Miracle-dz/UniFAR-FTUs huggingface dataset:
Null-homologous unknottings

Every knot can be unknotted with two generalized twists; this was first proved by Ohyama. Here we prove that any knot of genus g can be unknotted with 2g null-homologous twists and that there exist genus g knots that cannot be unknotted with fewer than 2g null-homologous twists.
Charged wormhole solutions in Einstein-Cartan gravity

Static solutions representing wormhole configurations in Einstein-Cartan theory ({{\sf ECT}}) in the presence of electric charge are obtained. The solutions are described by a constant redshift function with matter content consisting of a Weyssenhoff fluid along with an anisotropic matter and energy momentum tensor ({\sf EMT}) of the electric field which together generalize the anisotropic energy momentum tensor in Einstein-Maxwell theory in order to include the effects of the intrinsic angular momentum (spin) of the particles. Assuming the equation of state ({{\sf EoS}}) $p_r={\sf w}_1\rho$ and $p_t={\sf w}_2\rho$, we derive exact wormhole solutions satisfying weak and null energy conditions. Depending on the value of the spin square density at the wormhole throat these solutions can be asymptotically flat, de-Sitter or anti de-Sitter. Observational aspects of the wormhole solutions are also studied, using gravitational lensing effects. It is found that the throat can act as a photon sphere near which the light deflection angle has arbitrarily large values. Moreover, for a particular class of solutions, when ${\sf w}_1\rightarrow-{\sf w}_2$ the lensing features of the present model mimic those of the Ellis wormhole in the weak field limit.
Global analysis of protein expression in muscle tissues of dermatomyositis/polymyosisits patients demonstrated an association between dysferlin and human leucocyte antigen A.

OBJECTIVES DM and PM are characterized by myofibre damage with inflammatory cell infiltration due to the strong expressions of MHC class I HLA-A and monocyte chemoattractant protein-1 (MCP-1). Dysferlin (DYSF) is a transmembrane glycoprotein that anchors in the sarcolemma of myofibres. DYSF mutation is closely associated with inherited myopathies. This study aimed to determine the role of DYSF in the development of DM/PM. METHODS Mass spectrometry was performed in muscle tissues from DM/PM patients and controls. The DYSF levels in muscle tissue, peripheral blood cells and serum were detected by Western blotting, IF, flow cytometry or ELISA. Double IF and co-immunoprecipitation were used to investigate the relationship between DYSF and HLA-A. RESULTS Mass spectrometry and bioinformatics analysis findings suggested the dysregulated proteins in DM/PM patients participated in common biological processes and pathways, such as the generation of precursor metabolites and energy. DYSF was upregulated in the muscle tissue and serum of DM/PM patients. DYSF was mainly expressed in myofibres and co-localized with HLA-A and MCP-1. DYSF and HLA-A expressions were elevated in myocytes and endothelial cells after being stimulated by patient serum and IFN-β. However, no direct interactions were found between DYSF and HLA-A by co-immunoprecipitation. CONCLUSION Our study revealed the dysregulated proteins involved in common and specific biological processes in DM/PM patient samples. DYSF is upregulated and exhibits a potential role along with that of HLA-A and MCP-1 in inflammatory cell infiltration and muscle damage during the development of DM/PM.
A non-Gaussian option pricing model with skew

Closed-form option pricing formulae explaining skew and smile are obtained within a parsimonious non-Gaussian framework. We extend the non-Gaussian option pricing model of Borland (2002 Quant. Finance 2 415–31) to include volatility–stock correlations consistent with the leverage effect. A generalized Black–Scholes partial differential equation for this model is obtained, together with closed-form approximate solutions for the fair price of a European call option. In certain limits, the standard Black–Scholes model is recovered, as is the Constant Elasticity of Variance (CEV) model of Cox and Ross. Alternative solution methods for that model are thereby also discussed. The model parameters are partially fit from empirical observations of the underlying distribution. The option pricing model then predicts European call prices which fit well to empirical market data over several maturities.
THE PL SPECTROSCOPY OF CdTe POLYCRYSTALLINE THIN FILMS BY VAPOR TRANSPORT DEPOSITION

