Miracle-dz/UniFAR-FTUs huggingface dataset:
Metric Learning for Novelty and Anomaly Detection

When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works.
A Risk-Sensitive Approach for Packet Inter-Delivery Time Optimization in Networked Cyber-Physical Systems

In networked cyber-physical systems, the inter-delivery time of data packets becomes an important quantity of interest. However, providing a guarantee that the inter-delivery times of the packets are “small enough” becomes a difficult task in such systems due to the unreliable communication medium and limited network resources. We design scheduling policies that meet the inter-delivery time requirements of multiple clients connected over wireless channels. We formulate the problem as an infinite-state risk-sensitive Markov decision process, where large exceedances of inter-delivery times for different clients over their design thresholds are severely penalized. We reduce the infinite-state problem to an equivalent finite-state problem and establish the existence of a stationary optimal policy and an algorithm for computing it in a finite number of steps. However, its computational complexity makes it intractable when the number of clients is of the order of 100 or so that is found in applications such as in-vehicle networks. To design computationally efficient optimal policies, we, therefore, develop a theory based on the high reliability asymptotic scenario, in which the channel reliability probabilities are close to one. We thereby obtain an algorithm of relatively low computational complexity for determining an asymptotically optimal policy. To address the remaining case when the channels are not relatively reliable, we design index-based policies for the risk sensitive case, which extends key ideas for index policies in risk-neutral multi-armed bandit problems. Simulation results are provided to show the effectiveness of our policies.
Isoliquiritigenin Protects Against Pancreatic Injury and Intestinal Dysfunction After Severe Acute Pancreatitis via Nrf2 Signaling

Severe acute pancreatitis (SAP) is a digestive system disease that is associated with a range of complications including intestinal dysfunction. In this study, we determined that the chalcone compound, isoliquiritigenin (ISL), reduces pancreatic and intestinal injury in a mouse model of SAP. These effects were achieved by suppressing oxidative stress and the inflammatory responses to SAP. This was evidenced by a reduction in histological score, and malondialdehyde (MDA), interleukin (IL)-6, tumor necrosis factor (TNF)-α and cleaved-caspase-3 (c-caspase-3) protein along with an increase in Nrf2, hemeoxygenase-1 (HO-1), quinone oxidoreductase 1 (NQO1), and superoxide dismutase (SOD). We then used Nrf2-/- mice to test the protective effect of Nrf2 during ISL treatment of SAP. Our results indicated that Nrf2-/- mice had greater pancreatic injury and intestinal dysfunction than wild-type mice. They also had reduced adherens junctions (P120-catenin) and tight junctions (occludin), and increased activated nuclear factor-κB (NF-κB) protein. In Nrf2-/- mice, ISL was less effective at these functions than in the WT mice. In conclusion, this study demonstrated that ISL exerts its protective effects against oxidative stress and inflammatory injury after SAP via regulation of the Nrf2/NF-κB pathway. It also showed that the efficacy of ISL in repairing the intestinal barrier damage caused by SAP is closely related to the Nrf2 protein. Our findings demonstrated that Nrf2 is an important protective factor against SAP-induced injuries in the pancreas and intestines.
Drosophila mef2 is essential for normal mushroom body and wing development

ABSTRACT MEF2 (myocyte enhancer factor 2) transcription factors are found in the brain and muscle of insects and vertebrates and are essential for the differentiation of multiple cell types. We show that in the fruit fly Drosophila, MEF2 is essential for the formation of mushroom bodies in the embryonic brain and for the normal development of wings in the adult. In embryos mutant for mef2, there is a striking reduction in the number of mushroom body neurons and their axon bundles are not detectable. The onset of MEF2 expression in neurons of the mushroom bodies coincides with their formation in the embryo and, in larvae, expression is restricted to post-mitotic neurons. In flies with a mef2 point mutation that disrupts nuclear localization, we find that MEF2 is restricted to a subset of Kenyon cells that project to the α/β, and γ axonal lobes of the mushroom bodies, but not to those forming the α’/β’ lobes. Summary: Drosophila mef2 expression is restricted to subsets of mushroom body neurons, from the time of their differentiation to adulthood, and is essential for mushroom body formation.
Fair and Efficient Channel Observation-Based Listen-Before Talk (CoLBT) for LAA-WiFi Coexistence in Unlicensed LTE

