Miracle-dz/UniFAR-FTUs huggingface dataset:
Femtoscopic correlations of identical charged pions and kaons in pp collisions at $\sqrt{s}=13$ TeV with event-shape selection

Collective behavior has been observed in high-energy heavy-ion collisions for several decades. Collectivity is driven by the high particle multiplicities that are produced in these collisions. At the Large Hadron Collider (LHC), features of collectivity have also been seen in high-multiplicity proton-proton collisions that can attain particle multiplicities comparable to peripheral Pb-Pb collisions. One of the possible signatures of collective behavior is the decrease of femtoscopic radii extracted from pion and kaon pairs emitted from high-multiplicity collisions with increasing pair transverse momentum. This decrease can be described in terms of an approximate transverse mass scaling. In the present work, femtoscopic analyses are carried out by the ALICE collaboration on charged pion and kaon pairs produced in pp collisions at $\sqrt{s}=13$ TeV from the LHC to study possible collectivity in pp collisions. The event-shape analysis method based on transverse sphericity is used to select for spherical versus jet-like events, and the effects of this selection on the femtoscopic radii for both charged pion and kaon pairs are studied. This is the first time this selection method has been applied to charged kaon pairs. An approximate transverse-mass scaling of the radii is found in all multiplicity ranges studied when the difference in the Lorentz boost for pions and kaons is taken into account. This observation does not support the hypothesis of collective expansion of hot and dense matter that should only occur in high-multiplicity events. A possible alternate explanation of the present results is based on a scenario of common emission conditions for pions and kaons in pp collisions for the multiplicity ranges studied.
Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels

Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often trained through supervision from human annotations of opinionated texts. These fine-grained annotations include identifying aspects towards which a user expresses their sentiment, and their associated polarities (aspect-based sentiments). Such fine-grained annotations can be expensive and often infeasible to obtain in real-world settings. There is, however, an abundance of scenarios where user-generated text contains an overall sentiment, such as a rating of 1-5 in user reviews or user-generated feedback, which may be leveraged for this task. In this paper, we propose a VAE-based topic modeling approach that performs ABSA using document-level supervision and without requiring fine-grained labels for either aspects or sentiments. Our approach allows for the detection of multiple aspects in a document, thereby allowing for the possibility of reasoning about how sentiment expressed through multiple aspects comes together to form an observable overall document-level sentiment. We demonstrate results on two benchmark datasets from two different domains, significantly outperforming a state-of-the-art baseline.
A Systematic Review of Machine Learning Enabled Phishing

Developments in artificial intelligence (AI) are likely to affect social engineering and change cyber defense operations. The broad and sweeping nature of AI impact means that many aspects of social engineering could be automated, potentially giving adversaries an advantage. In this review, we assess the ways phishing and spear-phishing might be affected by machine learning techniques. By performing a systematic review of demonstrated ML-enabled phishing campaigns, we take a broad survey the space for current developments. We develop a detailed approach for evaluation by creating a risk framework for analyzing and contextualizing these developments. The object of this review is to answer the research questions: (1) Are there high-risk ML-enabled phishing use cases? (2) Is there a meaningful difference between traditional targeted phishing campaigns and ML-enabled phishing campaigns? Practitioners may use this review to inform standards, future research directions, and cyber defense strategies.
Context-Enhanced Detector For Building Detection From Remote Sensing Images

The field of building detection from remote sensing images has made significant progress, but faces challenges in achieving high-accuracy detection due to the diversity in building appearances and the complexity of vast scenes. To address these challenges, we propose a novel approach called Context-Enhanced Detector (CEDet). Our approach utilizes a three-stage cascade structure to enhance the extraction of contextual information and improve building detection accuracy. Specifically, we introduce two modules: the Semantic Guided Contextual Mining (SGCM) module, which aggregates multi-scale contexts and incorporates an attention mechanism to capture long-range interactions, and the Instance Context Mining Module (ICMM), which captures instance-level relationship context by constructing a spatial relationship graph and aggregating instance features. Additionally, we introduce a semantic segmentation loss based on pseudo-masks to guide contextual information extraction. Our method achieves state-of-the-art performance on three building detection benchmarks, including CNBuilding-9P, CNBuilding-23P, and SpaceNet.
Comparing the sets of volume polynomials and Lorentzian Polynomials

Given n convex bodies in the real space of dimension d, we consider the set of homogeneous polynomials of degree d in n variables that can be represented as their volume polynomial. This set is a subset of the set of Lorentzian polynomials. Using our knowledge of operations that preserve the Lorentzian property, we give a complete classification of the cases when the two sets are equal.
Hybrid Arrays: How Many RF Chains Are Required to Prevent Beam Squint?

