Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-invasive analyses of a multi-stratified archaeological and historical site: the Crypta Balbi case-study

A number of non-invasive techniques were used to study the state of conservation of an important archaeological site in the centre of Rome, the Crypta Balbi (part of the Museo Nazionale Romano), so-called after proconsul Lucio Cornelio Balbo, who in the year 13 BC built here a theatre in the Campus Martius [1]. Excavations are still ongoing, so new relevant archaeological findings could still be discovered. At the same time, this involves that each new finding must be conserved and preserved. To this aim, extensive monitoring campaigns are essential. The reconstructions in Figure 1 and 2 show the structure of the monument during the II century (imperial age) and after, until the 15th century [1]. Figure 3 shows the studied site as it is today. The theatre and the Crypta (a kind of courtyard) are located within the red dotted circles. Today it is close to a tramway in the city centre, that induces air pollution and vibrations potentially putting at risk this unique archaeological site. In order to study its various layers, a number of non-invasive techniques have been used. Among them: infrared cameras for the thermographic analysis, velocimeters to evaluate impact of vibrations, microclimatic parameters acquisition, kinematic analysis, digital video elaboration by motion magnification method. Substantially, this monitoring campaign was based on non-contact investigations in order to eliminate any danger to damage the elements of the monument, which provided useful insights. Thermal images of the monument were captured using a Flir T440 thermal infrared camera. Microclimatic parameters and data of weather stations were collected for correlation to experimental ambient vibration and NDTs data. In particular, air temperature and humidity were monitored by two MSR145 mini data loggers. Seismographs type SL06 SARA Instruments digital recorders equipped with SS02 triaxial velocimeters were used for the vibration monitoring. Each seismograph is independent and provided with battery for the energy supply and with a GPS antenna for synchronization, so that data from different instruments can be compared in the time domain. The instrumentation is very accurate, in fact the sensitivity is 400 V/m/s and the frequency range from 0.2 to 50 Hz, sampling frequency set at 200 Hz. Data were acquired from 10:00 a.m. to 12:00 a.m. Motion Magnification (MM) is a recently developed video processing methodology [2, 6, 5]. MM [2 - 6] acts like a microscope in digital videos, magnifying motions hardly visible with the naked eye, but leaving unchanged the topology of the images.
Prediction of CO2 emissions in Saudi Arabia using Nonlinear Grey Bernoulli Model NGBM (1,1) compared with GM (1,1) model

One of the most critical solution for tackling the challenges of global warming and climate change is to study and know the accurate prediction of carbon dioxide (CO2) emissions. Thus, aid to develop appropriate strategic plans that will reduce future damages caused by these emissions into the atmosphere. This study utilizes annual time series data on CO2 emissions in Saudi Arabia from 1970 to 2016. The goal of this study is to predict CO2 emissions using the Nonlinear Grey Bernoulli model NGBM (1,1), and compared with the GM (1,1) model based on MAPE metrics to achieve a high-accuracy prediction. The NGBM (1,1) is a newly created grey model with wide ranging applications in diverse fields due to its precision in handling small time-series datasets with nonlinear variations. The NGBM (1,1) with power γ is a nonlinear differential equation that can control the predicted result and adjust the solution to fit the 1-AGO of previous raw data. Thus, the findings show that at sample sizes of N=10 and N=5, the Nonlinear Grey Bernoulli Model (NGBM) is more precise than the Grey Model GM (1, 1). The findings could help the government develop future economic policies.
Self-organized neuronal subpopulations and network morphology underlying superbursts

Neural bursts are an important phenomenon that needs to be understood for their relevance to many different neurological diseases as well as neural computations. While there are different types of neuronal bursts, in this study we investigate the nature of population (as opposed to intrinsic cell-level) bursts, in particular, superbursts (SBs) that are a small (∼100 ms) packet of several population bursts (PBs). It has been suggested that neuronal PBs occur when there exists a delicate balance of system-wide excitation and inhibition and when recurrent excitation loops exist in the network. However, there has been no rigorous investigation on the relation between network morphology and (super)burst dynamics. Here we investigate the important issue based on a well-established Izhikevich network model of integrate-fire neurons. We have employed the overall conduction delay as our control parameter for tuning network morphology as well as its matching burst dynamics. Interestingly, we found that initially identical neurons self-organize to develop several distinct neuronal subpopulations, which are characterized by different spike firing patterns as well as local network properties. Moreover, a few different motifs of SB emerge according to a distinct mixture of neuronal subpopulations that, on average, fire at slightly different phases. Our analyses suggest that recurring motifs of different SBs reflect complex yet organized modular structures of different subpopulations.
Re-evaluation of the composite milk somatic cell count as a predictor of intramammary infections in dairy cattle

