Miracle-dz/UniFAR-FTUs huggingface dataset:
The Total Energy Norm in a Quasigeostrophic Model

Abstract Total energy E as the sum of kinetic and available potential energies is considered here for quasigeostrophic (QG) dynamics. The discrete expression for E is derived for the QG model formulation of Marshall and Molteni. While E is conserved by the nonlinear unforced model equations, an analogous expression in terms of perturbed fields is, in general, not conserved for tangent-linearized versions of the model, thereby allowing for growth (or decay) in this total energy norm. Examples for structures linearly growing optimally (i.e., the so-called singular vectors) in terms of either the total energy or just the kinetic energy norm are briefly illustrated and contrasted. It is argued that E might preferably be used (rather than kinetic energy) in predictability and data assimilation studies that are based on the QG model considered here.
Unscented Kalman Filter-based Beam Tracking in NR MIMO System using Hybrid Beamforming

Unmanned aerial vehicles (UAVs) and millimeter wave frequencies play a key role in supporting 5G wireless communication systems. They expand the area of wireless communication by increasing the data capacity in communication systems and supporting high data rates. However, short wavelengths, due to their high millimeter wave frequencies cause problems arising from signal attenuation and path loss. To address these limitations, research centered on high directional beamforming technology continues to gather interest. Furthermore, due to the mobility of UAVs, it is essential to track the beam angle accurately to obtain full beamforming gain. In this study, we propose a beam tracking method based on the unscented Kalman filter using hybrid beamforming. By expanding analog beamforming to hybrid beamforming, our proposed algorithm can be used even in multi-user and multi-stream environments, increasing the data capacity, and, thus increasing utilization in new radio multiple-input multiple-output orthogonal frequency diversity multiplexing systems.
Transformer Based on Deconstruction and Dot Product for Time Series Forecasting of Air Quality

Air quality prediction has been considered a key factor in air pollution warning and control management. Artificial intelligence-based methods have been widely used for air quality prediction. Forecasting problems generally deal with time series data and recent studies have shown that transformers per-form well in managing such data. However, for air quality prediction, the performance of the self-attentive mechanism in the standard transformer is limited owing to the nonlinear and dynamic characteristics of the multivariate air quality time series data. In this study, we propose a self-attentive mechanism based on deconstruction and dot product (DDPformer) to replace the self-attentive mechanism in the standard transformer. To highlight the features of the partial head in the multi-headed attention mechanism, we introduced and extended the channel attention mechanism and used the power normalization (PN) method instead of the layer normalization (LN) method. In addition to conducting a large number of experiments on two multivariate serial air quality datasets, we also conducted multiple experiments on four datasets in other fields. The results showed that DDPformer performed better than most baseline models.
PCR Radar-Based Counting System for Packaged Objects

Counting the number of packaged objects is a process that the fulfillment center must go through before shipping it to the customer. With the development of computer vision and deep learning, many studies are being conducted to recognize objects and determine quantity using cameras, but it is impossible to identify objects in a sealed package due to the occlusion problem of not seeing objects that are occluded. Among the nondestructive inspection methods using wavelengths that use penetrating properties, radar that is harmless to the human body does not require contact with objects, and uses low-power radio frequency is the most suitable for counting objects. In this paper, we propose a system that achieves 99.33% counting accuracy of packed objects by removing background noise of radio frequency measured by 60GHz pulsed coherent radar.
A Data Augmentation Approach to 28GHz Path Loss Modeling Using CNNs

Millimeter waves are easily influenced by the surrounding environment, making it difficult to predict path loss values for 28GHz communication systems. Recently, deep learning approaches have become popular mainly thanks to their superior performance in terms of prediction accuracy, generalizability as well as local adaptability. These deep learning approaches require a sufficient number of training data which often lacks variability with respect to the parameter values of base station configuration if not unavailable at all. This paper proposes to use the data augmentation approach to address these two issues by using a simulator to generate predicted data for the arbitrary values of base station parameters. It is shown that a Convolution Neural Network (CNN) trained with both measurement and augmented data outperforms a vanilla CNN model trained with measurement data only and that it can make accurate predictions for arbitrary base station configurations.
Universal Thompson Sampling

