Reviewerly/exHarmony huggingface dataset:
Differential Privacy: Exploring Federated Learning Privacy Issue to Improve Mobility Quality

The increasing number of vehicles within cities has been stimulating intense research for smart tools to avoid traffic jams and improve mobility quality. To get the most accurate data, urban mobility applications consume data generated by users as they explain the mobility pattern of a studied region, entailing the meantime vulnerability of privacy of these latter. Thus, new techniques are considered to share the minimum information possible to train models and keep privacy. Federated Learning (FL) is one approach that exploits this concept, sharing only the gradients produced by local models trained at each device. Nonetheless, information can be learned during the training, exposing vulnerable content. In this context, this study aimed to validate the FL in a given application and check the data leakage in this decentralized training architecture through the differential privacy (DP) method, removing potential clients that make the architecture vulnerable. Finally, we ended up having a final FL model with 71% of accuracy and removing two processing units from the training process.
Photonic VCSEL-based Time-Delay Reservoir Computer for Classification of RADAR Signals

We report a high-speed, low-power and hardware-friendly Photonic Reservoir Computer (RC), built with a single Vertical-Cavity Surface Emitting Laser (VCSEL), and demonstrate its ability to classify with high accuracy (~97%) observations of birds and drones obtained from RADAR data.
Pruning Binarized Neural Networks Enables Low-Latency, Low-Power FPGA-Based Handwritten Digit Classification

As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt; 9\text{ns}$</tex> latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.
Hybrid Multi-Strategy Improvements for The Aquila Optimizer

Although the original Aquila optimizer shows a strong optimization ability, it will fall into a local optimal solution in many cases. The design of the algorithm is flawed, the switch between exploration and exploitation strategies is extremely rigid, and each strategy is very dependent on the location of the prey. To address these issues, this paper proposes the Hybrid Multi-Strategy Aquila Optimizer (HMAO). This algorithm balances the exploration stage and the exploitation stage in the optimization process, introduces a variety of improvement strategies, enhances the global search ability, improves the convergence speed, and greatly avoids the problem of falling into local optimum, and it has a good performance on 23 benchmark functions Algorithm performance is verified.
AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation

Test-time adaptation is a promising research direction that allows the source model to adapt itself to changes in data distribution without any supervision. Yet, current methods are usually evaluated on benchmarks that are only a simplification of real-world scenarios. Hence, we propose to validate test-time adaptation methods using the recently introduced datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift, often resulting in degraded performance that falls below that of the source model. We noticed that the root of the problem lies in the inability to preserve the knowledge of the source model and adapt to dynamically changing, temporally correlated data streams. Therefore, we enhance well-established self-training framework by incorporating a small memory buffer to increase model stability and at the same time perform dynamic adaptation based on the intensity of domain shift. The proposed method, named AR-TTA, outperforms existing approaches on both synthetic and more real-world benchmarks and shows robustness across a variety of TTA scenarios.
Covert and Quantum-Safe Tunneling of Multi-Band Military-RF Communication Waveforms Through Non-Cooperative 5G Networks

We have built a prototype universal radio adapter which furnishes seamless and secure wireless communication through non-cooperative indigenous 5G networks for military and government users. The adapter consists of a waveform-agnostic hardware add-on that tunnels DoD terrestrial and satellite data. The adapter uses secure protocols for cross-connecting military-grade wireless RF communications equipment using spectrum in the range from UHF to Ka-band. A 5G data transport channel replaces the captured spectrum for transporting information at the IQ-sample level. In a sense, we replace the antenna-air interface and wireless channel with a transparent 5G data network. A plurarity of legacy military systems can operate through modern 5G networks in a seamless way without any knowledge of the characteristics of military waveforms. The adapter incorporates AI/ML based methods for smart spectrum sensing and autonomous radio reconfiguration. This enables intelligent interconnection of a number of military radios through non-cooperative (potentially adversarial) 5G commercial cellular networks. The adapter is built on four technical pillars: 1) ultra-wideband apertures for multi-functional and flexible software-defined radios (SDRs) with agile, wideband, and dual-band tunable RF transceivers for FR1/FR2 bands; 2) physical layer operation that involve device authentication via deep-learning based RF fingerprinting and compression of acquired IQ data; 3) secure and reconfigurable cryptographic co-processors employing the new quantum-safe algorithms selected by NIST to achieve authentication, key exchange, and encryption with focus on resource-constrained low size, weight, power, and cost (SWaP-C) devices; and 4) generative artificial intelligence and spread-spectrum steganography to hide DoD traffic passed through 5G networks and improve resiliency against real-time traffic analysis by nation-state carriers and intelligence agencies.
DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering

