Miracle-dz/UniFAR-FTUs huggingface dataset:
Misty-A Development Platform for Socially Assistive Robots [Student's Corner]

Socially assistive robots (SARs) serve as assistants that help people through social interactions. To completely understand what that means, we need to look at two distinct areas of robotics that have been around for quite some time. SARs are an amalgam of the capabilities of socially interactive robots and the intentions of assistive robots. The main purpose of socially interactive robots is to interact with people through speech, gestures, and other modes of communication that humans can understand. These robots have been deployed in hospitality settings, such as hotel lobbies and welcome stations, where they serve as interactive information kiosks and guides.
Investigating the Stability of Concrete Nouns in Word Embeddings

We know that word embeddings trained using neural-based methods (such as word2vec SGNS) are sensitive to stability problems and that across two models trained using the exact same set of parameters, the nearest neighbors of a word are likely to change. All words are not equally impacted by this internal instability and recent studies have investigated features influencing the stability of word embeddings. This stability can be seen as a clue for the reliability of the semantic representation of a word. In this work, we investigate the influence of the degree of concreteness of nouns on the stability of their semantic representation. We show that for English generic corpora, abstract words are more affected by stability problems than concrete words. We also found that to a certain extent, the difference between the degree of concreteness of a noun and its nearest neighbors can partly explain the stability or instability of its neighbors.
Host Immune Response to ZIKV in an Immunocompetent Embryonic Mouse Model of Intravaginal Infection

Zika virus (ZIKV) only induces mild symptoms in adults; however, it can cause congenital Zika syndrome (CZS), including microcephaly. Most of the knowledge on ZIKV pathogenesis was gained using immunocompromised mouse models, which do not fully recapitulate human pathology. Moreover, the study of the host immune response to ZIKV becomes challenging in these animals. Thus, the main goal of this study was to develop an immunocompetent mouse model to study the ZIKV spread and teratogeny. FVB/NJ immune competent dams were infected intravaginally with ZIKV during the early stage of pregnancy. We found that the placentae of most fetuses were positive for ZIKV, while the virus was detected in the brain of only about 42% of the embryos. To investigate the host immune response, we measured the expression of several inflammatory factors. Embryos from ZIKV-infected dams had an increased level of inflammatory factors, as compared to Mock. Next, we compared the gene expression levels in embryos from ZIKV-infected dams that were either negative or positive for ZIKV in the brain. The mRNA levels of viral response genes and cytokines were increased in both ZIKV-positive and negative brains. Interestingly, the levels of chemokines associated with microcephaly in humans, including CCL2 and CXCL10, specifically increased in embryos harboring ZIKV in the embryo brains.
Parameters Specification of the 3D STEP Object Format in Modular Multiphysical Modeling

This paper provides a brief description of the basic procedures for specifying the geometry of an object, within the boundaries of a multiphysical approach, through the standardized format STEP. The paper considers and gives examples of electrodynamic problems in which the specification is relevant for geometry: deformation, shapeshifting, translation, rotation, stratification and segmentation. Examples are given of using procedures for meaningful keywords. The general structure of the developed specification module for the distribution of the simulation functional cycle is given.
Automatic Detection of Latent Software Component Relationships from Online Q&A Sites

Modern software system stacks are composed of large numbers of software components. These components may include a broad range of entities such as services, libraries, and frameworks, all intended to address specific requirements. It is not only necessary that these components satisfy respective functional and non-functional concerns, but also that the combinations of selected components work well together. The space of component combinations to explore is huge. Together with the almost universal lack of formal documentation suggesting desirable combinations and cautioning against undesirable ones, this renders the proper selection of combinations very challenging. For this reason, software engineers often solicit advice and document their experience on online forums such as community Q&A sites. In this paper, we show that these Q&A sites contain valuable knowledge about inter-component relations. We develop an approach using information extraction techniques to automatically identify three different types of compatibility relations from unstructured text on Q&A site postings. Our work demonstrates that identifying such relations is valuable for the design of component-based systems and that automatic relation extraction is a promising technique to systematically harness such community knowledge.
The Role of Physiological Vitamin C Concentrations on Key Functions of Neutrophils Isolated from Healthy Individuals

