Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing

In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving. Despite great success achieved in various human intelligence tasks, similar to traditional software, DNNs could also exhibit incorrect behaviors caused by hidden defects causing severe accidents and losses. In this paper, we propose DeepHunter, an automated fuzz testing framework for hunting potential defects of general-purpose DNNs. DeepHunter performs metamorphic mutation to generate new semantically preserved tests, and leverages multiple plugable coverage criteria as feedback to guide the test generation from different perspectives. To be scalable towards practical-sized DNNs, DeepHunter maintains multiple tests in a batch, and prioritizes the tests selection based on active feedback. The effectiveness of DeepHunter is extensively investigated on 3 popular datasets (MNIST, CIFAR-10, ImageNet) and 7 DNNs with diverse complexities, under a large set of 6 coverage criteria as feedback. The large-scale experiments demonstrate that DeepHunter can (1) significantly boost the coverage with guidance; (2) generate useful tests to detect erroneous behaviors and facilitate the DNN model quality evaluation; (3) accurately capture potential defects during DNN quantization for platform migration.
Dynamics of cardiac re-entry in micro-CT and serial histological sections based models of mammalian hearts.

Cardiac re-entry regime of self-organised abnormal synchronisation underlie dangerous arrhythmias and fatal fibrillation. Recent advances in the theory of dissipative vortices, experimental studies, and anatomically realistic computer simulations, elucidated the role of cardiac re-entry interaction with fine anatomical features in the heart, and anatomy induced drift. The fact that anatomy and structural anisotropy of the heart is consistent within a species suggested its possible functional effect on spontaneous drift of cardiac re-entry. A comparative study of the anatomy induced drift could be used in order to predict evolution of atrial arrhythmia, and improve low-voltage defibrillation protocols and ablation strategies. Here, in micro-CT based model of rat pulmonary vein wall, and in sheep atria models based on high resolution serial histological sections, we demonstrate effects of heart geometry and anisotropy on cardiac re-entry anatomy induced drift, its pinning to fluctuations of thickness in the layer. The data sets of sheep atria and rat pulmonary vein wall are incorporated into the BeatBox High Performance Computing simulation environment. Re-entry is initiated at prescribed locations in the spatially homogeneous mono-domain models of cardiac tissue. Excitation is described by FitzHugh-Nagumo kinetics. In the in-silico models, isotropic and anisotropic conduction show specific anatomy effects and the interplay between anatomy and anisotropy of the heart. The main objectives are to demonstrate the functional role of the species hearts geometry and anisotropy on cardiac re-entry anatomy induced drift. In case of the rat pulmonary vein wall with ~90 degree transmural fibre rotation, it is shown that the joint effect of the PV wall geometry and anisotropy turns a plane excitation wave into a re-entry pinned to a small fluctuation of thickness in the wall.
Suppressing ROS‐TFE3‐dependent autophagy enhances ivermectin‐induced apoptosis in human melanoma cells

Melanoma is an aggressive skin malignancy with a high mortality rate; however, successful treatment remains a clinical challenge. Ivermectin, a broad‐spectrum antiparasitic drug, has recently been characterized as a potential anticancer agent due to its observed antitumor effects. However, the molecular mechanisms of ivermectin remain poorly understood. In the current study, we tested the involvement of autophagy in the ivermectin mechanism of action in human melanoma cells. We exposed SK‐MEL‐28 cells to different concentrations of ivermectin (2.5, 5, and 10 μM) for 24 hours. Here, ivermectin‐induced apoptosis, as evidenced by the upregulation of cleaved poly (ADP‐ribose) polymerase, BAX expression, and caspase‐3 activity and downregulation of BCL‐2 expression. In line with the apoptosis response, ivermectin triggered autophagy. Pharmacological or genetic inhibition of autophagy further sensitized SK‐MEL‐28 cells to ivermectin‐induced apoptosis. Mechanistically, ivermectin‐induced TFE3(Ser321) dephosphorylation, activated TFE3 nuclear translocation and increased TFE3 reporter activity, which contributed to lysosomal biogenesis and the expression of autophagy‐related genes, and subsequently, initiated autophagy in SK‐MEL‐28 cells. Moreover, N‐acetyl‐cysteine, an reactive oxygen species (ROS) scavenger, abrogated the effects of ivermectin on TFE3‐dependent autophagy. Taken together, we demonstrated that ivermectin increases TFE3‐dependent autophagy through ROS signaling pathways in human melanoma cells and that inhibiting autophagy enhances ivermectin‐induced apoptosis in human melanoma cells.
MET4F: A Tool for Providing in Near Real-Time Weather Information on Board

