Miracle-dz/UniFAR-FTUs huggingface dataset:
PairwiseNet: Pairwise Collision Distance Learning for High-dof Robot Systems

: Motion planning for robot manipulation systems operating in complex environments remains a challenging problem. It requires the evaluation of both the collision distance and its derivative. Owing to its computational complexity, recent studies have attempted to utilize data-driven approaches to learn the collision distance. However, their performance degrades significantly for complicated high-dof systems, such as multi-arm robots. Additionally, the model must be re-trained every time the environment undergoes even slight changes. In this paper, we propose PairwiseNet, a model that estimates the minimum distance between two geometric shapes and overcomes many of the limitations of current models. By dividing the problem of global collision distance learning into smaller pairwise sub-problems, PairwiseNet can be used to efficiently calculate the global collision distance. PairwiseNet can be deployed without further modifications or training for any system comprised of the same shape elements (as those in the training dataset). Experiments with multi-arm manipulation systems of various dof indicate that our model achieves significant performance improvements concerning several performance metrics, especially the false positive rate with the collision-free guaranteed threshold. Results further demonstrate that our single trained Pair-wiseNet model is applicable to all multi-arm systems used in the evaluation. The code is available at https://github.com/kjh6526/PairwiseNet .
Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk

Cervical cancer is a life-threatening disease and one of the most prevalent types of cancer affecting women worldwide. Being able to adequately identify and assess factors that elevate risk of cervical cancer is crucial for early detection and treatment. Advances in machine learning have produced new methods for predicting cervical cancer risk, however their complex black-box behaviour remains a key barrier to their adoption in clinical practice. Recently, there has been substantial rise in the development of local explainability techniques aimed at breaking down a model’s predictions for particular instances in terms of, for example, meaningful concepts, important features, decision tree or rule-based logic, among others. While these techniques can help users better understand key factors driving a model’s decisions in some situations, they may not always be consistent or provide faithful predictions, particularly in applications with heterogeneous outcomes. In this paper, we present a critical analysis of several existing local interpretability methods for explaining risk factors associated with cervical cancer. Our goal is to help clinicians who use AI to better understand which types of explanations to use in particular contexts. We present a framework for studying the quality of different explanations for cervical cancer risk and contextualise how different explanations might be appropriate for different patient scenarios through an empirical analysis. Finally, we provide practical advice for practitioners as to how to use different types of explanations for assessing and determining key factors driving cervical cancer risk.
MResT: Multi-Resolution Sensing for Real-Time Control with Vision-Language Models

Leveraging sensing modalities across diverse spatial and temporal resolutions can improve performance of robotic manipulation tasks. Multi-spatial resolution sensing provides hierarchical information captured at different spatial scales and enables both coarse and precise motions. Simultaneously multi-temporal resolution sensing enables the agent to exhibit high reactivity and real-time control. In this work, we propose a framework, MResT (Multi-Resolution Transformer), for learning generalizable language-conditioned multi-task policies that utilize sensing at different spatial and temporal resolutions using networks of varying capacities to effectively perform real time control of precise and reactive tasks. We leverage off-the-shelf pretrained vision-language models to operate on low-frequency global features along with small non-pretrained models to adapt to high frequency local feedback. Through extensive experiments in 3 domains (coarse, precise and dynamic manipulation tasks), we show that our approach significantly improves (2X on average) over recent multi-task baselines. Further, our approach generalizes well to visual and geometric variations in target objects and to varying interaction forces.
Scheduling conditional task graphs with deep reinforcement learning

Industrial applications often depend on costly computation infrastructures. Well optimised schedulers provide cost efficient utilization of these computational resources, but they can take significant effort to implement. It can also be beneficial to split the application into a hierarchy of tasks represented as a conditional task graph. In such case, the tasks in the hierarchy are conditionally executed, depending on the output of the earlier tasks. While such conditional task graphs can save computational resources, they also add complexity to scheduling. Recently, there has been research on Deep Re-inforcement Learning (DRL) based schedulers, but they mostly do not address conditional task graphs. We design a DRL based scheduler for conditional task graphs in a heterogeneous execution environment. We measure how the probabilities of a conditional task graph affects the scheduler and how these adverse effects can be mitigated. We show that our solution learns to beat traditional baseline schedulers in a fraction of an hour.
KeySearchWiki: An Automatically Generated Dataset for Keyword Search over Wikidata

