Miracle-dz/UniFAR-FTUs huggingface dataset:
A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.
VideoClick: Video Object Segmentation with a Single Click

Annotating videos with object segmentation masks typically involves a two stage procedure of drawing polygons per object instance for all the frames and then linking them through time. While simple, this is a very tedious, time consuming and expensive process, making the creation of accurate annotations at scale only possible for well-funded labs. What if we were able to segment an object in the full video with only a single click? This will enable video segmentation at scale with a very low budget opening the door to many applications. Towards this goal, in this paper we propose a bottom up approach where given a single click for each object in a video, we obtain the segmentation masks of these objects in the full video. In particular, we construct a correlation volume that assigns each pixel in a target frame to either one of the objects in the reference frame or the background. We then refine this correlation volume via a recurrent attention module and decode the final segmentation. To evaluate the performance, we label the popular and challenging Cityscapes dataset with video object segmentations. Results on this new CityscapesVideo dataset show that our approach outperforms all the baselines in this challenging setting.
Yet Another Representation of Binary Decision Trees: A Mathematical Demonstration

A decision tree looks like a simple computational graph without cycles, where only the leaf nodes specify the output values and the non-terminals specify their tests or split conditions. From the numerical perspective, we express decision trees in the language of computational graph. We explicitly parameterize the test phase, traversal phase and prediction phase of decision trees based on the bitvectors of non-terminal nodes. As shown, the decision tree is a shallow binary network in some sense. Especially, we introduce the bitvector matrix to implement the tree traversal in numerical approach, where the core is to convert the logical ‘AND’ operation to arithmetic operations. And we apply this numerical representation to extend and unify diverse decision trees in concept. gradients during More
Tsallis holographic dark energy for inflation

The application of holographic principle in very early time is studied. The consideration of the principle in the late-time evolution will be a good motivation to study its role at the time of inflation. Since the length scale is expected to be small during inflation, the resulted energy density form the holographic principle is expected to be large enough to drive inflation. The entropy of the system is the main part of the holographic principle, in which modifying entropy will leads to a modified energy density. Here, instead of the original entropy, we are going to apply a modified entropy, known as Tsallis entropy which includes quantum corrections. The length scale is assumed to be GO length scale. Finding an analytical solution for the model, the slow-roll parameters, scalar spectral index, and tensor-to-scalar ratio are calculated. Comparing the result, with Planck $r-n_s$ diagram, we could find a parametric space for the constants of the model. Then, a correspondence between the holographic dark energy and the scalar field is constructed, and the outcome potentials are investigated.
Light-Edge: A Lightweight Authentication Protocol for IoT Devices in an Edge-Cloud Environment

Due to the ever-growing use of active Internet devices, the Internet has achieved good popularity at present. The smart devices could connect to the Internet and communicate together that shape the Internet of Things (IoT). Such smart devices are generating data and are connecting to each other through edge-cloud infrastructure. Authentication of the IoT devices plays a critical role in the success of the integration of IoT, edge, and cloud computing technologies. The complexity and attack resistance of the authentication protocols are still the main challenges. Motivated by this, this article introduces a lightweight authentication protocol for IoT devices named Light-Edge using a three-layer scheme, including IoT device layer, trust center at the edge layer, and cloud service providers. The results show the superiority of the proposed protocol against other approaches in terms of attack resistance, communication cost, and time cost.
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feedback connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features. However, this inference is a major computational bottleneck. It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes learning stagnation. We also demonstrate that it permits constructing deep and wide predictive-coding networks. Such convolutional networks implement receptive fields that capture well the entire classes of objects on which the networks are trained. This improves the feature representations compared with our lab’s previous nonconvolutional and convolutional DPCNs. It yields unsupervised object recognition that surpass convolutional autoencoders and is on par with convolutional networks trained in a supervised manner.
Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

