Miracle-dz/UniFAR-FTUs huggingface dataset:
Stand by me: Learning to keep cohesion in the navigation of heterogeneous swarms

A robotic swarm is a particular type of Multiagent System that employs a large number of simpler agents in order to cooperatively perform diﬀerent tasks. Oftentimes, the implementation of complex swarm behaviors is a challenging task, and researchers have started to rely on machine learning techniques, which normally require large and complex training setups. In this paper, we explore a segregated navigation task, in which diﬀerent groups of robots should navigate in a shared environment without mixing with others. Specially, we investigate if a robot trained in simpler scenarios, using a smaller number of robots and groups, can use the learned behavior in more complex scenarios. We performed a series of simulations varying the number of robots and groups and discuss that learning in simpler scenarios can be eﬀective in the segregated navigation task.
Determination of the effective of chemical mutagenesis using sodium azide to improvement of vegetative growth and flowering characteristics in Helichrysum bracteatum L. plant

This study were carried out on Helichrysum bracteatum L. plant at the Experimental nursery of the Ornamental Hort. Dept., Fac. Agric. , Cairo Univ., Giza, Egypt, during the two successive seasons (2014/15 and 2015/16). The aim of this study was to investigate the effect of on some vegetative and flowering parameters and anatomical structure of leaves and flowers. Seeds of H. bracteatum L. were exposed to four concentrations of (0.05, 0.1, 0.2 and 0.3%) of sodium azide (NaN3) solution for six hours; one set of seeds was kept without sodium azide treatment (untreated) to act as control. The obtained data revealed that the low concentration of sodium azide (SA) increased plant height, number of leaves/ plant, stem diameter (cm) number of flowers, flowers fresh weight (g)/plant and flowers dry weight (g)/plant in M1 and M2. The ratio of chlorophyll (a and b) showed a markedly decrease as a result of increasing sodium azide concentrations. On contrary, in case of carotenoids showed a markedly increase as a result of decreasing sodium azide concentrations. Were obtained on many morphological mutants in flowers through two generations. Some parameters of anatomical structure of leaves and flowers and to confirm the stability of some of the mutations were obtained during the second generations.
“CHURN PREDICTION IN TELECOM INDUSTRY”

: Customers are the base for any business success and that is why firms become aware of the significance of acquiring satisfaction of customers. Customer churn is an essential issue and it is regarded as one of the most essential concerns among firms because of increasing rivalry among firms, increased significance of marketing strategies and customers conscious behaviour in present years. Organizations must develop different strategies to resolve the churn issues relying on the service they offer. Customer churn practise is essential in competitive and rapidly developing telecom sector. The process of migrating from one service provider to another telecom services provider occurs due to good services or rates or due to various advantages which the rivalry firm provides customers when signing up, Due to the greater cost related with acquiring new customers the prediction of customer churn has developed as an indispensable part of planning process and strategic decision making in telecom sector.
Inflation with moderately sharp features in the speed of sound: GSR and in-in formalism for power spectrum and bispectrum

We continue the study of mild transient reductions in the speed of sound of the adiabatic mode during inflation, of their effect on the primordial power spectrum and bispectrum, and of their detectability in the Cosmic Microwave Background (CMB). We focus on the regime of \emph{moderately sharp} mild reductions in the speed of sound during uninterrupted slow-roll inflation, a theoretically well motivated and self-consistent regime that admits an effective single-field description. The signatures on the power spectrum and bispectrum were previously computed using a slow-roll Fourier transform (SRFT) approximation, and here we compare it with generalized slow-roll (GSR) and in-in methods, for which we derive new formulas that account for moderately sharp features. The agreement between them is excellent, and also with the power spectrum obtained from the numerical solution to the equation of motion. We show that, in this regime, the SRFT approximation correctly captures with simplicity the effect of higher derivatives of the speed of sound in the mode equation, and makes manifest the correlations between power spectrum and bispectrum features. In a previous paper we reported hints of these correlations in the Planck data and here we perform several consistency checks and further analyses of the best fits, such as polarization and local significance at different angular scales. For the data analysis, we show the excellent agreement between the CLASS and CAMB Boltzmann codes. Our results confirm that the theoretical framework is consistent, and they suggest that the predicted correlations are robust enough to be searched for in CMB and Large Scale Structure (LSS) surveys.
Supplementary for “FBNet: Feedback Network for Point Cloud Completion”

