Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Action Unit Detection With Transformers

The Facial Action Coding System is a taxonomy for fine-grained facial expression analysis. This paper proposes a method for detecting Facial Action Units (FAU), which de-fine particular face muscle activity, from an input image. FAU detection is formulated as a multi-task learning problem, where image features and attention maps are input to a branch for each action unit to extract discriminative feature embeddings, using a new loss function, the center contrastive (CC) loss. We employ a new FAU correlation net-work, based on a transformer encoder architecture, to capture the relationships between different action units for the wide range of expressions in the training data. The resulting features are shown to yield high classification performance. We validate our design choices, including the use of CC-loss and Tversky loss functions, in ablative experiments. We show that the proposed method outperforms state-of-the-art techniques on two public datasets, BP4D and DISFA, with an absolute improvement of the F1-score of over 2% on each.
Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling

In medical image analysis, it is typical to collect multiple annotations, each from a different clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated. Meanwhile, from the computer vision practitioner viewpoint, it has been a common practice to adopt the ground-truth labels obtained via either the majority-vote or simply one annotation from a preferred rater. This process, however, tends to overlook the rich information of agreement or disagreement ingrained in the raw multi-rater annotations. To address this issue, we propose to explicitly model the multi-rater (dis-)agreement, dubbed MRNet, which has two main contributions. First, an expertise-aware inferring module or EIM is devised to embed the expertise level of individual raters as prior knowledge, to form high-level semantic features. Second, our approach is capable of reconstructing multi-rater gradings from coarse predictions, with the multi-rater (dis-)agreement cues being further exploited to improve the segmentation performance. To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation. Extensive empirical experiments are conducted across five medical segmentation tasks of diverse imaging modalities. In these experiments, superior performance of our MRNet is observed comparing to the state-of-the-arts, indicating the effectiveness and applicability of our MRNet toward a wide range of medical segmentation tasks. Source code is publicly available.
A new family of copula-based concordance orderings of random pairs: Properties and nonparametric tests

The formal assessment of the stochastic dominance of a random pair with respect to another one is a question of interest in the economic analysis of populations. For example, a manager may wonder if the components of a portfolio are more associated than that of another competing portfolio, in which case the former is generally considered more at risk. In this paper, a new family of copula-based concordance orderings in the spirit of increasing convex and concave orderings of random pairs is introduced as a natural extension of the well-known concordance ordering. In addition, a complete statistical methodology to test the stochastic dominance of a random pair with respect to another one according to the new concordance orderings is developed. The proposed tests are nonparametric, consistent against all alternatives, and valid under serially dependent data satisfying the α-mixing assumption. The sampling properties of the tests are investigated with the help of Monte–Carlo simulations and their usefulness is illustrated on real multivariate data. MSC2020 subject classifications: Primary 60E15; secondary 62G10.
NDT_A_282208 2707..2717

Department of Psychiatry, Faculty of Medicine, Medical University of Gdańsk, Gdańsk, Poland Abstract: Bipolar disorder (BD) is a psychiatric illness associated with high morbidity, mortality and suicide rate. It has neuroprogressive course and a high rate of treatment resistance. Hence, there is an unquestionable need for new BD treatment strategies. Ketamine appears to have rapid antidepressive and antisuicidal effects. Since most of the available studies concern unipolar depression, here we present a novel insight arguing that ketamine might be a promising treatment for bipolar disorder.
CD8+ T cells specific for conserved coronavirus epitopes correlate with milder disease in patients with COVID-19

A central feature of the SARS-CoV-2 pandemic is that some individuals become severely ill or die, whereas others have only a mild disease course or are asymptomatic. Here we report development of an improved multimeric αβ T cell staining reagent platform, with each maxi-ferritin “spheromer” displaying 12 peptide-MHC complexes. Spheromers stain specific T cells more efficiently than peptide-MHC tetramers and capture a broader portion of the sequence repertoire for a given peptide-MHC. Analyzing the response in unexposed individuals, we find that T cells recognizing peptides conserved amongst coronaviruses are more abundant and tend to have a “memory” phenotype, compared to those unique to SARS-CoV-2. Significantly, CD8+ T cells with these conserved specificities are much more abundant in COVID-19 patients with mild disease versus those with a more severe illness, suggesting a protective role. T cells specific for conserved coronavirus antigens are preferentially expanded in COVID-19 patients with mild disease.
The Role of Data for One-Shot Semantic Segmentation

