Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust selective weighted field mapping using multi-echo gradient echo-based MRI

In 3D gradient echo (GRE) and echo planar imaging (EPI), strong macroscopic field gradients are observed at air/tissue interfaces. The respective field gradients lead to an apparent increase in intravoxel dephasing, and, subsequently, to signal loss or image distortion. We propose an analytical approximation and a consequent method to compute low and high resolution field maps over all field map regimes (small and large echo spacing). A number of approaches which compute field maps from reconstructed phase data rely upon optimized linear least square fit and complex division approaches owing to the simplicity of their implementation. Most of these techniques, however, have historically considered only the phase signal when computing off-resonance maps while ignoring magnitude data. This latter may be of notable interest since the presence of noise is well depicted and interpreted. The presence of noise and phase aliasing that increase with increasing echo time (TE) and echo spacing (ΔTE) may seriously challenge the off-resonance map accuracy. These techniques still remain subject to the trade-off during the choice of GRE sequences, TE and ΔTE. In this work, we explore a novel model that considers any type of TE and ΔTE regime (small or large) and high phase wraps complexity. The field offset is weighted by the magnitude signal decay quality, to make the field mapping procedure as noise independent as possible. The performance of the proposed method was tested using simulated, experimental phantoms and in vivo human studies. The proposed approach markedly outperforms conventional techniques. It provides a correction equivalent to that of the conventional techniques in regions with high SNR (20), yielding a mean error of about 0.1 Hz, but appearing more robust in regions with low SNR (10), such as near the sinus cavity and at the very edge of the brain (mean error less than 1 Hz), where phase wraps and noise are highly present. The proposed technique shows promise to enhance field map generation over any acquisition regime and in regions of both high and low SNR and it can be easily implemented for rapid computation and used in a clinical setting.
Cost-Benefit Analysis of Li-Ion Batteries in a Distribution Network

The paper is focused on economic feasibility analysis of battery energy storage providing load shifting to the end-user. Electro-chemical storage technology is considered, as it is envisioned to play an important role in future power systems by reducing costs, deferring investments and increasing reliability of system operation. In the study, cash-flows arising from battery investment and operation in a representative distribution grid are considered for a planning period of 33 years. Forecasting methodology based on Fourier transform for the hourly electricity market price is proposed and performed for a particular bidding area of the Nord Pool day-ahead market. Results of the case study demonstrate that with the current trends of market price and tariff structure of electricity paid by the end-users, investments in BESS may not be profitable and some additional incentives should be introduced.
Tracking Authentic and In-the-wild Emotions Using Speech

This first-of-its-kind study aims to track authentic affect representations in-the-wild. We use the ‘Graz Real-life Affect in the Street and Supermarket (GRAS2)’ corpus featuring audiovisual recordings of random participants in non-laboratory conditions. The participants were initially unaware of being recorded. This paradigm enabled us to use a collection of a wide range of authentic, spontaneous and natural affective behaviours. Six raters annotated twenty-eight conversations averaging 2.5 minutes in duration, tracking the arousal and valence levels of the participants. We generate the gold standards through a novel robust Evaluator Weighted Estimator (EWE) formulation. We train Support Vector Regressors (SVR) and Recurrent Neural Networks (RNN) with the low-level-descriptors (LLDs) of the ComParE feature-set in different derived representations including bag-of-audio-words. Despite the challenging nature of this database, a fusion system achieved a highly promising concordance correlation coefficient (CCC) of.372 for arousal dimension, while RNNs achieved a top CCC of.223 in predicting valence, using a bag-of-features representation.
Taxonomic status of Populuswulianensis and P.ningshanica (Salicaceae)

