Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.
Generalized Rank-Breaking: Computational and Statistical Tradeoffs

For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of rank aggregation, for the Plackett-Luce model, we provide a hierarchy of rank-breaking mechanisms ordered by the complexity in thus generated sketch of the data. This allows the number of data points collected to be gracefully traded offs against computational resources available, while guaranteeing the desired level of accuracy. Theoretical guarantees on the proposed generalized rank-breaking implicitly provide such trade-offs, which can be explicitly characterized under certain canonical scenarios on the structure of the data. Further, the proposed generalized rank-breaking algorithm involves set-wise comparisons as opposed to traditional pairwise comparisons. The maximum likelihood estimate of pairwise comparisons is computed efficiently using the celebrated minorization maximization algorithm (Hunter, 2004). To compute the pseudo-maximum likelihood estimate of the set-wise comparisons, we provide a generalization of the minorization maximization algorithm and give guarantees on its convergence.
Prediction of Lung Tumor Motion Based on Recurrent Neural Network

This paper handles the prediction of the future lung tumor position which exhibits respiratory-induced motion inside the body of the patient. The prediction of the future tumor position is a necessary step for dynamic tumor tracking radiotherapy to compensate the positioning lag of the gantry of the clinical linear accelerator. This lag is known to be almost one second and the objective of the current study is accordingly to generate the one second future position estimate of the tumor with the tolerance of 1mm prediction error in the three-dimensional space. Several researches have been done to calculate predicted position of the tumor by establishing mathematical models. The current study focuses on the development of a mathematical model which provides one-second future position of the tumor using the recurrent neural network (RNN). The result of the numerical validation has been reported to justify the choice of the model structure and to show that the model can be trained to have the sufficient prediction accuracy necessary for use in real-time dynamic tumor tracking radiotherapy.
Discovering Fine-Grained Spatial Pattern From Taxi Trips: Where Point Process Meets Matrix Decomposition and Factorization

As increasing volumes of urban data are being available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based policies. In particular, taxi trip is an important urban sensor that provides unprecedented insights into many aspects of a city, from economic activity, human mobility to land development. However, analyzing these data presents many challenges, e.g., sparse data for fine-grained patterns, and the regularity submerged by seemingly random data. Inspired by above challenges, we focus on Pick-Up (PU)/Drop-Off (DO) points from taxi trips, and propose a fine-grained approach to unveil a set of low spatio-temporal patterns from the regularity-discovered intensity. The proposed method is conceptually simple yet efficient, by leveraging point process to handle sparsity of points, and by decomposing point intensities into the low-rank regularity and the factorized basis patterns, our approach enables domain experts to discover patterns that are previously unattainable for them, from a case study motivated by traffic engineers.
Approaches to Determine the Transfer Function of TEM Waveguides

The standard IEC 61000-4-20 [1] describes the validation of TEM waveguides in respect to the present TEM mode and uniform area, which implies the deviation of the primary E-field component for a constant forward power. Concerning immunity tests, for which transient high-altitude electromagnetic pulses (HEMP) are supplied to TEM waveguides [2], and general measurements with the topic of intentional and unintentional electromagnetic interference (IEMI/EMI), for which arbitrary ultra wideband signals are used, the specifications according to the IEC 61000-4-20 are not sufficient. The transmission characteristic of the TEM waveguide has to be validated for any arbitrary signal, what results in a huge effort in measurement and time. The knowledge of the transfer function would reduce this effort. The advantages and disadvantages of time and frequency domain approaches for evaluating the transfer function of TEM waveguides, especially G TEM cells, are discussed in this paper.
Service function chain orchestration across multiple clouds

Network function virtualization is a new network concept that moves network functions from dedicated hardware to software-defined applications running on standard high volume severs. In order to accomplish network services, traffic flows are usually processed by a list of network functions in sequence which is defined by service function chain. By incorporating network function virtualization in inter-data center (DC) network, we can use the network resources intelligently and deploy network services faster. However, orchestrating service function chains across multiple data centers will incur high deployment cost, including the inter-data center bandwidth cost, virtual network function cost and the intra-data center bandwidth cost. In this paper, we orchestrate SFCs across multiple data centers, with a goal to minimize the overall cost. An integer linear programming (ILP) model is formulated and we provide a meta-heuristic algorithm named GBAO which contains three modules to solve it. We implemented our algorithm in Python and performed side-by-side comparison with prior algorithms. Simulation results show that our proposed algorithm reduces the overall cost by at least 21.4% over the existing algorithms for accommodating the same service function chain requests.
Efficacy of the combination of carteolol hydrochloride + latanoprost in the treatment of glaucoma and ocular hypertension