With increasing frequencies, bandwidths, and array apertures, the phenomenon of beam squint arises as a serious impairment to beamforming. Fully digital arrays with true time delay per antenna element are a potential solution, but they require downconversion at each element. This paper shows that hybrid arrays can perform essentially as well as digital arrays once the number of radio-frequency chains exceeds a certain threshold that is far below the number of elements. The result is robust, holding also for suboptimum but highly appealing beamspace architectures.
WINDII measurements of nightglow enhancements in the South Atlantic Magnetic Anomaly Zone

Enhancements of the O(¹D) 630 nm and O(¹S) 558 nm volume emission rates were detected by WINDII, the Wind Imaging Interferometer on board the Upper Atmosphere Research Satellite, UARS, as its limb‐locked fields of view crossed the South Atlantic magnetic anomaly zone during periods of high geomagnetic activity. The enhancement attributed to electron precipitation is 20 R for the 630 nm red line and 3 R for the 558 nm green line, less than the natural variability of these nightglow emissions as seen from below. Nevertheless, statistical tests indicate with 95% confidence that the enhancements are significantly distinguishable from the natural variability. The red enhancement lies west of the green because eastward drifting trapped electrons move toward lower mirror altitudes in this region. These enhancements are energetically consistent with earlier measurements of electron fluxes in this region; we estimate the total precipitation energy deposition rate at a few megawatts.
Design and finite element analysis of a 3D-printed packaging insert

Packaging inserts play a crucial role in protecting products during transportation. However, their design and production processes often rely on conventional methods limiting equipment capabilities. Moreover, the empirical nature of their design can result in a lack of reliability in the final product. To address these challenges, this study aimed to validate the design of a packaging insert using the finite element method and subsequently create it using 3D printing. The chosen material is a thermoplastic polyurethane (TPU) filament commonly used in fused deposition filament printers for 3D printing. This process demonstrates the feasibility of using 3D printing to create cushioning inserts for packaging and employing finite element analysis to simulate the insert behavior. The main findings of this research highlight the potential benefits of numerical simulation, revealing the areas where the insert is primarily impacted by weight. Furthermore, the forces load and displacement simulation results confirm that the TPU elastic limit (3.9x106 MPa) is sufficient to handle the weight this insert intends to hold. These tools determine the viability of the proposed design for its intended application. Therefore, this study verifies that 3D printing is a reliable option for producing packaging inserts, offering significant advantages over traditional methods. These advantages include increased design flexibility and the ability to create custom inserts on demand.
Genetics of Vesicoureteral Reflux

Vesicoureteral reflux (VUR) is the retrograde passage of urine from the bladder to the upper urinary tract. It is the most common congenital urological anomaly affecting 1-2% of children and 30-40% of patients with urinary tract infections. VUR is a major risk factor for pyelonephritic scarring and chronic renal failure in children. It is the result of a shortened intravesical ureter with an enlarged or malpositioned ureteric orifice. An ectopic embryonal ureteric budding development is implicated in the pathogenesis of VUR, which is a complex genetic developmental disorder. Many genes are involved in the ureteric budding formation and subsequently in the urinary tract and kidney development. Previous studies demonstrate an heterogeneous genetic pattern of VUR. In fact no single major locus or gene for primary VUR has been identified. It is likely that different forms of VUR with different genetic determinantes are present. Moreover genetic studies of syndromes with associated VUR have revealed several possible candidate genes involved in the pathogenesis of VUR and related urinary tract malformations. Mutations in genes essential for urinary tract morphogenesis are linked to numerous congenital syndromes, and in most of those VUR is a feature. The Authors provide an overview of the developmental processes leading to the VUR. The different genes and signaling pathways controlling the embryonal urinary tract development are analyzed. A better understanding of VUR genetic bases could improve the management of this condition in children.
AAPG Memoir 74, Chapter 22: Libya, Petroleum Potential of the Underexplored Basin Centers--A Twenty-First-Century Challenge

Recoverable reserves in approximately 320 fields in Libya's Sirt, Ghadamis, Murzuq, and Tripolitania Basins exceed 50 billion barrels of oil and 40 trillion cubic feet of gas. Approximately 80% of these reserves were discovered prior to 1970. Since then, there has been a less active and more conservative exploration effort. Complex, subtle and, in particular, deep plays were rarely pursued during the 1970s and 1980s because of definitive imaging technologies, limited knowledge of the petroleum systems, high costs, and risk adversity.
Methodology for Determining the Optimal Operating Strategies for a Chilled Water Storage System

