Miracle-dz/UniFAR-FTUs huggingface dataset:
Previously rewarding environments enhance incidental memory formation

Declarative memory performance is superior for items that were encoded in temporal proximity to reward delivery or expectancy. How reward-predicting contexts affect subsequent declarative memory formation in those contexts are, however, unknown. Using an ecological experimental setup in the form of a naturalistic driving simulator task, we examined the effect that previously rewarded environments may have on incidental memory formation. After driving in two distinct environments, one of which associated with monetary reward, participants drove again in the environments, which were embedded with unique images on billboards. A recognition test 24 h later demonstrated that incidental memory was superior for items presented in the reward-associated environment. These findings suggest that environmental cues imbued with incentive salience promote memory processes even in the absence of reward.
Refinement of protein‐protein complexes in contact map space with metadynamics simulations

Accurate protein‐protein complex prediction, to atomic detail, is a challenging problem. For flexible docking cases, current state‐of‐the‐art docking methods are limited in their ability to exhaustively search the high dimensionality of the problem space. In this study, to obtain more accurate models, an investigation into the local optimization of initial docked solutions is presented with respect to a reference crystal structure. We show how physics‐based refinement of protein‐protein complexes in contact map space (CMS), within a metadynamics protocol, can be performed. The method uses 5 times replicated 10 ns simulations for sampling and ranks the generated conformational snapshots with ZRANK to identify an ensemble of n snapshots for final model building. Furthermore, we investigated whether the reconstructed free energy surface (FES), or a combination of both FES and ZRANK, referred to as CSα, can help to reduce snapshot ranking error.
Analyzing the Coexistence of Wi-Fi and LAA-LTE Towards a Proportional Throughput Fairness

Utilizing the unlicensed spectrum for the Long Term Evolution (LTE), enables the service providers to significantly increase the capacity of the network. However, LTE has to coexist with other network technologies and especially Wi-Fi. LTE employs the Licensed Assisted Access (LAA) protocol and applies the Listen Before Talk (LBT) technique to efficiently share the communication channel with Wi-Fi. Nonetheless, there is still a big gap in throughput performance between the two technologies, with the LAA-LTE considerably outperforming Wi-Fi. This unfair behavior becomes more severe in a saturation mode scenario, where the two networks are fully loaded. In this paper, we try to ensure the fair coexistence between Wi-Fi and LTE by proportionally changing the maximum packet length for each network by applying an optimization and a heuristic approach. The ultimate goal is to equalize the overall throughput for Wi-Fi and LAA or equalize the individual throughput attained for each communicating device. An admission control scheme is also studied to allocate incoming users between the two networks, while trying to balance the throughput distribution. Our results show that by changing the ratio of packet length between Wi-Fi and LAA-LTE a fair share of the medium can be achieved.
Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron

Motion classification based on pattern recognition of surface EMG (sEMG) signals is a promising approach for prosthetic control. We present a pattern recognition model that combines long short-term memory (LSTM) network with multiplayer perceptron (MLP) for sEMG signals feature learning and classification. The LSTM network captures temporal dependencies of the sEMG signals while the MLP has no inherent temporal dynamics but focuses on the static characteristics. The combination of the two networks would learn a feature space that contains both the dynamic and static information of the sEMG signals, which helps to improve the motion classification accuracy. The architecture of the proposed network was optimized by investigating the appropriate width and depth of the neural network as well as the dropout to achieve the best classification results. The performance of the proposed pattern recognition model was evaluated using Ninapro database. The results show that the proposed model can produce better classification accuracy than most of the well-known recognition techniques.
WiP: Are Cracked Applications Really Free? An Empirical Analysis on Android Devices

Android is among the popular platforms running on millions of smart devices, like smartphones and tablets, whose widespread adoption is seen as an opportunity for spreading malware. Adding malicious payloads to cracked applications, often popular ones, downloaded from untrusted third markets is a prevalent way for achieving the aforementioned goal. In this paper, we compare 25 applications from the official and third-party application stores delivering cracked applications. The behavioral analysis of applications is carried out on three real devices equipped with different Android versions by using five indicators: requested permissions, CPU usage, RAM usage and the number of opened ports for TCP and HTTP. Based on these indicators, we compute an application intention score and classify cracked applications as malicious or benign. The experimental results show that cracked applications utilize on average more resources and request access to more (dangerous) permissions than their official counterparts.
Prediction of Defect Formation during Resin Impregnation Process through a Multi-Layered Fiber Preform in Resin Transfer Molding by a Proposed Analytical Model