Vitamin C (ascorbate) is important for neutrophil function and immune health. Studies showing improved immune function have primarily used cells from scorbutic animals or from individuals with infectious conditions or immune cell disorders. Few studies have focused on the requirements of neutrophils from healthy adults. Therefore, we have investigated the role of vitamin C, at concentrations equivalent to those obtained in plasma from oral intakes (i.e., 50–200 µmol/L), on key functions of neutrophils isolated from healthy individuals. Cells were either pre-loaded with dehydroascorbic acid, which is rapidly reduced intracellularly to ascorbate, or the cells were activated in the presence of extracellular ascorbate. We measured the effects of enhanced ascorbate uptake on the essential functions of chemotaxis, oxidant production, programmed cell death and neutrophil extracellular trap (NET) formation. We found that neutrophils isolated from healthy individuals already had replete ascorbate status (0.35 nmol/106 cells), therefore they did not uptake additional ascorbate. However, they readily took up dehydroascorbic acid, thus significantly increasing their intracellular ascorbate concentrations, although this was found to have no additional effect on superoxide production or chemotaxis. Interestingly, extracellular ascorbate appeared to enhance directional mobilityin the presence of the chemoattractant formyl-methionyl-leucyl-phenylalanine (fMLP). Stimulation of the cells in the presence of ascorbate significantly increased intracellular ascorbate concentrations and, although this exhibited a non-significant increase in phosphatidylserine exposure, NET formation was significantly attenuated. Our findings demonstrate the ability of neutrophils to regulate their uptake of ascorbate from the plasma of healthy humans to maintain an optimal level within the cell for proper functioning. Higher oral intakes, however, may help reduce tissue damage and inflammatory pathologies associated with NET formation.
Boosting the Bitrate of Cross-Technology Communication on Commodity IoT Devices

The cross-technology communication (CTC) is a promising technique proposed recently to bridge heterogeneous wireless technologies in the ISM bands. Existing solutions use only the coarse-grained packet-level information for CTC modulation, suffering from a low throughput (e.g., 10 b/s). Our approach, called BlueBee, explores the dense PHY-layer information for CTC by emulating legitimate ZigBee frames with the Bluetooth radio. Uniquely, BlueBee achieves dual-standard compliance and transparency for its only modifying the payload of Bluetooth frames, requiring neither hardware nor firmware changes at either the Bluetooth sender or the ZigBee receiver. Our implementation on both USRP and commodity devices shows that BlueBee can achieve standard ZigBee bit rate of 250 kb/s at more than 99% accuracy, which is over $10000 \times$ faster than the state-of-the-art packet-level CTC technologies.
A Comparison of the Accuracy of Parts-of-Speech Tagging Systems Based on a Mathematical Model

Abstract Formulae are proposed for measuring the efficiency of part-of-speech tagging systems. The efficiency is a modification of tagging accuracy, which enables the comparison of tagging systems in the situation when they are applied to different corpora and/or use different tag-sets. The approach is based on a mathematical model of tagging accuracy and genotypes.
Essential Role of Interferon Response in Containing Human Pathogenic Bourbon Virus

Bourbon virus (BRBV) is a recently discovered tick-transmitted viral pathogen that is prevalent in the Midwest and southern United States. Since 2014, zoonotic BRBV infections have been verified in several human cases of severe febrile illness, occasionally with fatal outcomes, indicating a possible public health threat. We analyzed the pathology of BRBV infection in mice and found a high sensitivity of the virus to the host interferon system. Infected standard laboratory mice did not show clinical signs or virus replication. However, in mice carrying defects in the type I and type II interferon system, the virus grew to high titers and caused severe pathology. In cell culture, BRBV was blocked by antiviral agents like ribavirin and favipiravir (T705). Our data suggest that persons having severe BRBV infection might have a deficiency in their innate immunity and could benefit from an already approved antiviral treatment.
Revisiting NMT for Normalization of Early English Letters

This paper studies the use of NMT (neural machine translation) as a normalization method for an early English letter corpus. The corpus has previously been normalized so that only less frequent deviant forms are left out without normalization. This paper discusses different methods for improving the normalization of these deviant forms by using different approaches. Adding features to the training data is found to be unhelpful, but using a lexicographical resource to filter the top candidates produced by the NMT model together with lemmatization improves results.
Recombinant Antibodies against Mycolactone

In the past, it has proved challenging to generate antibodies against mycolactone, the primary lipidic toxin A of Mycobacterium ulcerans causing Buruli ulcer, due to its immunosuppressive properties. Here we show that in vitro display, comprising both phage and yeast display, can be used to select antibodies recognizing mycolactone from a large human naïve phage antibody library. Ten different antibodies were isolated, and hundreds more identified by next generation sequencing. These results indicate the value of in vitro display methods to generate antibodies against difficult antigenic targets such as toxins, which cannot be used for immunization unless inactivated by structural modification. The possibility to easily generate anti-mycolactone antibodies is an exciting prospect for the development of rapid and simple diagnostic/detection methods.
Fast and Discriminative Semantic Embedding

