Miracle-dz/UniFAR-FTUs huggingface dataset:
A Geometric Deep Learning Framework for Accurate Indoor Localization

Recent advances in (deep) machine learning offer new opportunities to solve indoor fingerprint-based localization problems. However, the majority of localization solutions employing popular machine learning models, such as k-nearest neighbors ($k$-NN), support vector machine (SVM), multi-layer perceptron (MLP), and convolutional neural network (CNN), do not sufficiently realize inability of these models to fully represent the non-Euclidean nature of fingerprint data, which consequently degrades their performance. In this paper, we first explain how these commonly-used models fail to effectively encode the fingerprint data due to their assumption (or lack of it) regarding fingerprints and/or geometric and topology information hidden within the RSSI measurements. Based on this, we provide our motivation to use geometric deep learning for indoor fingerprint-based localization. We then present a systematic approach to transform fingerprints into graphs, accounting for the co-existence of multiple radio frequency signal technologies. Finally, we present our localization approach based on a GraphSAGE estimator. Through extensive performance evaluation, using two different case studies (datasets), we show to what extent our proposed localization approach improves upon the state-of-the-art localization solutions. We also conclude that the best configuration of our approach requires both the edge features in the graphs and the pooling aggregator in the GraphSAGE model.
An Encoded LSTM Network Model for WiFi-based Indoor Positioning

WiFi received signal strength (RSS)-based finger-printing has been widely adopted in many indoor positioning systems due to its implementation simplicity and low computational complexity. Recently, some studies have explored the potential temporal features among WiFi data to provide better positioning accuracy using Long Short-Term Memory (LSTM). However, the large volume of invalid RSS signals/values in the radio map degrades the training performance and the positioning accuracy. Some recent research has shown effort to reduce the impact of the problem mentioned above using deep learning. However, these either lack efficiency (repeated training needed or training resource wasted) or cannot provide precision position estimations. This paper proposes a novel Encoded LSTM network model to efficiently extract main features from the WiFi RSS data and provide high positioning accuracy. Experimental results based on an open-source crowdsourced dataset show that the encoded LSTM can reduce the dimension of the WiFi fingerprints from 992 to 64 and achieve a mean positioning error of 7.37m. Compared to the benchmark results based on the same dataset, the encoded LSTM shows the lowest mean error, which outperforms 13 popular positioning algorithms. The proposed encoded LSTM can also provide a 10% improvement in positioning accuracy in comparison to other conventional LSTM models.
Towards Resilience by Self-Adaptation of Industrial Control Systems

Resilience is a critical quality of future Industrial Control Systems (ICS). The ability to detect and react to unanticipated attacks, bugs, and failures is crucial. Self-adaptation can provide this ability, yet it is difficult to achieve in safety-critical real-time systems, since strict safety and timing requirements must be guaranteed. Recent results indicate that automated adaptation of ICS using the IEC 61499 is possible, however it has not been analyzed how much dynamic adaptation can contribute to overall system resilience. In this paper, we analyze how dynamic adaptation can be embedded into industrial control architectures, and quantify its advantage over a traditional restart. We propose a self-adaptive architecture using the MAPE-K model and merge it with the existing models for ICS. Using measurements on a real system, we estimate the expected adaptation time of selected adaptation scenarios and calculate the loss of productivity depending on the reaction time and adaptation complexity. The results show that using current dynamic adaptation mechanisms, minor to moderate adaptations can be completed within 10 ms, while larger adaptations can take up to a second from initialisation to cleanup. The resilience gain is larger the faster the reaction is initiated, which indicates that once dynamic adaptation is available, a faster detection and decision-making becomes more important. Dynamic adaptation can provide ICS the means to evolve and react rapidly, preparing them for an agile, flexible, and resilient future.
3D-FPIM: An Extreme Energy-Efficient DNN Acceleration System Using 3D NAND Flash-Based In-Situ PIM Unit

