Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-Aided Jamming for Secure Ground Communication With Unknown Eavesdropper Location

This paper investigates unmanned aerial vehicle (UAV)-aided jamming technique for enabling physical layer keyless security in scenarios where the exact eavesdropper location is unknown. We assume that the unknown eavesdropper location is within an ellipse characterizing the coverage region of the transmitter. By sequentially optimizing the transmit power, the flight path of the UAV and its jamming power, we aim at maximizing the average secrecy rate with arbitrary eavesdropper location. Simulation results demonstrate that the optimal flight path obtains better secrecy rate performance compared to that using direct UAV flight path encasing the transmitter and the legitimate receiver. Most importantly, even with the unknown eavesdropper location, we obtained a secrecy rate that is comparable to a scenario when the eavesdropper’s location is known. However, the average secrecy rate with the unknown eavesdropper location varies depending on the proximity of the eavesdropper to the known location of the transmitter. We also observe that due to the UAV-aided jamming, the average secrecy rate stabilizes at some point even though the average received envelope power of the eavesdropper increases. This essentially demonstrates the effectiveness of the proposed scheme.
On the fundamental group of open Richardson varieties

We compute the fundamental group of an open Richardson variety in the manifold of complete flags that corresponds to a partial flag manifold. Rietsch showed that these log Calabi-Yau varieties underlie a Landau-Ginzburg mirror for the Langlands dual partial flag manifold, and our computation verifies a prediction of Hori for this mirror. It is log Calabi-Yau as it isomorphic to the complement of the Knutson-Lam-Speyer anti-canonical divisor for the partial flag manifold. We also determine explicit defining equations for this divisor.
ALMA and ROSINA detections of phosphorus-bearing molecules: the interstellar thread between star-forming regions and comets

To understand how phosphorus (P)-bearing molecules are formed in star-forming regions, we have analysed the Atacama Large Millimeter/Submillimeter Array (ALMA) observations of PN and PO towards the massive star-forming region AFGL 5142, combined with a new analysis of the data of the comet 67P/Churyumov–Gerasimenko taken with the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) instrument onboard Rosetta. The ALMA maps show that the emission of PN and PO arises from several spots associated with low-velocity gas with narrow linewidths in the cavity walls of a bipolar outflow. PO is more abundant than PN in most of the spots, with the PO/PN ratio increasing as a function of the distance to the protostar. Our data favour a formation scenario in which shocks sputter phosphorus from the surface of dust grains, and gas-phase photochemistry induced by UV photons from the protostar allows efficient formation of the two species in the cavity walls. Our analysis of the ROSINA data has revealed that PO is the main carrier of P in the comet, with PO/PN > 10. Since comets may have delivered a significant amount of prebiotic material to the early Earth, this finding suggests that PO could contribute significantly to the phosphorus reservoir during the dawn of our planet. There is evidence that PO was already in the cometary ices prior to the birth of the Sun, so the chemical budget of the comet might be inherited from the natal environment of the Solar system, which is thought to be a stellar cluster including also massive stars.
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets

For an unknown (new) classification dataset, choosing an appropriate deep learning architecture is often a recursive, time-taking, and laborious process. In this research, we propose a novel technique to recommend a suitable architecture from a repository of known models. Further, we predict the performance accuracy of the recommended architecture on the given unknown dataset, without the need for training the model. We propose a model encoder approach to learn a fixed length representation of deep learning architectures along with its hyperparameters, in an unsupervised fashion. We manually curate a repository of image datasets with corresponding known deep learning models and show that the predicted accuracy is a good estimator of the actual accuracy. We discuss the implications of the proposed approach for three benchmark images datasets and also the challenges in using the approach for text modality. To further increase the reproducibility of the proposed approach, the entire implementation is made publicly available along with the trained models.
A Continuation Method in Bayesian Inference

We present a continuation method that entails generating a sequence of transition probability density functions from the prior to the posterior in the context of Bayesian inference for parameter estimation problems. The characterization of transition distributions, by tempering the likelihood function, results in a homogeneous nonlinear partial integro-differential equation whose existence and uniqueness of solutions are addressed. The posterior probability distribution comes as the interpretation of the final state of the path of transition distributions. A computationally stable scaling domain for the likelihood is explored for the approximation of the expected deviance, where we manage to hold back all the evaluations of the forward predictive model at the prior stage. It follows the computational tractability of the posterior distribution and opens access to the posterior distribution for direct samplings. To get a solution formulation of the expected deviance, we derive a partial differential equation governing the moments generating function of the log-likelihood. We show also that a spectral formulation of the expected deviance can be obtained for low-dimensional problems under certain conditions. The computational efficiency of the proposed method is demonstrated through three differents numerical examples that focus on analyzing the computational bias generated by the method, assessing the continuation method in the Bayesian inference with non-Gaussian noise, and evaluating its ability to invert a multimodal parameter of interest.
Smart Grid State Estimation with PMUs Time Synchronization Errors

