Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Knowledge Engineering Using MatFlow for Inverse Quantum Dye Design

Inverse materials design can be viewed as a reverse engineering process of material constituents from a user given set of targets characterizing material properties. Machine learning techniques could be used to map a feature space <tex>$x$</tex> of material properties to a contextual latent space <tex>$z$</tex> over an object space <tex>$O$</tex>, and then reconstruct a distinct object space <tex>$O^{\prime}$</tex> over the latent space <tex>$z$</tex> to maximize the context <tex>$c$</tex>. In this paper, we introduce a new machine learning tool, called MatFlow, for inverse material design using machine learning. In MatFlow, we first learn the latent space <tex>$z$</tex> characterizing a context feature set <tex>$c$</tex>. MatFlow then helps identify novel materials <tex>$O^{\prime}$</tex> over the latent space <tex>$z$</tex> with a potential to exceed the contextual threshold <tex>$\theta$</tex> without destabilizing the latent space. We explain MatFlow features and its capabilities using an application in quantum dye material discovery. The main focus of this paper is developing a computational strategy to identify <tex>$z$</tex> in the context of <tex>$c$</tex>, and inform the characteristics of <tex>$z$</tex> space values of <tex>$O^{\prime}$</tex> most likely to meet the contextual threshold <tex>$\theta$</tex>.
IMPROVING N-FERTILIZATION EFFICIENCY FOR BARLEY PLANT GROWN ON SANDY CALCAREOUS SOIL BY USING ORGANIC AND BIOFERTILIZERS

: A pot experiment was carried out for investigating the possibility of partial or entire replacement of mineral nitrogen fertilizers with organic and biofertilizers, aiming to improve the efficiency of nitrogen fertilization for barley plant grown on sandy calcareous soil collected from Agriculture Research Station in Nubariya. Mineral nitrogen fertilizer was added at the rates 30, 60, 90 and 120 mg N kg soil -1 . Phosphorus and potassium were added at the recommended rates of 15 mg P and 30 mg K kg soil -1 , respectively. Organic fertilizers were added from different sources "compost, chicken manure and pigeon dung" at a rate of 30 mg kg N soil -1 based on their nitrogen content. Biofertilization was achieved through the addition of 5g pot -1 serialin or/and 5g pot -1 phosphorene; mixed thoroughly with soil. Upon the observed data, the highest dry matter yield was obtained when "compost + serialin" were applied to the soil in combination with mineral-N at addition level of "90 mg N kg soil -1 ". The highest nitrogen uptake was obtained when the soil was treated with "chicken manure + serialin" combined with mineral-N at addition level of 60 mg N kg soil -1 . The highest phosphorus uptake was obtained when soil treated with "chicken manure + phosphorine" combined with mineral-N at level of 60 mg N kg soil -1 . The highest potassium uptake was obtained when soil was treated with "chicken manure + serialin" combined with mineral-N at level of 90 mg N kg soil -1 . The highest yield of straw and grains as well as protein content were obtained when soil was treated with "chicken manure + serialin" combined with mineral-N at level of 60 mg N kg soil -1 .
The Application of Luminescence to Tectonics: Improvements to Luminescent Dating and its Utilization in High-Energy Depositional Environments

Author(s): Lawson, Michael | Advisor(s): Rhodes, Edward J.; Yin, An | Abstract: The dissertation presents improvements made to luminescent dating in tectonic areas, with examples of the application of luminescence to tectonic questions north of Death Valley, CA. The study of tectonics is incomplete without an understanding of fault processes through time. This can be difficult in areas with limited Quaternary age control. The technique of luminescence dating can provide absolute ages, but tectonic areas tend to exhume fresh material that can have poor luminescent properties. The “quartziness test” is outlined which numerically accesses the contamination of feldspar grains within quartz samples for small aliquots and single grain measurements. A new illumination protocol called Sinusodially Modulated Optically Stimulated Luminescence (SM-OSL) is presented which can also address contamination through trap component analysis and provides more rapid measurement then tranditional Linearly Modulated Optically Stimulated Luminescence (LM-OSL) Post- infrared infrared-stimulated luminescence (pIRIR) has been shown to be effective dating protocol that does not fade to the same degree as traditional IRSL measurements. Post-IRIR is applied to several studies pairing luminescence dating with geomorphic analysis. In Deep Springs Valley, a series of shorelines have been rotated vertically due to local extension. Two shorelines are dated as having ages of 7.51 i? ½ 0.67 ka and 10.94 i? ½ 0.95 ka, suggesting ~0.3 mm/yr vertical slip on the Deep Springs fault. These are comparable to long-term rates of 0.2 mm/yr (vertical and horizontal). In Eureka Valley, a tectonically controlled basin east of Deep Springs, slip recurrence is analyzed by pairing pIRIR with geomorphic modeling to produce a new mass diffusivity constant (κ) of 1.79 m2ka-1. In this area, a 6.1 moment magnitude earthquake occurred on May 17, 1993. Geomorphic modeling of the northern strand of this oblique-normal fault suggests a vertical slip rate of 0.6 mm/yr. Field mapping in the area uncovered a right lateral fault that may be the cause of an observed decrease of slip on the Fish Lake Valley Fault Zone (FLVFZ).
DDoS Attack Classification Leveraging Data Balancing and Hyperparameter Tuning Approach Using Ensemble Machine Learning with XAI

