Miracle-dz/UniFAR-FTUs huggingface dataset:
Squamous Cell Carcinomas of the Head and Neck Cancer Response to Programmed Cell Death Protein-1 Targeting and Differential Expression of Immunological Markers: A Case Report

Targeting the programmed cell death protein-1 (PD-1)/PD-1 ligand (PD-L1) pathway has been shown to enhance T cell-mediated antitumor immunity. Clinical responses are limited to subgroups of patients. The search for biomarkers of response is a strategy to predict response and outcome of PD-1/PD-L1 checkpoint intervention. The NY-ESO-1 cancer testis antigen has been considered as a biomarker in head and neck squamous cell carcinoma (HNSCC) patients and can induce both specific NY-ESO-1 antibody and T cells responses. Here, we correlated clinical responsiveness to anti-PD-1 (nivolumab) treatment with immunity to NY-ESO-1 in a patient with recurrent HNSCC. The patient was treated with second-line treatment of nivolumab and had a stable disease for over 7 months. His NY-ESO-1 antibody was found to be lower after the third (****p < 0.0001) and the fifth (****p < 0.0001) cycles of treatment compared to base line, and this was in line with the stability of the disease. The NY-ESO-1-specific T cells response of the patient was found to be increased after the third and the fifth (**p = 0.002) cycles of treatment but had a significant decline after progression (**p = 0.0028). The PD-1 expression by the patient’s T cells was reduced 15-folds after nivolumab treatment and was uniquely restricted to the CD8+ T cells population. Several cytokines/chemokines involved in immune activation were upregulated after nivolumab treatment; two biomarkers were reduced at progression [interleukin (IL)-10: ****p < 0.0001 and CX3CL1: ****p < 0.0001]. On the other hand, some cytokines/chemokines contributing to immune inhibition were downregulated after nivolumab treatment; two biomarkers were increased at progression (IL-6: ****p < 0.0001 and IL-8: ****p < 0.0001). This data support the notion that the presence of anti-NY-ESO-1 integrated immunity and some cytokines/chemokines profile may potentially identify a response to PD-1 blockade in HNSCC patients.
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method

We propose a novel algorithmic framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem. Its iteration complexities and local linear convergence rate are provided, which theoretically demonstrate that a large over-relaxed step-size contributes to accelerating the proposed VMOR-HPE as a byproduct. Specifically, we find that a large class of primal and primal-dual operator splitting algorithms are all special cases of VMOR-HPE. Hence, the proposed framework offers a new insight into these operator splitting algorithms. In addition, we apply VMOR-HPE to the Karush-Kuhn-Tucker (KKT) generalized equation of linear equality constrained multi-block composite convex optimization, yielding a new algorithm, namely nonsymmetric Proximal Alternating Direction Method of Multipliers with a preconditioned Extra-gradient step in which the preconditioned metric is generated by a blockwise Barzilai-Borwein line search technique (PADMM-EBB). We also establish iteration complexities of PADMM-EBB in terms of the KKT residual. Finally, we apply PADMM-EBB to handle the nonnegative dual graph regularized low-rank representation problem. Promising results on synthetic and real datasets corroborate the efficacy of PADMM-EBB.
GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks

This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic. We build a a Deep Neural Network that combines word and character-based representations in convolutional and recurrent networks with a CRF layer. The model is augmented with stacked layers of enriched information such pre-trained embeddings, Brown clusters and named entity gazetteers. Our system is ranked second among those participating in the shared task achieving an FB1 average of 70.09%.
Joint User Association and Resource Optimization for Unlicensed LTE Systems