Potential crowd flow prediction for new planned transportation sites is a fundamental task for urban planners and administrators. Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites. However, the transportation modes of nearby sites (e.g. bus stations, bicycle stations) might be different from the target site (e.g. subway station), which results in severe data scarcity issues. To this end, we propose a data-driven approach, named MOHER, to predict the potential crowd flow in a certain mode for a new planned site. Specifically, we first identify the neighbor regions of the target site by examining the geographical proximity as well as the urban function similarity. Then, to aggregate these heterogeneous relations, we devise a cross-mode relational GCN, a novel relation-specific transformation model, which can learn not only the correlation but also the differences between different transportation modes. Afterward, we design an aggregator for inductive potential flow representation. Finally, an LTSM module is used for sequential flow prediction. Extensive experiments on real-world data sets demonstrate the superiority of the MOHER framework compared with the state-of-the-art algorithms.
A Solution for Large Scale Optimization Problems Based on Gravitational Search Algorithm

One of the challenges in optimization of high dimensional problems is finding appropriate solutions in a way that are as close as possible to the global optima. In this regard, one of the most common phenomena that occurs is the curse of dimensionality in which a large scale feature space generates more parameters that need to be estimated. Heuristic algorithms, such as Gravitational Search Algorithm (GSA), are among the tools proposed for optimizing largescale problems, but in this case, they cannot solve the problem on their own. This paper proposes a novel method for optimizing large scale problems by improving the gravitational search algorithm’s performance. In order to increase the efficiency of the gravitational search algorithm in solving large scale problems, the proposed method combines this algorithm with the cooperative-coevolution methods. For the evaluation of the performance of the proposed algorithm, we consider two approaches. In the first approach, the proposed algorithm is compared with the gravitational search algorithm, and in the second approach, it is compared with some of the most significant research in this field. In the first approach, our method was able to improve the performance of the original gravitational algorithm to solve large scale problems, and in the second one, the results indicate more favorable performance, in some benchmark functions, compared with other cooperative methods.
Counterexample-Guided Prophecy for Model Checking Modulo the Theory of Arrays

We develop a framework for model checking infinite-state systems by automatically augmenting them with auxiliary variables, enabling quantifier-free induction proofs for systems that would otherwise require quantified invariants. We combine this mechanism with a counterexample-guided abstraction refinement scheme for the theory of arrays. Our framework can thus, in many cases, reduce inductive reasoning with quantifiers and arrays to quantifier-free and array-free reasoning. We evaluate the approach on a wide set of benchmarks from the literature. The results show that our implementation often outperforms state-of-the-art tools, demonstrating its practical potential.
Tuning the Frequency of Periodic Data Movements over Hybrid Memory Systems

Emerging hybrid memory systems that comprise technologies such as Intel's Optane DC Persistent Memory, exhibit disparities in the access speeds and capacity ratios of their heterogeneous memory components. This breaks many assumptions and heuristics designed for traditional DRAM-only platforms. High application performance is feasible via dynamic data movement across memory units, which maximizes the capacity use of DRAM while ensuring efficient use of the aggregate system resources. Newly proposed solutions use performance models and machine intelligence to optimize which and how much data to move dynamically; however, the decision of when to move this data is based on empirical selection of time intervals, or left to the applications. Our experimental evaluation shows that failure to properly configure the data movement frequency can lead to 10%-100% slowdown for a given data movement policy; yet, there is no established methodology on how to properly configure this value for a given workload, platform and policy. We propose Cori, a system-level tuning solution that identifies and extracts the necessary application-level data reuse information, and guides the selection of data movement frequency to deliver gains in application performance and system resource efficiency. Experimental evaluation shows that Cori configures data movement frequencies that provide application performance within 3% of the optimal one, and that it can achieve this up to 5x more quickly than random or brute-force approaches. System-level validation of Cori on a platform with DRAM and Intel's Optane DC PMEM confirms its practicality and tuning efficiency.
PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts

The identification and counting of small graph patterns, called network motifs, is a fundamental primitive in the analysis of networks, with application in various domains, from social networks to neuroscience. Several techniques have been designed to count the occurrences of motifs in static networks, with recent work focusing on the computational challenges provided by large networks. Modern networked datasets contain rich information, such as the time at which the events modeled by the networks edges happened, which can provide useful insights into the process modeled by the network. The analysis of motifs in temporal networks, called temporal motifs, is becoming an important component in the analysis of modern networked datasets. Several methods have been recently designed to count the number of instances of temporal motifs in temporal networks, which is even more challenging than its counterpart for static networks. Such methods are either exact, and not applicable to large networks, or approximate, but provide only weak guarantees on the estimates they produce and do not scale to very large networks. In this work we present an efficient and scalable algorithm to obtain rigorous approximations of the count of temporal motifs. Our algorithm is based on a simple but effective sampling approach, which renders our algorithm practical for very large datasets. Our extensive experimental evaluation shows that our algorithm provides estimates of temporal motif counts which are more accurate than the state-of-the-art sampling algorithms, with significantly lower running time than exact approaches, enabling the study of temporal motifs, of size larger than the ones considered in previous works, on billion edges networks.
A deterministic matching method for exact matchings to compare the outcome of different interventions

Statistical matching methods are widely used in the social and health sciences to estimate causal effects using observational data. Often the objective is to find comparable groups with similar covariate distributions in a dataset, with the aim to reduce bias in a random experiment. We aim to develop a foundation for deterministic methods which provide results with low bias, while retaining interpretability. The proposed method matches on the covariates and calculates all possible maximal exact matchesfor a given dataset without adding numerical errors. Notable advantages of our method over existing matching algorithms are that all available information for exact matches is used, no additional bias is introduced, it can be combined with other matching methods for inexact matching to reduce pruning and that the result is calculated in a fast and deterministic way. For a given dataset the result is therefore provably unique for exact matches in the mathematical sense. We provide proofs, instructions for implementation as well as a numerical example calculated for comparison on a complete survey.
Exponential Decay of Sensitivity in Nonlinear Model Predictive Control: A Graph-Theoretic Approach

: Exponential decay of sensitivity (EDS) is a property of nonlinear model predictive control (NMPC) problems; the property indicates that the sensitivity of the solution at time i against a data perturbation at time j decays exponentially with | i − j | . We use a graph-theoretic analysis of the optimality conditions of NMPC problems to prove that EDS holds under uniform boundedness of the Lagrangian Hessian, a uniform second order suﬃciency condition (uSOSC), and a uniform linear independence constraint qualiﬁcation (uLICQ). Furthermore, we prove that uSOSC and uLICQ can be obtained from uniform controllability and observability (well-known system-theoretic properties). These results provide insights into how perturbations propagate along the horizon and enable the development of approximation and solution schemes. We illustrate the developments with numerical examples.
Bayesian Inference Forgetting

The right to be forgotten has been legislated in many countries but the enforcement in machine learning would cause unbearable costs: companies may need to delete whole models trained from massive resources because of single individual requests. Existing works propose to remove the influence of the requested datums on the learned models via its influence function which is no longer naturally well-defined in Bayesian inference. To address this problem, this paper proposes a {\it Bayesian inference forgetting} (BIF) framework to extend the applicable domain to Bayesian inference. In the BIF framework, we develop forgetting algorithms for variational inference and Markov chain Monte Carlo. We show that our algorithms can provably remove the influence of single datums on the learned models. Theoretical analysis demonstrates that our algorithms have guaranteed generalizability. Experiments of Gaussian mixture models on the synthetic dataset and Bayesian neural networks on the Fashion-MNIST dataset verify the feasibility of our methods. The source code package is available at \url{https://github.com/fshp971/BIF}.
Data@Hand: Fostering Visual Exploration of Personal Data on Smartphones Leveraging Speech and Touch Interaction

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed Data@Hand, a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices.
X-Ray Pulsar XTE J1858+034: Discovery of the Cyclotron Line and the Revised Optical Identification

We present the results of a detailed investigation of the poorly studied X-ray pulsar (XRP) XTE J1858+034 based on the data obtained with the NuSTAR observatory during the outburst of the source in 2019. The spectral analysis resulted in the discovery of a cyclotron absorption feature in the source spectrum at ∼48 keV in both the pulse phase-averaged and resolved spectra. Accurate X-ray localization of the source using the NuSTAR and Chandra observatories allowed us to accurately determine the position of the X-ray source and identify the optical companion of the pulsar. The analysis of the counterpart properties suggested that the system is likely a symbiotic binary hosting an XRP and a late-type companion star of the K-M classes rather than a Be X-ray binary as previously suggested.
X-ray analysis of SDSS J165202.60+172852.4, an obscured quasar with outflows at peak galaxy formation epoch

