Miracle-dz/UniFAR-FTUs huggingface dataset:
On Entropy Regularized Path Integral Control for Trajectory Optimization

In this article, we present a generalized view on Path Integral Control (PIC) methods. PIC refers to a particular class of policy search methods that are closely tied to the setting of Linearly Solvable Optimal Control (LSOC), a restricted subclass of nonlinear Stochastic Optimal Control (SOC) problems. This class is unique in the sense that it can be solved explicitly yielding a formal optimal state trajectory distribution. In this contribution, we first review the PIC theory and discuss related algorithms tailored to policy search in general. We are able to identify a generic design strategy that relies on the existence of an optimal state trajectory distribution and finds a parametric policy by minimizing the cross-entropy between the optimal and a state trajectory distribution parametrized by a parametric stochastic policy. Inspired by this observation, we then aim to formulate a SOC problem that shares traits with the LSOC setting yet that covers a less restrictive class of problem formulations. We refer to this SOC problem as Entropy Regularized Trajectory Optimization. The problem is closely related to the Entropy Regularized Stochastic Optimal Control setting which is often addressed lately by the Reinforcement Learning (RL) community. We analyze the theoretical convergence behavior of the theoretical state trajectory distribution sequence and draw connections with stochastic search methods tailored to classic optimization problems. Finally we derive explicit updates and compare the implied Entropy Regularized PIC with earlier work in the context of both PIC and RL for derivative-free trajectory optimization.
Time-frequency analysis of wakes produced by turning ships

The use of time-frequency analysis to study ship wakes is the subject of much interest [1, 3, 8, 9, 10, 11, 12]. In particular, the use of short-time Fourier transforms to decompose a ship’s wake into a time-frequency heatmap known as a spectrogram has seen some success in identifying different features of ship wakes (eg., their transverse and divergent waves) observed in real shipping channels and in experimental towing tanks [3, 8, 11]. A spectrogram has the advantage that it only requires information from the surface elevation at one physical location, necessitating a single stationary sensor [3, 5]. The practical applications of accurately identifying properties of a ship wake from such easily accessible data range from environmental conservation (eg. determining if a group of strong waves that negatively effect a coastline originated from a ship [10]) to security (eg. identifying the presence of unmonitored ships). Recently, we studied spectrograms of steady ship wakes [6, 8]. Using linear water wave theory, we showed that regions of the highest colour intensity on the spectrograms followed a curve referred to as the linear dispersion curve. In [6], for an infinitely deep fluid, we also used numerical solutions to the fully nonlinear problem (computed with the scheme outlined in [7]) to help predict the location of higher order modes. In the present study, we shall extend the linear framework to apply for a ship travelling along a circular path with constant angular velocity. In Section 2 we use geometric arguments and the linear dispersion relation to derive a relationship between the frequency of the waves travelling at the group velocity and the time they reach a fixed sensor (this is the dispersion curve). Then, in Section 3, we propose a very simple model for a ship which involves a pressure distribution applied to the surface. Assuming this disturbance starts moving from a point in space and then travels in a circular path, we use Fourier transforms to derive an exact solution for the wave signal. With this signal, we compute spectrograms and discuss the results briefly in Section 4.
Extracting Kinematic Information Using Pose Estimation

We present a preliminary step in building a lightweight system to extract kinematic information from a video ﬁle. Much of our work is based on the latest advancements in the domain of pose estimation. We begin with an examination of the current state of the art network for estimating human pose and then discuss methods for shrinking the network while maintaining accuracy. While they are simple, our experiments show great potential for achieving a substantially smaller network with comparable accuracy and open the door for more complicated methods to be used.
Image Co-segmentation with Multi-Scale Dual-Cross Correlation Network

Considering that the global correlation between images is very important for image co-segmentation, we propose a multi-scale Dual-Cross Correlation Network (DCNet) that can efficiently capture global matching information across images to obtain segmentation results. Specifically, the low-dimensional index feature is used to calculate the correlation and the highdimensional content features are combined with the correlation matrix for final segmentation. Meanwhile, we specially design a Dual-Cross Correlation Module (DCCM) which harvests the spatial and channel correlation with the adjacent pixels of another image on the cross path to enhance the representation of correlation efficiently. By utilizing a further loop operation, each feature can capture the global dependencies from all pixels of another feature. Furthermore, we fuse multi-scale correlation and features into the decoder, which is called Multi-scale Correlation Fusing Decoder (MCFD), to refine the final segmentation results. Moreover, we introduce a new dice loss function to train the whole network by averaging the dice loss value of the foreground and background. Finally, we validate our method on three cosegmentation benchmarks and the results show that our method achieves the state-of-the-art performance.
BRST–BFV and BRST–BV descriptions for bosonic fields with continuous spin on R1,d−1