ABSTRACT Introduction: The only evidence-based mechanism for prevention and treatment of glaucomatous optic neuropathy is decreasing the intraocular pressure (IOP). Prescribing multiple ocular hypotensive agents, such as the combination of carteolol and latanoprost, may synergistically improve IOP; however, doing so may increase the complexity of a medication regimen, in turn, impairing patient adherence. Fixed-combination glaucoma medications offer convenience and effectiveness. New to this class of glaucoma medication is fixed combination carteolol-latanoprost (FCCL). Area covered: This review intends to give the reader a better understanding of the efficacy of the combination of carteolol and latanoprost separately, and where FCCL fits into the vast medical arsenal of IOP drops. Furthermore, it outlines the particular pharmacologic mechanisms targeted, the pharmacokinetics, effectiveness, the advantages of fixed-combination administration, and tolerability. Expert opinion: The combination of carteolol and latanoprost, separately or in a fixed-combination, is more effective than either drug alone. Given the early stage in development of FCCL, it has yet to be determined how FCCL compares to other fixed-combination medications. However, pending further approval, fixed-combination carteolol-latanoprost may represent a reasonable alternative for a patient whose IOP is inadequately controlled on a prostaglandin analog alone and for whom a simplified combination is preferred.
Radiated Susceptibility Tests in Thermal Vacuum Chambers Working as Reverberation Chambers

This paper presents recent investigations about the possibility to use thermal vacuum chambers as reverberation chambers for performing radiated susceptibility tests in the space industry above 1 GHz. The aim is to realize radiated susceptibility tests (in parallel to thermal ones) on equipments working at very low operational temperature. Two suitable stirring techniques overcoming all the constraints of such tests are proposed. The originality of the approach lies in the simplicity of the proposed calibration process which requires only one broadband measurement of the $S_{11}$ parameter of the emitting antenna. Tests performed in the so-called GAIA thermal vacuum chamber located at Airbus D&S in Toulouse validates the relevance of this approach.
Modulation of B Cells and Homing Marker on NK Cells Through Extracorporeal Photopheresis in Patients With Steroid-Refractory/Resistant Graft-Vs.-Host Disease Without Hampering Anti-viral/Anti-leukemic Effects

Graft-vs.-host disease (GvHD), a severe complication of allogeneic hematopoietic stem cell transplantation, significantly affects the post-transplant morbidity and mortality. Systemic steroids remain the gold standard for the initial management of GvHD. However, up to 60% of patients will not sufficiently respond to steroids. Extracorporeal photopheresis (ECP), a cell-based immunotherapy, has shown good clinical results in such steroid-refractory/resistant GvHD patients. Given its immunomodulatory, but not global immunosuppressive and steroid-sparing capacity, ECP constitutes an attractive option. In the case of GvHD, the balance of immune cells is destroyed: effector cells are not any longer efficiently controlled by regulatory cells. ECP therapy may restore this balance. However, the precise mechanism and the impact of ECP on anti-viral/anti-leukemic function remain unclear. In this study, 839 ECP treatments were performed on patients with acute GvHD (aGvHD) and chronic GvHD (cGvHD). A comprehensive analysis of effector and regulatory cells in patients under ECP therapy included multi-parametric flow cytometry and tetramer staining, LuminexTM-based cytokine, interferon-γ enzyme-linked immunospot, and chromium-51 release assays. Gene profiling of myeloid-derived suppressor cells (MDSCs) was performed by microarray analysis. Immunologically, modulations of effector and regulatory cells as well as proinflammatory cytokines were observed under ECP treatment: (1) GvHD-relevant cell subsets like CD62L+ NK cells and newly defined CD19hiCD20hi B cells were modulated, but (2) quantity and quality of anti-viral/anti-leukemic effector cells were preserved. (3) The development of MDSCs was promoted and switched from an inactivated subset (CD33−CD11b+) to an activated subset (CD33+CD11b+). (4) The frequency of Foxp3+CD4+ regulatory T cells (Tregs) and CD24+CD38hi regulatory B cells was considerably increased in aGvHD patients, and Foxp3+CD8+ Tregs in cGvHD patients. (5) Proinflammatory cytokines like IL-1β, IL-6, IL-8, and TNF-α were significantly reduced. In summary, ECP constitutes an effective immunomodulatory therapy for patients with steroid-refractory/resistant GvHD without impairment of anti-viral/leukemia effects.
The Analysis and Monitoring of Ecological Risks on the Basis of Fuzzy Petri Nets