We introduce a non-Bayesian variant of Thompson sampling whose belief is based on the Central Limit Theorem. As such, it is a parameter-less and prior-free algorithm for the stochastic multi-armed bandit problem. We analyze its asymptotical behavior by proving that it is greedy in the limit of infinite exploration. Further, we empirically evaluate its performance in the fixed-budget best-arm identification problem. In a suite of empirical tests, including both bounded and unbounded reward distributions, our approach exhibits in many cases the lowest misidentification rate among the state-of-the-art algorithms.
Design and Performance Evaluation of OAM-Antennas: A Comparative Review

For many communication systems, including wireless networks, mobile, satellite, radar, remote sensing, and many more applications, antenna arrays are very necessary. Utilizing effective beamforming techniques, antenna arrays have recently played a major part in driving data rates and system capacity to extremely high levels. The beamforming capabilities and system performance are both greatly influenced by the array configuration. Numerous array structures, including linear, two-dimensional, circular, conical, cylindrical, spherical, and even non-uniform array shapes, can be used in different applications. Modern communication systems pay a lot of attention to reconfigurable antennas because of the many applications they may serve and the added functionality they provide. A single structure that has the ability to change between one or a number of features, such as frequency, pattern, and polarization, is referred to as a reconfigurable antenna. Utilizing a specific circular phased microstrip antenna array, the orbital angular moment (OAM) vortex electromagnetic wave is created, allowing for more effective and dependable data transmission. When propagating, the vortex electromagnetic wave has a spiral phase wavefront structure, and its many OAM modes are orthogonal to one another. The helical antenna offers the benefits of light weight, small size, and excellent circular polarization when compared to other OAM radio producing techniques. This paper reviews various state-of-the-art antennas for 5G applications. Specifically, the single microstrip, array, traveling wave and metasurface antennas have been discussed and their important aspects were elaborated. Finally, the applications and future prospects were described.
PerMTL: A Multi-Task Learning Framework for Skilled Human Performance Assessment

Intelligent and complex human motion analysis can help design the next generation IoT and AR/VR systems for automated human performance assessment. Such an automated system can help advocate the interpretability and translatability of complex human motions, intelligent motion feedback, and fine-grained motion skill assessment to design next-generation interactive human-machine teaming systems. Motivated by this, we design a wearable sensing framework for assessing the players’ performance and consider a live badminton game as our use case. Generally, the players on the field try to improve their performance by focusing on fast and synchronous coordination of their limbs’ reflex actions to have the ideal body postures to perform the desired shot. Learning the minute dissimilarities and distinctive traits from each limb of the players simultaneously can help assess the players’ performance and specific skillsets during a game. This paper proposes a multi-task learning framework, PerMTL to learn the shared features from each player’s limb. The PerMTL comprises a task-specific regressor output layer that helps to determine the dissimilarities and distinctive traits between the player’s limbs for collective inference in a body sensor network (BSN) environment. We evaluate the PerMTL framework using publicly available Badminton Activity Recognition (BAR) and Daily and Sports Activities (DSA) datasets. Empirical results indicate that PerMTL achieves R2 Score of ≈ 82% in predicting the players’ performance.
ESD: An ECC-assisted and Selective Deduplication for Encrypted Non-Volatile Main Memory