We report on deep XMM-Newton and NuSTAR observations of the high redshift, z = 2.94, extremely red quasar (ERQ), SDSS J165202.60+172852.4, with known galactic ionized outflows detected via spatially-resolved [OIII] emission lines. X-ray observations allow us to directly probe the accretion disk luminosity and the geometry and scale of the circumnuclear obscuration. We fit the spectra from the XMM-Newton/EPIC and NuSTAR detectors with a physically motivated torus model and constrain the source to exhibit a near Compton-thick column density of NH = (1.02 −0.41)×10 cm−2, a near edge-on geometry with the line-of-sight inclination angle of θi = 85◦, and a scattering fraction of fsc ∼ 3 %. The absorption-corrected, intrinsic 2-10 keV X-ray luminosity of L2-10= (1.4 −1)× 10 erg s−1 reveals a powerful quasar that is not intrinsically X-ray weak, consistent with observed trends in other ERQs. We also estimate the physical properties of the obscuration, although highly uncertain: the warm ionized scattering density of ne ∼ 7.5× (10−10) cm−3 and the obscuration mass of Mobsc ∼ 1.7× (10−10)M . As previously suggested with shallower X-ray observations, optical and infrared selection of ERQ has proved effective in finding obscured quasars with powerful outflow signatures. Our observations provide an in-depth view into the X-ray properties of ERQs and support the conclusions of severely photon-limited studies of obscured quasar populations at high redshifts.
Leveraging AI to optimize website structure discovery during Penetration Testing

Dirbusting is a technique used to brute force directories and file names on web servers while monitoring HTTP responses, in order to enumerate server contents. Such a technique uses lists of common words to discover the hidden structure of the target website. Dirbusting typically relies on response codes as discovery conditions to find new pages. It is widely used in web application penetration testing, an activity that allows companies to detect websites vulnerabilities. Dirbusting techniques are both time and resource consuming and innovative approaches have never been explored in this field. We hence propose an advanced technique to optimize the dirbusting process by leveraging Artificial Intelligence. More specifically, we use semantic clustering techniques in order to organize wordlist items in different groups according to their semantic meaning. The created clusters are used in an ad-hoc implemented next-word intelligent strategy. This paper demonstrates that the usage of clustering techniques outperforms the commonly used brute force methods. Performance is evaluated by testing eight different web applications. Results show a performance increase that is up to 50% for each of the conducted experiments.
Multidimensional Information Assisted Deep Learning Realizing Flexible Recognition of Vortex Beam Modes

Due to countless orthogonal eigenstates, light beams with orbital angular momentum(OAM) have a large potential information capacity. Recently, deep learning has been extensively applied in recognition of OAM mode. However, previous deep learning methods require a constant distance between laser and receiver. The accuracy will drop quickly if the distance of testing set deviates from the training set. Previous deep learning methods also have difficulty distinguishing OAM modes with positive and negative topological charges. In order to further exploit the huge potential of the countless dimension of state space, we proposed multidimensional information assisted deep learning flexible recognition (MIADLFR) method to make use of both intensity and angular spectrum information for the first time to achieve recognition of OAM modes unlimited by the sign of TC and distance with high accuracy. Also, MIADLFR can reduce the computational complexity significantly and requires much smaller training set.
Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions

This work explores two approaches to event-driven predictive maintenance in Industry 4.0 that cast the problem at hand as a classification or a regression one, respectively, using as a starting point two state-of-the-art solutions. For each of the two approaches, we examine different data preprocessing techniques, different prediction algorithms and the impact of ensemble and sampling methods. Through systematic experiments regarding the aspectsmentioned above,we aimto understand the strengths of the alternatives, and more importantly, shed light on how to navigate through the vast number of such alternatives in an informed manner. Our work constitutes a key step towards understanding the true potential of this type of data-driven predictive maintenance as of to date, and assist practitioners in focusing on the aspects that have the greatest impact.