State Estimation (SE) is one of the essential tasks to monitor and control the smart power grid. This paper presents a method to estimate the state variables combining the measurement of power demand at each bus with the data collected from a limited number of Phasor Measurement Units (PMUs). Although PMU data are usually assumed to be perfectly synchronized with the Coordinated Universal Time (UTC), this work explicitly considers the presence of time-synchronization errors due, for instance, to the actual performance of GPS receivers and the limited stability of the internal oscillator. The proposed algorithm is a recursive Kalman filter which not only estimates the state variables of the power system, but also the frequency deviations causing clock offsets which eventually affect the timestamps of the measures returned by different PMUs. The proposed solution was tested and compared with alternative approaches using both synthetic data applied to the IEEE 123 bus distribution feeder and real-field data collected from a small-size medium-voltage (MV) distribution system located inside the EPFL campus in Lausanne. Results show the validity of the proposed method in terms of state estimation accuracy. In particular, when some synchronization errors are present, the proposed algorithm can estimate and compensate for them.
Enhanced UV radiation and dense clumps in the molecular outflow of Mrk 231

We present interferometric observations of the CN(1–0) line emission in Mrk 231 and combine them with previous observations of CO and other H2 gas tracers to study the physical properties of the massive molecular outflow. We find a strong boost of the CN/CO(1–0) line luminosity ratio in the outflow of Mrk 231, which is unprecedented compared to any other known Galactic or extragalactic astronomical source. For the dense gas phase in the outflow traced by the HCN and CN emissions, we infer XCN ≡ [CN]/[H2]> XHCN by at least a factor of three, with H2 gas densities of nH2 ∼ 105−6 cm−3. In addition, we resolve for the first time narrow spectral features in the HCN(1–0) and HCO+(1–0) high-velocity line wings tracing the dense phase of the outflow. The velocity dispersions of these spectral features, σv ∼ 7−20 km s−1, are consistent with those of massive extragalactic giant molecular clouds detected in nearby starburst nuclei. The H2 gas masses inferred from the HCN data are quite high, Mmol ∼ 0.3−5 × 108 M⊙. Our results suggest that massive complexes of denser molecular gas survive embedded into the more diffuse H2 phase of the outflow, and that the chemistry of these outflowing dense clouds is strongly affected by UV radiation.
DDNet: Dual-path Decoder Network for Occlusion Relationship Reasoning

Occlusion relationship reasoning based on convolution neural networks consists of two subtasks: occlusion boundary extraction and occlusion orientation inference. Due to the essential differences between the two subtasks in the feature expression at the higher and lower stages, it is challenging to carry on them simultaneously in one network. To address this issue, we propose a novel Dual-path Decoder Network, which uniformly extracts occlusion information at higher stages and separates into two paths to recover boundary and occlusion orientation respectively in lower stages. Besides, considering the restriction of occlusion orientation presentation to occlusion orientation learning, we design a new orthogonal representation for occlusion orientation and proposed the Orthogonal Orientation Regression loss which can get rid of the unfitness between occlusion representation and learning and further prompt the occlusion orientation learning. Finally, we apply a multi-scale loss together with our proposed orientation regression loss to guide the boundary and orientation path learning respectively. Experiments demonstrate that our proposed method achieves state-of-the-art results on PIOD and BSDS ownership datasets.
A Neural Rendering Framework for Free-Viewpoint Relighting

We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs. Existing neural rendering (NR) does not explicitly model the physical rendering process and hence has limited capabilities on relighting. RNR instead models image formation in terms of environment lighting, object intrinsic attributes, and light transport function (LTF), each corresponding to a learnable component. In particular, the incorporation of a physically based rendering process not only enables relighting but also improves the quality of view synthesis. Comprehensive experiments on synthetic and real data show that RNR provides a practical and effective solution for conducting free-viewpoint relighting.
Reheating in 
R2
 Palatini inflationary models

