Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite convergence of an active signature method to local minima of piecewise linear functions

ABSTRACT We previously derived first-order (KKT) and second-order (SOSC) optimality conditions for functions defined by evaluation programs involving smooth elementals and absolute values. For this class of problems we showed that the algorithm of successive abs-linear minimization with a proximal term (SALMIN) achieves a linear or even quadratic rate of convergence under suitable assumptions. An implementation of SALMIN called LiPsMin has been implemented and tested. It uses a bundle-type method direction finder for the inner loop, i.e. the minimization of the local piecewise linear model with a quadratic proximal term. As a consequence the inner solver can get stuck at stationary points of the piecewise linearization and the overall algorithm can only guarantee Clarke stationarity of its cluster points. Here we present a new method that computes a local minimizer of the proximal model objective. As a consequence all cluster points of the outer iteration must be first-order minimal, which is also known as criticality in nonsmooth optimization. The active signature strategy employed very much resembles the classical method for convex QOPs and utilizes the same numerical linear algebra techniques. The new custom-made algorithm provides opportunities for structure exploitation like warm starts in the context of the nonlinear, outer loop.
A novel port-Hamiltonian based design of stabilizing controller for DC-DC buck converter

In this work we present a port-Hamiltonian supported control system design aimed at stabilizing a DC-DC Buck converter driving a nonlinear dissipative load. A desired closed-loop dynamics in the form of a port- Hamiltonian system is proposed, whose parameterization enforces the asymptotic stability of the desired equilibrium point. Moreover, the closed loop incorporates a first order dynamic extension allowing to reject constant disturbances on the load side. We prove that the closed loop is ISS respect to unmatched disturbances. To extend this property to disturbances acting on the supply side we add a standard PI output regulator to the previous closed loop. The performance of the closed loop is verified via simulation.
Halfway Home? Residential Housing and Reincarceration

Every year, hundreds of thousands of people are released from prison. For many, the transition back to society includes a mandatory stay in residential housing. I estimate the effect of residential housing on reincarceration using administrative data from Iowa. I address selection into residential housing by instrumenting for residential housing assignment with the recommendation rate of randomly assigned case managers. I find no evidence that Iowa’s costly investment in residential housing results in reduced reincarceration relative to parole. Instead, residential housing increases reincarceration due to violent crimes and technical violations, while decreasing drug and public order crimes. (JEL K42, R23, R31, R38)
Approaches, Applications, and Challenges in Physiological Emotion Recognition—A Tutorial Overview

An automatic emotion recognition system can serve as a fundamental framework for various applications in daily life from monitoring emotional well-being to improving the quality of life through better emotion regulation. Understanding the process of emotion manifestation becomes crucial for building emotion recognition systems. An emotional experience results in changes not only in interpersonal behavior but also in physiological responses. Physiological signals are one of the most reliable means for recognizing emotions since individuals cannot consciously manipulate them for a long duration. These signals can be captured by medical-grade wearable devices, as well as commercial smart watches and smart bands. With the shift in research direction from laboratory to unrestricted daily life, commercial devices have been employed ubiquitously. However, this shift has introduced several challenges, such as low data quality, dependency on subjective self-reports, unlimited movement-related changes, and artifacts in physiological signals. This tutorial provides an overview of practical aspects of emotion recognition, such as experiment design, properties of different physiological modalities, existing datasets, suitable machine learning algorithms for physiological data, and several applications. It aims to provide the necessary psychological and physiological backgrounds through various emotion theories and the physiological manifestation of emotions, thereby laying a foundation for emotion recognition. Finally, the tutorial discusses open research directions and possible solutions.
Association of memory terms in a power amplifier modeling functions to the circuit components

ABSTRACT The main purpose of this study is to find a relation between power amplifier (PA) circuit component values and delay terms of the PA model polynomial by using measurement results. A PA designer can use this relation to determine component values while designing memoryless/linear PAs. For this purpose, a behavioral model with memory is worked on; the model, modeling approach, experimental set-up and results are presented. Memory depths are determined for a 1–2 GHz 10 W Class AB power amplifier with parametric results. The advantages of the elaborated model are presented. Relations between the bias circuit components and the memory terms are investigated.
Exact rate of decay for solutions to damped second order ODE's with a degenerate potential