Reducing write data to encrypted Non-Volatile Main Memory (NVMM) can directly improve NVMM’s endurance, performance, and energy efficiency. However, existing works that straightforwardly apply inline deduplication on encrypted NVMM can significantly lead to system performance degradation due to high computing, memory footprint, and index-lookup overhead to generate, store, and query the cryptographic hash (fingerprint). This paper proposes ESD, an ECC-assisted and Selective Deduplication for encrypted NVMM by exploiting both the device characteristics (ECC mechanism) and the workload characteristics (content locality). First, ESD utilizes the ECC information associated with each cache line evicted from the Last-Level Cache (LLC) as the fingerprint to identify data similarity and avoids the costly hash calculating overhead on the non-duplicate cache lines. Second, ESD leverages selective deduplication to exploit the content locality within cache lines by only storing the fingerprints with high reference counts in the memory cache to reduce the memory space overhead and avoid fingerprints NVMM_lookup operations. The experimental results show that ESD can significantly speed up the writes by up to 3.4x, 4.3x, and 2.6x, speed up the reads by up to 5.3x, 5.0x, and 2.0x, and reduce the energy consumption by up to 96.3%, 96.2%, and 56.6% than Baseline, Dedup SHA1, and DeWrite, respectively. Meanwhile, ESD also can significantly outperform other schemes in tail latency.
Hawkes Process Multi-armed Bandits for Search and Rescue

We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be undersampled or spatially biased. In particular, we introduce an upper confidence bound algorithm using Bayesian spatial Hawkes process estimation for balancing the trade-off between exploiting geographic regions where data has been collected and exploring geographic regions where data is unobserved. We first validate our model using simulated data. We then apply it to the problem of disaster search and rescue using calls for service data from hurricane Harvey in 2017 and the problem of detection and clearance of improvised explosive devices (IEDs) using IED attack records in Iraq. Our model outperforms state-of-the-art baseline spatial MAB algorithms in terms of cumulative reward and several other ranking evaluation metrics.
31.8 A 0.4-to-0.95GHz Distributed N-Path Noise-Cancelling Ultra-Low-Power RX with Integrated Passives Achieving −85dBm/100kb/s Sensitivity, −41dB SIR and 174dB RX FoM in 22nm CMOS

Commercially viable deployment of ubiquitous ultra-low-power (ULP) radios requires ULP RX that can operate in crowded ISM environments across geographies while minimizing off-chip components for low cost and for satisfying wearable/IoT device footprints (Fig. 31.8.1). ULP RX based on envelope detectors (ED) have been proposed for $\mu \mathrm{W}$-power; however high-Q off-chip passives are required for passive gain and interferer tolerance necessitating a change in components with change in operating frequency [1], [2]. While mixer-first RX with uncertain IF architectures [3] provide higher linearity and interferer tolerance, implementations have required off-chip components, such as inductors for matching, filtering [3] and gain [4] or low-loss wideband hybrid couplers for noise cancellation [5], that are challenging to integrate. Additionally, ULP ED-first and LNA/mixer-first schemes rely on ED for demodulation/detection and hence, are susceptible to modulated and pulsed interferers. Two-tone signals modulated using orthogonal codes [5], [6] or frequency chirps [4] have been proposed for interferer-tolerant ED detection, demonstrating robustness to modulated blockers (Fig. 31.8.1). In this paper, we present a novel distributed N-path mixer-first noise-cancelling ULP RX for two-tone spread-spectrum BPSK signals that (i) does not require off-chip passives for matching or noise cancellation, (ii) operates over a wide LO-defined frequency range, and (iii) provides modulated-blocker tolerance. While the RX architecture can be scaled across power consumption and data-rates, 100kb/s and $100\mu \mathrm{W}$-scale power consumption is targeted in this work for wearable/IoT applications. The proposed distributed mixer-first scheme is implemented in 22nm FinFET CMOS leading to excellent ULP RX performance of −85dBm sensitivity for 100kb/s data-rates with $118\mu \mathrm{W}$ power consumption $(2\times$ to $4\times$ figure-of-merit (FoM) improvement compared to state-of-the-art (SoA) ULP RX without signal-path off-chip passives [7], [8]) and operation across a ULP SoA frequency range of 400MHz to 950MHz.
Maximizing Bigdata Retrieval: Block as a Value for NoSQL over SQL

