Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional programming for modular Bayesian inference

We present an architectural design of a library for Bayesian modelling and inference in modern functional programming languages. The novel aspect of our approach are modular implementations of existing state-of-the-art inference algorithms. Our design relies on three inherently functional features: higher-order functions, inductive data-types, and support for either type-classes or an expressive module system. We provide a performant Haskell implementation of this architecture, demonstrating that high-level and modular probabilistic programming can be added as a library in sufficiently expressive languages. We review the core abstractions in this architecture: inference representations, inference transformations, and inference representation transformers. We then implement concrete instances of these abstractions, counterparts to particle filters and Metropolis-Hastings samplers, which form the basic building blocks of our library. By composing these building blocks we obtain state-of-the-art inference algorithms: Resample-Move Sequential Monte Carlo, Particle Marginal Metropolis-Hastings, and Sequential Monte Carlo Squared. We evaluate our implementation against existing probabilistic programming systems and find it is already competitively performant, although we conjecture that existing functional programming optimisation techniques could reduce the overhead associated with the abstractions we use. We show that our modular design enables deterministic testing of inherently stochastic Monte Carlo algorithms. Finally, we demonstrate using OCaml that an expressive module system can also implement our design.
Fluid–structure interaction analysis of cerebrospinal fluid with a comprehensive head model subject to a rapid acceleration and deceleration

ABSTRACT Primary Objective: Closed brain injuries are a common danger in contact sports and motorized vehicular collisions. Mild closed brain injuries, such as concussions, are not easily visualized by computed imaging or scans. Having a comprehensive head/brain model and using fluid–structure interaction (FSI) simulations enable us to see the exact movement of the cerebrospinal fluid (CSF) under such conditions and to identify the areas of brain most affected. Research Design: The presented work is based on the first FSI model capable of simulating the interaction between the CSF flow and brain. Methods and Procedures: FSI analysis combining smoothed-particle hydrodynamics and high-order finite-element method is used. Main Outcomes and Results: The interaction between the CSF and brain under rapid acceleration and deceleration is demonstrated. The cushioning effect of the fluid and its effect on brain are shown. Conclusions: The capability to locate areas (down to the exact gyri and sulci) of the brain the most affected under given loading conditions, and therefore assess the possible damage to the brain and consequently predict the symptoms, is shown.
Role of Phytohormones in Piriformospora indica-Induced Growth Promotion and Stress Tolerance in Plants: More Questions Than Answers

Phytohormones play vital roles in the growth and development of plants as well as in interactions of plants with microbes such as endophytic fungi. The endophytic root-colonizing fungus Piriformospora indica promotes plant growth and performance, increases resistance of colonized plants to pathogens, insects and abiotic stress. Here, we discuss the roles of the phytohormones (auxins, cytokinin, gibberellins, abscisic acid, ethylene, salicylic acid, jasmonates, and brassinosteroids) in the interaction of P. indica with higher plant species, and compare available data with those from other (beneficial) microorganisms interacting with roots. Crosstalks between different hormones in balancing the plant responses to microbial signals is an emerging topic in current research. Furthermore, phytohormones play crucial roles in systemic signal propagation as well as interplant communication. P. indica interferes with plant hormone synthesis and signaling to stimulate growth, flowering time, differentiation and local and systemic immune responses. Plants adjust their hormone levels in the roots in response to the microbes to control colonization and fungal propagation. The available information on the roles of phytohormones in beneficial root–microbe interactions opens new questions of how P. indica manipulates the plant hormone metabolism to promote the benefits for both partners in the symbiosis.
Using Algorithm Configuration Tools to Generate Hard SAT Benchmarks

Algorithm configuration tools have been successfully used to speed up local search satisfiability (SAT) solvers and other search algorithms by orders of magnitude. In this paper, we show that such tools are also very useful for generating hard SAT formulas with a planted solution, which is useful for benchmarking SAT solving algorithms and also has cryptographic applications. Our experiments with state-of-the-art local search SAT solvers show that by using this approach we can randomly generate satisfiable formulas that are considerably harder than uniform random formulas of the same size from the phase-transition region or formulas generated by state-of-the-art approaches. Additionally, we show how to generate small satisfiable formulas that are hard to solve by CDCL solvers.
Roles of Specialized Pro-Resolving Lipid Mediators in Cerebral Ischemia Reperfusion Injury

