Miracle-dz/UniFAR-FTUs huggingface dataset:
SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations

Although deep language representations have become the dominant form of language featurization in recent years, in many settings it is important to understand a model's decision-making process. This necessitates not only an interpretable model but also interpretable features. In particular, language must be featurized in a way that is interpretable while still characterizing the original text well. We present SenteCon, a method for introducing human interpretability in deep language representations. Given a passage of text, SenteCon encodes the text as a layer of interpretable categories in which each dimension corresponds to the relevance of a specific category. Our empirical evaluations indicate that encoding language with SenteCon provides high-level interpretability at little to no cost to predictive performance on downstream tasks. Moreover, we find that SenteCon outperforms existing interpretable language representations with respect to both its downstream performance and its agreement with human characterizations of the text.
This Land is {Your, My} Land: Evaluating Geopolitical Biases in Language Models

We introduce the notion of geopolitical bias – a tendency to report different geopolitical knowledge depending on the linguistic context. As a case study, we consider territorial disputes between countries. For example, for the widely contested Spratly Islands, would an LM be more likely to say they belong to China if asked in Chinese, vs. to the Philippines if asked in Tagalog? To evaluate if such biases exist, we first collect a dataset of territorial disputes from Wikipedia, then associate each territory with a set of multilingual, multiple-choice questions. This dataset, termed B ORDER L INES , consists of 250 territories with questions in 45 languages. We pose these question sets to language models, and analyze geopolitical bias in their responses through several proposed quantitative metrics. The metrics compare between responses in different question languages as well as to the actual geopolitical situation. The phenomenon of geopolitical bias is a uniquely cross-lingual evaluation, contrasting with prior work’s mono-lingual (mostly English) focus on bias evaluation. Its existence shows that the knowledge of LMs, unlike multilingual humans, is inconsistent across languages. DISCLAIMER: This paper contains examples that are politically sensitive.
FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery

The FLAIR #2 dataset hereby presented includes two very distinct types of data, which are exploited for a semantic segmentation task aimed at mapping land cover. The data fusion workflow proposes the exploitation of the fine spatial and textural information of very high spatial resolution (VHR) mono-temporal aerial imagery and the temporal and spectral richness of high spatial resolution (HR) time series of Copernicus Sentinel-2 satellite images. The French National Institute of Geographical and Forest Information (IGN), in response to the growing availability of high-quality Earth Observation (EO) data, is actively exploring innovative strategies to integrate these data with heterogeneous characteristics. IGN is therefore offering this dataset to promote innovation and improve our knowledge of our territories.
Un outil multilingue de construction semi-automatique de grammaires d'arbres adjoints. Application au français et à l'italien : Multilinguisme

Nous presentons la metagrammaire (MG) : un cadre de description linguistique modulaire, permettant, via compilation, la representation de TAG Lexicalisees. MG permet l'expression de generalisations syntaxiques et formalise des principes rendant compte de la bonne formation des structures lexicalisees, exprimes en utilisant les fonctions syntaxiques. Un outil implemente compile une MG en une LTAG, en operant les croisements valides de phenomenes. Nous decrivons une MG pour le francais (MGf) et une pour l'italien (MGi). La MGf a ete contruite a partir d'une LTAG existante (Abeille 1991) et a permis d'en augmenter la couverture. La MGi a ete construite a partir de la MGf. La compilation automatique fournit deux LTAG paralleles, compatibles pour des applications multilingues
Kenyan farmers appreciate the higher yield of 50% non-pollen producing Maize (Zea mays) hybrids

Summary Incorporating the dominant male sterile gene, Ms44, in new maize varieties results in 50% non-pollen producing (FNP) varieties. This makes the varieties more nitrogen efficient and increases yield directly by an average of 200 kg ha−1 across yield levels. However, as half of the plants do not shed pollen, the presence of Ms44 in an FNP variety is clearly visible. This technology can improve food production and security in the African maize-based agri-food systems, but only if accepted by farmers. Farmers were therefore invited to 11 on-farm, researcher managed trial sites of FNP varieties in Kenya over 2 years. They were asked to identify the traits they find important in evaluating maize varieties and to score the FNP varieties, as well as their conventional counterparts, on these criteria (including yield, resistance to pests, and cob size) and overall, using a five-point hedonic scale. In total, 2,697 farmers participated, of which 62% were women. Farmers mentioned many traits they find important, especially yield and related traits, early maturity, and drought resistance, but also tassel and pollen formation. In 2017, mid-season, participants scored FNP varieties lower than conventional varieties on tassel and pollen formation, indicating that farmers could distinguish the trait. FNP varieties still received higher scores for yield and overall evaluation. In mid-season 2018, participants no longer scored FNP varieties lower for pollen formation as they now understood the technology. In both years, at the end-season evaluation, scores for tassel formation were not different, but participants scored FNP varieties higher for yield and overall. We conclude that farmers recognized the FNP trait but did not mind it as they clearly favored its yield advantage. The FNP technology, therefore, has high potential not only to increase maize yields, food production, and food security in the agricultural systems of Africa but also to increase varietal turnover and the adoption of new, high-yielding, climate-smart maize hybrids.
The Use of Regression and Classification Algorithms for Layer Productivity Prediction in Naturally Fractured Reservoirs