The crossbar structure of the nonvolatile memory enables highly parallel and energy-efficient analog matrix-vector-multiply (MVM) operations. To exploit its efficiency, existing works design a mixed-signal deep neural network (DNN) accelerator, which offloads low-precision MVM operations to the memory array. However, they fail to accurately and efficiently support the low-precision networks due to their naive ADC designs. In addition, they cannot be applied to the latest technology nodes due to their premature RRAM-based memory array.In this work, we present 3D-FPIM, an energy-efficient and robust mixed-signal DNN acceleration system. 3D-FPIM is a full-stack 3D NAND flash-based architecture to accurately deploy low-precision networks. We design the hardware stack by carefully architecting a specialized analog-to-digital conversion method and utilizing the three-dimensional structure to achieve high accuracy, energy efficiency, and robustness. To accurately and efficiently deploy the networks, we provide a DNN retraining framework and a customized compiler. For evaluation, we implement an industry-validated circuit-level simulator. The result shows that 3D-FPIM achieves an average of 2.09x higher performance per area and 13.18x higher energy efficiency compared to the baseline 2D RRAM-based accelerator.
Music Classification Model Development Based on Audio Recognition using Transformer Model

There are several music genres, such as blues, classical, disco, and more. Music genres can be predicted by supervised machine learning using extracted features. This research explores the Transformer deep learning model for audio classification using music genres as labels. The dataset used in this paper is the GTZAN dataset, which contains music in a waveform audio file (WAV) format. The features of this dataset are extracted using Mel-frequency Cepstral Coefficients (MFCC). This study finds that the Transformer model's performance gives higher accuracy than other neural network architectures. Furthermore, the Transformer model's performance is compared with related research results that utilize various traditional machine learning methods with the same dataset. The best result achieved was the accuracy of 75.1% for the Transformer model with the best fine-tune parameters.
A Flexible Yet Efficient DNN Pruning Approach for Crossbar-Based Processing-in-Memory Architectures

Pruning deep neural networks (DNNs) can reduce the model size and thus save hardware resources of a resistive-random-access-memory (ReRAM)-based DNN accelerator. For the tightly coupled crossbar structure, existing ReRAM-based pruning techniques prune the weights of a DNN in a structured manner, thereby attaining low pruning ratios. This article presents a novel pruning technique, SegPrune, for pruning the weights of a DNN flexibly on crossbar architectures in order to maximize the pruning ratio achieved while preserving crossbar efficiency. We observe that different filters of a weight matrix share a large number of matrix subcolumns (in the same rows), called segments, that can be pruned by using the same segment shape in the sense that the weights at the same column position of these segments are either simultaneously accuracy-sensitive (and should thus be reserved) or simultaneously accuracy-insensitive (and can thus be pruned). Due to the bit-line exchangeability in the crossbar, segments with the same pruning shape can be assembled together into the same crossbar to ensure crossbar execution efficiency. We propose a projection-based shape voting algorithm to select suitable segment shapes to drive the weight pruning process. Accordingly, we also introduce a low-overhead data path that can be easily integrated into any existing ReRAM-based DNN accelerator, achieving a high pruning ratio and a high execution efficiency. Our evaluation shows that SegPrune outperforms the state-of-the-art, Hybrid-P, and FORMAS, by up to <inline-formula> <tex-math notation="LaTeX">$14.6\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.6\times $ </tex-math></inline-formula> in pruning ratio, <inline-formula> <tex-math notation="LaTeX">$13.9\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.4\times $ </tex-math></inline-formula> in inference speedup, and <inline-formula> <tex-math notation="LaTeX">$12.5\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.1\times $ </tex-math></inline-formula> in energy reduction, respectively, while achieving an even higher accuracy at the cost of less than 0.27% extra hardware area overhead.
All-heavy tetraquarks