In this work we investigate the potential of larger datasets for one-shot semantic segmentation. While computer vision models are often trained on millions of diverse samples, current one-shot semantic segmentation datasets encompass only a small number of samples (PASCAL-5 i ), a small number of classes (PASCAL-5 i and COCO-20 i ) or have little variability (FSS-1000). To improve this situation, we introduce LVIS-OneShot, a one-shot variant of the LVIS dataset. With 718 classes and 114,347 images, it exceeds previous datasets substantially in terms of size. By controlled experiments we show that not only the number of images but also the number of different classes is crucial. We analyze transfer learning across common datasets and ﬁnd that by training on LVIS-OneShot we outperform current state-of-the-art models on PASCAL-5 i . In particular, we observe that a simple baseline model (MaRF) learns to perform one-shot segmentation when trained on a large dataset although it has a generic architecture without strong inductive biases. Code and dataset are available here: eckerlab.org/code/one-shot-segmentation
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation

How to learn effective features from large-scale point clouds for semantic segmentation has attracted increasing attention in recent years. Addressing this problem, we propose a learnable module that learns Spatial Contextual Features from large-scale point clouds, called SCF in this paper. The proposed module mainly consists of three blocks, including the local polar representation block, the dual-distance attentive pooling block, and the global contextual feature block. For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud. The proposed module could be easily embedded into various network architectures for point cloud segmentation, naturally resulting in a new 3D semantic segmentation network with an encoder-decoder architecture, called SCF-Net in this work. Extensive experimental results on two public datasets demonstrate that the proposed SCF-Net performs better than several state-of-the-art methods in most cases.
On asymptotic equivalence of the NPMLE of a monotone density and a Grenander-type estimator in multi-sample biased sampling models

Abstract: In this article, we show that the nonparametric maximum likelihood estimator (NPMLE) of the decreasing density function in the s-sample biased sampling models, is asymptotically equivalent to a Grenander-type estimator, namely the left-continuous slope of the least concave majorant of the NPMLE of the distribution function in the larger model without imposing the monotonicity assumption. Since the two estimators favor different proof directions in establishing weak convergence, we require additional results for both estimators so that the two estimators can be considered jointly in a unified approach. For instance, we employ an analytic argument for showing the tightness of an inverse processes associated with the NPMLE, since a conventional geometric approach used in the literature cannot be employed due to multiple biased samples. We demonstrate other results using numerical simulation and a real data illustration.
Attention in the overnight period and bidder abnormal returns

Based on the premise that there is a divergence of investors between the overnight and day periods, we propose absolute overnight returns (AOR) as a proxy for retail investor attention. AOR plays a vital role in the context of one of the largest and most significant corporate events merger announcements. The study finds that AOR positively affects the acquirer abnormal returns. The short-term overreaction is corrected by price reversals in the postannouncement period. The set of results are strongest for bidders with low institutional ownership and bidders that are hard to value. The results further hold for the overreaction hypothesis related to stock swap deals while rejecting the notion that our proxy AOR captures investor sentiment.
The FAST VIP (First Aid for Severe Trauma “Virtual” in-Person) Educational Study

Introduction Trauma is the leading cause of death for young Americans. Increased school violence, combined with an emphasis on early hemorrhage control, has boosted demand to treat injuries in schools. Meanwhile, coronavirus disease 2019 (COVID-19) has made educating the public about trauma more difficult. A federally funded high school education program in development, called First Aid for Severe Trauma™ (FAST™), will teach students to aid the severely injured. The program will be offered in instructor-led, web-based, and blended formats. We created a program to prepare high school teachers to become FAST instructors via “virtual” in-person (VIP) instruction. We used a webinar followed by VIP skills practice, using supplies shipped to participants’ homes. To our knowledge, no prior studies have evaluated this type of mass, widely distributed, VIP education. Methods This study is a prospective, single-arm, educational cohort study. We enrolled a convenience sample of all high school teachers attending FAST sessions at the Health Occupations Students of America–Future Health Professionals International Leadership Conference. Half of the participants were randomized to complete the Stop the Bleed Education Assessment Tool (SBEAT) prior to the webinar, and the other completed it afterward; SBEAT is a validated tool to measure learning of bleeding competencies. We then performed 76 VIP video-training sessions from June–August 2020. The FAST instructors assessed each participant’s ability to apply a tourniquet and direct pressure individually, then provided interactive group skills training, and finally re-evaluated each participant’s performance post-training. Results A total of 190 (96%) participants successfully applied a tourniquet after VIP training, compared to 136 (68%) prior to training (P < 0.001). Participants significantly improved their ability to apply direct pressure: 116 (56%) pre-assessment vs 204 (100%) post-assessment (P < 0.001). The mean score for the SBEAT increased significantly from pre-training to post-training: 2.09 with a standard deviation (SD) of 0.97 to 2.55 post-training with a SD of 0.72 (P < 0.001). Conclusion This study suggests that a webinar combined with VIP training is effective for teaching tourniquet and direct-pressure application skills, as well as life-threatening bleeding knowledge. VIP education may be useful for creating resuscitative medicine instructors from distributed locations, and to reach learners who cannot attend classroom-based instruction.
Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection

In recent years, several industries have registered an impressive improvement in technological advances such as Internet of Things (IoT), e-commerce, vehicular networks, etc. These advances have sparked an increase in the volume of information that gets transmitted from different nodes of a computer network (CN). As a result, it is crucial to safeguard CNs against security threats and intrusions that can compromise the integrity of those systems. In this paper, we propose a machine mearning (ML) intrusion detection system (IDS) in conjunction with the Genetic Algorithm (GA) for feature selection. To assess the effectiveness of the proposed framework, we use the NSL-KDD dataset. Furthermore, we consider the following ML methods in the modelling process: decision tree (DT), support vector machine (SVM), random forest (RF), extra-trees (ET), extreme gradient boosting (XGB), and naïve Bayes (NB). The results demonstrated that using the GA algorithm has a positive impact on the performance of the selected classifiers. Moreover, the results obtained by the proposed ML methods were superior to existing methodologies.
Using Genomics to Measure Phenomics: Repeatability of Bull Prolificacy in Multiple-bull Pastures

Phenotypes are necessary for genomic evaluations and management. Sometimes genomics can be used to measure phenotypes when other methods are difficult or expensive. Prolificacy of bulls used in multiple-bull pastures for commercial beef production is an example. A retrospective study of 79 bulls aged 2-year-old and older used 141 times in 4-5 pastures across 4 years was used to estimate repeatability from variance components. Traits available before each season’s use were tested for predictive ability. Sires were matched to calves using individual genotypes and evaluating exclusions. A lower cost method of measuring prolificacy was simulated for 5 pastures using the bulls’ genotypes and pooled genotypes to estimate average allele frequencies of calves and of cows. Repeatability of prolificacy was 0.62 ± 0.09. A combination of age-class and scrotal circumference accounted for less than 5 % of variation. Simulated estimation of prolificacy by pooling DNA of calves was accurate. Adding pooling of cow DNA or actual genotypes both increased accuracy about the same. Knowing a bull’s prior prolificacy would help predict future prolificacy for management purposes and could be used in genomic evaluations and research with coordination of breeders and commercial beef producers.
An Experimental Setup for Heterogeneous Catalysis on Atomically Defined Metal Nanostructures

The main objective of this PhD thesis is to link the atomic scale structure to the catalytic properties of self-assembled nanostructures. The growth and characterization of these nanostructures was carried out in an ultra-high vacuum (UHV) chamber initially designed for the study of the kinetics of epitaxial growth of thin films and nanostructures by variable temperature scanning tunneling microscopy. During this PhD thesis, we built a very sensitive and selective gas detector based on a quadrupole mass spectrometer and adapted to the geometry of this UHV system. The proper functioning of the gas detector is confirmed by temperature programmed desorption measurements of Xe on Ag(100) which demonstrate the very high sensitivity of the device up to about 10−6 monolayer (ML). The zero-order desorption parameters obtained for the first monolayer of Xe adsorbed on Ag(100) are Ed = 0.22− 0.23 eV with a prefactor ν0 between 4× 1012 s−1 and 1× 1013 s−1. An exchange process between Fe adatoms and Ag atoms from the Ag(100) surface is identified by means of temperature programmed desorption of N2. Desorption spectra suggest that the exchange is complete at an annealing temperature of 140 K. Our study of the catalytic activity of metal nanostructures focuses on the ability of passivated Fe clusters to adsorb N2 molecules without dissociating them, which is a fundamental step in the bio-inspired heterogeneous catalysis of ammonia. The first system investigated for the growth of Fe clusters is MgO/Ag(100). Diffusion of Fe adatoms deposited on a MgO monolayer is observed from an annealing temperature of 280 K onwards. Fe clusters with an average size of 12.1 ± 0.5 atoms are obtained on a MgO monolayer by thermal ripening of 0.072 ML of Fe deposited at 40 K. No sign of N2 adsorption was observed on these clusters by thermal programmed desorption investigation. The second system chosen for the growth of Fe clusters is graphene/Ir(111) obtained by chemical vapor deposition. The deposition of 0.3 and 0.4 ML of Fe on a graphene/Ir(111) sample at 50 K, followed by annealing at 300 K, results in a superlattice of Fe clusters which matches the periodicity of the moiré pattern formed by graphene/Ir(111). The expected mean cluster size is 26 atoms for 0.3 ML of Fe, and 35 atoms for 0.4 ML of Fe. The temperature stability of the clusters obtained with 0.4 ML Fe is investigated from 300 K to 600 K. It is established that Smoluchowski ripening takes place as the annealing temperature increases and the superlattice structure has completely disappeared after annealing at 600 K. Temperature programmed desorption of molecularly
Double fused Lasso regularized regression with both matrix and vector valued predictors

