Miracle-dz/UniFAR-FTUs huggingface dataset:
Transpetrosal Approach to a Ruptured Distal Basilar Perforating Artery Aneurysm

Basilar perforating artery aneurysms are rare and underreported vascular anomalies in the cerebrovascular literature. Various open and endovascular treatment approaches can be employed to treat these aneurysms based on several patient- and aneurysm-specific factors. Some authors have even advocated for conservative, nonoperative management. Here, we report a case of a ruptured distal basilar perforating artery aneurysm secured by an open transpetrosal approach. A 67-year-old male presented to our institution with a Hunt-Hess grade 2, modified Fisher grade 3 subarachnoid hemorrhage (SAH). Initial cerebral digital subtraction angiography (DSA) did not identify an intracranial aneurysm or other vascular lesions. However, the patient had a re-rupture event several days after presentation. DSA at this time revealed a posteriorly projecting distal basilar perforating artery aneurysm. Initial attempts with endovascular coil embolization were unsuccessful. Thus, an open transpetrosal approach was taken to gain access to the middle and distal basilar trunk to secure the aneurysm. This case underscores the unpredictability of basilar perforating artery aneurysms and the challenges encountered when considering active treatment. We demonstrate an open surgical approach with an intraoperative video for definitive management after failed attempted endovascular treatment.
Porting the Kitten Lightweight Kernel Operating System to RISC-V

Hardware design in high-performance computing (HPC) is often highly experimental. Exploring new designs is difficult and time-consuming, requiring lengthy vendor cooperation. RISC-V is an open-source processor ISA that improves the accessibility of chip design, including the ability to do hardware/software co-design using open-source hardware and tools. Co-design allows design decisions to easily flow across the hardware/software boundary and influence future design ideas. However, new hardware designs require corresponding software to drive and test them. Conventional operating systems like Linux are massively complex and modification is time-prohibitive. In this paper, we describe our port of the Kitten lightweight kernel operating system to RISC-V in order to provide an alternative to Linux for conducting co-design research. Kitten's small code base and simple resource management policies are well matched for quickly exploring new hardware ideas that may require radical operating system modifications and restructuring. Our evaluation shows that Kitten on RISC-V is functional and provides similar performance to Linux for single-core benchmarks. This provides a solid foundation for using Kitten in future co-design research involving RISC-V.
Spin‐glass behavior of spinel iron oxide particles on iron metal particle recording media

Small (30 A diameter) spinel iron oxide particles which form the protective layer on acicular iron metal particle recording media are found to exhibit no observable magnetization. The typical experimental decrease in sample magnetization for several media specimens is about 3% from 5 to 300 K. This change is the same as the thermal decrease in metal core magnetization, determined by Mossbauer spectroscopy. The predicted decrease in sample magnetization assuming normal spinel oxide behavior is typically about 20%. Two intraparticle models for reduced magnetization in oxide nanoparticles are found to be incompatible with the magnetization data. An interparticle ‘‘super‐spin‐glass’’ model is compatible with the magnetization data. The alignment between adjacent oxide nanoparticle moments is hypothesized to depend on the particular sublattices in contact, since the sublattice exchange interactions are all antiferromagnetic. An inverse particle size dependence is thus expected, in agreement with spinel oxide magnetization reduction and other phenomena seen in a wide range of studies on larger oxide particles and films.
Customer Momentum

This paper examines customer momentum, defined as a positive relationship between a firm’s returns and past returns of its customers. I confirm previous evidence (Cohen and Frazzini 2008) that customer momentum is both statistically and economically significant. Long-short equally-weighted (value-weighted) decile portfolio generates a monthly return of 122 (106) basis points and a t-statistic above 4 (2.8) with respect to Fama-French factor models. The paper reports that customer momentum neither explains nor is explained by price momentum and earnings momentum. Customer momentum is partially driven by the lead-lag relationship between small and large stocks. I find that in the post-discovery sample, customer momentum has a smaller magnitude and loses statistical significance. The results are consistent with the hypothesis that after its discovery, customer momentum decreased due to exploitation by investors. ∗Simon Business School, University of Rochester. Email: Mykola.Pinchuk@ur.rochester. edu. I would like to thank Yixin Chen, Bill Schwert, Giulio Trigilia, Shuaiyu Chen, David Swanson, Zhao Jin, Pingle Wang and Robert Mann for helpful comments. ar X iv :2 30 1. 11 39 4v 1 [ qfi n. PR ] 2 6 Ja n 20 23
Fourier ptychographic microscopy reconstruction with an untrained deep neural network