Three regression algorithms and three classification algorithms have been applied to forecast naturally fractured layer productivity. The three regression algorithms are the regression of support vector machine (R-SVM), the back-propagation neural network (BPNN), and the multiple regression analysis (MRA), while the three classification algorithms are the classification of support vector machine (C-SVM), the naive Bayesian (NBAY), and the Bayesian successive discrimination (BAYSD). In general, when all these six algorithms are used to solve a real-world problem, they often produce different solution accuracies. When an algorithm is applied to a real-world problem, its solution accuracy is expressed with the total mean absolute relative residual for all samples, R(%). In this paper, three criteria have been proposed: 1) nonlinearity degree of a studied problem based on R(%) of MRA (weak if R(%) 30); 2) solution accuracy of a given algorithm application based on its R(%) (high if R(%) 30); and 3) results availability of a given algorithm application (applicable if R(%)<10, and inapplicable if R(%)≥10). A case study of naturally fractured layer productivity at an oilfield in Sichuan Province of China has been used to validate the proposed approach. This case study consists of two problems: regression and classification. For the regression problem, R-SVM, BPNN and MRA are inapplicable because their R(%) values are 4620, 44 and 5980, respectively. For the classification problem, however, C-SVM, NBAY and BAYSD are all applicable since their R(%) values are 0, 5.9 and 9.1, respectively. From the case study, it is concluded that: a) the three proposed criteria, and the rules of conversion from real number to integer number are practical; b) R(%) of MRA is used to measure the nonlinearity degree of a studied problem, and thus MRA should be used as a first choice; c) for classification problems, the preferable algorithm is C-SVM, NBAY, or BAYSD if the problems nonlinearity is weak or moderate, and BAYSD can also serve as a promising dimension-reduction tool; d) for regression problems, the preferable algorithm is BPNN, but MRA can serve as a promising dimension-reduction tool only when the problems are linear; e) if BPNN is inapplicable for a regression problem, it is proposed to change the problem from regression to classification by reasonable conversion rules, then apply C-SVM, NBAY, or BAYSD; and f) comparing with C-SVM, BAYSD is conditionally better.
Small Values of |L'/L(1,χ)|

Abstract In this article, we investigate the quantity , as over the primes, where is the Dirichlet L-function attached to a non-principal Dirichlet character modulo q. Our main result shows that . We also compute mq for every odd prime q up to 10 . As a consequence we numerically verified that for every prime q, , we have , with . In particular, this shows that for every non-principal Dirichlet character χ mod q where is prime, answering a question of Gun, Murty and Rath in this range. We also provide some statistics and scatter plots regarding the mq-values. The programs used and the computational results described here are available at the following web address: http://www.math.unipd.it/∼languasc/smallvalues.html.
Antiracism Approaches Through an Outsider-Within Positionality: Making Black Women’s Lives Matter in Art Education

This study investigated the narratives and racial experiences of Black women K–12 art educators navigating the White field of art education. Drawing from a Black feminist outsider-within positionality framework, their shared stories reveal how Black women art educators are unprivileged insiders within their schools and outsiders to a social system that privileges White people as the dominant group in art education. Outsider-within positionality is foundational in providing a platform for revealing the exclusion of Black women’s identities in White spaces. A Black feminist approach to narrative inquiry is a crucial methodology in identifying why Black women matter in art education. Their interviews expose the experiences of racist obstacles that hinder their progress as art educators. As a result, approaches to implementing gender equity with antiracist strategies are presented for an inclusive and safe environment for Black women art educators and create a critical dialogue in art education at large.
Probabilistic visual tracking based on adaptive appearance model

