Miracle-dz/UniFAR-FTUs huggingface dataset:
An automatic procedure to determine groups of nonparametric regression curves

In many situations it could be interesting to ascertain whether nonparametric regression curves can be grouped, especially when confronted with a considerable number of curves. The proposed testing procedure allows to determine groups with an automatic selection of their number. A simulation study is presented in order to investigate the finite sample properties of the proposed methods when compared to existing alternative procedures. Finally, the applicability of the procedure to study the geometry of a tunnel by analysing a set of cross-sections is demonstrated. The results obtained show the existence of some heterogeneity in the tunnel geometry.
EPIHC: Improving Enhancer-Promoter Interaction Prediction by Using Hybrid Features and Communicative Learning

Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, which help facilitate understanding of gene regulation, cell differentiation and disease mechanisms. EPI identification approaches through wet experiments are often costly and time-consuming, leading to the design of high-efficiency computational methods is in demand. In this paper, we propose a deep neural network-based method named EPIHC to predict Enhancer-Promoter Interactions with Hybrid features and Communicative learning. EPIHC extracts enhancer and promoter sequence-derived features using convolutional neural networks (CNN), and then we design a communicative learning module to capture the communicative information between enhancer and promoter sequences. Besides, EPIHC takes the genomic features of enhancers and promoters into account, incorporating with the sequence-derived features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveals that the communicative learning module can bring explicit information about EPIs, which is ignored by CNN, and provide explainability about EPIs to some degree. Moreover, we consider two strategies to improve the performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on some cell lines can exhibit good performances for other cell lines. The codes and data are available at https://github.com/BioMedicalBigDataMiningLab/EPIHC.
Robust Asymmetric Learning in POMDPs

Policies for partially observed Markov decision processes can be efficiently learned by imitating policies for the corresponding fully observed Markov decision processes. Unfortunately, existing approaches for this kind of imitation learning have a serious flaw: the expert does not know what the trainee cannot see, and so may encourage actions that are sub-optimal, even unsafe, under partial information. We derive an objective to instead train the expert to maximize the expected reward of the imitating agent policy, and use it to construct an efficient algorithm, adaptive asymmetric DAgger (A2D), that jointly trains the expert and the agent. We show that A2D produces an expert policy that the agent can safely imitate, in turn outperforming policies learned by imitating a fixed expert.
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning

Trajectory planning is a fundamental task on various autonomous driving platforms, such as social robotics and self-driving cars. Many trajectory planning algorithms use a reference curve based Frenet frame with time to reduce the planning dimension. However, there is a common implicit assumption in classic trajectory planning approaches, which is that the generated trajectory should follow the reference curve continuously. This assumption is not always true in real applications and it might cause some undesired issues in planning. One issue is that the projection of the planned trajectory onto the reference curve maybe discontinuous. Then, some segments on the reference curve are not the image of any part of the planned path. Another issue is that the planned path might self-intersect when following a simple reference curve continuously. The generated trajectories are unnatural and suboptimal ones when these issues happen. In this paper, we firstly demonstrate these issues and then introduce an efficient trajectory generation method which uses a new transformation from the Cartesian frame to Frenet frames. Experimental results on a simulated street scenario demonstrated the effectiveness of the proposed method.
Vehicular Network Slicing for Reliable Access and Deadline-Constrained Data Offloading: A Multi-Agent On-Device Learning Approach

Efficient data offloading plays a pivotal role in computational-intensive platforms as data rate over wireless channels is fundamentally limited. On top of that, high mobility adds an extra burden in vehicular edge networks (VENs), bolstering the desire for efficient user-centric solutions. Therefore, unlike the legacy inflexible network-centric approach, this paper exploits a software-defined flexible, open, and programmable networking platform for an efficient user-centric, fast, reliable, and deadline-constrained offloading solution in VENs. In the proposed model, each active vehicle user (VU) is served from multiple low-powered access points (APs) by creating a noble virtual cell (VC). A joint node association, power allocation, and distributed resource allocation problem is formulated. As centralized learning is not practical in many real-world problems, following the distributed nature of autonomous VUs, each VU is considered an edge learning agent. To that end, considering practical location-aware node associations, a joint radio and power resource allocation non-cooperative stochastic game is formulated. Leveraging reinforcement learning's (RL) efficacy, a multi-agent RL (MARL) solution is proposed where the edge learners aim to learn the Nash equilibrium (NE) strategies to solve the game efficiently. Besides, real-world map data, with a practical microscopic mobility model, are used for the simulation. Results suggest that the proposed user-centric approach can deliver remarkable performances in VENs. Moreover, the proposed MARL solution delivers near-optimal performances with approximately 3% collision probabilities in case of distributed random access in the uplink.
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient Optimization