LTE-Unlicensed (LTE-U) has been widely considered as one of the most up-to-date promising innovations to achieve more ambitious data rate under a unified networking architecture by extending the LTE network into the bandwidth-rich unlicensed spectrum. In this paper, we study the user association optimization for LTE-U systems with multiple small cell base stations (SBSs) and WiFi access points (APs). Aiming at maximizing the whole throughput of LTE-U systems, a non-convex combinatorial optimization problem is modeled, which is NP-hard. By relaxing the integer variables to continuous variables, an upper-bound algorithm is developed based on the theory of sum-of- ratios optimization. Moreover, a heuristic algorithm based on the student project allocation (SPA) modeling is also developed, whose computational complexity is linear. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithms.
Preparation and Evaluation of Oseltamivir Molecularly Imprinted Polymer Silica Gel as Liquid Chromatography Stationary Phase

To improve the chromatographic performance of an oseltamivir (OS) molecularly imprinted polymer (MIP), silica gel coated with an MIP layer for OS (OSMIP@silica gel) was prepared by the surface molecular imprinting technology on the supporter of porous silica gel microspheres. A nonimprinted polymer with the silica gel (NIP@silica gel) was also prepared for comparison. The obtained particles were characterized through FT–IR, scanning electron microscopy, specific surface area analysis, and porosity measurements. The results indicated that the polymer was successfully synthesized and revealed the structural differences between imprinted and nonimprinted polymers. The results of static adsorption experiments showed that adsorption quantity of the OSMIP@silica gel for OS was higher than that for NIP@silica gel, and the OSMIP@silica gel had two kinds of affinity sites for OS but the NIP@silica gel had one. The chromatographic performance of the OSMIP@silica gel column had significant improvement. The imprinting factor of the OSMIP@silica gel column for OS was 1.64. Furthermore, the OSMIP@silica gel column showed good affinity and selectivity for template OS and another neuraminidase inhibitor, peramivir, but not for quinocetone. These results indicated that the prepared OSMIP could be used to simulate the activity center of neuraminidase, and the OSMIP@silica gel column could be also employed in future studies to search for more active neuraminidase inhibitor analogues from traditional Chinese herbs.
Computational Modeling of Perovskite Structured Methylammonium Lead Iodide as a Solar Absorber

Halide Perovskite materials play a prominent role in solar cell industry as they comprise with advanced photovoltaic properties. Research is mainly based on the objective of investigating basic fundamental properties of CH3NH3PbI3 using Viana Ab-Initio Simulation Package(VASP). Before go to the experimental data analysis we have simulated fundamental photovoltaic properties of Methyl Ammonium Lead Iodide using computational method. CH3NH3PbI3 perovskite undergoes a phase transition between orthorhombic, tetragonal and cubic around 165K and 327K. Then the most stable structures are tetragonal and cubic phases. Therefore, in this research we have focused to study the basic crystallographic orientations, Energy band gap variation, Density of states in P, S orbitals and effective mass values of cubic and tetragonal phases in Methyl Ammonium Lead Iodide. Basic Density Function (DFT) theories like Local-density approximation of Kohn-Sham theory, Exchange correlation functional are basically used for theoretical calculations in this computational study.
Development of a Vibration Energy Harvesting Device Using Piezoelectric Sensors

Piezoelectric energy harvesting has become popular among researchers because of the ease of converting mechanical energy into electrical energy using piezoelectric materials. In this context, a vibration energy harvesting device was developed to harvest vibration energy from vehicles. Lead zirconate titanate (PZT) was selected as the piezoelectric material. First the vibration sources were analyzed to identify the resonant frequency. Piezoelectric energy harvester was developed using the cantilever type configuration. Euler–Bernoulli beam theory was used to analyze the cantilever beam under free vibration. Finite element analysis (FEA) was used to identify the design parameters of the prototype which includes the metal beam, tip mass and the PZT plate. The maximum theoretical voltage was obtained as 5.99V according to the FEA modeling. The prototype was developed and voltage was measured fixing the prototype to the motor bike. The average voltage output was 3.65V.
Privacy Preserving Distributed Computation of Private Attributes for Collaborative Privacy Aware Usage Control Systems