Ischemic stroke contributes to ~80% of all stroke cases. Recanalization with thrombolysis or endovascular thrombectomy are currently critical therapeutic strategies for rebuilding the blood supply following ischemic stroke. However, recanalization is often accompanied by cerebral ischemia reperfusion injury that is mediated by oxidative stress and inflammation. Resolution of inflammation belongs to the end stage of inflammation where inflammation is terminated and the repair of damaged tissue is started. Resolution of inflammation is mediated by a group of newly discovered lipid mediators called specialized pro-resolving lipid mediators (SPMs). Accumulating evidence suggests that SPMs decrease leukocyte infiltration, enhance efferocytosis, reduce local neuronal injury, and decrease both oxidative stress and the production of inflammatory cytokines in various in vitro and in vivo models of ischemic stroke. In this review, we summarize the mechanisms of reperfusion injury and the various roles of SPMs in stroke therapy.
Companion Fog Computing: Supporting Things Mobility Through Container Migration at the Edge

Due to their intrinsic resource constraints, the mobile Internet of Things (IoT) devices are not able to provide intensive services by just relying on their own facilities. Fog Computing effectively helps overcome this hurdle. Indeed, it extends the Cloud toward the network edge, distributing resources and services of computing, storage, and networking close to the end devices. This topological proximity is the key enabler of several advantages that are essential in many emerging ICT domains. Nonetheless, the mobility of an IoT device compromises such benefits as it increases the topological distance to the serving Fog node. Therefore, the Fog service has to be migrated in order to be always close enough to the served IoT device. We name this Companion Fog Computing (CFC), since the Fog service behaves as a "companion" of the correspondent application on the mobile device. In this paper, we present a Fog Computing Platform that performs stateful container (i.e., Fog service) migrations in order to enable CFC. Specifically, we introduce a CFC model from which we derive a reference architecture comprising all the functionalities required in a platform to make migration decisions and carry them out. Moreover, we demonstrate the soundness of the proposed reference architecture by discussing a proof-of-concept implementation based on the Stack4Things (S4T) platform, and we report a set of conducted experiments to show the feasibility of stateful container migrations.
Analyzing Residue Surface Proximity to Interpret Molecular Dynamics

The surface of a molecule holds important information about the interaction behavior with other molecules. In dynamic folding or docking processes, residues of amino acids with different properties change their position within the molecule over time. The atoms of the residues that are accessible to the solvent can directly contribute to binding interactions, while residues buried within the molecular structure contribute to the stability of the molecule. Understanding patterns and causality of structural changes is important for experts in the pharmaceutical domain, e.g., in the process of drug design. We apply an iterative computation of the Solvent Accessible Surface in order to extract virtual layers of a molecule. The extraction allows to track the movement of residues in the body of the molecule, with respect to the distance of the residue to the surface or the core during dynamics simulations. We visualize the obtained layer information for the complete time span of the molecular dynamics simulation as a 2D‐map and for individual time‐steps as a 3D‐representation of the molecule. The data acquisition has been implemented alongside with further analysis functionality in a prototypical application, which is available to the public domain. We underline the feasibility of our approach with a study from the pharmaceutical domain, where our approach has been used for novel insights into the folding behavior of μ‐conotoxins.
Spiking Neural Networks for early prediction in human–robot collaboration

This article introduces the Turn-Taking Spiking Neural Network (TTSNet), which is a cognitive model to perform early turn-taking prediction about a human or agent’s intentions. The TTSNet framework relies on implicit and explicit multimodal communication cues (physical, neurological and physiological) to be able to predict when the turn-taking event will occur in a robust and unambiguous fashion. To test the theories proposed, the TTSNet framework was implemented on an assistant robotic nurse, which predicts surgeon’s turn-taking intentions and delivers surgical instruments accordingly. Experiments were conducted to evaluate TTSNet’s performance in early turn-taking prediction. It was found to reach an F 1 score of 0.683 given 10% of completed action, and an F 1 score of 0.852 at 50% and 0.894 at 100% of the completed action. This performance outperformed multiple state-of-the-art algorithms, and surpassed human performance when limited partial observation is given (<40%). Such early turn-taking prediction capability would allow robots to perform collaborative actions proactively, in order to facilitate collaboration and increase team efficiency.
Health‐related quality of life in AYA cancer survivors who underwent HSCT compared with healthy peers

