Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on Edge Devices

This paper considers the task of appearance-based localization: visual place recognition from omnidirectional images obtained from catadioptric cameras. The focus is on designing an efficient neural network architecture that accurately and reliably recognizes indoor scenes on distorted images from a catadioptric camera, even in self-similar environments with few discernible features. As the target application is the global localization of a low-cost service mobile robot, the proposed solutions are optimized toward being small-footprint models that provide real-time inference on edge devices, such as Nvidia Jetson. We compare several design choices for the neural network-based architecture of the localization system and then demonstrate that the best results are achieved with embeddings (global descriptors) yielded by exploiting transfer learning and fine tuning on a limited number of catadioptric images. We test our solutions on two small-scale datasets collected using different catadioptric cameras in the same office building. Next, we compare the performance of our system to state-of-the-art visual place recognition systems on the publicly available COLD Freiburg and Saarbrücken datasets that contain images collected under different lighting conditions. Our system compares favourably to the competitors both in terms of the accuracy of place recognition and the inference time, providing a cost- and energy-efficient means of appearance-based localization for an indoor service robot.
Qualitative phytochemical analysis of Curcuma longa root extract

The active group (s) content of Curcuma longa rhizomes (CLR) makes it a medicinal plant with a reasonable therapeutic potential. Therefore, in this study, we attempted to clarify these groups using standard preliminary phytochemical screening tests. Results revealed detection of Alkaloids, Glycosides, Carbohydrates, Cardiac Glycoside(s), Saponin, Gallo-tannins, or Catecho-Tannins (hydrolysable or condensed), respectively, Flavonoids, Resins, Steroids, Fixed oils, and Protein compounds. Anthraquinones, Gums or Mucilage and Terpenoids were not noticeable. Phlobatannins content is suspect. These findings may prove CLR is a rich source of active groups, suggesting that it may be a nutraceutical-based medicine in the pharmaceutical and cosmetic industries.
X-ray doppler velocimetry for diagnosis of fluid motion in ICF implosions

We are developing a novel diagnostic for measurement of bulk fluid motion in materials, that is particularly applicable to very hot, x-ray emitting plasmas in the High Energy Density Physics (HEDP) regime. The X-ray Doppler Velocimetry (XDV) technique relies on monochromatic imaging in multiple x-ray energy bands near the center of an x-ray emission line in a plasma, and utilizes bent imaging crystals. Higher energy bands are preferentially sensitive to plasma moving towards the viewer, while lower energy bands are preferentially sensitive to plasma moving away from the viewer. Combining multiple images in different energy bands allows for a reconstruction of the fluid velocity field integrated along the line of sight. We review the technique, and we discuss progress towards benchmarking the technique with proof-of-principle HEDP experiments.
Direct Competitive Assay for ERBB2 Detection in Breast Cancer Cell Lysates Using 1-D Photonic Crystals-Based Biochips

ERBB2 is a member of the tyrosine kinase receptor family, a major cancer driver, and a recognized therapeutic target in many cancers, most importantly breast cancer. Its presence in soluble form in blood correlates with tumor expression and may have implications for diagnostic and therapeutic decisions. Herein, we develop a new approach that makes use of a direct competitive ERBB2 assay and 1-D photonic crystal-based biochips. Such biochips permit to operate in a combined label-free/fluorescence mode enabling ERBB2 biosensing in lysates from selected breast cancer cells, either ERBB2-positive (SK-BR 3, BT474) or ERBB2-negative (T47D). Moreover, our biochips allow the collection of enhanced fluorescence spectra, providing a highly specific ERBB2 detection in the three model cell lines. The main advantage of the assay developed in this work lies in the single-step detection procedure that reduces assay turnaround time to less than 20 min. This latter feature confirms the great potential of the technique in the rapid detection of ERBB2 in complex biological media.
Electroacupuncture Attenuates Neuropathic Pain in a Rat Model of Cervical Spondylotic Radiculopathy: Involvement of Spinal Cord Synaptic Plasticity