Keyword search is an intuitive method to access knowledge graphs without requiring technical expertise or knowledge of the underlying data schema. In this context, various methods for keyword search over knowledge graphs have been developed. However, only few evaluation datasets have been created, mostly based on a time-consuming manual generation. We present KeySearchWiki, an automatically generated dataset for keyword search over Wikidata, containing over 16 thousand queries and their relevant results. It is based on Wikidata and Wikipedia set categories which are refined and combined to derive more complex queries. We explain the dataset generation workflow, highlight some dataset characteristics, present experiments using baseline retrieval methods, and evaluate the accuracy of relevant results.
TransEHR: Self-Supervised Transformer for Clinical Time Series Data

Deep neural networks, including the Trans-former architecture, have achieved remarkable performance in various time series tasks. However, their effectiveness in handling clinical time series data is hindered by specific challenges: 1) Sparse event sequences collected asynchronously with multivariate time series, and 2) Limited availability of labeled data. To address these challenges, we propose TransEHR 1 , a self-supervised Transformer model designed to encode multi-sourced asynchronous sequential data, such as structured Electronic Health Records (EHRs), efficiently. We introduce three pretext tasks for pre-training the Transformer model, utilizing large amounts of unlabeled structured EHR data, followed by fine-tuning on downstream prediction tasks using the limited labeled data. Through extensive experiments on three real-world health datasets, we demonstrate that our model achieves state-of-the-art performance on benchmark clinical tasks, including in-hospital mortality classification, phenotyping, and length-of-stay prediction. Our findings highlight the efficacy of TransEHR in effectively addressing the challenges associated with clinical time series data, thus contributing to advancements in healthcare analytics.
Beyond Accuracy: Fairness, Scalability, and Uncertainty Considerations in Facial Emotion Recognition

Facial emotion recognition (FER) from images or videos is an emerging subfield of emotion recognition that in recent years has achieved increased traction resulting in a wide range of models, datasets, and applications. Benchmarking computer vision methods often provide accuracy rates above 90% in controlled settings. However, little focus has been given to aspects of fairness, uncertainty, and scalability within facial emotion recognition systems. The increasing applicability of FER models within assisted psychiatry and similar domains underlines the importance of fair and computational resource compliant decision-making. The primary objective of this paper is to propose methods for assessment of existing open source FER models to establish a thorough understanding of their current fairness, scalability, and robustness.
The Effect of Pulsatile Flow on the Peristaltic Transport of a Newtonian Fluid in a Cylindrical Tube with Permeable Wall

Peristaltic pumping is a form of the fluid transport in a flexible tube caused by a progressive wave of contraction or expansion from a region of lower pressure to higher pressure. Peristalsis is one of the major mechanisms for fluid in many biological systems. It is an automatic and vital process that moves food through the digestive tract, urine from the kidneys through the ureters into the bladder, and bile from the gall bladder into the duodenum and transport of blood through the artery with mild stenosis. In addition, the mechanism of   ,     ,
International Journal of Data and Network Science

The objective of this research is to comprehend how these elements interact to influence young people's subjective happiness and to provide insightful information about their perspectives. Significant discoveries are shown by the study's findings. This research paper conducts a thorough analysis of the many factors that affect youth happiness, including GDP, loneliness, longevity, autonomy, generosity
changes in extreme floods across multiple continents