Methodology for Determining the Optimal Operating Strategies for a Chilled Water Storage System. (May 2010) Zhiqin Zhang, B.S.; M.S., Tsinghua University Chair of Advisory Committee: Dr. William D. Turner This dissertation proposed a new methodology for determining the optimal operating strategies for a chilled water storage system under a Time-of-Use electricity rate structure. It is based on a new classification of operating strategies and an investigation of multiple search paths. Each operating strategy consists of a control strategy and the maximum number of chillers running during the off-peak and on-peak periods. For each month, the strategy with the lowest monthly billing cost and minimal water level higher than the setpoint is selected as the optimal operating strategy for the current month. A system model is built to simulate the tank water level at the end of each time step and the system total power during each time step. This model includes six sub-models. Specifically, the plant model is a forward model using a wire-to-water concept to simulate the plant total power. For the Thermal Energy Storage (TES) model, the tank state is described with total chilled water volume in the tank and its derivation is the tank charging or discharging flow rate. A regression model is adopted to simulate the loop supply and return temperature
The good earthworm: How natural processes preserve upland Archaic archaeological sites of western Illinois, U.S.A.

In western Illinois, many soil profiles developed into upland loess deposits (Peoria Silt) contain Archaic period artifacts greater than 3500 yr B.P. in stone zones below plow level. For artifacts in prairie and prairie‐forest transition soils (not forest soils), depth distribution curves suggest they were buried in biomantles by small soil fauna. Artifacts of sizes archaeologists routinely collect generally move down while retaining fine‐scale horizontal integrity. The process results in stratigraphic separation of Archaic and Woodland period components that otherwise would commingle at the surface. The characteristic distribution of known upland sites, narrowly rimming stream valley headwaters, reflects incomplete burial of materials on steeper forested slopes. On adjacent gently sloping upper shoulder segments of valleys where grassy cover more strongly influences soil development (and by inference on broad level uplands where true prairie soils occur), Archaic artifacts will be buried in the biomantle and go undetected by surface surveyors. © 2002 John Wiley & Sons, Inc.
A Bathymetric Model for the South China Sea from Satellite Altimetry and Depth Data

A bathymetric model for the South China Sea is computed from altimeter-derived gravity anomalies, shipborne depths, ETOPO5, and the GMT shorelines using a procedure that includes downward continuation, linear regression, and data merging. The model best fits the GMT shorelines and is a compromise between smoothness, degree of agreement with ship data, and the seafloor features we wish to retain. The model is proven useful in studying the tectonics and modeling the ocean tide of the South China Sea.
A slow slip event triggered by teleseismic surface waves

In recent years slow slip events (SSE) have been observed to occur at regular intervals on the deep portions of subduction zone interfaces. These are accompanied by seismic tremor that occurs over their duration. It has been observed that tremor activity shows transient modulations in response to earth tides and the passage of seismic waves from distant earthquakes. Here we show, for the first time, geodetic evidence for the triggering of an interplate SSE itself by teleseismic surface waves. This SSE, in southwest Japan, which had an equivalent magnitude Mw 5.3 and duration of 1.5 days, was triggered by the surface waves of a Mw 7.6 earthquake in Tonga. This evidence was captured by a newly deployed sensitive strainmeter network. The triggered SSE occurred on a place on the plate interface where the recurrence time for such events had almost expired, whereas other regions, at up to 90% of the recurrence time, were not triggered. This provides information for the conditions for triggering and generation of SSEs and, perhaps, for regular earthquakes.
Fully Distributed Minimal-order Attack-free Protocol for Nonlinear Multi-agent Networks

Achieving fully distributed consensus in multi-agent networks (MANs) based on relative output information is of significant importance in the field of distributed cooperative control. Despite the recent development of consensus theory, there is still lack of an efficient approach to constructing fully distributed consensus protocols for MANs using only relative output measurements, especially for the case with inherent nonlinear agent dynamics under directed communication graphs. This paper studies fully distributed minimal-order protocol design for consensus of MANs with Lipschitz-type nonlinear agent dynamics under general directed communication graphs. It is revealed that the minimal order of the observer estimating consensus error equals the difference between the dimension of a single agent's state vector and the rank of the output matrix of the agent. Based on the designed distributed minimal-order observer, fully distributed consensus protocols are developed. The proposed protocols have the abilities of reducing communication burden and also can avoid network attacks on the internal states. Numerical simulations are presented to illustrate the analytical results.
A Traffic Police Gesture Recognition Method Based on Nine-axis IMU and Random Forest Algorithm

