Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing Self-Reducible Samplers

Samplers are the backbone of the implementations of any randomized algorithm. Unfortunately, obtaining an efficient algorithm to test the correctness of samplers is very hard to find. Recently, in a series of works, testers like Barbarik, Teq, Flash for testing of some particular kinds of samplers, like CNF-samplers and Horn-samplers, were obtained. However, their techniques have a significant limitation because one can not expect to use their methods to test for other samplers, such as perfect matching samplers or samplers for sampling linear extensions in posets. In this paper, we present a new testing algorithm that works for such samplers and can estimate the distance of a new sampler from a known sampler (say, the uniform sampler). Testing the identity of distributions is the heart of testing the correctness of samplers. This paper's main technical contribution is developing a new distance estimation algorithm for distributions over high-dimensional cubes using the recently proposed subcube conditioning sampling model. Given subcube conditioning access to an unknown distribution P, and a known distribution Q defined over an n-dimensional Boolean hypercube, our algorithm CubeProbeEst estimates the variation distance between P and Q within additive error using subcube conditional samples from P. Following the testing-via-learning paradigm, we also get a tester that distinguishes between the cases when P and Q are close or far in variation distance with high probability using subcube conditional samples. This estimation algorithm CubeProbeEst in the subcube conditioning sampling model helps us to design the first tester for self-reducible samplers. The correctness of the tester is formally proved. Moreover, we implement CubeProbeEst to test the quality of three samplers for sampling linear extensions in posets.
Implicit Affordance Acquisition via Causal Action–Effect Modeling in the Video Domain

Affordance knowledge is a fundamental aspect of commonsense knowledge. Recent findings indicate that world knowledge emerges through large-scale self-supervised pretraining, motivating our exploration of acquiring affordance knowledge from the visual domain. To this end, we augment an existing instructional video resource to create the new Causal Action-Effect (CAE) dataset and design two novel pretraining tasks -- Masked Action Modeling (MAM) and Masked Effect Modeling (MEM) -- promoting the acquisition of two affordance properties in models: behavior and entity equivalence, respectively. We empirically demonstrate the effectiveness of our proposed methods in learning affordance properties. Furthermore, we show that a model pretrained on both tasks outperforms a strong image-based visual-linguistic foundation model (FLAVA) as well as pure linguistic models on a zero-shot physical reasoning probing task.
Power-duality in path integral formulation of quantum mechanics

Power duality in Feynman's path integral formulation of quantum mechanics is investigated. The power duality transformation consists of a change in coordinate and time variables, an exchange of energy and coupling, and a classical angular momentum replacement. Two physical systems connected by the transformation form a power-dual pair. The propagator (Feynman's kernel) expressed by Feynman's path integral cannot be form-invariant under the transformation, whereas the promotor constructed by modifying Feynman's path integral is found form-invariant insofar as the angular momentum is classical. Upon angular quantization, the power duality breaks down. To save the notion of power duality, the idea of quasi power duality is proposed, which constitutes of an ad hoc angular momentum replacement. The power-dual invariant promotor leads to the quasi-dual invariant Green function. A formula is proposed, which determines the Green function for one of a dual pair by knowing the Green function for the other. As examples, the Coulomb-Hooke dual pair and a family of two-term confinement potentials for a zero-energy state are discussed.
Warping the Residuals for Image Editing with StyleGAN

StyleGAN models show editing capabilities via their semantically interpretable latent organizations which require successful GAN inversion methods to edit real images. Many works have been proposed for inverting images into StyleGAN's latent space. However, their results either suffer from low fidelity to the input image or poor editing qualities, especially for edits that require large transformations. That is because low-rate latent spaces lose many image details due to the information bottleneck even though it provides an editable space. On the other hand, higher-rate latent spaces can pass all the image details to StyleGAN for perfect reconstruction of images but suffer from low editing qualities. In this work, we present a novel image inversion architecture that extracts high-rate latent features and includes a flow estimation module to warp these features to adapt them to edits. The flows are estimated from StyleGAN features of edited and unedited latent codes. By estimating the high-rate features and warping them for edits, we achieve both high-fidelity to the input image and high-quality edits. We run extensive experiments and compare our method with state-of-the-art inversion methods. Qualitative metrics and visual comparisons show significant improvements.
CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions

Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely established to assess the viability of myocardial tissue of patients after acute myocardial infarction (MI). We propose the Cascading Refinement CNN (CaRe-CNN), which is a fully 3D, end-to-end trained, 3-stage CNN cascade that exploits the hierarchical structure of such labeled cardiac data. Throughout the three stages of the cascade, the label definition changes and CaRe-CNN learns to gradually refine its intermediate predictions accordingly. Furthermore, to obtain more consistent qualitative predictions, we propose a series of post-processing steps that take anatomical constraints into account. Our CaRe-CNN was submitted to the FIMH 2023 MYOSAIQ challenge, where it ranked second out of 18 participating teams. CaRe-CNN showed great improvements most notably when segmenting the difficult but clinically most relevant myocardial infarct tissue (MIT) as well as microvascular obstructions (MVO). When computing the average scores over all labels, our method obtained the best score in eight out of ten metrics. Thus, accurate cardiac segmentation after acute MI via our CaRe-CNN allows generating patient-specific models of the heart serving as an important step towards personalized medicine.
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are often wrongly classified into head classes and/or 2) tail-class samples are treated as OOD samples. To address these issues, current studies fit a prior distribution of auxiliary/pseudo OOD data to the long-tailed in-distribution (ID) data. However, it is difficult to obtain such an accurate prior distribution given the unknowingness of real OOD samples and heavy class imbalance in LTR. A straightforward solution to avoid the requirement of this prior is to learn an outlier class to encapsulate the OOD samples. The main challenge is then to tackle the aforementioned confusion between OOD samples and head/tail-class samples when learning the outlier class. To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence. Extensive empirical results on three popular benchmarks CIFAR10-LT, CIFAR100-LT, and ImageNet-LT demonstrate that COCL substantially outperforms existing state-of-the-art OOD detection methods in LTR while being able to improve the classification accuracy on ID data. Code is available at https://github.com/mala-lab/COCL.
Optimize and Reduce: A Top-Down Approach for Image Vectorization

Vector image representation is a popular choice when editability and flexibility in resolution are desired. However, most images are only available in raster form, making raster-to-vector image conversion (vectorization) an important task. Classical methods for vectorization are either domain-specific or yield an abundance of shapes which limits editability and interpretability. Learning-based methods, that use differentiable rendering, have revolutionized vectorization, at the cost of poor generalization to out-of-training distribution domains, and optimization-based counterparts are either slow or produce non-editable and redundant shapes. In this work, we propose Optimize&Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing B\'ezier curve parameters and significantly reducing the number of shapes, using a devised importance measure. We contribute a benchmark of five datasets comprising images from a broad spectrum of image complexities - from emojis to natural-like images. Through extensive experiments on hundreds of images, we demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes. Moreover, we show that our algorithm is $\times 10$ faster than the state-of-the-art optimization-based method.
A generalization of the relaxation-based matrix splitting iterative method for solving the system of generalized absolute value equations

By incorporating a new matrix splitting and the momentum acceleration into the relaxed-based matrix splitting (RMS) method \cite{soso2023}, a generalization of the RMS (GRMS) iterative method for solving the generalized absolute value equations (GAVEs) is proposed. Unlike some existing methods, by using the Cauchy's convergence principle, we give some sufficient conditions for the existence and uniqueness of the solution to the GAVEs and prove that our method can converge to the unique solution of the GAVEs. Moreover, we can obtain a few new and weaker convergence conditions for some existing methods. Preliminary numerical experiments show that the proposed method is efficient.
Observation of subdiffusive dynamic scaling in a driven and disordered box-trapped Bose gas