The paper presents the theoretical analysis of fuzzy production systems of decision support in the frame of geoinformation monitoring ecological risks. A way of representing some fuzzy production rules in a fuzzy system of the ecological risk analysis and reduction, based on the application of fuzzy Petri nets, is considered. A fuzzy production system of ecological risk analysis on the basis of using modified fuzzy Petri nets is implemented.
Endocrinology of bone/brain crosstalk

Bone metabolism is regulated by the action of two skeletal cells: osteoblasts and osteoclasts. This process is controlled by many genetic, hormonal and lifestyle factors, but today more and more studies have allowed us to identify a neuronal regulation system termed ‘bone–brain crosstalk’, which highlights a direct relationship between bone tissue and the nervous system. The first documentation of an anatomic relationship between nerves and bone was made via a wood cut by Charles Estienne in Paris in 1545. His diagram demonstrated nerves entering and leaving the bones of a skeleton. Later, several studies were conducted on bone innervation and, as of today, many observations on the regulation of bone remodeling by neurons and neuropeptides that reside in the CNS have created a new research field, that is, neuroskeletal research.
Jumps, folds and singularities of Kodaira moduli spaces

For any integer k we construct an explicit example of a twistor space which contains a one‐parameter family of jumping rational curves, where the normal bundle changes from O(1)⊕O(1) to O(k)⊕O(2−k) . For k>3 the resulting anti‐self‐dual Ricci‐flat manifold is a Zariski cone in the space of holomorphic sections of O(k) . In the case k=2 we recover the canonical example of Hitchin's folded hyper‐Kähler manifold, where the jumping lines form a three‐parameter family. We show that in this case there exist normalisable solutions to the Schrödinger equation which extend through the fold.
Robust MPC for Constrained Uncertain Systems with Data-Driven Improvement

T0640. Generally, the adaptive model predictive control needs model identification. The process of identification demands a certain amount of data and places some requirements on data which makes it difficult to be implemented. To handle it, this paper proposes a novel data-driven approach without model identification. The data of previous time is utilized to describe state space and input space of the uncertain system instead of identifying the true model. Based on the data-driven method, the Quasi-Min-Max control strategy is used to design the robust data-driven MPC controller which directly calculates the input from the past data. Combined with the data-driven method, a free control variable is introduced to compensate for the insufficiency of past data. It is shown that adopting the data-driven controller can reduce conservatism by lessening model uncertainty and improve control performance. Meanwhile, the proposed design is proven to be recursively feasible and stabilizing. A numerical example demonstrates the effectiveness and advantages of the proposed control method.
Developing Hispolon-based novel anticancer therapeutics against human (NF-κβ) using in silico approach of modelling, docking and protein dynamics

Abstract Hispolon is a polyphenolic compound derived from black hoof mushroom (Phellinus linteus) or shaggy bracket mushroom (Inonotus hispidus) which induces the inhibition of cancer-promoting nuclear factor-kappa beta (NF-κβ) complex. To develop more potent lead molecules with enhanced anticancer efficiency, the mechanism of hispolon-mediated nuclear factor-κβ inhibition has been investigated by molecular modelling and docking. Ten derivatives of hispolon (DRG1-10) have been developed by pharmacophore-based design with a view to enhance the anticancer efficacy. Hispolon and its derivatives were further screened for different pharmacological parameters like binding free energy, drug likeliness, absorption–digestion–metabolism–excretion (ADME), permeability, mutagenicity, toxicity and inhibitory concentration 50 (IC50) to find a potent lead molecule. Based on pharmacological validation, comparative molecular dynamics (MD) simulations have been performed for three lead molecules: Hispolon, DRG2 and DRG7 complexed with human NF-κβ up to 50 ns. By analysing different factors like root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA) and principal component analysis (PCA), Gibb’s free energy plots DRG2 have more binding efficiency compared to hispolon and DRG7. In RMSD plot, hispolon-bound NF-κβ has the most deviation within a range between 0.125 and 0.45 nm, and DRG2-bound complex showed the range between 0.125 and 0.25 nm. The residues of NF-κβ responsible for hydrophobic interactions with ligand, e.g. Met469, Leu522 and Cys533, have the lowest fluctuation values in DRG2-bound complex. The average Rg fluctuation for DRG2-bound NF-κβ has been recorded under 2.025 nm for most of the simulation time which is much less compared to hispolon and DRG7. Gibb’s free energy plots also define the highest stability of DRG2-bound NF-κβ. Communicated by Ramaswamy H. Sarma
Improved United Multi-Operator Algorithm for Solving Optimization Problems