Fourier ptychographic microscopy (FPM) is a developed microscopic imaging technique which can break the limitation of the space-bandwidth-product and realize wide field of view, high-resolution and quantitative phase imaging. In recent years, application of deep learning in FPM effectively improved the imaging resolution of the amplitude and phase. However, these methods always require the construction of enormous datasets to train the network. To overcome the above problems, an untrained deep neural network (DNN) based on the physical model of FPM is proposed. Different from the traditional deep learning methods, this method aims to discard thousands of label data. According to the known frequency shift, the output images in DNN are virtually imaged with the forward model of FPM and a series of low-resolution images that have been simply fused are calculated to update the network by comparing errors between them and the experimental data. For a small reconstruction task, the proposed network can be treated as the iterative phase retrieval procedure, the amplitude and phase can be well retrieved with untrained parameters. The simulation results verify the feasibility of proposed physics-driven DNN. Compared with traditional deep learning methods, this method discard thousands of label data in the case of limited resolution loss.
Cooperative UAV Resource Allocation and Task Offloading in Hierarchical Aerial Computing Systems: A MAPPO-Based Approach

This article investigates a hierarchical aerial computing system, where both high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) provision computation services for ground devices (GDs). Different from the existing works which ignored UAV task offloading to HAPs and suffered long transmission delay between HAPs and GDs, in our system, UAVs are responsible for collecting the tasks generated by GDs. Considering limited resources and constrained coverage, UAVs need to cooperatively allocate their resources (including spectrum, caching, and computing) to GDs. After collecting GD tasks, UAVs are allowed to offload part of these tasks to the HAP, in order to minimize task processing delay and then better satisfy GD delay requirement. Our objective is to maximize the amount of computed tasks while satisfying tasks’ heterogeneous Quality-of-Service (QoS) requirements through the joint optimization of UAV resource allocation and task offloading. To this end, a joint optimization problem is first formulated as a partially observable Markov decision process (POMDP) under the constraints of available resources, UAV energy, and collision avoidance. Then, we design a multiagent proximal policy optimization (MAPPO)-based algorithm to solve the optimization problem. By introducing the centralized training with decentralized execution framework, UAVs acting as agents can cooperatively make decisions on GDs association, resource allocation, and task offloading according to their local observations. In addition, state normalization and action mask are also adopted to improve training efficiency. Experimental results verify the efficiency of the proposed algorithm and the system performance is also analyzed by the numerical results.
Round Table. Mission impossible? How to increase collective bargaining coverage in Germany and the EU

The Directive on fair and adequate minimum wages represents a paradigm shift in EU economic governance. It marks a profound reorientation away from an economic growth strategy that priori-tises liberalisation of labour markets and decentralisation of collective bargaining, which has dom-inated the policy orientation of most EU Member States and the European Commission for at least 20 years. The Directive reinforces the governance and the level of statutory minimum wages in EU Member States. In a second step, the Directive requests that Member States support collective bargaining and find concrete measures to increase bargaining coverage for those countries where coverage is less than 80 per cent. This second aspect is just as relevant to the Directive and even more ambitious than the first. While statutory minimum wages are in the control of governments, collective bargaining is based on voluntary negotiations between trade unions and employers’ associations. The role of governments and public policy towards collective bargaining is limited and only indirect. In this comment I will focus on the latter aspect of increasing collective bargaining coverage, but will argue first that the wider picture of a changing EU growth strategy is important to understand the significance of the shift. I will, secondly, discuss ways of strengthening collective bargaining, with a focus on the German case.
Testing for a Change in the Parameter Values and Order of an Autoregressive Model

