Miracle-dz/UniFAR-FTUs huggingface dataset:
Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code is publicly accessible.
Recoverable and Detectable Self-Implementations of Swap

Recoverable algorithms tolerate failures and recoveries of processes by using non-volatile memory. Of particular interest are self-implementations of key operations, in which a recoverable operation is implemented from its non-recoverable counterpart (in addition to reads and writes). This paper presents two self-implementations of the SWAP operation. One works in the system-wide failures model, where all processes fail and recover together, and the other in the independent failures model, where each process crashes and recovers independently of the other processes. Both algorithms are wait-free in crash-free executions, but their recovery code is blocking. We prove that this is inherent for the independent failures model. The impossibility result is proved for implementations of distinguishable operations using interfering functions, and in particular, it applies to a recoverable self-implementation of swap.
Those Flowing Locks - Creating Large Scale Water Hair for Ruby Gillman Teenage Kraken

One of the most unique features of the villain Nerissa in DreamWorks Animation’s Ruby Gillman Teenage Kraken is her hair. In the climatic battle, Nerissa transformed into a 150-foot-tall Titan Mermaid, whose hair is made of flowing water drawn from the surrounding ocean. While other films have created characters based on water, the sheer scale and the required action of the character interaction with the ocean, and the number of shots involved added significant challenges to the work. Working closely with multiple departments across the pipeline, a robust rig for the water hair was developed based on a combination of procedural and simulated elements. Furthermore, a Fluid Implicit Particle (FLIP) simulation is used to simulate the character’s interaction with the surrounding ocean. Finally, an additional level set fillet generation technique was used to seamlessly blend the ocean mesh with the water hair geometry.
Diagnostic performance of 18F-labeled PSMA PET/CT in patients with biochemical recurrence of prostate cancer: a systematic review and meta-analysis

Background After initial treatment of prostate cancer, increases in prostate-specific antigen (PSA) levels commonly signify potential relapse or metastasis. 18F-labeled prostate-specific membrane antigen (PSMA) is considered a promising treatment due to its favorable physical properties. Purpose To investigate the diagnostic value of 18F-PSMA PET/CT for the recurrence and/or metastasis of biochemical recurrence of prostate cancer (BRPca). Material and Methods A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library databases. Combined sensitivity and specificity values for the use of 18F-PSMA PET/CT in patients with BRPca were obtained. The quality of the studies was tested using the Diagnostic Accuracy Research Quality Assessment tool. Meta-analysis was performed using STATA 15 software, and heterogeneity was subsequently tested. Results A total of 16 studies (1162 patients) were enrolled and had significant heterogeneity. The pooled sensitivity, specificity, and AUC values for 18F-PSMA PET/CT in the diagnosis of prostate recurrence and/or metastasis were 0.93 (0.89–0.95), 0.94 (0.85–0.98), and 0.96 (0,94–0.98), respectively. Meta-regression analyses showed that the sources of heterogeneity did not relate to ligands, study designs, or participants. The pooled sensitivity and specificity values of 18F-DCFPyL PET/CT were 0.90 (0.85–0.94) and 0.89 (0.85–0.93), respectively. The pooled sensitivity and specificity values of 18F-PSMA-1007 PET/CT were 0.89 (0.85–0.93) and 0.93 (0.70–0.99), respectively. The per-patient pooled sensitivity and specificity values were 0.92 (0.86–0.96) and 0.83 (0.41–0.97), respectively. The per-lesion pooled sensitivity and specificity values were 0.91 (0.86–0.94) and 0.91 (0.86–0.94), respectively. Conclusion According to our meta-analysis, 18F-PSMA PET/CT has the potential to be critical for the diagnosis of recurrence and/or metastasis in patients with BRPca.
Differences in efficacy and safety among CAR-Ts anti-CD19/CD22, anti-CD19, and anti-CD22, in adult patients with relapse/refractory B-cell acute lymphoblastic leukemia: a meta-analysis and systematic review

Abstract Relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL) is a challenging disease with low rates of remission and survival in adult patients. Anti-CD19 Chimeric Antigen Receptor T-cells (CAR-Ts) therapies have been approved for these patients. Dual-target CAR-Ts against CD19 and CD22 have recently been developed to improve the efficacy of the single-target therapy; however, extent of the improvement using this dual-target therapy has yet to be determined. We performed a meta-analysis of the outcome and safety of CAR-Ts, comparing anti-CD19 vs anti-CD22 vs dual-target anti-CD19/CD22 CAR-Ts, to elucidate the differences and limitations of these therapies in adult patients with R/R B-ALL. Although the limitations of our study derived from heterogeneity in the included publications, our results suggest that anti-CD19/CD22 CAR-Ts generate lower incidence of relapse and neurotoxicity, but similar results were obtained regarding complete remission, minimal residual disease, overall survival, and cytokine release syndrome compared with single-target anti-CD19 and anti-CD22 CAR-Ts.
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph

The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers. In this paper, we propose a novel contrastive multi-view learning method named CMT for crowdsourcing fraud detection over the heterogeneous temporal graph (HTG) of MMMA. CMT captures both heterogeneity and dynamics of HTG and generates high-quality representations for crowdsourcing fraud detection in a self-supervised manner. We deploy CMT to detect crowdsourcing frauds on an industry-size HTG of a representative MMMA WeChat and it significantly outperforms other methods. CMT also shows promising results for fraud detection on a large-scale public financial HTG, indicating that it can be applied in other graph anomaly detection tasks.
Different facets of the same niche: Integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers

Citizen science initiatives have been increasingly used by researchers as a source of occurrence data to model the distribution of alien species. Since citizen science presence‐only data suffer from some fundamental issues, efforts have been made to combine these data with those provided by scientifically structured surveys. Surprisingly, only a few studies proposing data integration evaluated the contribution of this process to the effective sampling of species' environmental niches and, consequently, its effect on model predictions on new time intervals. We relied on niche overlap analyses, machine learning classification algorithms and ecological niche models to compare the ability of data from citizen science and scientific surveys, along with their integration, in capturing the realized niche of 13 invasive alien species in Italy. Moreover, we assessed differences in current and future invasion risk predicted by each data set under multiple global change scenarios. We showed that data from citizen science and scientific surveys captured similar species niches though highlighting exclusive portions associated with clearly identifiable environmental conditions. In terrestrial species, citizen science data granted the highest gain in environmental space to the pooled niches, determining an increased future biological invasion risk. A few aquatic species modelled at the regional scale reported a net loss in the pooled niches compared to their scientific survey niches, suggesting that citizen science data may also lead to contraction in pooled niches. For these species, models predicted a lower future biological invasion risk. These findings indicate that citizen science data may represent a valuable contribution to predicting future spread of invasive alien species, especially within national‐scale programmes. At the same time, citizen science data collected on species poorly known to citizen scientists, or in strictly local contexts, may strongly affect the niche quantification of these taxa and the prediction of their future biological invasion risk.
Circulating Neuropeptide Y May Be a Biomarker for Diagnosing Atrial Fibrillation

Introduction: Sympathetic nervous system disorder promotes atrial fibrillation (AF), and neuropeptide Y (NPY) is an important neurotransmitter. This study aimed to explore the predictive value of plasma NPY in patients with AF. Methods: Five hundred seventy-six patients were divided into AF (including paroxysmal and long-standing persistent AF; 360) and sinus rhythm (SR) groups (216). NPY level was detected using enzyme-linked immunosorbent assay, and NPY mRNA expression level was detected using quantitative polymerase chain reaction. Logistic regression was used to analyse the risk factors for AF; the correlations between blood NPY level and age, body mass index (BMI), left ventricular ejection fraction, left atrial diameter (LAD), and European Heart rate Association (EHRA) score in patients with AF were determined. The receiver operating characteristic (ROC) curve was utilised to predict AF. Results: Plasma NPY levels were found to be higher in patients with AF than in patients with SR and in patients with long-standing persistent AF than in patients with paroxysmal AF; blood NPY mRNA levels were higher in the paroxysmal and long-standing persistent AF groups compared to the SR group (p < 0.05). Increased age {odds ratio (OR) = 1.201 (95% confidence interval [CI]: 1.01, 1.427)} and high NPY [OR = 1.239 (95% CI: 1.022, 1.501)] were factors found to affect AF detrimentally. NPY was associated with BMI (r = 0.5856, p < 0.05), LAD (r = 0.4023, p < 0.05), and EHRA score (r = 0.898, p < 0.05). The ROC curve for the predictive value of plasma NPY levels for AF showed an area under the curve (AUC) value of 0.919 (p < 0.05), while that for long-standing persistent AF showed an AUC of 0.784 (p < 0.05). Conclusion: Circulating NPY may be a promising molecular biomarker of AF.
A Simple Implicit Measure of the Effective Bid‐Ask Spread in an Efficient Market