In order to increase the weather awareness on board, at CIRA (Italian Aerospace Research Center) it is under development a general system able to provide, in near-real time, forecast and monitored weather information along the flight route, gathering and integrating data from different sources. MET4F (METeorological data for FLARE) is a new tool, within this system, devoted to provide the over mentioned weather information through the dispatch of a binary message from an on ground control station to an Unmanned Air Vehicle (UAV). The message format follows the protocol adopted by the CIRA Remotely Piloted Aircraft System and allows an automatic flight reconfiguration, also on the basis of the weather information.
A Complete Power Allocation Framework for Multiple Target Tracking with the Purpose of Minimizing the Transmit Power

In this paper, a new power allocation framework is proposed with the task of multiple target tracking (MTT), in which an adaptive cost function (ACF) with respect to the transmit power and tracking accuracy requirements is first designed. Then we take the ACF as an objective function and formulate the proposed framework as a mathematical optimization problem. In this problem, the posterior Cramér-Rao lower bound (PCRLB) provides us with a lower bound on the estimated error of the targets state. Numerical simulation demonstrates that in the scenario where the common method is not applicable, an effective and robust power allocation scheme can be obtained by the proposed method.
Non intrusive load monitoring: analytic expressions as load signature

Since 1992 (G. W. Hart [5]) the researchers started to take interest (focus) in developing a link between energetic signatures and household devices. In the ideal case, the main objective of the signature is to uniquely identify a device. One important characteristic of the signature is to be as compact as possible. In the present context, this paper proposes to use analytic expressions in form of transcendent function as signature for devices from the same consumption category: laptop, LCD-TV, washing machine, air conditioner. The paper presents a method to determine a signature based on the tangent function and genetic algorithm. Two case studies are shown. They will prove that it is possible to obtain an individual signature for household devices, and separate them from a common consumption.
Epigenome-Wide Analyses Identify Two Novel Associations With Recurrent Stroke in the Vitamin Intervention for Stroke Prevention Clinical Trial

DNA methylation, a well-characterized epigenetic modification that is influenced by both environment and genetic variation, has previously been implicated in a number of complex diseases, including cardiovascular disease and stroke. The goal of this study was to evaluate epigenome-wide associations with recurrent stroke and the folate one-carbon metabolism-related trait, plasma homocysteine (hcy). Differential methylation analyses were performed on 473,864 autosomal CpG loci, using Illumina HumanMethylation 450K array data in 180 ischemic stroke cases from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial. Linear regression was used to assess associations between number of strokes prior to VISP enrollment and measures of hcy with degree of methylation (β-values), while logistic regression was used to evaluate recurrent stroke status and incident recurrent stroke associations. All regression analyses were stratified by race. Two differentially methylated CpG sites exceeded epigenome-wide significance (p ≤ 1.055 × 10−7) for prior number of strokes (PNS) in European Americans. The top locus, cg22812874, was located in the ankyrin repeat and SOCS box containing 10 gene (ASB10; p = 3.4 × 10−9; β = −0.0308; 95% CI = −0.040, −0.002). Methylation locus cg00340919, located in an intron of the tetratricopeptide repeat domain 37 gene, was also statistically significant (TTC37; p = 8.74 × 10−8; β = −0.0517; 95% CI = −0.069, −0.034). An additional 138 CpG sites met our threshold for suggestive significance (p ≤ 5 × 10−5). We evaluated DNA methylation associated with recurrent stroke and hcy phenotypes across the epigenome. Hypermethylation at two CpG sites located in ASB10 and TTC37 was associated with fewer strokes prior to VISP enrollment. Our findings present a foundation for additional epigenome-wide studies, as well as mechanistic studies into epigenetic marks that influence recurrent stroke risk.
Semantic Segmentation on Radar Point Clouds

Semantic segmentation on radar point clouds is a new challenging task in radar data processing. We demonstrate how this task can be performed and provide results on a large data set of manually labeled radar reflections. In contrast to previous approaches where generated feature vectors from clustered reflections were used as an input for a classifier, now the whole radar point cloud is used as an input and class probabilities are obtained for every single reflection. We thereby eliminate the need for clustering algorithms and manually selected features.
Personal reflections on the early contributions of Gus Born to platelet research

ABSTRACT Professor GVR Born, Gus to his friends, was one of the great pioneers of platelet research. My early memories of him have enabled me to look back at his early years in Oxford and London. A brilliant and generous man with always the time to discuss and advise he was instrumental in deciphering the principle stages of the aggregation of blood platelets by ADP, a path aided by his development and use of the platelet aggregometer. He applied his knowledge to the real time analysis of platelet and leukocyte involvement in thrombus formation in animal models and to the development of atherosclerosis and thrombosis and their pharmacological inhibition. What follows is a personal account of the major steps in this early work and of the actors involved.
Forensic Camera Model Identification: Highlights from the IEEE Signal Processing Cup 2018 Student Competition [SP Competitions]

