Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Abnormal classes are not present during training meaning that models must learn effective rep-resentations solely across normal class data samples. Deep Autoencoders (AE) have been widely used foranomaly detection tasks, but suffer from overfitting to a null identity function. To address this problem, weimplement a training scheme applied to a Denoising Autoencoder (DAE) which introduces an efficient methodof producing Adversarially Learned Continuous Noise (ALCN) to maximally globally corrupt the input priorto denoising. Prior methods have applied similar approaches of adversarial training to increase the robustnessof DAE, however they exhibit limitations such as slow inference speed reducing their real-world applicabilityor producing generalised obfuscation which is more trivial to denoise. We show through rigorous evaluationthat our ALCN method of regularisation during training improves AUC performance during inference whileremaining efficient over both classical, leave-one-out novelty detection tasks with the variations-: 9 (normal)vs. 1 (abnormal)&1 (normal) vs. 9 (abnormal); MNIST - AUCavg: 0.890&0.989, CIFAR-10 - AUCavg: 0.670&0.742, in addition to challenging real-world anomaly detection tasks: industrial inspection (MVTEC-AD -AUCavg: 0.780) and plant disease detection (Plant Village - AUC: 0.770) when compared to prior approaches.
Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods

Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.
New Centre/Surround Retinex-like Method for Low-Count Image Reconstruction

: The work is devoted to the issues of synthesizing a new method for low-count images reconstruction based on a realistic distortion model associated with quantum (Poisson) noise. The proposed approach to the synthesis of the reconstruction methods is based on the principles and concepts of statistical learning, understood as input learning (cf. adaptive smoothing). The synthesis is focused on a special representation of images using sample of counts of controlled size (sampling representation). Based on the specifics of this representation, a generative model of an ideal image is formulated, which is then concretized to a probabilistic parametric model in the form of a system of receptive fields. This model allows for a very simple procedure for estimating the count probability density, which in turn is an estimate of the normalized intensity of the registered radiation. With the help of the latter, similarly to the scheme of wavelet thresholding algorithms, a procedure for extracting contrast in the image is built. From the perception point of view, the contrast carries the main information about the reconstructed image, so such a procedure would provide a high image perception quality. The contrast extraction is carried out by comparing the number of counts in the centre and in the concentric surround of ON/OFF receptive fields and turns out to be very similar to wavelet thresholding.
Printed Packaging Authentication: Similarity Metric Learning for Rotogravure Manufacture Process Identification

: The number of medicine counterfeits increases each year due to the accessibility of printing devices and the weak protection of medicine blister foils. The medicine blisters are often produced using the rotogravure printing process. In this paper, we address the problem of rotogravure press identification and printed support identification using similarity metric learning. Both identification problems are difficult as the impact of printing press or of printing support are minimal, moreover the classical techniques (for example, the use of Pearson correlation) cannot identify the rotogravure press or the printing support used for the packaging production. We show that the similarity metric learning can easily identify the press used and the printing support used. Additionally, we explore the possibility to use the proposed approach for packaging authentication.
Chirped fibre Bragg grating optical wear sensor

A novel sensor is demonstrated to allow a real-time measurement of the physical wear applied to the surface of an object. Two different measurement methods are presented, both utilizing the reflected power from a sacrificial chirped fibre Bragg grating to give the wear measurement. The measurement systems are simple to implement with the possibility of low cost designs depending on the application. The sensor can measure wear with a resolution of 120 μm. © 2004 IOP Publishing Ltd.
Shifts in Above‐ Versus Below‐Ground Carbon Gains to Terrestrial Ecosystems Carbon Sinks Under Excess Nitrogen Inputs

Although elevated atmospheric nitrogen (N) deposition can increase terrestrial carbon (C) sinks, the persistence of this C acquisition depends partly on the responses of plant biomass and soil organic carbon (SOC) to long‐term exogenous N input. Experiments with N‐additions show increases in above‐ground biomass (AGB), although C dynamics between above‐ and below‐ground under N enrichment are unconnected in many studies. Here, we meta‐analyzed data from 362 N‐addition experiments globally and found that both AGB (+34%) and below‐ground biomass (BGB, +13%) positively but root‐to‐shoot ratios negatively (−15%) responded to N addition. These responses were reversed, however, when the experimental duration was over 20 years, wherein BGB increased and AGB was unchanged or even declined, during which time plants invested more C into roots. The effects of increased AGB were weak and negative, whereas those of increased BGB were robust and positive on SOC. Under chronic N deposition, the significant increase in SOC (5%) was associated with the increased root C inputs and decreased soil C losses rather than with increased above‐ground C inputs. The results suggest that the sequestration of below‐ground C exceeds that of above‐ground C to sustain terrestrial C gain during long‐term N deposition.
Leveraging Out-of-the-Box Retrieval Models to Improve Mental Health Support