Analyses of trends in observed ﬂoods often focus on relatively frequent events, whereas changes in rare ﬂoods are only studied for a small number of locations that have exceptionally long observational records. Understanding changes in rare ﬂoods is especially relevant as these events are often most damaging and inﬂuence the design of major structures. Here, we provide an assessment of changes in the largest ﬂood events ( ∼ 0.033 annual exceedance probability) observed during the period 1980 − 2009 for 1744 catchments located in Australia, Brazil, Europe and the United States. The occurrence of rare ﬂoods in spatial aggregate shows strong temporal variability and peaked around 1995. During the 30 year period, there are overall increases in both the frequency and magnitude
: a longitudinal study of a

Background: The interest for young people ’ s movement ability has increased. Literature shows that many factors, apart from movement ability, in ﬂ uence the development of and sustained interest in or taste for physical activity and the learning thereof. Purpose: This longitudinal study illustrates, through the lens of Bourdieu, whether and how assessed movement ability at the age of 15, together with cultural capital and sports habitus, is re ﬂ ected in exercise habits and a taste for participation in sport and physical activity as a young adult. Data collection and analysis: A multidisciplinary project called School-Sport-Health (Skola-idrott-hälsa) was established in 2001 in Sweden to investigate the physical and health status of children and youth, as well as attitudes to and participating in school physical education. Schools were randomly selected by the agency Statistics Sweden. In total, 1,975 pupils aged 9, 12 and 15 answered a life style questionnaire, and took part in at least one sub study of the baseline study in 2001. Movement ability was examined using the NyTid test. 560 pupils (283 girls, 277 boys) aged 15 (born in 1985, ±1) completed the test. In the follow-up study, lifestyle questionnaires, were answered nine years apart by the pupils who had participated in the NyTid test at the age of 15. The response rate was 61 per cent (341 young adults: 186 females, 155 males). Group and gender di ﬀ erences between those who had a high level of assessed movement ability (HLG) and those with a low level (LLG) were analysed. Data were analysed through Bourdieu ’ s analytical concepts habitus and capital. Findings: The assumption that an acquired high level of movement ability plays a central role in being physically active later in life is both con ﬁ rmed and challenged by the results. The HLG view themselves as more physically active and with a higher intensity (71 per cent) than the LLG (44 per cent). A low level of assessed movement ability at the age of 15 did not prevent young adults from acquiring a taste for sport and physical activity later in life. Habitus is a generative principle and can, according to the logic of practice, give action meaning, which may go against earlier habitus and trigger actions. Earlier described gender di ﬀ erences appear to be less evident in young adults. Conclusions: Further research is needed to understand how pupils ’ movement narratives are constructed to support young people ’ s taste for movement and development of movement competence
Zeolithic tuffs from Greece and their commercial potential in the cement industry

La Grece presente de vastes depots de zeolites dans les roches tufacees de l'ere tertiaire. Des zeolites se sont formees dans divers milieux sedimentaires par diagenese du verre volcanique acide a intermediaire. Les depots de zeolite les plus importants rencontres en Grece peuvent etre classes en deux categories : ○ Depots de tuf lacustre salin-alcalin. ○ Depots de tuf marin peu profond. La premiere categorie englobe les roches zeolitiques de Samos qui datent du Miocene Superieur et sont essentiellement composees d'assemblages d'analcite/clinoptilolite/feldspath-K, interstratifies avec de l'argilite. De petits lits de tuf clinoptilolite monominerale sont egalement rencontres. La deuxieme categorie, et la plus importante du point de vue commercial en raison de la taille des depots, est representee par les tufs zeolitises du Pliocene, que l'on trouve sur les iles de Kimolos, Milos, Poliegos et Santorin, et par les tufs zeolitises du Paleogene des bassins d'Orestias et d'Alexandroupolis au nord-est du pays. Les depots de zeolite de cette categorie contiennent de la clinoptilolite (+eulandite) ou de la mordenite. ou des melanges de mordenite/clinoptilolite. Pour determiner l'effet de l'adjonction de tuf zeolitique dans le ciment. on a selectionne trois depots representatifs de zeolite a des fins de production de ciment et d'essais. Ces depots presente la mineralogie zeolitique suivante : ○ Tuf riche en mordenite - Depot de l'ile de Kimolos. au centre de la mer Egee. ○ Tuf riche en clinoptilolite - Depot de Palestre/Pentalofo, Bassin d'Orestias, au nord-est. ○ Tuf riche en mordenite/clinoptilolite - Depot de Doriskos/Kavissos, Bassin d'Alexandroupolis. au nord-est. Les ciments de laboratoires ont ete prepares en cobroyant divers pourcentages de chaque tuf avec du clinker et du gypse. Parmi les tufs, ceux de Palestre et de Kavissos sont les plus difficiles a broyer. Les melanges de ciment/zeolite ont ete soumis a plusieurs essais concemant la surface specifique, le temps de prise et la resistance. Les resultats indiquent que les tufs n'ont que peu d'impact sur le temps de prise et la surface specifique du ciment. En ce qui conceme la resistance du ciment, l'emploi de tuf zeolite en provenance de Palestre a donne de meilleurs resultats. Parmi les tufs de Doriskos-Kavissos et Kimolos. le premier a donne des valeurs de resistance tardive legerement superieures.
Production of coriander in the NFT hydroponic using brackish waters to replace the evapotranspiration loss.

