Miracle-dz/UniFAR-FTUs huggingface dataset:
Acquired Pellicle Engineering Using a Combination of Organic (Sugarcane Cystatin) and Inorganic (Sodium Fluoride) Components against Dental Erosion

This study evaluated the combination of a sugarcane cystatin (CaneCPI-5) and sodium fluoride (NaF) in acquired pellicle engineering for the prevention of dental erosion in vitro. Seventy-five human enamel specimens were prepared and divided into 5 treatment groups (n = 15/group): Deionized water (Control); Elmex™ (SnCl2/NaF/AmF); 0.1 mg/mL CaneCPI-5; 500 ppm NaF; and CaneCPI-5+NaF (Combination). The specimens were individually treated (200 μL; 2 min; 37°C), then incubated in human saliva (200 μL; 1 h, at 37°C) for acquired pellicle formation. Afterward, the specimens were submitted to an erosive challenge (1% citric acid [CR], pH 3.6, 10 mL, 2 min, 25 °C). This sequence was conducted 5 times. Percentage of surface microhardness change (%SMC), relative surface reflection intensity (rSRI), and calcium released to the CR were measured and analyzed by one-way ANOVA followed by Tukey’s test (p < 0.05). In general, all the treatments (SnCl2/NaF/AmF, CaneCPI-5, NaF, and Combination) significantly protected the enamel when compared the control group. Regarding %SMC and rSRI, the Combination was the most effective treatment, reducing the %SMC significantly (p < 0.01) when compared to all the other treatments, although this difference was not significant in the CR analysis. All treatments demonstrated a protective effect on enamel against dental erosion; however, the combination of CaneCPI-5 with NaF showed a greater protection.
Genetics of Inherited thrombocytopenias.

The inherited thrombocytopenia syndromes are a group of disorders characterized primarily by quantitative defects in platelet number, though with a variety demonstrating qualitative defects and/or extra-hematopoietic findings. Through collaborative international efforts applying next generation sequencing approaches, the list of genetic syndromes that cause thrombocytopenia has expanded significantly in recent years now with over 40 genes implicated. In this review, we focus on what is known about the genetic etiology of inherited thrombocytopenia syndromes, and how the field has worked to validate new genetic discoveries. We highlight the important role for the clinician in identifying a germline genetic diagnosis, and strategies for identifying novel causes through research-based endeavors.
Trauma bay virtual reality—A game changer for ATLS instruction and assessment

Trauma VR Simulation is a game changer for teaching ATLS principles. Participants enjoyed the platform and found it enhanced their learning. This technology is part of the future of surgical education. Background Medical educational research highlights the need for high-fidelity, multidisciplinary simulation training to teach complex decision-making skills, such as those taught in Advanced Trauma Life Support (ATLS). This approach is, however, expensive and time-intensive. Virtual reality (VR) education simulation may improve skill acquisition in a cost-effective and time-sensitive manner. We developed a novel trauma VR simulator (TVRSim) for providers to apply ATLS principles. We hypothesized in this pilot study that TVRSim could differentiate practitioner competency with increasing experience and would be well accepted. Methods Providers at a Level I trauma center (acute care surgeons, novice (MS4 & PGY1), junior (PGY2 & 3), senior (PGY4-6) residents) ran a blunt, polytrauma VR code. Ten critical decision points were assessed: intubation, cricothyroidotomy, chest tube, intravenous access, focused abdominal sonography for trauma examination, pelvic binder, activation of massive transfusion protocol, administration of hypertonic saline, hyperventilation and decision to go to the operating room (OR). Learner assessment was based on frequency and time to correct decisions. Participant satisfaction was measured using validated surveys. Results All 31 providers intubated and obtained intravenous access. Novices and juniors frequently failed at hypertonic saline and hyperventilation decisions. Juniors often failed at cricothyroidotomy (60%) and OR (100%) decisions. Mean time to all decisions except going to the OR was longer for all groups compared to acute care surgeons. Mean number of decisions/min was significantly higher for surgeons and seniors compared to juniors and novices. Mortality was 92.3% for novices, 80% for juniors, 25% for seniors and 0% for the attendings. Participants found TVRSim comfortable, easy to use/interact with/performance enhancing, and helped develop skills and learning. Conclusions In this pilot study using a sample of convenience, TVRSim was able to discern decision-making abilities among trainees with increasing experience. All trainees felt that the platform enhanced their performance and facilitated skill acquisition and learning. TVRSim could be a useful adjunct to teach and assess ATLS skills. LEVEL OF EVIDENCE Diagnostic Test or Criteria; Level IV.
First evidence of SOX2 mutations in Peters' anomaly: Lessons from molecular screening of 95 patients

