Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyper-Accelerated Learning for Brain-Computer Interfaces via Partial Target-Aware Optimal Transport

Brain-computer interfaces (BCIs) have surfaced as a powerful modality in human-machine interaction and wearable technology with powered futuristic applications like virtual reality, robot control, gaming, etc. Using BCIs, the brain's intent can be harnessed without explicit communication. Despite the vast promise, systems designed for BCIs generalize poorly to new or unseen individuals due to high variability in brain signals among different subjects, resulting in long retraining/calibration sessions. This lack of generalization is typically attributed to a covariate shift of signals in the probability space, which manifests itself as disparate marginal and class conditional distributions. In this paper, we overview the factors contributing to poor generalization on a more granular level by analyzing a specific brain signal called the Error Potential (ErrP), a signal well-known for its noisy characteristics and high variability, and propose a novel algorithm to mitigate the associated covariate shift using partial target-aware optimal transport. We demonstrate our method on an ErrP dataset collected in our lab. Our method outperforms state-of-the-art models for cross-user generalization which translates to a reduction in calibration time by an order of magnitude.
The Gibbons of Khao Yai: Seasonal Variation in Behavior and Ecology

1 History of Gibbon Field Studies: Monogamy, Frugivory and Territoriality 2 Study Animals, Study Site and Methods 3 Activity Budgets and Social Behavior 4 Diet and Feeding Behavior 5 Ranging Behavior 6 Territoriality and Intergroup Encounters 7 Gibbon Socioecology 8 Summary and Directions for Future Research References Cited Index
Positive by Design: The Next Big Challenge in Rethinking Media as Agents?

Hypertext and Web pioneers had high aspirations and expectations about the potential positive impact of technology. However, studies in the last decades have shown how widely adopted social and intelligent media are either amplifiers or a source of adverse detrimental effects on their users. On the one hand, we now have a better understanding of these negative phenomena and strategies to identify and quantify their effects. On the other hand, as a community, we should take on the challenge of steering hypertext technologies toward positive applications. This position paper argues for a proactive role of the hypertext community in the design of agent media result of combining social media with intelligent algorithms. This silent paradigm shift introduced third-party proactive agents in a wide range of human-to-human interactions. We are today at a point where social media and global web applications cannot operate without such systems and demand, in the author's opinion, a similar proactive role of academia and scholars in understanding and driving their design for positive goals. This position paper outlines the need for this challenge to be taken on, and how and why the Hypertext community could lead in its own way this vision forward.
Forms of C, N and P accumulation under different pasture soil carbon sequestration scenarios

Sequestering carbon and managing nutrients in soils are key issues in global agriculture. Understanding the quantities involved and the major processes of change are vital for improved utility of soils. Fertilised pasture soils potentially contain large pools of carbon, nitrogen and phosphorus. A knowledge of the amount and distribution of nutrients stored in such soils can have positive implications for sequestering carbon in order to mitigate anthropogenic climate change, reducing nutrient runoff and improving soil fertility. In this study, the soil carbon, nitrogen and phosphorus contents were determined for six fields of varying fertility, across two dairy farms in the Camden region of NSW. Three profiles were examined to 100 cm depth, to investigate the distribution and nutrient forms in the different soil layers. Profiles were described and sampled, and samples were analysed for total carbon, total nitrogen, organic and inorganic phosphorus. Ancillary data such as particle size distribution and bulk densities were also measured. Total quantities of C, N and P and nutrient ratios were calculated for each of the fields. Results showed large amounts of nutrients were contained in the topsoils, and also in the deeper 30-70 cm profile layers that are not commonly analysed. Total carbon contents ranged between 170,184 292,278 kg/ha, total nitrogen from 11,562 21,780 kg/ha and total phosphorus from 1,586 4,035 kg/ha. Large variations were found between the different fertility fields, particularly in the inorganic phosphorus content where some soils contained a possible P surplus. An average C:N:P ratio of 192:12:1 was calculated across all soils sampled. This is similar to values found elsewhere for pasture soils. Well managed pasture soils were shown to have potential to sequester large additional amounts of carbon. It is recommended that fertiliser application rates be based on regular soil testing, and appropriate management strategies be developed to effectively manage nutrient accumulation and distribution.
Feasibility, effectiveness and acceptability of two perturbation-based treadmill training protocols to improve reactive balance in fall-prone older adults (FEATURE): protocol for a pilot randomised controlled trial

