimize the parameters required by this natural language processing technique. From this perspective, we introduce an Interactive Topic Model Trainer (ITMT) developed within the EU-funded project IntelComp. ITMT is a user-in-the-loop topic modeling tool presented with a graphical user interface that allows the training and curation of different state-of-the-art topic extraction libraries, including some recent neural-based methods, oriented toward the usage by domain experts. This paper reviews ITMT’s functionalities and key implementation aspects in this paper, including a comparison with other tools for topic modeling analysis.
Towards Generation and Recognition of Humorous Texts in Portuguese

Dealing with humor is an important step to develop Natural Language Processing tools capable of handling sophisticated semantic and pragmatic knowledge. In this context, this PhD thesis focuses on the automatic generation and recognition of verbal punning humor in Portuguese, which is still an underdeveloped language when compared to English. One of the main goals of this research is to conciliate Natural Language Generation computational models with existing theories of humor from the Humanities while avoiding mere generation by including contextual information into the generation process. Another point that is of utmost importance is the inclusion of the listener as an active part in the process of understanding and creating humor; we hope to achieve this by using concepts from Recommender Systems in our methods. Ultimately, we want to not only advance the current state-of-the-art in humor generation and recognition, but also to help the general Portuguese-speaking research community with methods, tools and resources that may aid in the development of further techniques for this language. We also expect our systems to provide insightful ideas about how humor is created and perceived by both humans and machines.
Exceptional curves on Del Pezzo surfaces

We classify all cases of exceptional curves on Del Pezzo surfaces, which turn out to be the smooth plane curves and some other cases with Clifford dimension 3. Moreover, the property of being exceptional holds for all curves in the complete linear system. We use this study to extend the results of Pareschi [17] on the constancy of the gonality and Clifford index of smooth curves in a complete linear system on Del Pezzo surfaces of degrees ≥ 2 to the case of Del Pezzo surfaces of degree 1, where we explicitly classify the cases where the gonality and Clifford index are not constant.
Memory-efficient Temporal Moment Localization in Long Videos

Temporal Moment Localization is a challenging multi-modal task which aims to identify the start and end timestamps of a moment of interest in an input untrimmed video, given a query in natural language. Solving this task correctly requires understanding the temporal relationships in the entire input video, but processing such long inputs and reasoning about them is memory and computationally expensive. In light of this issue, we propose Stochastic Bucket-wise Feature Sampling (SBFS), a stochastic sampling module that allows methods to process long videos at a constant memory footprint. We further combine SBFS with a new consistency loss to propose Locformer, a Transformer-based model that can process videos as long as 18 minutes. We test our proposals on relevant benchmark datasets, showing that not only can Locformer achieve excellent results, but also that our sampling is more effective than competing counterparts. Concretely, SBFS consistently improves the performance of prior work, by up to 3.13{% in the mean temporal IoU, leading to a new state-of-the-art performance on Charades-STA and YouCookII, while also obtaining up to 12.8x speed-up at testing time and reducing memory requirements by up to 5x.
Brain network flexibility as a predictor of skilled musical performance

Interactions between the body and the environment are dynamically modulated by upcoming sensory information and motor execution. To adapt to this behavioral state-shift, brain activity must also be flexible and possess a large repertoire of brain networks so as to switch them flexibly. Recently, flexible internal brain communications, i.e., brain network flexibility, have come to be recognized as playing a vital role in integrating various sensorimotor information. Therefore, brain network flexibility may be one of the key factors that define sensorimotor skill. However, little is known about how flexible communications within a brain characterizes inter-individual variation of sensorimotor skill and trial-by-trial variability within individuals. To address this, we recruited highly skilled musical performers (i.e. brass instrumentalists) and used a novel approach that combined multichannel-scalp electroencephalography (EEG) recordings, behavioral measurements of musical performance, and mathematical approaches to extract brain network flexibility. We found that brain network flexibility immediately before initiating the performance predicted inter-individual differences in the precision of tone timbre (as represented by spectral centroid of the sound), but not trial-by-trial variability at the individual level. Furthermore, brain network flexibility in broader cortical regions, rather than specific local cortical regions, predicted skilled musical performance, indicating that whole-cortical fluctuations determine individual skill. Our results provide novel evidence that brain network flexibility during movement preparation plays an important role in skilled sensorimotor performance and our findings have potentials for designing a new approach to predict an individual’s skill from neural dynamics and a new intervention tool to facilitate physical education.
TFOFinder: Python program for identifying purine-only double-stranded stretches in the predicted secondary structure(s) of RNA targets

