Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing flowDensity for Automated Analysis of Bone Marrow Lymphocyte Population

Introduction: Manual gating of flow cytometry (FCM) data for marrow cell analysis is a standard approach in current practice, although it is time- and labor-consuming. Recent advances in cytometry technology have led to significant efforts in developing partially or fully automated analysis methods. Although multiple supervised and unsupervised FCM data analysis algorithms have been developed, they have not been widely adopted by the clinical and research laboratories. In this study, we evaluated flowDensity, an open source freely available algorithm, as an automated analysis tool for classification of lymphocyte subsets in the bone marrow biopsy specimens. Materials and Methods: FlowDensity-based gating was applied to 102 normal bone marrow samples and compared with the manual analysis. Independent expression of each cell marker was assessed for comprehensive expression analysis and visualization. Results: Our findings showed a correlation between the manual and flowDensity-based gating in the lymphocyte subsets. However, flowDensity-based gating in the populations with a small number of cells in each cluster showed a low degree of correlation. Comprehensive expression analysis successfully identified and visualized the lymphocyte subsets. Discussion: Our study found that although flowDensity might be a promising method for FCM data analysis, more optimization is required before implementing this algorithm into day-to-day workflow.
Impact of corporate board and external audit attributes on earnings quality: Experience from Nigeria quoted foods and beverages firms

The recent audit failures has pointed to weaknesses in financial report and give occasion of doubt to users about the reliability of earnings declared by firms. This study examines the effects of corporate board and external audit attributes on earnings quality of listed foods and beverages firms in Nigeria. Secondary data were employed through audited annual reports and accounts of eight (8) selected foods and beverages firms listed on the Nigerian Stock Exchange using judgmental sampling technique. Data collected were analyse using descriptive statistics like table, percentage and inferential statistics such as panel regression analysis. All Analysis were tested at 5% level of significance. The results revealed that audit firm size (β=14485.32, P=0.000) which is external audit attribute and board size (β=-2741.887, P=0.000), audit committee presence (β= -8225.11, P=0.044) and company size (β= 5454.20, P=0.000) were the significant determinants of external audit and corporate board attributes of listed foods and beverages firms in Nigeria. Also, the result of panel regression revealed that audit independence (β= -6.57e-06, P=0.035) and audit firm size (β=0.1141479, P= 0.018) were the external audit attributes that had significant effect on discretionary accruals. The study therefore concluded that corporate board attributes (audit committee) and external audit attributes (auditor independence, audit firm size) had significant effect on earnings quality as measured by earnings management of selected foods and beverages firms in Nigeria. The study recommends that in order to enhance auditor’s independence, uniform audit fee should be formulated, and also, disclosure should be made on other corporate board attributes like board members’ year of experience and gender and for effective monitoring system.
COMPORTEMENTS : ÉVALUATION DE LA RECONNAISSANCE DES COMPORTEMENTS ET DE LA MOTIVATION À CHANGER CHEZ LES ÉLÈVES AUSTRALIENS DE L’ENSEIGNEMENT SECONDAIRE À HAUT RISQUE DE DÉCROCHAGE SCOLAIRE

Secondary students exhibiting severe aggressive, disengaged or avoidant behaviours are at high risk of negative psychosocial outcomes. The assessment of motivation for self-directed change remains an important consideration within intervention planning. The Behaviour Change Checklist (BCC) was designed to assess student: (1) recognition of behaviours, (2) recognition of behaviours as “problems” and (2) motivation for intentional and self-directed change. An 18-item behavioural checklist, with a rating scale aligned to the Transtheoretical Model, was reviewed by experts and refined through a pilot study (n = 71). Psychometric validation occurred through a broader pretest (n = 504) and post-test (n = 439) evaluation study of a wilderness intervention for youth at risk. The article reports on the design, piloting and validation of the BCC.
Understanding barriers to diagnosis in a rare, genetic disease: Delays and errors in diagnosing schwannomatosis

