Miracle-dz/UniFAR-FTUs huggingface dataset:
Deterministic Initialization of $k$-means Clustering by Data Distribution Guide

Clustering by the k-means is the most widely used method because of its ease of use. But the disadvantage of the k-means algorithm is that it relies on a random initialization. Therefore, the results obtained from each clustering are not stable depending on the starting point, affecting the results obtained in other applications. This paper, therefore, presents a method for determining the initialization of the k-means algorithm using the Data Distribution Guide (DDG). And use it as an aid in determining the starting point without random. Make the results of clustering always equal. And from the experimental results, We found that the accuracy obtained from clustering using the initialization from this method was good. Compared to the commonly used initialization designation.
Improved List-Decodability and List-Recoverability of Reed-Solomon Codes via Tree Packings: [Extended Abstract]

This paper shows that there exist Reed-Solomon (RS) codes, over large finite fields, that are combinatorially list-decodable well beyond the Johnson radius, in fact almost achieving list-decoding capacity. In particular, we show that for any ε E (0,1] there exist RS codes with rate $\Omega(\frac{\varepsilon}{1\not\varepsilon(1/_{\in})+1})$ that are list-decodable from radius of 1-ε. We generalize this result to list-recovery, showing that there exist $(1-\varepsilon,\ell, O(\ell/\varepsilon))$ -list-recoverable RS codes with rate $\Omega\left(\frac{\varepsilon}{\sqrt{\ell}(\log(1/\varepsilon)+1)}\right)$. Along the way we use our techniques to give a new proof of a result of Blackburn on optimal linear perfect hash matrices, and strengthen it to obtain a construction of strongly perfect hash matrices. To derive the results in this paper we show a surprising connection of the above problems to graph theory, and in particular to the tree packing theorem of Nash-Williams and Tutte. We also state a new conjecture that generalizes the tree-packing theorem to hypergraphs, and show that if this conjecture holds, then there would exist RS codes that are optimally (non-asymptotically) list-decodable.11A full version of this paper is available online at https://arxiv.org/abs/2011.04453.
Slow Wave Structure’ Internal Temperature Prediction Based on Experimental Data

At present, there are many researches on the thermal characteristics of traveling wave tubes (TWT), but few on the measurement of its internal temperature are involved because of the difficulty to monitor. The internal temperature, however, is closely related with the output power, reliability and stability of the tube. In this study, a simplified slow wave structure model was built and an RBF neural network was used to predict the internal temperature. The network was trained using the simulation temperature data from the corresponding ANSYS model. To verify the feasibility of the network, the external temperature of the simplified TWT model measured in the experiment were input into the model, and the error between the predicted and actual internal temperature was very small.
Achieving High Gain Performance for 5G Millimeter-Wave Transmission with Large Communication Transmission Loss

Millimeter wave has become a research hotspot due to its rich spectrum resources, and the deployment of 5G millimeter- wave has been put on the agenda recently. Substrate integrated waveguide (SIW) is often used for the high frequency transmission because of its advantages of low loss, small size, ease of integration. This work addresses the problems of millimeter-wave transmission with large communication transmission loss, susceptibility to water and oxygen absorption, and reduced signal coverage after increasing gain. Broadband high-gain SIW millimeter-wave antenna that meets the requirements of “Global Communications” and multi-beam antenna that realizes spatial scanning have been investigated in this study. In order to deal with the defect of high transmission loss in millimeter-wave communication, a SIW slot coupling microstrip patch antenna is designed in this paper. Besides, to solve the problem that the single antenna cannot meet the higher gain requirements in terminal applications, a four-way power divider network has been designed for the array. Through simulation, the array realized the peak gain of 13.21 dBi. Finally, in order to solve the problem that the signal coverage decreases with the increase of the gain, the double-layer 4×4 Butler matrix beamforming network designed from the single-aperture and the double-aperture cross coupler were designed successively.
Communication-Efficient and Fault-Tolerant Social Learning

We study the problem of decentralized non-Bayesian (social) learning with compressed communications in the presence of adversarial agents, i.e., Byzantine. The non-adversarial agents seek to collaboratively agree on a hypothesis that best describes an unknown state of the world. We propose a robust update rule where, under appropriate assumptions, non-faulty agents can reach a consensus on the true state of the world by sharing arbitrarily compressed messages over the network. We show almost sure asymptotic convergence of the beliefs of non-faulty agents around the optimal hypothesis and provide numerical evidence for the communication efficiency and robustness of the proposed algorithm.
Long-term Human Video Activity Quantification of Student Participation