Nucleic acid probes are valuable tools in biology and chemistry and are indispensable for PCR amplification of DNA, RNA quantification and visualization, and downregulation of gene expression. Recently, triplex forming oligonucleotides (TFO) have received increased attention due to their improved selectivity and sensitivity in recognizing purine-rich double-stranded RNA regions at physiological pH by incorporating backbone and base modifications. For example, triplex forming peptide nucleic acid (PNA) oligomers have been used for imaging structured RNA in cells and inhibiting influenza A replication. Although a handful of programs are available to identify triplex target sites (TTS) in DNA, none are available that find such regions in structured RNAs. Here, we describe TFOFinder, a Python program that facilitates the identification of intramolecular purine-only RNA duplexes that are amenable to forming parallel triple helices (pyrimidine/purine/pyrimidine). We performed genome- and transcriptome-wide analyses of TTS in Drosophila melanogaster and found that only 0.3% (123) of total unique transcripts (35,642) show the potential of forming 12-purine long triplex forming sites that contain at least one guanine. Using minimization algorithms, we predicted the secondary structure(s) of these transcripts, and using TFOFinder, we found that 97 (79%) of the identified 123 transcripts are predicted to fold to form at least one TTS for parallel triple helix formation. The number of transcripts with potential purine TTS increases when the strict search conditions are relaxed by decreasing the length of the probes or by allowing up to two pyrimidine inversions or 1-nucleotide bulge in the target site. These results are encouraging for the use of modified triplex forming probes for live imaging of endogenous structured RNA targets, such as pre-miRNAs, and inhibition of target-specific translation and viral replication.
A high-throughput nematode sensory assay reveals an inhibitory effect of ivermectin on parasite gustation

Sensory pathways first elucidated in Caenorhabditis elegans are conserved across free-living and parasitic nematodes, even though each species responds to a diverse array of compounds. Most nematode sensory assays are performed by tallying observations of worm behavior on two-dimensional planes using agarose plates. These assays have been successful in the study of volatile sensation but are poorly suited for investigation of water-soluble gustation or parasitic nematodes without a free-living stage. In contrast, gustatory assays tend to be tedious, often limited to the manipulation of a single individual at a time. We have designed a nematode sensory assay using a microfluidics device that allows for the study of gustation in a 96-well, three-dimensional environment. This device is suited for free-living worms and parasitic worms that spend their lives in an aqueous environment, and we have used it to show that ivermectin inhibits the gustatory ability of vector-borne parasitic nematodes.
A Quadrivalent mRNA immunization elicits potent immune responses against vaccinia and monkeypox viral antigens – a step closer to a broad orthopoxvirus vaccine

The global outbreak of the 2022 monkeypox virus infection of human raised the public health concerns of the threat of human-to-human transmission of zoonotic diseases. Given the evidence that other orthopoxviruses including cowpox and camelpox were also reported infectious to human, and that the reemerging risk of smallpox as a bioterrorist or accidental laboratory escape exists, there is an urgent need to develop a poxvirus vaccine with a broad protection of orthopoxviruses to stockpile for future emergency. Extensive studies of vaccinia virus (VACV) suggested that multiple VACV antigens, such as A27, L1, A33 and B5, showed high level similarity in terms of immunogenicity to their respective homologous antigens of other orthopoxviruses. These findings paved the ground for VACV antigens to be used as potential vaccine targets for development of a universal poxvirus vaccine. In this study, we construct a novel poxvirus vaccine candidate, mRNA-ALAB-LNP, encoding four vaccinia viral antigens A27, L1, A33 and B5. Strong anti-L1-specific antibody and moderate anti-A33-, anti-A27- and anti-B5-specific antibody responses were induced in mice after a single immunization. The antibody responses to all four antigens were significantly boosted after the second shot with all IgG titers >5 logs and highest being anti-A33 IgG. The high level of binding antibodies showed potent neutralizing capability against vaccinia virus. Specific IFN-γ responses were detected to all four antigens with the highest cellular response being that induced by the same antigen, A33. When evaluating the cross reactivity, equivalent or better serum IgG responses were seen in responses to corresponding monkeypox antigens A35, M1, A29 and B6, in comparison to vaccinia antigens. Apparently, the mRNA vaccine encoding four vaccinia antigens induced immunity not only to vaccinia virus but also to monkeypox, suggesting that the mRNA-ALAB may be a candidate for potential vaccine development against infection of monkeypox, smallpox and possibly other orthopoxviruses.
Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex

Abstract Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.
Research on Synthetic Aperture Radar Image Denoising with Double Density Dual-Tree Complex Wavelet Transform

This paper presents an improved algorithm for suppressing the synthetic aperture radar image speckle noise,in which the double density dual tree complex wavelet transform(DD-DT CWT) was combined with the bivariate shrinkage function(BSF) with local variance estimation.The SAR image was firstly decomposed by the DD-DT CWT,the bivariate probability density function was used as statistic correlation model for wavelet coefficients and their parent,and the corresponding bivariate shrinkage function was obtained by Bayes′ estimation theory.Then the wavelet coefficients were shrunk by the BSF,in which different orientations were nonlinear processed adaptively,finally the denoised image was reconstructed by all the update coefficients.The algorithm presented was tested by simulated and actual SAR image,and compared with other methods.Results are presented to verify that,the new algorithm significantly outperforms the traditional wavelet-transformbased denoising algorithm,and effectively denoises the noisy image as well as preserves the particulars.The performance index of the denoised image were simultaneously improved.
Convectively unstable wave packets in spatially developing open flows and media with algebraically decaying tails

In this paper we extend our recently developed theory of global and absolute instabilities of spatially developing open flows and media with algebraically decaying tails to treating propagating unstable wave packets in such flows. No restriction is imposed on the rate of variability of the spatially developing state in the finite domain. In the case when the limit state in infinity, i.e. the associated uniform state, is stable, but the inhomogeneous flow is absolutely unstable, a relatively simple complete description of the characteristics of the instability in a moving frame of reference is obtained in terms of the characteristics of the absolute instability and of the dispersion–relation function of the associated uniform state. When the associated uniform state is stable and the inhomogeneous flow is absolutely stable the inhomogeneous flow is convectively stable. Hence, any unstable inhomogeneous flow having a stable associated uniform state is absolutely unstable.
A Review of Factors Affecting Cognitive Load in Immersive Virtual Learning Environment

Immersive virtual learning environment have great potential for application in education by virtue of its unique advantages. However, the complex information elements in immersive virtual learning environment are more likely to distract learners' attention, leading to higher cognitive load in learning process compared to traditional environment, which would reduce learners' interest and motivation. According to Cognitive Theory of Multimedia Learning (CTML), cognitive overload is detrimental to learners' internalization and transfer of knowledge. To reduce and optimize cognitive load, we must first know what exactly affect cognitive load. In this study, systematic literature review was used to figure out the factors that affect cognitive load in immersive virtual learning environment. For the presentation of results, this study reported the factors from three dimensions: learner, instructional design, and environmental design. This study further discussed how these factors affect cognitive load and the possible interaction between them. In addition, we gave practical suggestions from three perspectives: learners, educational implementers, and environmental designers. And a series of suggestions for future research are given at last, which hope to provide some avenues to explore.
Economic Dispatch with Valve, Ramp and Zone Constraints based on Particle Swarm Optimization

Power system operation can be save the energy using power Economic Dispatch (ED). It is used to be operation at optimal generation unit power for minimum fuel cost to meeting the specific load demand. The outlines of this article are the essential foundation for applying the Particle Swarm Optimization (PSO) algorithm for Economic Dispatch operation. It considers generating cost function parameters with practical constraints of ramp rate limits, prohibited operating zones, generation operating limits and valve-point loading. Several test systems of 6 units, 10 units, 13 units, and 15 generating units have been used to satisfied the propose algorithm.