Abstract Species delimitation in the genus Populus is particularly challenging due to high levels of intraspecific polymorphism as well as frequent interspecific hybridisation and introgression. In this study, we aimed to examine the taxonomic status of Populusningshanica and P.wulianensis using an integrative taxonomy that considers multiple operational criteria. We carried out morphometric analyses of leaf traits and genetic examinations (including sequence variations at five barcoding DNAs and polymorphisms at 14 nuclear microsatellite SSR primers) at the population level between them and two closely related species P.adenopoda and P.davidiana. Results suggest that P.wulianensis belongs to the polymorphic species, P.adenopoda and should be considered as a synonym of the latter. P.ningshanica may have arisen as a result on the hybridisation between P.adenopoda and P.davidiana and therefore should be treated as P.×ningshanica. This study highlights the importance of the integrated evidence in taxonomic decisions of the disputed species.
Video Emotion Recognition using Hand-Crafted and Deep Learning Features

In this paper, we present our system designed for the video emotion recognition task of the Multimodal Emotion Challenge (MEC 2017). Histogram of Oriented Gradients (HOG), face shape (SHAPE), and geometric (GEO) features are extracted from the detected face images as hand-crafted video features. A pre-trained VGG-Face model is fine-tuned with the face images and emotion labels from the training set of CHEAVD 2.0, the outputs of the penultimate fully-connected layer (FC6) and the last fully-connected layer (FC7) are adopted as Deep Convolutional Neural Network (DCNN) based features. For each video clip, the hand-crafted features and DCNN based features are input into corresponding hidden Markov models (HMMs, one for each emotion class), respectively, for the initial emotion recognitions. The output logarithm likelihood probabilities from the HMMs are then ranked, and the orders constitute an eight-dimensional feature vector as inputs to a Naive Bayes classifier for decision fusion. Experimental results on the CHEAVD 2.0 database show that the combination of FC6, GEO, SHAPE and HOG features obtains the highest macro average precisions (MAPs) on both the validation set (46.61%) and test set (43.88%), which are 12.51% and 22.18% higher than the baseline results, respectively.
Examples of model visualization cards Workflow status Examples of problem discovery cards

A recent advancement in the machine learning community is the development of automated machine learning (autoML) systems, such as autoWeka or Googles Cloud AutoML, which automate the model selection and tuning process. However, while autoML tools give users access to arbitrarily complex models, they typically return those models with little context or explanation. Visual analytics can be helpful in giving a user of autoML insight into their data, and a more complete understanding of the models discovered by autoML, including differences between multiple models. In this work, we describe how visual analytics for automated model discovery differs from traditional visual analytics for machine learning. First, we propose an architecture based on an extension of existing visual analytics frameworks. Then we describe a prototype system Snowcat, developed according to the presented framework and architecture, that aids users in generating models for a diverse set of data and modeling tasks.
On the molecular and morphological evolution of continental and insular Cryptorchestia species, with an additional description of C.garbinii (Talitridae)

Abstract Semi-terrestrial talitrid amphipods of the genus Cryptorchestia (sensu Lowry and Fanini 2013) associated with freshwater-soaked leaf litter were known to occur in inland lakes of Turkey and at the shores of the Black Sea. Before 2013 they had been reported as Orchestiacavimana and later as Cryptorchestiacavimana. In our phylogenetic tree, inferred from a mitochondrial and nuclear gene dataset (cytochrome oxidase I (COI), and histone H3 (H3), respectively), we show that these Turkish populations belong to Cryptochestiagarbinii, a common and widespread continental species, which is closely related to C.cavimana (endemic to Cyprus) and C.ruffoi (endemic to Rhodes). For the Turkish and European populations of C.garbinii, we found low levels of both genetic differentiation and morphological variation, and an age-related size variability (increasing at each moult) of the small lobe in the male gnathopod I merus, the main taxonomically diagnostic character for Cryptorchestia. A mainland (C.garbinii) versus insular isolation and in situ speciation (C.cavimana, and C.ruffoi) in the two east Mediterranean islands of Cyprus and Rhodes is discussed in relation to terrestrial Cryptorchestia species endemic to North East Atlantic volcanic islands (Azores, Canary Islands, and Madeira). The incorporation of five Mediterranean and Atlantic Orchestia species in the Bayesian analysis of the two genes (COI, and H3) indicated that both genera Orchestia and Cryptorchestia are not monophyletic.
Is a “Pan-Scan” Indicated in the Older Patient with a Ground Level Fall?