: This work compares the performance of several information retrieval (IR) models in the search for relevant mental health documents based on relevance to forum post queries from a fully-moderated online mental health service. Three different architectures are assessed: a sparse lexical model, BM25, is used as a base-line, alongside two neural SBERT-based architectures - the bi-encoder and the cross-encoder. We highlight the credibility of using pretrained language models (PLMs) out-of-the-box, without an additional fine-tuning stage, to achieve high retrieval quality across a limited set of resources. Error analysis of the ranking results suggested PLMs make errors on documents which contain so called red-herrings - words which are semantically related but irrelevant to the query - whereas human judgements were found to suffer when queries are vague and present no clear information need. Further, we show that bias towards an author’s writing style within a PLM affects retrieval quality and, therefore, can impact on the success of mental health support if left unaddressed.
Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle International Airport

: Punctuality is a sensitive issue in large airports and hubs for passenger experience and for controlling operational costs. This paper presents a real and challenging problem of predicting and explaining flight off-block delays. We study the case of the international airport Paris Charles de Gaulle (Paris-CDG) starting from the specificities of this problem at Paris-CDG until the proposal of modelings then solutions and the analysis of the results on real data covering an entire year of activity. The proof of concept provided in this paper allows us to believe that the proposed approach could help improving the management of delays and reduce the impact of the resulting consequences.
Direct measurement of winds on Titan

We report the first direct measurement of wind velocity in the atmosphere of Titan, one of only two examples in our solar system of a slowly‐rotating body with a dense atmosphere and a prime target of the Cassini mission. Zonal wind velocity was determined from Doppler shift of ethane lines emitted from Titan's stratosphere (∼0.1–7 mbar) measured by infrared heterodyne spectroscopy near 12 µm (λ/Δ λ ≥ 106). Prograde zonal circulation, in the direction of global rotation, is established with 94% statistical confidence. Results provide information regarding Titan meteorology constraining dynamical theories for slowly‐rotating bodies, provide otherwise unobtainable data to optimize the Cassini Huygens Probe investigations, and demonstrate the capability for remotely measuring winds on a small, distant object.
SiameseBERT: A Bert-Based Siamese Network Enhanced with a Soft Attention Mechanism for Arabic Semantic Textual Similarity

: The assessment of semantic textual similarity (STS) is a challenging task in natural language processing. It is crucial for many applications, including question answering, plagiarism detection, machine translation, information retrieval, and word sense disambiguation. The STS task evaluates the similarity of data pairs of text. For high high-resource languages (e.g. English), several approaches for STS have been proposed. In this paper, we are interested in measuring the semantic similarity of texts for Arabic, a low-resource language. A standard approach for STS is based on vector embedding of the input text and application of similarity metric on space embedding. In this contribution, we propose a BERT-based Siamese Network (SiameseBERT) and investigate the most available Arabic BERT models to embed the input sentences. We validate our approach via Arabic STS datasets. The araBERT-based Siamese Network model achieves a Pearson correlation of 0.925. The results obtained demonstrate the superiority of integrating the BERT embedding, the attention mechanism, and the Siamese neural network for the semantic textual similarity task.
Towards Automatic Medical Report Classification in Czech

: This paper deals with the automatic classiﬁcation of medical reports in the form of unstructured texts in Czech. The outcomes of this work are intended to be integrated into a coding assistant, a system that will help the clinical coders with the manual coding of the diagnoses. To solve this task, we compare several approaches based on deep neural networks. We compare the models in two different scenarios to show their advantages and drawbacks. The results demonstrate that hierarchical GRU with attention outperforms all other models in both cases. The experiments further show that the system can signiﬁcantly reduce the workload of the operators and thus also saves time and money. To the best of our knowledge, this is the ﬁrst attempt at automatic medical report classiﬁcation in the Czech language.
Effect of dietary cation–anion difference on dry matter intake, digestibility, body weight gain, blood parameters, and carcass traits in Zandi lambs

