Miracle-dz/UniFAR-FTUs huggingface dataset:
A magyar határkerítés korszerűsítése felügyelet nélküli szenzorhálózatok

: A continuous border control is a costly task demanding a big human source. It is not enough to rely solely on current solutions to achieve the right level of border protection; in addition to human strength, physical barriers and intelligent signalling systems, it would be beneficial to adapt to further technological developments. A possible way to modernize the current border protection system is to supplement the system with unattended sensor networks. The first part of the two-part article describes the essence of sensor technology, the challenges of storing and processing sensor data. The second part reveals the applicability of sensor technology in border protection along the experiences of studying various international examples, and then outlines the possibilities of application in Hungary.
Strategic applications of nano-fertilizers for sustainable agriculture: Benefits and bottlenecks

Abstract The application of nano-fertilizers (NFs) is an emerging research field in agriculture. These are materials in the size range of 1–100 nm that support the nutrition of the plants. It is a novel way to optimize the nutrient supply, either alone or in combination. NFs are an economical alternative to ordinary chemical fertilizers that can increase global food production in a sustainable way. NFs are made up of nutrients and micronutrients and may act as carriers for nutrients. The nanocarriers deliver the nutrients to the right place, reducing the additional amount of active chemicals deposited in the plant, besides a slow release. Although nano-coated materials manage to penetrate through the stomata with a size exclusion limit greater than 10 nm, the nanoparticles appear to be able to make holes and enter the vascular system. This review addresses the potential benefits of NFs to agriculture, synthesis, mode of entry, mechanisms of action, and the fate of nanomaterials in soil. Finally, policy makers will have the bases to regulate the dose, frequency, and time period of NF applications for food production. We suggest formulating the integrated risk management frameworks for the possible applications of NFs in agriculture.
Design and analysis of CXL performance models for tightly-coupled heterogeneous computing

Truly heterogeneous systems enable partitioned workloads to be mapped to the hardware that nets the best performance. However, current practice requires that inter-device communication between different vendors' hardware use host memory as an intermediary step. To date, there are no widely adopted solutions that allow accelerators to directly transfer data. A new cache-coherent protocol, CXL, aims to facilitate easier, fine-grained sharing between accelerators. In this work we analyze existing methods for designing heterogeneous applications that target GPUs and FPGAs working collaboratively, followed by an exploration to show the benefits of a CXL-enabled system. Specifically, we develop a test application that utilizes both an NVIDIA P100 GPU and a Xilinx U250 FPGA to show current communication limitations. From this application, we capture overall execution time and throughput measurements on the FPGA and GPU. We use these measurements as inputs to novel CXL performance models to show that using CXL caching instead of host memory results in a 1.31X speedup, while a more tightly-coupled pipelined implementation using CXL-enabled hardware would result in a speedup of 1.45X.
ON/OFF current of nano-node field-effect transistors on p-substrate or SOI substrate

Higher drive current (ON current or ION) is desired on high performance (HP) chips, especially in advanced computing electronic products. In the meanwhile, the OFF current (IOFF) representing the standby performance is very important for the portable electronic products. How to handle or balance these two effects in nano-node process manufacturing is necessary to be precisely managed. In the recent advanced processes, the high-k (HK) gate dielectric or the 3-D fin shape on p-type substrate (p-substrate) or silicon-on-insulator (SOI) substrate is a feasible method to fit these needs. In this work, these two ON/OFF factors will be compared in manufacturing process and electrical performance.
Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and Applications

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.
Multi-objective Nonlinear Control Allocation for Flying-wing UAV using Kriging Model