To encourage farmers and veterinarians to sample cows for bacteriological culture, the probability of isolating a mastitis pathogen from the selected cows should be maximized. The objectives of this study are to re-evaluate the test characteristics of the composite milk somatic cell count (cSCC) in diagnosing intramammary infections (IMI) caused by either minor or major pathogens and to identify to what extent the herd SCC (hSCC) and cow factors such as parity and chronicity of the IMI affect the predictive values. A total of 1834 quarter milk samples from 291 lactating heifers and cows were collected for bacteriological culturing through 23 cross-sectional dairy herd screenings. Additionally, monthly cSCC data were available from the cows. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for different cSCC-thresholds using the information included in the last four recordings relative to sampling for bacteriological culturing. Preliminary results indicate that both parity and hSCC affect the PPV of cSCC, hampering the selection of one specific SCC-threshold for separating infected from non-infected animals. The PPV for a specific SCC-threshold seems to be lower in heifers than in multiparous cows and higher in herds with a higher hSCC . The results of this study will positively contribute to a more accurate selection of subclinically infected cows for bacteriological culturing.
Performance Analysis of Parity Cache enabled RAID Level 5 for DDR Memory Storage Device

In this paper, we analyze the performance of the parity cache enabled RAID level-5 via the simulation. This RAID system consists of the DDR memory-based storage devices. To do this, we develop the simulation model and suggest the basic performance analysis data which we want to get via the simulation. And we implement the simulator based on the simulation model and execute the simulator. From the result of the simulation, we expect that the parity cache enabled RAID level-5 configured by the DDR memory based storage devices has the positive effectiveness to the enhancing of the storage system performance if the storage access patterns of applications are tuned.
Techno-Economic analysis of the potential utilization of a hybrid PV-wind turbine system for commercial buildings in Jordan.

The Middle East has about 45% of global crude oil and natural gas reserves, making it highly energy independent. However, not all countries in this region are endowed with such fossil fuel potentials. Jordan is one of such country with energy dependency of about 96%. , On the other hand Jordan has huge renewable energy potentials which abound in the solar belt region with solar irradiance ranging between 4 kWh/m² and 7 kWh/m² and good wind potential with wind resources exceeding 7 m/s annually. Electricity tariff for commercial buildings is at $0.33 per kWh and with governments’ introduction of feed-in-tariff and net energy metering systems in December 2013, the potential of renewable energy systems looks promising for grid connected systems. This study simulates the potential of a stand-alone hybrid system comprising PV and wind turbine to adequately meet the annual electricity need of 34.4 MWh of a hotel in Jordan, the technical feasibility and economic viability of the system is also analyzed. Its economic potentials are compared to an on-grid system of PV-wind turbine to feed the same load observing the effect of the implemented feed-in policy. The technical analysis shows that a 20 kW PV system and a 10 kW wind turbine can sufficiently supply the demand. In addition, the economic indices obtained from the stand alone system showed a net present cost of $147,485 for its 25 years life span, a savings-investment ratio (SIR) of 1.924 and a payback period of 11 years was obtained. The grid connected hybrid system showed greater economic potentials with NPC of $98,712, SIR of 2.84 and payback time of 8.07 years. Also, the analysis shows that policy improvement has the potential of saving commercial buildings of similar load thousands of dollars annually while eliminating environmental greenhouse gas emissions.
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data