A distributed denial-of-service (DDoS) attack is a cyber-attack that aims to disrupt the regular traffic of a targeted server, service, or network by inundating the target or its surrounding infrastructure with a flood of Internet traffic. DDoS attacks can cause significant harm to the security of the network environment. There are several works on the classification of DDoS attacks using Machine Learning (ML) and Deep Learning (DL). However, some improvement is needed, and in-depth research is necessary with the rapidly changing DDoS attack types. This study presents a novel ensemble model that can identify DDoS attacks. The approach leverages ML algorithms such as Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost) classifiers to detect and classify these malicious attacks effectively. The hyper-tuning process plays a significant role in increasing the performance of our proposed model and reducing overfitting. In our research, we use the potent eXplainable Artificial Intelligence (XAI) models SHAP (Shapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). By utilizing SHAP and LIME's capabilities, we improve our ML models' readability and transparency, giving us a better understanding of difficult predictions and model behavior. The evaluation results demonstrate that the XGBoost ensemble model outperforms other classifiers, achieving an impressive accuracy rate of 97 %, with an outstanding F -score of 97%. The precision and recall are accordingly 98% and 96%.
Localized Delaunay Refinement for Sampling and Meshing

The technique of Delaunay refinement has been recognized as a versatile tool to generate Delaunay meshes of a variety of geometries. Despite its usefulness, it suffers from one lacuna that limits its application. It does not scale well with the mesh size. As the sample point set grows, the Delaunay triangulation starts stressing the available memory space which ultimately stalls any effective progress. A natural solution to the problem is to maintain the point set in clusters and run the refinement on each individual cluster. However, this needs a careful point insertion strategy and a balanced coordination among the neighboring clusters to ensure consistency across individual meshes. We design an octtree based localized Delaunay refinement method for meshing surfaces in three dimensions which meets these goals. We prove that the algorithm terminates and provide guarantees about structural properties of the output mesh. Experimental results show that the method can avoid memory thrashing while computing large meshes and thus scales much better than the standard Delaunay refinement method.
OptFog: Optimized Mobility-Aware Task Offloading and Migration Model for Fog Networks

Due to the increased usage of mobile devices, there are now diverse application needs that favor the use of Fog computing architecture over the Cloud's centralized architecture. Fog Computing arises in this circumstance, which moves certain processing resources to the edge or among themselves and provides low-latency access to networked devices. However, minimizing the number of migrations to utilize the limited resources at the fog devices is essential and challenging. In this regard, this paper presents OptFog (optimized mobility-aware predicted task migration model for fog-assisted networks) as a migration assistance model. Three machine learning models are employed to anticipate mobility and make proactive migration decisions. The data used in OptFog is from active users provided Luxembourg SUMO Traffic (LuST) from the city of Luxembourg. Machine learning models like Decision Tree, k-Nearest Neighbors (k-NN), and Random Forest are evaluated to estimate the user's future position. Thereafter, the best approach is determined by comparing them. The Random Forest model produces a significant result, with an overall accuracy of 97.47%. OptFog shows the overall time, downtime and energy consumption when employed with a prediction-based migration strategy and also analyzes the most elevated scenarios for task migration. Simulations have shown that migration can result in satisfactory effectiveness for supporting mobility in fog-assisted networks.
A Time Backoff-Based Energy-Efficient Geographical Forwarding for Wireless Sensor Networks

A geographical forwarding algorithm is an attractive localized data forwarding scheme for wireless sensor networks (WSNs) due to its directional routing properties and scalability. Also, the inherent constraints of WSNs such as limited battery power and network resources require the simplicity and energy efficiency of protocols. In this paper, we propose a Time Backoff-based energy-efficient Geographical Forwarding (TBGF) for WSNs. TBGF is a combined MAC and routing protocol based on geographical forwarding that achieves energy efficiency and minimization of control overhead. TBGF uses the energy-aware time backoff scheme with the optimal geographical information to make the next forwarding decisions. Our simulation results show that the proposed protocol outperforms other existing schemes in terms of energy efficiency and network performance.
Fast simulation of vehicles with non-deformable tracks

