Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed peak power management for many-core architectures

Recently proposed techniques for peak power management involve centralized decision-making and assume quick evaluation of the various power management states. These techniques do not prevent instantaneous power from exceeding the peak power budget, but instead trigger corrective action when the budget has been exceeded. Similarly, they are not suitable for many-core architectures (processors with tens or possibly hundreds of cores on the same die) due to an exponential explosion in the number of global power management states. In this paper, we look at a hierarchical and a gradient ascent-based technique for decentralized peak power management for many-core architectures. The proposed techniques prevent power from exceeding the peak power budget and enable the placement of several more cores on a die than what the power budget would normally allow. We show up to 47% (33% on average) improvements in throughput for a given power budget. Our techniques outperform the static oracle by 22%.
Non-equilibrium Dynamics as Source of Asymmetries in High Energy Physics

Understanding the origin of certain symmetry breaking scenarios in high-energy physics remains an open challenge. Here we argue that, at least in some cases, symmetry violation is an effect of non-equilibrium dynamics that is likely to develop somewhere above the energy scale of electroweak interaction. We also find that, imposing Poincare symmetry in non-equilibrium field theory, leads to fractalization of space-time continuum via period-doubling transition to chaos.
Dynamic Clustering Page Allocation for Read-Intensive Multimedia Streaming Applications

CFDP and PFCD page allocation schemes are commonly adopted for datacenter SSDs. However, they have limitations on improving read performance as their fixed allocation unit of a single page causes the layout of related data to be dispersed. This paper proposes the Dynamic Clustering Page Allocation scheme, which dynamically adjusts the allocation unit by considering the characteristics of application-level I/Os to find an optimal trade-off between the utilization of channel-level parallelism and internal flash-chip features. Our simulation study shows that the DCPA scheme improves throughput for read-intensive applications compared to CFDP/PFCD by a factor of 1.59 – 3.15.
Development of a Smart Infant Monitoring System for Working Mothers

Infants must be monitored around the clock because they are at risk for various health issues (breathing difficulties and feeding problems) and cannot communicate their needs properly. This becomes more essential for mothers working outside, keeping their child or infants at home or a daycare center. Though several infant monitoring systems exist to monitor infants, most of these do not have all possible intelligent features like detecting infant cries, which is the primary form of infant communication. Therefore, this research offers a machine learning-based smart infant monitoring system for working mothers. The proposed system can provide the infant's pulse rate, temperature, location, and alerts for crying and abnormal health condition. The system was also evaluated with 15 working mothers, and found that the proposed system could provide an easier and more convenient way to monitor their infants.
Safety Preventive Protection in Micro and Small-and Medium-Sized Enterprises Sector: A Systematic Literature Review

This study aims to deepen the understanding on the importance of safety preventive protection at work in micro and small- and medium-sized enterprises (MSMEs) sector. The systematic literature review (SLR) method was used to evaluate 60 literatures. The scope of the literature reviewed was in form of journals in English and indexed by Scopus and Scholar. A total of 60 articles were selected after being filtered based on international coverage. The results show that there is a positive and statistically significant relationship between these variables and the company's OHS performance level. In addition, there are insights that provided to occupational safety improvement covered by 15 articles (25%). The results of the review on 60 articles reflect research gaps that may become the basis for developing knowledge and practice in an effort to improve occupational safety and security. By exploring these findings, SLR contributes to a richer understanding of the implementation of safety prevention in various contexts, opening the door for further research and improving the practices in the future. By following these suggestions, the use of the SLR method can be an effective tool to guide further research.
Research on visual odometer based on improved ORB-GMS

For the ORB-SLAM2 in complex environments is prone to position estimation errors due to illumination, scale, rotation and other factors. In this paper, based on ORB-SLAM2, a Visual Odometry calculation method is proposed to improve ORB-GMS. By calculating the adaptive threshold of image block feature points, feature extraction is realized based on this threshold, and false matches are eliminated based on the homography and smoothing constraint relationship of neighboring frames. Comparison experiments are carried out in different scenes using the dataset, and the experimental results show that the algorithm in this paper can effectively improve the repeatability and discretization of feature extraction, the number of precision matched feature points and precision of feature matching. It has better RMSE performance and higher trajectory precision in scenes with obvious changes in illumination and scale.
Probabilistic validation of aggregated data in vehicular ad-hoc networks