Diagnosis of rare, genetic diseases is challenging, but conceptual frameworks of the diagnostic process can guide quality improvement initiatives. Using the National Academy of Medicine diagnostic framework, we assessed the extent of, and reasons for diagnostic delays and diagnostic errors in schwannomatosis, a neurogenetic syndrome characterized by nerve sheath tumors and chronic pain. We reviewed the medical records of 97 people with confirmed or probable schwannomatosis seen in two US tertiary care clinics. Time‐to‐event analysis revealed a median time from first symptom to diagnosis of 16.7 years (95% CI, 7.5–26.0 years) and median time from first medical consultation to diagnosis of 9.8 years (95% CI, 3.5–16.2 years). Factors associated with longer times to diagnosis included initial signs/symptoms that were intermittent, non‐specific, or occurred at younger ages (p < 0.05). Thirty‐six percent of patients were misdiagnosed; misdiagnoses were of underlying genetic condition (18.6%), pain etiology (16.5%), and nerve sheath tumor presence/pathology (11.3%) (non‐mutually exclusive categories). One‐fifth (19.6%) of patients had a clear missed opportunity for genetics workup that could have led to an earlier schwannomatosis diagnosis. These results suggest that interventions in clinician education, genetic testing availability, expert review of pathology findings, and automatic triggers for genetics referrals may improve diagnosis of schwannomatosis.
Genetic overlap between Parkinson’s disease and inflammatory bowel disease

Abstract Parkinson’s disease and inflammatory bowel disease have been increasingly associated, implying shared pathophysiology. To explore biological explanations for the reported connection, we leveraged summary statistics of updated genome-wide association studies and characterized the genetic overlap between the two diseases. Aggregated genetic association data were available for 37 688 cases versus 981 372 controls for Parkinson’s disease and 25 042 cases versus 34 915 controls for inflammatory bowel disease. Genetic correlation was estimated with the high-definition likelihood method. Genetic variants with joint association to both diseases were identified by conditional false discovery rate framework and further annotated to reveal shared loci, genes, and enriched pathways. For both Crohn’s disease and ulcerative colitis, the two main subtypes of inflammatory bowel disease, we detected weak but statistically significant genetic correlations with Parkinson’s disease (Crohn’s disease: rg = 0.06, P = 0.01; ulcerative colitis: rg = 0.06, P = 0.03). A total of 1290 variants in 27 independent genomic loci were detected to associate with Parkinson’s disease and Crohn’s disease at conjunctional false discovery rate under 0.01 and 1359 variants in 15 loci were pleiotropic to Parkinson’s disease and ulcerative colitis. Among the identified pleiotropic loci, 23 are novel and have never been associated with both phenotypes. A mixture of loci conferring either same or opposing genetic effects on two phenotypes was also observed. Positional and expression quantitative trait loci mapping prioritized 296 and 253 genes for Parkinson’s disease with Crohn’s disease and ulcerative colitis, respectively, among which only <10% are differentially expressed in both colon and substantia nigra. These genes were identified to overrepresent in pathways regulating gene expression and post-translational modification beyond several immune-related pathways enriched by major histocompatibility complex genes. In conclusion, we found robust evidence for a genetic link between Parkinson’s disease and inflammatory bowel disease. The identified genetic overlap is complex at the locus and gene levels, indicating the presence of both synergistic and antagonistic pleiotropy. At the functional level, our findings implied a role of immune-centered mechanisms in the reported gut-brain connection.
STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization

Learning-based edge detection has hereunto been strongly supervised with pixel-wise annotations which are tedious to obtain manually. We study the problem of self-training edge detection, leveraging the untapped wealth of large-scale unlabeled image datasets. We design a self-supervised framework with multilayer regularization and self-teaching. In particular, we impose a consistency regularization which enforces the outputs from each of the multiple layers to be consistent for the input image and its perturbed counterpart. We adopt L0-smoothing as the "perturbation" to encourage edge prediction lying on salient boundaries following the cluster assumption in self-supervised learning. Meanwhile, the network is trained with multilayer supervision by pseudo labels which are initialized with Canny edges and then iteratively refined by the network as the training proceeds. The regularization and self-teaching together attain a good balance of precision and recall, leading to a significant performance boost over supervised methods, with lightweight refinement on the target dataset. Through extensive experiments, our method demonstrates strong cross-dataset generality and can improve the original performance of edge detectors after self-training and fine-tuning.
Binary response model with many weak instruments