Introduction Perturbation-based balance training (PBT) targets the mechanism of falls (eg, slipping, tripping) to specifically train the recovery actions needed to avoid a fall. This task-specific training has shown great promise as an effective and efficient intervention for fall prevention in older adults. However, knowledge about the dose–response relationship of PBT, as well as its feasibility and acceptability in older adults with increased risk of falling is still limited. Thus, the aim of this study is to compare the effectiveness of two different treadmill PBT protocols for improving reactive balance control in fall-prone older adults, and to evaluate the feasibility and acceptability of these protocols. Methods and analysis The study is designed as a pilot randomised controlled trial with a 6-week intervention and 6-week follow-up period. Thirty-six community-dwelling, fall-prone (Timed Up and Go >12 s, habitual gait speed <1.0 m/s and/or fall history) older adults will be randomised (1:1) to receive six (weeks 1–6) or two treadmill PBT sessions (weeks 1+6) plus four conventional treadmill training sessions (weeks 2–5). Training sessions are conducted 1×/week for 30 min. Each PBT will include 40 perturbations in anterior–posterior and mediolateral directions. Reactive balance after perturbations in standing (Stepping Threshold Test (STT)) and walking (Dynamic Stepping Threshold Test (DSTT)) will be assessed as the primary outcome for effectiveness. Secondary outcomes are spatiotemporal and kinematic parameters collected during STT, DSTT and PBT, maximum perturbation magnitude for each PBT session, static and dynamic balance, physical capacity, physical activity, concerns with falling and executive functions. Feasibility will be assessed via training adherence, drop-out rate, perturbations actually performed and adverse events; and acceptability via self-designed questionnaire and focus groups. Ethics and dissemination The study has been approved by the Ethics Committee of the Medical Faculty Heidelberg (S-602/2022). Findings will be disseminated through publications in peer-reviewed journals and conference presentations. Trial registration number DRKS00030805.
HUMAN'23: 6th Workshop on Human Factors in Hypertext

HUMAN 2023 is the 6th workshop of a series for the ACM Hypertext conferences. The HUMAN workshop has a strong focus on the user and thus is complementary to the strong machine analytics research direction that could be experienced in previous conferences. The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains as well as human-centered AI. Furthermore, the workshop raises the question of how original hypertext ideas (e. g., Doug Engelbart's "augmenting human intellect" [7] or Jeff Conklin's "hypertext as a computer-based medium for thinking and communication" [6]) can improve today's hypertext systems.
Characterization of Identified Dopaminergic Neurons in the Mouse Forebrain and Midbrain

Dopaminergic (DA) neurons play pivotal roles in diverse brain functions, spanning movement, reward processing, and sensory perception. DA neurons are most abundant in the midbrain (Substantia Nigra pars compacta, SNC, and Ventral Tegmental Area, VTA) and the olfactory bulb (OB) in the forebrain. Interestingly, a subtype of OB DA neurons is capable of regenerating throughout life, while a second class is exclusively born during embryonic development. Emerging evidence in SNC and VTA also indicates substantial heterogeneity in terms of morphology, connectivity, and function. To further investigate this heterogeneity and directly compare form and function of midbrain and forebrain DA neurons, we performed immunohistochemistry and whole-cell patch-clamp recordings in ex vivo brain slices from juvenile DAT-tdTomato mice. After confirming the penetrance and specificity of the dopamine transporter (DAT) Cre line, we compared soma shape, passive membrane properties, voltage sags and action potential firing across midbrain and forebrain DA subtypes. We found that each DA subgroup within midbrain and forebrain was highly heterogeneous, and that DA neurons across the two brain areas are also substantially different. These findings complement previous work in rats as well as gene expression and in vivo datasets, further questioning the existence of a single “dopaminergic” neuronal phenotype.
Abnormal gastrointestinal motility is a major factor in explaining symptoms and a potential therapeutic target in patients with disorders of gut–brain interaction