Image segmentation is a fundamental task in computer vision. Data annotation for training supervised methods can be labor-intensive, motivating unsupervised methods. Current approaches often rely on extracting deep features from pre-trained networks to construct a graph, and classical clustering methods like k-means and normalized-cuts are then applied as a post-processing step. However, this approach reduces the high-dimensional information encoded in the features to pair-wise scalar affinities. To address this limitation, this study introduces a lightweight Graph Neural Network (GNN) to replace classical clustering methods while optimizing for the same clustering objective function. Unlike existing methods, our GNN takes both the pair-wise affinities between local image features and the raw features as input. This direct connection between the raw features and the clustering objective enables us to implicitly perform classification of the clusters between different graphs, resulting in part semantic segmentation without the need for additional post-processing steps. We demonstrate how classical clustering objectives can be formulated as self-supervised loss functions for training an image segmentation GNN. Furthermore, we employ the Correlation-Clustering (CC) objective to perform clustering without defining the number of clusters, allowing for k-less clustering. We apply the proposed method for object localization, segmentation, and semantic part segmentation tasks, surpassing state-of-the-art performance on multiple benchmarks <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding

While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically-consistent segments of variable length. A common approach to process long videos is applying a short-form video model over uniformly sampled clips of fixed temporal length and aggregating the outputs. This approach neglects the underlying nature of long videos since fixed-length clips are often redundant or uninformative. In this paper, we aim to provide a generic and adaptive sampling approach for long-form videos in lieu of the de facto uniform sampling. Viewing videos as semantically-consistent segments, we formulate a task-agnostic, unsupervised, and scalable approach based on Kernel Temporal Segmentation (KTS) for sampling and tokenizing long videos. We evaluate our method on long-form video understanding tasks such as video classification and temporal action localization, showing consistent gains over existing approaches and achieving state-of-the-art performance on long-form video modeling.
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers

When it comes to clinical images, automatic segmentation has a wide variety of applications and a considerable diversity of input domains, such as different types of Magnetic Resonance Images and Computerized Tomography scans. This heterogeneity is a challenge for cross-modality algorithms that should equally perform independently of the input image type fed to them. Often, segmentation models are trained using a single modality, preventing generalization to other types of input data without resorting to transfer learning techniques. Furthermore, the multi-modal or cross-modality architectures proposed in the literature frequently require registered images, which are not easy to collect in clinical environments, or need additional processing steps, such as synthetic image generation. In this work, we propose a simple framework to achieve fair image segmentation of multiple modalities using a single conditional model that adapts its normalization layers based on the input type, trained with non-registered interleaved mixed data. We show that our framework outperforms other cross-modality segmentation methods, when applied to the same 3D UNet baseline model, on the Multi-Modality Whole Heart Segmentation Challenge. Furthermore, we define the Conditional Vision Transformer encoder, based on the proposed cross-modality framework, and we show that it brings significant improvements to the resulting segmentation, up to 6.87% of Dice accuracy, with respect to its baseline reference. The code to reproduce our experiments and the trained model weights are publicly available at https://github.com/matteo-bastico/MI-Seg.
Raising the Bar on the Evaluation of Out-of-Distribution Detection