HGNet. The hierarchical graph-based encoder stacks 3 EdgeConv and 2 AdaptGP layers. Both EdgeConv and AdaptGP use k-nearest neighbors (kNN) as the grouping operation, where we set k = 16 throughout the paper. The k of kNN strategy in our cross transformer is set to 16. We use 3 full-connected layers (1024 × 1024, 1024 × 1024, 1024 × 128 ∗ 3) to decode the global feature to coarse shape Pc with Nc = 128. Besides, we aggregate Pc and the partial input, and downsample it to a new coarse output P ′ c with size 512×3, which is the input of 0-th FBAC block at first time step(t = 0). FBAC block. The FBAC block in the FBNet is a lightweight sub-network and its weight parameters are shared across time steps. For feature extraction, feedback exploitation and feature expansion modules, channel dimensions of these modules’ output features are set to 128. The NodeShuffle layer [2] is used to expand features to higher resolution ones. The EdgeConv and MLPs are used to expand input features by r times on channel dimension, and then a shuffle operation is used to rearrange the feature map. Time Steps and Up-sampling Ratios.We set the input size of first FBAC block to 512× 3, and the upsampling ratio in each FBAC block is set to expand point clouds and generate fine-grained shapes. The detailed upsampling ratios of different resolution completion task is shown in Table 1. We set T = 3 for 2048 points completion task, and for other higher resolution tasks, we set T = 2 to shorten training time and get competitive inference cost compared with recent SOTA works.
The Flavour Problem and the Family Symmetry Beyond the Standard Model

In the frame of two Higgs doublet model we try to explain the lepton masses and mixing matrix elements assuming that neutrinos are Dirac particles. Discrete family symmetry groups, which are subgroups of U(3) up to the 1025 order are considered. Like in the one Higgs Standard Model, we found that discrete family symmetries do not give satisfactory answer for this basic questions in the flavour problem.
Reduced-Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training

A multi-fidelity framework is established and demonstrated for prediction of combustion instabilities in rocket engines. The major idea is to adapt appropriate fidelity modeling approaches for different components in a rocket engine to ensure accurate and efficient predictions. Specifically, the proposed framework integrates projection-based Reduced Order Models (ROMs) that are developed using bases generated on truncated domain simulations. The ROM training is performed on truncated domains, and thus does not require full order model solutions on the full rocket geometry, thus demonstrating the potential to greatly reduce training cost. Geometry-specific training is replaced by the response generated by perturbing the characteristics at the boundary of the truncated domain. This training method is shown to enhance predictive capabilities and robustness of the resulting ROMs, including at conditions outside the training range. Numerical tests are conducted on a quasi-1D model of a single-element rocket combustor and the present framework is compared to traditional ROM development approaches.
Supplementary Material for D 3 Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding

3D dense captioning We compare our 3D dense captioning and object detection results against the baseline Scan2Cap [3] in Tab. 1. Our method trained with the speaker MLE loss (marked “Ours (MLE)”) outperforms Scan2Cap by a big margin, leveraging the improved object detection backbone. After training with the CIDEr reward (marked “Ours (CIDEr)”), our dense captioning results are further boosted. Training with the listener loss as the additional reward (marked “Ours (CIDEr+lis.)”) further improves our results due to the explicit reinforcement of the discriminability of generated object descriptions. Here, our object detection mAP is also improved due to the end-to-end joint fine-tuning of our speaker-listener architecture. We showcase the effectiveness of training with extra ScanNet data in the last row in Tab. 1, where 3D dense captioning and object detection results are improved simultaneously.
Statin Use Is Associated with Better Prognosis of Patients with Prostate Cancer after Definite Therapies: A Systematic Review and Meta-Analysis of Cohort Studies