The objective of this article is to review the evidence of abnormal gastrointestinal (GI) tract motor functions in the context of disorders of gut–brain interaction (DGBI). These include abnormalities of oesophageal motility, gastric emptying, gastric accommodation, colonic transit, colonic motility, colonic volume and rectal evacuation. For each section regarding GI motor dysfunction, the article describes the preferred methods and the documented motor dysfunctions in DGBI based on those methods. The predominantly non-invasive measurements of gut motility as well as therapeutic interventions directed to abnormalities of motility suggest that such measurements are to be considered in patients with DGBI not responding to first-line approaches to behavioural or empirical dietary or pharmacological treatment.
Disruption of immunoglobulin heavy and light chain assembly by antisense oligonucleotides impairs protein homeostasis and myeloma cell survival

Multiple myeloma (MM) is related to the accumulation of malignant plasma cells (PCs) in the bone marrow. MM accounts for approximatively 10% of hematological malignancies and despite major improvement in therapies and outcomes, relapses will virtually occur in all patients. Usually, the disease goes along with an excess production of a monoclonal immunoglobulin (Ig) component by the tumor PC clone. However, many questions remain regarding the consequences of a deregulated Ig production on PC survival. Recent advances in RNA-based therapy using antisense oligonucleotides (ASO) prompted us to examine the impact of altered Ig heavy to light chain (HC/LC) ratios in MM cells. We designed a pan IgG subclasses specific ASO targeting a consensus sequence found in the polyadenylation signal (PAS) of all secreted IGHG mRNAs (IgG-ASO). Remarkably, treatment with this compound strongly decreased IgG secretion in MM cell lines and patient cells. Consistent with a deregulated HC/LC ratio, a dose-dependent excess of free-LCs (as monomers and dimers) was observed in myeloma cells treated with IgG-ASO, compared to an irrevelant control ASO (CTRL). RNA-seq profiles further indicated that the expression of genes involved in cellular metabolism, unfolded protein response (UPR) and cell death pathways were altered after treatment with IgG-ASO. Interestingly, impaired survival of primary IgG-expressing cells isolated from MM patients was achieved upon treatment with IgG-ASO, whereas no major effect was observed for healthy cells. Altogether, our data provide evidence for efficient inhibition of IgG secretion upon ASO treatment and suggest that an excess of free-LC due to disruption of HC/LC stoichiometry is toxic for MM cells expressing complete Ig. Such RNA-based strategies targeting PC in an Ig isotype-dependent manner could open new avenues for selective therapeutic approaches in PC dyscrasias.
Linking genomic prediction for muscle fat content in Atlantic salmon to underlying changes in lipid metabolism regulation

Muscle fat content is an important production trait in Atlantic salmon (Salmo salar) because it influences the flavor, texture, and nutritional properties of the fillet. Genomic selection can be applied to alter muscle fat content, however how such selection changes the underlying molecular physiology of these animals is unknown. Here, we examine the link between genomic prediction and underlying molecular physiology by correlating genomic breeding values for fat content to liver gene expression in 184 fish. We found that Salmon with higher genomic breeding values had higher expression of genes in lipid metabolism pathways. This included key lipid metabolism genes hmgcrab, fasn-b, fads2d5, and fads2d6, and lipid transporters fatp2f, fabp7b, and apobc. We also found several regulators of lipid metabolism with negative correlation to genomic breeding vales, including pparg-b, fxr-a, and fxr-b. A quantitative trait loci analysis for variation in gene expression levels (eQTLs) for 167 trait associated genes found that 71 genes had at least one eQTL, and that most were trans eQTLs. Closer examination revealed distinct eQTL clustering on chromosomes 3 and 6, indicating the presence of putative common regulator in these regions. Taken together, these results suggest that increased fat content in high genomic breeding value salmon is associated with elevated lipid synthesis, elevated lipid transport, and reduced glycerolipid breakdown; and that this is at least partly achieved by selection on genetic variants that impact the function of top-level transcription factors involved in liver metabolism. Our study sheds light on how genomic selection alters lipid content in Atlantic salmon, and the results could be used to prioritize SNPs to improve the efficiency of genomic selection in the future.
Changes in Species Composition and Growth in a Restored Maple-Basswood Forest after 30 Years