Purpose Cervical spondylotic radiculopathy (CSR) is a common neurologic condition that causes chronic neck pain and motor functions, with neuropathic pain (NP) being the primary symptom. Although it has been established that electroacupuncture (EA) can yield an analgesic effect in clinics and synaptic plasticity plays a critical role in the development and maintenance of NP, the underlying mechanisms have not been fully elucidated. In this study, we explored the potential mechanisms underlying EA’s effect on synaptic plasticity in CSR rat models. Materials and Methods The CSR rat model was established by spinal cord compression (SCC). Electroacupuncture stimulation was applied to LI4 (Hegu) and LR3 (Taichong) acupoints for 20 min once a day for 7 days. Pressure pain threshold (PPT) and mechanical pain threshold (MPT) were utilized to detect the pain response of rats. A gait score was used to evaluate the motor function of rats. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), immunohistochemistry (IHC), immunofluorescence (IF), and transmission electron microscopy (TEM) were performed to investigate the effects of EA. Results Our results showed that EA alleviated SCC-induced spontaneous pain and gait disturbance. ELISA showed that EA could decrease the concentration of pain mediators in the cervical nerve root. WB, IHC, and IF results showed that EA could downregulate the expression of synaptic proteins in spinal cord tissues and promote synaptic plasticity. TEM revealed that the EA could reverse the synaptic ultrastructural changes induced by CSR. Conclusion Our findings reveal that EA can inhibit SCC-induced NP by modulating the synaptic plasticity in the spinal cord and provide the foothold for the clinical treatment of CSR with EA.
Apo state pore opening as functional basis of increased EAAT anion channel activity in episodic ataxia 6

SLC1A2 and SLC1A3 encode the glial glutamate transporters EAAT2 and EAAT1, which are not only the predominant glutamate uptake carriers in our brain, but also function as anion channels. Two homologous mutations, which predict substitutions of prolines in the center of the fifth transmembrane helix by arginine (P289R EAAT2, P290R EAAT1), have been identified in patients with epileptic encephalopathy (SLC1A2) or with episodic ataxia type 6 (SLC1A3). Both mutations have been shown to impair glutamate uptake and to increase anion conduction. The molecular processes that link the disease-causing mutations to two major alterations of glutamate transporter function remain insufficiently understood. The mutated proline is conserved in every EAAT. Since the pathogenic changes mainly affect the anion channel function, we here study the functional consequences of the homologous P312R mutation in the neuronal glutamate transporter EAAT4, a low capacity glutamate transporter with predominant anion channel function. To assess the impact of charge and structure of the inserted amino acid for the observed functional changes, we generated and functionally evaluated not only P312R, but also substitutions of P312 with all other amino acids. However, only exchange of proline by arginine, lysine, histidine and asparagine were functionally tolerated. We compared WT, P312R and P312N EAAT4 using a combination of cellular electrophysiology, fast substrate application and kinetic modelling. We found that WT and mutant EAAT4 anion currents can be described with a 11-state model of the transport cycle, in which several states are connected to branching anion channel states to account for the EAAT anion channel function. Substitutions of P312 modify various transitions describing substrate binding/unbinding, translocation or anion channel opening. Most importantly, P312R generates a new anion conducting state that is accessible in the outward facing apo state and that is the main determinant of the increased anion conduction of EAAT transporters carrying this mutation. Our work provides a quantitative description how a naturally occurring mutation changes glutamate uptake and anion currents in two genetic diseases.
Boiling of saturated water on grooved surface