Objective Although the prognostic effect of statins on patients with prostate cancer (PCa) has been frequently evaluated, a consistent result is still lacking. We aimed to evaluate the association between statin use and mortality among patients with PCa after definite therapies. Methods A systematic search of PubMed and other databases for cohort studies about the effect of statins on patients with PCa was performed until April 2022. Meta-analysis was performed using R software version 4.1. 2. Results 24 cohort studies involving 369, 206 participants were finally included. We found statin use significantly reduced the risk of prostate cancer-specific mortality (PCSM) with a pooled hazard ratio (pHR) = 0.76 (95% CI: 0.69–0.84, 18 studies), especially for postdiagnostic statin users: pHR = 0.81 (95% CI: 0.77–0.85) and patients who accepted androgen deprivation therapy (ADT): pHR = 0.69 (95% CI: 0.59–0.81). Statin use was also associated with a 24% reduction in the risk of all-cause mortality (ACM): pHR = 0.76 (95% CI: 0.68–0.85, 17 studies), especially for postdiagnostic statin users: pHR = 0.81 (95% CI: 0.78–0.85) and patients treated with ADT: pHR = 0.72 (95% CI: 0.63–0.82) or radiotherapy (RT): pHR = 0.68 (95% CI: 0.50–0.93). Conclusion In conclusion, the use of statins could promote the prognosis of patients with PCa, especially for postdiagnostic users. For patients who received either ADT or radical prostatectomy (RP), statin use could decrease the PCSM. As for those who received either ADT or RT, statin use could decrease the ACM.
Relation Extraction from Texts Containing Pharmacologically Significant Information on base of Multilingual Language Models

In this paper we estimate the accuracy of the relation extraction from texts containing pharmacologically significant information on base of the expanded version of RDRS corpus, which contains texts of internet reviews on medications in Russian. The accuracy of relation extraction is estimated and compared for two multilingual language models: XLM-RoBERTa-large and XLM-RoBERTa-large-sag. Earlier research proved XLM-RoBERTa-large-sag to be the most efficient language model for the previous version of the RDRS dataset for relation extraction using a ground-truth named entities annotation. In the current work we use two-step relation extraction approach: automated named entity recognition and extraction of relations between predicted entities. The implemented approach has given an opportunity to estimate the accuracy of the proposed solution to the relation extraction problem, as well as to estimate the accuracy at each step of the analysis. As a result, it is shown, that multilingual XLM-RoBERTa-large-sag model achieves relation extraction macro-averaged f1-score equals to 86.4% on the ground-truth named entities, 60.1% on the predicted named entities on the new version of the RDRS corpus contained more than 3800 annotated texts. Consequently, implemented approach based on the
Bridging fermionic and bosonic short range entangled states

In this paper we construct bosonic short range entangled (SRE) states in all spatial dimensions by coupling a Z 2 ? > gauge field to fermionic SRE states with the same symmetries, and driving the Z 2 ? > gauge field to its confined phase. We demonstrate that this approach allows us to construct many examples of bosonic SRE states, and we demonstrate that the previous descriptions of bosonic SRE states such as the semiclassical nonlinear sigma model field theory and the Chern–Simons field theory can all be derived using the fermionic SRE states.
Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines

Rapid and reliable optimal control of injection molding machines (IMMs) is critical for the effective production of injection-molded goods, especially in the situation of restricted computer resources of embedded equipment in IMMs. In this paper, an optimal tracking injection velocity control problem arising in a typical IMM is studied. An effective hybrid intelligent control approach with less computing resources for real-time implementation based on the deep learning (DL) method to mimic the classical model predictive control rule is developed to deal with the tracking control of the injection speed. The proposed method utilizes the gated recurrent unit neural network to learn and predict the optimal time series control process data produced by the traditional model predictive controller. The benefits of this approach over the conventional optimization method are illustrated through simulation results, which show that the convergent DL-based controller can effectively avoid the complex calculation in the control process of IMMs and meet the requirements of more robustness and resist environmental uncertainty to a certain level and can be potentially implemented in embedded hardware much more efficiently and conveniently with a smaller memory footprint and faster computation time.
Non-Abelian dual Meissner effect in SU(3) Yang-Mills theory and confinement/deconfinement phase transition at finite temperature