License Assisted Access-WiFi (LAA-WiFi) coexistence allows the operations on the unlicensed spectrum for Long Term Evolution (LTE) along with existing unlicensed wireless local area networks (WLANs). The current spectrum access process of legacy WLANs uses clear channel assessment (CCA) and carrier sense multiple access with collision avoidance (CSMA/CA), where the spectrum is sensed before use and a random binary exponential backoff (BEB) mechanism is employed for collision avoidance. While LAA uses a listen-before-talk (LBT) mechanism, moderately similar to the CCA CSMA/CA for channel access. However, there is a fairness issue when these two technologies coexist. In this paper, we propose a channel observation-based LBT (CoLBT) mechanism for fairness in LAA-Wi-Fi coexistence scenarios. Specifically, we introduce a more realistic practical channel observation-based collision probability observed by the LAA evolved Node B (eNB) to adaptively scale-up and scale-down the backoff contention window for channel contention, to reduce the waste of resources and improve LAA-WiFi coexistence performance. Simulation results validate that the proposed CoLBT mechanism is effective in LAA-WiFi coexistence scenario and can improve fairness performance, compared with the current mechanism of LBT.
3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition

Speech emotion recognition (SER) is a difficult task due to the complexity of emotions. The SER performances are heavily dependent on the effectiveness of emotional features extracted from the speech. However, most emotional features are sensitive to emotionally irrelevant factors, such as the speaker, speaking styles, and environment. In this letter, we assume that calculating the deltas and delta-deltas for personalized features not only preserves the effective emotional information but also reduces the influence of emotionally irrelevant factors, leading to reduce misclassification. In addition, SER often suffers from the silent frames and emotionally irrelevant frames. Meanwhile, attention mechanism has exhibited outstanding performances in learning relevant feature representations for specific tasks. Inspired by this, we propose a three-dimensional attention-based convolutional recurrent neural networks to learn discriminative features for SER, where the Mel-spectrogram with deltas and delta-deltas are used as input. Experiments on IEMOCAP and Emo-DB corpus demonstrate the effectiveness of the proposed method and achieve the state-of-the-art performance in terms of unweighted average recall.
Fizzy-Related dictates A cell cycle switch during organ repair and tissue growth responses in the Drosophila hindgut

Ploidy-increasing cell cycles drive tissue growth in many developing organs. Such cycles, including endocycles, are increasingly appreciated to drive tissue growth following injury or activated growth signaling in mature organs. In these organs, the regulation and distinct roles of different cell cycles remains unclear. Here, we uncover a programmed switch between cell cycles in the Drosophila hindgut pylorus. Using an acute injury model, we identify mitosis as the response in larval pyloric cells, whereas endocycles occur in adult pyloric cells. By developing a novel genetic method, DEMISE (Dual-Expression-Method-for-Induced-Site-specific-Eradication), we show the cell cycle regulator Fizzy-related dictates the decision between mitosis and endocycles. After injury, both cycles accurately restore tissue mass and genome content. However, in response to sustained growth signaling, only endocycles preserve epithelial architecture. Our data reveal distinct cell cycle programming in response to similar stimuli in mature vs. developmental states and reveal a tissue-protective role of endocycles.
New Constructions of MDS Codes with Asymptotically Optimal Repair

An <tex>$(n,k,\ l)$</tex> MDS code of length <tex>$n$</tex>, dimension <tex>$k$</tex>, and sub-packetization <tex>$l$</tex> over a finite field <tex>$F$</tex> is a set of <tex>$n$</tex> column vectors of length <tex>$l$</tex> over <tex>$\mathrm{F}$</tex> with the property that any <tex>$k$</tex> vectors can recover the entire data of <tex>$kl$</tex> symbols. If one of the <tex>$n$</tex> nodes fails, we can recover it by downloading symbols from the surviving nodes, and the total number of symbols downloaded in the worst case is the repair bandwidth of the code. By the cut-set bound, the repair bandwidth of an <tex>$(n,k,\ l)$</tex> MDS code is at least <tex>$(n-1)l/(n-k)$</tex>. There are several constructions of <tex>$(n,k,\ l)$</tex> MDS codes whose repair bandwidths meet or asymptotically meet the cut-set bound. For example, letting <tex>$r=n-k$</tex> denote the number of parities, Ye and Barg constructed <tex>$(n,k,r^{n})$</tex> Reed-Solomon codes that asymptotically meet the cut-set bound. Ye and Barg also constructed optimal bandwidth and optimal update <tex>$(n,\ k,\ r^{n})$</tex> MDS codes. Wang, Tamo, and Bruck constructed optimal bandwidth <tex>$(n,k,r^{n/(r+{1})})$</tex> MDS codes, and these codes have the smallest known sub-packetization for optimal bandwidth MDS codes. A key idea in all these constructions is to expand integers in base <tex>$r$</tex>. When <tex>$r$</tex> is an integral power, we demonstrated in a previous paper how this technique can be refined to improve the sub-packetization of the two <tex>$(n,k,\ l)$</tex> MDS code constructions by Ye and Barg while achieving asymptotically optimal repair bandwidth. Herein, we present an extension of this idea that leads to a significant reduction in the sub-packetization of the Wang-Tamo-Bruck construction while achieving a repair-by-transfer scheme that has asymptotically optimal repair bandwidth. Specifically, when <tex>$r=s^{m}$</tex>, we obtain an <tex>$(n,k,\mathrm{s}^{k/r+m-1})$</tex> MDS code which has a repair-by-transfer scheme with asymptotically optimal repair bandwidth. If <tex>$r=2^{m}$</tex>, for example, we achieve the sub-packetization of <tex>$2^{k/r+m-1}$</tex>, which improves upon the sub-packetization of <tex>$2^{mn/(r+1)}$</tex> in the Wang- Tamo-Bruck construction. Having demonstrated how to improve the sub-packetizations of three quite different <tex>$(n,k,\ l)$</tex> MDS code constructions, we believe that our approach will be generally useful in reducing the sub-packetizations of <tex>$(n,k,\ l)$</tex> MDS code constructions that utilize r-ary expansion.
Perfusion-CT analysis for assessment of hepatocellular carcinoma lesions: diagnostic value of different perfusion maps

