Miracle-dz/UniFAR-FTUs huggingface dataset:
Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization

Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-domain overfitting, resulting in models generating acceptable narratives only for hatred similar to training data, with little portability to other targets or to real-world toxic language. This paper introduces novel attention regularization methodologies to improve the generalization capabilities of PLMs for counter narratives generation. Overfitting to training-specific terms is then discouraged, resulting in more diverse and richer narratives. We experiment with two attention-based regularization techniques on a benchmark English dataset. Regularized models produce better counter narratives than state-of-the-art approaches in most cases, both in terms of automatic metrics and human evaluation, especially when hateful targets are not present in the training data. This work paves the way for better and more flexible counter-speech generation models, a task for which datasets are highly challenging to produce.
Self-Similarity-Based and Novelty-based loss for music structure analysis

Music Structure Analysis (MSA) is the task aiming at identifying musical segments that compose a music track and possibly label them based on their similarity. In this paper we propose a supervised approach for the task of music boundary detection. In our approach we simultaneously learn features and convolution kernels. For this we jointly optimize -- a loss based on the Self-Similarity-Matrix (SSM) obtained with the learned features, denoted by SSM-loss, and -- a loss based on the novelty score obtained applying the learned kernels to the estimated SSM, denoted by novelty-loss. We also demonstrate that relative feature learning, through self-attention, is beneficial for the task of MSA. Finally, we compare the performances of our approach to previously proposed approaches on the standard RWC-Pop, and various subsets of SALAMI.
Addressing Imperfect Symmetry: a Novel Symmetry-Learning Actor-Critic Extension

Symmetry, a fundamental concept to understand our environment, often oversimplifies reality from a mathematical perspective. Humans are a prime example, deviating from perfect symmetry in terms of appearance and cognitive biases (e.g. having a dominant hand). Nevertheless, our brain can easily overcome these imperfections and efficiently adapt to symmetrical tasks. The driving motivation behind this work lies in capturing this ability through reinforcement learning. To this end, we introduce Adaptive Symmetry Learning (ASL) $\unicode{x2013}$ a model-minimization actor-critic extension that addresses incomplete or inexact symmetry descriptions by adapting itself during the learning process. ASL consists of a symmetry fitting component and a modular loss function that enforces a common symmetric relation across all states while adapting to the learned policy. The performance of ASL is compared to existing symmetry-enhanced methods in a case study involving a four-legged ant model for multidirectional locomotion tasks. The results demonstrate that ASL is capable of recovering from large perturbations and generalizing knowledge to hidden symmetric states. It achieves comparable or better performance than alternative methods in most scenarios, making it a valuable approach for leveraging model symmetry while compensating for inherent perturbations.
Patched Line Segment Learning for Vector Road Mapping

This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and predict a suitable line segment within each patch. This strategy enables us to capture spatial and structural cues from these patch-based line segments, simplifying the process of constructing the road network graph without the necessity of additional neural networks for connectivity. In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture. Furthermore, our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs in terms of GPU hours.
Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks

In this study, we investigated whether self-supervised pretraining could produce a neural network feature extractor applicable to multiple classification tasks in B-mode lung ultrasound analysis. When fine-tuning on three lung ultrasound tasks, pretrained models resulted in an improvement of the average across-task area under the receiver operating characteristic curve (AUC) by 0.032 and 0.061 on local and external test sets respectively. Compact nonlinear classifiers trained on features outputted by a single pretrained model did not improve performance across all tasks; however, they reduced inference time by 49% compared to the serial execution of separate fine-tuned models. When training using 1% of the available labels, pretrained models consistently outperformed fully supervised models, with a maximum observed test AUC increase of 0.396 for the task of view classification. Overall, the results indicate that self-supervised pretraining is a useful strategy for producing initial weights for lung ultrasound classifiers.
Strain Engineering of Magneto-optical Properties in C$_3$B/C$_3$N van der Waals Heterostructure

Carbon-based bilayer van der Waals (vdW) materials are attracting much attention due to their predicted interesting physical properties. Here, we theoretically investigate electronic and optical properties of C$_3$B/C$_3$N vdW heterostructure (HTS) under external magnetic field and mechanical strain. The tight-binding model of the system is constructed to include the strain-induced modification of the hopping interactions. The influence of a uniform perpendicular magnetic field is included by using the Peierls substitution method. We observe the intriguing electronic and optical characteristics of the HTS under mechanical strain, covering the band inversion, alteration of band gap and optical gap, distortion of band-edge states, as well as significant enhancement of optical absorption. Furthermore, the interplay between external magnetic field and biaxial strain leads to exotic features of quantization and optical spectra. This work provides important information for the comprehension of the engineering of materials by external effects. Our study suggests that C$_3$B/C$_3$N vdW HTS is a promising candidate for next-generation electronic and optoelectronic devices.
Investigating the Effect of Winds and Storms on Shoreline Erosion along the Coast of Tanzania