The dual superconductivity is a promising mechanism for quark confinement. We have proposed the non-Abelian dual superconductivity picture for SU(3) Yang-Mills theory, and showed the restricted field dominance (called conventionally Abelian dominance), and non-Abelian magnetic monopole dominance in the string tension. We have further demonstrated by measuring the chromoelectric flux that the non-Abelian dual Meissner effect exists and determined that the dual superconductivity for SU(3) case is of type I, which is in sharp contrast to the SU(2) case: the border of type I and type II. In this talk, we focus on the confinement/deconfinement phase transition and the non-Abelian dual superconductivity at a finite temperature: We measure the Polyakov loop average and correlator and investigate the restricted field dominance in the Polyakov loop. Then, we measure the chromoelectric flux between a pair of static quark and antiquark created by a pair of Polyakov loops, and investigate the non-Abelian dual Meissner effect and its relevance to the phase transition.
A topology-motivated mixed finite element method for dynamic response of porous media

In this paper, we propose a numerical method for computing solutions to Biot's fully dynamic model of incompressible saturated porous media [Biot;1956]. Our spatial discretization scheme is based on the three-field formulation (u-w-p) and the coupling of a lowest order Raviart-Thomas mixed element [Raviart,Thomas;1977] for fluid variable fields (w, p ) and a nodal Galerkin finite element for skeleton variable field (u). These mixed spaces are constructed based on the natural topology of the variables; hence, are physically compatible and able to exactly model the kind of continuity which is expected. The method automatically satisfies the well known LBB (inf-sup) stability condition and avoids locking that usually occurs in the numerical computations in the incompressible limit and very low hydraulic conductivity. In contrast to the majority of approaches, our three-field formulation can fully capture dynamic behavior of porous media even in high frequency loading phenomena with considerable fluid acceleration such as liquefaction and biomechanics of porous tissues under rapid external loading. Moreover, we address the importance of consistent initial conditions for poroelasticity equations with the incompressibility constraint, which represent a system of differential algebraic equations. The energy balance equation is derived for the full porous medium and used to assess the stability and accuracy of our time integration. To highlight the capabilities of our method, a variety of numerical studies are provided including verification with analytical and boundary element solutions, wave propagation analyses, hydraulic conductivity effects on damping and frequency content, energy balance analyses, mass lumping considerations, effects of mesh pattern and size, and stability analyses. We also explain some discrepancies commonly found in dynamic poroelasticity results in the literature.
Supplementary:BRNet: Exploring Comprehensive Features for Monocular Depth Estimation

The evaluation method introduced by Eigen [1] for KITTI uses the projected LIDAR points. However, it does not handle occlusions or moving objects, which are very common because the cars are usually travelling. To alleviate these problems, [10] introduced a set of high-quality depth maps for the KITTI dataset, which is made by five consecutive frames, and the moving objects are handled by stereo pairs. The improved ground truth contains 652 frames from the Eigen split, which is 93% of the total test frames (697). Following [3], we evaluate our methods on these frames with improved ground truth and compare them to several representative networks. We employ the same error metrics from the standard evaluation and clip the predicted depth to 80m to match the Eigen evaluation. As shown in Table 1, our methods trained by all the three supervision types achieve significant improvements from our baseline work, outperforming all existing methods.
A Variable Metric Extension of the Forward–Backward–Forward Algorithm for Monotone Operators

