bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving polygon image segmentation by enhancing U-Net architecture
Authors: Li Da 
Published: February 2024

Abstract
The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model’s capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer.
An improved DDPG algorithm based on evolution-guided transfer in reinforcement learning
Authors: Bai Xueqian , Wang Haonian 
Published: February 2024

Abstract
Deep Reinforcement Learning (DRL) algorithms help agents take actions automatically in sophisticated control tasks. However, it is challenged by sparse reward and long training time for exploration in the application of Deep Neural Network (DNN). Evolutionary Algorithms (EAs), a set of black box optimization techniques, are well applied to single agent real-world problems, not troubled by temporal credit assignment. However, both suffer from large sets of sampled data. To facilitate the research on DRL for a pursuit-evasion game, this paper contributes an innovative policy optimization algorithm, which is named as Evolutionary Algorithm Transfer - Deep Deterministic Policy Gradient (EAT-DDPG). The proposed EAT-DDPG takes parameters transfer into consideration, initializing the DNN of DDPG with the parameters driven by EA. Meanwhile, a diverse set of experiences produced by EA are stored into the replay buffer of DDPG before the EA process is ceased. EAT-DDPG is an improved version of DDPG, aiming at maximizing the reward value of the agent trained by DDPG as much as possible within finite episodes. The experimental environment includes a pursuit-evasion scenario where the evader moves with the fixed policy, and the results show that the agent can explore policy more efficiently with the proposed EAT-DDPG during the learning process.
Knee replacement patients and wearable knee pads
Authors: Zhang Yishen 
Published: February 2024

Abstract
This paper proposes a novel solution to the common problem of knee stiffness experienced by patients following knee replacement surgery. The paper suggests designing a wearable knee pad that is fitted with three 6-axis IMU sensors to monitor, evaluate, and process the movement data of the patient’s knee in real-time. The data collected would then be used to provide appropriate recovery methods and encouragement to the patients. This paper highlights the advancements in wearable technology and remote patient monitoring, which allow for the improvement of postoperative care and behavioural change in knee replacement surgery patients. The literature review section examines the role of mHealth technologies and wearable sensors in remote patient monitoring and behaviour change for total knee arthroplasty patients. The research recommends integrating mobile health and wearable sensor technologies for remote patient monitoring and behaviour change interventions in these patients for enhanced postoperative care and improved patient outcomes. The proposed methodology includes user-friendly interfaces that provide continuous monitoring, personalized rehabilitation programs, and enhanced patient engagement using machine learning algorithms to recognize patterns and anomalies in knee motion data. The data analysis section employs various filtering, segmentation, normalization, and statistical methods to ensure accurate and meaningful data analysis. The document concludes by highlighting the need for further research to optimize and validate these technologies and interventions.
Current and future challenges when operating the ATLAS Liquid Argon Calorimeter
Authors: Kreße T. ,   the ATLAS Liquid Argon Calorimeter Group
Published: February 2024

Abstract
The Liquid Argon (LAr) Calorimeters are employed by ATLAS for precision electromagnetic calorimetry and for hadronic and forward calorimetry. They also provide inputs to the first level of the ATLAS trigger system.

The LAr Calorimeters have been operated during regular ATLAS data taking since 2008. All critical parameters, like LAr purity and temperature, have been kept at stable values resulting in reliable signal pulses. In 2022, the LHC collider has increased the luminosity leading to a pile-up of up to 70 interactions per bunch crossing. In the high luminosity phase, HL-LHC, planned to be fully functional in 2029, the pile-up will increase up to 200 interactions per bunch crossing.