We prove exact rate of decay for solutions to a class of second order ordinary differential equations with degenerate potentials, in particular, for potential functions that grow as different powers in different directions in a neigborhood of zero. As a tool we derive some decay estimates for scalar second order equations with non-autonomous damping.
In Vitro Biological Activity of Three Marine Sponges From Theonella and Haliclona Genera Collected From Bidong Island, Terengganu, Malaysia

Marine sponges are primitive sessile animals that are rich sources of biologically active compounds. This paper aimed to assess the in-vitro biological activity of marine sponges from Theonella and Haliclona genera collected from Bidong Island, Terengganu, Malaysia. Biological activities such as antibacterial (discs diffusion assay), antioxidant (DPPH free-radical scavenging assay), and cytotoxicity activity (against cancerous HeLa, MCF-7, HepG-2 cell lines and the normal cell line Vero) were evaluated using MTT cytotoxicity assay. The bioassays were done on methanol extracts at different concentrations. Results indicate that T. swinhoei, and T. cf cupola showed low antibacterial capabilities ranging from 0 to 50 mg/mL and exhibited medium antioxidant activity with the IC50 value of 23.25 ± 1.57 and 18.52 ± 0.86 mg/mL, respectively. Cytotoxicity activities indicate that both species of T. swinhoei and T. cf cupola possesses toxic capabilities to inhibit the proliferation of all cancer cell lines used and demonstrated no significant toxicity for the normal cell line used in this study. Haliclona fascigera showed medium antibacterial activity against all Gram-positive bacteria and low activity against Gram-negative bacteria used. Haliclona fascigera exhibited antioxidant activity with an IC50 value of 1.80 ± 0.08 mg/mL and outcomes of the cytotoxicity activity assay against all cancer cells showed IC50 below 30 µg/mL. Marine sponges evaluated in this study indicate promising bioactive compounds that can be an excellent candidate for drug discovery in prospecting novel antibiotics and anticancer. Despite showing low antibacterial and medium antioxidant activity, species from both Theonella can be further studied in other assays to explore other biological activities whilst marine sponge H. fascigera possesses excellent capabilities in antibacterial, antioxidant, and cytotoxicity activities that can be further studied its chemical compositions for future research.
Progesterone Receptor Expression Level Predicts Prognosis of Estrogen Receptor-Positive/HER2-Negative Young Breast Cancer: A Single-Center Prospective Cohort Study

Simple Summary Although ER expression levels affect the prognosis of breast cancer, studies about PR expression levels are insufficient. Furthermore, there is a knowledge gap between single HR-positive and double HR-positive, especially according to PR expression. As HR positivity is an important prognostic factor, particularly in YBC patients, this research was conducted in a prospective cohort with only YBC patients in order to find out whether the expression of PR modifies the clinical course of breast cancer. We investigated clinicopathologic features and prognosis of ER-positive/HER2-negative breast cancer after stratifying them according to PR expression levels. Conclusively, low PR expression was correlated with worse clinicopathologic characteristics, and associated with increased risk of recurrence, distant metastasis, and death compared with strong PR expression group. Low PR might be a prognostic factor of ER-positive/HER2-negative YBC. Abstract Background: Although estrogen receptor (ER) expression levels affect the prognosis of breast cancer, studies about progesterone receptor (PR) expression levels are insufficient, especially in young breast cancer (YBC). The purpose of this study was to compare clinical characteristics and prognosis according to PR expression levels in invasive breast cancer patients. Methods: A prospective cohort study was conducted to identify YBC patients with invasive carcinoma diagnosed at an age of less than 40 years old between 2013 and 2018. Clinicopathologic features and prognosis of ER-positive and human epidermal growth factor receptor 2 (HER2)-negative patients were investigated. Patients were stratified into strong PR (PR-positive cell proportion > 10%), low PR (PR-positive cell proportion = 1~10%), and PR-negative (PR-positive cell proportion < 1%). Results: Among 458 patients enrolled, 386 (84.3%), 26 (5.7%), and 46 (10.0%) were categorized into strong PR, low PR, and PR-negative groups, respectively. The median follow-up duration was 58.6 months. Compared with the strong PR group, low PR and PR-negative groups were more likely to have high Ki-67 and a high nuclear grade. Low R and PR-negative groups had significantly worse disease-free survival (DFS) and distant metastasis-free survival (DMFS) than the strong PR group (p = 0.0033, p = 0007). Low PR group had an even higher risk of distant metastasis than PR-negative patients. Low PR patients and PR-negative had significantly lower overall survival (OS) rates than strong PR. Conclusion: Low PR might be a prognostic factor of ER-positive/HER2-negative in YBC.
Phytochemical, Antibacterial and Antioxidant Activities of Schefflera elliptica Leaves