Research on video activity recognition has been primarily focused on differentiating among many diverse activities defined using short video clips. In this paper, we introduce the problem of reliable video activity recognition over long videos to quantify student participation in collaborative learning environments (45 minutes to 2 hours). Video activity recognition in collaborative learning environments contains several unique challenges. We introduce participation maps that identify how and when each student performs each activity to quantify student participation. We present a family of low-parameter 3D ConvNet architectures to detect these activities. We then apply spatial clustering to identify each participant and generate student participation maps using the resulting detections.We demonstrate the effectiveness by training over about 1,000 3-second samples of typing and writing and test our results over ten video sessions of about 10 hours. In terms of activity detection, our methods achieve 80% accuracy for writing and typing that match the recognition performance of TSN, SlowFast, Slowonly, and I3D trained over the same dataset while using 1200x to 1500x fewer parameters. Beyond traditional video activity recognition methods, our video activity participation maps identify how each student participates within each group.
Deep Anomaly Detection for Network Traffic

Canonical anomaly detection has been achieved through various means ranging from statistical tests and clustering methods to categorical decision-making and rule-based systems. Each method has its own pros and cons; however, many depend on assumptions. These assumptions can be model driven, such as assuming white Gaussian inputs, or method driven such as linear regression. In any case, assumptions are being made either about the structure of the data or its relationship with other random variables.This work presents a deep learning methodology for anomaly detection, a sampling technique for large data sets, and feature importance analysis. The anomaly detection technique uses an ensemble of learners to predict relationships between benign features and characterizes deviations from these patterns as "surprisal" scores. This method identifies malicious network traffic without previous attack behavior knowledge and is applied to data from the Canadian Institute for Cybersecurity.
Preconditioning the Regression of Power Amplifier Behavioral Models and Digital Predistorters

This work presents insights into the regression of power amplifier behavioral models and digital predistorters. First, the least squares regression equation is split into its basic terms and these are analyzed from a correlation approach. Techniques such as normalization, whitening, coefficient selection, coefficient exclusion and Ridge regularization and their effect on the condition number are discussed. Experimental results highlight how these techniques affect the performance of the predistortion of a class J PA working in its saturation regime in a dual band scenario, concluding that the presented methods are beneficial in the regression.
A tensor-based approach for training flexible neural networks

Despite excellent prediction performance, state-of-the-art neural network (NN) architectures are very large, up to several millions of weights. In particular, running them on systems with limited computational capacity (embedded systems) becomes a difficult task. In this work, we focus on the compression of NNs, considering a tensor-based approach. In particular, we are interested in the estimation of flexible activation functions using tensor-based solutions. The proposed method is based on a coupled matrix-tensor factorization (CMTF), fusing the zeroth and first order information of the NN. The proposed learning algorithm is based on a constrained alternating least squares (ALS) approach. The activation functions (AFs) are expressed as a weighted sum of pre-defined basis elements. Our method allows to strongly compress large NN layers, with a very slight degradation of the classification accuracy. This also provides insights into the functioning of NNs, which is a problem of utmost interest in NN domain.
Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor

Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System–denied environments. This article presents a computationally efficient visual–inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.
Evaluation of Detection Accuracy and Efficiency of Considered Beam Alignment Strategies for mmWave Massive MIMO Systems

The recent deployment of millimeter wave (mmWave) massive MIMO systems starts a new era in mobile communications by offering connectivity advantages such as energy efficiency, higher system capacity, reduced latency and higher data rates. Large antenna arrays with strongly anisotropic radiation patterns are mandatory at these frequencies to compensate for higher path loss resulting from smaller antenna elements. Hence, the initial access step requires solving the beam alignment problem prior to data transmission, i.e. finding a pair of strongly coupled narrow beams from predefined beam codebooks at transmitter and/or receiver side. The beam selection problem can be understood as a composite multi-hypothesis testing problem. In this work we compare three main types of such codebooks in terms of alignment accuracy and acquisition time while using fixed vs. variable length detection frameworks. Our numerical results indicate that a variable length test using either the frequency dependent codebook or a Butler codebook are the most promising methods achieving better efficiency, adaptivity and robustness under varying SNR in practical scenarios. A hierarchical scheme with fixed length measurements shows lesser efficiency. It would only be advantageous if no power constraint would apply at lower levels.
Android GUI Test Generation with SARSA