In this work we study the mass spectra of the fully-heavy tetraquark systems, i.e. $cc\bar{c}\bar{c}$, $bb\bar{b}\bar{b}$, $bb\bar{c}\bar{c}/cc\bar{b}\bar{b}$, $bc\bar{c}\bar{c}/cc\bar{b}\bar{c}$, $bc\bar{b}\bar{b}/bb\bar{b}\bar{c}$, and $bc\bar{b}\bar{c}$, within a potential model by including the linear confining potential, Coulomb potential, and spin-spin interactions. It shows that the linear confining potential has important contributions to the masses and is crucial for our understanding of the mass spectra of the fully-heavy tetraquark systems. For the fully-heavy tetraquarks $Q_1Q_2\bar{Q}_3\bar{Q}_4$ our explicit calculations suggest that no bound states can be formed below the thresholds of any meson pairs $(Q_1\bar{Q}_3)$-$(Q_2\bar{Q}_4)$ or $(Q_1\bar{Q}_4)$-$(Q_2\bar{Q}_3)$. Thus, we do not expect narrow fully-heavy tetraquark states to be existing in experiments.
Comparative Evaluation of Millimeter-Wave Beamsteering Algorithms Using Outdoor Phased Antenna Array Measurements

The use of high-gain directional communications is key to enable the capacity enhancement of millimeter-wave (mm-wave) for 5G-and-beyond networks. However, robust mm-wave coverage using directional beams entails significant beamsteering effort for maintaining precise beam alignment between the base station and the user. Numerous mm-wave beamsteering algorithms have been proposed in the literature, but verified largely using statistical channel models or limited measurements. Importantly this still leaves open the question of whether these studies can be directly translated to real outdoor mm-wave network deployments. In this paper, we present the results of the first extensive comparative evaluation of eight state-of-the-art mm-wave beamsteering algorithms based on outdoor mm-wave measurements using phased antenna arrays. We collect received signal strength data over fine-grained 3D angular orientations for 78 spatially-dense user positions in a European city, comprising an open-source dataset of over 421,000 individual measurements. We then perform an empirical evaluation of the beamsteering algorithms in the context of link establishment for static and link maintenance for mobile users. Overall, our results show that these state-of-the-art mm-wave beamsteering algorithms, when tested on real measurement data, perform far from optimal and worse than originally reported. A key takeaway from our evaluation is that the practical feasibility of beamsteering algorithms strongly depends on the trade-off between the incurred beam training delay and the antenna gains facilitated in the initial beam training stage which limit the established link budget. Our study of link maintenance for mobile users showed significant deviations from the maximum achievable performance for all algorithms, including those that leverage correlation of the mm-wave sparse link opportunities or historical link information for link recovery. This shows that, to enable seamless connectivity in future mm-wave networks, more sophisticated beamsteering algorithms must be designed that intelligently adapt to the site-specific mm-wave channel while taking into account the realistic antenna beams of commercially-viable phased antenna arrays.
90Y-Scintigraphy of small animals A study of imaging parameters