Vehicular ad-hoc networks present great opportunity for information exchange and equal opportunity for abuse. Validating traffic information without imposing significant communication overheads is a hard problem. In this paper, we propose a solution for validating aggregated data. The main idea is to use random checks to probabilistically catch the attacker, and thereby discourage attacks in the network. Our solution relies on PKI based authentication and assumes a tamper-proof service in each car to carry out certain secure operations such as signing and timestamping. We try to keep the set of secure operations as small as possible, in accordance with the principle of economy of mechanism. We show that our solution provides security without significant communication overheads.
Adjust-RippleNet Recommendation Algorithm for Big Data Analysis in Technology

The integration of recommendation systems with knowledge graphs to perform hybrid recommendations has significant research value and implications. Addressing the technological service needs of Qinghai, we have researched and established a method for technological service recommendations. By harnessing the distinct characteristics of Qinghai's technological data and the recommendation requirements for technological services, we introduced the Adjust-RippleNet algorithm, building upon the foundational RippleNet recommendation algorithm. This method replaces the user's historical behavior data in the original algorithm with the most recent background data of entity objects. The accumulated latent preferences of entity objects across different ripple sets are then represented as vectors of the entity objects, forming an entity object-background interaction matrix. Ultimately, based on the labels of entities in the knowledge graph, we executed categorized recommendations and confirmed the efficacy of this algorithm through comparative experiments.
MSCR-HRNetV2: High-Resolution Remote Sensing Image Segmentation for Railway Scenes

Remote sensing image semantic segmentation not only enables the acquisition of valuable category information regarding surface objects but also provides crucial spatial information. This foundational data, with its high research value, plays a pivotal role in advancing the understanding and utilization of spatial environmental resources. To construct a suitable space for railway transportation and improve the channel information mismatch problem in the multi-scale fusion unit of HRNetV2, in terms of data, a high-resolution remote sensing image dataset of two railway sections of Kaha Railway and Ganquan Railway was collected, and annotated, including 210 original remote sensing images with a resolution of $4800 \times 2986$, a viewing altitude of 2km, and a spatial resolution of 0.24m. In terms of methods, an embedded self-attention mechanism module was introduced and a multi-scale consistency regularization (MSCR) method was introduced to propose MSCR-HRNetV2, achieving pixel-level semantic segmentation representation of high-resolution remote sensing images. The experimental results show that the MIoU results of MSCR-HRNetV2 on the self-built railway dataset and the public Potsdam remote sensing image dataset reached 0.8137 and 0.7596 respectively, which were 0.0313 and 0.0201 higher than the original HRNetV2. Compared with other typical remote sensing image semantic segmentation methods such as Deeplabv3+, the segmentation accuracy has also been improved by 0.0386 and 0.0219. The high-resolution remote sensing image dataset constructed in this study provides an important data source for studying the segmentation problem of high-resolution remote sensing images around railways. The proposed MSCR-HRNetV2 benchmark method lays the foundation for the full development and utilization of railway transportation space resources.
An Architecture to Research Aggregated Vertical Web Search Results

Conventional search engines often present users with ranked lists of search results, necessitating manual sifting through documents to extract information. This approach restricts exploring web-based multimedia content, mainly using vertical search engines. Interacting with influences from various verticals can lead to a loss of exploration context, making it challenging to assemble relevant information and requiring extensive scrolling and clicking. To address these limitations, we propose an architecture for investigating vertical web search results, enabling a comprehensive exploration of aggregated multimedia documents. Our system employs advanced techniques, including clustering and summarization, to efficiently organize search results and enhance user interaction. It incorporates non-linear representations, such as tree maps and pie-chart visualizations, to offer an intuitive and interactive exploration experience. The designed Search User Interface (SUI) evaluates user behavior while interacting with multimedia documents and verticals. This enables seamless exploration across disjoint verticals, maximizing contextual understanding and cognitive engagement. The evaluation of our proposed system showcased promising results as it attained a B+ grade, placing it within the 80 to 84 percentile range, validating the effectiveness and efficiency of the proposed approach.
Tibetan Few-Shot Learning Model Based on Matching Networks