Gauge-invariant descriptions for a free bosonic scalar field of continuous spin in a $d$-dimensional Minkowski space-time using a metric-like formulation are constructed on the basis of a constrained BRST-BFV approach we propose. The resulting BRST-BFV equations of motion for a scalar field augmented by ghost operators contains different sets of auxiliary fields, depending on the manner of a partial gauge-fixing and a resolution of some of the equations of motion for a BRST-unfolded first-stage reducible gauge theory. To achieve an equivalence of the resulting BRST-unfolded constrained equations of motion with the initial irreducible Poincare group conditions of a Bargmann--Wigner type, it is demonstrated that one should replace the field in these conditions by a class of gauge-equivalent configurations. Triplet-like, doublet-like constrained descriptions, as well as an unconstrained quartet-like non-Lagrangian and Lagrangian formulations, are derived using both Fronsdal-like and new tensor fields. In particular, the BRST--BV equations of motion and Lagrangian using an appropriate set of Lagrangian multipliers in the minimal sector of the respective field and antifield configurations are constructed in a manifest way.
First-principles discovery of stable two-dimensional materials with high-level piezoelectric response

The rational design of two-dimensional (2D) piezoelectric materials has recently garnered great interest due to their increasing use in technological applications, including sensor technology, actuating devices, energy harvesting, and medical applications. Several materials possessing high piezoelectric response have been reported so far, but a high-throughput first-principles approach to estimate the piezoelectric potential of layered materials has not been performed yet. In this study, we systematically investigated the piezoelectric (e 11, d 11) and elastic (C11 and C12) properties of 128 thermodynamically stable 2D semiconductor materials by employing first-principle methods. Our high-throughput approach demonstrates that the materials containing Group-V elements produce significantly high piezoelectric strain constants, d 11 > 40 pm V−1, and 49 of the materials considered have the e 11 coefficient higher than MoS2 insomuch as BrSSb has one of the largest d 11 with a value of 373.0 pm V−1. Moreover, we established a simple empirical model in order to estimate the d 11 coefficients by utilizing the relative ionic motion in the unit cell and the polarizability of the individual elements in the compounds.
MUNet: A Multi-scale U-Net Framework for Medical Image Segmentation

Artificial intelligence has once again become the focus of attention in all fields, of which deep learning has brought a series of changes in the field of computer vision. In this paper, we propose the MUNet model, which is a more general convolutional neural network framework for medical image segmentation. The framework proposed in this paper is essentially a fully convolutional encoder-decoder network based on feature pyramids, in which the encoder and decoder are connected by skip connections. Not only is it suitable for image segmentation, but it can also identify categories of regions of interest. The full convolutional neural network architecture can implement multi-scale image input and prediction. We compared the MUNet and UNet models in cervical lymph node localization and benign and malignant diagnosis in ultrasound images. Experiments show that on our dataset, MUNet with multi-scale segmentation has achieved a Dice score improvement of 4.1% and an AUC score improvement of 8.1% compared to U-Net.
EPAS1 targeting by miR-152-3p in Paclitaxel-resistant Breast Cancer

Background: Paclitaxel plays a pivotal role in the chemotherapy of breast cancer, but resistance to this drug is an important obstacle in the treatment. It is reported that microRNA-152-3p (miR-152-3p) is involved in tamoxifen resistance in breast cancer, but whether it is involved in paclitaxel resistance in breast cancer remains unknown. Materials and methods: We examined the expression of miR-152-3p in breast cancer tissues and cells by qRT-PCR. After transfecting paclitaxel-resistant MCF-7/TAX cells with miR-152-3p mimics, we analyzed the function of miR-152-3p in these cells by MTT assay and flow cytometry. We screened the target gene, endothelial PAS domain-containing protein 1 (EPAS1), using bioinformatics analysis and verified it with the dual luciferase reporter gene experiment. The relationship between EPAS1 and miR-152-3p and their roles in paclitaxel resistance of breast cancer were further investigated using RNA interference and transfection techniques. Results: The expression of miR-152-3p in normal breast tissues and cells was markedly higher than that in breast cancer. Overexpression of miR-152-3p decreased the survival rate and increased the apoptosis rate and sensitivity of MCF-7/TAX cells to paclitaxel. We confirmed that EPAS1 is the target of miR-152-3p and is negatively regulated by this miRNA. Moreover, transfection with EPAS1 siRNA enhanced the susceptibility and apoptosis rate of MCF-7/TAX cells to paclitaxel. Co-transfection of miR-152-3p mimics and EPAS1 increased paclitaxel sensitivity and apoptosis induced by the drug. Conclusion: miR-152-3p inhibits the survival of MCF-7/TAX cells and promotes their apoptosis by targeting the expression of EPAS1, thereby, enhancing the sensitivity of these breast cancer cells to paclitaxel.
Thermodynamics curvature in the phase transition for the AdS black hole