The problem of testing whether or not a change has occurred in the parameter values and order of an autoregressive model is considered. It is shown that if the white noise in the AR model is weakly stationary with finite fourth moments, then under the null hypothesis of no changepoint, the normalized Gaussian likelihood ratio test statistic converges in distribution to the Gumbel extreme value distribution. An asymptotically distribution-free procedure for testing a change of either the coefficients in the AR model, the white noise variance or the order is also proposed. The asymptotic null distribution of this test is obtained under the assumption that the third moment of the noise is zero. The proofs of these results rely on Horvath's extension of Darling-Erdos' result for the maximum of the norm of a $k$-dimensional Ornstein-Uhlenbeck process and an almost sure approximation to partial sums of dependent random variables.
Analytical relation of band gaps to both chirality and diameter of single-wall carbon nanotubes

We present a tight-binding model of single-wall carbon nanotubes with curvature-modified hopping parameter ${\ensuremath{\gamma}}_{i},$ from which an analytical relation of band gaps to both chirality and diameter is derived by developing a transfer matrix method. The results are in agreement with the experimental results obtained from scanning tunneling microscopy and scanning tunneling spectroscopy measurements by three groups.
An only metal, compact, and circular polarizer designed with helical array rods

This paper presents a simple method to realize a circular polarizer by inserting rods arranged helically in the cylindrical waveguide. In the proposed polarizer, the rods are installed to excite the electric ﬁelds on the perpendicular axis to the input ﬁeld. In addition, the rod locations create a 900 phase diﬀerence between two perpendicular axes to convert the polarization from linear to circular. In the proposed structure, the axial ratio is smaller than 0.4 dB and the phase diﬀerence is about 900 at 10 GHz frequency. In addition, the reﬂection coeﬃcients are better than -14 dB for both E x and E y polarizations at the same frequency. Also, this structure is very compact compared to similar structures. Furthermore, this is made only of metal. Therefore, there is no dielectric loss and it has high endurance. The validation results have a good agreement with the simulation.
Plasmonically enhanced diffusive and subdiffusive metal nanoparticle-dye random laser

We report on surface plasmon (SP)-enhanced random laser emission from a suspension of silver nanoparticles in a laser dye operating at diffusive and subdiffusive scattering strengths. SP resonance enhances the scattering cross section, while the geometrical cross section remains small, thus providing a large gain volume. The localized electromagnetic field near the particle surface leads to enhanced absorption of excitation light and larger amplification of fluorescence. The metal-nanoparticle-based random laser yields larger linewidth narrowing at lower pump fluence threshold than a dielectric-scatterer-based random laser under equivalent conditions. These findings open the door to studies of applications related to light amplification assisted by SP in metallic nanoparticles.
Ultrafast-laser driven micro-explosions in transparent materials

We initiate micro-explosions inside fused silica, quartz, sapphire, and other transparent materials using tightly focused 100 fs laser pulses. In the micro-explosions, material is ejected from the center, forming a cavity surrounded by a region of compacted material. We examine the resulting structures with optical microscopy, diffraction, and atomic force microscopy of internal cross sections. We find the structures have a diameter of only 200–250 nm, which we attribute to strong self-focusing of the laser pulse. These experiments probe a unique regime of light propagation inside materials at intensities approaching 1021 W/m2, the electron ionization that accompanies it, and the material response to extreme pressure and temperature conditions. The micro-explosions also provide a novel technique for internal microstructuring of transparent materials.
Frequency of visual impairment in autistic children of autism school of Lahore, Pakistan