This paper highlights the low-resource Tibetan few-shot learning model and establishes accuracy benchmarks utilizing Matching Networks (MN). Addressing the issues of low quality and limited usability in the existing Tibetan-Chinese parallel corpus, this paper constructs a small-scale and high-quality Tibetan-Chinese parallel corpus containing 110,000 sentence pairs using data filtering, deduplication, deletion of blank lines, and special symbol processing. Based on full context embeddings, Softmax-based attention mechanisms, and similarity metrics, the Tibetan few-shot learning model based on MN is proposed to help the model learn logical Tibetan knowledge rapidly from few-shot data. Different distance metrics on Cosine, Euclidean, Poincare, and Minkowski distances are used to test the accuracy of the Tibetan k-shot learning model, which provides a unified and reliable benchmark for the Tibetan few-shot learning tasks. This research provides substantial assistance in solving the issues of machine learning performing poorly in low-resource settings. The experiment results demonstrate that the accuracy of all metrics is improved with the number of shots increased, and Euclidean distance performs well in 5-way 1-shot learning, cosine performs well in 5-way 2-shot learning, and Minkowski distance $(\mathrm{p}=3)$ shows excellent accuracy in 5-way 3-shot learning. Notably, we used our own Tibetan data set and succeeded in enhancing accuracy by a noteworthy 1.6% in a 5-way 3-shot setup.
Transfer of conflict and cooperation from experienced games to new games: a connectionist model of learning

The question of whether, and if so how, learning can be transfered from previously experienced games to novel games has recently attracted the attention of the experimental game theory literature. Existing research presumes that learning operates over actions, beliefs or decision rules. This study instead uses a connectionist approach that learns a direct mapping from game payoffs to a probability distribution over own actions. Learning is operationalized as a backpropagation rule that adjusts the weights of feedforward neural networks in the direction of increasing the probability of an agent playing a myopic best response to the last game played. One advantage of this approach is that it expands the scope of the model to any possible n × n normal-form game allowing for a comprehensive model of transfer of learning. Agents are exposed to games drawn from one of seven classes of games with significantly different strategic characteristics and then forced to play games from previously unseen classes. I find significant transfer of learning, i.e., behavior that is path-dependent, or conditional on the previously seen games. Cooperation is more pronounced in new games when agents are previously exposed to games where the incentive to cooperate is stronger than the incentive to compete, i.e., when individual incentives are aligned. Prior exposure to Prisoner's dilemma, zero-sum and discoordination games led to a significant decrease in realized payoffs for all the game classes under investigation. A distinction is made between superficial and deep transfer of learning both—the former is driven by superficial payoff similarities between games, the latter by differences in the incentive structures or strategic implications of the games. I examine whether agents learn to play the Nash equilibria of games, how they select amongst multiple equilibria, and whether they transfer Nash equilibrium behavior to unseen games. Sufficient exposure to a strategically heterogeneous set of games is found to be a necessary condition for deep learning (and transfer) across game classes. Paradoxically, superficial transfer of learning is shown to lead to better outcomes than deep transfer for a wide range of game classes. The simulation results corroborate important experimental findings with human subjects, and make several novel predictions that can be tested experimentally.
Drivers of Wetland Conversion: a Global Meta-Analysis

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.
Finapres tracking of systolic pressure and baroreflex sensitivity improved by waveform filtering.

OBJECTIVE Arterial pressure waveforms distort between brachial and finger arteries, causing differences mainly in systolic pressure. Distortion, reportedly, can be removed by applying a waveform filter to the finger pressure. DESIGN We analysed the data from two studies that detected discrepancies in systolic tracking between Finapres and brachial pressures. The first set comprised waveforms of seven volunteers during incremental bicycle exercise to exhaustion and the second set comprised waveforms of eight volunteers during increasing phenylephrine infusion. METHODS We applied the filter and compared 1 min averaged unfiltered and waveform-filtered finger and brachial pressures. RESULTS During exercise, finger systolic pressure overestimated brachial increasingly, from 7(SD 10) mmHg at rest to 27(17) mmHg at maximal exertion. Differences were reduced by waveform filtering from 3 (SD 9)mmHg at rest to 1 (SD 15)mmHg at maximal exertion. During phenylephrine infusion finger systolic pressure overestimated brachial pressure, but the magnitude of the overestimate decreased from 14 (SD 15)mmHg at baseline to -1(SD 16)mmHg at maximal rate. After waveform filtering overestimation was an almost constant 6(SD 11)mmHg. Median baroreflex sensitivities from brachial, unfiltered and waveform-filtered finger pressure were 5.8, 7.5 and 5.3 ms/mmHg and correlation increased after filtering. The results indicate improved systolic pressure tracking after waveform filtering. CONCLUSIONS Finger pressure distortion follows a general pattern correctable by waveform filtering. Waveform filtering allows a 'brachial' view to be obtained from Finapres data.
Digital Tools for Aircraft Maintenance: Prototyping Location-Aware AOI for Engine Assessment and Cable Routing Solutions