Abstract The dietary cation–anion difference (DCAD) has gotten much attention recently; however, there is not much evidence on organic matter digestibility, blood parameters, dry matter intake, body weight, and carcass features of male sheep fed with different DCAD diets. The effects of dietary cation–anion difference (DCAD) on these traits in male lambs under the environmental high temperatures were investigated in this study. Forty male lambs (average body weight of 39 kg) were randomly assigned to one of five treatments with eight replicates. Lambs were fed diets with DCAD levels ranging from 150 (control group) to 300, 450, 600, and 750 mEq/kg dry matter. This study lasted 100 d and used a 21-d adaptation. The results showed that the control group had the highest dry matter intake, dry matter digestibility, and crude protein digestibility (P = 0.02). Also, the lowest amount of average body weight was observed in the control group (P = 0.01). The results showed the different DCAD levels affected the statistical significance in terms of live weight, carcass weight, length and width of muscle cross section, lung weight, spleen weight, and abdominal fat (P = 0.04). As well, the highest ruminal pH was observed in the control group (P = 0.4). The results of the blood glucose parameter showed that control group had a significant effect on the blood glucose level (P = 0.04). Furthermore, the highest abdominal fat weight was observed in the control group (P = 0.04). There was no statistically significant difference between other traits, including skin weight, head weight, leg weight, carcass length, liver weight, kidney weight, heart weight, testicle weight, tail weight, rumen weight, and lactation weight. In summary, increasing DCAD in the diet could improve the production and carcass quality in lambs under environmental high temperatures.
Non-contrast-Enhanced Functional Lung MRI to evaluate treatment response of allergic bronchopulmonary aspergillosis in patients with cystic fibrosis: a pilot study

Background: Allergic bronchopulmonary aspergillosis (ABPA) in cystic fibrosis (CF) patients is associated with severe lung damage and requires specific therapeutic management. Repeated imaging is recommended to both diagnose and follow-up the response to treatment of ABPA in CF. Purpose: To evaluate whether Fourier decomposition (FD) functional lung MRI can detect the response to treatment of ABPA in CF patients. Study type: Retrospective longitudinal. Population: A total of 12 CF patients. Field strength/sequence: 2D balanced steady-state free precession (bSSFP) sequence with Fourier decomposition (FD) at 1.5T scanner. Assessment: Ventilation weighted (V) and perfusion weighted (Q) maps were obtained after FD processing of the 2D coronal bSSFP time resolved images before and after treatment of ABPA. Defects extent was assessed on the functional maps using a visual semi quantitative score. Mean and coefficient of variation (cv) of the ventilation signal intensity (VSI) and the perfusion signal intensity (QSI) were calculated. Measurements were performed independently by two readers and averaged. The reproducibility of the measurements was also assessed. Pulmonary function tests (PFTs) were performed as markers of the airflow limitation severity. Statistical tests: Comparisons of medians were assessed using paired Wilcoxon test. Reproducibility was assessed using the intraclass correlation coefficient (ICC). Correlations were assessed using Spearman test. A p value <0.05 was considered as significant. Results: Defects extent on both V and Q maps showed a significant reduction after ABPA treatment (p<0.01). VSI_mean was significantly increased after treatment (p<0.01). Visual analyses reproducibility showed an ICC >0.93. ICC of the quantitative measurements was almost perfect (>0.99). VSI_cv and QSI_cv variations correlated inversely with the variation of obstructive parameters of PFTs (rho = -0.68, p=0.01). Data conclusion: Non-contrast enhanced FD lung MRI appears to be able to reproducibly assess response to treatment of ABPA in CF patients and correlates with PFTs obstructive parameters. Keywords: MRI; ABPA; CF; ventilation; perfusion; Fourier decomposition
Pre-main-sequence disk accretion in Z Canis Majoris

It is suggested that the pre-main-sequence object Z CMa is a luminous accretion disk, similar in many respects to the FU Orionis variables. Z CMa shows the broad, doubled optical absorption lines expected from a rapidly rotating accretion disk. The first overtone CO absorption detected in Z CMa is blue-shifted, suggesting line formation in a disk wind. Accretion at rates about 0.001 solar mass/yr over 100 yr is required to explain the luminosity of Z CMa. The large amount of material accreted (0.1 solar mass/yr) indicates that Z CMa is in a very early stage of stellar evolution, possibly in an initial phase of massive disk accretion. 41 references.
Cataract surgical training among residents in India: Results from a survey