Determining the make and model of the camera that captured an image has been an important research area in information forensics for more than a decade [1]-[3] (see Figure 1). Information about which type of camera captured an image can be used to help determine or verify the origin of an image and can form an important piece of evidence in some scenarios, such as analyzing images involved in child exploitation investigations. While metadata may contain information about an image's source camera, metadata is both easy to falsify and frequently missing from an image. As a result, signal processing researchers have developed information forensic algorithms that can exploit traces intrinsically present in the image itself.
Evolution of pollination by frugivorous birds in Neotropical Myrtaceae

Bird pollination is relatively common in the tropics, and especially in the Americas. In the predominantly Neotropical tribe Myrteae (Myrtaceae), species of two genera, Acca and Myrrhinium, offer fleshy, sugary petals to the consumption of birds that otherwise eat fruits, thus pollinating the plants in an unusual plant-animal interaction. The phylogenetic position of these genera has been problematic, and therefore, so was the understanding of the evolution of this interaction. Here we include new sequences of Myrrhinium atropurpureum in a comprehensive molecular phylogeny based on a balanced sample of two plastid and two nuclear markers, with the aim of providing the historical framework of pollination by frugivorous birds in Myrteae. We developed 13 flower and inflorescence characters that comprehensively depict the macroscopic morphological components of this interaction. Bayesian and parsimony phylogenies concur in placing both Acca and Myrrhinium in a clade with Psidium species; with Myrrhinium sister to Psidium. Mapping of morphological characters indicated some degree of convergence (e.g., fleshy petals, purplish display) but also considerable divergence in key characters that point to rather opposing pollination strategies and also different degrees of specialization in Acca versus Myrrhinium. Pollination by frugivorous birds represents a special case of mutualism that highlights the evolutionary complexities of plant-animal interactions.
Parallel Analytical Model for Frequent Itemset Mining

The data generated in real world applications in recent days are quite huge and processing and analyzing those data has become a challenging task. For any analytical and inference engine finding frequent itemsets turns out to be a major functionality. Frequent itemset mining is performed usually with the help of association rule mining technique in data mining. Generally the results obtain from these techniques are large and diverse which makes it difficult for an inference engine to reach a conclusion. One of the ideal solutions for handling such datasets is by devising a parallel processing system to efficiently run the data mining approaches and obtain a more accurate and easily analyzable output. The research carried out here aims at designing and developing parallel analytical model for frequent itemset mining in big data by integrating R on Hadoop.
Digitally Designed Ultrathin Metasurfaces for Multiwavelength Optics in the Visible

Metasurface-based optical elements have the ability to shape wavefronts by locally changing the properties of the incident illumination. They hold great potential to promote a new generation of wearable devices and thin optical systems for imaging and sensing. However, due to the diffractive nature of these metasurfaces, their performance is highly sensitive to the wavelength of operation. In this paper, we present a new approach to design single-layer multi-wavelength metasurfaces using ultrathin dielectric resonators (thickness $<<$ wavelength) as structural elements. The electromagnetic response of these resonators can be tailored and used to manipulate the transmitted light. We introduce a digital transmission modulation scheme which enables opportunities to design novel multi-wavelength optics beyond conventional binary diffractive optics. The potential of the proposed approach is demonstrated with achromatic meta-lenses in the visible band, one of the most challenging functionalities to achieve with a single-layer flat optical element.
An Area Efficient 1024-Point Low Power Radix-22 FFT Processor With Feed-Forward Multiple Delay Commutators

Radix- $2^{k}$ delay feed-back and radix-K delay commutator are the most well-known pipeline architecture for FFT design. This paper proposes a novel radix-22 multiple delay commutator architecture utilizing the advantages of the radix-22 algorithm, such as simple butterflies and less memory requirement. Therefore, it is more hardware efficient when implementing parallelism for higher throughput using multiple delay commutators or feed-forward data paths. Here, we propose an improved input scheduling algorithm based upon memory to eliminate energy required to shift data along the delay lines. A 1024-point FFT processor with two parallel data paths is implemented in 65-nm CMOS process technology. The FFT processor occupies an area of 3.6 mm2, successfully operates in the supply voltage range from 0.4–1 V and the maximum clock frequency of 600 MHz. For low voltage, high performance applications, the processor is able to operate at 400 MHz and consumes 60.3 mW or 77.2 nJ/FFT generating 800 Msamples/s at 0.6 V supply.
Regularized aggregation of statistical parametric maps

Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group‐level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap‐based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.
Ranking RDF Instances in Degree-decoupled RDF Graphs