Aircraft maintenance plays a vital role in ensuring not only public safety but also aircraft availability and financial viability. However, carrying out such activities traditionally leaves gaps for processual optimizations with the potential of increasing the overall performance and satisfaction of operators and engineers who deal with these responsibilities. Currently, available technologies may enable the increment of quality in aircraft maintenance processes, namely, the ones based on xReality and deep learning. Therefore, this paper proposes two use-cases based on real aircraft maintenance context requirements: one employing augmented reality for cable routing, enabling cataloguing and also previewing the position and paths of cable systems inside the fuselage kit; and another making use of deep learning and virtual environments to aid in the activities related with engine baroscopic inspections, with position awareness capabilities. Results regarding a few functional and usability tests are also presented and discussed. In spite of their still incipient maturity, both these tools showed potential for being involved in the pipelines of aircraft maintenance in the future.
In-Network Local Distributed Estimation for Power-Constrained Wireless Sensor Networks

In this paper, we consider the problem of power-efficient distributed estimation of a localized event in the large-scale Wireless Sensor Networks (WSNs). In order to increase the power efficiency in these networks, we develop a joint optimization problem that involves both selecting a subset of active sensors and the routing structure so that the quality of estimation at a given querying node is the best possible subject to a total imposed communication cost. We first formulate our problem as an optimization problem and show that it is NP-Hard. Then, we propose a local distributed optimization algorithm that is based on an Estimate-and-Forward (EF) strategy, which allows to perform sequentially this joint optimization in an efficient way. We also provide a lower bound for our optimization problem and show that our local distributed optimization algorithm provides a performance that is close to this bound. Although there is no guarantee that the gap between this lower bound and the optimal solution of the main problem is always small, our numerical experiments support that this gap is actually very small in many cases. An important result from our work is that because of the interplay between the communication cost over the links and the gains in estimation accuracy obtained by choosing certain sensors, the traditional Shortest Path Tree (SPT) routing structure, widely used in practice, is no longer optimal, that is, our routing structures provide a better trade-off between the overall power efficiency and the final estimation accuracy obtained at the querying node. Our experimental results show that our algorithms yield a significant energy saving.
A Miniaturized Design of Frequency Selective Absorber for S, C and X bands

In this paper, a study for realizing a miniaturized frequency selective absorber (FSA) capable of wide performance in S, C, and X bands is presented. The proposed absorber consists of a resistive layer on which a hexagonal shaped ring resonator and lumped resistors mounted rectangular strips are printed. A wide range of absorption is achieved in the proposed study with 90% bandwidth ranging from 2.9 to 11.2 GHz, along with the miniaturized unit dimension of $0.074 \lambda \mathrm{L}$ (where $\lambda_{L}$ is associated with the lowest absorption frequency). The proposed structure is a 2-layer design comprising of 4 lumped elements per unit cell and a thickness of $0.079 \lambda_{L}$. The absorption process is thoroughly investigated through comprehensive parametric analysis.
Multi-Path Spatial Detail-Aware Network for Semantic Segmentation

In the existing semantic segmentation, small objects and slender strip objects that are easy to blend into the surrounding background are generally difficult to be accurately identified and segmented. We propose a Multi-Path Spatial Detail-Aware Network (MSDNet) for semantic segmentation of traffic scenes. Specifically, an interactive Dual-branch Parallel Complementary module (DPC) based on the Transformer-CNN framework is firstly proposed to enhance the discriminative representation of similar feature encodings between different objects. Then, a spatial detail assistant module (SDA) with attention mechanism is designed to help the network obtain more detailed spatial detail information. Moreover, a Cross-layer Feature Aggregation module (CFA) is constructed for further aligning and aggregating feature maps of different resolutions in adjacent two layers, which can improve the segmentation performance of small objects. Extensive experimental results show that the proposed method achieves the accuracy of 81.16% mean Intersection over Union (mIoU) on the challenging Cityscapes dataset.