Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case of Regeneration in Polychoete Worms

The facts recorded in the following note were ascertained in the course of my observations made upon a marine rock-boring polychaete worm found at Tenby in the spring of the present year. This worm, a species of Potamilla, is living in limestone rock, in which another species of the same genus, Potamilla reniformis , is also burrowing. It differs from the latter in various particulars, amongst which may be noted the absence of eyes on the branchial filaments, the colour of the blood (which is red instead of being sometimes green), the form of the setae, and the character of the external tube, which is largely covered, especially at the tip, with minute pieces of shell attached edgewise, imparting to it a white, rugged appearance, somewhat similar to that of the tube of Owenia . The worm is sometimes as much as 3½ inches long, and is seldom extracted entire from the rock, fragments only, of varying length, usually being obtained. It occurred to me that this material might be utilised for the study of the regeneration of the lost parts, and my experiments in that direction succeeded beyond my expectations. Not only did the fragments renew these parts (both anterior and posterior) but they demonstrated the existence of a power to economise labour in this respect, by changing the arrangements of certain of the old parts , so as to complete the model of the original animal.
Implementation of the CNN-SVM Hybrid Model for Leukocoria Detection

Retinoblastoma (Rb) is a tumor that is a primary intraocular malignancy of the retina that generally affects infants and children. The most common symptom of Rb is a white reflection emitted from the retina of the eye through the pupil called leukocoria. This disease must be treated earlier, otherwise, it can lead to dangerous diseases. Based on this, an early diagnosis will be very important, and this will be done by detecting leukocoria. With the development of technology, image classification has become easier. Convolutional Neural Network (CNN) is an advanced image processing algorithm, and Support Vector Machine (SVM) is an advanced algorithm for general classification. For this reason, the authors propose a CNN-SVM hybrid method to classify eyes with leukocoria. The model with the CNN-SVM hybrid method will extract features from the eye photographic image on the CNN layer and perform the final classification using the SVM algorithm. The model has good performance with accuracy, precision, recall, and F1-score values reaching 96.2%, 90.9%, 100.0%, and 95.2%.
A 12/16 GSps Time-Interleaved Pipelined-SAR ADC with Temperature Robust Performance at 0.75V Supply in 7nm FinFET Technology

This paper presents a 10.2b time-interleaved ADC in 7nm FinFET technology with 45/59 dBSNDR/SFDR at 12GSps and 41/55 dBSNDR/SFDR at 16GSps, while consuming 290 and 334mW, respectively. A temperature-stabilized dynamic residual amplifier achieves a low 11% gain deviation from -25C to 105C. This is the first pipelined-SAR ADC that operates above 10GSps with robust performance over temperature with a 0. 75V supply. The ADC has 1. 3x lower input range than state-of-the-art ADCs to relax RF front-end linearity and yet achieves a competitive FoM of 148 dB.
A 49 year hindcast of surface winds over the Iberian Peninsula

The use of hindcast climatic data is quite extended for multiple applications. However, this approach needs the support of a validation process to allow its drawbacks and, therefore, confidence levels to be assessed. In this work, the strategy relies on an hourly wind database resulting from a dynamical downscaling experiment, with a spatial resolution of 10 km, covering the Iberian Peninsula (IP), driven by the ERA40 reanalysis (1959–2001) extended by European Centre for Medium‐Range Weather Forecast (ECMWF) analysis (2002–2007) and comprising two main steps.
A 53-62 GHz Two-channel Differential 6-bit Active Phase Shifter in 55-nm SiGe Technology

In this paper, a two-channel 6-bit active differential phase shifter is presented. A novel optimization technique is proposed to achieve accurate I/Q signals from the poly-phase filter, ultimately resulting in low gain and phase errors for the phase shifter. Unlike conventional matching methods between poly-phase filter and vector modulator, a capacitive loading is proved to reduce the sensitivity of phase error to impedance variation of the vector modulator. With the appropriate selection of circuit parameters for the poly-phase filter, reasonable phase and gain errors can be achieved without making any unnecessary modifications to the vector modulator. Using this technique, a two-channel phase shifter is fabricated in a 55-nm SiGe process. The peak differential gain of the phase shifter is around 2.6 dB. Moreover, in the frequency range of 53-62GHz, both channels exhibit maximum gain and phase errors of 1.6 dB and 3.3°, respectively.
ScrapeIOC: Designing a Web-scraping Tool for Malware Detection based on Indicators of Compromise