CdTe thin films on CdS coated FTO glasses were prepared by vapor transport deposition (VTD). The photoluminescence spectra excited both from CdTe film side and the glass side were investigated in order to study the effect of deposition parameters in VTD process such as substrate temperature, pressure and atmosphere on the defects in CdTe films. The results show that the substrate temperature and the atmosphere w/o oxygen have great influence on the number and variety of defects in CdTe films as well as at CdTe/CdS interface, which leads to significant difference in CdTe film properties and device performances. The effect of the pressure in this paper is identical to the effect of the substrate surface temperature. During the VTD deposition process, the real substrate temperature and the oxygen in atmosphere are the first two important parameters to control.
Efficient Tracking Proposals using 2D-3D Siamese Networks on LIDAR

Tracking vehicles in LIDAR point clouds is a challenging task due to the sparsity of the data and the dense search space. The lack of structure in point clouds impedes the use of convolution and correlation filters usually employed in 2D object tracking. In addition, structuring point clouds is cumbersome and implies losing fine-grained information. As a result, generating proposals in 3D space is expensive and inefficient. In this paper, we leverage the dense and structured Bird Eye View (BEV) representation of LIDAR point clouds to efficiently search for objects of interest. We use an efficient Region Proposal Network and generate a small number of object proposals in 3D. Successively, we refine our selection of 3D object candidates by exploiting the similarity capability of a 3D Siamese network. We regularize the latter 3D Siamese network for shape completion to enhance its discrimination capability. Our method attempts to solve both for an efficient search space in the BEV space and a meaningful selection using 3D LIDAR point cloud. We show that the Region Proposal in the BEV outperforms Bayesian methods such as Kalman and Particle Filters in providing proposal by a significant margin and that such candidates are suitable for the 3D Siamese network. By training our method end-to-end, we outperform the previous baseline in vehicle tracking by 12% / 18% in Success and Precision when using only 16 candidates.
An Evolutionary Constraint-Handling Technique for Parametric Optimization of a Cancer Immunotherapy Model

Recent studies have shown that evolutionary constraint-handling techniques are capable of solving optimization problems with constraints. However, these techniques are often evaluated based on benchmark test functions instead of real-world problems. This paper presents an application of evolutionary constrained parametric optimization for a breast cancer immunotherapy model formulated based on biological principles and limited clinical results. It proposes a new constraint-handling technique that partitions the population into different sections to enhance the evolutionary search diversity. In addition, the upper bound of each section is reduced dynamically to drive the convergence of individuals toward the feasible solution region. Experimental results show the effectiveness and robustness of the proposed constraint-handling approach in solving parametric optimization problems. Moreover, the evolutionary optimized cancer immunotherapy model can be used for prognostic outcomes in clinical trials and the predictability is considered significant for such a parametric optimization approach.
Bipartite graphs as polynomials and polynomials as bipartite graphs

The aim of this paper is to show that any finite undirected bipartite graph can be considered as a polynomial [Formula: see text], and any directed finite bipartite graph can be considered as a polynomial [Formula: see text], and vise verse. We also show that the multiplication in the semirings [Formula: see text], [Formula: see text] corresponds to an operation of the corresponding graphs. This operation is exactly the product of Petri nets in the sense of Winskel [G. Winskel and M. Nielsen, Models of concurrency, in Handbook of Logic in Computer Science, Vol. 4, eds. Abamsky, Gabbay and Maibaum (Oxford University Press, 1995), pp. 1–148]. As an application, we give an approach to dividing in the semirings [Formula: see text], [Formula: see text], and a criteria for parallalization of Petri nets. Finally, we endow the set of all bipartite graphs with the Zariski topology.
A Formalization of Robustness for Deep Neural Networks

Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation. This process depends on the goals and capabilities of the adversary, In this paper, we propose a unifying formalization of the adversarial input generation process from a formal methods perspective. We provide a definition of robustness that is general enough to capture different formulations. The expressiveness of our formalization is shown by modeling and comparing a variety of adversarial attack techniques.
Biomechanical Energy Harvesting by Single Electrode-based Triboelectric Nanogenerator

Recently, soft electronic has achieved fast growth in the form of miniaturization, but the main challenge is to develop consistent energy devices. Triboelectric nanogenerator offers the best choice to utilize the prior wearable devices which integrate the uninterrupted power generator and the self-powered small sensor. Here we present the silicon rubber based mechanical energy harvesting single electrode triboelectric nanogenerator (TENG), a flexible electrical uninterrupted power source, which is self-powered and can be assembled on any part of the human body where the contact separation occurs. The two triboelectric layered fabricated TENG enclosed with the elastomer i-e silicon rubber prepared from (Ecoflex-00-30) and the copper layer which acts as the electrode, human skin has been used here as the second triboelectric layer of the device. The fabricated device generated the output voltage 45V and the short circuit current 17µA, while the maximum output power achieved 19µW at the external load resistance of the 60MΩ. 30 LEDs were directly powered by the generated power from the fabricated TENG.
The influence of forest view through a window on job satisfaction and job stress

