Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records

Simple Summary Breast cancer is a heterogeneous disease characterized by different risks of relapse, which makes it challenging to predict progression and select the most appropriate follow-up strategies. With the ever-growing adoption of Electronic Health Records, there are great opportunities to leverage the amount of data collected routinely in electronic format for secondary purposes. Machine Learning algorithms offer the ability to analyze large amounts of data and reveal insights that might otherwise go undetected. In this study, we have applied several algorithms to predict 5-year breast cancer recurrence from health data. We compared whether taking advantage of both structured and unstructured data from health records yields better prediction results than using any of the sources separately. These algorithms are valuable tools to help clinicians effectively integrate large amounts of data into their decision-making and are key to improving risk stratification and providing personalized assistance to patients. Abstract Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.
Characterization of red blood cells (RBCs) using dual Brillouin/Raman micro-spectroscopy

Erythrocytes, or red blood cells, transport oxygen to and carbon dioxide from the body's tissues and organs. Red blood cell mechanical properties are altered in a number of diseases such as sickle cell anaemia and malaria. Additionally, mechanically modified red blood cell ghosts are being considered as a long-term, biocompatible carrier for drug delivery and for blood analyte sensing. Brillouin spectroscopy enables viscoelastic characterization of samples at the microscale. In this report, Brillouin spectroscopy is applied to characterize the mechanical properties of red blood cells and red blood cell ghosts.
Hybrid Tuning of Sub‐Filaments to Improve Analog Switching Performance in Memristive Devices

Memristive devices are promising candidates for analog computing applications such as neuromorphic computation. Larger dynamic ranges and more sufficient multilevel states can enable the significant development of memristor‐based utilizations. Herein, a method to improve the analog switching performance of memristors through a hybrid tuning (coarse and fine tuning) of two sub‐filaments is demonstrated. The creation of sub‐filaments inside the dielectric switching layer is realized by deploying Pt metal islands in the switching layer. Given the different material stack configurations of the two sub‐filaments, they exhibit different switching properties to play the roles of coarse and fine tuning respectively in the memristor. Based on the above mechanism, a Pt/Ta/Al2O3/Pt island/Al2O3‐x/TiOy/Al2O3‐x/Pt memristor is proposed and fabricated. Through the hybrid tuning of two sub‐filaments, a combined dynamic range of 600 Ω to 50 kΩ is achieved. Compared to the reference Pt/Ta/Al2O3/Pt memristors (dynamic range: 600 Ω to 8 kΩ), both dynamic range and multilevel resistance states are increased significantly. Meanwhile, the energy efficiency is improved because the resistance of tunable states can be set to larger values. Furthermore, this mechanism can be incorporated into various existing memristors to improve their dynamic range and multilevel states, which extensively enriches the applications of memristors.
Lipoxin A4 (LXA4) Reduces Alkali-Induced Corneal Inflammation and Neovascularization and Upregulates a Repair Transcriptome

Purpose: To investigate the anti-inflammatory and anti-angiogenic effects of the bioactive lipid mediator LXA4 on a rat model of severe corneal alkali injury. Methods: To induce a corneal alkali injury in the right eyes of anesthetized Sprague Dawley rats. They were injured with a Φ 4 mm filter paper disc soaked in 1 N NaOH placed on the center of the cornea. After injury, the rats were treated topically with LXA4 (65 ng/20 μL) or vehicle three times a day for 14 days. Corneal opacity, neovascularization (NV), and hyphema were recorded and evaluated in a blind manner. Pro-inflammatory cytokine expression and genes involved in cornel repair were assayed by RNA sequencing and capillary Western blot. Cornea cell infiltration and monocytes isolated from the blood were analyzed by immunofluorescence and by flow cytometry. Results: Topical treatment with LXA4 for two weeks significantly reduced corneal opacity, NV, and hyphema compared to the vehicle treatment. RNA-seq and Western blot results showed that LXA4 decreased the gene and protein expression of pro-inflammatory cytokines interleukin (IL)-1β and IL-6 and pro-angiogenic mediators matrix metalloproteinase (MMP)-9 and vascular endothelial growth factor (VEGFA). It also induces genes involved in keratinization and ErbB signaling and downregulates immune pathways to stimulate wound healing. Flow cytometry and immunohistochemistry showed significantly less infiltration of neutrophils in the corneas treated with LXA4 compared to vehicle treatment. It also revealed that LXA4 treatment increases the proportion of type 2 macrophages (M2) compared to M1 in blood-isolated monocytes. Conclusions: LXA4 decreases corneal inflammation and NV induced by a strong alkali burn. Its mechanism of action includes inhibition of inflammatory leukocyte infiltration, reduction in cytokine release, suppression of angiogenic factors, and promotion of corneal repair gene expression and macrophage polarization in blood from alkali burn corneas. LXA4 has potential as a therapeutic candidate for severe corneal chemical injuries.
Medical Waste Classification using Deep Learning and Convolutional Neural Networks