Android applications are often challenging to test because of large event spaces with an exponential number of event sequences. Several studies employ reinforcement learning to generate test suites in an effort to optimize code coverage and fault-finding effectiveness under limited testing budgets. In this paper, we generate test cases using the SARSA rein-forcement learning algorithm for seven Android applications, each with a two-hour testing window. The SARSA generated test suites achieve 9.87% to 24.79% better line coverage, 6.9% to 20.09% better branch coverage, 7.88% to 28.48% better method coverage and 3.74% to 35.02% better class coverage than the test suites generated at random by the Monkey tool.
Implementation of spatially consistent channel models for real-time full stack C-ITS V2X simulations

Vehicle-to-everything (V2X) wireless communication is expected to play a key role in enabling complex Advanced Driving Assistance Applications, including the support of emerging autonomous vehicles. The development of V2X applications, especially safety-related, requires rigorous verification. Field trials, though, have to be supplemented with less expensive and more repeatable methods. Thorough V2X modeling could be an efficient approach to boost V2X development. However, to be helpful, V2X modeling has to be realistic and properly capture the signal propagation in a complex V2X environment. To this end, Geometry Based Stochastic Models (GSCM) have been derived to account for the influence of the environment as well as small yet critical details, where we typically lack information about their physical and geometrical properties. In this paper, we present an implementation of a GSCM in the Unity3D game engine. Due to extensive signal path search in a 3D environment with ray-tracing, the GSCM computation has to be further optimized to enable real-time V2X simulations. Thus, we also propose the approach for GSCM computation optimization and explain the practicalities of its implementation. The results show that optimization can accelerate the simulation framework up to a few thousand times without losing the precision in modeling channel characteristics.
Multi-agent Policy Optimization for Pilot Selection in Delay-constrained Grant-free Multiple Access

Grant-free multiple access (GFMA) mitigates the uplink handshake overhead to support low-latency communication by transmitting payload data together with the pilot (preamble). However, the channel capacity with random access is limited by the number of available orthogonal pilots and the incoordination among devices. We consider a delay-constrained GFMA system, where each device with randomly generated data traffic needs to deliver its data packets before some pre-determined deadline. The pilot selection problem is formulated to minimize the average packet drop rate of the worst user. A priority-sorting based centralized policy is derived by introducing a fairness promoting function. For decentralization, we propose a multi-agent policy optimization algorithm with improved sample efficiency by exploring the model structure. Simulation results show that our proposed scheme facilitates near-optimal coordination between devices by using only partial state information.
Contextual Shortest Path with Unknown Context Distributions

This paper proposes a class of stochastic decision problems, which we call Contextual Shortest Path (CSP) problems. The problem is motivated by dynamic path planning and obstacle avoidance for UAV and drone applications. More specifically, in path planning applications, there is a need to compute the path between two pre-determined locations in space while avoiding various spontaneous obstacles in the environment. In general, finding the optimal path in a stochastic environment with time-varying obstacles generalizes the problem of finding the shortest path over an undirected graph with stochastic edges, where random edge realizations are augmented with richer contexts that can change over time and require online inspection.Under perfect knowledge of context distributions, we provide an extended Dijkstra’s algorithm to solve the associated dynamic program efficiently. When the context distributions are unknown and need to be learned online, we first adapt two algorithms as our baselines, one based on Thompson Sampling and the other based on ϵ-greedy exploration. We then propose a novel reinforcement learning algorithm, RL-CSP, which intelligently distributes exploration episodes over the time horizon and ensures the agent visits under-explored states. We bound the regret for RL-CSP, and augment the theoretical results with simulations over various network topologies. We further demonstrate the improved robustness of our RL-based algorithm in the stochastic shortest path setting.
Controlled Query Evaluation over Ontologies through Policies with Numerical Restrictions

We study Controlled Query Evaluation (CQE), a declarative approach to privacy-preserving query answering. In particular, we focus on the application of CQE to ontologies and analyze the possibility of using role cardinality restrictions in the formulas expressing a data protection policy. We start from an existing framework for CQE over DL-Lite ontologies, and extend the policy language of the existing framework defining a class of formulas (called numerical restriction axioms) with role number restrictions. We show that the computational properties of the existing framework are not affected by the extension of the policy language. In particular, conjunctive query answering over $DL - Lite_{horn}^{\mathcal{H}}$ ontologies under the CQE semantics (IGA) is still FO-rewritable and is in AC0 with respect to data complexity.
Drastic differential CRISPR-Cas9 induced mutagenesis influenced by DNA methylation and chromatin features