This paper considers an endogenous binary response model with many weak instruments. We in the current paper employ a control function approach and a regularization scheme to obtain better estimation results for the endogenous binary response model in the presence of many weak instruments. Two consistent and asymptotically normally distributed estimators are provided, each of which is called a regularized conditional maximum likelihood estimator (RCMLE) and a regularized nonlinear least square estimator (RNLSE) respectively. Monte Carlo simulations show that the proposed estimators outperform the existing estimators when many weak instruments are present. We apply our estimation method to study the effect of family income on college completion.
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks

We study the dynamics of a neural network in function space when optimizing the mean squared error via gradient flow. We show that in the underparameterized regime the network learns eigenfunctions of an integral operator TK∞ determined by the Neural Tangent Kernel (NTK) at rates corresponding to their eigenvalues. For example, for uniformly distributed data on the sphere Sd−1 and rotation invariant weight distributions, the eigenfunctions of TK∞ are the spherical harmonics. Our results can be understood as describing a spectral bias in the underparameterized regime. The proofs use the concept of “Damped Deviations”, where deviations of the NTK matter less for eigendirections with large eigenvalues due to the occurence of a damping factor. Aside from the underparameterized regime, the damped deviations point-of-view can be used to track the dynamics of the empirical risk in the overparameterized setting, allowing us to extend certain results in the literature. We conclude that damped deviations offers a simple and unifying perspective of the dynamics when optimizing the squared error.
Comments on the mass sheet degeneracy in cosmography analyses

We make a number of comments regarding modeling degeneracies in strong lensing measurements of the Hubble parameter H 0. The first point concerns the impact of weak lensing associated with different segments of the line of sight. We show that external convergence terms associated with the lens-source and observer-lens segments need to be included in cosmographic modeling, in addition to the usual observer-source term, to avoid systematic bias in the inferred value of H 0. Specifically, we show how an incomplete account of some line of sight terms biases stellar kinematics as well as ray tracing simulation methods to alleviate the mass sheet degeneracy. The second point concerns the use of imaging data for multiple strongly-lensed sources in a given system. We show that the mass sheet degeneracy is not fully resolved by the availability of multiple sources: some degeneracy remains because of differential external convergence between the different sources. Similarly, differential external convergence also complicates the use of multiple sources in addressing the approximate mass sheet degeneracy associated with a local (“internal”) core component in lens galaxies. This internal-external degeneracy is amplified by the non-monotonicity of the angular diameter distance as a function of redshift. For a rough assessment of the weak lensing effects, we provide estimates of external convergence using the nonlinear matter power spectrum, paying attention to non-equal time correlators.
Computing with Injection-Locked Spintronic Diodes

Spintronic diodes (STDs) are emerging as a technology for the realization of high-performance microwave detectors. The key advantages of such devices are their high sensitivity, capability to work at low input power, and compactness. In this work, we show a possible use of STDs for neuromorphic computing expanding the realm of their functionalities to implement analog multiplication, which is a key operation in convolutional neural networks (CNN). In particular, we introduce the concept of degree of rectification (DOR) in injection-locked STDs. Micromagnetic simulations are used to design and identify the working range of the STDs for the implementation of the DOR. Previous experimental data confirm the applicability of the proposed solution, which is tested in image processing and in a CNN that recognizes handwritten digits.
The Recent LMC–SMC Collision: Timing and Impact Parameter Constraints from Comparison of Gaia LMC Disk Kinematics and N-body Simulations

We present analysis of the proper-motion (PM) field of the red clump stars in the Large Magellanic Cloud (LMC) disk using the Gaia Early Data Release 3 catalog. Using a kinematic model based on old stars with 3D velocity measurements, we construct the residual PM field by subtracting the center-of-mass motion and internal rotation motion components. The residual PM field reveals asymmetric patterns, including larger residual PMs in the southern disk. Comparisons of the observed residual PM field with those of five numerical simulations of an LMC analog that is subject to the tidal fields of the Milky Way and the Small Magellanic Cloud (SMC) show that the present-day LMC is not in dynamical equilibrium. We find that both the observed level of disk heating (PM residual rms of 0.057 ± 0.002 mas yr−1) and kinematic asymmetry are not reproduced by Milky Way tides or if the SMC impact parameter is larger than the size of the LMC disk. This measured level of disk heating provides a novel and important method to validate numerical simulations of the LMC–SMC interaction history. Our results alone put constraints on an impact parameter ≲10 kpc and impact timing <250 Myr. When adopting the impact timing constraint of ∼140–160 Myr ago from previous studies, our results suggest that the most recent SMC encounter must have occurred with an impact parameter of ∼5 kpc. We also find consistent radial trends in the kinematically and geometrically derived disk inclination and line-of-node position angles, indicating a common origin.
Can Dark Energy Emerge from a Varying G and Spacetime Geometry?