We explore the dynamics of a tuneable box-trapped Bose gas under strong periodic forcing in the presence of weak disorder. In absence of interparticle interactions, the interplay of the drive and disorder results in an isotropic nonthermal momentum distribution that shows subdiffusive dynamic scaling, with sublinear energy growth and the universal scaling function captured well by a compressed exponential. For increasing interaction strength, the gas behavior crosses over to wave turbulence characterized by a power-law momentum distribution
Enhancing Motor Sequence Learning via Transcutaneous Auricular Vagus Nerve Stimulation (taVNS): An EEG Study

Motor learning plays a crucial role in human life, and various neuromodulation methods have been utilized to strengthen or improve it. Transcutaneous auricular vagus nerve stimulation (taVNS) has gained increasing attention due to its non-invasive nature, affordability and ease of implementation. Although the potential of taVNS on regulating motor learning has been suggested, its actual regulatory effect has yet been fully explored. Electroencephalogram (EEG) analysis provides an in-depth understanding of cognitive processes involved in motor learning so as to offer methodological support for regulation of motor learning. To investigate the effect of taVNS on motor learning, this study recruited 22 healthy subjects to participate a single-blind, sham-controlled, and within-subject serial reaction time task (SRTT) experiment. Every subject involved in two sessions at least one week apart and received a 20-minute active/sham taVNS in each session. Behavioral indicators as well as EEG characteristics during the task state, were extracted and analyzed. The results revealed that compared to the sham group, the active group showed higher learning performance. Additionally, the EEG results indicated that after taVNS, the motor-related cortical potential amplitudes and alpha-gamma modulation index decreased significantly and functional connectivity based on partial directed coherence towards frontal lobe was enhanced. These findings suggest that taVNS can improve motor learning, mainly through enhancing cognitive and memory functions rather than simple movement learning. This study confirms the positive regulatory effect of taVNS on motor learning, which is particularly promising as it offers a potential avenue for enhancing motor skills and facilitating rehabilitation.
Novel five nucleotide deletion in dysferlin leads to autosomal recessive limb‐girdle muscular dystrophy

Muscular dystrophy (MD) is a genetic disorder that causes progressive muscle weakness and degeneration. Limb‐girdle muscular dystrophy (LGMD) is a type of MD that mainly causes muscle atrophy within the shoulder and pelvic girdles. LGMD is classified into autosomal dominant (LGMD‐D) and autosomal recessive (LGMD‐R) inheritance patterns. Mutations in the Dysferlin gene (DYSF) are common causes of LGMD‐R. However, genetic screening of DYSF mutations is rare in Taiwan. Herein, we identified a novel c. 2867_2871del ACCAG deletion and a previously reported c.937+1G>A mutation in DYSF from a Taiwanese family with LGMD. The primary symptoms of both siblings were difficulty climbing stairs, walking on the toes, and gradually worsening weakness in the proximal muscles and increased creatine kinase level. Through pedigree analysis and sequencing, two siblings from this family were found to have compound heterozygous DYSF mutations (c. 937+1G>A and c. 2867_2871del ACCAG) within the separated alleles. These mutations induced early stop codons; if translated, truncated DYSF proteins will be expressed. Or, the mRNA products of these two mutations will merit the nonsense‐mediated decay, might result in no dysferlin protein expressed. To our knowledge, this is the first report of a novel c. 2867_2871del ACCAG deletion in DYSF. Further research is required to examine the effects of the novel DYSF mutation in Taiwanese patients with LGMD.
Genomic Tools in Biological Invasions: Current State and Future Frontiers

Abstract Human activities are accelerating rates of biological invasions and climate-driven range expansions globally, yet we understand little of how genomic processes facilitate the invasion process. Although most of the literature has focused on underlying phenotypic correlates of invasiveness, advances in genomic technologies are showing a strong link between genomic variation and invasion success. Here, we consider the ability of genomic tools and technologies to (i) inform mechanistic understanding of biological invasions and (ii) solve real-world issues in predicting and managing biological invasions. For both, we examine the current state of the field and discuss how genomics can be leveraged in the future. In addition, we make recommendations pertinent to broader research issues, such as data sovereignty, metadata standards, collaboration, and science communication best practices that will require concerted efforts from the global invasion genomics community.
Epidemiological and Clinical Characteristics of Confirmed Cases of Severe Fever With Thrombocytopenia Syndrome in Jeju Province, Korea, 2014-2018