Yttrium-90 is a pure β-emitting radionuclide with a high energy (Emax = 2.28 MeV) and a short half-life (T½ = 64.1 h). It has shown to be a promising isotope to use in radio-immunotherapy. In the absence of direct photon emission scintillation camera imaging can be obtained by acquiring events from bremsstrahlung photons created at or near the decay site of the radionuclide. It is, however, not evident how the energy window has to be defined, because the photon energy spectrum from the interacting β-particles extends from zero to the maximum energy of the β-particle. The aim of this master’s work was to examine the imaging parameters of the gamma camera (energy window, collimator) to achieve information in order to determine the optimum conditions for bremsstrahlung imaging. Methods: A Siemens DIACAM scintillation camera and a water phantom simulating a rat were used for the experimental studies. Spheres, filled with 90Y, simulated tumours. Three collimators denoted LEAP (Low-Energy All Purpose), HE (High-Energy) and UHE (Ultra High Energy) were investigated for various settings of the energy window. The system sensitivity [cps/MBq] and the primary-to-total radiation ratio were compared for the three collimators and the different energy window settings. In addition, Monte Carlo simulations using the SIMIND code were made to demonstrate how the image quality degrades as function of physical parameters. The simulations were setup in such a way that the components in the images from geometrical penetrating and scatter events were separated. Results: The LEAP collimator showed the highest sensitivity as expected, but also the lowest image quality because of the septum penetration. The UHE collimator has, generally, lower sensitivity and lower ratio of primary-to-total radiation than the LEAP collimator but the septal penetration was less than the LEGP. The HE collimator with an energy window between 86-254 keV was selected as optimal acquisition setting with consideration of both the sensitivity and primary-to-total ratio. The lower limit of 86 keV for the energy windows was chosen to eliminate the characteristic K X-ray photons, with energies 74.97 keV (46.2%), 72.81 keV (27.7%), 84.94 keV (10.7%) and 84.45 keV (5.58%). Results from the simulations also showed that a large fraction of the recorded events when using the LEAP collimator originates from photons that have penetrated the septal walls. As the energy window decreased the ratio primary-to-total increased which means that fewer scattering and penetrating events are registered. The simulations also show that the penetration is a significant problem for the LEAP collimator, while the UHE collimator shows the smallest penetration fraction. LUJI-RADFYS-EX-4/2003
Architecting Decentralization and Customizability in DNN Accelerators for Hardware Defect Adaptation

The efficiency of machine intelligence techniques has improved noticeably in the embedded application domains thanks to the dedicated hardware accelerators for deep neural networks (DNNs). Despite the economic criticality of yield and reliability problems in advanced semiconductor nodes, these concerns have attracted limited attention in the context of embedded machine intelligence devices. The micro-architectural features of deep learning accelerators, when paired with the algorithmic characteristics of DNNs, unlock novel opportunities to tackle semiconductor reliability problems in embedded deep learning devices. While the fine-grained bypassing of the faulty processing elements reins the computational impact of hardware defects, a one-time training of DNNs with Hardware-Aware Dropout/Dropconnect techniques boosts model decentralization and facilitates accurate neural network inference in the degraded computational fabrics. Furthermore, on-device calibration methods can improve resilience even further without necessitating expensive defect compensation methods such as device-specific training. Our work confirms the potential for improving the yield, reliability, and operational lifetime of embedded machine intelligence devices through a highly practical co-design of DNNs and configurable hardware architectures.
Mapping the stellar age of the Milky Way bulge with the VVV

Context. The bulge represents the best compromise between old and massive Galactic components, and as such its study is a valuable opportunity to understand how the bulk of the Milky Way formed and evolved. In addition, being the only bulge in which we can individually resolve stars in all evolutionary sequences, the properties of its stellar content provide crucial insights into the formation of bulges.Aims. We are providing a detailed and comprehensive census of the Milky Way bulge stellar populations by producing deep and accurate photometric catalogs of the inner ∼300 deg2of the Galaxy.Methods. We performed DAOPHOT/ALLFRAME point spread function (PSF) fitting photometry of multi-epochsJandKsimages provided by the VISTA Variables in the Vía Láctea (VVV) survey to obtain deep photometric catalogs. Artificial star experiments have been conducted on all images to properly assess the completeness and the accuracy of the photometric measurements.Results. We present a photometric database containing nearly 600 million stars across the bulge area surveyed by the VVV. Through the comparison of derived color-magnitude diagrams of selected fields representative of different levels of extinction and crowding, we show the quality, completeness and depth of the new catalogs. With the exception of the fields located along the plane, this new photometry samples stars down to ∼1–2 mag below the old main sequence turnoff with unprecedented accuracy. To demonstrate the tremendous potential inherent to this new dataset, we give a few examples of possible applications, including (i) star count studies through the dataset completeness map; (ii) surface brightness map; and (iii) cross-correlation withGaiaDR2.Conclusions. The database presented here represents an invaluable collection for the whole community, and we encourage its exploitation. The photometric catalogs including completeness information are publicly available through the ESO Science Archive as part of the MW-BULGE-PSPHOT release.
Multi-User Reconfigurable Intelligent Surface-Aided Communications Under Discrete Phase Shifts