In an efficient market, the fundamental value of a security fluctuates randomly. However, trading costs induce negative serial dependence in successive observed market price changes. In fact, given market efficiency, the effective bid-ask spread can be measured by Spread=2−cov where “cov” is the first-order serial covariance of price changes. This implicit measure of the bid-ask spread is derived formally and is shown empirically to be closely related to firm size.
VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs

Data-driven approaches have been proven effective in solving combinatorial optimization problems over graphs such as the traveling salesman problems and the vehicle routing problem. The rationale behind such methods is that the input instances may follow distributions with salient patterns that can be leveraged to overcome the worst-case computational hardness. For optimization problems over graphs, the common practice of neural combinatorial solvers consumes the inputs in the form of adjacency matrices. In this paper, we explore a vision-based method that is conceptually novel: can neural models solve graph optimization problems bytaking a look at the graph pattern - Our results suggest that the performance of such vision-based methods is not only non-trivial but also comparable to the state-of-the-art matrix-based methods, which opens a new avenue for developing data-driven optimization solvers.
High-precision interferometric measurement of rapid temperature changes in convective flow

Using interferometry, we measure rapid temperature variations in convective flow to within a few mK. This is enabled by the fast response time and high sensitivity of the interferometer to minute changes in the refractive index of the test fluid caused by temperature variations. These features allow us to detect the signature of fine fluid dynamical patters in convective flow in a fully non-intrusive manner. For example, we are able to observe an asymmetry in the rising thermal plume (i.e. an asynchronous arrival of two counter-rotating vortices at the measurement location), which is not possible to resolve with more traditional (and invasive) techniques, such as RTD (Resistance Temperature Detector) sensors. These findings, and the overall reliability of our method, are further corroborated by means of Particle Image Velocimetry and Large Eddy Simulations. The non-intrusive nature of this method, along with its robustness and the ability to readily yield real-time, highly accurate measurements, render this technique very attractive for a wide range of applications in experimental fluid dynamics.
POSIT: Promotion of Semantic Item Tail via Adversarial Learning

In many recommender problems, a handful of popular items (e.g. movies/TV shows, news etc.) can be dominant in recommendations for many users. However, we know that in a large catalog of items, users are likely interested in more than what is popular. The dominance of popular items may mean that users will not see items they would likely enjoy. In this paper, we propose a technique to overcome this problem using adversarial machine learning. We define a metric to translate user-level utility metric in terms of an advantage/disadvantage over items. We subsequently use that metric in an adversarial learning framework to systematically promote disadvantaged items. The resulting algorithm identifies semantically meaningful items that get promoted in the learning algorithm. In the empirical study, we evaluate the proposed technique on three publicly available datasets and four competitive baselines. The result shows that our proposed method not only improves the coverage, but also, surprisingly, improves the overall performance.
Decoding of translation‐regulating entities reveals heterogeneous translation deficiency patterns in cellular senescence

Cellular senescence constitutes a generally irreversible proliferation barrier, accompanied by macromolecular damage and metabolic rewiring. Several senescence types have been identified based on the initiating stimulus, such as replicative (RS), stress‐induced (SIS) and oncogene‐induced senescence (OIS). These senescence subtypes are heterogeneous and often develop subset‐specific phenotypes. Reduced protein synthesis is considered a senescence hallmark, but whether this trait pertains to various senescence subtypes and if distinct molecular mechanisms are involved remain largely unknown. Here, we analyze large published or experimentally produced RNA‐seq and Ribo‐seq datasets to determine whether major translation‐regulating entities such as ribosome stalling, the presence of uORFs/dORFs and IRES elements may differentially contribute to translation deficiency in senescence subsets. We show that translation‐regulating mechanisms may not be directly relevant to RS, however uORFs are significantly enriched in SIS. Interestingly, ribosome stalling, uORF/dORF patterns and IRES elements comprise predominant mechanisms upon OIS, strongly correlating with Notch pathway activation. Our study provides for the first time evidence that major translation dysregulation mechanisms/patterns occur during cellular senescence, but at different rates depending on the stimulus type. The degree at which those mechanisms accumulate directly correlates with translation deficiency levels. Our thorough analysis contributes to elucidating crucial and so far unknown differences in the translation machinery between senescence subsets.
Dynamic fronto‐amygdalar interactions underlying emotion‐regulation deficits in women at higher weight