Modern malware profoundly impacts our digitized planet; its victims range from private individuals to governments to international corporations. Therefore, it is essential to collect as much malware-related data as possible. Such data advances our understanding of malware and equips us to mitigate its noxious effects. In this work, we present a web-scraping tool, dubbed ScrapeIOC, which was written in Python3 and captures hashed malware samples, tagging them with either a malware type or a malware family. Additional information about the hashed malware sample is also recorded. Scraping was conducted via API calls to various websites; ultimately, a database containing more than 20,000 hashed samples was produced. The hashed malware samples included MD5, SHA1, and SHA256 hashes. This work details each part of the tool’s code, including its auxiliary features, such as information display and database backups. Toward the end of this work, a second database was also generated. For the second database, the ten most common malware families of spring 2020 were used as tags. The second database comprises over 4,000 hashed samples.
Deep neuroevolution to predict astrocytoma grade from functional brain networks

Whereas MRI produces anatomic information about the brain, functional MRI (fMRI) tells us about neural activity within the brain, including how various regions communicate with each other. The full chorus of conversations within the brain is summarized elegantly in the adjacency matrix. Although information-rich, adjacency matrices typically provide little in the way of intuition. Whereas trained radiologists viewing anatomic MRI can readily distinguish between different kinds of brain cancer, a similar determination using adjacency matrices would exceed any expert’s grasp. Artificial intelligence (AI) in radiology usually analyzes anatomic imaging, providing assistance to radiologists. For non-intuitive data types such as adjacency matrices, AI moves beyond the role of helpful assistant, emerging as indispensable. We sought here to show that AI can learn to discern between two important brain tumor types, high-grade glioma (HGG) and low-grade glioma (LGG), based on adjacency matrices. We trained a convolutional neural networks (CNN) with the method of deep neuroevolution (DNE), because of the latter’s recent promising results; DNE has produced remarkably accurate CNNs even when relying on small and noisy training sets, or performing nuanced tasks. After training on just 30 adjacency matrices, our CNN could tell HGG apart from LGG with perfect testing set accuracy. Saliency maps revealed that the network learned highly sophisticated and complex features to achieve its success. Hence, we have shown that it is possible for AI to recognize brain tumor type from functional connectivity. In future work, we will apply DNE to other noisy and somewhat cryptic forms of medical data, including further explorations with fMRI.
ON THE SAFETY OF USE OF BACILLUS SPECIES IN ANIMAL NUTRITION

In the light of its previous report and of newly available scientific data, SCAN is requested to reassess the safety of the use of bacteria of the genus Bacillus (Bacillus cereusand other species) in animal nutrition. SCAN is also requested to identify the scientific data which should be provided for the safety evaluation of products using strains ofBacillusspecies submitted for authorisation as feed additives.
An assessment of the use of Bacillariophyceae as biological monitors of heavy metal pollution in Australian tropical streams.

xi THESIS DECLARATION.xiii ACKNOWLEDGMENTS.xiv CHAPTER 1 INTRODUCTION . 15 1.1 AUSTRALIAN WATER RESOURCES AND WATER QUALITY . 15 1.2 MINING IN THE NORTHERN TERRITORY . 15 1.3 MONITORING CHANGE IN AQUATIC SYSTEMS . 16 1.4 MONITORING AND BIOMONITORING OF AUSTRALIAN AQUATIC SYSTEMS . 20 1.5 DIATOMS AS BIOLOGICAL INDICATORS . 21 1.6 DIATOM TAXONOMIC ASSUMPTIONS . 23 1.7 METHODS OF DIATOM MONITORING . 26 1.8 DIATOM STUDIES IN AUSTRALIA AND THE TROPICS . 28 1.9 DIATOM MONITORING OF HEAVY METAL POLLUTION . 29 1.10 STUDY AIMS . 300 1.11 THESIS STRUCTURE. 31 CHAPTER 2 CHARACTERISTICS OF STUDY SITES . 34 2.1 CHARACTERISTICS OF AUSTRALIAN TROPICAL RIVERS . 34 2.1.1 Hydrology . 34 2.1.2 Climate . 34 2.1.3 Water chemistry . 35 2.1.4 Biota . 35 2.2 MINING IN THE NORTHERN TERRITORY . 35 2.2.1 Rum Jungle Mine . 38 2.2.2 Tom’s Gully Mine . 38 2.2. 3 Redbank Mine . 39 2.2. 4 Cosmo Howley Mine . 39 2.2. 4.1 Cosmo Howley Mine . 39 2.2. 4.2 Brocks Creek Mine . 40 CHAPTER 3 FIELD, LABORATORY, GENETIC AND STATISTICAL TECHNIQUES . 41 3.1 FIELD METHODS . 41 3.1.1 Selection of sites . 41 3.1.2 Selection and measurement of environmental variables . 46 3.1.3 AUSRIVAS observations . 47 3.1.4 Diatom sampling . 48 3.2 LABORATORY METHODS . 48 3.2.1 Chemical Analysis . 48 3.2.2 Diatom preparation techniques . 50
A DC-DC Converter with Multistage Boost Input and High Voltage Gain for Solar PV Applications