This paper focuses on studying orthogonal and non-orthogonal multiple access in intelligent reflecting surface (IRS)-aided systems. Unlike most prior works assuming continuous phase shifts, we employ the practical setup where only a finite number of discrete phase shifts are available. To maximize the sum rate, active beamforming and discrete reflection need to be jointly optimized. We therefore propose an alternative optimization method to get the optimal continuous phase shifts iteratively, and then quantize each phase shift to its nearest discrete value. The sum-rate maximization of different schemes is theoretically analyzed and numerically evaluated with different numbers of phase-control bits.
Data Analysis and Synthesis of COVID-19 Patients using Deep Generative Models: A Case Study of Jakarta, Indonesia

Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset.
Improved Passivity of Grid-Connected Impedance using Asymmetric Dual-Edge Modulators

This paper proposes double-sampling asymmetric dual-edge digital pulse-width modulation (ADE-DPWM) as a means of passivization of the power electronic converter’s (PEC’s) admittance. As a consequence of control-loop delays, PEC’s admittance may exhibit non-passive zones around and above the cross-over frequency of the inner control loop, possibly reducing the stability margin of the interconnected microgrid. An effective way of preventing destabilization of poorly damped grid resonances is to shape PEC’s admittance to improve the passivity region below half of the switching frequency. This paper shows that, due to its zero phase delay, double-sampling ADE-DPWM improves the passivity of the input admittance. The experimental validation performed on a single-phase current-controlled voltage-source converter showcased passivity benefits of the ADE-DPWM compared to the state-of-the-art double-sampling trailing-triangle edge DPWM.
ICE: An Intelligent Cognition Engine with 3D NAND-based In-Memory Computing for Vector Similarity Search Acceleration

Vector similarity search (VSS) for unstructured vectors generated via machine learning methods is a promising solution for many applications, such as face search. With increasing awareness and concern about data security requirements, there is a compelling need to store data and process VSS applications locally on edge devices rather than send data to servers for computation. However, the explosive amount of data movement from NAND storage to DRAM across memory hierarchy and data processing of the entire dataset consume enormous energy and require long latency for VSS applications. Specifically, edge devices with insufficient DRAM capacity will trigger data swap and deteriorate the execution performance. To overcome this crucial hurdle, we propose an intelligent cognition engine (ICE) with cognitive 3D NAND, featuring non-volatile in-memory computing (nvIMC) to accelerate the processing, suppress the data movement, and reduce data swap between the processor and storage. This cognitive 3D NAND features digital nvIMC techniques (i. e., ADClDAC-free approach), high-density 3D NAND, and compatibility with standard 3D NAND products with minor modifications. To facilitate parallel INT8/INT4 vector-vector multiplication (VVM) and mitigate the reliability issue of 3D NAND, we develop a bit-error-tolerance data encoding and a two’s complement-based digital accumulator. VVM can support similarity computations (e.g., cosine similarity and Euclidean distance), which are required to search “the most similar data” right where they are stored. In addition, the proposed solution can be realized on edge storage products, e.g., embedded Multi-Media Card (eMMC). The measured and simulated results on real 3D NAND chips show that ICE enhances the system execution time by $17\times to 95\times$ and energy efficiency by $11\times to 140\times$, compared to traditional von Neumann approaches using state-of-the-art edge systems with MobileFaceNet on CASIA-WebFace dataset. To the best of our knowledge, this work demonstrates the first 3D NAND-based digital nvIMC technique with measured silicon data.