∗ ABSTRACT. The Innovation Control Effectors (ICEs) of flying-wing Unmanned Aerial Vehicle (UAV) typically has the characteristics of multi-axis coupling and highly nonlinear aerodynamic. The conventional linear control allocation methods are not accurate enough for this nonlinear control allocation problem. In this paper, we propose a high accuracy, Kriging-based multi-objective nonlinear control allocation method. Firstly, the control allocation problem is transformed into a constrained multi-objective optimization problem, which considers the cost function of control energy cost, lift and drag. Secondly, Kriging algorithm is exploited to build the nonlinear aerodynamic models for each control surface. Then, relying on the Kriging aerodynamic models, Multi-Objective Particle Swarm Optimization (MOPSO) is applied to solve the multi-objective optimization problem. Finally, we compare the performance of our Kriging-based control allocation method to a piecewise linear allocation approach in a same flight simulation. Simulation results show that the proposed Kriging-based allocation method has a higher accuracy of the control allocation and is capable of satisfying the multi-objective control requirements.
Not All Features are Equal: Selection of Robust Features for Speech Emotion Recognition in Noisy Environments

Speech emotion recognition (SER) system deployed in real-world applications often encounters noisy speech. While most noise compensation techniques consider all acoustic features to have equal impact on the SER model, some acoustic features may be more sensitive to noisy conditions. This paper investigates the noise robustness of each feature in the acoustic feature set. We focus on low-level descriptors (LLDs) commonly used in SER systems. We firstly train SER models with clean speech by only using a single LLD. Then, we rank each LLD with respect to the absolute performance on a development set contaminated with noise, and the relative performance decrease from the results from the models trained with the clean set. Our experiment shows that using all the LLDs leads to worse performance than training the system with a single robust LLD. We propose to select a group of robust features according to their performance and robustness in noisy condition. Without using any compensation method, our feature selection methods improve the performance by 24.4% (arousal), 23.9% (dominance), and 43.2% (valence) in the 10dB noisy condition. Moreover, even though the selection is conducted with the 10dB condition, our selection methods also yield performance improvements in unseen noisy recording conditions.
A Few-Sample Strategy for Guitar Tablature Transcription Based on Inharmonicity Analysis and Playability Constraints

The prominent strategical approaches regarding the problem of guitar tablature transcription rely either on fingering patterns encoding or on the extraction of string-related audio features. The current work combines the two aforementioned strategies in an explicit manner by employing two discrete components for string-fret classification. It extends older few-sample modeling strategies by introducing various adaptation schemes for the first stage of audio processing, taking advantage of the inharmonic characteristics of guitar sound. Physical limitations and common standards of human performers are incorporated in a genetic algorithm which constitutes a second contextual-based module that further processes the initial audio-based predictions. The proposed methods are evaluated on both annotated guitar performances and isolated note recordings.
Multi-Modal Acoustic-Articulatory Feature Fusion For Dysarthric Speech Recognition

Building automatic speech recognition (ASR) systems for speakers with dysarthria is a very challenging task. Although multi-modal ASR has received increasing attention recently, incorporating real articulatory data with acoustic features has not been widely explored in the dysarthric speech community. This paper investigates the effectiveness of multi-modal acoustic modelling for dysarthric speech recognition using acoustic features along with articulatory information. The proposed multi-stream architectures consist of convolutional, recurrent and fully-connected layers allowing for bespoke per-stream pre-processing, fusion at the optimal level of abstraction and post-processing. We study the optimal fusion level/scheme as well as training dynamics in terms of cross-entropy and WER using the popular TORGO dysarthric speech database. Experimental results show that fusing the acoustic and articulatory features at the empirically found optimal level of abstraction achieves a remarkable performance gain, leading to up to 4.6% absolute (9.6% relative) WER reduction for speakers with dysarthria.
Transformer-Based Estimation of Spoken Sentences Using Electrocorticography

Invasive brain–machine interfaces (BMIs) are a promising neurotechnological venture for achieving direct speech communication from a human brain, but it faces many challenges. In this paper, we measured the invasive electrocorticogram (ECoG) signals from seven participating epilepsy patients as they spoke a sentence consisting of multiple phrases. A Transformer encoder was incorporated into a "sequence-to-sequence" model to decode spoken sentences from the ECoG. The decoding test revealed that the use of the Transformer model achieved a minimum phrase error rate (PER) of 16.4%, and the median (±standard deviation) across seven participants was 31.3% (±10.0%). Moreover, the proposed model with the Transformer achieved significantly better decoding accuracy than a conventional long short-term memory model.
Multi-Scale Refinement Network Based Acoustic Echo Cancellation