The embedding of words and documents in compact, semantically meaningful vector spaces is a crucial part of modern information systems. Deep Learning models are powerful but their hyperparameter selection is often complex and they are expensive to train, and while pre-trained models are available, embeddings trained on general corpora are not necessarily well-suited to domain specific tasks. We propose a novel embedding method which extends random projection by weighting and projecting raw term embeddings orthogonally to an average language vector, thus improving the discriminating power of resulting term embeddings, and build more meaningful document embeddings by assigning appropriate weights to individual terms. We describe how updating the term embeddings online as we process the training data results in an extremely efficient method, in terms of both computational and memory requirements. Our experiments show highly competitive results with various state-of-the-art embedding methods on different tasks, including the standard STS benchmark and a subject prediction task, at a fraction of the computational cost.
Experimental Approaches and Computational Modeling of Rat Serum Albumin and Its Interaction with Piperine

The bioactive piperine (1-piperoyl piperidine) compound found in some pepper species (Piper nigrum linn and Piper sarmentosum Roxb) has been shown to have therapeutic properties and to be useful for well-being. The tests used to validate these properties were performed in vitro or with small rats. However, in all these assays, the molecular approach was absent. Although the first therapeutic trials relied on the use of rats, no proposal was mentioned either experimentally or computationally at the molecular level regarding the interaction between piperine and rat serum albumin (RSA). In the present study, several spectroscopic techniques were employed to characterize rat serum albumin and, aided by computational techniques, the protein modeling was proposed. From the spectroscopic results, it was possible to estimate the binding constant (3.9 × 104 M−1 at 288 K) using the Stern–Volmer model and the number of ligands (three) associated with the protein applying interaction density function model. The Gibbs free energy, an important thermodynamic parameter, was determined (−25 kJ/mol), indicating that the interaction was spontaneous. This important set of experimental results served to parameterize the computational simulations. The results of molecular docking and molecular dynamics matched appropriately made it possible to have detailed microenvironments of RSA accessed by piperine.
P300-based deception detection of mock network fraud with modified genetic algorithm and combined classification

To detect network fraud, a three-stimulus paradigm was used in a mock crime P300-based concealed information test. A P300-based deception detection method based on a modified genetic algorithm and a confidence-coefficient-based combined classifier was created for mock network fraud detection. After the multi-domain integrated signal preprocessing and feature extraction, a modified logistic equation based multi-population genetic algorithm was adopted for feature selection to obtain an optimal feature subset. Then the confidence coefficient was proposed to determine the classification difficulty levels of samples. A combined classifier based on confidence coefficient was proposed for classification. Compared with the component classifiers and other individual classifiers, the combined classifier requires 34% less computing time and the mean classification accuracy rate is 0.2 to 2.23 percentage points higher for twelve subjects using leave-one-out cross validation. Experiment results confirm that the proposed method is effective to detect deception during network fraud simulation.
Development and Characterization of Acellular Caprine Choncal Cartilage Matrix for Tissue Engineering Applications

Because of poor regenerative capabilities of cartilage, reconstruction of similar rigidity and flexibility is difficult, challenging, and restricted. The aim of the present investigation was to develop cost-effective acellular xenogeneic biomaterial as cartilage substitution. Two novel biometrics have been developed using different chemical processes (Na-deoxycholate + SDS and GndHCl + NaOH) to decellularize caprine (goat) ear cartilage and further extensively characterized before preclinical investigation. Complete cell removal was ascertained by hematoxylin and eosin staining followed by DNA estimation. No adverse effect on extracellular matrix (ECM) was found by quantifying collagen and sulfated glycosaminoglycans (sGAG) content as well as collagen, sGAG and elastin staining. Results showed no drastic changes in ECM structure apart from desired sGAG loss. Scanning electron microscopy images confirmed cellular loss and unaltered orientation. Nano-indentation study on cartilage matrices indicated interesting output showing better results among decellularized groups. Increased elastic modulus and hardness indicated better stiffness and more active energy dissipation mechanism due to decellularization. Fluid uptake and retention property remained unchanged after decellularization as analyzed by swelling behavior study. Additionally, acellular materials were confirmed to be nonreactive and nonhemolytic as assessed by in vitro hemocompatibility study. In vivo study (up to 3 months) on rabbits showed no symptoms of graft rejection/ tissue necrosis, established through postoperative histology and biochemical analyses of tissue explants. With regard to size, shape, biomechanics, source of origin and nonimmunogenic properties, these developed materials can play versatile role in biomedical/ clinical applications and pave a new insight as alternatives in cartilage reconstruction.
Phase Noise Suppression Efficiency for InSAR Interferograms

