Miracle-dz/UniFAR-FTUs huggingface dataset:
Stacked rigid and compliant dielectric structures for increasing force range in soft capacitive sensors

Soft force sensors have many applications in robotics and industrial environments. One challenge is obtaining a wide force range from common compliant materials. The capacitive sensor presented expands the range of measurable forces, while also detecting normal and shear forces. Mutual capacitance sensors are created from patterned silicone with stretchable electrodes. Capacitance changes between electrodes measure displacements and forces. A two-layer dielectric between the electrodes has one layer that is compliant to small forces, and stiff at higher forces. A second stiffer layer enables reasonable sensitivity at higher forces. This dielectric design results in a soft sensor with a normal force range of 0.03 N–50 N, with distinct low- and high-force ranges. At low forces, the sensor is able to distinguish normal forces of 31 mN and shear displacements of 10.8 µm. This approach enables a soft sensor for combining high normal and shear force measurement.
EVOLUTION OF THE DUST SIZE DISTRIBUTION OF COMET 67P/CHURYUMOV–GERASIMENKO FROM 2.2 au TO PERIHELION

The Rosetta probe, orbiting Jupiter-family comet 67P/Churyumov–Gerasimenko, has been detecting individual dust particles of mass larger than 10−10 kg by means of the GIADA dust collector and the OSIRIS Wide Angle Camera and Narrow Angle Camera since 2014 August and will continue until 2016 September. Detections of single dust particles allow us to estimate the anisotropic dust flux from 67P, infer the dust loss rate and size distribution at the surface of the sunlit nucleus, and see whether the dust size distribution of 67P evolves in time. The velocity of the Rosetta orbiter, relative to 67P, is much lower than the dust velocity measured by GIADA, thus dust counts when GIADA is nadir-pointing will directly provide the dust flux. In OSIRIS observations, the dust flux is derived from the measurement of the dust space density close to the spacecraft. Under the assumption of radial expansion of the dust, observations in the nadir direction provide the distance of the particles by measuring their trail length, with a parallax baseline determined by the motion of the spacecraft. The dust size distribution at sizes >1 mm observed by OSIRIS is consistent with a differential power index of −4, which was derived from models of 67P’s trail. At sizes <1 mm, the size distribution observed by GIADA shows a strong time evolution, with a differential power index drifting from −2 beyond 2 au to −3.7 at perihelion, in agreement with the evolution derived from coma and tail models based on ground-based data. The refractory-to-water mass ratio of the nucleus is close to six during the entire inbound orbit and at perihelion.
The Study of XML Functional Dependency and Multi-Valued Dependency and Inference Rules Set

This paper discusses and defines XML functional dependency and XML multi-valued dependency, making the formal definition of XML functional dependency and XML multivalued dependency and further defines the XML-trivial functional dependency and multivalued dependency. It defines the logical implication, base closures and minimal dependence and gives the set of inference rules that effectiveness and completeness are proved when functional dependency and multi-valued dependency exist simultaneously.
Coupling of One Dimensional and Three Dimensional Simulation Models

A software tool has been developed that allows one dimensional and three dimensional fluid flow simulation packages to interact in a heterogeneous (UNIX -NT/2000/98) environment. This allows for the implementation of a “co-simulation” methodology in which the internal flow in a component simulated in three dimensions is incorporated into a network containing components represented by bulked loss coefficients in a one dimensional analysis. This methodology enables the details of the internal flow to be retained whilst conserving overall mass flow in the network, thus eliminating uncertainties in boundary values prescribed in the three dimensional model and reducing the overall simulation time.
The Relationship Between Cognitive Impairment And Functionality In Euthymic Bipolar Patients: A Case Controlled Study