The aim of this study was to evaluate of coriander cultivation in NFT (nutrient film technique) hydroponic system with different availabilities of nutrient solution and levels of 1 Tecnologo em Irrigacao e Drenagem, Doutorando em Engenharia Agricola (Agricultura Irrigada e Recursos Hidricos), Nucleo de Engenharia de Agua e Solo/NEAS, Universidade Federal do Reconcavo da Bahia/UFRB, Rua Rui Barbosa, 710, Centro, CEP: 44380-000, Cruz das Almas-BA, e-mail: mairtong@hotmail.com 2 Engenheiro Agronomo, Prof. Doutor, Nucleo de Engenharia de Agua e Solo/NEAS, Universidade Federal do Reconcavo da Bahia/UFRB, Cruz das Almas-BA, e-mail: talesmiler@gmail.com 3 Agronomos pela Universidade Federal do Reconcavo da Bahia/UFRB, Cruz das Almas-BA, e-mail: itamar25souza@gmail.com; jeandinoite@hotmail.com 4 Estudantes de Agronomia, Nucleo de Engenharia de Agua e Solo/NEAS, Universidade Federal do Reconcavo da Bahia/UFRB, Cruz das Almas-BA, e-mail: jozelmopinho@gmail.com; oibaf.freitas@hotmail.com
Biomedical Signal Processing and Control

Myocardial infarction (MI) is the leading cause of mortality in the world. Its early diagnosis can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. The regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in that can be captured by echocardiography. However, assessing the motion only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study
DECAY LAW AND TIME DILATATION ∗

We study the decay law for a moving unstable particle. The usual time-dilatation formula states that the decay width for an unstable state moving with a momentum p and mass M is ˜ Γ p = ΓM/ (cid:112) p 2 + M 2 with Γ being the decay width in the rest frame. In agreement with previous studies, we show that in the context of QM as well as QFT, this equation is not correct provided that the quantum measurement is performed in a reference frame in which the unstable particle has momentum p (note, a momentum eigenstate is not a velocity eigenstate in QM). We then give, to our knowledge for the ﬁrst time, an analytic expression of an improved formula and we show that the deviation from ˜ Γ p has a maximum for p/M = (cid:112) 2 / 3 , but is typically very small. Then, the result can be easily generalized to a momentum wave packet and also to an arbitrary initial state. Here, we give a very general expression of the non-decay probability. As a next step, we show that care is needed when one makes a boost of an unstable state with zero momentum/velocity: namely, the resulting state has zero overlap with the elements of the basis of unstable states (it is already decayed! ). However, when considering a spread in velocity, one ﬁnds again that ˜ Γ p is typically a very good approximation. The study of a velocity wave packet represents an interesting subject which constitutes one of the main outcomes of the present manuscript. In the end, it should be stressed that there is no whatsoever breaking of special relativity, but as usual in QM, one should specify which kind of measurement on which kind of state is performed.
Besov-Spaces on Domains in RD Besov-Spaces on Domains in RD