A method of space-based interferometric synthetic aperture radar measurements (InSAR and DInSAR techniques) allows to obtain different types of altitude data for the Earth surface, i.e. terrain heights and displacements. Phase noise is an intrinsic random uncorrelated additive process, which complicates data processing in such systems. Different processing approaches (on example of ALOS PALSAR scenes) are proposed from the phase noise suppression efficiency point-of-view for digital elevation models creation tasks. An accuracy estimation for these approaches based on a reference digital elevation model is made, and some optimal processing parameters and conditions are found.
Novel Spatiospectral Features of ERPs Enhances Brain-Computer Interfaces

Constructing accurate predictive models for the detection of event-related potentials (ERPs) is a crucial step to obtain robust Brain-Computer Interface (BCI) systems. In this regard, the majority of previous studies have used spatiotemporal features of ERPs for classification. Recently, we showed that the spatiospectral features of ERP signals also contain significant discriminatory effects in predicting users’ mental intent. In this study, we compare the discriminatory effect of spatiospectral features and spatiotemporal features of electroencephalographic signals. Spectral features are extracted by modeling ERP signals as a sum of sinusoids with unknown amplitudes, frequencies, and phases. Temporal features are the magnitude of ERP waveforms across time. As the classification rule Logistic Regression with L2-Ridge penalty (LRR) is used. We chose this classifier as we recently showed it could achieve high performance using spatiospectral features. We observe that generally by directly using temporal features rather than extracted spectral features even a higher classification performance is achieved.
Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence

Purpose To use supervised machine learning to predict visual function from retinal structure in retinitis pigmentosa (RP) and apply these estimates to CEP290- and NPHP5-associated Leber congenital amaurosis (LCA) to determine the potential for functional improvement. Methods Patients with RP (n = 20) and LCA due to CEP290 (n = 12) or NPHP5 (n = 6) mutations were studied. A patient with CEP290 mutations but mild retinal degeneration was included. RP patients had cone-mediated macular function. A machine learning technique was used to associate perimetric sensitivities to local structure in RP patients. Models trained on RP data were applied to predict visual function in LCA. Results The RP and LCA patients had comparable retinal structure. RP patients had peak sensitivity at the fovea surrounded by decreasing sensitivity. Machine learning could successfully predict perimetry results from segmented or unsegmented optical coherence tomography (OCT) input. Application of machine learning predictions to LCA within the residual macular island of photoreceptor structure showed differences between predicted and measured sensitivities defining treatment potential. In patients with retained vision, the treatment potential was 4.6 ± 2.9 dB at the fovea but 16.4 ± 4.4 dB at the parafovea. In patients with limited or no vision, the treatment potential was 17.6 ± 9.4 dB. Conclusions Cone vision improvement potential in LCA due to CEP290 or NPHP5 mutations is predictable from retinal structure using a machine learning approach. This should allow individual prediction of the maximal efficacy in clinical trials and guide decisions about dosing. Similar strategies can be used in other retinal degenerations to estimate the extent and location of treatment potential.
RTOP: Optimal User Grouping and SFN Clustering for Multiple eMBMS Video Sessions

Evolved Multimedia Broadcast Multicast Service (eMBMS) is a 3GPP standard that improves the utilization of scarce wireless resources and the quality of the received content. eMBMS uses a Single Frequency Network (SFN) to transmit real-time videos over synchronized resources across neighboring base stations (eNBs) and allows users to share wireless spectrum across multiple cell sites. However the user with the worst channel condition and the eNB with the least available resources limit the throughput of a session. To overcome such limitations, the SFN can be divided into non-overlapping clusters of eNBs and in each cluster users can be split into groups. We formulate an optimization problem that maximizes an operator-defined utility for multiple eMBMS sessions served at multiple bitrates by choosing the optimal set of SFN clusters and user groups for each session. We propose an algorithm, RTOP, that finds the optimal or a near-optimal solution in real-time regardless of the number of eMBMS users. Our extensive simulations indicate that, in comparison to state-of-the-art schemes, RTOP improves the system utility and average user bitrate by up to 14% and 90% respectively. Additionally, we show that the utility of RTOP always stays within a 1% gap from the optimal solution.