Abstract Windows have been found to be a particularly salient feature of the workplace, not only as a matter of preference but also for health and well-being. Depending on what is in the view, looking out of the window may provide numerous opportunities for restoration. This study investigated the effect of window views on job satisfaction and stress. The impact of two specific influencing mechanisms was examined: existence of forest views through windows in workplaces, and absence of forest views through windows in workplaces. The sample consisted of 931 office workers in Seoul, South Korea, 481 who could see forest views from their workplaces and 450 who could not see forest views. A set of self-administered questionnaires including job satisfaction and job stress measures was distributed to the sample from April to September 2004. The results showed a significant direct effect of forest views from windows on job satisfaction and stress. Respondents’ personal information such as gender, age and job category did not influence on the window view effects. As expected, employees’ job satisfaction and job stress were highly and negatively correlated.
Redundancy-Reducing and Holiday Speed Prediction Based on Highway Traffic Speed Data

The accurate prediction of the highway traffic speed plays an important role in improving the production efficiency and the convenience of lives, and it is also one of the important issues in vehicle social big data analyses. Aiming at the problem of highway traffic speed data preprocessing, this paper proposes the algorithm of Redundant Data Reducing (RDR) that can greatly reduce the amount of data in model training of long short-term memory (LSTM) and improve the training speed under the condition that the influence on prediction accuracy is small and controllable. Aiming at the problem of low prediction accuracy for holiday traffic speeds due to small data volume, this paper proposes the Segment Prediction Algorithm (SPA) based on the speed features, which can effectively improve the prediction accuracy. The experimental results show that RDR can reduce the training data by up to 60% without notably affecting the prediction accuracy, while it improves the training speed by 60%. Compared with the current LSTM algorithm, the prediction accuracy of SPA improves significantly.
Retrieving the Lake Trophic Level Index with Landsat-8 Image by Atmospheric Parameter and RBF: A Case Study of Lakes in Wuhan, China

The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states.
Fluctuations and response in financial markets: the subtle nature of ‘random’ price changes

Using trades and quotes data from the Paris stock market, we show that the random walk nature of traded prices results from a very delilcated interplay between two opposite tendencies: long-range correlated market orders that lead to super-diffusion (or persistence), and mean revrting limit orders that lead to sub-diffusion (or anti-persistence). We define and study a model where the price, at any instant, is the result of the impact of all past trades, mediated by a non-constant ‘propagator’ in time that describes the response of the market to a single trade. Within this model, the market is shown to be, in a precise sense, at a critical point, where the price is purely diffusive and the average response function almost constant. We find empirically, and discuss theoretically, a fluctuation-response relation. We also discuss the fraction of truly informed market orders, that correctly anticipate short-term moves, and find that it is quite small.
MeshGAN: Non-linear 3D Morphable Models of Faces

Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data. Certain GAN architectures and training methods have demonstrated exceptional performance in generating realistic synthetic images (in particular, of human faces). However, for 3D object, GANs still fall short of the success they have had with images. One of the reasons is due to the fact that so far GANs have been applied as 3D convolutional architectures to discrete volumetric representations of 3D objects. In this paper, we propose the first intrinsic GANs architecture operating directly on 3D meshes (named as MeshGAN). Both quantitative and qualitative results are provided to show that MeshGAN can be used to generate high-fidelity 3D face with rich identities and expressions.
Semantically Secure Lattice Codes for Compound MIMO Channels

We consider compound multi-input multi-output (MIMO) wiretap channels where minimal channel state information at the transmitter (CSIT) is assumed. Code construction is given for the special case of isotropic mutual information, which serves as a conservative strategy for general cases. Using the flatness factor for MIMO channels, we propose lattice codes universally achieving the secrecy capacity of compound MIMO wiretap channels up to a constant gap (measured in nats) that is equal to the number of transmit antennas. The proposed approach improves upon existing works on secrecy coding for MIMO wiretap channels from an error probability perspective, and establishes information theoretic security (in fact semantic security). We also give an algebraic construction to reduce the code design complexity, as well as the decoding complexity of the legitimate receiver. Thanks to the algebraic structures of number fields and division algebras, our code construction for compound MIMO wiretap channels can be reduced to that for Gaussian wiretap channels, up to some additional gap to secrecy capacity.
Group Sparse Representation Based Dictionary Learning for SAR Image Despeckling