This paper presents a novel technique that allows for both computationally fast and sufficiently plausible simulation of vehicles with non-deformable tracks. The method is based on an effect we have called Contact Surface Motion. A comparison with several other methods for simulation of tracked vehicle dynamics is presented with the aim to evaluate methods that are available off-the-shelf or with minimum effort in general-purpose robotics simulators. The proposed method is implemented as a plugin for the open-source physics-based simulator Gazebo using the Open Dynamics Engine.
Enhancing Inter-Satellite Data Relay in Dynamic Space Communication

In this paper, we explore the design and operations of an inter-satellite data relay network, which includes Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geo-stationary Orbit (GEO) satellites. Due to the different orbiting speeds and dynamic behaviors of these satellite constellations, we consider Delay-Tolerant Networking (DTN) between them and implement contact graph routing (CGR) at the layer-3 and Licklider Transmission Protocol (LTP) at layer-4. We base our analysis on a realistic space scenario where satellite locations and movements over time are extracted from a real-time satellite database. We demonstrate end-to-end latency and goodput of a file transfer through the satellite network under consideration.
Experimental investigation on effects of karanja biodiesel (B100) on performance, combustion, and regulated and GHG emissions characteristics of an automotive diesel engine

ABSTRACT In the present study, the effects of base diesel and biodiesel (karanja, B100) on combustion, performance and emissions including greenhouse gas emissions (CO2, CH4 and N2O) of an automotive diesel engine were experimentally investigated. The regulated emissions such CO, hydrocarbon (HC) and smoke decrease with B100; however, NOx emission increases at all loads. The GHG emissions such as CH4 and N2O are less at all loads but CO2 emission is higher. The in-cylinder temperature is higher with B100. Combustion characteristics indicate the peak heat release rate is lower and combustion duration is higher with B100. An important conclusion that emerged from the study is that the CH4 and N2O emissions are inversely proportional to in-cylinder temperature. Although total CO2-equivalent GHG emission in the engine is moderately higher with B100, some quantity of the gas (with consideration of the uncertainty in the amount of carbon-neutral fuel) in the atmosphere will be recycled by the karanja plants.
Container Privilege Escalation and Escape Detection Method Based on Security-First Architecture

Privilege escalation and escape attacks pose the greatest security threats for containers since they have a direct impact on the confidentiality, integrity, and availability of the underlying host and the entire container cloud platform. Meanwhile, there are numerous privilege escalation and container escape exploits. Existing works can only partially identify privilege escalation and escape attacks. Additionally, as they are at the same privilege level as the operating system, mal ware can easily get around them. In this work, we propose CPEED, a method based on security-first architecture to detect container privilege escalation and escape attacks caused by memory corruption exploits. First, We create a general attack model by studying the 25 memory corruption exploits and determine what to measure. Then, we address the difficulties in transferring semantic information from the computing subsystem to the security subsystem due to hardware isolation, and perform integrity measurements in the security subsystem. Experimental results demonstrate that CPEED can detect the privilege escalation and escape attacks effectively with minor performance impacts on computing workloads, and it is difficult for malware to evade detection.
Neuroanatomy of space, body, and posture perception in patients with right hemisphere stroke

Objective: To specify the neuroanatomical correlates of biases in the representations of the gravitational vertical (subjective vertical [SV]) and body axis (subjective straight ahead [SSA]), as well as postural difficulties, in patients with hemispheric stroke. Methods: The analysis focused on right hemisphere lesions in 21 neglect patients and 21 non-neglect patients (using MRIcro software) and related performance in 2 experimental tasks (SV and SSA) and a clinical balance assessment. Voxel-based lesion-symptom mapping was used to highlight brain areas in which lesions best explained the severity of task biases (p < 0.01). Results: The bias in the representation of body orientation was found to be strongly related to lesions of the anterior parietal cortex and the middle part of the superior temporal gyrus. The SV errors were associated with more widespread lesions of the posterior parietal and temporal cortices. Imbalance was preferentially associated with lesions of the posterior insula and the adjacent temporoparietal cortex. Conclusion: This study evidenced a cortical dissociation for body-centered and gravitational representations biases, which may reflect the differential involvement of these brain regions in spatial information processing. The lesions involved in representation biases (especially of the SV) and postural difficulties overlapped to some extent in the temporoparietal, superior temporal, and posterior insular regions of the cortex.
Base lung sound in diffuse interstitial pneumonia analyzed by linear and nonlinear techniques