This study was aimed at comparing adolescent and young adult (AYA) Hematopoietic Stem Cell Transplantation (HSCT) paediatric cancer survivors and a control group of healthy peers in terms of Health-Related Quality of Life (HRQOL) and frequency of posttraumatic stress symptoms (PTSS). The participants were 32 AYA HSCT survivors and 28 matched healthy peers. The survivors were, on average, 19.4 years old (SD = 3.8), with an average age of 8.1 years (SD = 4.3) at diagnosis, and with a mean time, since treatment was completed, of 8.5 years (SD = 3.2). The majority of survivors (78.1%) did not show clinical PTSS, with intrusion symptoms most frequently reported in those who had undergone autologous HSCT (F = 3.3; df = 2; p = 0.05) and relapse presence in their treatment associated with more PTSS avoidance symptoms (r = 0.4; p = 0.002). Women reported more problems in the SF-36 pain scale (t = 2.1; df = 31; p = 0.04) than men. Additionally, 87.5% of survivors fell below the 25th percentile in the SF-36 general well-being scale, and 70.8% had the same trend for the SF-36 fatigue scale. Survivors reported better emotional well-being (t = 2.6, df = 27, p = 0.01) and fewer limitations than their healthy peers (t = 2.5, df = 27, p = 0.02), while they perceived a lower life satisfaction referring to the past (t = -2.8, df = 27, p = 0.009).
Model-Guided 3D Sketching

We present a novel 3D model-guided interface for in-situ sketching on 3D planes. Our work is motivated by evolutionary design, where existing 3D objects form the basis for conceptual re-design or further design exploration. We contribute a novel workflow that exploits the geometry of an underlying 3D model to infer 3D planes on which 2D strokes drawn that are on and around the 3D model should be meaningfully projected. This provides users with the nearly modeless fluidity of a sketching interface, and is particularly useful for 3D sketching over planes that are not easily accessible or do not preexist. We also provide an additional set of tools, including sketching with explicit plane selection and model-aware canvas manipulation. Our system is evaluated with a user study, showing that our technique is easy to learn and effective for rapid sketching of product design variations around existing 3D models.
Investigating Nuisances in DCNN-Based Face Recognition

Face recognition “in the wild” has been revolutionized by the deployment of deep learning-based approaches. In fact, it has been extensively demonstrated that deep convolutional neural networks (DCNNs) are powerful enough to overcome most of the limits that affected face recognition algorithms based on hand-crafted features. These include variations in illumination, pose, expression, and occlusion, to mention some. The DCNNs discriminative power comes from the fact that low-and high-level representations are learned directly from the raw image data. As a consequence, we expect the performance of a DCNN to be influenced by the characteristics of the image/video data that are fed to the network, and their preprocessing. In this paper, we present a thorough analysis of several aspects that impact on the use of DCNN for face recognition. The evaluation has been carried out from two main perspectives: the network architecture and the similarity measures used to compare deeply learned features; and the data (source and quality) and their preprocessing (bounding box and alignment). The results obtained on the IARPA Janus Benchmark-A, MegaFace, UMDFaces, and YouTube Faces data sets indicate viable hints for designing, training, and testing DCNNs. Considering the outcomes of the experimental evaluation, we show how competitive performance with respect to the state of the art can be reached even with standard DCNN architectures and pipeline.
A study of the dental treatment needs of special patients: cerebral paralysis and Down syndrome.

AIM The aim of the present study was to compare the dental characteristics and the oral health care needs of patients with Cerebral Paralysis (CP) and Down Syndrome (DS). MATERIALS AND METHODS The selected sample consisted of 28 patients of both sexes between 10 and 20 years of age. STUDY DESIGN observational, descriptive and cross-sectional study. STATISTICS The statistical analysis was carried out with the SPSS 19.0 program for Windows. The frequency distribution and contingency tables were analysed, as was interobserver concordance. non mi è chiaro cosa intendono qui. RESULTS Fifty percent of the patients with CP presented dental traumas, compared to 15% of the patients with DS. Dental prophylaxis was the most prevalent treatment in both groups (77% in CP compared to 86.7% in DS). The most frequent habit was oral breathing, which was found in 69.2% of the patients with CP and 80% of those with DS. CONCLUSIONS Patients with CP and DS require early dental care in order to prevent and limit the severity of the pathologies observed.
Learning a Mixture of Two Multinomial Logits

The classical Multinomial Logit (MNL) is a be-havioral model for user choice. In this model, a user is offered a slate of choices (a subset of a ﬁ-nite universe of n items), and selects exactly one item from the slate, each with probability proportional to its (positive) weight. Given a set of observed slates and choices, the likelihood-maximizing item weights are easy to learn at scale, and easy to interpret. However, the model fails to represent common real-world behavior. As a result, researchers in user choice often turn to mixtures of MNLs, which are known to approximate a large class of models of rational user behavior. Unfortunately, the only known algorithms for this problem have been heuristic in na-ture. In this paper we give the ﬁrst polynomial-time algorithms for exact learning of uniform mixtures of two MNLs. Interestingly, the parameters of the model can be learned for any n by sampling the behavior of random users only on slates of sizes 2 and 3; in contrast, we show that slates of size 2 are insufﬁcient by themselves.
Evaluating the Effects of Hand-gesture-based Interaction with Virtual Content in a 360° Movie