Objectives: Jeju Province is well known as the region showing the highest incidence of severe fever with thrombocytopenia syndrome (SFTS) in South Korea. The aim of this study was to evaluate the epidemiological and clinical characteristics of SFTS patients in Jeju Province. Methods: The primary data for this study were obtained from the Integrated Diseases and Health Control System of the Korea Centers for Disease Control and Prevention (KCDCIS). The selection criteria were confirmed cases of SFTS with a residence listed in Jeju Province at the time of diagnosis, reported to the KCDCIS between July 16, 2014 and November 30, 2018. Results: Of 55 confirmed cases of SFTS, the case fatality rate was 10.9% (95% confidence interval [CI], 4.1 to 22.2). The most common presenting symptoms at diagnosis of severe fever, myalgia, and diarrhea had incidences of 83.6% (95% Cl, 71.2 to 92.2), 45.5% (95% Cl, 32.0 to 59.5), and 40.0% (95% CI, 27.0 to 54.1), respectively. Conclusions: Compared to SFTS patients nationwide in 2013-2015, the subjects of this study exhibited a lower case fatality rate and had a lower incidence of severe fever, myalgia, and confusion.
Preventing chronic malnutrition in children under 2 years in rural Angola (MuCCUA trial): protocol for the economic evaluation of a three-arm community cluster randomised controlled trial

Introduction Chronic malnutrition is a serious problem in southern Angola with a prevalence of 49.9% and 37.2% in the provinces of Huila and Cunene, respectively. The MuCCUA (Mother and Child Chronic Undernutrition in Angola) trial is a community-based randomised controlled trial (c-RCT) which aims to evaluate the effectiveness of a nutrition supplementation plus standard of care intervention and a cash transfer plus standard of care intervention in preventing stunting, and to compare them with a standard of care alone intervention in southern Angola. This protocol describes the planned economic evaluation associated with the c-RCT. Methods and analysis We will conduct a cost-efficiency and cost-effectiveness analysis nested within the MuCCUA trial with a societal perspective, measuring programme, provider, participant and household costs. We will collect programme costs prospectively using a combined calculation method including quantitative and qualitative data. Financial costs will be estimated by applying activity-based costing methods to accounting records using time allocation sheets. We will estimate costs not included in accounting records by the ingredients approach, and indirect costs incurred by beneficiaries through interviews, household surveys and focus group discussions. Cost-efficiency will be estimated as cost per output achieved by combining activity-specific cost data with routine data on programme outputs. Cost-effectiveness will be assessed as cost per stunting case prevented. We will calculate incremental cost-effectiveness ratios comparing the additional cost per improved outcome of the different intervention arms and the standard of care. We will perform sensitivity analyses to assess robustness of results. Ethics and dissemination This economic evaluation will provide useful information to the Angolan Government and other policymakers on the most cost-effective intervention to prevent stunting in this and other comparable contexts. The protocol was approved by the República de Angola Ministério da Saúde Comité de Ética (27C.E/MINSA.INIS/2022). The findings of this study will be disseminated within academia and the wider policy sphere. Trial registration number ClinicalTrials.gov Registry (NCT05571280).
Recruitment Barriers of an mHealth Pediatric Asthma Pilot Study