Recent technological progress in the development of Unmanned Aerial Vehicles (UAVs) together with decreasing acquisition costs make the application of drone fleets attractive for a wide variety of tasks. In agriculture, disaster management, search and rescue operations, commercial and military applications, the advantage of applying a fleet of drones originates from their ability to cooperate autonomously. Multi-Agent Reinforcement Learning approaches that aim to optimize a neural network based control policy, such as the best performing actor-critic policy gradient algorithms, struggle to effectively back-propagate errors of distinct rewards signal sources and tend to favor lucrative signals while neglecting coordination and exploitation of previously learned similarities. We propose a Multi-Critic Policy Optimization architecture with multiple value estimating networks and a novel advantage function that optimizes a stochastic actor policy network to achieve optimal coordination of agents. Consequently, we apply the algorithm to several tasks that require the collaboration of multiple drones in a physics-based reinforcement learning environment. Our approach achieves a stable policy network update and similarity in reward signal development for an increasing number of agents. The resulting policy achieves optimal coordination and compliance with constraints such as collision avoidance.
Isostasy with Love: II Viscoelastic relaxation

In modern geodynamics, isostasy can be viewed either as the static equilibrium of the crust that minimizes deviatoric stresses, or as a dynamic process resulting from the viscous relaxation of the non-hydrostatic crustal shape. Paper I gave a general formulation of Airy isostasy as a loading problem solved with Love numbers, and applied it to the case of minimum stress isostasy. In this sequel, the same framework is used to study Airy isostasy as the long-time evolution of a viscoelastic shell submitted to surface and internal loads. Isostatic ratios are defined in terms of time-dependent deviatoric Love numbers. Dynamic isostasy depends on the loading history, two flavours of which are the constant load applied at a specific time and the constant shape maintained by addition or removal of material at the compensation depth. Whereas the former model has no elastic analog, the latter is equivalent to elastic isostasy with zero deflection at the surface, which was shown in Paper I to be dual to minimum stress isostasy. Viscoelastic and viscous approaches are completely equivalent. Isostatic models thus belong to two independent groups: the elastic/stationary approaches and the time-dependent approaches. If the shell is homogeneous, all models predict a similar compensation of large-scale gravity perturbations. If the shell rheology depends on depth, stationary models predict more compensation at long wavelengths, whereas time-dependent models result in negligible compensation. Analytical formulas for the isostatic ratios of an incompressible body with three homogeneous layers are given in complementary software. 1 ar X iv :2 01 2. 15 22 6v 1 [ as tr oph . E P] 3 0 D ec 2 02 0
Sparse PCA via $l_{2, p}$-Norm Regularization for Unsupervised Feature Selection

In the field of data mining, how to deal with high-dimensional data is an inevitable problem. Unsupervised feature selection has attracted more and more attention because it does not rely on labels. The performance of spectral-based unsupervised methods depends on the quality of constructed similarity matrix, which is used to depict the intrinsic structure of data. However, real-world data contain a large number of noise samples and features, making the similarity matrix constructed by original data cannot be completely reliable. Worse still, the size of similarity matrix expands rapidly as the number of samples increases, making the computational cost increase significantly. Inspired by principal component analysis, we propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_{2,p}$-norm regularization. The projection matrix, which is used for feature selection, is learned by minimizing the reconstruction error under the sparse constraint. Then, we present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically. Finally, extensive experiments on real-world data sets demonstrate the effectiveness of our proposed method.
Estimating Solution Smoothness and Data Noise with Tikhonov Regularization

Abstract A main drawback of classical Tikhonov regularization is that often the parameters required to apply theoretical results, e.g., the smoothness of the sought-after solution and the noise level, are unknown in practice. In this paper we investigate in new detail the residuals in Tikhonov regularization viewed as functions of the regularization parameter. We show that the residual carries, with some restrictions, the information on both the unknown solution and the noise level. By calculating approximate solutions for a large range of regularization parameters, we can extract both parameters from the residual given only one set of noisy data and the forward operator. The smoothness in the residual allows to revisit parameter choice rules and relate a-priori, a-posteriori, and heuristic rules in a novel way that blurs the lines between the classical division of the parameter choice rules. All results are accompanied by numerical experiments.
Exact Moment Representation in Polynomial Optimization