Objective: We sought to compare differences in cognitive function between patients with bipolar disorder type 1 (BPD-1) and healthy controls (HC) and to investigate the relationship between cognitive functions and functional areas of patients with BPD-1. Methods: This study included 93 euthymic patients with BPD-1 and 64 healthy ageand sex-matched individuals. All participants completed a sociodemographic form; took the Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), and Montreal Cognitive Assessment Scale (MoCA) assessments; and underwent a neuropsychometric battery. Results pertaining to the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, fourth revision Axis I Disorders (SCID-I), Young Mania Rating Scale score, and Bipolar Disorder Functioning Questionnaire score were obtained for the patients; the HC group was investigated using the SCID nonpatient version. Results: All of the cognitive domain scores were significantly lower in the BPD-1group than in the HC group (p < 0.05). A statistically significant positive correlation was observed between the neurocognitive domains of attention, verbal memory, visual memory, and BPD-1 total functioning (p < 0.05). Discussion: Impairments in verbal memory, visual memory, and attention among euthymic patients with BPD-1 may cause impaired functionality. Özlem BU / Arch Clin Psychiatry. 2021;48(3):147-154
DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition

Named entity recognition is one of the cornerstones of Danish NLP, essential for language technology applications within both industry and research. However, Danish NER is inhibited by a lack of available datasets. As a consequence, no current models are capable of fine-grained named entity recognition, nor have they been evaluated for potential generalizability issues across datasets and domains. To alleviate these limitations, this paper introduces: 1) DANSK: a named entity dataset providing for high-granularity tagging as well as within-domain evaluation of models across a diverse set of domains; 2) DaCy 2.6. 0 that includes three generalizable models with fine-grained annotation; and 3) an evaluation of current state-of-the-art models' ability to generalize across domains. The evaluation of existing and new models revealed notable performance discrepancies across domains, which should be addressed within the field. Shortcomings of the annotation quality of the dataset and its impact on model training and evaluation are also discussed. Despite these limitations, we advocate for the use of the new dataset DANSK alongside further work on the generalizability within Danish NER.
C AUSAL B ENCH C HALLENGE : D IFFERENCES IN M EAN E XPRESSION

In this write-up, we describe our solution to the 2023 CausalBench Challenge. We describe our approaches to preprocessing the data, parameterizations of DCDI and GRNBoost, and modifications to the baseline algorithms. 1 DATA PRE-PROCESSING AND POST-PROCESSING Parallel to developing modifications of the baseline DCDI and GRNBoost algorithms, we considered modifications to the input and output data of these algorithms. In particular, we analyzed good initial values for the gene expression threshold and output graph size. Gene expression threshold. The gene expression threshold is used to remove genes that have a non-zero expression in less than a user-defined fraction of the samples. The default value of 0.25 resulted in DCDI performance that was visibly worse than that reported in (Chevalley et al., 2022). Therefore, we changed the default expression threshold to 0.5 and used this value in further experiments. Moreover, we omitted samples labeled as ‘excluded‘. Figure 1: Mean Wasserstein distance for different sizes of GRNBoost output graphs. Output graph size. The challenge submissions are evaluated based on the mean Wasserstein distance between the expression distributions of connected pairs of nodes in the output graph. Seeing that not all pairs are equally important and methods such as GRNBoost rely on sorting pairs according to importance and then selecting only a subset of them using a threshold, we assumed that smaller graphs will be more likely to have a higher value of the mean Wasserstein distance. To verify this hypothesis, we plotted the mean Wasserstein distance for GRNBoost graphs of different sizes. As can be noticed by looking at Figure 1, the mean Wasserstein distance indeed decreases as the number of edges in the graph grows. Although GRNBoost does not perfectly sort gene pairs according to differences between expression distributions, the results are still very good. Therefore, in further experiments, we have always limited the number of edges to 1,000, which was the smallest allowed output graph according to the competition rules.
Attach That There: Investigating 3D Virtual Assembly Assistants That Point Into the Real World