In image classification, a lot of development has happened in detecting out-of-distribution (OoD) data. However, most OoD detection methods are evaluated on a standard set of datasets, arbitrarily different from training data. There is no clear definition of what forms a "good" OoD dataset. Furthermore, the state-of-the-art OoD detection methods already achieve near perfect results on these standard benchmarks. In this paper, we define 2 categories of OoD data using the subtly different concepts of perceptual/visual and semantic similarity to in-distribution (iD) data. We define Near OoD samples as perceptually similar but semantically different from iD samples, and Shifted samples as points which are visually different but semantically akin to iD data. We then propose a GAN based framework for generating OoD samples from each of these 2 categories, given an iD dataset. Through extensive experiments on MNIST, CIFAR-10/100 and ImageNet, we show that a) state-of-the-art OoD detection methods which perform exceedingly well on conventional benchmarks are significantly less robust to our proposed benchmark. Moreover, we observe that b) models performing well on our setup also perform well on conventional real-world OoD detection benchmarks and vice versa, thereby indicating that one might not even need a separate OoD set, to reliably evaluate performance in OoD detection.
HISTORY OF THE RIVER NETWORK DEVELOPMENT, GEOMORPHIC STRUCTURE AND PROSPECTS OF GOLD MINERALIZATION IN THE CENTRAL PART OF SALAIR

Geological and geomorphic works were carried out with a geomorphological survey with a detail of 1: 10,000 in the central part of Salair. The middle of the Salair peneplanation planes is widely distributed here – the Late Cretaceous-Early Paleogene, formed at elevations slightly exceeding the denudation basis – the level of the World Ocean at that time, which was 250 m above the modern one. Its current absolute marks are about 400 m, that corresponds to the neotectonic rise of the territory with an amplitude of 150 m. At the beginning of the Quaternary,a vigorous downcutting of hydrographic network occurred with its stabilization and transition to a perstratal regime at elevations 10–15 m below the сurrent encroachment line of stream flows as a result of neotectonic bulging up of the territory and further drawdown in the level of the World Ocean. Massive deposition of aeolian material occurred in the Middle Quaternary. The thickness of the cover loams on watersheds is currently up to 55 m. From valley slopes aeolian loams entered the hydraulic network and overlapped the Early Quaternary alluvium, with which the main gold-bearing alluvial placers of the territory are associated. During reoccurring downcutting, there was an abstraction of watercourse heads of the southwestern slope by watercourses of the northeastern slope of Salair with a displacement of the main watershed in some areas by 5–6 km to the southwest. Emerging buried areas of paleovalleys represent prospective targets in search for placers. The hydrographic network that arose during reoccurrung downcutting does not repeat in detail the Early Quaternary one and ancient alluvium oft en underlays slope deposits with a displacement beyond the bottom of modern valleys to the first tens of meters. Conducting a large-scale geomorphological survey makes it possible to identify areas of ancient valleys with potentially gold-bearing alluvium, which usually remain untested during prospecting and exploration.
Interoperability of the MIITRA atlas with complementary atlases: development of a comprehensive array of gray matter labels

Abstract Background In order to enhance the functionality of the MIITRA atlas and its interoperability with complementary atlases, a comprehensive set of gyral‐based, cytoarchitecture‐based, and functional connectivity‐based gray matter labels were constructed in MIITRA space. Method T1w images from the 400 older‐adults included in the construction of MIITRA atlas were nonlinearly registered using ANTs SyN to the MNI152 6thgen,MNI Colin, and the ICBM2009c templates, and volumetric labels from Harvard‐Oxford,Julich,AAL3,Buckner,CoBrALab,Hammersmith and Princeton Visual atlases were warped to the space of each participant. The ANTs‐derived transformations used to build the MIITRA T1w template were used to map the corresponding gray matter labels from raw space to exact physical locations in the final MIITRA space following a multi‐atlas correction scheme to correct for residual misregistration. T1w images from a separate group of 100 older‐adults for evaluation were nonlinearly normalized to the MIITRA template and the inverse transformation was applied to the gray matter labels to transform them to each individual’s space. The T1w image of each individual was nonlinearly registered to every other individual and the labels from the 99 other older‐adults were transformed to each individual. Pair‐wise Jaccard index for each label was calculated in every individual’s native space. The same procedure was followed for the labels obtained directly from the respective source templates. Result The overlap was higher for the AAL3 labels obtained from MIITRA with an average pair‐wise Jaccard index of 0.59 and standard deviation of 0.08, compared to 0.55±0.1 for the AAL3(Fig1A) labels obtained from the MNI Colin template. The pair‐wise Jaccard index for the Julich(Fig1C) and CoBrALab labels(Fig2A) was 0.54±0.13 and 0.75±0.17 from MIITRA and, 0.53±0.14 and 0.74±0.17 from ICBM2009c. For the Harvard‐Oxford(Fig2B), Buckner(Fig2C), Hammersmith(Fig1B) and Princeton Visual(Fig2D), the pairwise Jaccard was 0.68±0.14, 0.61±0.07, 0.71±0.10 and 0.35±0.06 respectively from MIITRA and 0.66±0.11, 0.61±0.08, 0.67±0.11 and 0.32±0.08 respectively from MNI152 template. Heat maps representing the Jaccard‐index for each template are shown in Fig3. Conclusion Gray matter labels from MIITRA showed higher pair‐wise overlap suggesting that labels from MIITRA space allows more consistent segmentation of the gray matter of the older‐adult brain compared to the labels obtained from templates constructed using younger or middle‐aged data.
Relation between quantum advantage in supervised learning and quantum computational advantage