Head-mounted displays are becoming increasingly popular as home entertainment devices for viewing 360 ◦ movies. This paper explores the effects of adding gesture interaction with virtual content and two different hand-visualisation modes for 360 ◦ movie watching experience. The system in the study comprises of a Leap Motion sensor to track the user’s hand and ﬁnger motions, in combination with a SoftKinetic RGB-D camera to capture the texture of the hands and arms. A 360 ◦ panoramic movie with embedded virtual objects was used as content. Four conditions, displaying either a point-cloud of the real hand or a rigged computer-generated hand, with and without interaction, were evaluated. Presence, agency, embodiment, and ownership, as well as the overall participant preference were measured. Results showed that participants had a strong preference for the conditions with interactive virtual content, and they felt stronger embodiment and ownership. The comparison of the two hand visualisations showed that the display of the real hand elicited stronger ownership. There was no overall difference for presence between the four conditions. These ﬁndings suggest that adding interaction with virtual content could be beneﬁcial to the overall user experience, and that interaction should be performed using the real hand visualisation instead of the virtual hand if higher ownership is desired.
Efficient feature learning and multi-size image steganalysis based on CNN

For steganalysis, many studies showed that convolutional neural network has better performances than the two-part structure of traditional machine learning methods. However, there are still two problems to be resolved: cutting down signal to noise ratio of the steganalysis feature map and steganalyzing images of arbitrary size. Some algorithms required fixed size images as the input and had low accuracy due to the underutilization of the noise residuals obtained by various types of filters. In this paper, we focus on designing an improved network structure based on CNN to resolve the above problems. First, we use 3x3 kernels instead of the traditional 5x5 kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to utilize channel correlation of the residuals, compress the image content and increase the signal-to-noise ratio (between the stego signal and the image signal). Then, we use spatial pyramid pooling (SPP) to aggregate the local features, enhance the representation ability of features, and steganalyze arbitrary size image. Finally, data augmentation is adopted to further improve network performance. The experimental results show that the proposed CNN structure is significantly better than other four methods such as SRM, Ye-Net, Xu-Net, and Yedroudj-Net, when it is used to detect two spatial algorithms such as WOW and S-UNIWARAD with a wide variety of datasets and payloads.
Efficient Large-scale Image Search With a Vocabulary Tree

The task of searching and recognizing objects in images has become an important research topic in the area of image processing and computer vision. Looking for similar images in large datasets given an input query and responding as fast as possible is a very challenging task. In this work the Bag of Features approach is studied, and an implementation of the visual vocabulary tree method from Nist´er and Stew´enius is presented. Images are described using local invariant descriptor techniques and then indexed in a database using an inverted index for further queries. The descriptors are quantized according to a visual vocabulary, creating sparse vectors, which allows to compute very eﬃciently, for each query, a ranking of similarity for indexed images. The performance of the method is analyzed varying diﬀerent factors, such as the parameters for the vocabulary tree construction, diﬀerent techniques of local descriptors extraction and dimensionality reduction with PCA. It can be observed that the retrieval performance increases with a richer vocabulary and decays very slowly as the size of the dataset grows.
Deep Neural Network for Device Modeling

High quality and high accuracy device model is an essential link from device simulation to circuit simulation. However, device model using artificial neural network (ANN) often causes unphysical behaviors. A novel deep neural network (DNN) approach for device modeling is proposed in this article. A novel regression method is used to eliminate unphysical behaviors. The developed DNN model has been verified by a complete set of physical measured data with smooth and accurate predicted results.
Normalizing Spontaneous Reports Into MedDRA: Some Experiments With $\mathsf{MagiCoder}$

Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of <inline-formula><tex-math notation="LaTeX">$\mathsf{MagiCoder}$</tex-math></inline-formula>, an NLP application designed to extract <inline-formula><tex-math notation="LaTeX">$\mathsf{MedDRA}$</tex-math></inline-formula> terms from narrative clinical text. Given a narrative description, <inline-formula><tex-math notation="LaTeX">$\mathsf{MagiCoder}$</tex-math></inline-formula> proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested <inline-formula><tex-math notation="LaTeX">$\mathsf{MagiCoder}$</tex-math></inline-formula> performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about <inline-formula><tex-math notation="LaTeX">$\mathsf{MagiCoder}$</tex-math></inline-formula>. Moreover, we do a change of language, moving to English documents. In particular, we tested <inline-formula><tex-math notation="LaTeX">$\mathsf{MagiCoder}$</tex-math></inline-formula> on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.