This contribution will highlight the present and future challenges in the operation of such a detector.
Pipeline for performance evaluation of flavour tagging dedicated Graph Neural Network algorithms
Authors: Brianti Greta , Iuppa Roberto , Cristoforetti Marco 
Published: February 2024

Abstract
Machine Learning is a rapidly expanding field with a wide range of applications in science. In the field of physics, the Large Hadron Collider, the world's largest particle accelerator, utilizes Neural Networks for various tasks, including flavour tagging. Flavour tagging is the process of identifying the flavour of the hadron that initiates a jet in a collision event, and it is an essential aspect of various Standard Model and Beyond the Standard Model studies. Graph Neural Networks are currently the primary machine-learning tool used for flavour tagging. Here, we present the AUTOGRAPH pipeline, a completely customizable tool designed with a user-friendly interface to provide easy access to the Graph Neural Networks algorithm used for flavour tagging.
A prototype 4D-tracking demonstrator based on the TimeSPOT developments
Authors: Loi A. , Addison M. , Anderlini L. , Brundu D. , Boscardin M. , Cadeddu S. , Cardini A. , Cossu G.M. , Dalla Betta G.-F. , Frontini L. , Garau M. , Lai A. , La Delfa L. , Lampis A. , Liberali V. , Passaleva G. , Piccolo L. , Ronchin S. , Stabile A. , Veltri M. 
Published: February 2024

Abstract
We present first results obtained with a prototype 4D-tracking demonstrator, using sensors and electronics developed within the TimeSPOT project, and tested on a positive charged pion beam at CERN SPS. The setup consists of five small tracking layers in a row, having area of about 3 mm2 each, three of which equipped with 3D-trench silicon sensors and two with 3D-column diamond sensors. The five layers are then read-out by a KC705 Xilinx board on a PC. We describe the demonstrator structure and operation and illustrate preliminary results on its tracking capabilities.
Hough Transform FPGA solution for High Energy Physics online fast tracking
Authors: Alfonsi F. , Del Corso F. , Gabrielli A. 
Published: February 2024

Abstract
In the coming years, significant upgrades are planned for ATLAS and other High Energy
Physics experiments at CERN. Both the technologies and methodologies employed will undergo
changes for the scheduled runs at the end of the decade. The LHC accelerator itself will also
undergo multiple modifications, allowing it to achieve a peak of instantaneous luminosity up to
5–7.5 × 1034 cm-2 s-1. These enhancements will necessitate the
experiments to handle a greater number of events at the conclusion of the data acquisition
chain. For instance, ATLAS will be compelled to employ online tracking for its inner detector,
aiming to achieve a final event rate of 10 kHz from the 1 MHz originating from the Calorimeters
and the Muon Spectrometer trigger discrimination. Among the architectures explored to expedite
fast tracking, there is consideration of a “hardware accelerator” farm, an infrastructure made
of interconnected accelerators such as GPUs and FPGAs, designed to accelerate the tracking
processes. The project presented here proposes a tuned Hough Transform algorithm implementation on
high-end FPGA technology, specifically designed to adapt to various tracking situations. A
development platform comprising software and firmware tools has been created to study different
datasets. This platform utilizes software to simulate the firmware and to perform hardware
tests. AMD-Xilinx FPGAs were chosen to implement and asses the system, with specific boards such
as the VC709, the VCU1525 and the Alveo U250. Strategies such as low-level design for the firmware
architecture, leveraging the card's features like PCI Express data transfer, and the > 1 million
gates array available have been exploited. The system underwent testing using internally simulated
events generated within the ATLAS environment. Simulated 200 pile up events were used to evaluate
the algorithm effectiveness. The average processing time was estimated to be below 5 μs,
with the capability to concurrently process two events per algorithm instance. Internal efficiency
tests have shown conditions where track finding performance for single muon tracking exceeded
95%.
Test bench of a 100 Gbps radiation hardened link for future particle accelerators
Authors: Martina F. , Baron S. , Moreira P. , Baszczyk M. , Biereigel S. , Klekotko A. , Kulis S. 
Published: February 2024