The accelerated expansion of the universe implies the existence of an energy contribution known as dark energy. Associated with the cosmological constant in the standard model of cosmology, the nature of this dark energy is still unknown. We will discuss an alternative gravity model in which this dark energy contribution emerges naturally, as a result of allowing for a time-dependence on the gravitational constant, G, in Einstein’s field equations. With this modification, Bianchi’s identities require an additional tensor field to be introduced so that the usual conservation equation for matter and radiation is satisfied. The equation of state of this tensor field is obtained using additional constraints, coming from the assumption that this tensor field represents the space-time response to the variation of G. We will also present the predictions of this model for the late-universe data, and show that the energy contribution of this new tensor is able to explain the accelerated expansion of the universe without the addition of a cosmological constant. Unlike many other alternative gravities with varying gravitational strength, the predicted G evolution is also consistent with local observations and therefore this model does not require screening. We will finish by discussing possible other implications this approach might have for cosmology and some future prospects.
Privacy Amplification by Subsampling in Time Domain

Aggregate time-series data like traffic flow and site occupancy repeatedly sample statistics from a population across time. Such data can be profoundly useful for understanding trends within a given population, but also pose a significant privacy risk, potentially revealing e.g., who spends time where. Producing a private version of a time-series satisfying the standard definition of Differential Privacy (DP) is challenging due to the large influence a single participant can have on the sequence: if an individual can contribute to each time step, the amount of additive noise needed to satisfy privacy increases linearly with the number of time steps sampled. As such, if a signal spans a long duration or is oversampled, an excessive amount of noise must be added, drowning out underlying trends. However, in many applications an individual realistically cannot participate at every time step. When this is the case, we observe that the influence of a single participant (sensitivity) can be reduced by subsampling and/or filtering in time, while still meeting privacy requirements. Using a novel analysis, we show this significant reduction in sensitivity and propose a corresponding class of privacy mechanisms. We demonstrate the utility benefits of these techniques empirically with real-world and synthetic time-series data.
Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets.
Sensitivity of superconducting states to the impurity location in layered materials

The family of multi-layered superconductors derived from the doped topological insulator Bi$_2$Se$_3$ has been found to be unusually robust against non-magnetic disorder. Recent experimental studies have highlighted the fact that the location of impurities could play a critical role for this puzzling robustness. Here we investigate the effects of four different types of impurities, on-site, interstitial, intercalated and polar, on the superconducting critical temperature. We find that different components of the scattering potential are active depending on the impurity configuration and choice of orbitals for the effective low-energy description of the normal state. For the specific case of Bi$_2$Se$_3$-based superconductors, we find that only the symmetric share of impurity configurations contribute to scattering, such that polar impurities are completely inactive. We also find that a more dominant mass-imbalance term in the normal-state Hamiltonian can make the superconducting state more robust to intercalated impurities, in contrast to the case of on-site or interstitial impurities.
UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning

It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. Although 3D convolution can efficiently aggregate local context to suppress local redundancy from a small 3D neighborhood, it lacks the capability to capture global dependency because of the limited receptive field. Alternatively, vision transformers can effectively capture long-range dependency by self-attention mechanism, while having the limitation on reducing local redundancy with blind similarity comparison among all the tokens in each layer. Based on these observations, we propose a novel Unified transFormer (UniFormer) which seamlessly integrates merits of 3D convolution and spatiotemporal self-attention in a concise transformer format, and achieves a preferable balance between computation and accuracy. Different from traditional transformers, our relation aggregator can tackle both spatiotemporal redundancy and dependency, by learning local and global token affinity respectively in shallow and deep layers. We conduct extensive experiments on the popular video benchmarks, e.g., Kinetics-400, Kinetics-600, and Something-Something V1&V2. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1 accuracy respectively. Code is available at https://github.com/Sense-X/UniFormer.