We investigate the problem of representing moment sequences by measures in the context of Polynomial Optimization Problems. This consists in ﬁnding the inﬁmum of a real polynomial on a real semialgebraic set deﬁned by polynomial inequalities. We analyze the exactness of Moment Matrix (MoM) relaxations, dual to the Sum of Squares (SoS) relaxations, which are hierarchies of convex cones introduced by Lasserre to approximate measures and positive polynomials. We investigate in particular ﬂat truncation properties, which allow testing e ﬀ ectively when MoM exactness holds. We consider the quadratic module Q generated by the inequalities. We show that the dual of the MoM relaxation coincides with the SoS relaxation extended with the real radical of the support of Q , and focus on the zero-dimensional case, generalizing results for equations deﬁning a ﬁnite real variety. We deduce su ﬃ cient and necessary conditions for ﬂat truncation, under the ﬁnite convergence assumption: ﬂat truncation happens if and only if the support of the quadratic module associated with the minimizers is of dimension zero. We also bound the order of the relaxation at which ﬂat truncation holds. As corollaries, we conclude that ﬂat truncation holds: E ﬀ ective numerical computations illustrate these properties.
A Puzzle Concerning String Dualities at Order α ′ 3

We show that the cosmological reduction of the fourth powers of the Riemann tensor claimed to arise in string theory at order α 3, with overall coefficient proportional to ζ(3), is not invariant under standard O(9, 9) transformations. This is in conflict with the general result in string theory, due to Sen, that classical string theory with d-dimensional translation invariance admits an O(d, d) symmetry to all orders in α. Possible resolutions of this puzzle are discussed.
Facile and Selective Synthesis of Chloromethylpyridines and Chloropyridines Using Diphosgene/Triphosgene

Abstract Diphosgene and triphosgene in the presence of amines were found to be an excellent chlorinating agents with high selectivity for the preparation of chloromethylpyridines and chloropyridines from picoline‐N‐oxides and pyridine‐N‐oxides respectively. #IICT Communication No: 030808.
AraGPT2: Pre-Trained Transformer for Arabic Language Generation

Recently, pre-trained transformer-based architectures have proven to be very efficient at language modeling and understanding, given that they are trained on a large enough corpus. Applications in language generation for Arabic are still lagging in comparison to other NLP advances primarily due to the lack of advanced Arabic language generation models. In this paper, we develop the first advanced Arabic language generation model, AraGPT2, trained from scratch on a large Arabic corpus of internet text and news articles. Our largest model, AraGPT2-mega, has 1.46 billion parameters, which makes it the largest Arabic language model available. The mega model was evaluated and showed success on different tasks including synthetic news generation, and zero-shot question answering. For text generation, our best model achieves a perplexity of 29.8 on held-out Wikipedia articles. A study conducted with human evaluators showed the significant success of AraGPT2-mega in generating news articles that are difficult to distinguish from articles written by humans. We thus develop and release an automatic discriminator model with a 98% percent accuracy in detecting model-generated text. The models are also publicly available, hoping to encourage new research directions and applications for Arabic NLP.
Testing the general theory of relativity using gravitational wave propagation from dark standard sirens

Alternative theories of gravity predict modifications in the propagation of gravitational waves (GW) through space-time. One of the smoking-gun predictions of such theories is the change in the GW luminosity distance to GW sources as a function of redshift relative to the electromagnetic (EM) luminosity distance expected from EM probes. We propose a multi-messenger test of the theory of general relativity from the propagation of gravitational waves by combining EM and GW observations to resolve these issues from GW sources without EM counterparts (which are also referred to as dark standard sirens). By using the relation between the geometric distances accessible from baryon acoustic oscillation measurements, and luminosity distance measurements from the GW sources, we can measure any deviation from the general theory of relativity via the GW sources of unknown redshift that will be detectable by networks of GW detectors such as LIGO, Virgo, and KAGRA. Using this technique, the fiducial value of the frictional term can be measured to a precision Ξ0 = 0.98 +0.04 −0.23 after marginalizing over redshift dependence, cosmological parameters, and GW bias parameters with ∼ 3500 dark standard sirens of masses 30 M each distributed up to redshift z = 0.5. For a fixed redshift dependence, a value of Ξ0 = 0.99 +0.02 −0.02 can be measured with a similar number of dark sirens. Application of our methodology to the far more numerous dark standard sirens detectable with next generation GW detectors, such as LISA, Einstein Telescope and Cosmic Explorer, will allow achievement of higher accuracy than possible from use of bright standard sirens.
Indoor Air Quality Improvement