It is very important to predict any defects occurring by undesired fiber deformations to improve production yields of resin transfer molding, which has been widely used for mass production of carbon fiber reinforced composite parts. In this study, a simple and efficient analytic scheme was proposed to predict deformations of a multi-layered fiber preform by comparing the forces applied to the preform in a mold of resin transfer molding. Friction coefficient of dry and wet states, permeability, and compressive behavior of unidirectional (UD) and plain woven (PW) carbon fabrics were measured, which were used to predict deformations of the multi-layered fiber preforms with changing their constitution ratios. The model predicted the occurrence, type, and position of fiber deformation, which agreed with the experimental results of the multi-layered preforms.
Mixer: Efficient Many-to-All Broadcast in Dynamic Wireless Mesh Networks

Many-to-all communication is a prerequisite for many applications and network services, including distributed control and data replication. However, current solutions do not meet the scalability and latency requirements of emerging applications. This paper presents Mixer, a many-to-all broadcast primitive for dynamic wireless mesh networks. Mixer integrates random linear network coding (RLNC) with synchronous transmissions and approaches the order-optimal scaling in the number of messages to be exchanged. To achieve an efficient operation in real networks, we design Mixer in response to the theory of RLNC and the characteristics of physical-layer capture. Our experiments demonstrate, for example, that Mixer outperforms the state of the art by up to 3.8x and provides a reliability greater than 99.99 % even at a node moving speed of 60 km/h.
Artificial intelligence and deep learning in ophthalmology

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.
Combining Neural and Non-Neural Methods for Low-Resource Morphological Reinflection

We describe our systems and results in the type-level low-resource setting of the CoNLL– SIGMORPHON 2018 Shared Task on Universal Morphological Reinﬂection. We test non-neural transduction models, as well as more recent neural methods. We also investigate the effect of leveraging unannotated corpora to improve the performance of selected methods. Our best system obtains the highest accuracy on 34 out of 103 languages.
Malware Variants Detection Using Behavior Destructive Features

The variants of malware are a major threat to the security of computer systems. Millions of hosts on the Internet have been infected by malwares variants. Accurate detection of malware variants has become a key challenge for malware detection. The existing static detection is susceptible to file shelling and code obfuscation, while the dynamic detection is subject to anti-debugging and anti-virtual machine technology. Therefore, by combining the static and dynamic detection, we designed a malicious variants detection method based on behavior destructive features to analyze malicious samples.
Impact of sarcopenia on prognostic value of cirrhosis: going beyond the hepatic venous pressure gradient and MELD score

Sarcopenia has been reported as a prognostic factor. We evaluated the impact of sarcopenia to the conventional prognostic factors [Model for End‐Stage Liver Disease (MELD) score, Child–Turcotte–Pugh (CTP) score, hepatic venous pressure gradient (HVPG)] in cirrhosis.
The Effects of Lower Body Positive Pressure Treadmill Training on Dynamic Balance of Children with Cerebral Palsy

We aimed to characterize the therapeutic effects of Anti-gravity (AlterG) body weight supported treadmill training on the impaired balance of children with cerebral palsy (CP). Four spastic CP children participated; two received AlterG training 3 times a week for 8 weeks and the other two received typical occupational therapy accordingly. Their dynamic balance was evaluated before the start of the treatment and 2 months after it. Features related to the center of pressure (COP) and the center of mass (COM) were considered as dynamic balance parameters. Our results showed that the maximum velocity and acceleration of the COP and COM, the average variability (RMS) and peak to peak of the COM-COP separation, and RMS of velocity and acceleration of the COM and COP were all improved for both AlterG training patients (15–90%), though there was a limited improvement of 0.2–24% in some features of the control patients. Our results demonstrate that intensive sessions of the AlterG training program could have the potential to be used as a therapeutic tool that can produce dynamic balance improvements in CP children compared to that of typical occupational therapy.
A 45- $\mu$ W, 162.1-dBc/Hz FoM, 490-MHz Two-Stage Differential Ring VCO Without a Cross-Coupled Latch

An ultra-low-power, start-up and phase noise performance improved 2-stage differential ring voltage-controlled oscillator (VCO) is proposed. The proposed delay cell architecture reduces power consumption by eliminating the cross-coupling latch of the conventional ring VCO. Instead of the latch, the inverters in the proposed delay cell produce negative conductance by insertion of an additional RC network, which improves the start-up performance of the proposed VCO. The proposed delay cell architecture also improves the phase noise performance of the VCO as it sharpens the drain current of the inverter stage such that it resembles an impulse waveform, which reduces the root-mean-square value of impulse sensitivity function. Implemented in a 65-nm CMOS technology, the measured phase noise of the proposed VCO at 1 MHz offset is −94.84 dBc/Hz at 490 MHz oscillation frequency while dissipating 45 $\mu \text{W}$ from a 0.6-V supply. The figure of merit is 162.1 dBc/Hz.
Added Values of Linked Data in Education: A Survey and Roadmap