Wind data spanning 28 years (1977-2004) from four meteorological stations (Tanga, Zanzibar, Dar es Salaam and Mtwara) were analyzed to elucidate the effect of winds on coastal erosion along the coast of Tanzania. The investigation entailed wavelet analysis of the time series of winds which were related with past erosion events at each site. The study also analyzed qualitatively the incidences of tropical storms in relation to coastal erosion events along the coast. The results revealed that most of the critical erosion incidents in the past few decades were associated with periods of occurrence of extreme wind speeds and tropical storms. Furthermore, the results indicated that the wind speeds and the frequency and intensity of tropical storms had generally been increasing during the past three decades. The findings were consistent to those recently reported for the coast of Dar es Salaam. The increasing trends of winds and storms are considered to have direct impact on the wave climate with increased risk of shoreline wave-induced erosion that is already affecting relatively many parts of Tanzania.
Estimation of spectral functionals for Levy-driven continuous-time linear models with tapered data

The paper is concerned with the nonparametric statistical estimation of linear spectral functionals for Levy-driven continuous-time stationary linear models with tapered data. As an estimator for unknown functional we consider the averaged tapered periodogram. We analyze the bias of the estimator and obtain sufficient conditions assuring the proper rate of convergence of the bias to zero, necessary for asymptotic normality of the estimator. We prove a a central limit theorem for a suitable normalized stochastic process generated by a tapered Toeplitz type quadratic functional of the model. As a consequence of these results we obtain the asymptotic normality of our estimator.
Impact of Phylogenetically Diverse Bacterial Endophytes of Bergenia pacumbis on Bergenin Production in the Plant Cell Suspension Cultures

Abstract Plant in vitro cultures are potential sources for secondary metabolites. However, low productivity is often a major drawback for industrial application. Elicitation is an important strategy to improve product formation in vitro . In this context, endophytes are of special interest as biotic elicitors due to their possible interaction with the metabolism of the host plant. A total of 128 bacterial endophytes were isolated from the medicinal plant Bergenia pacumbis and taxonomically classified using 16S rRNA gene sequencing. Five strains belonging to different genera were grown in lysogeny broth and tryptic soy broth medium and cells as well as spent media were used as elicitors in cell suspension cultures of B. pacumbis . Production of the main bioactive compound bergenin was enhanced 3-fold (964 µg/g) after treatment with cells of Moraxella sp. or spent tryptic soy broth medium of Micrococcus sp. These results indicate that elicitation of plant cell suspension cultures with endophytic bacteria is a promising strategy for enhancing the production of desired plant metabolites.
Topology Identification of Multiplex Delayed Networks

Despite the increasing attention of multiplex networks in recent years, topology identification of the network is challenging. This brief addresses the problem of topology identification of multiplex delayed network, where each layer has its own time delay. An algorithm for identifying the topologies of all the layers and the system parameters of the nodes is proposed. A duplex network is provided to validate the effectiveness of the theoretical results. Numerical results show that the inner couplings and the time delays have significant impact on topology identification. With the fixed inner couplings (time delay) of one layer, the logarithmic growth of other layer’s inner couplings (time delay) in a certain range is beneficial to the identification performance.
A closer look at the relationship of cognitive and metacognitive strategy use to EFL reading achievement test performance

This article reports on an investigation into the relationship of test-takers’ use of cognitive and metacognitive strategies to the EFL (English as a foreign language) reading test performance. The study employed both quantitative and qualitative data analyses. The 384 students enrolled in a fundamental English course at a Thai university took an 85-item, multiple-choice reading comprehension achievement test, followed by a cognitive-metacognitive questionnaire on how they thought while completing the test. Eight of these students (4 highly successful and 4 unsuccessful)were selected for retrospective interviews. The results suggested that (1) the use of cognitive and metacognitive strategies had a positive relationship to the reading test performance; and (2) highly successful test-takers reported significantly higher metacognitive strategy use than the moderately successful ones who in turn reported higher use of these strategies than the unsuccessful test-takers. Discussion of the findings and implications for further research are articulated.
Exploring the supervisory relationship in the context of culturally responsive supervision: a supervisee’s perspective

