Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPROVING PHASE FIELD MODELS OF RECRYSTALLIZATION OF GERMANIUM ANTIMONY TELLURIDE THIN FILMS FOR TRANSMISSION MODELING

Infrared Spectral Imaging is a fundamental imaging technique used in atmospheric and astronomy imaging fields. While filter wheels are one of the primary pieces of equipment used in infrared imaging, these filter wheels can have a significant Size Weight and Power (SWaP) burden. One way to decrease this burden is to change from these traditional filter wheel assemblies to filters based on phase changing materials. One of the more appealing technologies for implementation are Midwave infrared (MWIR) range are filters based on phase changing material like Germanium Antimony Telluride (GST). GST exploits the difference in refractive index between its various phases to act as a band pass filter. While these filters have been successfully created, over multiple heating cycles inhomogeneities could potentially form in the film and cause various problems. The following proceedings compound on optical and recrystallization simulations previously reported with the primary goal of refining the quality of the phase field simulations.
Least Squares Conformal Maps

We present LSCM (Least Squares Conformal Maps), a new quasi-conformal parameterization method for triangulated surfaces, based on a least-squares approximation of the Cauchy-Riemann equations. We show the following properties: Our criterion minimizes angle deformations and non-uniform scaling. The objective function is a quadratic form, that can be efficiently minimized. We prove that this quadratic form is definite positive, therefore the existence and uniqueness of the minimum is ensured. The borders of the charts do not need to be fixed. Therefore, large charts with arbitrarily shaped borders can be parameterized.
Performance Evaluation of Various Hyperspectral Nonlinear Unmixing Algorithms

Nonlinear unmixing of hyperspectral images has shown considerable attention in image and signal processing research areas. Hyperspectral unmixing identifies endmembers spectral signatures and the abundance fractions of each endmemeber within each pixel of an observed hyperspectral scene. Over the last few years, several nonlinear unmixing algorithms have been proposed. This paper presents an empirical comparison of several popular and recent algorithms of supervised nonlinear unmixing algorithms. Namely, we compared Kernel-based algorithms, graph Laplacian regularization algorithm, and nonlinear unmixing algorithm using a generalized bilinear model (GBM). These unmixing algorithms estimate the abundances of linear, bilinear and intimate mixtures of hyperspectral data. We assessed the performance of these algorithms using Root Mean SquareError on the same data sets.
On the Temporal Relations between Logging and Code

Prior work shows that misleading logging texts (i.e., the textual descriptions in logging statements) can be counterproductive for developers during their use of logs. One of the most important types of information provided by logs is the temporal information of the recorded system behavior. For example, a logging text may use a perfective aspect to describe a fact that an important system event has finished. Although prior work has performed extensive studies on automated logging suggestions, few of these studies investigate the temporal relations between logging and code. In this work, we make the first attempt to comprehensively study the temporal relations between logging and its corresponding source code. In particular, we focus on two types of temporal relations: (1) logical temporal relations, which can be inferred from the execution order between the logging statement and the corresponding source code; and (2) semantic temporal relations, which can be inferred based on the semantic meaning of the logging text. We first perform qualitative analyses to study these two types of logging-code temporal relations and the inconsistency between them. As a result, we derive rules to detect these two types of temporal relations and their inconsistencies. Based on these rules, we propose a tool named TempoLo to automatically detect the issues of temporal inconsistencies between logging and code. Through an evaluation of four projects, we find that TempoLo can effectively detect temporal inconsistencies with a small number of false positives. To gather developers' feedback on whether such inconsistencies are worth fixing, we report 15 detected instances from these projects to developers. 13 instances from three projects are confirmed and fixed, while two instances of the remaining project are pending at the time of this writing. Our work lays the foundation for describing temporal relations between logging and code and demonstrates the potential for a deeper understanding of the relationship between logging and code.
DEVELOPMENT OF A VECTOR MAGNETOMETER BASED ON ELECTROMAGNETICALLY INDUCED TRANSPARENCY IN 87 Rb ATOMIC VAPOR

We present progress towards the development of an atomic magnetometer capable of accurate scalar and vector magnetic field measurements with high sensitivity and no need for external calibration. The proposed device will use the interaction between a bi-chromatic laser field and rubidium vapor to derive magnetic field strength and direction from measured amplitudes of Electromagnetically Induced Transparency (EIT) resonances. Since the proposed method requires precision control of light polarization, we observe the performance capabilities of a liquid crystal device to rotate the polarization of the laser field. Another goal in this project is to optimize and characterize EIT resonances by vary-ing laser frequency, modulation strength, and polarization locking parameters to most precisely measure and accurately describe magnetic fields. Finally, we realize a method to derive field direction from comparisons of EIT resonance strengths. The work completed herein will inform the greater project’s noise reduction, component manufacturing, and device refinement steps that aim to produce the first compact and unobtrusive vector magnetometer.
SOK: Side Channel Monitoring for Additive Manufacturing - Bridging Cybersecurity and Quality Assurance Communities