The regulation of negative emotions entails the modulation of subcortical regions, such as the amygdala, by prefrontal regions. There is preliminary evidence suggesting that individuals at higher weight may present with hypoactivity in prefrontal regulatory systems during emotional regulation, although the directionality of these pathways has not been tested. In this study, we compared fronto‐amygdalar effective connectivity during cognitive reappraisal as a function of BMI in 48 adult women with obesity and 54 control participants.
Safe Multimodal Communication in Human-Robot Collaboration

The new industrial settings are characterized by the presence of human and robots that work in close proximity, cooperating in performing the required job. Such a collaboration, however, requires to pay attention to many aspects. Firstly, it is crucial to enable a communication between this two actors that is natural and efficient. Secondly, the robot behavior must always be compliant with the safety regulations, ensuring always a safe collaboration. In this paper, we propose a framework that enables multi-channel communication between humans and robots by leveraging multimodal fusion of voice and gesture commands while always respecting safety regulations. The framework is validated through a comparative experiment, demonstrating that, thanks to multimodal communication, the robot can extract valuable information for performing the required task and additionally, with the safety layer, the robot can scale its speed to ensure the operator's safety.
3D-EX: A Unified Dataset of Definitions and Dictionary Examples

Definitions are a fundamental building block in lexicography, linguistics and computational semantics. In NLP, they have been used for retrofitting word embeddings or augmenting contextual representations in language models. However, lexical resources containing definitions exhibit a wide range of properties, which has implications in the behaviour of models trained and evaluated on them. In this paper, we introduce 3D-EX, a dataset that aims to fill this gap by combining well-known English resources into one centralized knowledge repository in the form of triples. 3D-EX is a unified evaluation framework with carefully pre-computed train/validation/test splits to prevent memorization. We report experimental results that suggest that this dataset could be effectively leveraged in downstream NLP tasks. Code and data are available at https://github.com/F-Almeman/3D-EX.
Optic Disc Detection by Earth Mover's Distance Template Matching

This paper presents a method for the detection of OD in the retina which takes advantage of the powerful preprocessing techniques such as the contrast enhancement, Gabor wavelet transform for vessel segmentation, mathematical morphology and Earth Mover's distance (EMD) as the matching process. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Vessel segmentation method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and 2D Gabor wavelet transform responses taken at multiple scales. A simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity using the EMD. The minimum distance provides an estimate of the OD center coordinates. The method's performance is evaluated on publicly available DRIVE and STARE databases. On the DRIVE database the OD center was detected correctly in all of the 40 images (100%) and on the STARE database the OD was detected correctly in 76 out of the 81 images, even in rather difficult pathological situations. Keywords—Diabetic retinopathy, Earth Mover's distance, Gabor wavelets, optic disc detection, retinal images
Intravitreal Bevacizumab for Treatment of Diabetic Macular Edema

Purpose: To evaluate the effects of Intra-vitreal Bevacizumab on visual function and macular edema in diabetic patients Material and Methods: This study was conducted in the Department of Ophthalmology, Madina Teaching Hospital, University Medical and Dental College, Faisalabad. Three months from 1 June 2010 to 31 August 2010. A total of 26 eyes of 26 diabetic patients (mean age 48.92 years) with diabetic macular edema were included in this study. Best – corrected visual acuity, slitlamp biomicroscopic examination of anterior segment, fundus examination and fundus flourescein angiography, were done at baseline and at each follow up visits. All patients were treated with 0.05 ml intravitreal injection containing 1.25 mg of bevacizumab (IVB). Results: All patients completed 3 months follow up. The mean BCVA at baseline was 0.726 + log MAR. It improved to 0.515, 0.461, and 0.452 + log MAR at 1 week, 1 month, and 3 months respectively. Final BCVA analysis demonstrated that 17 (65.38%) patients improved ≥ 2 lines of Senellen’s visual acuity chart, 7 (26.38%) improved by one line, one (3.84%) remained stable, and one (3.84%) showed deterioration of one line. Macular edema at 1 week resolved completely in 18 (69.23%) and moderately in 7 (26.92%) patients at 1 month and 3 month follow up resolved moderately in 24 (92.30%). Flourecein leakage stopped in 25 (96.15%) patients. No ocular toxicity or adverse effects to drug were observed. Conclusions: IVB injection resulted in improvement of visual acuity, resolution of macular edema and stoppage of Flourescein leakage as early as 1 week after injection in patients with DME. The slight reduction in visual improvement at 3 months suggests wearing of effect of drug. Further clinical trials will be needed to evaluate the long term safety and efficacy of this drug.