Abstract Clinical supervision is a relationship-based education, considered crucial in providing clinicians with emotional support, skill development and improving client outcomes. Culturally responsive supervision assumes that culture permeates clinical practice and supervision. Culturally responsive supervisors promote the development of cultural competence in supervision, through modelling, reflective discussion and responsivity. Research has demonstrated that greater perceived cultural responsivity in supervision may result in greater satisfaction for supervisees, particularly those from racially or ethnically minoritised (REM) backgrounds. The current study explores supervisee perceptions of culturally responsive supervision and supervisory relationships between different supervisory dyads, comprising supervisees from REM and White backgrounds. This was a cross-sectional design incorporating a between-groups comparison. Trainee and qualified clinical psychologists, counselling psychologists and CBT therapists (n = 222) completed an online survey. Perceptions of cultural responsivity and the supervisory relationship were explored. Participants provided information about their supervisor’s race and ethnicity and their own, and were organised into four supervisory dyads. Participants from REM backgrounds in dyads with White supervisors perceived their supervision as significantly less culturally responsive, with significantly lower quality supervisory relationships. Greater perceived cultural responsivity in supervision significantly predicted better supervisory relationships (regardless of participant cultural background). Findings suggest that culturally responsive supervisory practices may play an important role in developing cultural competence and strengthening the supervisory relationship, particularly in cross-cultural supervisory dyads. Findings present important clinical and theoretical implications. Key learning aims (1) To understand the need for cultural responsivity within the context of clinical supervision. (2) To explore the differences between cross-cultural and culturally similar supervisory dyads in perceptions of cultural responsivity in supervision. (3) To explore the differences between cross-cultural and culturally similar supervisory dyads in perceptions of the quality of the supervisory relationship. (4) How does culturally unresponsive supervision impact supervisee experiences?
Stewardship 2.0: Embracing elements of implementation science to enhance everyday antimicrobial Stewardship efforts

Abstract This article explores the utility of implementation science (IS) as a method to promote the effective uptake of antimicrobial stewardship processes. Elements of IS can be readily incorporated into QI work and used as a platform to extend stewardship reach. As stewards are stretched to do more, IS can be a potential vehicle to ensure that our collective work is impactful, sustainable, and contributes more broadly to clinically relevant improvements.
Rewiring of the TCR signalosome in natural intestinal Intraepithelial T lymphocytes drives non-deletional tolerance

Intraepithelial T lymphocytes (T-IEL) are a large population of cytotoxic T cells that protect the small intestinal epithelium against pathogens. Based on ontogeny, T-IEL can be categorized into two major subsets: induced and natural. Natural T-IEL are agonistically selected in the thymus on self-antigens before migrating directly to the small intestine. Despite having self-reactive T cell antigen receptors (TCR), natural T-IEL are maintained in a tolerized state in the gut by unknown mechanisms. We therefore investigated TCR signaling in T-IEL using multiplexed fluorescent cell barcoding, phosphoproteomics and TCR signaling reporter mouse models, which revealed that TCR signaling is intrinsically suppressed in natural, but not induced, T-IEL. Unexpectedly, we discover that this cell intrinsic suppression was mediated through altered TCR signalosome components. Specifically, downregulation of the key signaling adaptor, Linker for activation of T cells (LAT) during thymic selection is a vital checkpoint for the development and tolerization of natural IELs. Thus, TCR signaling is rewired in self-reactive natural T-IEL to promote tolerance and prevent inappropriate inflammation in the gut. One sentence summary Self-reactive natural intestinal intraepithelial T lymphocytes are developmentally tolerized by rewiring the T cell antigen receptor signaling pathway through the downregulation of the adaptor protein, LAT.
Soil Prokaryotic and Fungal Biome Structures Associated with Crop Disease Status across the Japan Archipelago