Recently, deep encoder-decoder networks have shown outstanding performance in acoustic echo cancellation (AEC). However, the subsampling operations like convolution striding in the encoder layers significantly decrease the feature resolution lead to fine-grained information loss. This paper proposes an encoder-decoder network for acoustic echo cancellation with mutli-scale refinement paths to exploit the information at different feature scales. In the encoder stage, high-level features are obtained to get a coarse result. Then, the decoder layers with multiple refinement paths can directly refine the result with fine-grained features. Refinement paths with different feature scales are combined by learnable weights. The experimental results show that using the proposed multi-scale refinement structure can significantly improve the objective criteria. In the ICASSP 2022 Acoustic echo cancellation Challenge, our submitted system achieves an overall MOS score of 4.439 with 4.37 million parameters at a system latency of 40ms.
Learning Approach For Fast Approximate Matrix Factorizations

Efficiently computing an (approximate) orthonormal basis and low-rank approximation for the input data X plays a crucial role in data analysis. One of the most efficient algorithms for such tasks is the randomized algorithm, which proceeds by computing a projection XA with a random sketching matrix A of much smaller size, and then computing the orthonormal basis as well as low-rank factorizations of the tall matrix XA. While a random matrix A is the de facto choice, in this work, we improve upon its performance by utilizing a learning approach to find an adaptive sketching matrix A from a set of training data. We derive a closed-form formulation for the gradient of the training problem, enabling us to use efficient gradient-based algorithms. We also extend this approach for learning structured sketching matrix, such as the sparse sketching matrix that performs as selecting a few number of representative columns from the input data. Our experiments on both synthetical and real data show that both learned dense and sparse sketching matrices outperform the random ones in finding the approximate orthonormal basis and low-rank approximations.
Deep Adaptive Aec: Hybrid of Deep Learning and Adaptive Acoustic Echo Cancellation

In this paper we integrate classic adaptive filtering algorithms with modern deep learning to propose a new approach called deep adaptive AEC. The main idea is to represent the linear adaptive algorithm as a differentiable layer within a deep neural network (DNN) framework. This enables the gradients to flow through the adaptive layer during back propagation and the inner layers of the DNN are trained to estimate the playback reference signal and the time-varying learning factors. The proposed approach combines the power of DNNs with adaptive filters. Experimental results show the effectiveness of the proposed method in scenarios where the echo path changes continuously and signal-to-echo ratio (SER) and signal-to-noise ratio (SNR) are low. Furthermore, compared to fully DNN-based baseline methods, integrating adaptive algorithm consistently improves performance and leads to easier training using smaller models.
Safari from Visual Signals: Recovering Volumetric 3d Shapes

In this paper we propose a convex approach for recovering a detailed 3D volumetric geometry of several objects from visual signals. To this end, we first present a minimal detailed surface energy that is optimized together with a volume constraint by considering some geometrical priors, and without requiring neither additional training data nor templates in order to constrain the solution. Our problem can be efficiently solved by means of a gradient descent, and be applied for single RGB images or monocular videos even with very small rigid motions. Temporal-aware solutions and driven by point correspondences are incorporated without assuming any 2D tracking data over time. Thanks to this formulation, both rigid and non-rigid objects can be considered. We have extensively validated our approach in a wide variety of scenarios in the wild, recovering challenging type of shapes that have not been previously attempted without assuming any training data.
Hypergraphs with Edge-Dependent Vertex Weights: Spectral Clustering Based on the 1-Laplacian

We propose a flexible framework for defining the 1-Laplacian of a hypergraph that incorporates edge-dependent vertex weights. These weights are able to reflect varying importance of vertices within a hyperedge, thus conferring the hypergraph model higher expressivity than homogeneous hypergraphs. We then utilize the eigenvector associated with the second smallest eigenvalue of the hypergraph 1-Laplacian to cluster the vertices. From a theoretical standpoint based on an adequately defined normalized Cheeger cut, this procedure is expected to achieve higher clustering accuracy than that based on the traditional Laplacian. Indeed, we confirm that this is the case using real-world datasets to demonstrate the effectiveness of the proposed spectral clustering approach. Moreover, we show that for a special case within our framework, the corresponding hypergraph 1-Laplacian is equivalent to the 1-Laplacian of a related graph, whose eigenvectors can be computed more efficiently, facilitating the adoption on larger datasets.
Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology

Human cardiac function is characterized by a complex interplay of mechanical deformation and electrophysiological conduction. Similar to the underlying cardiac anatomy, these interconnected physiological patterns vary considerably across the human population with important implications for the effectiveness of clinical decision-making and the accuracy of computerized heart models. While many previous works have investigated this variability separately for either cardiac anatomy or physiology, this work aims to combine both aspects in a single data-driven approach and capture their intricate interdependencies in a multi-domain setting. To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a single model. Each VAE branch is specifically designed to address the particular challenges of the respective input domain, enabling efficient encoding, reconstruction, and synthesis of multi-domain cardiac signals. Our method achieves high reconstruction accuracy on a United Kingdom Biobank dataset, with Chamfer Distances between reconstructed and input anatomies below the underlying image resolution and ECG reconstructions outperforming multiple single-domain benchmarks by a considerable margin. The proposed VAE is capable of generating realistic virtual populations of arbitrary size with good alignment in clinical metrics between the synthesized and gold standard anatomies and Maximum Mean Discrepancy (MMD) scores of generated ECGs below those of comparable single-domain approaches. Furthermore, we observe the latent space of our VAE to be highly interpretable with separate components encoding different aspects of anatomical and ECG variability. Finally, we demonstrate that the combined anatomy and ECG representation improves the performance in a cardiac disease classification task by 3.9% in terms of Area Under the Receiver Operating Characteristic (AUROC) curve over the best corresponding single-domain modeling approach.
In Search for the Retrievable Memory Trace in an Insect Brain

The search strategy for the memory trace and its semantics is exemplified for the case of olfactory learning in the honeybee brain. The logic of associative learning is used to guide the experimental approach into the brain by identifying the anatomical and functional convergence sites of the conditioned stimulus and unconditioned stimulus pathways. Two of the several convergence sites are examined in detail, the antennal lobe as the first-order sensory coding area, and the input region of the mushroom body as a higher order integration center. The memory trace is identified as the pattern of associative changes on the level of synapses. The synapses are recruited, drop out, and change the transmission properties for both specifically associated stimulus and the non-associated stimulus. Several rules extracted from behavioral studies are found to be mirrored in the patterns of synaptic change. The strengths and the weaknesses of the honeybee as a model for the search for the memory trace are addressed in a comparison with Drosophila. The question is discussed whether the memory trace exists as a hidden pattern of change if it is not retrieved and whether an external reading of the content of the memory trace may ever be possible. Doubts are raised on the basis that the retrieval circuits are part of the memory trace. The concept of a memory trace existing beyond retrieval is defended by referring to two well-documented processes also in the honeybee, memory consolidation during sleep, and transfer of memory across brain areas.
Investigating Intrinsically Disordered Proteins With Brownian Dynamics

Intrinsically disordered proteins (IDPs) have recently become systems of great interest due to their involvement in modulating many biological processes and their aggregation being implicated in many diseases. Since IDPs do not have a stable, folded structure, however, they cannot be easily studied with experimental techniques. Hence, conducting a computational study of these systems can be helpful and be complementary with experimental work to elucidate their mechanisms. Thus, we have implemented the coarse-grained force field for proteins (COFFDROP) in Browndye 2.0 to study IDPs using Brownian dynamics (BD) simulations, which are often used to study large-scale motions with longer time scales and diffusion-limited molecular associations. Specifically, we have checked our COFFDROP implementation with eight naturally occurring IDPs and have investigated five (Glu-Lys)25 IDP sequence variants. From measuring the hydrodynamic radii of eight naturally occurring IDPs, we found the ideal scaling factor of 0.786 for non-bonded interactions. We have also measured the entanglement indices (average C α distances to the other chain) between two (Glu-Lys)25 IDP sequence variants, a property related to molecular association. We found that entanglement indices decrease for all possible pairs at excess salt concentration, which is consistent with long-range interactions of these IDP sequence variants getting weaker at increasing salt concentration.
Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.