©2023 by the Board of Regents of the University of Wisconsin System. Changes in Species Composition and Growth in a Restored Maple-Basswood Forest after 30 Years Kathleen L. Shea (corresponding author: Departments of Biology and Environmental Studies, St. Olaf College, 1520 St. Olaf Ave., Northfield, MN 55057, USA, sheak@stolaf. edu) and Sean R.D. Meagher (Department of Biology, St. Olaf College, Northfield, MN, USA)
Development of a tool for anomaly detection and power load forecasting: the case of Politecnico di Torino

Il settore degli edifici e uno dei piu energivori a livello mondiale. In quest'ottica, l'incremento dell'efficienza dei sistemi energetici degli edifici e di primaria importanza. Una delle soluzioni piu promettenti in materia e rappresentata dall'integrazione di sistemi di gestione dell'energia all'interno degli edifici, capaci di ridurre i consumi e gli sprechi, mantenendo ottime condizioni ambientali per gli occupanti. Questi sistemi necessitano di strumenti in grado di prevedere i consumi energetici futuri nel breve periodo su intervalli variabili, al fine di migliorare la loro gestione delle risorse e dei carichi. I cosiddetti modelli inversi "data-driven", sono tra i piu promettenti per adempiere a questo compito e, insieme alle tecniche di "data mining", possono essere efficacemente utilizzati per analizzare gli edifici sia a scopi diagnostici che per sviluppare tecnologie in grado di controllarne direttamente i sistemi energetici. In questo lavoro viene svolta un'analisi di letteratura circa i principali modelli inversi di tipo "data-driven" e le relative applicazioni in campo di previsione dei fabbisogni energetici, controllo degli impianti, rilevazione di anomalie, gestione della domanda e valutazione comparativa. Infine, a partire dai dati riguardanti la sottostazione elettrica C del Politecnico, forniti dal Living Lab, viene sviluppato uno strumento in grado di rilevare le anomalie di consumo e di classificarle in quattro diverse categorie appartenenti a una libreria di dati anomali, identificati grazie ad analisi di "data mining" effettuate sui dati storici. Questo strumento e, inoltre, in grado di fornire previsioni orarie del consumo di energia elettrica su un orizzonte di ventiquattro ore, quantificandone le quote adibite a ciascun uso finale, in modo da facilitare il monitoraggio dei consumi e da permettere un'analisi preventiva della forma del profilo e del relativo orario e picco di potenza.
Classification de surfaces dans une image hyperspectrale urbaine par réseau de neurones pour la qualité de l'air

Résumé Cet article est la première étape d’un processus complexe de mesure de la concentration de particules fines par des caméras spectrales. Pour ce faire, une classification pré-cise et rapide de tous les pixels d’une image hyperspec-trale est cruciale pour déterminer la nature des objets et leurs propriétés optiques. Les méthodes traditionnellement utilisées reposent sur la richesse de l’information spectrale avec des centaines de longueurs d’onde habituellement me-surées par les caméras. Cependant, négliger la dimension spatiale durant une classification conduit à certaines situations où les classes obtenues ne sont pas spatialement cohérentes. Nous proposons ici une méthode de classification optimisée pour la mesure de la qualité de l’air (Spatial Spectral Optimised Classification) basée sur l’appren-tissage, qui prend en compte les caractéristiques spectrales mais aussi l’information spatiale via les pixels voisins.
Étude du comportement à haute température d'une fissuration instable dans l'acier 16MND5 et application au calcul de la rupture d'un fond de cuve en cas d'accident grave