Archaea, bacteria, and fungi in the soil are increasingly recognized as determinants of agricultural productivity and sustainability. A crucial step for exploring soil microbiomes with high ecosystem functions is to perform statistical analyses on potential relationship between microbiome structure and functions based on comparisons of hundreds or thousands of environmental samples collected across broad geographic ranges. In this study, we integrated agricultural field metadata with microbial community analyses by targeting > 2,000 soil samples collected along a latitudinal gradient from cool-temperate to subtropical regions in Japan (26.1– 42.8 °N). The data involving 632 archaeal, 26,868 bacterial, and 4,889 fungal operational taxonomic units detected across the fields of 19 crop plant species allowed us to conduct statistical analyses (permutational analyses of variance, generalized linear mixed models, and randomization analyses) on relationship among edaphic factors, microbiome compositions, and crop disease prevalence. We then examined whether the diverse microbes form species sets varying in potential ecological impacts on crop plants. A network analysis suggested that the observed prokaryotes and fungi were actually classified into several species sets (network modules), which differed substantially in associations with crop disease prevalence. Within the network of microbe-to-microbe coexistence, ecologically diverse microbes, such as an ammonium-oxidizing archaeum, an antibiotics-producing bacterium, and a potentially mycoparasitic fungus, were inferred to play key roles in shifts between crop-disease-promotive and crop-disease-suppressive states of soil microbiomes. The bird’s-eye view of soil microbiome structure will provide a basis for designing and managing agroecosystems with high disease-suppressive functions. IMPORTANCE Understanding how microbiome structure and functions are organized in soil ecosystems is one of the major challenges in both basic ecology and applied microbiology. Given the ongoing worldwide degradation of agroecosystems, building frameworks for exploring structural diversity and functional profiles of soil microbiomes is an essential task. Our study provides an overview of cropland microbiome states in light of potential crop-disease-suppressive functions. The large dataset allowed us to explore highly functional species sets that may be stably managed in agroecosystems. Furthermore, an analysis of network architecture highlighted species that are potentially used to cause shifts from disease-prevalent states of agroecosystems to disease-suppressive states. By extending the approach of comparative analyses towards broader geographic ranges and diverse agricultural practices, agroecosystem with maximized biological functions will be further explored.
Network effects of traumatic brain injury: from infra slow to high frequency oscillations

Traumatic brain injury (TBI) can have a multitude of effects on neural functioning. In extreme cases, TBI can lead to seizures both immediately following the injury as well as persistent epilepsy over years to a lifetime. However, mechanisms of neural dysfunctioning after TBI remain poorly understood. To address these questions, we analyzed experimental data and developed a biophysical network model implementing effects of ion concentration dynamics and homeostatic synaptic plasticity to test effects of TBI on the brain network dynamics. We focus on three primary phenomena that have been reported in vivo after TBI: an increase in infra slow oscillations (<0.1 Hz), increase in delta (0.1 - 4 Hz) power, and the emergence of high frequency oscillations (HFOs) in the gamma range (30 - 100 Hz). We show that the infra slow oscillations can be directly attributed to extracellular potassium fluctuations, while the existence and characterization of HFOs is related to the increase in strength of synaptic weights from homeostatic synaptic scaling. The experimentally found transient increase in delta power can be attributed to the inter-HFO timings. We then show that buildup of high frequency oscillations in the injured region can lead to seizure-like events that span all neurons in the network; additional seizures can then be initiated in previously healthy regions. This study brings greater understanding of network effects of TBI, and how they can give rise to epileptic activity. This lays the foundation to begin investigating how injured networks can be healed and seizures prevented. Significance Statement This project delineates and attempts to explain abnormalities seen in human brain following traumatic brain injury (TBI). TBI can lead to the development of seizures, which may last a lifetime and often become resistant to pharmaceutical treatments. The study identified key mechanisms responsible for occurrence of three characteristic changes in spatio-temporal network dynamics following TBI. This model provides predictions that can serve as a testing ground for potential therapeutic approaches.
Phage libraries screening on P53 : yield improvement by zinc and a new parasites-integrating analysis and rationale

P53 is a transcription factor that controls a variety of genes, primarily involved in cell cycle and other processes related to cell survival and death. We have isolated peptides targeting P53 (protein and domains) using the “phage display” technique. Interestingly, adding ZnCl2 at 5-10 mM in panning solutions helped to recover more plaque-forming units at least at round one of the screening. Subtractive docking analyses were designed by using a pool of common redundant peptides known as parasites. This rationale helped us differentiate between possibly specific and non-specific bindings. We found notable differences in docking characteristics between different sets of peptides either related to different targets or related to zinc-conditions. The set of zinc-related peptides shows advantageous docking profiles: sharper binding for some positions and distinct exclusive bound residues, including the relevant R248 and R273. Zinc would have modulating/helping role in the targeting of protein P53 by phage displayed peptides in addition to an enhancement action on bacterial infection.