The flow patterns and pool boiling heat transfer performance of rectangular grooved surface immersed in saturated water were experimentally investigated. The effect of the aspect ratio (groove depth/fin thickness) on boiling performance was examined. The test surfaces were manufactured on a copper block with a base diameter of 19 mm with four fin thickness (0.5 mm, 1 mm, 1.5 mm, and 2 mm) and three groove depths (1.0 mm, 2.0 mm, and 3.0 mm). All experiments were performed in the saturated state at atmospheric pressure. A plain surface was used as the reference standard and compared with the grooved surfaces. The photographic images showed different boiling flow patterns among the test surfaces at various heat fluxes. The test results indicated that closer and more number of grooves yielded a greater flow resistance against the bubble/vapour lift-off along the groove wall. At higher heat flux, numerous vapour mushrooms periodically appeared from the perimeter of the grooves. The developed correlation for Nusselt number predicts the experimental data with mean absolute error of 7.42%.
Mitigation of UV-Induced Propagation Loss in PECVD Silicon Nitride Photonic Waveguides

We observe a drastic increase in propagation loss at visible wavelengths in PECVD silicon nitride waveguides after exposure to ultraviolet (UV) light. Low temperature annealing or high intensity optical exposure at visible wavelengths brings the propagation loss back toward the original value before UV exposure or even lower. We postulate that these effects can be explained by the population and depopulation of defect centers in the silicon nitride. We demonstrate the importance of defect depopulation before using waveguides in any silicon nitride based visible photonic application and provide a cleaning procedure that yields reproducible, low-loss, initial waveguide conditions.
Changes in resting-state functional MRI connectivity during and after transcranial direct current stimulation in healthy adults

Introduction Transcranial direct current stimulation (tDCS) applied over the dorsolateral prefrontal cortex (DLPFC) at rest can influence behaviors. However, its mechanisms remain poorly understood. This study examined the effect of a single session of tDCS over the bilateral DLPFC on resting-state functional connectivity using fMRI (rs-fcMRI) during and after stimulation in healthy adults. We also investigated whether baseline rs-fcMRI predicted tDCS-induced changes in rs-fcMRI. Methods This was a randomized, sham-controlled, double-blind, crossover study. We delivered tDCS for 30 min at 1 mA with the anode and cathode over the left and right DLPFC, respectively. We used seed-based analyses to measure tDCS-induced effects on whole-brain rs-fcMRI using a 3 (before, during, after stimulation) × 2 (active, sham stimulation) ANOVA. Results There were four significant Time × Stimulation interactions on the connectivity scores with the left DLPFC seed (under the anode electrode) and no interactions for the right DLPFC seed (under the cathode electrode). tDCS changed rs-fcMRI between the left DLPFC seed and parieto-occipital, parietal, parieto-occipitotemporal, and frontal clusters during and after stimulation, as compared to sham. Furthermore, rs-fcMRI prior to stimulation predicted some of these tDCS-induced changes in rs-fcMRI during and after stimulation. For instance, rs-fcMRI of the fronto-parietooccipital network predicted changes observed after active stimulation, rs-fcMRI of the fronto-parietal network predicted changes during active stimulation, whereas rs-fcMRI of the fronto-parieto-occipitotemporal and the frontal networks predicted changes both during and after active stimulation. Discussion Our findings reveal that tDCS modulated rs-fcMRI both during and after stimulation mainly in regions distal, but also in those proximal to the area under the anode electrode, which were predicted by rs-fcMRI prior to tDCS. It might be worth considering rs-fcMRI to optimize response to tDCS.
International Accounting Convergence and Divergence: Towards a Framework for Understanding De Jure Adoption of IFRS