1 Motivation Good indoor air quality is a vital part and has great importance for of human health. If quality of indoor air quality is not good, it can contribute to the development of chronic respiratory diseases such as asthma, heart disease, and lung cancer. Poor air quality is extremely difficult for humans to detect through sight and smell alone and existing sensing equipment is designed to be used by and provide data for scientists rather than everyday citizens. We typically spend 90 percent of our time indoors eating, sleeping, working, cooking, and spending time with our loved ones. Yet, some of our activities degrade the environmental quality of these spaces. For example, cooking, lighting a fireplace emits carbon dioxide particles and dust into the air. Clean air can prevent many environmental health hazards such as asthma. Although CO2 is considered as nontoxic, higher levels can cause health problems to the occupants such as drowsiness, headaches, lower efficiency and aggravated problems under long term exposure. Thus, there is a need of proper ventilation system that can create a healthier environment by allowing clean air to circulate from other rooms. As sometimes the air in the other rooms may not be of good quality. In such case, we can vent the outside air at the same time taking into consideration the temperature of the room, but without it, the air in a building can actually be worse than the air outside. The design of such ventilation system should be energy efficient. Energy management is one of the important factors that must be considered while designing efficient ventilation system. Using such energy efficient system, we can reduce the cost required to achieve ideal conditions in the room. For reducing the power consumption we have to design a system based on occupancy patterns and use historical data prediction which will result in an energy efficient system.
Sensifi: A Wireless Sensing System for Ultrahigh-Rate Applications

Wireless sensor networks (WSNs) are being used in various applications, such as structural health monitoring and industrial control. Since energy efficiency is one of the major design factors, the existing WSNs primarily rely on low-power, low-rate wireless technologies, such as 802.15. 4 and Bluetooth. In this article, by proposing Sensifi, we strive to tackle the challenges of developing ultrahigh-rate WSNs based on the 802.11 (WiFi) standard. As an illustrative structural health monitoring application, we consider the spacecraft vibration test and identify system design requirements and challenges. Our main contributions are as follows. First, we propose packet encoding methods to reduce the overhead of assigning accurate timestamps to samples. Second, we propose energy-efficiency methods to enhance the system’s lifetime. Third, to enhance sampling rate and mitigate sampling rate instability, we reduce the overhead of processing outgoing packets through the network stack. Fourth, we study and reduce the delay of processing time synchronization packets through the network stack. Fifth, we propose a low-power node design, particularly targeting vibration monitoring. Sixth, we use our node design to empirically evaluate energy efficiency, sampling rate, and data rate. We leave large-scale evaluations as future work.
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings

Fact-based Visual Question Answering (FVQA), a challenging variant of VQA, requires a QA-system to include facts from a diverse knowledge graph (KG) in its reasoning process to produce an answer. Large KGs, especially common-sense KGs, are known to be incomplete, i.e., not all non-existent facts are always incorrect. Therefore, being able to reason over incomplete KGs for QA is a critical requirement in real-world applications that has not been addressed extensively in the literature. We develop a novel QA architecture that allows us to reason over incomplete KGs, something current FVQA state-of-the-art (SOTA) approaches lack due to their critical reliance on fact retrieval. We use KG Embeddings, a technique widely used for KG completion, for the downstream task of FVQA. We also employ a new image representation technique we call 'Image-as-Knowledge' to enable this capability, alongside a simple one-step CoAttention mechanism to attend to text and image during QA. Our FVQA architecture is faster during inference time, being O(m), as opposed to existing FVQA SOTA methods which are O(N log N), where m = number of vertices, N = number of edges = O(m^2). KG embeddings are shown to hold complementary information to word embeddings: a combination of both metrics permits performance comparable to SOTA methods in the standard answer retrieval task, and significantly better (26% absolute) in the proposed missing-edge reasoning task.
A Survey on Segment Routing with Emphasis on Use Cases in Large Provider Networks

Segment routing is heralded as an important technology innovation in large provider networks. In the domain of large telecom service providers, segment routing has the potential to be in the same league as MPLS and IPv6 as well as pave a way towards the successful implementation of SDNs. In this regard this paper is a survey on the existing segment routing work in the community. We begin by describing how segment routing works, inclusive of the various building blocks. The paper describes the segment routing architecture in terms of its positioning vis-à-vis existing networking technologies, as well as the data-plane and the control plane. Label encoding techniques that are central towards implementing segment routing are then discussed. In order to make segment routing relevant to the current domain of providers, we postulate various use cases, their working and issues of interoperability. The paper also reviews the current set of segment routing implementation cases in industry. Thereafter we position segment routing among the various provider manifestations.