Background Computed tomography liver perfusion (CTLP) has been improved in recent years, offering a variety of perfusion-parametric maps. A map that better discriminates hepatocellular carcinoma (HCC) is still to be found. Purpose To compare different CTLP maps, regarding their ability to differentiate cirrhotic or non-cirrhotic parenchyma from malignant HCC. Material and Methods Twenty-six patients (11 cirrhotic) with 50 diagnosed HCC lesions, underwent CTLP on a 128-row dual-energy CT system. Nine different maps were generated. Regions of interest (ROIs) were positioned on non-tumorous parenchyma and on HCCs found on previous magnetic resonance imaging. Perfusion parameters for non-cirrhotic and cirrhotic livers were compared. Receiver operating characteristic (ROC) analysis was employed to evaluate each map’s ability to discriminate HCCs from non-tumorous livers. Comparison of ROC curves was performed to evaluate statistical significance of differences in the discriminating efficiency of derived perfusion maps. Results Perfusion parameters for non-tumorous liver and HCCs recorded in cirrhotic patients did not significantly differ from corresponding values recorded in non-cirrhotic patients (P > 0.05). The highest power for HCC discrimination was found for the maximum-slope-of-increase (MSI) parametric map, with estimated the area under ROC curve of 0.997. An MSI cut-off criterion of 2.2 HU/s was found to provide 96% sensitivity and 100% specificity. Time to peak, blood flow, and transit time to peak were also found to have high discriminating power. Conclusion Among available CTLP-derived perfusion parameters, MSI was found to provide the highest diagnostic accuracy in discriminating HCCs from non-tumorous parenchyma. Perfusion parameters for non-tumorous livers and HCCs were not found to significantly differ between cirrhotic and non-cirrhotic patients.
Efficient Ranking and Selection for Stochastic Simulation Model Based on Hypothesis Test

This paper proposes an efficient ranking and selection algorithm for a stochastic simulation model. The proposed algorithm evaluates an uncertainty to assess whether the observed best design is truly optimal, based on hypothesis test. Then, it conservatively allocates additional simulation resources to reduce uncertainty with an intuitive allocation rule in each iteration of a sequential procedure. This conservative allocation provides a high robustness to noise for the algorithm. The results of several experiments demonstrated its improved performance compared to the other algorithms in the literature. The algorithm can be an efficient way to solve optimization problems in real-world systems where significant noise exists.
Colistin Resistance Mediated by mcr-1 in ESBL-Producing, Multidrug Resistant Salmonella Infantis in Broiler Chicken Industry, Italy (2016–2017)

Colistin-resistance mediated by mobilisable and plasmid-borne mcr genes has emerged worldwide, threatening the efficacy of colistin, a last resort antibiotic increasingly used for treating human invasive infections by multidrug-resistant or extensively drug-resistant Enterobacteriaceae. In this study, we report the first evidence of mcr-1-mediated colistin resistance in four multidrug resistant (MDR) out of 324 Salmonella infantis from the Italian antimicrobial resistance (AMR) monitoring (2001–2017) in broilers and broiler meat. Two were also Extended Spectrum Beta-Lactamases (ESBL)-producing isolates. Characterization by whole genome sequencing (WGS), located mcr-1.1 on an incX4 plasmid. Phylogenetic analysis of these isolates with selected Italian S. Infantis previously isolated from animals, meat and human clinical cases with unknown epidemiological relationship, demonstrated that ESBL-producing, mcr-1-positive isolates belonged to the emerging pESI-like-positive-ESBL-producing clone described in Italy in 2015.
Concentration Bounds for Single Parameter Adaptive Control