Routine full-body CT “pan-scan” use in older patients after ground level falls (GLFs) is of questionable benefit. Retrospective review of new diagnosis & changes in management in patients >55 years with Glasgow Coma Scale of 15 after a GLF who received a pan-scan (routine head, cervical spine/neck, chest abdomen, and pelvis CT). Head CT results were considered separately; results described in the following paragraph pertains to cervical spine/neck, chest, abdomen, and pelvis CT. One hundred and fifty-two patients received pan-scans; 96 (63%) had new findings. Thirty-five (23%) resulted in a minor change and three (2%) in a major change in management, defined as a procedural intervention. This included tube thoracostomy in one patient and cervical spine surgery in two. A further eight patients required the use of a cervical collar. In all patients requiring intervention, there were clinical signs present that should have led to directed CT scan of area of concern. Routine pan-scans in stable, alert older patients after a GLF result in new findings in most patients, with primarily nonprocedural interventions for these additional findings.
Utilizing End-User Flexibility for Demand Management Under Capacity Subscription Tariffs

The Norwegian regulator has proposed a new grid tariff, based on capacity subscription, where the consumer pays an excess fee whenever he exceeds the subscribed level. We compare this tariff with a variant of capacity subscription where demand is physcially limited to the subscribed level, but where the limitation is activated only when there is grid congestion. The results show that this can be an attractive option if demand can be flexibly controlled to stay below the subscribed limit, which is increasingly possible. Use of a battery is also attractive, but the investment costs are still much too high.
Influence of Composition and Preparation on Crystalline Phases and Morphology in Poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) Relaxor-Ferroelectric Terpolymer

The influence of chemical composition and crystallisation conditions on the ferroelectric and paraelectric phases and the resulting morphology in Poly(vinylidene fluoridetrifluoroethylene-chlorofluoroethylene) (P(VDF-TrFE-CFE)) terpolymer films with 55.4/37.2/7.3 mol% or with 62.2/29.4/8.4 mol% of VDF/TrFE/CFE was studied. Poly(vinylidene fluoride trifluoroethylene) (P(VDF-TrFE)) with 75/25 mol% VDF/TrFE was employed as reference material. Fourier-Transform Infrared Spectroscopy (FTIR) was used to determine the fractions of the relevant terpolymer phases, and X-Ray Diffraction (XRD) was employed to assess the crystalline morphology. The FTIR results show an increase of the fraction of paraelectric phases after annealing. On the other hand, XRD results indicate a more stable paraelectric phase in the terpolymer with higher CFE content.
Hearing, Emotion, Amplification, Research, and Training Workshop: Current Understanding of Hearing Loss and Emotion Perception and Priorities for Future Research

The question of how hearing loss and hearing rehabilitation affect patients’ momentary emotional experiences is one that has received little attention but has considerable potential to affect patients’ psychosocial function. This article is a product from the Hearing, Emotion, Amplification, Research, and Training workshop, which was convened to develop a consensus document describing research on emotion perception relevant for hearing research. This article outlines conceptual frameworks for the investigation of emotion in hearing research; available subjective, objective, neurophysiologic, and peripheral physiologic data acquisition research methods; the effects of age and hearing loss on emotion perception; potential rehabilitation strategies; priorities for future research; and implications for clinical audiologic rehabilitation. More broadly, this article aims to increase awareness about emotion perception research in audiology and to stimulate additional research on the topic.
Specific Learning Disabilities: Issues that Remain Unanswered

Specific learning disabilities (SLDs) are defined as “heterogeneous group of conditions wherein there is a deficit in processing language, spoken or written, that may manifest itself as a difficulty to comprehend, speak, read, write, spell, or to do mathematical calculations and includes such conditions as perceptual disabilities, dyslexia, dysgraphia, dyscalculia, dyspraxia and developmental aphasia. ”
Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences

We propose a generic framework to calibrate accuracy and confidence of a prediction in deep neural networks through stochastic inferences. We interpret stochastic regularization using a Bayesian model, and analyze the relation between predictive uncertainty of networks and variance of the prediction scores obtained by stochastic inferences for a single example. Our empirical study shows that the accuracy and the score of a prediction are highly correlated with the variance of multiple stochastic inferences given by stochastic depth or dropout. Motivated by this observation, we design a novel variance-weighted confidence-integrated loss function that is composed of two cross-entropy loss terms with respect to ground-truth and uniform distribution, which are balanced by variance of stochastic prediction scores. The proposed loss function enables us to learn deep neural networks that predict confidence calibrated scores using a single inference. Our algorithm presents outstanding confidence calibration performance and improves classification accuracy when combined with two popular stochastic regularization techniques---stochastic depth and dropout---in multiple models and datasets; it alleviates overconfidence issue in deep neural networks significantly by training networks to achieve prediction accuracy proportional to confidence of prediction.
Strong Coordination over Noisy Channels with Strictly Causal Encoding

We consider a network of two nodes separated by a noisy channel, in which the input and output signals have to be coordinated with the source and its reconstruction. In the case of strictly causal encoding and non-causal decoding, we prove inner and outer bounds for the strong coordination region and show that the inner bound is achievable with polar codes.
Hyperspectral Target Detection with CNN Using Subtraction Model

Recently, the convolutional neural network (CNN) has been widely used in the fields of hyperspectral image (HSI) processing. In this paper, a CNN-based hyperspectral target detection framework is presented. And subtraction model is used to sufficiently enlarge the number of training samples. The subtraction model is built from twenty-eight manually selected objects in several AVIRIS date following three aspects: 1) The new pixel made by subtraction of any two pixels between 27 different classes is labelled as 0; 2) the new pixel made by subtraction of any two pixels within per class is labelled as 0; 3) the new pixel made by subtraction of any two pixels, in which one pixel is from the target class and the other is from background classes, is labelled as 1. Theoretically, if the pixel under test belongs to the target class, the output label of the CNN will be the same as the label of the target class. The experiment results on three images all indicate that the proposed CNN-based detector outperforms the classical hyperspectral target detection algorithms.
Intestinal Inflammation and Dysregulated Immunity in Patients With Inherited Caspase-8 Deficiency.

Caspase-8 (CASP8) is a protease that initiates apoptosis and regulates inflammation and immune responses. We identified germline mutations in CASP8 in 3 unrelated patients with infant-onset inflammatory bowel disease: 2 patients were homozygous for the mutation 710A>G, p.Q237R, which resulted in reduced protein expression, and 1 patient carried the mutation 793C>T, p.R265W. We isolated peripheral blood mononuclear cells from our index patient and observed defects in T- and B-cell maturation, proliferation, and/or activation. Macrophages from 1 patient with CASP8 deficiency and monocytic BLaER1 cells with knockout of CASP8 or overexpression of CASP8 with the 710A>G mutation had altered inflammasome activity on stimulation with lipopolysaccharide. Patient-derived intestinal organoids and colon carcinoma cells with knockout of CASP8 had defects in cell death processes that involved loss of TRAIL signaling and increased necroptosis. These findings indicate that CASP8 controls inflammation, innate and adaptive immunity, and intestinal barrier integrity in humans.
Coconut E-Petition Implementation

In this dissertation project, we describe and implement a practical system application based on a selective disclosure credential scheme, namely the Coconut credential scheme\cite{sonnino_coconut:_2018}. The specific application here is an electronic petition system with the distinctive added feature of unlinkability as well as anonymity: such that no information about the anonymous petition voter is linkable back to the individual. In other words, there is no data leaked about who voted in the petition, just that the users who did, were indeed eligible and authorized to vote. As for the implementation, the client-side is done using JavaScript so that the client can trustlessly compute the cryptographic constructions individually, whereas the server-side is done using Node.js, but can easily be replaced by a more sophisticated and secure structure such as a permissionless blockchain platform.
Analysis of volatile profiles of green Hawai'ian coffee beans damaged by the coffee berry borer (Hypothenemus hampei).