Collaborative smart services provide functionalities which exploit data collected from different sources to provide benefits to a community of users. Such data, however, might be privacy sensitive and their disclosure has to be avoided. In this paper, we present a distributed multi-tier framework intended for smart-environment management, based on usage control for policy evaluation and enforcement on devices belonging to different collaborating entities. The proposed framework exploits secure multi-party computation to evaluate policy conditions without disclosing actual value of evaluated attributes, to preserve privacy. As reference example, a smart-grid use case is presented.
Data-Driven Parallel Video Transcoding for Content Delivery Network in the Cloud

Efficient video transcoding has become a critical technology in content delivery network (CDN) service for social networked video application. In this paper, we propose a data-driven parallel video transcoding solution for CDN. The key to the solution is to compute the ranking of popular videos through web log analysis and then transcode popular videos by mapreduce. Experimental results show that our solution can significantly improve the efficiency of video transcoding.
NanoLog: A Nanosecond Scale Logging System

NanoLog is a nanosecond scale logging system that is 1-2 orders of magnitude faster than existing logging systems such as Log4j2, spdlog, Boost log or Event Tracing for Windows. The system achieves a throughput up to 80 million log messages per second for simple messages and has a typical log invocation overhead of 8 nanoseconds in microbenchmarks and 18 nanoseconds in applications, despite exposing a traditional printf-like API. NanoLog achieves this low latency and high throughput by shifting work out of the runtime hot path and into the compilation and post-execution phases of the application. More specifically, it slims down user log messages at compile-time by extracting static log components, outputs the log in a compacted, binary format at runtime, and utilizes an offline process to re-inflate the compacted logs. Additionally, log analytic applications can directly consume the compacted log and see a performance improvement of over 8x due to I/O savings. Overall, the lower cost of NanoLog allows developers to log more often, log in more detail, and use logging in low-latency production settings where traditional logging mechanisms are too expensive.
Robot Program Construction via Grounded Natural Language Semantics & Simulation

Robots acting in semi-structured, human environments need to understand the effects of their actions and the instructions given by a human user. Simulation has been considered a promising reasoning technique to help tackle both problems. In this paper, we present a system that constructs an executable robot program from a linguistic semantic specification produced by parsing a natural language sentence; in effect, our system grounds the semantic specification into the produced robot plan. The plan can then be run in a simulated environment, which allows one to infer more about the plan than was present in the initial semantic specification. Our system allows modeling how actions can be modified by subclauses, which we showcase by a transport action. Simulation runs allow discovery of better parameters, either locally for a subtask or such that the entire task is better performed; simulation reveals these parameterizations may differ.
Brain stem modeling at a system level – chances and limitations

Abstract The topic of brain stem computational simulation still seems understudied in contemporary scientific literature. Current advances in neuroscience leave the brain stem as one of the least known parts of the human central nervous system. Brain stem lesions are particularly damaging to the most important physiological functions. Advances in brain stem modeling may influence important issues within the core of neurology, neurophysiology, neurosurgery, and neurorehabilitation. Direct results may include both development of knowledge and optimization and objectivization of clinical practice in the aforementioned medical areas. Despite these needs, progress in the area of computational brain stem models seems to be too slow. The aims of this paper are both to recognize the strongest limitations in the area of computational brain stem simulations and to assess the extent to which current opportunities may be exploited. Despite limitations, the emerging view of the brain stem provided by its computational models enables a wide repertoire of functions, including core dynamic behavior.
A boundary vector cell model of place field repetition

ABSTRACT Hippocampal place cells are thought to form the neural substrate of a global cognitive map. However, in multicompartment mazes, these cells exhibit locally repeating representations, undermining the global cognitive map view of place cells. This phenomenon appears to be related to the repetitive layout of these mazes, but still no hypothesis adequately explains it. Here, we use a boundary vector cell (BVC) model of place cell firing to model the activity of place cells in numerous multicompartment environments. The activity of modeled place cells bears a striking resemblance to experimental data, replicating virtually every major experimental result. Our results support the BVC model and indicate that locally repeating place cell firing could result purely from local geometry.
Communicative Cues for Reach-to-Grasp Motions: From Humans to Robots