Successful participant recruitment for pediatric studies can be challenging and even result in study discontinuation. In conducting a 2-year pilot study for a pediatric mobile health (mHealth) asthma intervention, a group of researchers experienced multiple barriers and failures in all 4 areas of recruitment: generating initial contacts, screening, consenting, and enrollment and retention. The main failures that resulted in minimal participant enrollment were poor participation, communication issues, and difficulties related to recruiting in an emergency department during a pandemic. The following is a report on this study’s recruitment efforts, including preliminary and adapted strategies, the results of these strategies, and considerations for researchers who conduct studies with children. The research team concluded that frequent and clear communication, flexibility, and a willingness to alter initial recruitment plans are essential elements for recruitment success in pediatric studies.
miR‐9 promotes canine endothelial‐like cell migration by targeting COL15A1

Abstract Background Endothelial cell migration is the initial stage of angiogenesis. In previous studies, miR‐9 has been found to regulate angiogenesis and cell migration in human medicine. Objectives This study aimed to reveal the regulatory effect of miR‐9 on canine endothelial cell migration. Methods Embryonic canine ventricle myocardium tissues were collected and induced to differentiate into endothelial‐like cells (ELCs). A transwell and invasion assay were used to evaluate the impact of miR‐9 on the migration capacity of ELCs, after which a luciferase reporter assay, western blotting, RNA sequencing and reverse transcription‐polymerase chain reaction were conducted to explore the regulatory mechanism. Results Our results showed that we successfully induced the primary cells derived from canine cardiac embryo tissues into ELCs. MiR‐9 also promoted the migration and invasion of canine ELCs, and inhibited the expression of collagen XV, an angiogenic inhibitor, at the translational level by targeting the 3′ untranslated region of COL15A1 gene. Furthermore, RNA sequencing showed that overexpression of miR‐9 impacted several signalling pathways and eight genes involved in angiogenesis and cell migration in canine ELCs. Conclusions These findings suggest that miR‐9 enhances the migration of canine ELCs and may serve as a potential diagnostic and therapeutic target for canine diseases involved in endothelial cells migration and angiogenesis, but more further studies are needed.
Evaluation of current performance of dairy cattle in ASAS and Kitulo farms in the Southern Highlands of Tanzania

This study was carried out to evaluate the current performance of dairy cattle at ASAS and Kitulo farms in the Southern Highlands of Tanzania. The objective was to assess the effects of genetic and non-genetic factors on lactation and reproductive performance, calf mortality rate, longevity traits and constraints affecting performance of dairy animals. Ayrshire and Friesian cows were involved in the study. Data were analysed using General Linear Models procedure of Statistical Analysis System (SAS). Average ages at first calving (AFC) at ASAS farm were 1059.5±19.41 and 1105.7±12.99 days for Ayrshires and Friesians, respectively. Breed and calving period significantly (P < 0.05) influenced AFC. At Kitulo farm AFC was 1151.7±9.63 days. Both parity and period of calving significantly (P < 0.001) influenced CI. Mean calving intervals (CI) at ASAS farms were 410.8±8.1 and 423.4±6.8 days for Ayrshire and Friesian cows, respectively. At Kitulo farm mean CI was 421.6±1.43 days. Mean lactation milk yields (LMY), lactation lengths (LL) and dry periods (DP) at the ASAS farm were 2696.8±75.4kg, 305.8±5.7 and 92.9±2.6 days, and 3000.1±62.9kg, 318.7±4.8 and 96.3±2.2 days for Ayrshires and Friesians, respectively. Breed (P < 0.001), parity (P < 0.001) and period of calving (P < 0.05) significantly influenced LMY and DP. At Kitulo farm mean LMY, LL and DP were 2608.3±24.0 kg, 322.3±1.4 and 91.2±0.52 days. Only period had a significant (P < 0.01) effect on LL. Overall abortion, stillbirth, pre-weaning and postweaning mortality rates were 4.8, 3.7, 6.5 and 11.0% for ASAS farm. Corresponding rates at Kitulo farm were 11.2, 7.6, 12.1 and 13.0%. Mean lifetime milk production at ASAS farm for Ayrshires and Friesians were 11 303.6 and 13 517.5 kg while at Kitulo farm it was 13 481.2 kg. Management improvement including testing and ii