We investigate the thermodynamic curvature in the Hawking-Page phase transition and the second-order phase transition of the AdS black hole. It is shown that the thermodynamic curvature has the same behavior in these two different phase transitions. Specifically, the thermodynamic curvature in the Hawking-Page phase transition is the power function of the Hawking-Page phase transition temperature with exponent related to the spacetime dimension, or is invariably proportional to the reciprocal of the Hawking-Page phase transition entropy. For the thermodynamic curvature in the second-order phase transition, the conclusion is similar. The more enlightening result is that the ratio of thermodynamic curvatures along the two different phase transitions tends to the natural constant in the limit that the number of spacetime dimensions is infinite. The universal and novel result provides an essential difference between the Hawking-Page phase transition and the second-order phase transition of the AdS black hole in the large dimension paradigm.
Use of Phenology for Examining Provenance Transfers in Reforestation of Douglas-Fir

'Seed transfer' in reforestation is the process of moving seed, or seedlings, from place of origin to alternative planting sites. Strong support for the theory of optimality of local populations has been presented by Langlet (1936) and Clausen, Keck & Hiesey (1948). Consequently, use of non-local seed is avoided if possible to minimize losses of vigour or fitness; but in cases where local seed is not available, transfer guides are necessary. For Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in western Washington and Oregon, present guides are rules of thumb taken from Swedish models. Therefore, a method for early examination of effects of moving seed would be helpful. This report presents a simple predictive model which uses timing of vegetative bud burst for examining responses of seedlings to transfer. The main assumptions included in the model were: (1) genetic differentiation in timing of bud burst is in response to adaptive selection; (2) the timing of bud burst in an adapted phenotype is a function of the physiological response of that provenance to temperature; (3) chilling requirements of all provenances will be satisfied by normal winter temperatures, regardless of the part of the region to which they are moved. Only the first assumption is examined in this report, by the method of provenancehabitat correlations. The other two assumptions are supported in the literature; for example, timing of bud burst appears to be almost exclusively temperature mediated if chilling requirements are met (Wommack 1964; Lavender & Hermann 1970). Also the chilling requirement of Douglas-fir is less than 80 days of temperatures lower than 10? C (Wommack 1964), whilst in any winter most of the region under consideration will have more than 100 days in which maximum temperatures will not rise above 120 C (Wakefield 1969). The exceptions are in south-coastal Oregon and California, a negligible part of the Douglas-fir region. The model required two sets of equations. The first, based on an experiment using forty-four provenances, describes timing of bud burst as a function of average daily temperature. The second describes pattern of average daily temperature at stations where provenances were collected. Together, the equations provided functions for predicting bud-burst dates. Predicted dates were then used to examine hypothetical effects of transferring seed along elevational and latitudinal gradients.
Testing evolutionary and ecological hypotheses using path analysis and structural equation modelling

The use of path analysis has grown rapidly in recent years, yet few studies employ recent advances in the field of structural equation modelling (SEM). Here I illustrate the capabilities of SEM for path analyses, using data from a study of hummingbird pollination. The main drawback to conventional path analysis is that the overall agreement between the path diagram (or model ) and the data is not quantified ; this also means that there is no clear way to directly compare competing models of the same system
Learning Behavior Rate Models on Social Network Data

Intensity is one of the main characteristics of human behavior, using data about behavior intensity we can make high enough quality predictions about future human behavior. But it is often impossible to get a direct behavior rate, because of high cost, time consumption or restrictions for monitoring private lives, so we need tools to estimate it indirectly. We offer two models for behavior rate evaluation with expert-defined and learned structures. These models are Bayesian belief networks. They include information about the intervals in days between the last three behavior episodes of the study period, the minimum and maximum intervals between episodes, and the interval between the last episode of the study period and the next episode, respectively, after the end of the study period. As we need for the models approbation an example of behavior allowing us to get direct behavior rate, we take users’ posting behavior in social network. For learning parameters and structure one of the models, testing models, data from the social network Vkontakte for December 2019 was collected. This data includes an information about posting on own users’ "walls" for this month, i.e. posts quantity, time of last three posts, maximum and minimum time interval between posts for December 2019, and time of the first post starting from January 2020.
Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