We study Besov spaces Bc(Lp(Q)), 0 < p, q, Ca < oo, on domains Q in Rd . We show that there is an extension operator X which is a bounded mapping from Bc(Lp(Q)) onto Bq(Lp(Rd)). This is then used to derive various properties of the Besov spaces such as interpolation theorems for a pair of Ba (Lp(Q)), atomic decompositions for the elements of B (Lp (Q)), and a description of the Besov spaces by means of spline approximation.
Generating Optimal Molecules with Synthesizability and 3D Equivariant Conformational Constraints

This thesis examines two forms of constraint-driven machine learning-based molecule generation techniques. The first is BBO-SYN, a generative framework based on black-box optimization (BBO), which predicts diverse molecules with desired properties together with corresponding synthesis pathways. BBO-SYN uses recent advances in a Monte Carlo Tree Search-based latent search algorithm to locate promising reactants that produce high-scoring products when fed to a pretrained language model for chemical reaction prediction. BBO-SYN is empirically shown to produce high-scoring and diverse synthesis trees while operating over a large continuous reactant space. Similarly, after exploring synthesizabil-ity constraints, CoarsenConf was developed to generate optimal 3D low-energy conformers in an SE(3) equivariant fashion. CoarsenConf is a hierarchical graph variational autoencoder that coarsens input molecule graphs based on torsion angles to learn a subgraph level latent distribution that is used for an efficient autoregressive generation via aggregated attention. CoarsenConf predominantly outperforms state-or-the-art methods with significantly less data and training iterations on more robust benchmarks.
: a prospective

Background: Experiencing a stillbirth can be a potent stressor for psychological distress in the subsequent pregnancy and possibly after the subsequent birth. The impact on women ’ s relationship with her partner in the subsequent pregnancy and postpartum remains uncertain. The objectives of the study were 1) To investigate the prevalence of anxiety and depression in the pregnancy following stillbirth and assess gestational age at stillbirth and inter-pregnancy interval as individual risk factors. 2) To assess the course of anxiety, depression and satisfaction with partner relationship up to 3 years after the birth of a live-born baby following stillbirth. Methods: This study is based on data from the Norwegian Mother and Child Cohort Study, a population-based pregnancy cohort. The sample included 901 pregnant women: 174 pregnant after a stillbirth, 362 pregnant after a live birth and 365 previously nulliparous. Anxiety and depression were assessed by short-form subscales of the Hopkins Symptoms Checklist, and relationship satisfaction was assessed by the Relationship Satisfaction Scale. These outcomes were measured in the third trimester of pregnancy and 6, 18 and 36 months postpartum. Logistic regression models were applied to study the impact of previous stillbirth on depression and anxiety in the third trimester of the subsequent pregnancy and to investigate gestational age and inter-pregnancy interval as potential risk factors. Results: Women pregnant after stillbirth had a higher prevalence of anxiety (22.5%) and depression (19.7%) compared with women with a previous live birth (adjusted odds ratio (aOR) 5.47, 95% confidence interval (CI) 2.90 – 10.32 and aOR 1.91, 95% CI 1.11 – 3.27) and previously nulliparous women (aOR 4.97, 95% CI 2.68 – 9.24 and aOR 1.91, 95% CI 1.08 – 3.36). Gestational age at stillbirth (> 30 weeks) and inter-pregnancy interval < 12 months were not associated with depression and/or anxiety. Anxiety and depression decreased six to 18 months after the birth of a live-born baby, but increased again 36 months postpartum. Relationship satisfaction did not differ between groups. Conclusion: Women who have experienced stillbirth face a significantly greater risk of anxiety and depression in the subsequent pregnancy compared with women with a previous live birth and previously nulliparous women.