Although many evolutionary algorithms (EAs) have successfully solved different optimization problems, no single EA has consistently been the best for all these problems. During the last decade, to alleviate this limitation, many proposals which utilize multiple EAs in a single algorithmic framework, called multi-methods or multi-operators, have been introduced. However, there is still room to enhance their performance. In this paper, an improved variant of a united multi-operator algorithm is introduced with few improvements that are capable of providing a balance between diversification and intensification properties during the optimization. The proposed algorithm is tested on the CEC2017 unconstrained benchmark problems, with the results revealing that the proposed algorithm is capable of producing high quality solutions compared with those of state-of-the-art algorithms.
A New Calibration Method of UWB Antenna Delay Based on the ADS-TWR

This paper presents a new Ultra-wideband (UWB) antenna delay calibration method which is based on the Asymmetric Double-sided Two-way Ranging (ADS-TWR) to get the precise information of distance measurement with different UWB devices. We use a similar derivation as ADS-TWR to build the objective function based on the antenna delay model. Then we use Particle Swarm Optimization (PSO) to optimize the objective function and get the independent antenna delay of each device according to combination of different devices antenna delay. Finally we use the least square positioning method to verify the performance of different calibration methods. The result shows that our method is effective to the distance measurement system and positioning system. Compared with rough calibration method and official calibration method, our proposed method greatly improves the accuracy of the systems.
Pre-breakdown streamer propagation and breakdown characteristics of refined bleached and deodorized palm oil under lightning impulse voltage

Vegetable oils are considered nowadays as a potential alternative to Mineral Oil (MO) for dielectric insulation fluids in transformers due to its high biodegradability, non-toxicity and high fire resistance. This paper presents an experimental investigation on the streamer propagation and breakdown of Refined, Bleached and Deodorized Palm Oil (RBDPO) under both positive and negative lightning impulse voltages. The streamer shape, stopping length, velocity and breakdown voltage of 2 types of RBDPO were examined. Needle-plane electrodes configuration with 50 mm gap was used in this study and streamer images were captured by a high speed camera. It is observed that the negative streamer shape patterns are thicker than positive streamer for both RBDPO and MO. At the same applied voltage level and polarity, both RBDPO have longer streamer stopping lengths and the streamers propagate faster than MO.
PSO based Multiobjective Optimization for parameter adaptation in CR based IoTs.

Cognitive Radio (CR) has emerged as a reliable technology to handle large number of connected devices in the upcoming Internet of Things (IoTs). Recent trends in communication technology are moving towards adapting the cognitive radio networks into IoT. To achieve this, spectrum sensing task should be followed by real time tuning of transmission parameters so that the objectives of minimum transmit power, minimum bit error rate (BER) and maximum throughput could be achieved for different service types.The decision making module for cognitive radio isresponsible to reach at some autonomous decision for a set of transmission parameters according to the transmission scenario. In this paper, Particle swarm optimization (PSO) based decision making module has been designed to support three modes of operation.The simulation results have been compared with Real coded Genetic Algorithm (GA) that has different encodingmechanism as compared to widely prevalent Binary coded Genetic Algorithm (BCGA) scheme used in the past. The results demonstrate that the parameter adaptation for PSO based engine outperforms the GA based implementation for all the transmission modes in CR based IoTs.
FAST OCCLUSION FILLING METHOD FOR MULTIVIEW VIDEO GENERATION

Occlusion filling is a basic problem for multiview video generation from existing monocular video. The essential goal of this problem is to recover missing information about a scenes 3D structure and corresponding texture.We propose a method for content-aware deformation of the source view that ensures no disoccluded regions are visible in the synthesized views while also keeping visible distortions to a minimum. We formulate this problem in terms of global energy min-imization. Furthermore, we introduce a similar variable-rejection algorithm that, along with other known optimization techniques, allows us to accelerate the energy function minimization by nearly 30 times and still maintain the visual quality of the synthesized views.
Oral antidiabetic agents in gestational diabetes: a narrative review of current evidence

Gestational diabetes mellitus (GDM) classically occurs when maternal glucose metabolism is unable to compensate the progressive development of insulin resistance that arises from the continuously rising diabetogenic placental hormones. Although most women can be treated satisfactorily with diet alone, some require more intensive treatment. Insulin has been the most reliable treatment strategy in GDM over several decades. Although a long time has passed since the publication of two randomized controlled trials suggesting comparable efficacy and safety of metformin and glibenclamide, international bodies have not yet approved these oral agents. However, with the consistently emerging efficacy and safety data of these two drugs in the past decade, they may perhaps open a rather new door. The aim of this narrative review is to critically evaluate the existing evidence regarding safety and efficacy of oral drugs in GDM accumulated since the first publication in year 2000, suggesting clinical equivalency of glibenclamide (glyburide).