Gestures are a fundamental part of human communication. However, commonly used voice assistants do not exploit the advantages of human-like nonverbal communication. We present an Embodied Conversational Agent (ECA) with the ability to explain assembly steps and point to indicate real-world targets. To enable accurate pointing into the real world, we implemented our ECA in a spherical Fish Tank Virtual Reality (FTVR) display. We evaluated the effect of a pointing ECA on the performance and experience in an assembly scenario, as well as investigated whether spherical FTVR displays provide an advantage over 2-dimensional (2D) flat displays. Results show that, while the spherical FTVR was preferred in all conditions, pointing to real pieces did not reduce assembly time or errors compared to showing virtual pieces by holding them up. Based on our findings, we provide design insights and research directions for ECAs with pointing gestures in an assembly scenario.
Robust Gaussian process regression with the trimmed marginal likelihood

Accurate outlier detection is not only a necessary preprocessing step, but can itself give important insights into the data. However, especially, for non-linear regression the detection of outliers is non-trivial, and actually ambiguous. We propose a new method that identiﬁes outliers by ﬁnding a subset of data points T such that the marginal likelihood of all remaining data points S is maximized. Though the idea is more general, it is particular appealing for Gaussian processes regression, where the marginal likelihood has an analytic so-lution. While maximizing the marginal likelihood for hyper-parameter optimization is a well established non-convex optimization problem, optimizing the set of data points S is not. Indeed, even a greedy approximation is computationally challenging due to the high cost of evaluating the marginal likelihood. As a remedy, we propose an efﬁcient projected gradient descent method with provable convergence guarantees. Moreover, we also establish the breakdown point when jointly optimizing hyper-parameters and S . For various datasets and types of outliers, our experiments demonstrate that the proposed method can improve outlier detection and robustness when compared with several popular alternatives like the student-t likelihood.
A Unet-based method for Cell Segmentation Challenge

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. The development of automated meth-ods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide microscopy image. In situations of crowded cells, some existing methods can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. For this challenge that contains more than one modality and are diverse in cell shape and color, we use a unet-based method to tackle this problem. The modified Unet can generate probability maps and distance maps in one forward step. We then utilize the probability maps and distance maps to obtain the final cell instance segmentation results.
ENTREPRENEURIAL AND INTRAPRENEURIAL INTENTIONS OF THE CURRENT LABOUR FORCE IN BOSNIA AND HERZEGOVINA: THE ROLE OF THE THEORY OF PLANNED BEHAVIOUR AND ENTREPRENEURIAL ORIENTATION

This article investigates the relationship between the dimensions of the Theory of Planned Behaviour (which includes attitude toward behavior, subjective norm, and perceived control behavior) and entrepreneurial intentions and intrapreneurial intentions, considering entrepreneurial orientation as a moderator. Using the snowball sampling method, cross-sectional data were collected from 437 respondents. After testing for reliability and validity using confirmatory factor analysis, hypotheses were tested using hierarchical regression. The results indicate that the attitudes toward entrepreneurship and perceived behavioral control positively relate to entrepreneurial intentions. In contrast, attitude toward intrapreneurship is the only dimension of the theory of planned behavior positively related to intrapreneurial intentions. The moderating role of entrepreneurial orientation is only significant for the relationship between attitude toward entrepreneurship and entrepreneurial intentions. Based on the results, several recommendations are made for businesses and policymakers to boost entrepreneurial activity among the current labor force.
Probing Reasoning of Language Models with Inductive In-Context Learning