Peters' anomaly (PA) is a rare anterior segment dysgenesis characterized by central corneal opacity and irido‐lenticulo‐corneal adhesions. Several genes are involved in syndromic or isolated PA (B3GLCT, PAX6, PITX3, FOXE3, CYP1B1). Some copy number variations (CNVs) have also been occasionally reported. Despite this genetic heterogeneity, most of patients remain without genetic diagnosis. We retrieved a cohort of 95 individuals with PA and performed genotyping using a combination of comparative genomic hybridization, whole genome, exome and targeted sequencing of 119 genes associated with ocular development anomalies. Causative genetic defects involving 12 genes and CNVs were identified for 1/3 of patients. Unsurprisingly, B3GLCT and PAX6 were the most frequently implicated genes, respectively in syndromic and isolated PA. Unexpectedly, the third gene involved in our cohort was SOX2, the major gene of micro‐anophthalmia. Four unrelated patients with PA (isolated or with microphthalmia) were carrying pathogenic variants in this gene that was never associated with PA before. Here we described the largest cohort of PA patients ever reported. The genetic bases of PA are still to be explored as genetic diagnosis was unavailable for 2/3 of patients. Nevertheless, we showed here for the first time the involvement of SOX2 in PA, offering new evidence for its role in corneal transparency and anterior segment development.
The quiet evolutionary response to cellular challenges

Like many evolutionary geneticists, I am fascinated by genes underlying potentially adaptive traits that differentiate populations. While such traits are obviously important, I have, in part through my own work on adaptation to whole‐ genome duplication, become interested in traits that do not differ in obvious ways between populations. There is good evidence that such traits are also important and can leave signatures of selection in genomes. This idea is not a new revelation—in the vast literature on protein biophysics there is keen awareness that evolutionary adjustments are often needed to keep essential proteins functioning in new conditions. However, this concept has not been employed extensively outside that field to, for example, interpret genome scans for selection. Things written off as false positives in genome scans may actually be critical for adaptation; evolutionary adjustment of proteins underlying conserved traits may explain otherwise puzzling footprints of selection and may help explain why adaptation is often multigenic. The general conclusion that selection can act on trait maintenance rather than change, is likely broadly relevant.
Paraphrasing Magritte's Observation

Contrast Sensitivity of the human visual system can be explained from certain low-level vision tasks (like retinal noise and optical blur removal), but not from others (like chromatic adaptation or pure reconstruction after simple bottlenecks). This conclusion still holds even under substantial change in stimulus statistics, as for instance considering cartoon-like images as opposed to natural images (Li, Gomez-Villa, Bertalmio, & Malo, 2022). In this note we present a method to generate original cartoon-like images compatible with the statistical training used in (Li et al., 2022). Following the classical observation in (Magritte, 1929), the stimuli generated by the proposed method certainly are not what they represent: Ceci n’est pas une pipe. The clear distinction between representation (the stimuli generated by the proposed method) and reality (the actual object) avoids eventual problems for the use of the generated stimuli in academic, non-profit, publications.
Explainable Predictive Process Monitoring: A User Evaluation

Williams Rizzi1,2 ̊, Marco Comuzzi4, Chiara Di Francescomarino1, Chiara Ghidini1, Suhwan Lee5, Fabrizio Maria Maggi2, Alexander Nolte3,6 1Fondazione Bruno Kessler, Via Sommarive, 18, 38123 Povo, Italy 2Free University of Bozen-Bolzano, Piazza Università, 5, 39100 Bolzano, Italy 3University of Tartu, Narva maantee 18, 51009 Tartu, Estonia 4Ulsan National Institute of Science and Technology, Ulsan, South Korea 5Utrecht University, Utrecht, The Netherlands 6Carnegie Mellon University, Pittsburgh, PA, USA ̊To whom correspondence should be addressed; E-mail: wrizzi@fbk.eu
Bias and unfairness in machine learning models: a systematic literature review

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to examine existing knowledge on bias and unfairness in Machine Learning models, identifying mitigation methods, fairness metrics, and supporting tools. A Systematic Literature Review found 40 eligible articles published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases. The results show numerous bias and unfairness detection and mitigation approaches for ML technologies, with clearly defined metrics in the literature, and varied metrics can be highlighted. We recommend further research to define the techniques and metrics that should be employed in each case to standardize and ensure the impartiality of the machine learning model, thus, allowing the most appropriate metric to detect bias and unfairness in a given context.
Modeling Strong Physically Unclonable Functions with Metaheuristics