: In many contemporary applications such as longitudinal studies, neuroimaging or civil engineering, a dataset can contain high dimensional measurements on both matrix-valued and vector-valued variables. Such structure demands statistical tools that can extract information from both types of measurements. In this paper, we propose a double fused Lasso regularized method to handle both matrix-valued and vector-valued predictors under the context of linear regression and logistic regression. An eﬃcient and scalable sGS-ADMM (symmetric Gauss-Seidel based alternating direction method of multipliers) algorithm is derived to obtain the estimator. Global convergence and the Q-linear rate of convergence for the algorithm is established. Consistency of the double fused Lasso estimators holds under mild conditions. Numerical experiments and examples show that the double fused Lasso estimators achieve eﬃcient gains in estimation and better prediction performance compared to existing estimators.
RMHP_A_291004 1541..1550

1School of Public Health, Southeast University, Nanjing, People’s Republic of China; 2Research School of Population Health, Australian National University, Canberra, Australia; 3Research School of Finance, Actuarial Studies & Statistics, Australian National University, Canberra, Australia; 4Massachusetts Eye and Ear Institute, Harvard University, Cambridge, USA; 5Faculty of Health Science, University of Sydney, Sydney, Australia Objective: To investigate factors in association with high psychological distress in people with disabilities. Methods: We used the 2015 national survey on disability in Australia to derive the representative study population of 7936 people with disabilities aged 18+ years. The Kessler Psychological Distress Scale (K10) was used to define high psychological distress (scores ≥22). The explanatory variables included socioeconomic status, physical health, social relationships and environment factors. Adjusted Odds Ratios (ORs) and 95% Confidence Intervals (CIs) were evaluated using weighted Logistic regression models with lasso techniques. Results: Approximately 21 in 100 study participants experienced high psychological distress. The risk of high psychological distress decreased with age and high educational attainment. Having non-English speaking background (2.31; 1.87–2.85) and need for assistance in cognitive or emotional tasks (3.25; 2.65–3.98) were independently significantly associated with high psychological distress in people with disabilities. Delay seeing a GP was associated with a 2-fold risk increase. Conclusion: Integrated healthcare and social support are warranted with appropriate targeting to improve mental health outcomes in people with disabilities.
Do CEOs Learn from their Deal-Making Experience? Evidence on M&A Performance and Acquisition Behaviour of Serial Acquiring CEOs

While existing studies document that deal performance of serial acquirers can deteriorate along their deal-order due to various reasons apart from their inability to learn, the learning curve theory suggests that the performance should actually improve as managers learn through experience. If deal-making experience has a positive effect on deal quality, then the rest of the effects responsible for the declining CARs in later deals will eventually subside as the CEO becomes a more experienced deal-maker. The positive learning effect of experience over other negative effects will be more prominent particularly when the CEO attains expertise through highly specialised experience. The paper therefore examines the value of CEO acquisition experience using a near exhaustive hand-collected sample and a research design that overcomes the methodological limitations of previous papers. The evidence found suggests that as acquiring CEOs gain acquisition experience, they learn and turnaround the initial decline in their deal performance such that they create more value in their subsequent deals. This learning is sharply enhanced when the CEO has highly specialised acquisition experience related to the characteristics of the target. The improvement in value-creation is not reversed over time and the source of turnaround in valuecreation is linked to the CEO's ability to pay lower premiums with their general acquisition experience, and to identify higher-synergy targets with their highly specialised acquisition experience. Furthermore, the evolution of CEOs’ deal characteristics with experience indicates cautious acquisition behaviour supported by rational decision-making.
The Role of Blockchain in Reducing the Cost of Financial Transactions in the Retail Industry

Blockchain is viewed as a game-changing technology by many researchers and consultants. The anticipated benefits of blockchain in several sectors opened the door for many trials, use-cases, and even live implementation across many sectors, including finance, real estate, supply chain, and retail. This paper presented a brief about the basic functionalities of blockchain and how the technology can reduce the cost of financial transactions in different ways. In addition, this paper gave an overview of all use-cases and implementation schemes done in different market sectors. The aim of this paper was to assess the role of blockchain in reducing the cost of financial transactions in the retail industry. To achieve the aim of this paper, a thorough literature review was conducted on the subject at hand, and six interviewees were conducted with matter experts. The analysis revealed that blockchain might have a direct and indirect reduction in transaction costs incurred in the retail sector.