This paper presents NoSQL Over SQL Block as a Value Database (NOSD), a system that speeds up data retrieval time and availability in very large relational databases. NOSD proposes a Block as a Value model (BaaV). Unlike a relational database model where a relation is $R(K,\ A_{1},\ A_{2},\ \ldots A_{n})$, with a key attribute $K$ and a set of attributes of the relation: $A_{1}, A_{2}, \ldots A_{n}$, BaaV represents a relation $R(K, r_{1}, r_{2}, \ldots r_{n})$ with a key attribute $K$ and a set of $n$ relations called blocks. Each $r$ contains a set of its own attributes denoted as $r(k,\ a_{1},\ a_{2},\ldots a_{n})$ with a key attribute $k$ and a set of $n$ attributes. The relations $r_{1}, r_{2}, \ldots r_{n}$ in $R$ are related through foreign key relationships to a super relation $R$ with primary key $K$. The BaaV model is then denoted in a keyed block format $R\{K,\ B\}$, where $K$ is a key to a block of values $B$ of partial relations implemented on NoSQL databases and replicating existing large relational database systems. As opposed to conventional systems such as Zidian, Google's Spanner, SparkSQL and Simple Buttom-Up (SBU) which implement SQL over NoSQL and replicate data into different nodes, NOSD implements NoSQL over SQL and uses Lucene functionality on NoSQL to enhance data retrieval costs. Experimenting with our proposed model, we demonstrated the performance of NOSD under the following conditions to prove its novelty (a) scan free queries, and (b) bounded queries on NoSQL databases. We showed that NOSD (a) performs excellently than ordinary relational databases (b) guarantees no scans for no scan queries (c) allows parallelization in query execution, and (d) can be deployed into existing SQL databases with guaranteed horizontal scalability, data retention and accurate autonomous data replication. Using existing benchmark systems, we demonstrated that NOSD outperforms existing SQL databases, SQL over NoSQL systems and is novel in ensuring that existing large SQL database systems utilize the functionalities of NoSQL databases without data loss. $A_{1}, A_{2}, \ldots A_{n}$
Localizationof dependence for solutionsof hyperbolicdierential equations

We survey several results that localize the dependence of solutions to hyperbolic equations. These observations address questions that are central to numerical simulation of solutions on unbounded spatial domains. One result shows that in principle it is possible to numerically compute (the restriction of) a solution to a wave equation on an unbounded domain using only a bounded computational domain. Other results provide implementations of this fact in particular situations. In addition, we introduce a new diagrammatic way to generate explicit solutions to multiple-time initial-value problems for the wave equation in one space dimension.
Portuguese Twitter Dataset on COVID-19

Over the last two years, the COVID-19 pandemic has affected hundreds of millions of people around the world. As in many crises, people turn to social media platforms, like Twitter, to communicate and share information. Twitter datasets have been used over the years in many research studies to extract valuable information. Therefore, several large COVID-19 Twitter datasets have been released over the last two years. However, none of these datasets contains only Portuguese Tweets, despite the Portuguese Language being reported as one of the top five languages used on Twitter. In this paper, we present the first large-scale Portuguese COVID-19 Twitter dataset. The dataset contains over 19 million Tweets spanning 2020 and 2021, allowing the entire pandemic to be analyzed. We also conducted a sentiment analysis on the dataset and correlated the various spikes in Tweet count and sentiment scores to various news articles and government announcements in Portugal and Brazil. The dataset is available at: https://github.com/bioinformatics-ua/Portuguese-Covid19-Dataset
Uncovering Coordinated Communities on Twitter During the 2020 U.S. Election

A large volume of content related to claims of election fraud, often associated with hate speech and extremism, was reported on Twitter during the 2020 US election, with evidence that coordinated efforts took place to promote such content on the platform. In response, Twitter announced the suspension of thousands of user accounts allegedly involved in such actions. Motivated by these events, we here propose a novel network-based approach to uncover evidence of coordination in a set of user interactions. Our approach is designed to address the challenges incurred by the often sheer volume of noisy edges in the network (i.e., edges that are unrelated to coordination) and the effects of data sampling. To that end, it exploits the joint use of two network backbone extraction techniques, namely Disparity Filter and Neighborhood Overlap, to reveal strongly tied groups of users (here referred to as communities) exhibiting repeatedly common behavior, consistent with coordination. We employ our strategy to a large dataset of tweets related to the aforementioned fraud claims, in which users were labeled as suspended, deleted or active, according to their accounts status after the election. Our findings reveal well-structured communities, with strong evidence of coordination to promote (i.e., retweet) the aforementioned fraud claims. Moreover, many of those communities are formed not only by suspended and deleted users, but also by users who, despite exhibiting very similar sharing patterns, remained active in the platform. This observation suggests that a significant number of users who were potentially involved in the coordination efforts went unnoticed by the platform, and possibly remained actively spreading this content on the system.
California Wildfire Prediction using Machine Learning