Evolutionary algorithms have been successfully applied to attack Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. In this paper, we take a step back and systematically evaluate several metaheuristics for the challenge-response pair-based attack on strong PUFs. Our results confirm that CMA-ES has the best performance, but we note several other algorithms with similar performance while having smaller computational costs.
Reversible data hiding with dual pixel-value-ordering and minimum prediction error expansion

Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual PVO (dPVO) for Prediction Error Expansion (PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward. In the forward phase, PVO with classic PEE is applied to every non-overlapping image block of size 1 × 3. In the backward phase, minimum-set and maximum-set of pixels are determined from the pixels predicted in the forward phase. The minimum set only contains the lowest predicted pixels and the maximum set contains the largest predicted pixels of each image block. Proposed dPVO with PEE is then applied to both sets, so that the pixel values of minimum set are increased and that of the maximum set are decreased by a unit value. Thereby, the pixels predicted in the forward embedding can partially be restored to their original values resulting in both a better embedded image quality and a higher embedding rate. Experimental results have recorded a promising rate-distortion performance of our scheme with a significant improvement of embedded image quality at higher embedding rates compared to the popular and state-of-the-art PVO-based RDH schemes.
Information Extraction in Low-Resource Scenarios: Survey and Perspective

Information Extraction (IE) seeks to derive structured information from unstructured texts, often facing challenges in low-resource scenarios due to data scarcity and unseen classes. This paper presents a review of neural approaches to low-resource IE from \emph{traditional} and \emph{LLM-based} perspectives, systematically categorizing them into a fine-grained taxonomy. Then we conduct empirical study on LLM-based methods compared with previous state-of-the-art models, and discover that (1) well-tuned LMs are still predominant; (2) tuning open-resource LLMs and ICL with GPT family is promising in general; (3) the optimal LLM-based technical solution for low-resource IE can be task-dependent. In addition, we discuss low-resource IE with LLMs, highlight promising applications, and outline potential research directions. This survey aims to foster understanding of this field, inspire new ideas, and encourage widespread applications in both academia and industry.
Active Uncertainty Learning for Human-Robot Interaction: An Implicit Dual Control Approach

Predictive models are effective in reasoning about human motion, a crucial part that affects safety and efﬁciency in human-robot interaction. However, robots often lack access to certain key parameters of such models, for example, human’s objectives, their level of distraction, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters as stochastic hidden states and identifying their values using information gathered during control of the robot. Despite its ability to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general human-in-the-loop motion planning, mainly due to nested trajectory optimization and human intent prediction. In this paper, we present a novel algorithmic approach to enable active uncertainty learning for human-in-the-loop motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for generic human predictive models with both continuous and categorical uncertainty. The efﬁcacy of our approach is demonstrated with simulated driving examples.
Holonomic control of a three-qubits system in an NV center using a near-term quantum computer

The holonomic approach to controlling (nitrogen-vacancy) NV-center qubits provides an elegant way of theoretically devising universal quantum gates that operate on qubits via calculable microwave pulses. There is, however, a lack of simulated results from the theory of holonomic control of quantum registers with more than two qubits describing the transition between the dark states. In light of this, we have been experimenting with the IBM Quantum Experience technology to determine the capabilities of simulating holonomic control of NV-centers for three qubits describing an eight-level system that produces a non-Abelian geometric phase. The tunability of the geometric phase via the detuning frequency is demonstrated through the high fidelity (~80%) of 3-qubit off-resonant holonomic gates over the on-resonant ones. The transition between the dark states shows the alignment of the gate’s dark state with the qubit’s initial state hence decoherence of the multi-qubit system is well-controlled through a /3 rotation. The electron return probability can exhibit spin-orbit coupling-like behavior as observed in topological materials based on the extra geometric phase.
RNGDet: Road Network Graph Detection by Transformer in Aerial Images

Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms. To find road network graphs, manually annotation is usually inefficient and labor-intensive. Automatically detecting road network graphs could alleviate this issue, but existing works still have some limitations. For example, segmentation-based approaches could not ensure satisfactory topology correctness, and graph-based approaches could not present precise enough detection results. To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper. In view of that high-resolution aerial images could be easily accessed all over the world nowadays, we make use of aerial images in our approach. Taken as input an aerial image, our approach iteratively generates road network graphs vertex-by-vertex. Our approach can handle complicated intersection points with various numbers of incident road segments. We evaluate our approach on a publicly available dataset. The superiority of our approach is demonstrated through the comparative experiments. Our work is accompanied with a demonstration video which is available at https://tonyxuqaq.github. io/projects/RNGDet/.
Measuring Unintended Memorisation of Unique Private Features in Neural Networks