Despite their recent success, Language Models (LMs) have brought to question whether they statistically repeat data (‘stochastic parrots’)[Bender et al. , 2021] or can learn the underlying generative process of the data. Current benchmarks used to probe the reasoning ability of LMs can be ambiguous as it is unclear whether the model has learned the benchmark or the generative process of the data. In this work we introduce a novel evaluation setting that we use with Inductive In-Context Learning (IIL) and a dataset, ReAnalogy , to probe the reasoning ability of LMs. ReAnalogy consists of sequences with positive examples, generated from regular expressions (regex), and contains quasi-natural language. We use regex to evaluate implicitly whether a LM can infer ‘Rules’ (regex) given limited sets of examples (‘Facts’). We use the LM to generate additional Facts to evaluate whether the generated Facts abide by the Rules. We evaluate a GPT model in our setting and compare with the same model where a Rule is injected during training to replace a Fact. We use IIL during evaluation to probe the model to infer the Rule given Facts. We then use the inferred Rule to synthesize an additional Fact. IIL improves ‘reason-ing’ performance by as much as 33%. Our results suggest that LMs can learn more than statistical patterns in the data and we support our findings with ablation studies. We evaluate our dataset with existing benchmarks and baselines in inductive programming and find that current state-of-the-art symbolic or neuro-symbolic approaches fail to the complexity of our dataset; while the existing dataset and benchmark in the domain are inapplicable for LMs. Our probing method and dataset are complex enough for LMs and applicable for evaluating the inductive reasoning abilities of LMs, while IIL
How Learning Experience Designers Make Design Decisions: The Role of Data, the Reliance on Subject Matter Expertise, and the Opportunities for Data-Driven Support

Learning Experience Designers (LXDs) play an increasingly consequential role in the creation of courses and training materials that meet the needs of diverse learner populations and the growing class scope. Emerging design requests for scalable and effective courseware introduce new challenges in Learning Experience (LX) design practice while providing an opportunity for researchers to understand LX workflows and design new tools to improve them. This paper presents an interview study with 21 LXDs from 18 different organizations with the goal of understanding LXDs' collaborative relationships with subject matter experts (SMEs), data needs, and contextual challenges. We further perform a survey study to validate the challenges and probe into LXDs' attitudes toward a suite of data-driven solutions. We find that LXDs demonstrate a strong desire to collect data to inform their design - including target learners' prior knowledge and relevant design precedents. LXDs want support in better collaborating with SMEs, acquiring and processing diverse learner data, identifying relevant research studies to communicate their design decisions, understanding domain-specific material, and creating quality materials (especially questions). We discuss LXDs' concerns regarding automated solutions such as the lack of contextual understanding, over-reliance on automation, and data privacy before elaborating on the implications for future work.
Visual Exploration of Indirect Bias in Language Models

Language models are trained on large text corpora that often include stereotypes. This can lead to direct or indirect bias in downstream applications. In this work, we present a method for interactive visual exploration of indirect multiclass bias learned by contextual word embeddings. We introduce a new indirect bias quantification score and present two interactive visualizations to explore interactions between multiple non-sensitive concepts (such as sports, occupations, and beverages) and sensitive attributes (such as gender or year of birth) based on this score.
Repeated Doses of Gadobutrol Gadolinium Deposition in Patients Receiving Impact of Kidney Function on CNS

BACKGROUND AND PURPOSE: Studies associate repeat gadolinium-based contrast agent administration with T1 shortening in the dentate nucleus and globus pallidus, indicating CNS gadolinium deposition, most strongly with linear agents but also reportedly with macrocyclics. Renal impairment effects on long-term CNS gadolinium deposition remain underexplored. We investigated the relationship between signal intensity changes and renal function in patients who received $ 10 administrations of the macrocyclic agent gadobutrol. MATERIALS AND METHODS: Patients who underwent $ 10 brain MR imaging examinations with administration of intravenous gado-butrol between February 1, 2014, and January 1, 2018, were included in this retrospective study. Dentate nucleus-to-pons and globus pallidus-to-thalamus signal intensity ratios were calculated, and correlations were calculated between the estimated glomerular ﬁ l-tration rate (minimum and mean) and the percentage change in signal intensity ratios from the ﬁ rst to last scan. Partial correlations were calculated to control for potential confounders. RESULTS: One hundred thirty-one patients (73 women; mean age at last scan, 55.9years) showed a mean percentage change of the dentate nucleus-to-pons of 0.31%, a mean percentage change of the globus pallidus-to-thalamus of 0.15%, a mean minimum estimated glomerular ﬁ ltration rate of 69.65 (range, 10.16 – 132.26), and a mean average estimated glomerular ﬁ ltration rate at 89.48 (range, 38.24 – 145.93). No signi ﬁ cant association was found between the estimated glomerular ﬁ ltration rate and percentage change of the dentate nucleus-to-pons (minimum estimated glomerular ﬁ ltration rate, r ¼ – 0.09, P ¼ . 28; average estimated glomerular ﬁ l-tration rate, r ¼ – 0.09, P
Locally Regularized Sparse Graph by Fast Proximal Gradient Descent