Education values such as knowledge sharing, and the linked data (LD) abilities such as interoperability are in perfect harmony. Much research has exploited that and provided important contributions and improvements in education through LD. International universities, large open education repositories, OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs) initiatives, educational search engines, blending and adaptive learning, learning analysis and other various areas were the targets of many works on leveraging LD. However, this research exists in a scattered way without any type of categorization or organization. In this paper, we present a survey on the current works in educational linked data (ELD) to provide a starting point and a comprehensive roadmap to help researchers in recognizing the main tracks in ELD area. In addition, the paper extracted the common life cycle, outcome datasets and vocabularies from the overall presented works. The paper also provides samples of applications that exhibit the practical benefit of adopting LD in the various tracks and highlights the challenges that each track faced during the utilization of LD. Pioneer ELD’s projects, other existing overviews and landscapes and the most used tools based on the stages and prevalent are presented. Lastly, discussion and recommendations were provided based on the overall study.
Laser-Ablated Antireflective Structures for Terahertz Radiation Focusing

Numerical simulations and experimental characterization of laser-ablated anti-reflective, phase shifting and focusing structures for terahertz frequencies are presented. Nearly 90 % transmittance of silicon wafers with anti-reflective structures on both sides was confirmed experimentally within 0.5-0.6 THz frequency range. Focusing binary zone plate for 0.6 THz was produced employing phase shift differences due to the dual function antireflective layer. Close to diffraction-limited focusing performance was demonstrated thus further confirming sufficient uniformity of the structured layer for reliable phase-shifting application.
Divergent influences of the locus coeruleus on migraine pathophysiology

Abstract Migraine is a common disabling neurological condition that is associated with several premonitory symptoms that can occur days before the headache onset. The most commonly reported premonitory symptom is marked fatigue that has been shown to be highly predictive of an ensuing migraine attack. The locus coeruleus (LC) is a key nucleus involved in arousal that has also been shown to impact pain processing. It provides one of the major sources of noradrenaline to the dorsal horn of the spinal cord and neocortex. Given the clinical association between migraine, sleep–wake regulation, and fatigue, we sought to determine whether LC modulation could impact migraine-related phenotypes in several validated preclinical models of migraine. To determine its role in migraine-related pain, we recorded dural nociceptive-evoked responses of neurons in the trigeminocervical complex, which receives trigeminal primary afferents from the durovascular complex. In addition, we explored the susceptibility to cortical spreading depression initiation, the presumed underlying phenomenon of migraine aura. Our experiments reveal a potent role for LC disruption in the differential modulation of migraine-related phenotypes, inhibiting dural-evoked activation of wide dynamic neurons in the trigeminocervical complex while increasing cortical spreading depression susceptibility. This highlights the potential divergent impact of LC disruption in migraine physiology, which may help explain the complex interactions between dysfunctional arousal mechanisms and migraine.
Terpenoid Metabolic Engineering in Photosynthetic Microorganisms

Terpenoids are a group of natural products that have a variety of essential and non-essential roles in metabolism, in biotic and abiotic interactions, as well as commercial applications such as pharmaceuticals, food additives, and chemical feedstocks. Economic viability for commercial applications is commonly not achievable by using natural source organisms or chemical synthesis. Engineered bio-production in suitable heterologous hosts is often required to achieve commercial viability. However, our poor understanding of regulatory mechanisms and other biochemical processes makes obtaining efficient conversion yields from feedstocks challenging. Moreover, production from carbon dioxide via photosynthesis would significantly increase the environmental and potentially the economic credentials of these processes by disintermediating biomass feedstocks. In this paper, we briefly review terpenoid metabolism, outline some recent advances in terpenoid metabolic engineering, and discuss why photosynthetic unicellular organisms—such as algae and cyanobacteria—might be preferred production platforms for the expression of some of the more challenging terpenoid pathways.
Representations of regular and irregular shapes by deep Convolutional Neural Networks, monkey inferotemporal neurons and human judgments