This paper proposes a traffic police gesture recognition algorithm based on a nine-axis Inertial measurement unit(IMU). Two wearable devices are attached to both hands of the tester, and then eight types of standard traffic police gestures are performed 150 times. During the experiment, the two-channel data of three-axis acceleration, three-axis angular velocity, and three-axis magnetometer from both hands is collected for model training and validation. A Butterworth filter is utilized for denoising and then a Kalman filter is utilized for the fusion of data from two channels. After that, the feature extraction is implemented using the Fast Fourier transform (FFT), and the extracted feature values are used for training on a random forest model. Experimental results demonstrate that the proposed method has higher classification accuracy than related works in traffic police gesture recognition and can be applied in various areas such as autonomous driving, traffic police gesture training, etc.
Real-time Plant Disease Segmentation and Identification using Deep Learning

Farmers usually face the common issue of plant pests and diseases that affect their crop productivity and yield. Misdiagnosis and mistreatment of the crop's condition will lead to a similar outcome further amplifying the harm of the problem. To take the guesswork out of farming and provide an immediate and convenient plant disease advisory, we propose a real-time plant disease segmentation and identification approach using transfer learning that accurately detects local plant diseases affecting crops and offers a corresponding step-by-step treatment measure. A real dataset of local plant diseases was used to train several CNN architectures such as EfficientNet, ResNet and VGG. Prior to the training stage, advanced preprocessing and segmentation techniques were used to enhance the images and efficiently extract the region of interest, which further enable transfer learning more effectively. The best-performing model when the accuracy and prediction time efficiency combined are considered is the EfficientNet-B0 model, resulted in a prediction time of 0.0145 seconds, an overall accuracy of 99.0%, and a mean F1 score of 98.7%. Moreover, we integrated the best-performing model into a mobile application (named PlantDoc) for further validation and usage in a real-world scenario.
Featherweight and high-precision closed-loop detection method

One of the key technologies for autonomous driving is localization. Precise localization is inseparable from high-precision maps. Simultaneous Localization And Mapping (SLAM) is technologies for building maps and locating in unknown environments. One of the mainstream directions of current autonomous driving research is how to carry out efficient closed-loop processing over long periods of time, eliminating accumulated error in the odometer. Improving the accuracy of the map and ensuring the accuracy of localization is crucial for ensuring the safety of autonomous driving. Therefore, this paper proposes a featherweight and high-precision method for multi-sensor fusion of laser and GPS for loop detection. The closed-loop results are optimized together with IMU to improve the mapping accuracy.
Model-Based Software Development for Predictive Battery Temperature Estimation

In modern vehicles, developing reliable and efficient software systems is crucial for optimal performance, safety, and cost-effectiveness. This paper suggests a model-driven method for developing automotive software, particularly emphasizing the creation of a predictive system for estimating battery temperature. The proposed approach employs Simulink models and the KF algorithm to develop a software system for the temperature control of the battery in electric cars. The accuracy and performance of the KF algorithm are assessed by comparing it with the EKF method for non-linear battery models. Furthermore, the reliability and performance of the proposed approach are assessed through MATLAB Simulink MIL, SIL, and PIL testing. The study findings suggest that the EKF is recommended for estimating battery temperature in rapidly fluctuating systems with non-linear behavior, where accurate estimation is critical. The developed system showed good scalability, efficiency, and outperformed other methods by providing more precise and timely temperature estimates for the battery.
A Trust Based Node Identification and Location Optimization for WSN using Cloud Infrastructure

The deployment of wireless sensor networks (WSNs) into the environment can increase the awareness of the environment in most cases but it is usually restricted by limited energy and computing resources in the network. To address this limitation, cloud infrastructure can be used as a platform for location optimization. With effective location optimization, the cloud infrastructure can use its computing capabilities to minimize the number of sensor displacements while maintaining the desired coverage of the WSN. This will allow the WSN nodes to extend the lifetime and make more efficient use of their limited energy and computing resources while still providing the necessary coverage. Location optimization algorithms can be used to determine the optimal locations for the network nodes. These algorithms consider the spatial characteristics of the environment and prioritize the placement of nodes based on their expected utility. Several methods have been proposed for location optimization based on the cloud infrastructure, such as Reinforcement Learning, Differential Evolution, and Swarm Optimization techniques. These techniques were compared in terms of their accuracy and scalability in optimizing a WSN's coverage performance. The results showed that Differential Evolution outperformed the other techniques in terms of accuracy and Swarm Optimization was the best for scalability. The use of the cloud for location optimization for WSNs can help extend the lifetime of the network and provide a viable solution for long-term data collection.