Sparse graphs built by sparse representation has been demonstrated to be effective in clustering high-dimensional data. Albeit the compelling empirical performance, the vanilla sparse graph ignores the geometric information of the data by performing sparse representation for each datum separately. In order to obtain a sparse graph aligned with the local geometric structure of data, we pro-pose a novel Support Regularized Sparse Graph, abbreviated as SRSG, for data clustering. SRSG encourages local smoothness on the neighborhoods of nearby data points by a well-deﬁned support regularization term. We propose a fast proximal gradient descent method to solve the non-convex optimization problem of SRSG with the convergence matching the Nesterov’s optimal convergence rate of ﬁrst-order methods on smooth and convex ob-jective function with Lipschitz continuous gradient. Extensive experimental results on various real data sets demonstrate the superiority of SRSG over other competing clustering methods.
Exploring alternative treatment options for a patient with heavy menstrual bleeding and mistrust of the healthcare system

A clinical decision report using: Tinelli A, Gustapane S, D’Oria O, Licchelli M, Panese G. Nutraceuticals in fibroid management after ulipristal acetate administration: An observational study on patients’ compliance. International Journal of Gynecology and Obstetrics; 2022;156(1):133-138. https://doi.org/10.1002/ijgo.13692 for a patient with chronic, symptomatic anemia with menorrhagia and uterine fibroid.
Artiﬁcial neural network optimization using diﬀerential evolution algorithm with adaptive weight bound adjustment for pattern classiﬁcation

Artiﬁcial Neural Network (ANN) is a popular machine learning technique applied to various advanced applications of Artiﬁcial Intelligence (AI). In this work, a Diﬀerential Evolution Algorithm with adaptive Weight bound adjustment (DEAW) is used to optimize the neural networks for solving classiﬁcation problems. The DEAW algo-rithm initials the weights in a small range of bounds and gradually expands them in the mutation step when needed. The experiments are performed on ﬁve synthesis scatter-points datasets and four real-world UCI datasets to compare with other well-known evolutionary algorithms: harmony search, ant colony optimization, and self-adaptive diﬀerential evolution. The results show that the DEAW method is comparable to the other algorithms and performs better in several cases.
Real‐Time Ray Tracing of Micro‐Poly Geometry with Hierarchical Level of Detail

In recent work, Nanite has demonstrated how to rasterize virtualized micro‐poly geometry in real time, thus enabling immense geometric complexity. We present a system that employs similar methods for real‐time ray tracing of micro‐poly geometry. The geometry is preprocessed in almost the same fashion: Nearby triangles are clustered together and clusters get merged and simplified to obtain hierarchical level of detail (LOD). Then these clusters are compressed and stored in a GPU‐friendly data structure. At run time, Nanite selects relevant clusters, decompresses them and immediately rasterizes them. Instead of rasterization, we decompress each selected cluster into a small bounding volume hierarchy (BVH) in the format expected by the ray tracing hardware. Then we build a complete BVH on top of the bounding volumes of these clusters and use it for ray tracing. Our BVH build reaches more than 74% of the attainable peak memory bandwidth and thus it can be done per frame. Since LOD selection happens per frame at the granularity of clusters, all triangles cover a small area in screen space.