Schefflera elliptica or locally called kayu tulak, is one of the Balinese herbal plants that have traditionally be used to reject (tulak) negative influences that exist in the human body. Althoug, S. elliptica has been routinely used as a part of a ritual in Bali, only a limited study has been reported on its bioactivity. This study was designed to analyze the phytochemical content, antibacterial activity, and antioxidant activity of S. elliptica. In this study, simplisia of S. elliptica was extracted using n-hexane and ethyl acetate solvents, then the viscous extracts of the two solvents were carried out for phytochemical tests, antibacterial activity tests with the Kirby-Bauer method and antioxidant activity tests with based on DPPH method. Phytochemical screening showed that the n-hexane extract contains active compounds in the form of phenols and steroids while ethyl acetate contains active compounds in the form of phenols, tannins, and steroids. Antibacterial screening showed ethyl acetate extract of S. elliptica displayed a diameter zone of inhibition of 10.72±0.71 mm against Staphylococcus aureus, 12.17±2.80 mm against Streptococcus mutans, 12.40±1.65 mm against Escherichia coli and 15.20±2.44 mm against Klebsiella pneumoniae. The DPPH analysis showed percentages of 61.17% and 67.42% from n-hexane and ethyl acetate extracts respectively, which indicated the antioxidant properties of S. elliptica. Overall, this research provides a preliminary report on the bioactivity potential of S. elliptica mainly in term of antibacterial and antioxidant properties which open up possibilities for future drug development.
Shoreline Extraction Based on LiDAR Data Obtained Using an USV

: This article explores the use of Light Detection And Ranging (LiDAR) derived point clouds to extract the shoreline of the Lake K ł odno (Poland), based on their geometry properties. The data collection was performed using the Velodyne VLP ‐ 16 laser scanner, which was mounted on the HydroDron Unmanned Surface Vehicle (USV). A modified version of the shoreline extraction method proposed by Xu et al. was employed, comprising of the following steps: (1) classifying the point cloud using the Euclidean cluster extraction with a tolerance parameter of 1 m and min. cluster size of 10,000 points, (2) further filtration of boundary points by removing those with height above 1 m from the measured elevation of water surface, (3) manual determination of a curve consisting of 5 points located along the entire shoreline extraction region at a relatively constant distant from the coast, (4) removal of points that are further from the curve than the average distance, repeated twice. The method was tested on the scanned section of the lake shoreline for which Ground Control Points (GCP) were measured using a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver. Then, the results were compared to the ground truth data, obtaining an average position error of 2.12 m with a standard deviation of 1.11 m. The max error was 5.54 m, while the min. error was 0.41 m, all calculated on 281 extracted shoreline points. Despite the limitations of this parametric, semi ‐ supervised approach, those preliminary results demonstrate the potential for accurate shoreline extraction based on LiDAR data obtained using an USV. Further testing and optimisation of this method for larger scale and better generalisation for different waterbodies are necessary to fully assess its effectiveness and feasibility. In this context, it is essential to develop computationally efficient methods for approximating shorelines that can accurately determine their course based on a set of points.
A High-Efficiency Integrated LCC/S WPT System With Constant Current Output

In some practical applications such as battery charging and LED driving, it is desirable for the wireless power transfer (WPT) systems to have load-independent constant current output (CCO) characteristic. However, the existing WPT systems with CCO function suffer from low efficiency, limited output current by the loosely coupled transformer (LCT) parameters, low space utilization, and high cost. In order to solve the above issues, this article proposes a new integrated LCC/S WPT system with CCO function, in which the transmitter-side compensation inductor and the transmitter-side coil are coaxially and symmetrically stacked on the same plane to save space occupied when they are placed separately in the traditional structure. A detailed mathematical derivation of the proposed integrated LCC/S WPT system with both load-independent CCO characteristic and zero phase angle (ZPA) operation is carried out, and the explicit corresponding parameter design method is provided. It is worth emphasizing that the output current of the proposed integrated LCC/S WPT system is not limited by the LCT parameters due to sufficient design freedom. Then, a comprehensive theoretical analysis on the realization of zero voltage switching (ZVS) operation for higher overall efficiency and the impact on the CCO characteristic by slightly adjusting the parameters of the compensation capacitors is performed. Furthermore, a detailed and thorough efficiency comparison between the proposed integrated LCC/S WPT system and the SS WPT system is performed under alignment, long-distance, and misalignment conditions to reflect the efficiency advantage of the proposed system. Finally, a demonstrative experimental prototype with 60-V dc input voltage and 4.9-A CCO is fabricated to validate the correctness and rationality of the proposed integrated LCC/S WPT system and the corresponding theoretical analysis.
Chitosan-Coated SLN: A Potential System for Ocular Delivery of Metronidazole