Intent communication is an important challenge in the context of human-robot interaction. The aim of this work is to identify subtle non-verbal cues that make communication among humans fluent and using them to generate intent expressive robot motion. A human-human reach-to-grasp experiment ( n =14) identified two temporal and two spatial cues: (1) relative time to reach maximum hand aperture ( MA ), (2) overall motion duration ( OT ), (3) exaggeration in motion ( Exg ), and (4) change in grasp modality ( GM ). Results showed there was statistically significant difference in the temporal cues between no-intention and intention conditions. A follow-up experiment ( n =30) was conducted based on these results. Reach-to-grasp motions of a simulated robot containing different cue combinations were shown to the participants. They were asked to guess the target object during robot's motion, based on the assumption that intent expressive motion would result in earlier and more accurate guesses. Results showed that, OT, GM and several cue combinations led to faster and more accurate guesses which imply they can be used to generate communicative motion. However, MA had no effect, and surprisingly Exg had a negative effect on expressiveness.
A Quantum Kinetic Monte Carlo Method for Quantum Many-Body Spin Dynamics

We propose a general framework of quantum kinetic Monte Carlo algorithm, based on a stochastic representation of a series expansion of the quantum evolution. Two approaches have been developed in the context of quantum many-body spin dynamics, using different decomposition of the Hamiltonian. The effectiveness of the methods is tested for many-body spin systems up to 40 spins.
Effect of Region of Interest Size on the Repeatability of Quantitative Brain Imaging Biomarkers

In the repeatability analysis, when the measurement is the mean value of a parametric map within a region of interest (ROI), the ROI size becomes important as by increasing the size, the measurement will have a smaller variance. This is important in decision-making in prospective clinical studies of brain when the ROI size is variable, e.g., in monitoring the effect of treatment on lesions by quantitative MRI, and in particular when the ROI is small, e.g., in the case of brain lesions in multiple sclerosis. Thus, methods to estimate repeatability measures for arbitrary sizes of ROI are desired. We propose a statistical model of the values of parametric map within the ROI and a method to approximate the model parameters, based on which we estimate a number of repeatability measures including repeatability coefficient, coefficient of variation, and intraclass correlation coefficient for an ROI with an arbitrary size. We also show how this gives an insight into related problems such as spatial smoothing in voxel-wise analysis. Experiments are conducted on simulated data as well as on scan–rescan brain MRI of healthy subjects. The main application of this study is the adjustment of the decision threshold based on the lesion size in treatment monitoring.
Genetic Determinants of Penicillin Tolerance in Vibrio cholerae

Many bacteria are resistant to killing (tolerant) by typically bactericidal antibiotics due to their ability to counteract drug-induced cell damage. Vibrio cholerae, the cholera agent, displays an unusually high tolerance to diverse inhibitors of cell wall synthesis. ABSTRACT Many bacteria are resistant to killing (tolerant) by typically bactericidal antibiotics due to their ability to counteract drug-induced cell damage. Vibrio cholerae, the cholera agent, displays an unusually high tolerance to diverse inhibitors of cell wall synthesis. Exposure to these agents, which in other bacteria leads to lysis and death, results in a breakdown of the cell wall and subsequent sphere formation in V. cholerae. Spheres readily recover to rod-shaped cells upon antibiotic removal, but the mechanisms mediating the recovery process are not well characterized. Here, we found that the mechanisms of recovery are dependent on environmental conditions. Interestingly, on agarose pads, spheres undergo characteristic stages during the restoration of rod shape. Drug inhibition and microscopy experiments suggest that class A penicillin binding proteins (aPBPs) play a more active role than the Rod system, especially early in sphere recovery. Transposon insertion sequencing (TnSeq) analyses revealed that lipopolysaccharide (LPS) and cell wall biogenesis genes, as well as the sigma E cell envelope stress response, were particularly critical for recovery. LPS core and O-antigen appear to be more critical for sphere formation/integrity and viability than lipid A modifications. Overall, our findings demonstrate that the outer membrane is a key contributor to beta lactam tolerance and suggest a role for aPBPs in cell wall biogenesis in the absence of rod-shape cues. Factors required for postantibiotic recovery could serve as targets for antibiotic adjuvants that enhance the efficacy of antibiotics that inhibit cell wall biogenesis.
On the Human Factor Consideration for VANETs Security Based on Social Networks