Wildfires in California are increasingly frequent and caused a vast range of damages to the land and society. Existing work in the field of machine learning aided fire susceptibility mapping contains promising results, however they focused on small fire prone areas or yielded less than impressive classification metrics, with even fewer groups focusing on California. In this paper, we applied five machine learning models for fire prediction using a series of remote sensing data and fire incident records of California. The fire incident records were used for labeling fire conditions, and remote sensing features (land surface temperature, normalized difference vegetation index, and thermal anomalies) were selected for model training. Two datasets were created for analysis of wildfires in California based on different sized regions: the whole state treated as one region, and California being broken into counties. For the state level dataset, fire prediction accuracies were 89% for artificial neural network (ANN), and 87% for Gaussian Naive Bayes (GaussianNB), k-nearest neighbors (KNN), and logistic regression (LR), and support vector machine (SVM). For the county level dataset, the prediction accuracies were 97% for KNN, 96% for ANN, SVM, and LR, and 94% for GuassianNB.
SecPB: Architectures for Secure Non-Volatile Memory with Battery-Backed Persist Buffers

The durability of data stored in persistent memory (PM) exposes data to potentially data leakage attacks. Recent research has identified the requirements for crash recoverable secure PM, but do not consider recent trends of the persistency domain extending on-chip to include cache hierarchies. In this paper, we explore this design space and identify performance and energy optimization opportunities.We propose secure persistent buffers (SecPB), a battery-backed persistent structure that moves the point of secure data persistency from the memory controller closer to the core. We revisit the fundamentals of how data in PM is secured and show how various subsets of security metadata can be generated lazily while still guaranteeing crash recoverability and integrity verification. We analyze the metadata dependency chain required in securing PM and expose optimization opportunities that allow for SecPB to reduce performance overheads by up to 32.8×, with average performance overheads as low as 1.3% observed for reasonable battery capacities.
Metasurface-Loaded Tri-Mode Wideband Monopole Antenna Using Characteristic Mode Analysis

Metasurfaces (MTSs) are often used for bandwidth enhancement of directional antennas. In this paper, an MTS-loaded tri-mode wideband monopole antenna is proposed, with an overall size of $\boldsymbol{0.52 \times 0.52\lambda_{0}^{2}}$ (where $\lambda_{0}$ is the free-space wavelength at 2.22 GHz). Characteristic mode analysis (CMA) shows that the loading of MTS improves the magnitudes of modal weighting coefficients (|MWC|s) of the monopole, leading to tri-mode wideband radiation. The measured impedance bandwidth of the proposed antenna is 61.7% (2.22-4.2 GHz) for| S11| < −10 dB with omnidirectional radiation.
AI in Classroom: Group Score Prediction System

Knowledge tracing and learning path optimization are active research fields in education with AI technologies. The purpose of knowledge tracing is to model student's knowledge state of a concept and to predict the percentage of correctly answer a next question. Using the technology of modeling a student's knowledge state, learning path optimization technologies recommend personalized learning path for efficient learning. These two research fields are implemented on learning management systems for individual learning. In this research paper, method of using knowledge tracing and learning path optimization in group learning environment is suggested. Group score prediction model predicts number of students who will answer their next question correctly by utilizing one-dimensional convolution neural network and fully connected layers. The model is adopted in a group score prediction system where instructors utilize the model's output to create a question set corresponding to their strategy and students' responses are used to re-train and evaluate the model.