Objective: To comparatively evaluate several transformer model architectures at identifying protected health information (PHI) in the i2b2/UTHealth 2014 clinical text de-identification challenge corpus. Methods: The i2b2/UTHealth 2014 corpus contains N=1304 clinical notes obtained from N=296 patients. Using a transfer learning framework, we fine-tune several transformer model architectures on the corpus, including: BERT-base, BERT-large, ROBERTA-base, ROBERTA-large, ALBERT-base and ALBERT-xxlarge. During fine-tuning we vary the following model hyper-parameters: batch size, number training epochs, learning rate and weight decay. We fine tune models on a training data set, we evaluate and select optimally performing models on an independent validation dataset, and lastly assess generalization performance on a held-out test dataset. We assess model performance in terms of accuracy, precision (positive predictive value), recall (sensitivity) and F1 score (harmonic mean of precision and recall). We are interested in overall model performance (PHI identified vs. PHI not identified), as well as PHI-specific model performance. Results: We observe that the ROBERTA-large models perform best at identifying PHI in the i2b2/UTHealth 2014 corpus, achieving>99% overall accuracy and 96.7% recall/precision on the heldout test corpus. Performance was good across many PHI classes; however, accuracy/precision/recall decreased for identification of the following entity classes: professions, organizations, ages, and certain locations. Conclusions: Transformers are a promising model class/architecture for clinical text de-identification. With minimal hyper-parameter tuning transformers afford researchers/clinicians the opportunity to obtain (near) state-of-the-art performance.
Decomposing income-related inequality in health-related quality of life in mainland China: a national cross-sectional study

Introduction Health equity is an important indicator measuring social development and solidarity. However, there is a paucity in nationwide studies into the inequity in health-related quality of life (HRQoL) in mainland China, in particular using the most recent data measuring HRQoL using the EuroQol 5-Dimension-5 Level (EQ-5D-5L). This study aimed to address the gap in the literature by estimating and decomposing income-related inequality of the utility index (UI) of EQ-5D-5L in mainland China. Methods Data were extracted from the Psychology and Behaviour Investigation of Chinese Residents (2022), including 19 738 respondents over the age of 18 years. HRQoL was assessed by the UI of the EQ-5D-5L. Concentration index (CI) was calculated to measure the degree of income-related inequality in the UI. The contributions of individual, behavioural and context characteristics to the CI were estimated using the Wagstaff decomposition method. Results The CI of the EQ-5D-5L UI reached 0.0103, indicating pro-rich inequality in HRQoL. Individual characteristics made the greatest contribution to the CI (57.68%), followed by context characteristics (0.60%) and health behaviours (−3.28%). The contribution of individual characteristics was mainly attributable to disparities in the enabling (26.86%) and need factors (23.86%), with the chronic conditions (15.76%), health literacy (15.56%) and average household income (15.24%) as the top three contributors. Educational level (−5.24%) was the top negative contributor, followed by commercial (−1.43%) and basic medical insurance (−0.56%). Higher inequality was found in the least developed rural (CI=0.0140) and western regions (CI=0.0134). Conclusion Pro-rich inequality in HRQoL is evident in mainland China. Targeted interventions need to prioritise measures that aim at reducing disparities in chronic conditions, health literacy and income.
Escaping Spurious Local Minima of Low-Rank Matrix Factorization Through Convex Lifting

This work proposes a rapid global solver for nonconvex low-rank matrix factorization (MF) problems that we name MF-Global . Through convex lifting steps, our method efﬁciently escapes saddle points and spurious local minima ubiquitous in noisy real-world data, and is guaranteed to always converge to the global optima. Moreover, the proposed approach adaptively adjusts the rank for the factorization and provably identiﬁes the optimal rank for MF automatically in the course of optimization through tools of manifold identiﬁcation, and thus it also spends sig-niﬁcantly less time on parameter tuning than existing MF methods, which require an exhaustive search for this optimal rank. On the other hand, when compared to methods for solving the lifted convex form only, MF-Global leads to signiﬁcantly faster convergence and much shorter running time. Experiments on real-world large-scale recommendation system problems conﬁrm that MF-Global can indeed effectively escapes spurious local solutions at which existing MF approaches stuck, and is magnitudes faster than state-of-the-art algorithms for the lifted convex form.
Deep Geometry Post-Processing for Decompressed Point Clouds

Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission. However, distortions can be introduced into the decompressed point clouds due to quantization. In this paper, we propose a novel learning-based post-processing method to enhance the decompressed point clouds. Specifically, a voxelized point cloud is first divided into small cubes. Then, a 3D convolutional network is proposed to predict the occupancy probability for each location of a cube. We leverage both local and global contexts by generating multi-scale probabilities. These probabilities are progressively summed to predict the results in a coarse-to-fine manner. Finally, we obtain the geometry-refined point clouds based on the predicted probabilities. Different from previous methods, we deal with decompressed point clouds with huge variety of distortions using a single model. Experimental results show that the proposed method can significantly improve the quality of the decompressed point clouds, achieving 9.30dB BDPSNR gain on three representative datasets on average.
Driving Mode Decision Making for Intelligent Vehicles in Stressful Traffic Events