With the rise of attention to healthcare since the start of the century, which the recent pandemic has emphasized, the number of hospitals and clinics has increased exponentially. The growth in hospitals and patients has also resulted in increased medical waste. The different kinds of medical waste must be segregated and disposed of properly to prevent the spread of bacteria and viruses and cross-contamination. However, it is not economically feasible to hire a workforce that can segregate said waste. With the perceived popularity of deep learning and image classification systems, creating a Deep Learning model to categorize the different kinds of medical waste is possible. Hence using a deep learning-based classification method in which an appropriate pre-trained model is selected for practical implementation, followed by transfer learning methods to improve classification results, is appropriate. Different types of medical waste are grouped into umbrella categories(general, hazardous, infectious). An ideal situation would be where images are uploaded, and the machine can classify the presented waste appropriately with little to no waiting times. Three out of four of the modified pre-trained models with different architectures were able to achieve an accuracy above 95 percent.
Multiple Neural Network architectures for visual emotion recognition using Song-Speech modality

Visual Emotion recognition is an active topic during the last decade. It belong to a different domains such as configuration of medical abnormalities, robotic tasks and security matters. The visual tasks became more and more difficult because of the variance of the information for the video processing. Treating this issues several neural network models can shows a different performance, specially the convolutional neural networks models that is most applied on image processing tasks. This latter is mostly known for it's ability for feature extraction and detecting patterns and it consider as the basic architecture for both VGG16 and ResNet50. This article describes a comparative study between a Convolutional Neural Network and a Deep Neural Network. After applying CNN model on both modalities song and speech, the accuracy results obtained are 64.76% and 71.92% respectively. Moreover for VGG16 the accuracy results reach 50.06% and 55%. While for ResNet50 the accuracy results reach 57.73% and 55.52%. Comparing the performance of the models, the shape of the accuracy curve for the Resnet model is smoother than other models for both modalities.
RAVEN: Stateless Rapid IP Address Variation for Enterprise Networks

Enterprise networks face increasing threats against the privacy of their clients. Existing enterprise services like Network Address Translation (NAT) offer limited privacy protection, at the cost of requiring per-flow state. In this paper, we introduce RAVEN (Rapid Address Variation for Enterprise Networks), a network-based privacy solution that is complementary to application-layer defenses. RAVEN protects privacy by frequently changing the client's public IP address. With RAVEN, a client is not limited to using a single IP address at a given time, or even for a given connection. RAVEN goes further, breaking the association between packets that belong to the same connection by frequently changing the client's IP address within a single connection. RAVEN achieves this through a novel division of labor: the client uses a transport protocol, like QUIC, that supports seamless connection migration, and decides when to switch its IP address, while the enterprise network actually changes the client's IP address in a stateless manner at line rate and ensures end-to-end packet delivery. We implement RAVEN using QUIC and off-the-shelf programmable switches. We deploy RAVEN in a test IPv6 network and evaluate its defense against webpage fingerprinting attacks. Even with a strong adversary, the average precision of the best adaptive attacks drops from 0.96 to 0.84, with a 0.5% degradation in client throughput. When RAVEN changes IP addresses at unpredictable frequency, the precision of the best attacks falls to 0.78---the same effectiveness as WTF-PAD.
AI/Lu/Lu2O3/ Al Memristor for Synaptic and Memory Applications

In this paper, we propose the designing of bipolar resistive switching Memristor using high-k dielectric material ‘Lu203’ as switching layer using combined software package of COMSOL multi-physics package tool based on Finite element method (FEM). The simulation results of Lu2O3 memristor reports a stable hysteresis I-V characteristics and observed lower C2C variability of 13.61 % and 14.54% for SET and RESET states respectively which noted lower values than earlier high-k dielectric Y2O3 memristor and also observed ‘Potentiations’ and ‘Depressions’ of synaptic weights with different Voltage ramp-rates for synaptic applications and CQ behavior phenomenon enhances the device for multi-level storage and high-density memory applications.
Impact of Barrier Metal Thickness on SRAM Reliability