Neural networks pose a privacy risk to training data due to their propensity to memorise and leak information. Focusing on image classification, we show that neural networks also unintentionally memorise unique features even when they occur only once in training data. An example of a unique feature is a person's name that is accidentally present on a training image. Assuming access to the inputs and outputs of a trained model, the domain of the training data, and knowledge of unique features, we develop a score estimating the model's sensitivity to a unique feature by comparing the KL divergences of the model's output distributions given modified out-of-distribution images. Our results suggest that unique features are memorised by multi-layer perceptrons and convolutional neural networks trained on benchmark datasets, such as MNIST, Fashion-MNIST and CIFAR-10. We find that strategies to prevent overfitting (e.g.\ early stopping, regularisation, batch normalisation) do not prevent memorisation of unique features. These results imply that neural networks pose a privacy risk to rarely occurring private information. These risks can be more pronounced in healthcare applications if patient information is present in the training data.
Introduction to Radio Astronomical Polarimetry

These workshop notes present an introduction to the concepts and mathematical foundations of polarimetry. One of the main goals of this workshop is to develop an understanding of the relationships between a physical description of the signal path (e.g. gain, delay, rotation, coupling, etc.), the corresponding transformations of the electric field vector, and the equivalent transformations of the Stokes parameters. The adopted algebraic/geometric approach is either directly copied from or heavily inspired by the work of Britton (2000) and Hamaker (2000), and some justification is provided for preferring these over other approaches and parameterizations. 1. POLARIZATION For a plane-propagating transverse electromagnetic wave, there exist two independent solutions to Maxwell’s equations, representing two orthogonal senses of polarization. Radio receiver systems must differentiate between these two senses in order to fully describe the vector state of the observed radiation. A dual-polarization receiver is therefore designed with two receptors, or probes, that are ideally sensitive to orthogonal polarizations. Define the transverse electric field vector,
Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru/Bilstm-Based Model

Depression is a global mental health problem, the worst case of which can lead to suicide. An automatic depression detection system provides great help in facilitating depression self-assessment and improving diagnostic accuracy. In this work, we propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants’ interviews. In addition, we establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers. To the best of our knowledge, EATD-Corpus is the first and only public depression dataset that contains audio and text data in Chinese. Evaluated on two depression datasets, the proposed method achieves the state-of-the-art performances. The outperforming results demonstrate the effectiveness and generalization ability of the proposed method. The source code and EATD-Corpus are available at https://github.com/speechandlanguageprocessing/ICASSP2022-Depression.
Unified Smoke and Fire Detection in An Evolutionary Framework with Self-Supervised Progressive Data Augment

Few researches have studied simultaneous detection of smoke and flame accompanying fires due to their different physical natures that lead to uncertain fluid patterns. In this study, we collect a large image data set to re-label them as a multi-label image classification problem so as to identify smoke and flame simultaneously. In order to solve the generalization ability of the detection model on account of the movable fluid objects with uncertain shapes like fire and smoke, and it not compactible natures as well as the complex backgrounds with high variations, we propose a data augment method by random image stitch to deploy resizing, deforming, position variation, and background altering so as to enlarge the view of the learner. Moreover, we propose a self-learning data augment method by using the class activation map to extract the highly trustable region as new data source of positive examples to further enhance the data augment. By the mutual reinforcement between the data augment and the detection model that are performed iteratively, both modules make progress in an evolutionary manner. Experiments show that the proposed method can effectively improve the generalization performance of the model for concurrent smoke and fire detection.
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective

Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance on out-of-distribution ones. Most of the existing debiasing methods often identify and weaken these samples with biased features (i.e., superficial surface features that cause such spurious correlations). However, down-weighting these samples obstructs the model in learning from the non-biased parts of these samples. To tackle this challenge, in this paper, we propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective. Specifically, we introduce Random Fourier Features and weighted re-sampling to decorrelate the dependencies between features to mitigate spurious correlations. After obtaining decorrelated features, we further design a mutual-information-based method to purify them, which forces the model to learn features that are more relevant to tasks. Extensive experiments on two well-studied NLU tasks demonstrate that our method is superior to other comparative approaches.