BACKGROUND The coffee berry borer (CBB), Hypothenemus hampei, is the most destructive insect pest of coffee globally, causing significant losses in yield and leading to 'off' flavors in damaged beans. Automated headspace sampling (AHS) and gas chromatography-mass spectrometry (GC-MS) were used to investigate changes in the volatile profiles of CBB-damaged green coffee beans. Green coffee from three coffee farms on the island of Hawai'i were sorted into three levels of CBB damage: non-damaged, slightly damaged (1-2 pinholes/bean), and heavily damaged (> 2 pinholes/bean). RESULTS Distinct differences were found between green coffee bean samples based on the amounts of eight prominent volatiles. The amount of CBB damage was particularly correlated with the amount of both hexanal and 2-pentylfuran. Principal component analysis showed clustering of non-damaged green beans, which did not overlap with the slightly or heavily damaged clusters. Good separation was also found between a mixture of 50% slightly damaged and non-damaged coffee. However, 20% slightly damaged and non-damaged coffee clusters showed strong overlap. CONCLUSION Understanding the effects of CBB damage on coffee flavor profiles is critical to quality control for this valuable agricultural product. The results of this study show that the volatile profiles of green coffee beans vary with CBB damage. With specific volatile profiles for CBB-damaged coffee identified, coffee samples can be tested in the lab, or potentially on the farm or in coffee mills, to identify high levels of CBB damage that may lead to off flavors and a reduction in product quality and value. © 2018 Society of Chemical Industry.
Preliminary Results of the Macedonian-Adapted Version of Ages and Stages Developmental Questionnaires

ABSTRACT Context: Early detection of developmental problems is critical, and interventions are more effective when they are carried out early in a child's life. In Macedonia, there are only four centers providing early intervention services. Aims: In this research, we determined the reliability of the translation and adaptation of Ages and Stages Questionnaires 3rd edition (ASQ-3-M) for assessment of children aged 3–5 years old in Macedonia, and reported preliminary results of the gender differences in the development. Materials and Methods: ASQ-3-M was completed by 165 parents and 40 educators in seven kindergarten classrooms. Children were 3–5 years old. Statistical Analysis Used: Cronbach's alpha, Intraclass Correlation coefficient (ICC), and interrater reliability (IRR) were used to assess ASQ-3-M psychometric properties. The Bayesian t-test was performed to estimate the difference in means between males and females. Results: The Cronbach's alpha ranged from 0.65 to 0.87. The overall ICC was 0.89 (ranged from 0.8 to 0.95), which indicates a strong to almost perfect strength of agreement between test-retest. IRR correlation revealed an average of 0.88 (ranged from 0.74 to 0.95), suggesting that ASQ-3-M is reliable and stable. Conclusions: The results from the comparison between males and females on all dimensions of ASQ-3-M were not statistically significant (BF10 <3), indicating no significant gender difference. That said, the ASQ-3 is recommended for routine use in screening children aged 3–5 years old.
Indoor Localization for Smart-handhelds with Stable Set of Wireless Access Points

Wi-Fi-based indoor localization plays a pivotal role in estimating the location of an indoor environment. This ap- proach depends upon fingerprinting effort. However, fingerprint data are susceptible to factors like time and device heterogeneity. Thus, identifying stable Access Points (APs) is important such that, the signal for those APs do not vary much even when training and test conditions change. Localization with stable APs not only provides sustainable localization accuracy but also saves location prediction time. Consequently, in this paper, we propose an algorithm for identifying stable APs in an real indoor environment of our university campus subject to time and device heterogeneity. Our proposed algorithm is found to select 20 stable APs irrespective of time and device variation out of all APs (46) which give an average error of 1.3 meter and 90.13% localization accuracy using KNN whereas, considering all APs KNN can achieve an accuracy of 84.20% with an average error of 2.10 meter.