To understand the effect of barrier metal thickness (BM THK) of metal gate (MG) on static random access memory (SRAM) reliability, we evaluated 3 different wafer-level reliability (WLR) methods; random telegraph noise (RTN) characteristics ($\tau_{\mathrm{c}}/\tau_{\mathrm{e}}$, or capture/ emission time constant) and BTI recovery are studied on single-bit transistors, and SRAM static noise margin (SNM) degradation is also investigated with various stress configuration. Using three different MG process splits, it is observed that RTN performance is modulated by BM THK. Through BM THK optimization, the best result (i.e., $\mathbf{RTN}\downarrow$, bias temperature instability (BTI) $\mathbf{recovery}\uparrow$, SRAM SNM $\mathbf{shift}\downarrow$) could be achieved, owing to less oxide damage by minimal trapping/de-trapping phenomenon. This clearly indicates the need of subtle process-reliability optimization. In addition, high temperature operating life (HTOL) is performed to confirm the SRAM Vmin shift at package-level test.
Vacuum Decay in a Flux Landscape

This thesis will examine vacuum decay in a flux landscape. We identify a number of effects that greatly enhance the decay to wildly distant minima—so much so that such decays can dominate over transitions to nearest neighbors. The charged branes that mediate decay form bound states that lower their effective tension, so that nucleating a stack of branes is made easier. We even construct an explicit example where the most likely decay path is to the bubble of nothing, where all the flux is removed, the construction becomes unstable, the extra dimensions shrink to zero size, and spacetime pinches off. Along the way, we will solve the case of the disappearing instanton, prove the connectedness of the landscape, and argue that it is exceedingly unlikely to see evidence of bubble collisions in the CMB.
Fostering a Student’s Abstraction of the Relationship Between Parallelogram and Trapezoid Within Quadrilateral Hierarchy

ABSTRACT This article provides an analysis of a data set coming from a two-phase qualitative study that focused on fostering primary students’ abstraction of interrelations among quadrilaterals (squares, rectangles, parallelograms, rhombuses, trapezoids). The pilot study consisted of work with eight primary students operating at van Hiele level 2 (e.g., understanding quadrilaterals without interrelations). We benefitted from the teaching experiment methodology to develop a task sequence applied in a dynamic geometry environment and a paper-pencil environment to help each participant develop quadrilateral hierarchy at van Hiele level 3 (e.g., understanding quadrilaterals with their interrelations). The main study used a case study approach to investigate two primary students’ progress (Efe and Ayla, age 10). After the pre-interviews, each participant was taught the developed task sequence individually during seven up-to-one-hour teaching sessions, followed by a post-interview. This article only details Efe’s case as he worked on developing the relationship between parallelogram and trapezoid. We analyzed Efe’s data (from the pre-interview, Teaching Session-7, and the post-interview) to describe how the different parts of the task sequence fostered his abstraction of the interrelation between parallelogram and trapezoid as he moved from van Hiele Level 2 to 3. This article provides initial evidence for the classification process.
Machine Learning Based Tesla Stock Analysis: A Theosophical Review

The paper aims at providing a comparative analysis on stock price determination of tesla company using Machine Learning Algorithms. The stock market is a very competitive environment where the determination of the future value will be highly beneficial to the share holder. By providing a solution to this we plan in cutting down on a certain percentage of risk factor that arises when buying and selling shares.
GaN HEMT based Ku band LNA and SPDT Switch for Transmit-Receive application

This paper presents Monolithic Microwave Integrated circuit low noise amplifier (LNA) and single pole double throw switch (SPDT) circuits designed and fabricated with using 150 nm Gallium Nitride on SiC process of UMS, France. This MMIC LNA exhibits a noise figure of 2.5 dB with an associated gain of 25 dB in a chip area of 3.5 mm x 1.8 mm at Ku band. The designed and fabricated MMIC SPDT switch has an insertion loss less than 1.2 dB with an isolation better than 45 dB throughout the operating frequency band of 15.5 GHz - 17.5 GHz. The chip dimensions of SPDT switch is 1.5 mm X 3.5 mm. These MMICs operating at Ku-Band are suitable for integration in RF front-end chip for phased array radar applications.
Design: High-Speed Block-Based Carry Speculative Adder for Error-Tolerant Applications

While compared to exact adders, approximate adders have a shorter propagation time and take up less space. For the approximate computing, we use two alternative strategies. The adder is split into certain non-overlapped summation blocks throughout the first structure, and also the substitute adders employed which is called as Knowles parallel prefix adders. The propagation delay will be reduced by implementing the Knowles adder construction. Full adder sum is built with one or gate and single x-or gate in the next structure. The amount of space available will be decreased as a result of this design. In terms of time and area, the novel method is compared with the previous method.
Towards a Modular Framework for Visco-Hyperelastic Simulations of Soft Material Manipulators with Well-Parameterised Material