We consider $R^2$ inflation in the Palatini gravity assuming the existence of scalar fields, coupled to gravity in the most general manner. These theories, in the Einstein frame, and for one scalar field $h$, share common features with $K$ - inflation models. We apply this formalism for the study of popular inflationary models, whose potentials are monomials, $ V \sim h^{n} $, with $ n $ a positive even integer. We also study the Higgs model non-minimally coupled to gravity. Although these have been recently studied, in the framework of the Palatini approach, we show that the scalar power spectrum severely constrains these models. Although we do not propose a particular reheating mechanism, we show that the quadratic $ \sim h^2$ and the Higgs model can survive these constraints with a maximum reheating temperature as large as $ \sim 10^{15} \, GeV$, when reheating is instantaneous. However, this can be only attained at the cost of a delicate fine-tuning of couplings. Deviations from this fine-tuned values can still yield predictions compatible with the cosmological data, for couplings that lie in very tight range, giving lower reheating temperatures.
A Statistical Model for Dynamic Networks with Neural Variational Inference

In this paper we propose a statistical model for dynamically evolving networks, together with a variational inference approach. Our model, which we call Dynamic Latent Attribute Interaction Model (DLAIM), encodes edge dependencies across different time snapshots. It represents nodes via latent attributes and uses attribute interaction matrices to model the presence of edges. Both are allowed to evolve with time, thus allowing us to capture the dynamics of the network. We develop a neural network based variational inference procedure that provides a suitable way to learn the model parameters. The main strengths of DLAIM are: (i) it is flexible as it does not impose strict assumptions on network evolution unlike existing approaches, (ii) it applies to both directed as well as undirected networks, and more importantly, (iii) learned node attributes and interaction matrices may be interpretable and therefore provide insights on the mechanisms behind network evolution. Experiments done on real world networks for the task of link forecasting demonstrate the superior performance of our model as compared to existing approaches.
A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis

Identifying the quality of free-text arguments has become an important task in the rapidly expanding field of computational argumentation. In this work, we explore the challenging task of argument quality ranking. To this end, we created a corpus of 30,497 arguments carefully annotated for point-wise quality, released as part of this work. To the best of our knowledge, this is the largest dataset annotated for point-wise argument quality, larger by a factor of five than previously released datasets. Moreover, we address the core issue of inducing a labeled score from crowd annotations by performing a comprehensive evaluation of different approaches to this problem. In addition, we analyze the quality dimensions that characterize this dataset. Finally, we present a neural method for argument quality ranking, which outperforms several baselines on our own dataset, as well as previous methods published for another dataset.
Learning Efficient Video Representation with Video Shuffle Networks

3D CNN shows its strong ability in learning spatiotemporal representation in recent video recognition tasks. However, inflating 2D convolution to 3D inevitably introduces additional computational costs, making it cumbersome in practical deployment. We consider whether there is a way to equip the conventional 2D convolution with temporal vision no requiring expanding its kernel. To this end, we propose the video shuffle, a parameter-free plug-in component that efficiently reallocates the inputs of 2D convolution so that its receptive field can be extended to the temporal dimension. In practical, video shuffle firstly divides each frame feature into multiple groups and then aggregate the grouped features via temporal shuffle operation. This allows the following 2D convolution aggregate the global spatiotemporal features. The proposed video shuffle can be flexibly inserted into popular 2D CNNs, forming the Video Shuffle Networks (VSN). With a simple yet efficient implementation, VSN performs surprisingly well on temporal modeling benchmarks. In experiments, VSN not only gains non-trivial improvements on Kinetics and Moments in Time, but also achieves state-of-the-art performance on Something-Something-V1, Something-Something-V2 datasets.
COOK: Chirp-OOK Communication with Self-reliant Bitrate Adaptation in Backscatter Networks.

For large-scale Internet of Things (IoT), backscatter communication is a promising technology to reduce power consumption and simplify deployment. However, backscatter communication lacks stability, along with limited communication range within a few meters. Due to the limited computation ability of backscatter tags, it is burdensome to effectively adapt the bitrate for the time-varying channel. Thus, backscatter tags are failed to fully utilize the optimal transmission rate. In this paper, we design a system named COOK with self-reliant bitrate adaptation in backscatter communication. Channel symmetry allows backscatter tags to adjust bitrate depending on the received signal strength of the excitation source (ES) without feedback. In addition, the chirp spreading signal is exploited as the ES signal to enable backscatter tags to work under noise floor. Our modulation approach is denoted as Chirp-OOK since the tags reflect the chirp signal by employing the on-off keying modulation. It allows that receiver can decode under the noise floor and the bitrate varies flexibly as the communication range changes. We have implemented the prototype system based on the universal software radio peripheral (USRP) platform. Extensive experiment results demonstrate the effectiveness of the proposed system. Our system provides valid communication distance up to $27m$, which is 7 times as compared with normal backscatter system. The system significantly increases the backscatter communication stability, by supporting bitrate adaptation ranges from 0.33kbps to 1.2Mbps, and guaranteeing the bit error rate (BER) is below 1%.
Electricity Load Forecasting - An Evaluation of Simple 1D-CNN Network Structures