Abstract
Pioneering physics experiments require increasingly faster data transfers and high-throughput electronics, which drives the research towards a new class of serialisers and optical links. In this framework, the DART28, a 100 Gbps radiation tolerant serialiser and driver, has been designed in 28 nm CMOS technology, submitted in April and delivered in August 2023. The development has been coupled with an FPGA based emulation, which provided an early assessment of its behaviour, a scalable system-level demonstrator and an effective evaluation tool for compatible commercial solutions. The challenges faced in this research and the architecture of both the hardware setup and the firmware will be described.
Novel developments on the OpenIPMC project
Authors: Calligaris Luigi , Dell'Aquila Carlos Ruben , Grossi Bassi Antono Vitor , Muller Cascadan André , Ardila-Perez Luis Eduardo , Fuchs Marvin , Akpinar Alp , Peck Andrew , Gastler Daniel , Fedi Giacomo 
Published: February 2024

Abstract
We present the recent developments in the context of the OpenIPMC project, which proposes a free and open-source Intelligent Platform Management Controller (IPMC) software and an associated controller mezzanine card for use in ATCA electronic boards. We discuss our experience in the operation of OpenIPMC on prototype boards designed for the upgrades of particle physics experiments at CERN and we show the addition of new features and support for new protocols in the firmware of the controller mezzanine card.
A high time resolution and high dynamic range ASIC for the micro-vertex detector in the PANDA experiment
Authors: Zhang H. , Hirono T. , Peric I. 
Published: February 2024

Abstract
A monolithic pixel sensor test chip for the PANDA
micro-vertex detector has been implemented in a 180 nm HVCMOS
technology on a high resistivity substrate. The sensor should have
very high time resolution (1 ns sigma) and high dynamic range (up to
1000). The pixel electronics contains a charge sensitive amplifier,
a feedback circuit and two comparators. One comparator receives the
fast signal and enables accurate time measurement. The other
comparator receives the low pass filtered signal and is used for
precise amplitude measurement. This publication presents several
novel features of the PANDA ASIC, its characterization and several
measurement results.
Analysis of compressive strength of sustainable fibre reinforced foamed concrete using machine learning techniques
Authors: Ayyanar Dhanalakshmi , Ali Shahul Hameed Masthan 
Published: February 2024

Abstract
This study emphasizes the usage of Silica Fume (SF) and Marble Sludge Powder (MSP) as a partial replacement for fly ash in Fibre Reinforced Foamed Concrete (FRFC). The compressive strength for various samples was analyzed using Artificial Neural Network (ANN) methods. In this research work, the utilization of silica fume, fly ash, marble sludge powder, polypropylene fiber, and foaming agent in fiber-reinforced foamed concrete is presented and a sincere attempt has been made to use silica fume and marble sludge powder for the replacement of fly ash with various percentages. In addition to that polypropylene fiber (PPF) was used in various proportions of 0%, 0.1%, 0.2%, 0.3%, 0.4%, and 0.5%. The Feed Forward Propagation (FFP) network of the machine learning method with one hidden layer was taken as the ANN structure of FRFC. In this ANN work, cement, silica fume, fly ash, marble sludge powder, foaming agent, water, and polypropylene fiber were used as input parameters and compressive strength is the output parameter. The correlation coefficient with the ANN methods was found as 0.940 for compressive strength. In machine learning techniques, the ANN method was found to be accurate in estimating and analyzing strength prediction responses with effective parameters.
Development of ambitious and realistic targets to reduce short-lived climate pollutant emissions in nationally determined contributions: case study for Colombia
Authors: Malley Christopher S , Pedraza Jenniffer I , González Luisa , del Carmen Cabeza Maria , Gaitan Mauricio , Melo John H , Ulloa Silvia , Kuylenstierna Johan C I , Haeussling Seraphine , Lefèvre Elsa N 
Published: February 2024