Additive Manufacturing (AM) is critical for the fourth industrial revolution (i.e., Industry 4.0). It involves printing a 3D object layer-by-layer from scratch. Fused filament fabrication (FFF), one of the most widely used AM technology, has been adopted by commercial and domestic consumers. With the recent addition of metal filaments, FFF caters to a broad spectrum of manufacturing industry requirements. Cybersecurity and Quality Assurance (QA) of the FFF process is an active research area. Like any other cyber-physical system, FFF exhibits many side channels (SCs), including acoustic and thermal emissions, vibrations, etc. Researchers in the QA domain use SCs to predict defects in the printed parts. Cybersecurity researchers, on the other hand, utilize SCs to identify malicious anomalies in the process. While the aims are different, there are definite overlaps in both communities’ acquisition and analysis methodologies. As the two communities bring distinct skill sets and expertise, we find an opportunity to bring them closer through a systematic study of available work and identifying the commonalities and distinctions to motivate the consumption of cross-domain knowledge. Our approach to systematizing the knowledge is based on identifying the available SC, the acquisition and analysis methodologies, performance statistics, associated challenges, and future research directions. This knowledge consolidation and systematization exercise will not only help the new researchers aiming to explore SCs in the FFF process but also highlight collaboration opportunities between QA and cybersecurity communities.
Analysis of Raman Spectra With a Machine Learning Approach for Improved Quantification of Microcystin-LR

Cyanobacterial harmful algal blooms (cyanoHABs) have increased in prevalence in recent years, threatening 0.5% of potable water on earth. Microcystins, a class of toxins that can be produced in a cyanoHAB, are particularly harmful to humans and ecosystems alike. Microcystin-LR (MC-LR) is among the most toxic and the most common microcystins. Though biosensors have recently shown impressive capabilities in detecting microcystins, exhibiting high sensitivity, selectivity, and portability, traditional Raman spectroscopy combined with modern machine learning may still offer a path to sensitive, portable detection of MC-LR. The objective of this project focuses on evaluating the efficacy of three machine learning algorithms at detecting MC-LR in water at concentrations near the EPA’s benchmark limit of 1 μg/L. Raman spectra were collected from MC-LR dilutions in water at concentrations ranging from 0.001 to 6.0 μg/L. A sample size of n=1000 Raman spectra was achieved, and spectral preprocessing methods including background subtraction of water, z-score feature normalization, and baseline removal were employed. Regression models to predict MC-LR concentration in water from Raman spectral data were built using three machine learning algorithms: kernel support vector machine for regression (SVR), regression deep neural network (DNN), and partial least squares regression (PLSR). These three models are compared using mean-square-error (MSE) and mean-absolute-error (MAE) to evaluate their efficacy for predicting MC-LR concentrations in the range of 0.001 to 6.0 μg/L. After validation of the models on test data (n=200), MSE values were found to increase in the order of PLSR without feature normalization (0.191) < PLSR with feature normalization (0.199) < SVR (0.432) < DNN (3.155).
Real-Time 6DoF Tracking of Rigid 3D Objects Using a Monocular RGB Camera

This project proposes to develop a novel real-time 6DoF tracking approach for 3D rigid objects using a monocular RGB camera. The proposal is to unite modern hardware with the traditional lightweight edge-based pipeline proposed by RAPID[1] in 1990. This pipeline will be incremented to better handle challenging scenarios, drawing inspiration and partial solutions from the current state of the art. Necessary adaptations will be made, as well as experimentation for original solutions. The work will be evaluated regarding its ro-bustness to common challenges (e.g., clutter, partial occlusions, illumination changes, motion blur). The evaluation will be made by direct comparison to the publicly-available state-of-the-art approaches RBOT[2] (region-based) and GOS[3] (edge-based, RAPID-like) through the popular benchmark datasets OPT[4], LAVAL[5], and RBOT’s own RBOT dataset. Performance and mem-ory footprint will also be evaluated using a commodity laptop. The idea is to improve upon the classic edge-based pipeline and develop a solution that is lightweight enough to be a viable alternative for a port to lesser hardware (such as mobile) while having precision somewhat comparable (or slightly inferior) to the state-of-the-art (albeit not necessarily being as robust to all aforementioned challenges).
MODELING AND CHARACTERIZATION OF THE EFFECTS OF PERCOLATION FLOW IN COMPOSITES WITH AN EMBEDDED FIBER TOW GAP