Abstract By bridging the two main approaches examining de jure adoption of IFRS, namely, convergence and divergence studies, we provide a framework that more fully captures the totality, dynamics, and complexity of voluntary adoption of IFRS by country. The framework offers an understanding of accounting regulators’ efforts to balance between pressure to adopt IFRS and national specific conditions that may conflict with IFRS requirements. The suggested framework depicts four propositions which are built on four institutional dimensions: the degree of IFRS diffusion, national accounting system compatibility with IFRS, country dependence on external financing, and accounting regulator’s international networking. These four propositions jointly predict national adoption level of IFRS, ranging from non-adoption to partial adoption, and to full adoption. The framework assumes that the current IFRS adoption status by country is not static and may change over time. The voluntary adoption of IFRS standards by country is understood as the result of tradeoffs among multiple factors. In doing so, the developed framework solves a theoretical dichotomy in IFRS studies: the tendency of using institutional isomorphism to examine convergence versus accounting classification to understand divergence.
Post-acute Sequelae of COVID-19 Case Fatality Rate and its Associated Covariates: A Systematic Review, Meta-Analysis and Meta-Regression

Background. Long COVID is a wide range of new, returning, or ongoing health problems experienced after primary COVID-19 infection, with a possibility of broad adverse outcomes. The aim of this study was to determine the case fatality of of post-acute sequelae of COVID-19 (PASC) and assess possible covariates. Population and Methods. We conducted a systematic review and meta-analysis from 43 studies (367,236 patients), (June, 2020 - August, 2022). PASC mortality was assessed from six studies. With random-effects model, the pooled case fatality was measured. Publication bias was ascertained and meta-regression analysis done on predetermined covariates. Results. The estimated prevalence of PASC was 42.5% (95% CI = 36.0 % - 49.3%). The pooled case fatality was 7.4% (95% CI = 7.4% to 11.2%). The funnel plot suggested the presence of publication bias. Hospital re-admission (P = 0.0034) (R² = 1.00) and the year 2021 (P = 0.0309) (R² = 0.55) were associated with fatalities from PASC. Discussion. PASC increased the case-fatality of COVID-19, particularly during the year 2021, reflecting a longer follow-up of patients and with hospital re-admission. It is recommended to monitor patients re-admitted to hospital post index COVID-19 closely monitor specific clinical parameters that may increase the risk of death.
Jitter generation model based on timing modulation and cross point calibration for jitter decomposition

High-speed serial standards are rapidly developing, and with a requirement for eﬀective compliance and characterization measurement methods. Jitter decomposition consists in troubleshooting steps based on jitter components from decomposition results. In order to verify algorithms with diﬀerent deterministic jitter (DJ) in actual circuits, jitter generation model by cross-point calibration and timing modulation for jitter decomposition is presented in this paper. The generated jitter is pre-processed by cross-point calibration which improves the accuracy of jitter generation. Precisely controllable DJ and random jitter (RJ) are generated by timing modulation such as data-dependent jitter (DDJ), duty cycle distortion (DCD), bounded uncorrelated jitter (BUJ), and period jitter (PJ). The beneﬁt of the cross-point calibration was veriﬁed by comparing generation of controllable jitter with and without cross-point calibration. The accuracy and advantage of the proposed method were demonstrated by comparing with the method of jitter generation by analog modulation. Then, the validity of the proposed method was demonstrated by hardware experiments where the jitter frequency had an accuracy of ± 20 ppm, the jitter amplitude ranged from 10 ps to 8.33 ns, a step of 2 ps or 10 ps, and jitter amplitude was independent of jitter frequency and data rate.
Learnable Swendsen-Wang Cuts for Image Segmentation