Abstract
Limiting global temperature increases to below 1.5 °C requires reductions in Short-Lived Climate Pollutants (SLCPs), like methane, black carbon, and hydrofluorocarbons (HFCs), which is rarely reflected in targets within Nationally Determined Contributions (NDCs). Colombia’s 2020 NDC is explored as a case study for how Governments can integrate SLCP mitigation targets into climate change commitments. Methane and HFC mitigation contribute approximately 9% of Colombia’s GHG reduction commitment, and a separate target is included to reduce black carbon emissions by 40% by 2030 compared to 2014 levels. These targets are shown to be ambitious, due to the inclusion of a new black carbon target, realistic due to the identification of mitigation measures to achieve them, and additional to CO2 mitigation. Analysis of the planning process establishing these targets emphasises the importance of long-term planning to obtain agreement between coordinating institutions and implementing institutions on the utility of SLCP targets, and capacity-building within national institutions.
Strain-induced magnetic anisotropy of multi-domain epitaxial EuPd2 thin films
Authors: Schuck Alfons G , Kölsch Sebastian , Valadkhani Adrian , Mazin Igor I , Valentí Roser , Huth Michael 
Published: February 2024

Abstract
Europium intermetallic compounds show a variety of different ground states and anomalous physical properties due to the interactions between the localized 4f electrons and the delocalized electronic states. Europium is also the most reactive of the rare earth metals which might be the reason why very few works are concerned with the properties of Eu-based thin films. Here we address the low-temperature magnetic properties of ferromagnetic EuPd2 thin films prepared by molecular beam epitaxy. The epitaxial (111)-oriented thin films grow on MgO (100) with eight different domain orientations. We analyze the low-temperature magnetic hysteresis behavior by means of micromagnetic simulations taking the multi-domain morphology explicitly into account and quantify the magnetic crystal anisotropy contribution. By ab initio calculations we trace back the microscopic origin of the magnetic anisotropy to thin film-induced uniform biaxial strain.
Diffusive and ballistic transport in thin InSb nanowire devices using a few-layer-graphene-AlO
x
gate
Authors: Shani Lior , Lueb Pim , Menning Gavin , Gupta Mohit , Riggert Colin , Littmann Tyler , Hackbarth Frey , Rossi Marco , Jung Jason , Badawy Ghada , Verheijen Marcel A , Crowell Paul A , Bakkers Erik P A M , Pribiag Vlad S 
Published: February 2024

Abstract
Quantum devices based on InSb nanowires (NWs) are a prime candidate system for realizing and exploring topologically-protected quantum states and for electrically-controlled spin-based qubits. The influence of disorder on achieving reliable quantum transport regimes has been studied theoretically, highlighting the importance of optimizing both growth and nanofabrication. In this work, we consider both aspects. We developed InSb NW with thin diameters, as well as a novel gating approach, involving few-layer graphene and atomic layer deposition-grown AlO
x
. Low-temperature electronic transport measurements of these devices reveal conductance plateaus and Fabry–Pérot interference, evidencing phase-coherent transport in the regime of few quantum modes. The approaches developed in this work could help mitigate the role of material and fabrication-induced disorder in semiconductor-based quantum devices.
The Aesthete as Monster
Authors: Hext Kate 
Published: February 2024

Abstract
This chapter shows how Wilde was brought back to the foreground of American culture by Leslie and Sewell Stokes’ play, Oscar Wilde, produced in Los Angeles and starring Laird Cregar (1940). It develops Harry M. Benshoff’s point that Wilde was the originator of the sexually suspect monster on-screen, by looking at the ways in which Wildean wit and effeminacy were repackaged, first, in B-movie horrors like The Old Dark House (1932) and then—with direct references to Wilde and decadence—in films noirs, such as Laura (1944), The Big Sleep (1946), and A Double Life (1947). It then illustrates how Albert Lewin’s adaptation of The Picture of Dorian Gray (1945) was filtered through the noir lens of its time, and how Wilde’s own descriptions helped to define noir lighting.
Oscar Wilde, Hollywood Rebel
Authors: Hext Kate 
Published: February 2024