This research paper presents a grid-connected DC/DC converter with multiple inputs, emphasizing a substantial voltage gain and non-isolated operation. The structure can accommodate a number of input stages and allows independently performing the MPPT function and power sharing. Since a higher voltage increase is obtained in the offered converter with more input sources, there will be no need to connect solar modules in series or parallel to achieve higher voltage and power ratings. The converter inherently offers higher reliability. The converter also draws current with minimal ripple for inputs, which is preferred by solar PV systems. These properties allow using this converter as a suitable and high-quality converter for high power to connect solar PV energy sources to distribution network.
Connectivity-Aware Redirected Walking in 5G mmWave Networks

Enabling immersive metaverse in virtual environments (VE) is one of the key applications for 5G networks and beyond. To achieve this, redirected walking (RDW) allows six-degree-of-freedom (6DoF) movements and guides each virtual reality (VR) user along a virtual path by imperceptibly manipulating the scene seen by the user, in a way to prevent collisions with obstacles in physical space. Generally, RDW allows users to traverse a vast virtual environment in a finite physical space via natural walking. This requires wireless VR utilizing wireless connections. Wireless VR applications have high demands on network bandwidth, requiring users to stay in areas with good wireless conditions. However, existing RDW solutions do not take into account connectivity constraints and may lead VR users to areas with poor network resources. To solve this problem, we model the redirected path-finding problem in 5G millimeter wave (mmWave) communication systems and solve it as a grid-based angle-constrained problem with dynamic programming. Simulation results show the performance of our solution, demonstrating that it is indeed an excellent solution for wireless RDW applications.
BGP Blockchain for Metaverse - A Distributed Consensus System for BGP

A Metaverse provides an immersive and linked experience for individuals similar to what is depicted in the book "Ready Player One". Experiences will be enabled by VR headsets, gaming systems, desktop computers, smartphones, etc., while there will likely emerge several metaverses which in turn will need interconnection. Thus, the Metaverse may be portrayed as a platform that utilizes networking to build an open ecosystem in which different platforms co-exist to serve all users. One key technology for the Metaverse is a distributed consensus system (DCS), underpinning a set of distributed applications, most notably for cryptocurrency and asset management in general, often characterized as blockchain-based trading platforms. But the use of DCSs is not limited to applications. We assert that for a true Metaverse experience, and to prevent metaverse silos, there will need to be DCS-based security between the various metaverse platforms interconnected by the Border Gateway Protocol (BGP). Key for such security is the permissionless notion in its participation to enable a truly open ecosystem, while also addressing key performance aspects such as low latency across the various interconnections. This position paper highlights various opportunities for integrating DCSs with BGP in order to provide a secure and interoperable Metaverse architecture, and we propose a BGP Blockchain system as one area for additional BGP route security. We further propose the necessary elements needed in order to address key performance aspects in such a BGP Blockchain environment.
A Compact 240 GHz Differential Fundamental Oscillator with -94.2dBc/Hz Phase Noise and 5.4% DC-to-RF Efficiency in 22nm FDSOI

This paper presents a 240-GHz differential fundamental oscillator. With a novel differential π-type topology, the oscillator achieves a compact area, while naturally separating the gate and drain of the transistor to realize biasing flexibility and enhance performance. Thanks to the co-design of the large-signal behavior of transistors and the losses of embedding and matching networks, the DC-to-RF efficiency is optimized. Fabricated in 22nm FDSOI process, the oscillator generates 0.63-mW output power with a 0.5-V gate bias voltage and 1-V power supply, corresponding to a 5.4% DC-to-R$\Gamma$ efficiency. The measured tuning range is 1.47%. To the best of the author’s knowledge, this oscillator has a minimal core area of only 0.003 m$\mathrm{m}^{2}$ and the lowest phase noise of -94.2dBc/Hz at 1 MHz offset among single-core fundamental oscillators with high efficiency.
Design Criteria for an Axial Flux Wind Generator with Halbach Array Permanent Magnets

This research focuses on the improvement of the Halbach magnet configuration within an axial flux generator for wind power applications. The use of a 2D parametric model is proposed as an effective approach to simplify the design optimisation process while maintaining accuracy and reducing computational complexity. The study aims to improve generator performance by maximising torque density and minimising the use of rare earth materials.
Task Allocation Optimization Strategy in UAV-enabled Mobile Edge Computing System

Unmanned Aerial Vehicles (UAVs) have been widely used as aerial computing devices in Internet of Things (IoT) to support time-sensitive and compute-intensive applications and services, e.g. automatic driving, data analysis, etc. In this paper, a UAV-enabled mobile edge computing (MEC) system is considered for computing offloading services, in which the offloaded tasks from ground devices are processed by UAVs. However, when multiple computing tasks exist, the task allocation for devices should be carried out at the lowest computing cost. To this end, we first propose a non-cooperative game model-based power allocation (NGPA) scheme, and the optimal power allocation strategy of devices is determined to minimize transmission energy consumption. Next, we model the problem of task allocation as a bilateral matching between devices and UAVs to minimize the overall computing cost. Then, a bilateral matching mechanism based on improved Hungarian algorithm is provided to obtain the optimal solution. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes on computing cost.
Forecasting Air Passengers in Phuket, Thailand: An Application of Machine Learning Algorithms

The COVID-19 pandemic occurred in 2020 had a significant impact on the global economy, resulting in a significant decrease in air passengers. As a result, time series trends experienced significant fluctuations that were difficult to predict. The objective of this research was to forecast the number of air passengers in Phuket, Thailand, from September to December 2022. Additional features, such as exchange rates and time-based properties, were included to enhance the accuracy of our predictions. We then compared the performance of statistical models and machine learning models using evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Our findings revealed that the SARIMAX model outperformed other models for air passenger forecasting. Nonetheless, machine learning models might be more appropriate for forecasting new data due to their stability with additional training data. This research emphasized the importance of selecting the appropriate model based on the available data and the particular needs of the forecasting problem.
The North American Cordillera—An Impediment to Growing the Continent-Wide Laurentide Ice Sheet

AbstractThis study examines the evolution of a continental-scale ice sheet on a triangular representation of North America, with and without the influence of the Cordilleran region. Simulations are conducted using a comprehensive atmospheric general circulation model asynchronously coupled to a three-dimensional thermomechanical ice-sheet model. The atmospheric state is updated for every 2 × 106 km3 increase in ice volume, and the coupled model is integrated to steady state. In the first experiment a flat continent with no background topography is used. The ice sheet evolves fairly zonally symmetric, and the equilibrium state is continent-wide and has the highest point in the center of the continent. This equilibrium ice sheet forces an anticyclonic circulation that results in relatively warmer (cooler) summer surface temperatures in the northwest (southeast), owing to warm (cold) air advection and radiative heating due to reduced cloudiness. The second experiment includes a simplified representation of t.
Vegetation, water and thermal stress index for study of drought in Nepal and central northeastern India

Drought is the degradation of land in arid, semi-arid and dry sub-humid regions caused primarily by human activity and climatic variations. The present study is the first attempt to identify and monitor drought using a vegetation index, a vegetation-water index and land surface temperature (LST) data for Nepal and central northeastern India. We propose a Vegetation Water Temperature Condition Index (VWTCI) for monitoring drought on a regional scale. The VWTCI includes the Normalized Difference Water Index (NDWI), which measures the water status in vegetation, the Normalized Difference Vegetation Index (NDVI) and LST data. To validate the approach, the VWTCI was compared with the Vegetation Temperature Condition Index (VTCI) and Tropical Rainfall Measuring Mission (TRMM) 3B31 Precipitation Radar (PR) data. The study revealed a gradual increase in the extent of drought in the central part of the study area from 2000 to 2004. Certain constant drought areas were also identified and the results indicate that these areas are spreading slowly towards the northeast into the central part of the study area. Comparison of the drought areas also shows a decrease in rainfall in June and July from 2000 to 2004.
r a f

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the Large Language Model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident’s root cause category, and provides an explanatory narrative. We evaluate RCA-Copilot using a real-world dataset consisting of a year’s worth of incidents from Transport service in Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years.
AtlFast3: the next generation of fast simulation in ATLAS

The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
The Erdﬁs-Ko-Rado properties of various graphs containing singletons

Let G = ( V, E ) be a graph. For r ≥ 1 , let I ( r ) G be the family of independent vertex r -sets of G . For v ∈ V ( G ) , let I ( r ) G ( v ) denote the star { A ∈ I ( r ) G : v ∈ A } . G is said to be r-EKR if there exists v ∈ V ( G ) such that |A| ≤ |I ( r ) G ( v ) | for any non-star family A of pair-wise intersecting sets in I ( r ) G . If the inequality is strict, then G is strictly r -EKR . Let Γ be the family of graphs that are disjoint unions of complete graphs, paths, cycles, including at least one singleton. Holroyd, Spencer and Talbot proved that, if G ∈ Γ and 2 r is no larger than the number of connected components of G , then G is r -EKR. However, Holroyd and Talbot conjectured that, if G is any graph and 2 r is no larger than µ ( G ) , the size of a smallest maximal independent vertex set of G , then G is r -EKR, and strictly so if 2 r < µ ( G ) . We show that in fact, if G ∈ Γ and 2 r is no larger than the independence number of G , then G is r -EKR; we do this by proving the result for all graphs that are in a suitable larger set Γ 0 ) Γ . We also con(cid:28)rm the conjecture for graphs in an even larger set Γ 00 ) Γ 0 .