A robust and adaptive appearance model is proposed,in which the value of each pixel over time is modeled by a mixture of Gaussians.To adapt to changes in object appearance,an online expectation maximization(EM) algorithm is developed to update the Gaussian parameters.When estimating the target state,particle filter is adopted,and the observation model is designed based on the adaptive appearance model.Occlusion is handled using a robust estimation technique.Numerous experimental results show that the proposed algorithm can track targets well under illumination changes,large pose variations,and partial or full occlusions.
Pluralistic Islamic Religious Education: A Vision for Indonesia

This essay argues for the development of a pluralistic Islamic religious education in Indonesia based on the principles of positive pluralism, in which students can hold tightly to their convictions while at the same time understand, accept, and engage with those who differ in beliefs. Specifically, this essay focuses on how to develop an Islamic Religious Education—Pendidikan Agama Islam (PAI) in Indonesian—that is pluralistic, shaping students who are open-minded, tolerant, and have a positive outlook on accepting and working together in the midst of differences.
Neurophysiological trajectories in Alzheimer’s disease progression

Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.
WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance

The rodent vibrissal system remains pivotal in advancing neuroscience research, particularly for studies of cortical plasticity, learning, decision-making, sensory encoding and sensorimotor integration. While this model system provides notable advantages for quantifying active tactile input, it is hindered by the labor-intensive process of curating touch events across millions of video frames. Even with the aid of automated tools like the Janelia Whisker Tracker, millisecond-accurate touch curation often requires >3 hours of manual review / million video frames. We address this limitation by introducing Whisker Automatic Contact Classifier (WhACC), a python package designed to identify touch periods from high-speed videos of head-fixed behaving rodents with human-level performance. For our model design, we train ResNet50V2 on whisker images and extract features. Next, we engineer features to improve performance with an emphasis on temporal consistency. Finally, we select only the most important features and use them to train a LightGBM classifier. Classification accuracy is assessed against three expert human curators on over one million frames. WhACC shows pairwise touch classification agreement on 99.5% of video frames, equal to between-human agreement. Additionally, comparison between an expert curator and WhACC on a holdout dataset comprising nearly four million frames and 16 single-unit electrophysiology recordings shows negligible differences in neural characterization metrics. Finally, we offer an easy way to select and curate a subset of data to adaptively retrain WhACC. Including this retraining step, we reduce human hours required to curate a 100 million frame dataset from ∼333 hours to ∼6 hours.
An RNA origami robot that traps and releases a fluorescent aptamer

RNA nanotechnology aims at using RNA as a programmable material to create self-assembling nanodevices for application in medicine and synthetic biology. RNA devices have been developed by adopting mechanisms such as allosteric binding and toehold-mediated strand displacement. There are, however, no examples of RNA “robotic” devices that sense, compute, and actuate through mechanical reconfiguration as has been demonstrated in DNA nanotechnology. Here we use the RNA origami method to prototype an RNA robotic device, named the “Traptamer”, that senses two RNA key strands, acts as a Boolean AND gate, and activates the fluorescent aptamer iSpinach through release from a mechanical trap. The Traptamer depends on binding of two different RNA key strands to achieve full activation and can be reversed by addition of two complementary RNA anti-key strands. Cryo-EM of the closed Traptamer structure at 5.45 Å resolution reveals a hinge-like mechanical distortion of the iSpinach motif. Our RNA robot prototype opens the door to build more sophisticated RNA machines that use sensing, computing, and acting modules to precisely control RNA functionalities.
Three-dimensional Shear Wave Elastography Using Acoustic Radiation Force and A 2-D Row-Column Addressing (RCA) Array