Abstract We propose a framework for Bayesian unsupervised image segmentation with descriptive, learnable models. Our approach is based on learning descriptive models for segmentation and applying Monte Carlo Markov chain to traverse the solution space. Swendsen-Wang cuts are adapted to make meaningful jumps in solution space. Keywords: Monte-Carlo Markov Chain, Swendsen-Wang Cut, Image Segmentation, Machine Learning, 1. INTRODUCTION We consider the task of unsupervised image segmentation, meaning we assume no user interaction during the segmentation process. Unsupervised segmentation algorithm can be a generic one and seek to generate reasonable segmentation results for any possible image observed. In practice and especially in computer vision one is often working with certain image class and is in need of task-oriented segmentation. For this case, algorithms that can focus on partitioning the image in the way that would provide better representation of image for the task at hand are needed. In this paper we consider inferring segmentation rules from human-labeled collection of images. We do not make any recognition or analysis of resulting region. Thus, our task is to partition an image into number of segments, inferring knowledge of what “good” segmentation is from a manually prepared training set. Problem of unsupervised image segmentation can be formulated as an optimization problem in graph cuts framework. For this end image is represented as a graph and one seeks to find such a graph partition (cut) that minimizes some given criterion. There is a large body of research dedicated to the problem of unsupervised image segmentation in graph cuts framework. First, algorithms based on graph spectral analysis [7][8] are proposed to find a minimal normalized cut. Also, for supervised segmentation mostly, algorithms based on maximum flow algorithm are proposed [6]. The main criticism of these approaches [2] is narrowness of segmentation models these methods can work with and that they do not have any clear statistical interpretation. Other approach [1][2][3] is to formulate Bayesian model of segmentation, with generative models for image segments and perform a stochastic search in space of all possible segmentations by Monte Carlo Markov chain. Swendsen-Wang graph cuts algorithm can be successfully applied for this purpose. All cited methods are generic and try to obtain reasonable results for any image class. Learning a descriptive model of segments with help of Gestalt features [9] provides more flexible framework, but using naive optimization method makes it very dependant on initial segmentation and is very computationally expensive.
Analyzing Iterations in Identification with Application to Nonparametric $mathcal{H}_infty$-Norm Estimation

In the last decades, many iterative approaches in the field of system identification for control have been proposed. Many successful implementations have been reported, despite the lack of a solid analysis with respect to the convergence and value of these iterations. The aim of this paper is to present a thorough analysis of a specific iterative algorithm that involves nonparametric H∞-norm estimation. The pursued approach involves a novel frequency domain approach that appropriately deals with additive stochastic disturbances and input normalization. The results of the novel convergence analysis are twofold: i) the presence of additive disturbances introduces a bias in the estimation procedure, and ii) the iterative procedure can be interpreted as experiment design for H∞-norm estimation, revealing the value of iterations and limits of accuracy in terms of the Fisher information matrix. The results are confirmed by means of a simulation example.
Demand forecasting and information platform in tourism

Abstract Information asymmetry and the bullwhip effect have been serious problems in the tourism supply chain. Based on platform theory, this paper established a mathematical model to explore the inner mechanism of a platform’s influence on stakeholders’ ability to forecast demand in tourism. Results showed that the variance of stakeholders’ demand predictions with a platform was smaller than the variance without a platform, which meant that a platform would improve predictions of demand for stakeholders. The higher information-processing ability of the platform also had other effects on demand forecasting. Research on the inner logic of the platform’s influence on stakeholders has important theoretical and realistic value. This area is worthy of further study.
Using collaborative critical autoethnography to decolonise through “seeing” and doing: Social work, community engagement, and ethical practice

INTRODUCTION: This article reports on a collaborative critical autoethnographic study that we, two white settler social workers, conducted about our engagement with Inuit youth in Nunavut. APPROACH: We facilitated three digital storytelling projects with youth living in three different Nunavut communities. By engaging in a collaborative critical autoethnography study, we were able to attend to the ways in which we were entering into communities, paying particular attention to the ways in which white supremist colonial thought has impacted our training and our locations within larger structures—shaped by colonising histories with consequences that mould day-to-day life and opportunity for the Inuit youth engaged in the digital storytelling. FINDINGS: Through collaborative critical autoethnography, using individual research memos and guided dialogue, we considered the ways in which commodification was structured into our relationships, how these structures continue to be colonising, and consider the impact of the past and current colonisation. We also encountered the many strengths and resistances of the Inuit of Nunavut. IMPLICATIONS: By bringing these considerations to light, we hope to enter into relationships with Inuit communities with fewer of the biases and assumptions that underlay and rationalise the structures that we have critically examined.