Recent studies suggest that deep Convolutional Neural Network (CNN) models show higher representational similarity, compared to any other existing object recognition models, with macaque inferior temporal (IT) cortical responses, human ventral stream fMRI activations and human object recognition. These studies employed natural images of objects. A long research tradition employed abstract shapes to probe the selectivity of IT neurons. If CNN models provide a realistic model of IT responses, then they should capture the IT selectivity for such shapes. Here, we compare the activations of CNN units to a stimulus set of 2D regular and irregular shapes with the response selectivity of macaque IT neurons and with human similarity judgements. The shape set consisted of regular shapes that differed in nonaccidental properties, and irregular, asymmetrical shapes with curved or straight boundaries. We found that deep CNNs (Alexnet, VGG-16 and VGG-19) that were trained to classify natural images show response modulations to these shapes that were similar to those of IT neurons. Untrained CNNs with the same architecture than trained CNNs, but with random weights, demonstrated a poorer similarity than CNNs trained in classification. The difference between the trained and untrained CNNs emerged at the deep convolutional layers, where the similarity between the shape-related response modulations of IT neurons and the trained CNNs was high. Unlike IT neurons, human similarity judgements of the same shapes correlated best with the last layers of the trained CNNs. In particular, these deepest layers showed an enhanced sensitivity for straight versus curved irregular shapes, similar to that shown in human shape judgments. In conclusion, the representations of abstract shape similarity are highly comparable between macaque IT neurons and deep convolutional layers of CNNs that were trained to classify natural images, while human shape similarity judgments correlate better with the deepest layers.
Exome sequencing in families with severe mental illness identifies novel and rare variants in genes implicated in Mendelian neuropsychiatric syndromes

Introduction Severe Mental Illnesses (SMI), such as bipolar disorder and schizophrenia, are highly heritable, and have a complex pattern of inheritance. Genome wide association studies detect a part of the heritability, which can be attributed to common genetic variation. Examination of rare variants with Next Generation Sequencing (NGS) may add to the understanding of genetic architecture of SMIs. Methods We analyzed 32 ill subjects (with diagnosis of Bipolar Disorder, n=26; schizophrenia, n=4; schizoaffective disorder, n=1 schizophrenia like psychosis, n=1) from 8 multiplex families; and 33 healthy individuals by whole exome sequencing. Prioritized variants were selected by a 4-step filtering process, which included deleteriousness by 5 in silico algorithms; sharing within families, absence in the controls and rarity in South Asian sample of Exome Aggregation Consortium. Results We identified a total of 42 unique rare, non-synonymous deleterious variants in this study with an average of 5 variants per family. None of the variants were shared across families, indicating a ‘private’ mutational profile. Twenty (47.6%) of the variant harboring genes identified in this sample have been previously reported to contribute to the risk of neuropsychiatric syndromes. These include genes which are related to neurodevelopmental processes, or have been implicated in different monogenic syndromes with a severe neurodevelopmental phenotype. Conclusion NGS approaches in family based studies are useful to identify novel and rare variants in genes for complex disorders like SMI. The study further validates the phenotypic burden of rare variants in Mendelian disease genes, indicating pleiotropic effects in the etiology of severe mental illnesses.
The vacuolated morphology of chordoma cells is dependent on cytokeratin intermediate filaments

Notochordal cells (NCs), characterized by their vacuolated morphology and coexpression of cytokeratin and vimentin intermediate filaments (IFs), form the immature nucleus pulposus (NP) of the intervertebral disc. As humans age, NCs give way to mature NP cells, which do not possess a vacuolated morphology and typically only express vimentin IFs. In light of their concomitant loss, we investigated the relationship between cytosolic vacuoles and cytokeratin IFs, specifically those containing cytokeratin‐8 proteins, using a human chordoma cell line as a model for NCs. We demonstrate that the chemical disruption of IFs with acrylamide, F‐actin with cytochalasin‐D, and microtubules with nocodazole all result in a significant (p < 0.001) decrease in vacuolation. However, vacuole loss was the greatest in acrylamide‐treated cells. Examination of the individual roles of vimentin and cytokeratin‐8 IFs in the existence of vacuoles was accomplished using small interfering RNA–mediated RNA interference to knock down either vimentin or cytokeratin‐8 expression. Reduction of cytokeratin‐8 expression was associated with a less‐vacuolated cell morphology. These data demonstrate that cytokeratin‐8 IFs are involved in stabilizing vacuoles and that their diminished expression could play a role in the loss of vacuolation in NCs during aging. A better understanding of the NCs may assist in preservation of this cell type for NP maintenance and regeneration.