Abnormal lung sounds in diffuse interstitial pneumonia have been characterized by the presence of crackles. However, few attempts have tried to analyze the sound where crackles are immersed. In this work base lung sounds (BLS) were analyzed by linear and nonlinear techniques to find possible differences between normal subjects and patients with diffuse interstitial pneumonia. In both groups, segments of lung sounds (crackles absent) and segments of BLS (lung sound in between crackles) were analyzed from acquired lung sounds from four points at the posterior chest, two apical and two basal. In this study, 8 healthy subjects and 8 patients participated and BLS were analyzed by spectral percentiles and sample entropy. Although spectral percentiles and sample entropy (SampEn) tended to be lower in the group of patients, statistical differences (p<0.05) between normal subjects and patients were found in BLS at the left hemithorax at basal and apical regions, while at the right hemithorax significant differences were found only at the basal region using the nonlinear technique. We conclude that in respect to normal subjects, BLS of patients with diffuse interstitial pneumonia present differences as assessed by SampEn, so that BLS by itself provides useful information. Moreover, it seems that nonlinear technique did a better discrimination of abnormal BLS than spectral percentile parameters.
Joint optimization of audible noise suppression and deep neural networks for single-channel speech enhancement

Improving the perceptual quality of speech signals is a key yet challenging problem for many real world applications. Taking into account the good performance of deep learning in signal representation, a novel single-channel speech enhancement technique is presented based on joint Deep Neural Networks and audible noise suppression as a whole network architecture. This new deep neural network jointly trains an audible noise suppression function which is used to estimate the magnitude spectrum of the clean speech and shape the spectrum of the audible noise at the same time. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the baselines, no matter whether the noise conditions are included in the training set or not.
Quantal Neurotransmitter Secretion Rate Exhibits Fractal Behavior

The rate of exocytic events from both neurons and non-neuronal cells exhibits fluctuations consistent with fractal (self-similar) behavior in time, as evidenced by a number of statistical measures. We explicitly demonstrate this for neurotransmitter secretion atXenopus neuromuscular junctions and for rat hippocampal synapses in culture; the exocytosis of exogenously supplied neurotransmitter from cultured Xenopus myocytes and from rat fibroblasts behaves similarly. The magnitude of the fluctuations of the rate of exocytic events about the mean decreases slowly as the rate is computed over longer and longer time periods, the periodogram decreases in power-law manner with frequency, and the Allan factor (relative variance of the number of exocytic events) increases as a power-law function of the counting time. These features are hallmarks of self-similar behavior. Their description requires models that exhibit long-range correlation (memory) in event occurrences. We have developed a physiologically plausible model that accords with all of the statistical measures that we have examined. The appearance of fractal behavior at synapses, as well as in systems comprising collections of synapses, indicates that such behavior is ubiquitous in neural signaling.
Comptes Rendus Mathématique

We prove that there exist two families in M 2 , M 3 of non-Galois covers of the projective line whose Jacobians trace out Shimura subvarieties of A 2 , A 3 . They provide the ﬁrst two explicit examples of Shimura subvarieties obtained by means of Jacobians carrying non-trivial endomorphisms not directly induced by the automorphisms of the curves. We also obtain a new example of a positive dimensional family of special Pryms in A δ 4 . 2020 Mathematics
OFDMA femtocells: A roadmap on interference avoidance

OFDMA femtocells have been pointed out by the industry as a good solution not only to overcome the indoor coverage problem but also to deal with the growth of traffic within macrocells. However, the deployment of a new femtocell layer may have an undesired impact on the performance of the macrocell layer. The allocation of spectrum resources and the avoidance of electromagnetic interference are some of the more urgent challenges that operators face before femtocells become widely deployed. In this article a coverage and interference analysis based on a realistic OFDMA macro/femtocell scenario is provided, as well as some guidelines on how the spectrum allocation and interference mitigation problems can be approached in these networks. Special attention is paid to the use of self-configuration and self-optimization techniques for the avoidance of interference.
Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model

To solve the problems of polysemy and feature extraction in the text sentiment analysis process, a BERT-CNN-BiLSTM-Att hybrid model has been proposed for text sentiment analysis. The BERT pre-training model was established to break up the text input into words and obtain a dynamic word vector that was then input into the CNN and the BiLSTM models respectively. Later, the local features of the word vector, extracted using CNN, and the global features, extracted using BiLSTM, were fused, and the key information of the Douban movie review dataset was highlighted using the attention mechanism to realize sentiment categorization of the dataset. The results of comparison between the constructed model and Word2Vec-BiLSTM, Word2Vec-CNN, Word2Vec-CNN-BiLSTM-Att, BERT, BERT-CNN and BERT-BiLSTM models show that the model that runs against the test dataset has an increased accuracy by 4.63%,4.37%,3.64%,2.63%,2.56% and 5.54% respectively. The experimental findings reveal that BERT-CNN-BiLSTM-Att’s sentiment analysis method is more accurate in performing sentiment classification.