This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future. In contrast to well established CNN architectures, the input data is one-dimensional. A parameter scanning of network parameters is conducted in order to gain information about the influence of the kernel size, number of filters, and dense size. The results show that a good forecast quality can already be achieved with basic CNN architectures.The method works not only for smooth sum loads of many hundred consumers, but also for the load of apartment buildings.
Contextual Combinatorial Conservative Bandits

The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range of practical scenarios. Various algorithms have been designed to achieve a high reward in a long term. However, its short-term performance might be rather low, which is injurious in risk sensitive applications. Building on previous work of conservative bandits, we bring up a framework of contextual combinatorial conservative bandits. An algorithm is presented and a regret bound of $\tilde O(d^2+d\sqrt{T})$ is proven, where $d$ is the dimension of the feature vectors, and $T$ is the total number of time steps. We further provide an algorithm as well as regret analysis for the case when the conservative reward is unknown. Experiments are conducted, and the results validate the effectiveness of our algorithm.
Multi-View Time Series Classification via Global-Local Correlative Channel-Aware Fusion Mechanism

Multi-view time series classification aims to fuse the distinctive temporal information from different views to further enhance the classification performance. Existing methods mainly focus on fusing multi-view features at an early stage (e.g., learning a common representation shared by multiple views). However, these early fusion methods may not fully exploit the view-specific distinctive patterns in high-dimension time series data. Moreover, the intra-view and inter-view label correlations, which are critical for multi-view classification, are usually ignored in previous works. In this paper, we propose a Global-Local Correlative Channel-AwareFusion (GLCCF) model to address the aforementioned issues. Particularly, our model extracts global and local temporal patterns by a two-stream structure encoder, captures the intra-view and inter-view label correlations by constructing a graph based correlation matrix, and extracts the cross-view global patterns via a learnable channel-aware late fusion mechanism, which could be effectively implemented with a convolutional neural network. Extensive experiments on two real-world datasets demonstrate the superiority of our approach over the state-of-the-art methods. An ablation study is furtherprovided to show the effectiveness of each model component.
Multi-View Multiple Clusterings using Deep Matrix Factorization

Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and interesting from different perspectives. However, how to find multiple, meaningful, and diverse clustering results from multi-view data is still a rarely studied and challenging topic in multi-view clustering and multiple clusterings. In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. DMClusts gradually factorizes multi-view data matrices into representational subspaces layer-by-layer and generates one clustering in each layer. To enforce the diversity between generated clusterings, it minimizes a new redundancy quantification term derived from the proximity between samples in these subspaces. We further introduce an iterative optimization procedure to simultaneously seek multiple clusterings with quality and diversity. Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions.
A Stochastic Trust-Region Framework for Policy Optimization

In this paper, we study a few challenging theoretical and numerical issues on the well known trust region policy optimization for deep reinforcement learning. The goal is to find a policy that maximizes the total expected reward when the agent acts according to the policy. The trust region subproblem is constructed with a surrogate function coherent to the total expected reward and a general distance constraint around the latest policy. We solve the subproblem using a preconditioned stochastic gradient method with a line search scheme to ensure that each step promotes the model function and stays in the trust region. To overcome the bias caused by sampling to the function estimations under the random settings, we add the empirical standard deviation of the total expected reward to the predicted increase in a ratio in order to update the trust region radius and decide whether the trial point is accepted. Moreover, for a Gaussian policy which is commonly used for continuous action space, the maximization with respect to the mean and covariance is performed separately to control the entropy loss. Our theoretical analysis shows that the deterministic version of the proposed algorithm tends to generate a monotonic improvement of the total expected reward and the global convergence is guaranteed under moderate assumptions. Comparisons with the state-of-the-art methods demonstrate the effectiveness and robustness of our method over robotic controls and game playings from OpenAI Gym.