Fiber reinforced composites are lightweight materials with potential uses in the aeronautics industry. However, novel manufacturing methods cause layup induced defects that propagate through its production A finite modelling framework is needed to properly simulate the complex non-linear deformations. A heat transfer and a hyperelastic constitutive model was used to simulate the heat transfer and compaction respectively. Experiments were conducted to measure the mass lost during compaction, and the evolution of the fiber volume fraction. Micrographs were taken of the composite for experimental measurements. The angle was measured and were compared between the experimental, a theoretical value and finite element model. Results shows that there is much more physics to be modeled for the compaction of a model with an embedded fiber tow gap. Percolation flow was also found to influence the final morphology from the measurements of the volume of the resin flowing into the gap compared to the reduction in volume of the finite element model and the determined volume of the micrograph from the experiments. This proves that for future finite element analysis work the presence of resin in the fiber tow gap needs to be considered for a proper simulation.
SMOOTH AND ANALYTIC ACTIONS OF SL(n,R) AND SL(n,Z) ON CLOSED n-DIMENSIONAL MANIFOLDS

The main theorem is a classiﬁcation of smooth actions of SL( n, R ), n ≥ 3, or connected groups locally isomorphic to it, on closed n -manifolds, extending a theorem of Uchida [33]. We also construct new exotic actions of SL( n, Z ) on the n -torus and connected sums of n -tori, and we formulate a conjectural classiﬁcation of actions of lattices in SL( n, R ) on closed n -manifolds. We prove some related results about invariant rigid geometric structures for SL( n, R )-actions.
General overview of AMD SEV-SNP and Intel TDX

Trusted execution environments (TEEs) have become increasingly common for the execution of security critical code. AMD SEV-SNP and Intel TDX are new hardware extensions developed to provide trusted execution for virtual machines. By providing additional integrity guarantees and building upon previous secure extensions, they enable confidential computing in cloud environments without risking sensitive user data. This work presents the additional components and processes which are used to accomplish these substantial security gains.
Empirical Evaluation of Predictive Models

I give a brief overview of my recent keynote at the 2022 European Conference on Information Retrieval that was held in Stavanger, Norway. I pay particular attention to some basic questions involving the F-score that appear to lead to confusion. I also settle a question raised at the conference by reconstructing an account from Van Rijsbergen's classic text Information Retrieval.
On S*g--Open Sets In Topological Spaces

In this paper, we introduce a new class of sets, namely , s*g--open sets and we show that the family of all s*g--open subsets of a topological space ) ,X(  from a topology on X which is finer than  . Also , we study the characterizations and basic properties of s*g-open sets and s*g--closed sets . Moreover, we use these sets to define and study a new class of functions, namely , s*g-  -continuous functions and s*g-  -irresolute functions in topological spaces . Some properties of these functions have been studied .
PCLDet: Prototypical Contrastive Learning for Fine-Grained Object Detection in Remote Sensing Images

The capacity of satellites to supply high-resolution imaging has promoted the fine-grained object detection task in remote sensing images. However, this type of object detection is challenging due to low interclass feature differences in objects. To address this issue, we propose a prototypical contrastive learning-based detector (PCLDet) for fine-grained object detection in remote sensing images. The PCLDet first introduces the prototype to learn the fine-grained objects’ features and then adopts contrastive learning to compare the target and the learned features, thus improving the differentiability of the fine-grained object. Specifically, we first introduce the prototype, which represents the feature centers of each class, and then construct a prototype bank to store the feature prototypes of each class. Then, we introduce contrastive learning to extract the discriminative features by maximizing the interclass distance and minimizing the intraclass distance. Furthermore, we propose the ProtoCL loss as a part of the model optimization, which enables more representative prototypes to be learned. Finally, to address the long-tail problem in the remote sensing fine-grained object detection dataset, we propose a new proposal sampler, the class-balanced sampler (CBS) that can sample each class equally. Extensive experiments demonstrate that our method can achieve the state-of-the-art performance on a commonly used aerial fine-grained object dataset (Fair1M) and aerial fine-grained ship dataset (OFSD) while maintaining high efficiency. The code will be available at https://github.com/G-Naughty/PCLDet.
Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy

This work investigates the effectiveness of various machine learning (ML) methods in classifying human gait patterns associated with cerebral palsy (CP) and examines the clinical relevance of the learned features using explainability approaches. We trained different ML models, including convolutional neural networks, self-normalizing neural networks, random forests, and decision trees, and generated explanations for the trained models. For the deep neural networks, Grad-CAM explanations were aggregated on different levels to obtain explanations at the decision, class and model level. We investigate which subsets of 3D gait analysis data are particularly suitable for the classification of CP-related gait patterns. The results demonstrate the superiority of kinematic over ground reaction force data for this classification task and show that traditional ML approaches such as random forests and decision trees achieve better results and focus more on clinically relevant regions compared to deep neural networks. The best configuration, using sagittal knee and ankle angles with a random forest, achieved a classification accuracy of 93.4 % over all four CP classes (crouch gait, apparent equinus, jump gait, and true equinus). Deep neural networks utilized not only clinically relevant features but also additional ones for their predictions, which may provide novel insights into the data and raise new research questions. Overall, the article provides insights into the application of ML in clinical practice and highlights the importance of explainability to promote trust and understanding of ML models.
A Stable Learning-Based Corner Detector for Event-Based Cameras

Event-based cameras have been raising much attention with their appealing features like high temporal resolution, reduced motion blur, low latency, and high dynamic range. These properties make event cameras more ideal than conventional cameras for computer vision tasks in demanding situations, including in future VR applications. However, event cameras produce an asynchronous stream of signals, which is incompatible with many classic computer vision algorithms relying on frame data. For example, corner detection is one of such algorithms. Corners are low-level and primitive features that can be used in downstream analysis and many applications. The current event-based corner detectors face the difficulty of balancing accuracy, speed, and robustness. In this study, we propose to utilize a machine learning-based method to learn a fast and stable corner detector for event cameras. The experiments show that the proposed corner detector based on Multi-layer Perceptron classifier outperforms current methods in terms of stableness and accuracy, while maintaining a comparable speed. We also propose an enhanced event representation method of time surface, which improves the classifier accuracy and robustness.
Automatic Flexible Cryptocurrency Trading System Based on Box Theory and K-Means Clustering Algorithm

Recently, due to the ease of buying and selling cryptocurrencies and the continuous influence of social media, people have invested in the cryptocurrency market to obtain passive income. However, the volatility of the cryptocurrency market has caused many investors to lose their money. Although most people are aware of the high risks of cryptocurrencies along with the high rate of return, investing in cryptocurrencies has always been a topic of continuous discussion among researchers and investors. With the development of artificial intelligence (AI) and machine learning, machine learning has been applied to financial investment, and the research effect is remarkable recently. Thus, we propose a new self-adaptive trading system based on box theory and the K-means clustering algorithm. In the box theory, good buying or selling points occur when the oscillation box is broken and falls upward or below to enter the next box. This system predicted the upper and lower boundaries of the Oscillation Box through the K-means clustering algorithm and the sliding window method. Because of the sliding window method, prediction becomes more flexible and can be used in the market with the obtained upper and lower boundaries in a trading system. We also evaluated various market conditions (bull market, bear market, and fluctuant market) to construct the best K-means trading algorithm. After using Ethereum for backtesting, in the 4-month of July to November 2022), the transaction showed a 75 % winning rate, the final Return on Investment (ROI) of 33%, and a market gain of around 6%. This trading model is equipped with the ability of self-adjustment so that investors do not need to put effort on the market while maintaining a stable and considerable return on investment.
Investigating EEG Connectivity Differences Between Right and Left-Handers

An extensive knowledge of cognitive development from early infancy to late adolescence is required to investigate brain neural pathways. The success in recognizing diverse stimuli is one of the remarkable frameworks that used to examine the essential variations in cognitive processes. Recognizing the intended stimuli has been proven to be influenced by several factors such as participants ‘gender and handedness. In this study, we investigated whether these cognitive variations regarding handedness take place at age nine-year. 33-EEG channels were recorded while seven right-handed and seven left-handed participants completed the Eriksen Flanker task-experimental paradigm with a variety of stimuli for evaluating attention and cognition. In each handedness group, the imaginary coherence was computed to evaluate the functional connectivity of each child. It was observed that left and right-handers activate distinct cortical regions, particularly in the alpha and beta bands. Additionally, the findings showed that brain connectivity at the incongruent Flanker stimuli can be utilized to differentiate the two targeted group.
Strategic Energy Trading Among Prosumers in a Smart Grid

A smart grid (SG) is a network of interconnected nodes that can generate, consume, and share energy. In this paper, we consider a game theoretic approach to its management aimed to balance the monetary and energy transactions between users. We analyze the efficiency of the proposed approach for a smart grid consisting of a set of prosumers connected to an energy router that manages energy and monetary transactions. We compare our game theoretic approach with alternative strategies available to a prosumer, i.e., to sell, buy or store the energy whenever possible. For each strategy, the monetary outcome was compared, showing some interesting results.