Abstract
This brief conclusion draws together the main points that the book makes on (1) how Wilde shaped Hollywood cinema (and the limitation of our understanding of this) and (2) how seeing Wilde through Hollywood can reshape how we read him. In the first case it discusses how Wilde and the ‘Wilde-ish’ helped to define the aesthetics of American cinema up to the early 1950s. In the second, it suggests that film-makers were able to make his works and, in consequence, Wilde himself the figure that they wanted him to be and so this Conclusion discusses how their version of Wilde can shift our perspective on his works, from their criminality and violent potential to their anticipations of the cinematic gaze and epigrammatic wit.
Radical prostatectomy cancer grade and percentage of Gleason pattern 4 estimated by global vs individual tumor grading correlate differently with the risk of biochemical recurrence in Grade Group 2 and 3 cancers
Authors: Kryvenko Oleksandr N , Epstein Jonathan I , Merhe Ali , Iakymenko Oleksii A , De Almeida E Silva Junior Ricardo , Chanamolu Dimple Kumar , Briski Laurence M , Kwon Deukwoo , Nemov Ivan , Punnen Sanoj , Pollack Alan , Stoyanova Radka , Parekh Dipen J , Jorda Merce , Gonzalgo Mark L 
Published: February 2024

Abstract

Objectives
There are 2 grading approaches to radical prostatectomy (RP) in multifocal cancer: Grade Group (GG) and percentage of Gleason pattern 4 (GP4%). We investigated whether RP GG and GP4% generated by global vs individual tumor grading correlate differently with biochemical recurrence.

Methods
We reviewed 531 RP specimens with GG2 or GG3 cancer. Each tumor was scored separately with assessment of tumor volume and GP4%. Global grade and GP4% were assigned by combining Gleason pattern 3 and 4 volumes for all tumors. Correlation of GG and GP4% generated by 2 methods with biochemical recurrence was assessed by Cox proportional hazard regression and receiver operating characteristic curves, with optimism adjustment using a bootstrap analysis.

Results
Median age was 63 (range, 42-79) years. Median prostate-specific antigen was 6.3 (range, 0.3-62.9) ng/mL. In total, the highest-grade tumor in 371 (36.9%) men was GG2 and in 160 (30.1%) men was GG3. Global grading was downgraded from GG3 to GG2 in 37 of 121 (30.6%) specimens with multifocal disease, and 145 of 404 (35.9%) specimens had GP4% decreased by at least 10%. Ninety-eight men experienced biochemical recurrence within a median of 13 (range, 3-119) months. Men without biochemical recurrence were followed up for a median of 47 (range, 12-205) months. Grade Group, GP4%, and margin status correlated with the risk of biochemical recurrence using highest-grade tumor and global grading, but the degrees of these correlations varied and were statistically significantly different between the 2 grading approaches.

Conclusions
Grade Group, GP4%, and margin status derived by global vs individual tumor grading predict postoperative biochemical recurrence statistically significantly differently. This difference has important implications if results derived from cohorts graded using different methods are compared.
The molecular modification, expression, and the antibacterial effects studies of human lysozyme
Authors: Xu Kewei , Ma Chuanyuan , Wu Changyun , Wu Dan 
Published: February 2024

ABSTRACT
Human lysozyme (hLYZ) has attracted considerable research attention due to its natural and efficient antibacterial abilities and widespread uses. In this study, hLYZ was modified to enhance its enzyme activity and expressed in a Pichia pastoris expression system. A combination mutant HZM(2R-K)-N88D/V110S demonstrated the highest enzyme activity (6213 ± 164 U/mL) in shake flasks, which was 4.07-fold higher when compared with the original strain. Moreover, the recombinant P. pastoris was inducted in a 3 L bioreactor plus methanol/sorbitol co-feeding. After 120 h induction, the antibacterial activity of hLYZ reached 2.23 ± 0.12 × 105 U/mL, with the specific activity increasing to 1.89 × 105 U/mg, which is currently the highest specific activity obtained through recombinant expression of hLYZ. Also, hLYZ supernatants showed 2-fold inhibitory effects toward Staphylococcus aureus and Micrococcus lysodeikticus when compared with HZM(2R-K). Our research generated a hLYZ mutant with high antibacterial capabilities and provided a method for screening of high-quality enzymes.