Purpose: To assess the quantum of cataract surgical training opportunities for trainees enrolled in ophthalmology residency programs in India. Methods: An anonymous online survey was sent across to resident ophthalmologists across India through various social media platforms. The results were tabulated and analyzed. Results: A total of 740 resident ophthalmologists participated in the survey. In all, 40.1% (297/740) were independently performing cataract surgeries. Of those who were not performing independent cataract surgeries, 62.5% (277/443) were in the third year of residency. A significantly higher proportion of trainees who were not operating independent cataract surgeries were enrolled in MD/MS programs as compared with DNB courses (65.6% vs. 43.7%; P < 0.0001). Of those who were operating independent cases; 97.1% had exposure to manual small incision cataract surgery (MSICS), whereas only 14.1% performed phacoemulsification. It was noted that 31.3% of residents reported that on an average a trainee in their training program performed less than 100 independent cataract surgeries throughout the residency. Apart from cataract surgery, the most performed surgeries by residents were pterygium excision (85.3%), followed by enucleation/evisceration (68.1%). When it came to training aids, 47.2% (349/740) of the respondents reported no access to wet lab, animal/cadaver eyes, or surgical simulators for training. Conclusion: The amount of surgical exposure in terms of cataract surgery across residency programs in India is low with most of the ophthalmology residents who participated in this survey not operating cataracts independently; even in their final year of residency. Exposure to phacoemulsification in residency programs is very limited across the country. Although some programs do provide well-rounded surgical exposure to trainees, such centers are scarce; the stark variations in infrastructure, training opportunities, and surgical numbers warrant an overhaul in the structure and curriculum of residency programs in India.
Predicting functional effects of ion channel variants using new phenotypic machine learning methods

Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables a timely diagnosis, facilitates precision therapy, and guides prognosis. Functional characterization presents a bottleneck in translational medicine. Machine learning models may be able to rapidly generate supporting evidence by predicting variant functional effects. Here, we describe a multi-task multi-kernel learning framework capable of harmonizing functional results and structural information with clinical phenotypes. This novel approach extends the human phenotype ontology towards kernel-based supervised machine learning. Our gain- or loss-of-function classifier achieves high performance (mean accuracy 0.853 SD 0.016, mean AU-ROC 0.912 SD 0.025), outperforming both conventional baseline and state-of-the-art methods. Performance is robust across different phenotypic similarity measures and largely insensitive to phenotypic noise or sparsity. Localized multi-kernel learning offered biological insight and interpretability by highlighting channels with implicit genotype-phenotype correlations or latent task similarity for downstream analysis.
Generalized Laplacian Distributions and Autoregressive Processes

Generalized Laplacian distribution is considered. A new distribution called geometric generalized Laplacian distribution is introduced and its properties are studied. First- and higher-order autoregressive processes with these stationary marginal distributions are developed and studied. Simulation studies are conducted and trajectories of the process are obtained for selected values of the parameters. Various areas of application of these models are discussed.
Multiple Resonant or Non-Resonant Parametric Excitations for Nonlinear Oscillators

The transient and steady state response of a very general non-linear oscillator subject to a finite number of parametric excitations is considered by the asymptotic perturbation method. Three main cases are examined: (1) the parametric excitations frequencies are not close to each other or close to the principal parametric resonance of the oscillator; (2) the parametric excitations frequencies are close to each other but not close to the principal resonance; and (3) all the parametric excitations frequencies are close to the principal resonance. Both the conditions for the quenching of the oscillation and the conditions for its persistence are determined. The main conclusion is that the oscillation in systems with one degree of freedom cannot be fully quenched due to the action of parametric excitation, because the only change is a shift in the oscillator frequencies. Analytical approximate results are checked by numerical integration.
Effect of high-energy ball milling time on superconducting properties of MgB2 with low purity boron powder

MgB2 bulks and tapes were fabricated using ball-milled precursor powders. The selected B powders have the β-rhombohedral crystalline structure as a main phase, with a small amount of MgO impurity. Milling time was tested from 0.5 to 120 h to get an optimized value. It is found that ball milling can facilitate MgB2 phase formation, refine the MgB2 grains and increase the defect density. Moreover, the active area, Bc2 and flux pinning are improved, which is of benefit to the enhancement of Jc performance. The 80 h milled MgB2 bulk sample has a Jc value of 2.8 × 104 A cm−2 at 5 K and 6 T, 41 times larger than that of the 0.5 h sample. At the same time, the transport Jc of the 80 h MgB2 tape reaches 2.3 × 103 A cm−2 at 4.2 K and 9 T, 32 times larger than that of the 0.5 h one. Due to the increase of impurities and the release of lattice deformation and strain, the Jc was decreased for the sample milled up to 120 h.