Autism is a condition in which abnormal development of the brain takes place, manifesting in the first 3 years of life. Visual impairment is one of the major factors of autism spectrum disorder (ASD), and measurement of visual impairment is necessary for proper management of the disorder. The purpose of this study is to determine the frequency of visual impairment associated with ASD in Lahore Pakistan. A descriptive cross-sectional study was conducted. A convenient and purposive sample of 127 students enrolled in various autism schools of Lahore was enrolled. Written informed consent of teacher’s autistic children was taken. Children having diseases like cerebral palsy, other developmental disorders, and/or serious cognitive disability were excluded from this study. Participant’s undergone ocular assessment including visual acuity, ocular motility examination, refraction, and fundoscopy. Data analysis was performed using SPSS-20 and Pearson’s chi-square analysis was conducted to determine the associations of refractive error and squint with ASD. A total of 127 participants were included in this study, of which 77 (60.6%) were male and 50 (39.4%) were female. More than 60% were emmetropes (having no refractive errors), 19 (15%) were having myopes, 15 (11.8%) were hypermetropic, and 9 (7.1%) were Astigmatic, while 20 were diagnosed with a squint. The major cause of visual impairment in autistic children was refractive errors 33.8% and squint 15.7%. A high frequency of visual impairment was present in autistic children in Lahore autistic schools. As vision is a basic right so we have to pay attention also toward autistic children, in that way every child should have screened for factors that are responsible for visual impairments, Identifying refractive errors in these children early and providing appropriate corrective lenses may help optimize their visual functioning and impact their activities of daily life positively.
Analysis of propagation loss of metallic photonic crystal waveguides

Metallic waveguides can be constructed with sidewalls consisting of metallic rods instead of sheets. The attenuation constants and bandwidths of such waveguides are calculated for a fixed cutoff frequency while the radii of the metallic rods are varied. The propagation losses are found to decrease when the radii of the rods is increased. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 2942–2945, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23844
Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Featured Application This article is focused on a literature review to provide a valuable resource for understanding the latest developments in the Maintenance 4.0 approach. The conducted research will be helpful for many people, including maintenance managers, maintenance engineers, and researchers, who are interested in the issues of maintenance performance in the context of Industry 4.0 technologies implementation. The conducted literature review intends to introduce the readers to the major up-to-date theory and practice in Maintenance 4.0 main research directions. The presented study makes it possible to identify the thematic structure related to maintenance performance. In addition, it shows which topics from the studied scientific area are the most investigated in a given country/region. At the same time, the conducted analysis allowed the development of future research directions in the areas identified as research and knowledge gaps. Abstract Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, (4) Maintenance system architecture, and (5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.
The spectrum of random magnetic fields in the mean field dynamo theory of the galactic magnetic field

The fluctuation spectrum that must arise in a mean field dynamo generation of galactic fields if the initial field is weak is considered. A kinetic equation for its evolution is derived and solved. The spectrum evolves by transfer of energy from one magnetic mode to another by interaction with turbulent velocity modes. This kinetic equation is valid in the limit that the rate of evolution of the magnetic modes is slower than the reciprocal decorrelation time of the turbulent modes. This turns out to be the case by a factor greater than 3. Most of the fluctuation energy concentrates on small scales, shorter than the hydrodynamic turbulent scales. The fluctuation energy builds up to equipartition with the turbulent energy in times that are short compared to the e-folding time of the mean field. The turbulence becomes strongly modified before the dynamo amplification starts. Thus, the kinematic assumption of the mean dynamo theory is invalid. Thus, the galactic field must have a primordial origin, although it may subsequently be modified by dynamo action.
Human–Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention

Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person’s behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker’s emotional state from their speech signal. SER is a growing discipline in human–computer interaction, and it has recently attracted more significant interest. This is because there are not so many universal emotions; therefore, any intelligent system with enough computational capacity can educate itself to recognise them. However, the issue is that human speech is immensely diverse, making it difficult to create a single, standardised recipe for detecting hidden emotions. This work attempted to solve this research difficulty by combining a multilingual emotional dataset with building a more generalised and effective model for recognising human emotions. A two-step process was used to develop the model. The first stage involved the extraction of features, and the second stage involved the classification of the features that were extracted. ZCR, RMSE, and the renowned MFC coefficients were retrieved as features. Two proposed models, 1D CNN combined with LSTM and attention and a proprietary 2D CNN architecture, were used for classification. The outcomes demonstrated that the suggested 1D CNN with LSTM and attention performed better than the 2D CNN. For the EMO-DB, SAVEE, ANAD, and BAVED datasets, the model’s accuracy was 96.72%, 97.13%, 96.72%, and 88.39%, respectively. The model beat several earlier efforts on the same datasets, demonstrating the generality and efficacy of recognising multiple emotions from various languages.