The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantage are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This paper investigates under which conditions they are found to be equivalent or, at least, highly related. This relation is analyzed by considering two definitions of learning speed-up: one tied to the distribution and another that is distribution-independent. In both cases, the existence of efficient algorithms to generate training sets emerges as the cornerstone of such conditions, although, for the distribution-independent definition, additional mild conditions must also be met. Finally, these results are applied to prove that there is a quantum speed-up for some learning tasks based on the prime factorization problem, assuming the classical intractability of this problem.
Light-Weight and Person-Independent Radar-Based Hand Gesture Recognition for Classification and Regression of Continuous Gestures

This paper proposes a novel pre-processing technique for radar-based short-range gesture sensing using a frequency modulated continuous wave (FMCW) radar. The pre-processing is light-weight and works without Fourier transformation. The signal after pre-processing represents the backscattering central dynamics of the hand as a complex-valued time signal of a point target. It is shown that the proposed processing provides competitive classification results compared to conventional frequency domain-based solutions, while being less computationally intensive and having better generalization performance. The pre-processed time domain signal preserves a high temporal resolution of the hand movement. Due to this fact, it is possible to integrate a periodic control gesture into the system. In doing so, the system not only detects that a gesture is performed continuously and periodically, but also estimates its speed. This is an essential property for controlling scalable parameters such as brightness or volume at different speeds. The real-time capability was proven on a Raspberry Pi 3B with an ARM Cortex-A53 CPU. The proposed processing causes a CPU utilization of only 6%. The nn inference is done within 75 ms with a classification accuracy of 96.7%.
Using Active Learning for the Computational Design of Polymer Molecular Weight Distributions

The design of the reaction conditions is essential for controlling polymerization to synthesize polymers with desired properties. However, the experimental screening of the reaction conditions can be time-consuming and costly. Computational methods such as kinetic Monte Carlo (KMC) simulations have been developed to assist with the design of experiments. Nevertheless, when the dimensions of the reaction conditions to be explored are large, the simulation models might still not be able to meet the demand for efficient screening and design. Active learning can be used to tackle this problem by designing data acquisition strategies that can minimize the labeling required to construct a good surrogate model in the design space. In this work, we combined a cluster-maximum model change (CMMC) model with KMC simulations, which can precisely design polymerization conditions at the lowest computational cost for the desired molecular weight distributions. The case study results show that the CMMC model only uses 50 KMC simulations to construct a predictive model with a 10% relative error for a system with 4 design parameters, which greatly reduces the computational cost while maintaining accuracy.
A New Hybrid Particle Swarm Optimization–Teaching–Learning-Based Optimization for Solving Optimization Problems

This research paper develops a novel hybrid approach, called hybrid particle swarm optimization-teaching-learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of "exploitation capabilities of PSO" is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, "exploration abilities of TLBO" means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications.