Miracle-dz/UniFAR-FTUs huggingface dataset:
Phasic Firing and Coincidence Detection by Subthreshold Negative Feedback: Divisive or Subtractive or, Better, Both

Phasic neurons typically fire only for a fast-rising input, say at the onset of a step current, but not for steady or slow inputs, a property associated with type III excitability. Phasic neurons can show extraordinary temporal precision for phase locking and coincidence detection. Exemplars are found in the auditory brain stem where precise timing is used in sound localization. Phasicness at the cellular level arises from a dynamic, voltage-gated, negative feedback that can be recruited subthreshold, preventing the neuron from reaching spike threshold if the voltage does not rise fast enough. We consider two mechanisms for phasicness: a low threshold potassium current (subtractive mechanism) and a sodium current with subthreshold inactivation (divisive mechanism). We develop and analyze three reduced models with either divisive or subtractive mechanisms or both to gain insight into the dynamical mechanisms for the potentially high temporal precision of type III-excitable neurons. We compare their firing properties and performance for a range of stimuli. The models have characteristic non-monotonic input-output relations, firing rate vs. input intensity, for either stochastic current injection or Poisson-timed excitatory synaptic conductance trains. We assess performance according to precision of phase-locking and coincidence detection by the models' responses to repetitive packets of unitary excitatory synaptic inputs with more or less temporal coherence. We find that each mechanism contributes features but best performance is attained if both are present. The subtractive mechanism confers extraordinary precision for phase locking and coincidence detection but only within a restricted parameter range when the divisive mechanism of sodium inactivation is inoperative. The divisive mechanism guarantees robustness of phasic properties, without compromising excitability, although with somewhat less precision. Finally, we demonstrate that brief transient inhibition if properly timed can enhance the reliability of firing.
Deep Learning Methods for Compressed Sensing Image Reconstruction

Deep Learning Methods for Compressive Sensing Image Reconstruction Deep learning (DL) has become, in the last years, the principal way to process images in all the fields of image elaboration and the same goes for Compressive Sensing (CS), the new technique that is making its way in the world of image processing. Compressive Sensing is a full of potential technique, above all for what concerns the acquisition of images, in the case of MRI for example, or the transmission of very big images, e.g. the transmission of the images from satellites. In fact, CS, allowing, together with the single-pixel camera, to directly acquire very compressed data, avoid a waste of time and computational resources to acquire and compress images. Traditional compression techniques are not universal techniques and they suit up for the data, understanding each time how to compress it in the better way. CS, instead, provides a universal way for data acquisition that will speed up these processes and will allows to better and more easily transmit these data too. Unfortunately, CS is not so good in reconstructing the acquired signals and this is its biggest problem. In fact, the iterative classical methods, like minimization of l1− norm, could also take days to exactly reconstruct an image and all the gain of the acquisition and transmission phase is lost. DL aims at solving this problem and, even if the accuracy could not be of 100% of course, the latest results show its great potential. In this thesis, we have evaluated the efficiency of these DL based methods for CS reconstruction and developed an algorithm considering various block size (an important parameter in image processing with NN), fixed and trained sensing matrices and various CS ratios. The results obtained far exceed the state of the art in terms of accuracy and PSNR and remain comparable in terms of elaboration time. Moreover, the algorithm developed works very well with every kind of image.
Longitudinal profiling of circulating miRNA during cardiac allograft rejection: a proof‐of‐concept study

Allograft rejection following heart transplantation (HTx) is a serious complication even in the era of modern immunosuppressive regimens and causes up to a third of early deaths after HTx. Allograft rejection is mediated by a cascade of immune mechanisms leading to acute cellular rejection (ACR) and/or antibody‐mediated rejection (AMR). The gold standard for monitoring allograft rejection is invasive endomyocardial biopsy that exposes patients to complications. Little is known about the potential of circulating miRNAs as biomarkers to detect cardiac allograft rejection. We here present a systematic analysis of circulating miRNAs as biomarkers and predictors for allograft rejection after HTx using next‐generation small RNA sequencing.
Signal Representations for Synthesizing Audio Textures with Generative Adversarial Networks

Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation consisting of log magnitude and instantaneous frequency (the"IFSpectrogram"). Many other synthesis systems use representations derived from the magnitude spectra, and then depend on a backend component to invert the output magnitude spectrograms that generally result in audible artefacts associated with the inversion process. However, for signals that have closely-spaced frequency components such as non-pitched and other noisy sounds, training the GAN on the 2-channel IFSpectrogram representation offers no advantage over the magnitude spectra based representations. In this paper, we propose that training GANs on single-channel magnitude spectra, and using the Phase Gradient Heap Integration (PGHI) inversion algorithm is a better comprehensive approach for audio synthesis modeling of diverse signals that include pitched, non-pitched, and dynamically complex sounds. We show that this method produces higher-quality output for wideband and noisy sounds, such as pops and chirps, compared to using the IFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable to using the IFSpectrogram, even while using a simpler representation with half the memory requirements.
Learning Long Term Style Preserving Blind Video Temporal Consistency

When trying to independently apply image-trained algorithms to successive frames in videos, noxious flickering tends to appear. State-of-the-art post-processing techniques that aim at fostering temporal consistency, generate other temporal artifacts and visually alter the style of videos. We propose a post-processing model, agnostic to the transformation applied to videos (e.g. style transfer, image manipulation using GANs, etc.), in the form of a recurrent neural network. Our model is trained using a Ping Pong procedure and its corresponding loss, recently introduced for GAN video generation, as well as a novel style preserving perceptual loss. The former improves long-term temporal consistency learning, while the latter fosters style preservation. We evaluate our model on the DAVIS and videvo.net datasets and show that our approach offers state-of-the-art results concerning flicker removal, and better keeps the overall style of the videos than previous approaches.
Machine Learning Assisted Orthonormal Basis Selection for Functional Data Analysis

In implementations of the functional data methods, the effect of the initial choice of an orthonormal basis has not gained much attention in the past. Typically, several standard bases such as Fourier, wavelets, splines, etc. are considered to transform observed functional data and a choice is made without any formal criteria indicating which of the bases is preferable for the initial transformation of the data into functions. In an attempt to address this issue, we propose a strictly data-driven method of orthogonal basis selection. The method uses recently introduced orthogonal spline bases called the splinets obtained by efficient orthogonalization of the B-splines. The algorithm learns from the data in the machine learning style to efficiently place knots. The optimality criterion is based on the average (per functional data point) mean square error and is utilized both in the learning algorithms and in comparison studies. The latter indicates efficiency that is particularly evident for the sparse functional data and to a lesser degree in analyses of responses to complex physical systems.
Can Single-Shuffle SGD be Better than Reshuffling SGD and GD?

We propose matrix norm inequalities that extend the Recht-R\'e (2012) conjecture on a noncommutative AM-GM inequality by supplementing it with another inequality that accounts for single-shuffle, which is a widely used without-replacement sampling scheme that shuffles only once in the beginning and is overlooked in the Recht-R\'e conjecture. Instead of general positive semidefinite matrices, we restrict our attention to positive definite matrices with small enough condition numbers, which are more relevant to matrices that arise in the analysis of SGD. For such matrices, we conjecture that the means of matrix products corresponding to with- and without-replacement variants of SGD satisfy a series of spectral norm inequalities that can be summarized as:"single-shuffle SGD converges faster than random-reshuffle SGD, which is in turn faster than with-replacement SGD. "We present theorems that support our conjecture by proving several special cases.
Adversarial Graph Disentanglement

A real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. However, most existing methods lack consideration of the intrinsic differences in relations between nodes caused by factor entanglement. In this paper, we propose an \underline{\textbf{A}}dversarial \underline{\textbf{D}}isentangled \underline{\textbf{G}}raph \underline{\textbf{C}}onvolutional \underline{\textbf{N}}etwork (ADGCN) for disentangled graph representation learning. To begin with, we point out two aspects of graph disentanglement that need to be considered, i.e., micro-disentanglement and macro-disentanglement. For them, a component-specific aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that cause the links between nodes. On the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer to improve the separability among component distributions, thus restricting the interdependence among components. Additionally, to reveal the topological graph structure, a diversity-preserving node sampling approach is proposed, by which the graph structure can be progressively refined in a way of local structure awareness. The experimental results on various real-world graph data verify that our ADGCN obtains more favorable performance over currently available alternatives. The source codes of ADGCN are available at \textit{\url{https://github.com/SsGood/ADGCN}}.
Microwave-Convective and Microwave-Vacuum Drying of Cranberries: A Comparative Study

Abstract Two drying methods of cranberries (microwave-vacuum and microwave-convective) are reviewed, and their advantages and disadvantages regarding the quality of dried product and the process performance are presented. Mechanically and osmotically pretreated berries were subjected to drying and quality evaluation. Quality parameters are color (in Hunter L*a*b* coordinates), textural characteristics (toughness and Young's modulus), and organoleptic properties (color, texture, taste, and overall appearance). Special emphasis was given to the energy performance of the process, monitoring of the real-time temperature profile, and the total microwave power-on time. Two microwave power densities are assessed, as well as different microwave power-on/power-off cycling periods. In almost all observed parameters, microwave-vacuum drying exhibited enhanced characteristics when compared to microwave-convective drying. Drying performance results (defined as mass of evaporated water per unit of supplied energy) showed that microwave-vacuum drying is more energy-efficient than microwave-convective. Tasting panel results exhibited slight preference in all parameters for microwave-convective dried samples.
SURFACTANT INTERACTIONS WITH HEUR ASSOCIATING POLYMERS

The interactions between a HEUR (hydrophobically-modified ethoxylated urethane) AP (associating polymer) and the anionic surfactant SDS (sodium dodecyl sulfate) or the cationic surfactant DTAB (dodecyltrimethylammonium bromide) were studied using a combination of rheology measurements plus PGSE (pulsed-gradient spin-echo) NMR self-diffusion and NMR relaxation time measurements. For comparison, parallel experiments were performed using poly(ethylene oxide) (PEO) plus surfactant mixtures. At low concentrations (4 SDS per HEUR AP hydrophobe) added surfactant enhanced HEUR AP network formation. At high concentrations (33 SDS per HEUR AP hydrophobe) added surfactant totally disrupted the HEUR AP network and the HEUR AP self-diffusion came to resemble that of PEO. Both SDS and DTAB bound with greater affinity to the HEUR AP than to PEO. SDS bound with greater affinity than DTAB to either the HEUR AP or PEO. Despite the association of SDS with the ethylene oxide backbone of the HEUR AP, the results support the h.
Offset-free Model Predictive Control: A Ball Catching Application with a Spherical Soft Robotic Arm

This paper presents an offset-free model predictive controller for fast and accurate control of a spherical soft robotic arm. In this control scheme, a linear model is combined with an online disturbance estimation technique to systematically compensate model deviations. Dynamic effects such as material relaxation resulting from the use of soft materials can be addressed to achieve offset-free tracking. The tracking error can be reduced by 35% when compared to a standard model predictive controller without a disturbance compensation scheme. The improved tracking performance enables the realization of a ball catching application, where the spherical soft robotic arm can catch a ball thrown by a human.
iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging

Indirect Time-of-Flight (iToF) cameras are a promising depth sensing technology. However, they are prone to errors caused by multi-path interference (MPI) and low signal-to-noise ratio (SNR). Traditional methods, after denoising, mitigate MPI by estimating a transient image that encodes depths. Recently, data-driven methods that jointly denoise and mitigate MPI have become state-of-the-art without using the intermediate transient representation. In this paper, we propose to revisit the transient representation. Using data-driven priors, we interpolate/extrapolate iToF frequencies and use them to estimate the transient image. Given direct ToF (dToF) sensors capture transient images, we name our method iToF2dToF. The transient representation is flexible. It can be integrated with different rule-based depth sensing algorithms that are robust to low SNR and can deal with ambiguous scenarios that arise in practice (e.g., specular MPI, optical cross-talk). We demonstrate the benefits of iToF2dToF over previous methods in real depth sensing scenarios.
Out‐of‐field dose assessment for a 1.5 T MR‐Linac with optically stimulated luminescence dosimeters

Purpose To assess the out‐of‐field surface and internal dose of the 1.5 T MR‐Linac compared to the conventional external beam linac using optically stimulated luminescence dosimeters (OSLDs), and evaluate the out‐of‐field dose calculation accuracy of the Monaco treatment planning system (TPS) of the 1.5T MR‐Linac. Methods A cubic solid water phantom, with OSLDs on the surface, was vertically irradiated by MR‐Linac square fields with different sizes. In addition, OSLDs were arranged out of the beam edges in four directions. An anthropomorphic adult phantom, with 125 cm3 simulated volume, was irradiated in four orthogonal directions by both MR‐Linac and conventional linac at the head, thoracic, and pelvic sites. Out‐of‐field doses were measured by OSLDs on both the surface and internal emulational organs at risk (OARs). The results were compared to the simulated dose from Monaco TPS. Results At different field sizes (5 × 5 to 20 × 20 cm2) and distances (1 to 10 cm) to beam edge, the out‐of‐field surface dose measured on MR‐Linac varied from 0.16 % (10 cm to 5 × 5 cm2 edge) to 7.02 % (1 cm to 20 × 20 cm2 edge) of the maximum dose laterally and from 0.14 % (10 cm to 5 × 5 cm2 edge) to 8.56 % (1 cm to 20 × 20 cm2 edge) of the maximum dose longitudinally. Compared to the OSLDs measured data, the Monaco TPS presented an overestimate of the out‐of‐field dose of OARs at 0–2 % isodose area on both surface and internal check points, and the overestimation gets greater as the distance increases. The underestimation was found to be 0–35% at 2–5% isodose area on both surface and internal check points. Compared to the conventional linac, MR‐Linac delivered higher average values of out‐of‐field dose on surface check points (20%, 19%, 21%) and internal simulated OARs (42%, 37%, 9%) of the anthropomorphic phantom at head, thoracic, and pelvic irradiations, respectively. Conclusions Compared to the conventional linac, MR‐Linac has the same out‐of‐field dose distribution. However, considering the absolute dose values, MR‐Linac delivered relatively higher out‐of‐field doses on both surface and internal OARs. Additional radiation shielding to patients undergoing MR‐Linac may provide protection from out‐of‐field exposure.
Establishing the protocols for the South Australian Emergency Department Admission Blood Psychoactive Testing (EDABPT) programme for drug surveillance

ED presentations because of illicit use of psychotropic drugs and pharmaceuticals result in significant medical harm and resource consumption. Patient assessment is complicated by the regular emergence of new psychoactive substances, difficulties associated with their identification and a lack of information about their effects. Here we report the protocol for the Emergency Department Admission Blood Psychoactive Testing (EDABPT) programme, an observational study utilising clinical data capture and definitive drug identification to assess the medical impact and patterns of illicit drug use in the community, and their geographic and temporal fluctuations. The study provides data to an early warning system targeting an improved public health response to emerging drugs of concern.
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks

Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes. Such embeddings are important for tasks such as link prediction and node classification. In the current paper, we consider low dimensional embeddings of dynamic networks, that is a family of time varying networks where there exist both temporal and spatial link relationships between nodes. We present novel embedding methods for a dynamic network based on higher order tensor decompositions for tensorial representations of the dynamic network. In one sense, our embeddings are analogous to spectral embedding methods for static networks. We provide a rationale for our algorithms via a mathematical analysis of some potential reasons for their effectiveness. Finally, we demonstrate the power and efficiency of our approach by comparing our algorithms' performance on the link prediction task against an array of current baseline methods across three distinct real-world dynamic networks.
Predicting Community Smells’ Occurrence on Individual Developers by Sentiments

Community smells appear in sub-optimal software development community structures, causing unforeseen additional project costs, e.g., lower productivity and more technical debt. Previous studies analyzed and predicted community smells in the granularity of community sub-groups using socio-technical factors. However, refactoring such smells requires the effort of developers individually. To eliminate them, supportive measures for every developer should be constructed according to their motifs and working states. Recent work revealed developers’ personalities could influence community smells’ variation, and their sentiments could impact productivity. Thus, sentiments could be evaluated to predict community smells’ occurrence on them. To this aim, this paper builds a developer-oriented and sentiment-aware community smell prediction model considering 3 smells such as Organizational Silo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developer quitted the community after being affected by any smell. The proposed model achieves cross- and within-project prediction F-Measure ranging from 76% to 93%. Research also reveals 6 sentimental features having stronger predictive power compared with activeness metrics. Imperative and indicative expressions, politeness, and several emotions are the most powerful predictors. Finally, we test statistically the mean and distribution of sentimental features. Based on our findings, we suggest developers should communicate in a straightforward and polite way.
CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration

This paper considers online object-level mapping using partial point-cloud observations obtained online in an unknown environment. We develop an approach for fully Convolutional Object Retrieval and Symmetry-AIded Registration (CORSAIR). Our model extends the Fully Convolutional Geo-metric Features model to learn a global object-shape embedding in addition to local point-wise features from the point-cloud observations. The global feature is used to retrieve a similar object from a category database, and the local features are used for robust pose registration between the observed and the retrieved object. Our formulation also leverages symmetries, present in the object shapes, to obtain promising local-feature pairs from different symmetry classes for matching. We present results from synthetic and real-world datasets with different object categories to verify the robustness of our method.
Efficient Pairwise Neuroimage Analysis Using the Soft Jaccard Index and 3D Keypoint Sets

We propose a novel pairwise distance measure between image keypoint sets, for the purpose of large-scale medical image indexing. Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry. A new kernel is proposed to quantify the variability of keypoint geometry in location and scale. Our distance measure may be estimated between ${O}\,{(}{N}^{{\,{2}}}{)}$ image pairs in ${O}\,{(}{N}\,\text {log}{N}\,{)}$ operations via keypoint indexing. Experiments report the first results for the task of predicting family relationships from medical images, using 1010 T1-weighted MRI brain volumes of 434 families including monozygotic and dizygotic twins, siblings and half-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalence and the keypoint geometry kernel improve upon standard hard set equivalence (HSE) and appearance kernels alone in predicting family relationships. Monozygotic twin identification is near 100%, and three subjects with uncertain genotyping are automatically paired with their self-reported families, the first reported practical application of image-based family identification. Our distance measure can also be used to predict group categories, sex is predicted with an AUC = 0.97. Software is provided for efficient fine-grained curation of large, generic image datasets.