Ensuring the required trustiness among communicating peers is an important task in Vehicular Adhoc Networks (VANETs), especially for safety-related applications where the margin of error is extremely undesired. Most the safety applications are a kind of decision aided system, and final decision is always taken by humans. Thus, in addition to securing inter-vehicle communication, the human factor must be also considered. With the appearance of 5G technology it became possible to connect VANET to any other network including Online Social Networks (OSNs). In this paper, we took advantage of this possibility to connect VANET and OSN, for the purpose of estimating the drivers honesty based on their OSN profiles. Afterward, we combined both inter-vehicle and OSN-based trust to compute the overall trust about the different vehicles and their drivers. Conducted simulation show that our proposal offers more than 5% detection ratio than the classical inter-vehicle solution. Furthermore, it also reduced the detection error ratio by about 3% with a reduced standard deviation for both detection and error ratios.
Orchestrating Data as a Services-Based Computing and Communication Model for Information-Centric Internet of Things

With the explosive growth of the Internet of Things, the gap between the rapidly growing demands of data rates and the existing bandwidth-limited network infrastructures has become increasingly prominent, leading to network congestion, high latency, and energy consumption and deterioration of the user’s quality of service. To narrow this gap, an orchestrating data as services-based computing and communication (ODAS-CC) model is proposed to reduce the data rate, latency, and energy consumption for information-centric Internet of Things. The main innovations of the ODAS-CC model that differ from previous strategies are as follows. First, the services-based network architecture proposed in this paper can run on the current network effectively. In the proposed architecture, the data are orchestrated to services when they route to the data center; therefore, the data are transmitted after the service conversion, thereby forming a service-based network, which can greatly reduce the amount of data transmitted in the network, latency, and energy consumption. Second, a service conversion is performed in the process of routing such that the user’s service request can be satisfied locally, thereby improving the user’s quality of experience. Third, based on the ODAS-CC model, the performance evaluation model for energy consumption, latency, and load is provided in detail, and the performance of the ODAS-CC model is evaluated comprehensively. The theoretical analyses and experimental results show that compared with the previous approach, the ODAS-CC model can reduce the network traffic, the average latency of data upload by 26.3%, the service latency by up to 30.1%, and the data center load, thereby reducing energy consumption from 20.0% to 30.0%.
An empirical rig for jaw animation

In computer graphics the motion of the jaw is commonly modelled by up-down and left-right rotation around a fixed pivot plus a forward-backward translation, yielding a three dimensional rig that is highly suited for intuitive artistic control. The anatomical motion of the jaw is, however, much more complex since the joints that connect the jaw to the skull exhibit both rotational and translational components. In reality the jaw does not move in a three dimensional subspace but on a constrained manifold in six dimensions. We analyze this manifold in the context of computer animation and show how the manifold can be parameterized with three degrees of freedom, providing a novel jaw rig that preserves the intuitive control while providing more accurate jaw positioning. The chosen parameterization furthermore places anatomically correct limits on the motion, preventing the rig from entering physiologically infeasible poses. Our new jaw rig is empirically designed from accurate capture data, and we provide a simple method to retarget the rig to new characters, both human and fantasy.