The purpose of this paper is to analyse transient dynamics in adaptive control using statistical concentration bounds. For maximal clarity, the study is limited to a linear first order system with a single uncertain parameter. Two types of bounds are given: First we prove probabilistic bounds on the parameter estimation error as a function of time. In particular, we prove that the estimation error has finite variance after three time steps and finite fourth moments after five time steps. These bounds are independent of how the parameter estimates are used for feedback. Secondly, we bound the “regret” as a function of time, i.e. the difference in control performance between a self-tuning adaptive controller and the best controller given full knowledge of the plant. The conservatism of the bounds is investigated through simulation.
Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap

OpenStreetMap (OSM) has proven to serve as a promising free global encyclopedia of maps with an increasing popularity across different user communities and research bodies. One of the unique characteristics of OSM has been the availability of the full history of users’ contributions, which can leverage our quality control mechanisms through exploiting the history of contributions. Since this aspect of contributions (i.e., historical contributions) has been neglected in the literature, this study aims at presenting a novel approach for improving the positional accuracy and completeness of the OSM road network. To do so, we present a five-stage approach based on a Voronoi diagram that leads to improving the positional accuracy and completeness of the OSM road network. In the first stage, the OSM data history file is retrieved and in the second stage, the corresponding data elements for each object in the historical versions are identified. In the third stage, data cleaning on the historical datasets is carried out in order to identify outliers and remove them accordingly. In the fourth stage, through applying the Voronoi diagram method, one representative version for each set of historical versions is extracted. In the final stage, through examining the spatial relations for each object in the history file, the topology of the target object is enhanced. As per validation, a comparison between the latest version of the OSM data and the result of our approach against a reference dataset is carried out. Given a case study in Tehran, our findings reveal that the completeness and positional precision of OSM features can be improved up to 14%. Our conclusions draw attention to the exploitation of the historical archive of the contributions in OSM as an intrinsic quality indicator.
Glucose Concentration Affects Fibrin Clot Structure and Morphology as Evidenced by Fluorescence Imaging and Molecular Simulations

Although in vivo studies have been conducted in the past to determine hyperglycemic effects and influence on clotting risk in patients with diabetes, the true extent of hyperglycemia on unstable and spontaneous clot formation remains highly debated. Factors such as increased glycation, elevated fibrinogen concentration, elevated prothrombin levels, and decreased plasminogen are known to influence fibrin conversion, clot morphology, and thrombus formation in these individuals. In this regard, the isolated effects of hyperglycemia on irregular fibrin clot formation were investigated in a controlled fibrinogen system. In this study, fibrin clot characteristic differences at 3 glucose concentrations were analyzed to determine the effects of glucose concentration on fibrinogen glycation and fibrin clot morphology using confocal microscopy, glycation quantification, molecular simulations, and image processing methods. Algorithms coupled with statistical analysis support in vivo findings that hyperglycemia increases fibrinogen glycation, with ensuing altered fibrin clot structure characteristics. Our experimental and molecular simulation results consistently show an increased glucose adsorption by fibrinogen with increased glucose concentration. Significant differences in clot structure characteristics were observed, and the results of this work can be used to further develop diagnostic tools for evaluating clotting risk in individuals with hypercoagulable and hyperglycemic conditions.
Dynamic Model for Magnetostrictive Systems With Applications to Damper Design

Magnetostrictive iron–gallium alloys are able to dissipate mechanical energy via eddy currents and magnetic hysteresis. The mechanically induced eddy current loss is determined by the piezomagnetic coefficient; the hysteresis loss is usually quantified by the phase lag. This study first characterizes these losses for research grade, <100>-oriented, highly textured, polycrystalline <inline-formula><tex-math notation="LaTeX"> $\mathrm{Fe_{81.6}Ga_{18.4}}$</tex-math></inline-formula> within the structural frequency range (up to 800 Hz). The magnetic biasing is provided by applying a constant current of 500 mA on a pair of electromagnets; the mechanical excitation is a sinusoidal stress wave (3 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 0.2 MPa) superimposed on a <inline-formula><tex-math notation="LaTeX">$-$</tex-math> </inline-formula>20 MPa constant stress. As stress frequency increases, the piezomagnetic coefficient decreases from 32.27 to 10.33 T/GPa and the phase lag <inline-formula><tex-math notation="LaTeX">$|\Delta \phi |$</tex-math> </inline-formula> increases from 11.38<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> to 43.87<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>. A rate-dependent finite element framework decoupling eddy current loss and hysteresis loss is then developed. The model accurately reproduces the experimental results in both quasi-static and dynamic regimes. Guided by the knowledge of material properties and the finite element model, a coil-less and solid-state damper is designed which can attenuate vibrations before they propagate and induce structure-borne noise and damage. Modeling results show that the loss factor of this damper can be continuously tuned from 0 to a maximum value of 0.107 by adjusting the precompression on the magnetostrictive component.