Since the sparse representation coefficients of synthetic aperture radar (SAR) images often appear in clusters with intrinsic structure, traditional sparse representation theory cannot capture this property. In this paper, the concept of group sparse representation (GSR) is utilized to exploit the intrinsic structure of SAR images. Different from traditional patch-based sparse representation theory, GSR is able to sparsely represent images in the domain of group which contains the image patches with similar structure. Based on the multiplicative speckle noise model, a novel dictionary learning algorithm based on GSR (GSR-DL) for SAR image despeckling is proposed. The proposed algorithm mainly consists of three steps. First, in order to realize the recovery of despeckled SAR image by the GSR model, a mean filter is included in the modeling process. Second, the proposed GSR-DL algorithm is used to calculate the optimal dictionary and group sparse representation coefficients. Third, the despeckled SAR image is reconstructed by the learned dictionary and coefficients. The experimental results on SAR images manifest that the proposed GSR-DL algorithm achieves a better performance than other state-of-the-art despeckling algorithms.
The effect of repeated whole-body cryostymulation on the HSP-70 and lipid metabolisms in healthy subjects.

The aim of this study was to evaluate the effects of exposure to 30 daily whole body cryostimulation (WBC) on lipid metabolic parameters and serum HSP-70 concentration. The study involved 45 volunteers, homogeneous in terms of diet and daily physical activity. Blood samples were collected before and after the 10th, the 20th, and the 30th session and one month after the intervention. Total cholesterol, HDL, TG concentrations and Apolipoprotein A-I, ApoB and HSP-70 protein levels were determined in serum. Additionally, the LI (Lipid Index) and the LDL level were calculated. During exposure, positive changes in the lipid profile that included a decrease in the TCh, initiated after the 20th WBCsession with a simultaneous decrease in TG and LDL levels, and an increase in the HDL concentration were observed. These changes were accompanied by a downward trend in the ApoB concentration and a decrease in the ApoB:ApoA-I ratio after 30 sessions. The nature of these changes persisted for a month after the exposure. The obtained results indicate metabolic benefits that result from prolonged exposure to cryogenic temperatures, confirming the postulate of using WBC in the regulation of lipid metabolism and the prevention of cardiovascular diseases.
Practical Resolution Methods for MDPs in Robotics Exemplified With Disassembly Planning

In this letter, we focus on finding practical resolution methods for Markov decision processes (MDPs) in robotics. Some of the main difficulties of applying MDPs to real-world robotics problems are: first, having to deal with huge state spaces; and second, designing a method that is robust enough to dead ends. These complications restrict or make more difficult the application of methods, such as value iteration, policy iteration, or labeled real-time dynamic programming (LRTDP). We see in determinization and heuristic search a way to successfully work around these problems. In addition, we believe that many practical use cases offer the opportunity to identify hierarchies of subtasks and solve smaller, simplified problems. We propose a decision-making unit that operates in a probabilistic planning setting through stochastic shortest path problems, which generalize the most common types of MDPs. Our decision-making unit combines: first, automatic hierarchical organization of subtasks; and second, on-line resolution via determinization. We argue that several applications of planning benefit from these two strategies. We exemplify our approach with a robotized disassembly application. The disassembly problem is modeled in probabilistic planning definition language, and serves to define our experiments. Our results show many advantages of our method over LRTDP, such as a better capability to handle problems with large state spaces and state definitions that change when new fluents are discovered.
A Technology Assisted Framework for Medical Emergencies Involving Formal and Informal Caregivers

With the technological advancements, particularly real time access to medical knowledge-base and improved healthcare facilities, the average life expectancy of people has increased significantly. This has also led to an ageing population worldwide, which in turn has increased the demand for quality health and social care. Although, technological advancements have enabled the provision of sophisticated healthcare facilities for people, those are not always accessible to a growing number of elderly people who may live in isolation or may not have the knowledge or capability of accessing technology for healthcare purpose. These people need more prompt and personal care particularly in case of emergencies. Thus, for elderly people, there is a need to have an integrated infrastructure that connects both formal caregivers, like, paramedics, nurses and trained medical professionals, and informal caregivers, like, friends and neighbours with the purpose of providing prompt medical and social care when required. This research work proposes a technology-assisted medical emergency framework involving the formal and informal caregivers to take care of elderly and other people in need in case of medical emergency situations. Minimizing the medical emergency response time is one of the primary aims of this proposed framework.