Background Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.
Screening for hepatocellular carcinoma

Koza Jarosław, Orłowski Krzysztof, Jurgoński Adam, Meder Agnieszka, Pujanek Małgorzata, Ameryk Monika, Kwiatkowska Renata, Świątkowski Maciej. Screening for hepatocellular carcinoma.Journal of Education, Health and Sport. 2017;7(11):152-161. eISSN 2391-8306. DOI http://dx.doi. org/10.5281/zenodo.1045341 http://ojs.ukw. edu.pl/index.php/johs/article/view/5030 https://pbn.nauka. gov.pl/sedno-webapp/works/837035
The generation of MHC class I restricted islet antigen specific regulatory T cells for the treatment of type 1 diabetes

Type I diabetes (T1D) is an autoimmune disease in which the insulin producing β cells of the pancreas are selectively targeted for destruction by autoimmune effector T cells. The aberrant effects of these effector T cells may in part be due to a dysfunction in the regulatory T cell (TREG) compartment and currently there is great interest in developing methods to strengthen the immunoregulation of patients with T1D. A potential way to boost immunoregulation in these patients would be the use of adoptive TREG therapy whereby expanded polyclonal TREG are transferred into patients. Although this treatment in mouse models of disease has shown promise it has been found that antigen specific TREG cells are much more efficacious of preventing disease, and can even reverse disease. The translation of these murine experiments into the human setting is however complex, since the generation of large numbers of antigen specific TREG from human patients is currently a major hurdle. One way to remove this barrier is to utilise lentiviral gene transfer technology, which can allow for the transfer of antigen specific T cell receptor (TCR) genes into a desired cell population. Specifically for T1D, it is hypothesised that TREG engineered to express a MHC Class I restricted (MHCI) TCR, although unconventional, would selectively function at the site of inflammation i.e. within the islets. This project, therefore, aims to generate MHCI islet antigen specific TREG with the hypothesis that these would confer islet antigen specific suppression. To test this hypothesis we engineered human CD4 TREG to express two MHCI islet antigen specific TCRs whilst using a third high affinity pathogenic MHCI TCR as a control. As others have shown, we demonstrate that the control TCR was effectively able to re-direct the antigen specificity of TREG cells through signalling and function. However, we discovered that transfer of autoimmune MHCI TCRs were unable to yield the same results as the control TCR due in part to their natural low affinity for antigen. To circumvent this, we engineered CD4 TREG to express an MHCI autoimmune TCR along with the CD8αβ co-receptor or a CD8αβ high affinity variant. Using this system, human TREG could be successfully re-directed towards an islet specific peptide and exhibit antigen specific suppression. Thus, this study is the first of its kind to use an autoimmune disease relevant, MHCI TCR to successfully re-direct the Ag specificity of TREG cells.
ROLA CZYNNIKA TRANSKRYPCYJNEGO HSF1 W PROCESIE NOWOTWORZENIA* THE ROLE OF HEAT SHOCK TRANSCRIPTION FACTOR 1 IN CARCINOGENESIS

Streszczenie: HSF1 (ang. Heat Shock Factor 1) jest czynnikiem transkrypcyjnym aktywowanym pod wpływem stresu środowiskowego, co prowadzi do wzmożonej ekspresji białek HSP (ang. Heat Shock Proteins). HSP to białka opiekuńcze biorące udział w fałdowaniu innych białek i chroniące komórki przed apopotozą wywołaną działaniem stresu. W wielu typach nowotworów HSF1 i HSP ulegają zwiększonej ekspresji. HSF1 może wspierać transformację nowotworową, a także ułatwiać przetrwanie komórek nowotworowo zmienionych, modulując ścieżki sygnałowe związane ze wzrostem i proliferacją, apoptozą, metabolizmem oraz ruchliwością. HSF1 może promować niestabilność genetyczną przez unikanie punktów kontrolnych cyklu komórkowego. Sekwencja regulatorowa HSE rozpoznawana przez HSF1 występuje nie tylko w promotorach genów HSP, ale także w innych genach, np. w genie oporności wielolekowej ABCB1 (MDR1), kodującym glikoproteinę P aktywnie usuwającą leki z komórki. Działanie białka ABCB1 może znacznie zmniejszać skuteczność terapii przeciwnowotworowej. Cytoprotekcyjne działanie HSF1 w warunkach wzrostu nowotworowego jest więc bardzo niepożądane i czyni z niego nowy, ważny cel w terapii nowotworów.