Ophthalmic drops for ocular delivery exhibit inadequate residence time, which often requires multiple daily dosing that may result in patient non-adherence. In this study, the development of a once-daily-dosed chitosan-coated metronidazole (MTZ)-loaded solid lipid nanoparticles (SLNs) for ocular delivery was undertaken. Melt emulsification and ultrasonication were used to manufacture MTZ-loaded SLN, which were subsequently coated with chitosan (CS) by mechanical stirring using a 0.1% w/v solution. Gelucire® 48/16 and Transcutol® HP were used as the solid lipid and synthetic solvent, respectively, with Tween® 20 included as a stabilizing agent. The critical quality attributes (CQA) of the optimized CS-coated SLN that was monitored included particle size, polydispersity index, Zeta potential, % entrapment efficiency, % MTZ loading, pH, and osmolarity. The optimized coated nanocarriers were evaluated using laser Doppler anemometry (LDA) and were determined to be stable, with particle sizes in the nanometre range. In vitro mucoadhesion, MTZ release and short-term stability, in addition to the determination of the shape of the optimized CS-coated SLN, were undertaken. The mucoadhesive properties of the optimized CS-coated MTZ-loaded SLN demonstrated increased ocular availability, which may allow dose reduction or longer intervals between doses by improving precorneal retention and ocular availability. Overall, our findings suggest that CS-coated MTZ-loaded SLNs have the potential for clinical application, to enhance ocular delivery through the release of MTZ.
3D simulation of acoustical shock waves propagation through a turbulent atmosphere. Application to sonic boom

This thesis deals with the effects of atmospheric turbulence on the propagation of acoustical shock waves. These effects are of major interest for applications such as sonic boom, buzz saw noise or thunder. A numerical one-way method is developed to model and to simulate three-dimensional nonlinear propagation of acoustical shock waves in a moving heterogeneous medium. It relies on a split-step approach that permits to take into account efficiently the different involved physical mechanisms. To tackle realistic 3D problems (of order of one billion of degree of freedom), the implementation of the method is done using the parallel single program multiple data paradigm. Validity of this method is assessed using multiple test cases. The method is applied to investigate the effects of atmospheric turbulence on sonic boom propagation through the Planetary Boundary Layer. Hence, both under-track boom and boom in the shadow zone are studied for a hypersonic configuration developed in the European project ATLLAS II. Finally, the focusing of weak shock waves on a cusped caustic is simulated. It is the first study of the stability of a nonlinear caustic to flow perturbations to our knowledge.
Toward Highly-Efficient and Accurate Services QoS Prediction via Machine Unlearning

Personalized Internet of Things (IoT) services prediction based on Quality-of-Service (QoS) is an indispensable technique for selecting appropriate services for each user. However, existing collaborative prediction models do not take into account the user’s authority to manage their own generated data. From the standpoint of users, the expectation is for models to eliminate the impact of their sensitive data to the greatest extent possible. Meanwhile, IoT service providers face the challenge of data contamination during service provision, which necessitates models to forget data quickly and accurately to restore performance. Furthermore, existing QoS prediction methods usually suffer from low model availability when handling unlearning requests by full retraining. This underscores the need to address security, availability, fidelity, privacy, and related issues, highlighting the urgency of unlearning. To solve the problem, we propose Context-Aware Data Driven Eraser (CADDEraser), a novel efficient machine unlearning framework for QoS prediction tasks. Firstly, we divide the training data into multiple shards to train submodels and obtain node embeddings by utilizing contextual information to derive graph embeddings. Then these embeddings are employed in a balanced clustering partition, ensuring the preservation of the QoS record between users and services. Finally, we use a concatenate aggregation method and stacking & attention-based aggregation methods to synthesize information from sub-models more efficiently. Experiments on large-scale datasets show that our CADDEraser framework not only improves efficiency but also enhances the accuracy of QoS prediction, achieving efficient unlearning and outperforms state-of-the-art unlearning approaches. Source codes are available at https://github.com/ZengYuXiang7/CADDEraser.
Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing

Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.