L'objet de la these porte sur la prevision de la ruine (dechirement) d'un reacteur a eau pressurisee sous chargement accidentel thermomecanique. Des essais reels etant inenvisageables, la methode utilisee consiste a reproduire ce type de chargement sur maquette instrumentee et de comparer les resultats obtenus sur deux grades d'acier de cuve (16MND5) a des simulations numeriques. Une part tres importante de ce travail a ete la conception et la mise au point de ce dispositif experimental. La maquette est un tube en traction et pression interne dont l'etat de contrainte et le volume de gaz disponible sont comparables au cas reel. Le tube est de plus chauffe a 900 ou 1000°C ce qui correspond au cas accidentel. Aux habituelles mesures de temperature, effort, deplacement et pression, on filme egalement le tube par deux cameras numeriques rapides et une infrarouge. On extrait de ces essais les conditions d'initiation et de propagation de fissure ainsi que la loi de depressurisation. A ces temperatures, le fluage induit de tres grandes deformations avant l'apparition des fissures qui peuvent dans le pire des cas se propager a des vitesses de plusieurs metres par seconde. Les simulations numeriques sont conduites en utilisant les elements finis, l'initiation et la propagation de la fissure est modelisee par une zone cohesive dont le trajet est connu a l'avance. Une formulation 3D d'element cohesif a ete developpee et implementee dans le code de calcul ABAQUS6. 6-1. Ces elements cohesifs supportent les tres grands deplacements et supposent une loi de traction-separation durcissante, seule capable de reproduire les essais reels. L'identification a ete faite sur des essais sur eprouvette CT realises au CEA. Enfin, la base de donnees de caracterisation en traction de l'acier 16MND5 a ete completee pour des temperatures voisines de 900-1000°C et des vitesses de deformation vraie superieures a 0. 1s-1. Ces vitesses de deformation sont representatives des vitesses de deformations locales lors du fluage tertiaire de la cuve
Shades of Black: Diversity in African American Identity

Preface Acknowledgments Part I: Rethinking Self-Hatred 1. Landmark Studies of Negro Identity 2. Empirical Research on Group Identity and Personal Identity 3. Testing the Lewinian Hypothesis 4. Issues of Imposition, Self-Hatred, and Celebration Part II: the Psychology of Nigrescence 5. Black Militancy and the Psychology of Nigrescence 6. Rethinking Nigrescence Appendix: Summary of Forty-Five Studies of Black Identity Bibliography Index
Learning from Textbook Knowledge: A Case Study

One of the "grand challenges for machine learning" is the problem of learning from textbooks. This paper addresses the problem of learning from texts including omissions and inconsistencies that are clarified by illustrative examples. To avoid problems in natural language understanding, we consider a simplification of this problem in which the text has been manually translated into a logical theory. This learning problem is solvable by a technique that we call analogical abductive explanation based learning (ANA-EBL). Formal evidence and experimental results in the domain of contract bridge show that the learning technique is both efficient and effective.
A Novel Mapping and Navigation Framework for Robot Autonomy in Orchards

Resumen —Target detection is a basic task to divide the object types in the orchard point cloud global map, which is used to count the overall situation of the orchard. And provide necessary information for unmanned navigation planning of agricultural vehicles. In order to divide the fruit trees and the ground in the point cloud global map of the standardized orchard, and provide the orchard overall information for the path planning of autonomous vehicles in the natural orchard environment. A fruit tree detection method based on the Yolo-V7 network is proposed, which can effectively detect fruit tree targets from multi-sensor fused radar point cloud, reduce the 3D point cloud information of the point cloud map to 2D for the fruit tree point cloud in the Yolo-V7 network detection map, and project the prediction results into the point cloud map. Generally, the target detection network based on PointNet has the problem of low speed and large computational load. The method proposed in this paper is fast and low computational load and is suitable for deployment in mobile robots. From the experimental results, the recall rate and accuracy rate of the proposed method in orchard fruit tree detection are 0.4 and 0.696 respectively, and its weight and reasoning time are 7.4 M and 28 ms respectively. The experimental results show that this method can achieve the robustness and efficiency of real-time detection of orchard fruit trees.