Controller design for continuum robots maintains to be a difficult task. Testing controllers requires dedicated work in manufacturing and investment into hardware as well as software, to acquire a test bench capable of performing dynamic control tasks. Typically, proprietary software for practical controller design such as Matlab/simulink is used but lacks specific implementations of soft material robots. This intermediate work presents the results of a toolchain to derive well-identified rod simulations. State-of-the-art methods to simulate the dynamics of continuum robots are integrated into an object-oriented implementation and wrapped into the Simulink framework. The generated S-function is capable of handling arbitrary, user-defined input such as pressure actuation or external tip forces as demonstrated in numerical examples. With application to a soft pneumatic actuator, stiffness parameters of a nonlinear hyperelastic material law are identified via finite element simulation and paired with heuristically identified damping parameters to perform dynamic simulation. To prove the general functionality of the simulation, a numerical example as well as a benchmark from literature is implemented and shown. A soft pneumatic actuator is used to generate validation data, which is in good accordance with the respective simulation output. The tool is provided as an open-source project****Code available under https://gitlab.com/soft_material_robotics/cosserat-rod-simulink-sfunction.
Clustering for metric and non-metric distance measures

We study a generalization of the <i>k</i>-median problem with respect to an arbitrary dissimilarity measure D. Given a finite set <i>P</i>, our goal is to find a set <i>C</i> of size <i>k</i> such that the sum of errors D(<i>P, C</i>) = Σ<i><sub>p∈P</sub></i> min<i><sub>c∈C</sub></i>{D(<i>p, c</i>)} is minimized. The main result in this paper can be stated as follows: There exists an <i>O</i>(<i>n</i>2<i><sup>k/ε)<sup>O(1)</sup></sup></i>) time (1 + ε)-approximation algorithm for the <i>k</i>-median problem with respect to D, if the 1-median problem can be approximated within a factor of (1 + ε) by taking a random sample of constant size and solving the 1-median problem on the sample exactly. Using this characterization, we obtain the first linear time (1 + ε)-approximation algorithms for the <i>k</i>-median problem in an arbitrary metric space with bounded doubling dimension, for the Kullback-Leibler divergence (relative entropy), for Mahalanobis distances, and for some special cases of Bregman divergences. Moreover, we obtain previously known results for the Euclidean <i>k</i>-median problem and the Euclidean <i>k</i>-means problem in a simplified manner. Our results are based on a new analysis of an algorithm from [20].
Optimized Mechanism For Dataset Generation to train Machine Learning Models for Rectangular Microstrip Antenna

In the current world, there is a surge in the amount of data getting generated along with the variety, and speed. This also applies to antenna design, for which large-scale data processing has become crucial. It is observed, there is still a gap between the traditional scientific computing ecosystem used in the area of antenna design with that of the modern approaches including big data analytics tools and frameworks. In this paper, we propose and evaluate the Direction Equation Method using Python(DEM) as an alternative and more efficient approach for generating datasets over simulation software for rectangular microstrip patch antenna. we compare our results with HFSS on a standard 2.3 GHz 8-Core Intel Core i9 RAM 16GB Laptop. Our results show that the DEM approach does the job at a very fast pace.
Mutations in the Second Alternative Oxidase Gene: A New Approach to Group Aspergillus niger Strains

Alternative oxidase is a terminal oxidase in the branched mitochondrial electron transport chain of most fungi including Aspergillus niger (subgenus Circumdati, section Nigri). A second, paralogous aox gene (aoxB) is extant in some A. niger isolates but also present in two divergent species of the subgenus Nidulantes—A. calidoustus and A. implicatus—as well as in Penicillium swiecickii. Black aspergilli are cosmopolitan opportunistic fungi that can cause diverse mycoses and acute aspergillosis in immunocompromised individuals. Amongst the approximately 75 genome-sequenced A. niger strains, aoxB features considerable sequence variation. Five mutations were identified that rationally affect transcription or function or terminally modify the gene product. One mutant allele that occurs in CBS 513.88 and A. niger neotype strain CBS 554.65 involves a chromosomal deletion that removes exon 1 and intron 1 from aoxB. Another aoxB allele results from retrotransposon integration. Three other alleles result from point mutations: a missense mutation of the start codon, a frameshift, and a nonsense mutation. A. niger strain ATCC 1015 has a full-length aoxB gene. The A. niger sensu stricto complex can thus be subdivided into six taxa according to extant aoxB allele, which may facilitate rapid and accurate identification of individual species
Personality Traits Classification from Text Using Deep Learning Model

A relative balance and consistent internal character-istics of an individual's critical features such as thoughts, feelings, and behaviors is important while characterized by a personality. Various theories and models exist for personality traits, like a Big Five Trait Model of personality commonly known as Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN), which puts a broad categories of the human personality that are made up of five basic dimensions. Personality trait classification using the deep learning models is an emerging area of research. In this paper, we present a Bidirectional LSTM network to perform the classification of personality traits based on the big five (OCEAN) model. Our proposed network effectiveness has been measured by Accuracy, Precision, Recall, and F1-Score as performance metrics.