The development of autonomous vehicles provides effective solutions and opportunities for reducing the probability of traffic accidents. However, because of technical limitations and economic and social challenges, achieving fully autonomous driving is a long-term endeavor. One principal research question is how to choose the suitable driving mode of an intelligent vehicle during stressful traffic events. For this purpose, an on-road experiment with 22 drivers was conducted in Wuhan, China; multisensor data were collected from the driver, the vehicle, the road, and the environment. Driving modes were classified into three categories on the basis of the driver’s self-reported records, and two physiological indexes that use the k-means cluster method were adopted to calibrate the self-reported driving modes. A feature-ranking algorithm based on the information gained was adopted to identify significant factors, and a driving mode decision-making model was established with the multiclass support vector machine algorithm. The results indicated that the SD of the front wheel angle, driver experience, vehicle speed, headway time, and acceleration had significant effects on the driving mode decision making. The driving mode decision-making model demonstrated a high predictive power with a prediction accuracy of 0.888 and area under the curve values of 0.918, 0.91, and 0.929 for the receiver operating characteristic curves. The conclusions provide theoretical support for decision making by the controller of a semiautomated vehicle.
Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast

We present an approach to learn voice-face representations from the talking face videos, without any identity labels. Previous works employ cross-modal instance discrimination tasks to establish the correlation of voice and face. These methods neglect the semantic content of different videos, introducing false-negative pairs as training noise. Furthermore, the positive pairs are constructed based on the natural correlation between audio clips and visual frames. However, this correlation might be weak or inaccurate in a large amount of real-world data, which leads to deviating positives into the contrastive paradigm. To address these issues, we propose the cross-modal prototype contrastive learning (CMPC), which takes advantage of contrastive methods and resists adverse effects of false negatives and deviate positives. On one hand, CMPC could learn the intra-class invariance by constructing semantic-wise positives via unsupervised clustering in different modalities. On the other hand, by comparing the similarities of cross-modal instances from that of cross-modal prototypes, we dynamically recalibrate the unlearnable instances' contribution to overall loss. Experiments show that the proposed approach outperforms state-of-the-art unsupervised methods on various voice-face association evaluation protocols. Additionally, in the low-shot supervision setting, our method also has a significant improvement compared to previous instance-wise contrastive learning.
User-controllable Recommendation Against Filter Bubbles

Recommender systems usually face the issue of filter bubbles: over-recommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention. This work proposes a new recommender prototype called User-Controllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r. t. both accuracy and diversity.
Evaluation of GNU Radio Platform Enhanced for Hardware Accelerated Radio Design

The advent of software radio technology has enabled radio developers to design and implement radios with great ease and flexibility. Software radios are effective in experimentation and development of radio designs. However, they have limitations when it comes to highspeed, high-throughput designs. This limitation can be overcome by introducing a hardware element to the software radio platform. Enhancing GNU Radio for Hardware Accelerated Radio Design project implements such a scheme by augmenting an FPGA co-processor to a conventional GNU Radio flow. In this thesis, this novel platform is evaluated in terms of performance of a radio design, as well as hardware and software system requirements. A simple and efficient Zigbee receiver design is presented. Implementation of this receiver is used as a proof-of-concept for the effectiveness and design methodology of the modified GNU Radio. This work also proposes a scheme to extend this idea for design of ultra-wideband radio systems based on multiband-OFDM. This work received financial support from the Strategic Technology Office (STO) at DARPA.
Reduction of Stratified Axi-Symmetric Euler-Poisson Equations Under Symmetry

The paper considers Euler-Poisson equations which govern the steady state of a self gravitating, rotating, axi-symmetric ﬂuid under the additional assumption that it is incompressible and stratiﬁed. In this setting we show that the original system of six nonlinear partial diﬀerential equations can be reduced to two equations, one for the mass density and the other for gravitational ﬁeld. This reduction is carried out in cylindrical coordinates. As a result we are able to derive also expressions for the pressure as a function of the density. The resulting equations are then solved analytically. These analytic solutions are used then to determine the shape of the rotating star (or interstellar cloud) by applying the boundary condition that the pressure is zero at the boundary.