We propose a variable metric extension of the forward–backward-forward algorithm for finding a zero of the sum of a maximally monotone operator and a monotone Lipschitzian operator in Hilbert spaces. In turn, this framework provides a variable metric splitting algorithm for solving monotone inclusions involving sums of composite operators. Monotone operator splitting methods recently proposed in the literature are recovered as special cases.
Nonterrestrial Communications Assisted by Reconfigurable Intelligent Surfaces

Nonterrestrial communications have emerged as a key enabler for seamless connectivity in the upcoming generation networks. This kind of network can support high data rate communications among aerial platforms (i.e., unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites) and cellular networks, achieving anywhere and anytime connections. However, there are many practical implementation limitations, especially overload power consumption, high probability of blockage, and dynamic propagation environment. Fortunately, the recent technology reconfigurable intelligent surface (RIS) is expected to be one of the most cost-efficient solutions to address such issues. RIS with low-cost elements can bypass blockages and create multiple line-of-sight (LoS) links and provide controllable communication channels. In this article, we present a comprehensive literature review on the RIS-assisted nonterrestrial networks (RANTNs). First, the framework of the RANTNs is introduced with detailed discussion about distinct properties of RIS in NTNs and the two deployment types of RIS, that is, terrestrial RISs (TRISs), and aerial RISs (ARISs), and the classification of RANTNs, including RIS-assisted air-to-ground (A2G)/ground-to-air (G2A), ARIS-assisted ground-to-ground (G2G), and RIS-assisted air-to-air (A2A) communications. In combination with next-generation communication technologies, the advanced technologies in RANTNs are discussed. Then, we overview the literature related to RANTNs from the perspectives of performance analysis and optimization, followed by the widely used methodologies. Finally, open challenges and future research direction in the context of the RANTNs are highlighted.
Tachyon cosmology with non-vanishing minimum potential: a unified model

We investigate the tachyon condensation process in the effective theory with non-vanishing minimum potential and its implications to cosmology. It is shown that the tachyon condensation on an unstable three-brane described by this modified tachyon field theory leads to lower-dimensional branes (defects) forming within a stable three-brane. Thus, in the cosmological background, we can get well-behaved tachyon matter after tachyon inflation, (partially) avoiding difficulties encountered in the original tachyon cosmological models. This feature also implies that the tachyon inflated and reheated universe is followed by a Chaplygin gas dark matter and dark energy universe. Hence, such an unstable three-brane behaves quite like our universe, reproducing the key features of the whole evolutionary history of the universe and providing a unified description of inflaton, dark matter and dark energy in a very simple single-scalar field model.
A COMPREHENSIVE SPECTROSCOPIC ANALYSIS OF DB WHITE DWARFS

We present a detailed analysis of 108 helium-line (DB) white dwarfs based on model atmosphere fits to high signal-to-noise optical spectroscopy. We derive a mean mass of 0.67 M☉ for our sample, with a dispersion of only 0.09 M☉. White dwarfs also showing hydrogen lines, the DBA stars, comprise 44% of our sample, and their mass distribution appears similar to that of DB stars. As in our previous investigation, we find no evidence for the existence of low-mass (M < 0.5 M☉) DB white dwarfs. We derive a luminosity function based on a subset of DB white dwarfs identified in the Palomar-Green Survey. We show that 20% of all white dwarfs in the temperature range of interest are DB stars, although the fraction drops to half this value above Teff ∼ 20,000 K. We also show that the persistence of DB stars with no hydrogen features at low temperatures is difficult to reconcile with a scenario involving accretion from the interstellar medium, often invoked to account for the observed hydrogen abundances in DBA stars. We present evidence for the existence of two different evolutionary channels that produce DB white dwarfs: the standard model where DA stars are transformed into DB stars through the convective dilution of a thin hydrogen layer and a second channel where DB stars retain a helium atmosphere throughout their evolution. We finally demonstrate that the instability strip of pulsating V777 Her white dwarfs contains no non-variables, if the hydrogen content of these stars is properly accounted for.