Acoustic radiation force (ARF)-based shear wave elastography (SWE) is a clinically available ultrasound imaging mode that noninvasively and quantitatively measures tissue stiffness. Current implementations of ARF-SWE are largely limited to 2-D imaging, which does not provide robust estimation of heterogeneous tissue mechanical properties. Existing 3-D ARF-SWE solutions that are clinically available are based on wobbler probes, which cannot provide true 3-D shear wave motion detection. Although 3-D ARF-SWE based on 2-D matrix arrays have been previously demonstrated, they do not provide a practical solution because of the need for a high channel-count ultrasound system (e.g., 1024-channel) to provide adequate volume rates and the delicate circuitries (e.g., multiplexers) that are vulnerable to the long-duration “push” pulses. To address these issues, here we propose a new 3-D ARF-SWE method based on the 2-D row-column addressing (RCA) array which has a much lower element count (e.g., 256), provides an ultrafast imaging volume rate (e.g., 2000 Hz), and can withstand the push pulses. In this study, we combined the comb-push shear elastography (CUSE) technique with 2-D RCA for enhanced SWE imaging field-of-view. In vitro phantom studies demonstrated that the proposed method had robust 3-D SWE performance in both homogenous and inclusion phantoms. An in vivo study on a breast cancer patient showed that the proposed method could reconstruct 3-D elasticity maps of the breast lesion, which was validated using a commercial ultrasound scanner. These results demonstrate strong potential for the proposed method to provide a viable and practical solution for clinical 3-D ARF-SWE.
Bearskin2 mediates the coordinated secretion of xylogalacturonan and root cap polygalacturonase in Arabidopsis border-like cells

Border-like cells (BLCs) are sheets of cells that are continuously sloughed off and replenished at the Arabidopsis root cap surface. ROOT CAP POLYGALACTURONASE (RCPG) encodes a putative pectinase involved in BLC shedding. Xylogalacturonan (XGA) is a pectic polysaccharide whose synthesis is associated with cell detachment and secreted separately from other cell wall polysaccharides. BEARSKIN1 (BRN1) and BRN2 are Arabidopsis NAC family transcription factors, and RCPG expression is inhibited in brn1/2. To explore the link between XGA and RCPG, we examined XGA synthesis in Arabidopsis lines with altered RCPG levels. We found that RCPG was contained in XGA-carrying vesicles budding from the trans-Golgi, but XGA synthesis was not affected in the rcpg mutant. XGA was absent in BLCs of brn2, but not of brn1, indicating that BRN2 is necessary for XGA synthesis. Overexpression of functional RCPG-GFP (oeRCPG-GFP) caused upregulation of BRN2, ectopic XGA synthesis, overaccumulation of endogenous RCPG, and accelerated BLC turnover, suggesting a positive regulatory loop between RCPG and BRN2. Inactivation of BRN2 in oeRCPG-GFP suppressed RCPG-GFP expression, excess RCPG, and XGA synthesis. Our data provide evidence that XGA and RCPG are secreted together and that BRN2 controls XGA synthesis, which facilitates RCPG export and BLC separation.
Exercise modulates human hippocampal-cortical ripple dynamics

Physical exercise acutely improves hippocampal-based learning and memory in rodents and humans. While animal studies have mainly offered cellular- and synaptic-level accounts of these effects, human neuroimaging studies show that exercise improves hippocampal-cortical connectivity at the macroscale level. However, the neurophysiological basis for exercise-induced effects on human hippocampal-cortical circuits remains unknown. A growing body of evidence supports the critical role of hippocampal sharp wave-ripples (SWRs) in learning and memory. Moreover, recent studies suggest that the coupling between ripples in the hippocampus and neocortex reflect acute modulations in inter-regional connectivity required by mnemonic processes. Here, we examine the hypothesis that exercise modulates hippocampal SWR events and their coupling with ripples in other cortical areas. We performed intracranial recordings in neurosurgery patients during awake resting state, before and after one session of aerobic exercise. Exercise elicited an increase in ripple rate and duration in mesio-temporal areas (hippocampus, amygdala and parahippocampal gyrus). These changes in ripple features were also observed in the limbic and the default mode (DMN) networks. Furthermore, after exercise, we observed an increase in coupling and phase synchrony between ripples in these two networks and hippocampal SWRs. Our results elucidate the potential mechanisms by which aerobic exercise elicits its reported short-term effects in cognition. Further investigations are needed to explore how these exercise-induced acute modulations contribute to long-term changes in neural plasticity. Significance Statement Physical activity is a modifiable lifestyle factor that improves cognitive function and prevents age-related cognitive decline. Even one session of exercise can enhance hippocampal-based memory and learning. However, the neurophysiological mechanisms by which exercise acutely affects human cognition remain unknown. Using intracranial recordings in neurosurgical patients we show that the hippocampus and neocortex often synchronize their activities via high-frequency neural synchrony events known as ripples. After exercise, hippocampal and neocortical ripples were prolonged and emerged more frequently. Moreover, hippocampal and neocortical ripples exhibited increased coupling and phase synchrony. These effects were neocortical region-specific, favoring structures of the limbic and default mode networks. Ultimately, our results shed light on the mechanisms behind the preventive and therapeutic potential of exercise interventions.