CRISPR-Cas9 mediated genome editing has been widely adopted for basic and applied biological research in eukaryotic systems. While many studies considered DNA sequences of CRISPR target sites as the primary determinant for CRISPR mutagenesis efficiency and mutation profiles, increasing evidence revealed the significant role of chromatin context. Nonetheless, most of the prior studies were limited by the lack of sufficient epigenetic resources and/or by only transiently expressing CRISPR-Cas9 in a short time window. In this study, we leveraged the wealth of high-resolution epigenomic resources in Arabidopsis thaliana to address the impact of chromatin features on CRISPR-Cas9 mutagenesis using stable transgenic plants. Our results indicated that DNA methylation and chromatin features could lead to significant variations in mutagenesis efficiency by up to 250 folds. Low mutagenesis efficiencies were mostly associated with repressive heterochromatic features. This repressive effect appeared to persist through cell divisions but could be alleviated through substantial reduction of DNA methylation at CRISPR target sites. Moreover, specific chromatin features, such as H3K4me1, H3.3, and H3.1, appear to be associated with significant variations in CRISPR-Cas9 mutation profiles reflected by the 1 bp insertion rates. Our findings provided strong evidence that specific chromatin features could have significant and lasting impacts on both CRISPR-Cas9 mutagenesis efficiency and DNA double strand break repair outcomes.
The nuclear receptor NR4A1 restrains neutrophil granulocyte mediated brain damage in cerebral ischemia

Immigration and activation of immune cells play a significant role in damage progression after ischemic stroke. It has been shown that the nuclear receptor NR4A1 exerts a crucial role within the inflammatory response of various immune diseases via regulating immune cell activation. In this study, we investigated the role of NR4A1 on the activation and recruitment of brain resident and peripheral immune cells after cerebral ischemia. Here, we show that NR4A1 mediates an anti-inflammatory and damage limiting effect after ischemic stroke through immigrating neutrophil granulocytes. Importantly, NR4A1-activation with its ligand Cytosporone-B improves functional outcome and diminishes brain damage. Therefore, modulation of NR4A1 is a promising therapeutic target in the treatment of stroke.
Blood–brain barrier permeability to enrofloxacin in Pengze crucian carp (Carassius auratus var. Pengze)

To explore the permeability of blood-brain barrier (BBB) to enrofloxacin (ENR) and the role of brain injury markers in the evaluation of brain damage, crucian carp (Carassius auratus var. Pengze) was employed in this work as the research object treated by a single oral administration of the antibiotic. Results showed that ENR residues could be detected in brain of crucian carp exposed to both half lethal dose (LD50) and safe dose (SD) of ENR throughout the trial period, indicating that ENR had an ability of permeating the BBB of crucian carp. Evans blue (EB) dispersively distributed in brain tissue of fish exposed to SD ENR, but not in the vehicle-treated fish brain, which suggested structural failure of BBB by ENR. Expression levels of BBB-related molecules, occludin and P-glycoprotein (P-gp), were significantly down-regulated by ENR. Brain injury markers, NSE, S100B and GFAP were improved that were demonstrated by elevated contents of proteins in brain and serum. According to the data of correlation analysis, it was observed that ENR residue was closely related to S100B and GFAP, indicating that S100B and GFAP could be used as evaluation indicators for brain injury of crucian carp. All findings mentioned above suggest that ENR may cause structural change of BBB, resulting in biological brain damage in crucian carp.
ATM Modulates Nuclear Mechanics by Regulating Lamin A Levels

Ataxia-telangiectasia mutated (ATM) is one of the three main apical kinases at the crux of DNA damage response and repair in mammalian cells. ATM activates a cascade of downstream effector proteins to regulate DNA repair and cell cycle checkpoints in response to DNA double-strand breaks. While ATM is predominantly known for its role in DNA damage response and repair, new roles of ATM have recently begun to emerge, such as in regulating oxidative stress or metabolic pathways. Here, we report the surprising discovery that ATM inhibition and deletion lead to reduced expression of the nuclear envelope protein lamin A. Lamins are nuclear intermediate filaments that modulate nuclear shape, structure, and stiffness. Accordingly, inhibition or deletion of ATM resulted in increased nuclear deformability and enhanced cell migration through confined spaces, which requires substantial nuclear deformation. These findings point to a novel connection between ATM and lamin A and may have broad implications for cells with ATM mutations—as found in patients suffering from Ataxia Telangiectasia and many human cancers—which could lead to enhanced cell migration and increased metastatic potential.