In the last decade, RDF emerged as a new kind of standardized data model, and a sizable body of knowledge from fields such as Information Retrieval was adapted to RDF graphs. One common task in graph databases is to define an importance score for nodes based on centrality measures, such as PageRank and HITS. The majority of the strategies highly depend on the degree of the node. However, in some RDF graphs, called degree-decoupled RDF graphs, the notion of importance is not directly related to the node degree. Therefore, this work first proposes three novel node importance measures, named InfoRank I, II and III, for degree-decoupled RDF graphs. It then compares the proposed measures with traditional PageRank and other familiar centrality measures, using with an IMDb dataset.
Silicon and Germanium Mid-Infrared Optical Modulators

The mid-infrared wavelength region contains absorption fingerprints of numerous molecules and therefore is mostly considered for sensing applications [1]. However, due to a need for more spectral bandwidth for communications, and because modulation can be used to enhance signal to noise ratio in sensing applications, photonic modulator devices operating at mid infrared (MIR) wavelengths would be useful functional devices [2]. However, to date there have been few reported investigations of modulation in Si or Ge in the mid-infrared. SOI carrier injection modulators operating at up to 3 Gb/s with pre-emphasis at a wavelength of 2165 nm have been reported [3]. By using Ge rib waveguides with lateral p-i-n junctions, optical intensity modulation based on free-carrier absorption was demonstrated in Ge by injecting carriers through a Ge p-i-n junction at 1950 nm [4]. Also, thermo-optic modulation has been demonstrated in SOI at 3.8 μm [5], and in Ge-on-Si (GOS) and Ge-on-SOI (GOSOI) at ∼5.3 μm [6]. An all optical modulator based on free-carrier absorption has also been demonstrated in GOS with bandwidth of around 55 MHz across the 2–3.2 μm wavelength range [7]. In addition, two photon absorption (TPA) cross absorption modulation has been reported on the same platform and in the same wavelength range [8].
A Learning-Based Hierarchical Control Scheme for an Exoskeleton Robot in Human–Robot Cooperative Manipulation

Exoskeleton robots can assist humans to perform activities of daily living with little effort. In this paper, a hierarchical control scheme is presented which enables an exoskeleton robot to achieve cooperative manipulation with humans. The control scheme consists of two layers. In low-level control of the upper limb exoskeleton robot, an admittance control scheme with an asymmetric barrier Lyapunov function-based adaptive neural network controller is proposed to enable the robot to be back drivable. In order to achieve high-level interaction, a strategy for learning human skills from demonstration is proposed by utilizing Gaussian mixture models, which consists of the learning and reproduction phase. During the learning phase, the robot observes and learns how a demonstrator performs a specific impedance-based task successfully, and in the reproduction phase, the robot can provide the subjects with just enough assistance by extracting human skills from demonstrations to prevent the motion of the robot end-effector deviating far from desired ones, due to variation in the interaction force caused by environmental disturbances. Experimental results of two different tasks show that the proposed control scheme can provide human subjects with assistance as needed during cooperative manipulation.
Interface Asymmetry Induced by Symmetric Electrodes on Metal–Al:TiO $_{x}$–Metal Structures

Emerging memory technologies have sparked great interest in studying a variety of materials that can be employed in metal–insulator–metal topologies to support resistive switching. While the majority of reports focus on identifying appropriate materials that can be used as active core layers, the selection of electrodes also impacts the performance of such memory devices. Here, both the top and the bottom interfaces of symmetric Metal–Al:TiO$_{x}$–Metal structures have been investigated by the analysis of their current versus voltage characteristics in the temperature range of 300–350 K. Three different metals were utilized as electrodes, Nb, Au, and Pt, for covering a wide range of work function and electronegativity values. Despite their symmetric structure, the devices were found to exhibit asymmetric performance with respect to the applied bias polarity. Clear signature plots indicating thermionic emission over the interface Schottky barriers have been obtained. The asymmetry between the top and the bottom interfaces was further evaluated by the values of the potential barrier heights and by the barrier lowering factors, both calculated from the experimental data. This study highlights the importance of the interface effects and proves that in addition to film doping, proper (top/bottom) metal selection, and interface engineering should also be exploited for developing thin film metal oxide based devices with tailored electrical characteristics.
Item-specific and relational processing both improve recall accuracy in the DRM paradigm

Using the Deese–Roediger–McDermott (DRM) paradigm, Huff and Bodner found that both item-specific and relational variants of a task improved correct recognition, but only the item-specific variants reduced false recognition, relative to a read-control condition. Here, we examined the outcome pattern when memory was tested using free recall, using the same item-specific versus relational task variants across three experiments as our previous study (processing instructions, pleasantness ratings, anagram generation). The outcome pattern in recall was similar to recognition, except relational processing at study actually reduced the DRM illusion, though not as much as item-specific processing. To reconcile this task difference, we suggest that the memory information laid down during relational encoding enhances the familiarity of the critical items at test. To the extent that familiarity is used less as a basis for responding in free recall than in recognition, relational processing ironically reduces rather than increases the DRM illusion in recall.